commit d5d633b6c740c8cdc3f130484ce53ef1f1e425a9
Author: Yao Wang <yao.wang@vis.uni-stuttgart.de>
Date:   Mon May 9 14:32:31 2022 +0200

    init commit

diff --git a/README.md b/README.md
new file mode 100644
index 0000000..0f23162
--- /dev/null
+++ b/README.md
@@ -0,0 +1,54 @@
+# VisRecall: Quantifying Information Visualisation Recallability via Question Answering
+
+*Yao Wang, Chuhan Jiao(Aalto University), Mihai Bâce and Andreas Bulling*
+
+submitted to The IEEE Transactions on Visualization and Computer Graphics (TVCG2022)
+
+This repository contains the dataset and models for predicting visualisation recallability.
+
+```
+$Root Directory
+│
+│─ README.md —— this file
+│
+|─ RecallNet —— Source code of the network to predict infovis recallability 
+│  │
+│  │─ environment.yaml —— conda environments
+│  │
+│  │─ notebooks 
+│  │  │
+│  │  │─ train_RecallNet.ipynb —— main notebook for training and validation
+│  │  │
+│  │  └─ massvis_recall.json —— saved recallability scores for MASSVIS dataset
+│  │
+│  └─ src
+│     │
+│     │─ singleduration_models.py —— RecallNet model
+│     │
+│     │─ sal_imp_utilities.py —— image processing utilities
+│     │
+│     │─ losses_keras2.py —— loss functions
+│     │
+│    ...
+│   
+└─ VisRecall —— The dataset
+   │
+   │─ answer_raw —— raw answers from AMT workers
+   │  
+   │─ merged
+   │  │
+   │  │─ src —— original images
+   │  │
+   │  │─ qa —— question annotations
+   │  │
+   │  └─ image_annotation —— other metadata annotations
+   │     
+   └─ training_data
+      │
+      │─ all —— all averaged questions
+      │
+      └─ X-question —— a specific type of question (T-, FE-, F-, RV-, U-)
+```
+
+
+contact: yao.wang@vis.uni-stuttgart.de
\ No newline at end of file
diff --git a/RecallNet/environment.yaml b/RecallNet/environment.yaml
new file mode 100644
index 0000000..e5fc038
--- /dev/null
+++ b/RecallNet/environment.yaml
@@ -0,0 +1,160 @@
+name: tf-cuda9
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _tflow_select=2.1.0=gpu
+  - absl-py=0.11.0=py37h06a4308_0
+  - argon2-cffi=20.1.0=py37h8f50634_2
+  - astor=0.8.1=py37_0
+  - async_generator=1.10=py_0
+  - attrs=20.2.0=pyh9f0ad1d_0
+  - backports=1.0=py_2
+  - backports.functools_lru_cache=1.6.1=py_0
+  - blas=1.0=mkl
+  - bleach=3.2.1=pyh9f0ad1d_0
+  - c-ares=1.16.1=h7b6447c_0
+  - ca-certificates=2021.4.13=h06a4308_1
+  - certifi=2020.12.5=py37h06a4308_0
+  - cffi=1.14.3=py37he30daa8_0
+  - cudatoolkit=9.0=h13b8566_0
+  - cudnn=7.3.1=cuda9.0_0
+  - cupti=9.0.176=0
+  - decorator=4.4.2=py_0
+  - defusedxml=0.6.0=py_0
+  - entrypoints=0.3=py37hc8dfbb8_1002
+  - gast=0.4.0=py_0
+  - google-pasta=0.2.0=py_0
+  - grpcio=1.31.0=py37hf8bcb03_0
+  - h5py=2.10.0=py37hd6299e0_1
+  - hdf5=1.10.6=hb1b8bf9_0
+  - importlib-metadata=2.0.0=py_1
+  - importlib_metadata=2.0.0=1
+  - intel-openmp=2020.2=254
+  - ipykernel=5.3.4=py37hc6149b9_1
+  - ipython=5.8.0=py37_1
+  - ipython_genutils=0.2.0=py_1
+  - ipywidgets=7.5.1=pyh9f0ad1d_1
+  - jinja2=2.11.2=pyh9f0ad1d_0
+  - jsonschema=3.2.0=py_2
+  - jupyter_client=6.1.7=py_0
+  - jupyter_core=4.6.3=py37hc8dfbb8_2
+  - jupyterlab_pygments=0.1.2=pyh9f0ad1d_0
+  - keras=2.3.1=0
+  - keras-applications=1.0.8=py_1
+  - keras-base=2.3.1=py37_0
+  - keras-preprocessing=1.1.0=py_1
+  - ld_impl_linux-64=2.33.1=h53a641e_7
+  - libedit=3.1.20191231=h14c3975_1
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=9.1.0=hdf63c60_0
+  - libgfortran-ng=7.3.0=hdf63c60_0
+  - libprotobuf=3.13.0.1=hd408876_0
+  - libsodium=1.0.18=h516909a_1
+  - libstdcxx-ng=9.1.0=hdf63c60_0
+  - markdown=3.3.2=py37_0
+  - markupsafe=1.1.1=py37hb5d75c8_2
+  - mistune=0.8.4=py37h8f50634_1002
+  - mkl=2020.2=256
+  - mkl-service=2.3.0=py37he904b0f_0
+  - mkl_fft=1.2.0=py37h23d657b_0
+  - mkl_random=1.1.1=py37h0573a6f_0
+  - nbclient=0.5.1=py_0
+  - nbconvert=6.0.7=py37hc8dfbb8_2
+  - nbformat=5.0.8=py_0
+  - ncurses=6.2=he6710b0_1
+  - nest-asyncio=1.4.1=py_0
+  - notebook=6.1.4=py37hc8dfbb8_1
+  - numpy=1.19.2=py37h54aff64_0
+  - numpy-base=1.19.2=py37hfa32c7d_0
+  - openssl=1.1.1k=h27cfd23_0
+  - packaging=20.4=pyh9f0ad1d_0
+  - pandoc=2.11.0.4=hd18ef5c_0
+  - pandocfilters=1.4.2=py_1
+  - pexpect=4.8.0=pyh9f0ad1d_2
+  - pickleshare=0.7.5=py_1003
+  - pip=20.2.4=py37_0
+  - prometheus_client=0.8.0=pyh9f0ad1d_0
+  - prompt_toolkit=1.0.15=py_1
+  - protobuf=3.13.0.1=py37he6710b0_1
+  - ptyprocess=0.6.0=py_1001
+  - pycparser=2.20=pyh9f0ad1d_2
+  - pygments=2.7.2=py_0
+  - pyparsing=2.4.7=pyh9f0ad1d_0
+  - pyrsistent=0.17.3=py37h8f50634_1
+  - python=3.7.9=h7579374_0
+  - python-dateutil=2.8.1=py_0
+  - python_abi=3.7=1_cp37m
+  - pyyaml=5.3.1=py37h7b6447c_1
+  - pyzmq=19.0.2=py37hac76be4_2
+  - readline=8.0=h7b6447c_0
+  - send2trash=1.5.0=py_0
+  - setuptools=50.3.0=py37hb0f4dca_1
+  - simplegeneric=0.8.1=py_1
+  - six=1.15.0=py_0
+  - sqlite=3.33.0=h62c20be_0
+  - tensorboard=1.14.0=py37hf484d3e_0
+  - tensorflow=1.14.0=gpu_py37hae64822_0
+  - tensorflow-base=1.14.0=gpu_py37h8f37b9b_0
+  - tensorflow-estimator=1.14.0=py_0
+  - tensorflow-gpu=1.14.0=h0d30ee6_0
+  - termcolor=1.1.0=py37_1
+  - terminado=0.9.1=py37hc8dfbb8_1
+  - testpath=0.4.4=py_0
+  - tk=8.6.10=hbc83047_0
+  - tornado=6.0.4=py37h8f50634_2
+  - traitlets=5.0.5=py_0
+  - wcwidth=0.2.5=pyh9f0ad1d_2
+  - webencodings=0.5.1=py_1
+  - werkzeug=1.0.1=py_0
+  - wheel=0.35.1=py_0
+  - widgetsnbextension=3.5.1=py37hc8dfbb8_4
+  - wrapt=1.12.1=py37h7b6447c_1
+  - xz=5.2.5=h7b6447c_0
+  - yaml=0.2.5=h7b6447c_0
+  - zeromq=4.3.3=he1b5a44_2
+  - zipp=3.4.0=pyhd3eb1b0_0
+  - zlib=1.2.11=h7b6447c_3
+  - pip:
+    - adjusttext==0.7.3
+    - alignment==0.0.1
+    - chardet==4.0.0
+    - cmasher==1.6.2
+    - colorspacious==1.1.2
+    - cycler==0.10.0
+    - e13tools==0.9.6
+    - editdistance==0.5.3
+    - fastdtw==0.3.4
+    - idna==2.10
+    - imageio==2.9.0
+    - joblib==1.0.0
+    - kiwisolver==1.2.0
+    - mat4py==0.5.0
+    - matplotlib==3.4.3
+    - natsort==7.1.1
+    - networkx==2.5
+    - opencv-python==4.4.0.44
+    - pandas==1.2.1
+    - patsy==0.5.1
+    - pillow==8.0.1
+    - pytz==2021.1
+    - pywavelets==1.1.1
+    - requests==2.25.1
+    - researchpy==0.3.2
+    - salicon==1.0
+    - scikit-image==0.18.1
+    - scikit-learn==0.24.1
+    - scipy==1.2.0
+    - seaborn==0.11.1
+    - simplejson==3.17.5
+    - sklearn==0.0
+    - spotlight==2.3.1
+    - statsmodels==0.12.2
+    - threadpoolctl==2.1.0
+    - tifffile==2021.3.17
+    - tqdm==4.51.0
+    - urllib3==1.26.4
+    - wget==3.2
+prefix: /netpool/homes/wangyo/.conda/envs/tf-cuda9
+
diff --git a/RecallNet/notebooks/.ipynb_checkpoints/train_RecallNet-checkpoint.ipynb b/RecallNet/notebooks/.ipynb_checkpoints/train_RecallNet-checkpoint.ipynb
new file mode 100644
index 0000000..c6f2d5c
--- /dev/null
+++ b/RecallNet/notebooks/.ipynb_checkpoints/train_RecallNet-checkpoint.ipynb
@@ -0,0 +1,984 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import keras\n",
+    "import sys\n",
+    "import os, glob\n",
+    "from keras.models import Model\n",
+    "import tensorflow as tf\n",
+    "from keras.utils import Sequence\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
+    "#from IPython.display import clear_output\n",
+    "import tqdm \n",
+    "import math\n",
+    "\n",
+    "sys.path.append('../src')\n",
+    "\n",
+    "from util import get_model_by_name\n",
+    "\n",
+    "from sal_imp_utilities import *\n",
+    "from cb import InteractivePlot\n",
+    "\n",
+    "config = tf.ConfigProto()\n",
+    "config.gpu_options.allow_growth = True\n",
+    "sess = tf.Session(config=config)\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mon May  9 14:23:41 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 470.103.01   Driver Version: 470.103.01   CUDA Version: 11.4     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  Off  | 00000000:29:00.0  On |                  N/A |\n",
+      "| 30%   41C    P2    38W / 184W |    975MiB /  7959MiB |      5%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A      1377      G   /usr/lib/xorg/Xorg                 95MiB |\n",
+      "|    0   N/A  N/A      2243      G   /usr/lib/xorg/Xorg                432MiB |\n",
+      "|    0   N/A  N/A      2378      G   /usr/bin/gnome-shell               72MiB |\n",
+      "|    0   N/A  N/A      3521      G   ...AAAAAAAAA= --shared-files       16MiB |\n",
+      "|    0   N/A  N/A      5912      G   ...RendererForSitePerProcess      103MiB |\n",
+      "|    0   N/A  N/A     18975      G   ...sktop/bin/mendeleydesktop       42MiB |\n",
+      "|    0   N/A  N/A    168057      G   ...178058406726361824,131072       78MiB |\n",
+      "|    0   N/A  N/A    178813      C   .../envs/tf-cuda9/bin/python       87MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ENV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = '/your/path/VisRecall/'\n",
+    "# 0: T, 1: FE, 2: F, 3: RV, 4: U\n",
+    "TYPE = 0\n",
+    "split = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TYPE_Q = ['T','FE','F','RV','U']\n",
+    "bp_imp = data_path + 'merged/src/'\n",
+    "training_set = np.load(data_path + 'training_data/%s-question/train_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)\n",
+    "val_set = np.load(data_path + 'training_data/%s-question/val_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_filename = []\n",
+    "train_label = []\n",
+    "train_mean = []\n",
+    "train_type = []\n",
+    "\n",
+    "for data in training_set:\n",
+    "    one_hot = [0,0,0,0,0,0]\n",
+    "    one_hot[data['vistype']] = 1\n",
+    "    train_filename.append(bp_imp+data['name'])\n",
+    "    train_label.append(one_hot)\n",
+    "    train_mean.append(data['norm_mean_acc_withD'])\n",
+    "    train_type.append(data['norm_%d_withD'%(TYPE)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_filename = []\n",
+    "val_label = []\n",
+    "val_mean = []\n",
+    "val_type = []\n",
+    "for data in val_set:\n",
+    "    one_hot = [0,0,0,0,0,0]\n",
+    "    one_hot[data['vistype']] = 1\n",
+    "    val_filename.append(bp_imp+data['name'])\n",
+    "    val_label.append(one_hot)\n",
+    "    val_mean.append(data['norm_mean_acc_withD'])\n",
+    "    val_type.append(data['norm_%d_withD'%(TYPE)])\n",
+    "#val_filename"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model and training params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# FILL THESE IN: set training parameters \n",
+    "ckpt_savedir = \"ckpt\"\n",
+    "\n",
+    "load_weights = False\n",
+    "weightspath = \"\"\n",
+    "\n",
+    "batch_size = 4\n",
+    "init_lr = 0.002\n",
+    "lr_reduce_by = .1\n",
+    "reduce_at_epoch = 3\n",
+    "n_epochs = 15\n",
+    "\n",
+    "opt = Adam(lr=init_lr) \n",
+    "\n",
+    "\n",
+    "model_name = \"RecallNet_xception\"\n",
+    "\n",
+    "model_inp_size = (240, 320)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_shape = model_inp_size + (3,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+      "\n",
+      "xception output shapes: (?, 30, 40, 2048)\n",
+      "Model: \"model_1\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            (None, 240, 320, 3)  0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1 (Conv2D)           (None, 119, 159, 32) 864         input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_bn (BatchNormaliza (None, 119, 159, 32) 128         block1_conv1[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_act (Activation)   (None, 119, 159, 32) 0           block1_conv1_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2 (Conv2D)           (None, 117, 157, 64) 18432       block1_conv1_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_bn (BatchNormaliza (None, 117, 157, 64) 256         block1_conv2[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_act (Activation)   (None, 117, 157, 64) 0           block1_conv2_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1 (SeparableConv2 (None, 117, 157, 128 8768        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1_bn (BatchNormal (None, 117, 157, 128 512         block2_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_act (Activation (None, 117, 157, 128 0           block2_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2 (SeparableConv2 (None, 117, 157, 128 17536       block2_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_bn (BatchNormal (None, 117, 157, 128 512         block2_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_1 (Conv2D)               (None, 59, 79, 128)  8192        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)      (None, 59, 79, 128)  0           block2_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_1 (BatchNor (None, 59, 79, 128)  512         conv2d_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_1 (Add)                     (None, 59, 79, 128)  0           block2_pool[0][0]                \n",
+      "                                                                 batch_normalization_1[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_act (Activation (None, 59, 79, 128)  0           add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1 (SeparableConv2 (None, 59, 79, 256)  33920       block3_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_bn (BatchNormal (None, 59, 79, 256)  1024        block3_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_act (Activation (None, 59, 79, 256)  0           block3_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2 (SeparableConv2 (None, 59, 79, 256)  67840       block3_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_bn (BatchNormal (None, 59, 79, 256)  1024        block3_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_2 (Conv2D)               (None, 30, 40, 256)  32768       add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)      (None, 30, 40, 256)  0           block3_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_2 (BatchNor (None, 30, 40, 256)  1024        conv2d_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_2 (Add)                     (None, 30, 40, 256)  0           block3_pool[0][0]                \n",
+      "                                                                 batch_normalization_2[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_act (Activation (None, 30, 40, 256)  0           add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1 (SeparableConv2 (None, 30, 40, 728)  188672      block4_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block4_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_act (Activation (None, 30, 40, 728)  0           block4_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block4_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block4_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_3 (Conv2D)               (None, 30, 40, 728)  186368      add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)      (None, 30, 40, 728)  0           block4_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_3 (BatchNor (None, 30, 40, 728)  2912        conv2d_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_3 (Add)                     (None, 30, 40, 728)  0           block4_pool[0][0]                \n",
+      "                                                                 batch_normalization_3[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_act (Activation (None, 30, 40, 728)  0           add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block5_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block5_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_act (Activation (None, 30, 40, 728)  0           block5_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block5_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block5_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_act (Activation (None, 30, 40, 728)  0           block5_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block5_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block5_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_4 (Add)                     (None, 30, 40, 728)  0           block5_sepconv3_bn[0][0]         \n",
+      "                                                                 add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_act (Activation (None, 30, 40, 728)  0           add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block6_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block6_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_act (Activation (None, 30, 40, 728)  0           block6_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block6_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block6_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_act (Activation (None, 30, 40, 728)  0           block6_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block6_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block6_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_5 (Add)                     (None, 30, 40, 728)  0           block6_sepconv3_bn[0][0]         \n",
+      "                                                                 add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_act (Activation (None, 30, 40, 728)  0           add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block7_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block7_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_act (Activation (None, 30, 40, 728)  0           block7_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block7_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block7_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_act (Activation (None, 30, 40, 728)  0           block7_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block7_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block7_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_6 (Add)                     (None, 30, 40, 728)  0           block7_sepconv3_bn[0][0]         \n",
+      "                                                                 add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_act (Activation (None, 30, 40, 728)  0           add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block8_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block8_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_act (Activation (None, 30, 40, 728)  0           block8_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block8_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block8_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_act (Activation (None, 30, 40, 728)  0           block8_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block8_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block8_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_7 (Add)                     (None, 30, 40, 728)  0           block8_sepconv3_bn[0][0]         \n",
+      "                                                                 add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_act (Activation (None, 30, 40, 728)  0           add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block9_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block9_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_act (Activation (None, 30, 40, 728)  0           block9_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block9_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block9_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_act (Activation (None, 30, 40, 728)  0           block9_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block9_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block9_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_8 (Add)                     (None, 30, 40, 728)  0           block9_sepconv3_bn[0][0]         \n",
+      "                                                                 add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block10_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block10_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_act (Activatio (None, 30, 40, 728)  0           block10_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2 (SeparableConv (None, 30, 40, 728)  536536      block10_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_bn (BatchNorma (None, 30, 40, 728)  2912        block10_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_act (Activatio (None, 30, 40, 728)  0           block10_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3 (SeparableConv (None, 30, 40, 728)  536536      block10_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_bn (BatchNorma (None, 30, 40, 728)  2912        block10_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_9 (Add)                     (None, 30, 40, 728)  0           block10_sepconv3_bn[0][0]        \n",
+      "                                                                 add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block11_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block11_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_act (Activatio (None, 30, 40, 728)  0           block11_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2 (SeparableConv (None, 30, 40, 728)  536536      block11_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_bn (BatchNorma (None, 30, 40, 728)  2912        block11_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_act (Activatio (None, 30, 40, 728)  0           block11_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3 (SeparableConv (None, 30, 40, 728)  536536      block11_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_bn (BatchNorma (None, 30, 40, 728)  2912        block11_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_10 (Add)                    (None, 30, 40, 728)  0           block11_sepconv3_bn[0][0]        \n",
+      "                                                                 add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block12_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block12_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_act (Activatio (None, 30, 40, 728)  0           block12_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2 (SeparableConv (None, 30, 40, 728)  536536      block12_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_bn (BatchNorma (None, 30, 40, 728)  2912        block12_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_act (Activatio (None, 30, 40, 728)  0           block12_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3 (SeparableConv (None, 30, 40, 728)  536536      block12_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_bn (BatchNorma (None, 30, 40, 728)  2912        block12_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_11 (Add)                    (None, 30, 40, 728)  0           block12_sepconv3_bn[0][0]        \n",
+      "                                                                 add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_11[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block13_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block13_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_act (Activatio (None, 30, 40, 728)  0           block13_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2 (SeparableConv (None, 30, 40, 1024) 752024      block13_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_bn (BatchNorma (None, 30, 40, 1024) 4096        block13_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_4 (Conv2D)               (None, 30, 40, 1024) 745472      add_11[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_pool (MaxPooling2D)     (None, 30, 40, 1024) 0           block13_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_4 (BatchNor (None, 30, 40, 1024) 4096        conv2d_4[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_12 (Add)                    (None, 30, 40, 1024) 0           block13_pool[0][0]               \n",
+      "                                                                 batch_normalization_4[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1 (SeparableConv (None, 30, 40, 1536) 1582080     add_12[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_bn (BatchNorma (None, 30, 40, 1536) 6144        block14_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_act (Activatio (None, 30, 40, 1536) 0           block14_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2 (SeparableConv (None, 30, 40, 2048) 3159552     block14_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_bn (BatchNorma (None, 30, 40, 2048) 8192        block14_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_act (Activatio (None, 30, 40, 2048) 0           block14_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "global_conv (Conv2D)            (None, 10, 14, 256)  4718592     block14_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "global_BN (BatchNormalization)  (None, 10, 14, 256)  1024        global_conv[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_1 (Activation)       (None, 10, 14, 256)  0           global_BN[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_1 (Dropout)             (None, 10, 14, 256)  0           activation_1[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "global_average_pooling2d_1 (Glo (None, 256)          0           dropout_1[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "global_dense (Dense)            (None, 256)          65792       global_average_pooling2d_1[0][0] \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_2 (Dropout)             (None, 256)          0           global_dense[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "out_mean_acc (Dense)            (None, 1)            257         dropout_2[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "out_type0_acc (Dense)           (None, 1)            257         dropout_2[0][0]                  \n",
+      "==================================================================================================\n",
+      "Total params: 25,647,402\n",
+      "Trainable params: 25,592,362\n",
+      "Non-trainable params: 55,040\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get model \n",
+    "model_params = {\n",
+    "    'input_shape': input_shape,\n",
+    "    #'n_outs': len(losses),\n",
+    "    'n_outs': 2\n",
+    "}\n",
+    "model_func, mode = get_model_by_name(model_name)\n",
+    "#assert mode == \"simple\", \"%s is a multi-duration model! Please use the multi-duration notebook to train.\" % model_name\n",
+    "model = model_func(**model_params)\n",
+    "\n",
+    "if load_weights: \n",
+    "    model.load_weights(weightspath)\n",
+    "    print(\"load\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checkpoints will be saved with format ckpt/RecallNet_xception/RecallNet_xception_ep{epoch:02d}_valloss{val_loss:.4f}.hdf5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set up data generation and checkpoints\n",
+    "if not os.path.exists(ckpt_savedir): \n",
+    "    os.makedirs(ckpt_savedir)\n",
+    "\n",
+    "\n",
+    "# Generators\n",
+    "gen_train = RecallNet_Generator(train_filename,\n",
+    "                           train_label,\n",
+    "                           train_mean,\n",
+    "                           train_type,\n",
+    "                           batch_size = 4)\n",
+    "\n",
+    "gen_val = RecallNet_Generator(val_filename,\n",
+    "                           val_label,\n",
+    "                           val_mean,\n",
+    "                           val_type,\n",
+    "                           1)\n",
+    "\n",
+    "# Callbacks\n",
+    "\n",
+    "# where to save checkpoints\n",
+    "#filepath = os.path.join(ckpt_savedir, dataset_sal + \"_\" + l_str + '_ep{epoch:02d}_valloss{val_loss:.4f}.hdf5')\n",
+    "filepath = os.path.join(ckpt_savedir, model_name, model_name+'_ep{epoch:02d}_valloss{val_loss:.4f}.hdf5')\n",
+    "\n",
+    "print(\"Checkpoints will be saved with format %s\" % filepath)\n",
+    "\n",
+    "cb_chk = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_weights_only=False, period=1)\n",
+    "cb_plot = InteractivePlot()\n",
+    "\n",
+    "def step_decay(epoch):\n",
+    "    lrate = init_lr * math.pow(lr_reduce_by, math.floor((1+epoch)/reduce_at_epoch))\n",
+    "    if epoch%reduce_at_epoch:\n",
+    "        print('Reducing lr. New lr is:', lrate)\n",
+    "    return lrate\n",
+    "cb_sched = LearningRateScheduler(step_decay)\n",
+    "\n",
+    "cbs = [cb_chk, cb_sched, cb_plot]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([0.74688797, 0.84647303, 0.93360996, 0.8340249 ]), array([0.7826087 , 0.95652174, 1.        , 0.86956522])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#test the generator \n",
+    "batch_img, out= gen_train.__getitem__(1)\n",
+    "print(out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.callbacks.History at 0x7ff9ed6f3950>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#model.compile(optimizer=opt, loss={'out_mean_acc': 'mean_squared_error', 'out_type0_acc':'mean_squared_error','out_classif':'binary_crossentropy'}, loss_weights={'out_mean_acc': 3, 'out_type0_acc': 3, 'out_classif': 1})\n",
+    "model.compile(optimizer=opt, loss={'out_mean_acc': 'mean_squared_error', 'out_type0_acc':'mean_squared_error'}, loss_weights={'out_mean_acc': 1, 'out_type0_acc': 1})\n",
+    "\n",
+    "print('Ready to train')\n",
+    "model.fit_generator(gen_train, epochs=n_epochs, verbose=1, callbacks=cbs, validation_data=gen_val, max_queue_size=10, workers=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_path = '/your/path/VisRecall/'\n",
+    "\n",
+    "if True:\n",
+    "    W = \"./ckpt/RecallNet/U-question/Fold4/RecallNet_xception_ep10_valloss0.0313.hdf5\"\n",
+    "    model.load_weights(W)\n",
+    "    print('load')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "avg, type:  0.02553353417062307 0.02617591625785007\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "mean_list = []\n",
+    "type_list = []\n",
+    "\n",
+    "num_t = 0.0\n",
+    "\n",
+    "for i in range(len(val_filename)):\n",
+    "    img = preprocess_images([val_filename[i]], 240, 320)\n",
+    "    preds = model.predict(img)\n",
+    "    #if(np.argmax(preds[2][0]) == np.argmax(val_label[i])):\n",
+    "    #    num_t += 1\n",
+    "    mean_acc = preds[0][0]\n",
+    "    type_acc = preds[1][0]\n",
+    "    mean_list.append(mean_squared_error([val_mean[i]], mean_acc))\n",
+    "    type_list.append(mean_squared_error([val_type[i]], type_acc))\n",
+    "    #print(val_label[i])\n",
+    "\n",
+    "#print('classificaion: ', num_t/len(val_filename))\n",
+    "print('avg, type: ', np.mean(mean_list),np.mean(type_list))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781), (0.5932855723937102, 0.38129302771926993)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781), (0.5932855723937102, 0.38129302771926993), (0.015003968232081454, 0.06507362662601399)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781), (0.5932855723937102, 0.38129302771926993), (0.015003968232081454, 0.06507362662601399), (0.02553353417062307, 0.02617591625785007)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 0: T, 1: FE, 2: F, 3: RV, 4: U\n",
+    "TYPE = 4\n",
+    "final = []\n",
+    "for split in range(5):\n",
+    "    TYPE_Q = ['T','FE','F','RV','U']\n",
+    "    bp_imp = data_path + 'merged/src/'\n",
+    "    training_set = np.load(data_path + 'training_data/%s-question/train_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)\n",
+    "    val_set = np.load(data_path + 'training_data/%s-question/val_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)\n",
+    "    train_filename = []\n",
+    "    train_label = []\n",
+    "    train_mean = []\n",
+    "    train_type = []\n",
+    "\n",
+    "    for data in training_set:\n",
+    "        one_hot = [0,0,0,0,0,0]\n",
+    "        one_hot[data['vistype']] = 1\n",
+    "        train_filename.append(bp_imp+data['name'])\n",
+    "        train_label.append(one_hot)\n",
+    "        train_mean.append(data['norm_mean_acc_withD'])\n",
+    "        train_type.append(data['norm_%d_withD'%(TYPE)])\n",
+    "    \n",
+    "    val_filename = []\n",
+    "    val_label = []\n",
+    "    val_mean = []\n",
+    "    val_type = []\n",
+    "    for data in val_set:\n",
+    "        one_hot = [0,0,0,0,0,0]\n",
+    "        one_hot[data['vistype']] = 1\n",
+    "        val_filename.append(bp_imp+data['name'])\n",
+    "        val_label.append(one_hot)\n",
+    "        val_mean.append(data['norm_mean_acc_withD'])\n",
+    "        val_type.append(data['norm_%d_withD'%(TYPE)])\n",
+    "\n",
+    "    mean_list = []\n",
+    "    type_list = []\n",
+    "\n",
+    "    num_t = 0.0\n",
+    "    if True:\n",
+    "        W = \"./ckpt/RecallNet_xception/U-question/Fold\"+str(split)+\"/RecallNet_xception_ep10.hdf5\"\n",
+    "        model.load_weights(W)\n",
+    "        print('load')\n",
+    "\n",
+    "    for i in range(len(val_filename)):\n",
+    "        img = preprocess_images([val_filename[i]], 240, 320)\n",
+    "        preds = model.predict(img)\n",
+    "        #if(np.argmax(preds[2][0]) == np.argmax(val_label[i])):\n",
+    "        #    num_t += 1\n",
+    "        mean_acc = preds[0][0]\n",
+    "        type_acc = preds[1][0]\n",
+    "        mean_list.append(mean_squared_error([val_mean[i]], mean_acc))\n",
+    "        type_list.append(mean_squared_error([val_type[i]], type_acc))\n",
+    "    final.append((np.mean(mean_list),np.mean(type_list)))\n",
+    "    print(final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.1716159 , 0.16309815]), array([0.21483601, 0.13730111]))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(final ,axis=0),np.std(final ,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar4.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie4.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar1.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie2.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar3.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line1.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar2.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie1.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line2.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line4.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line3.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie3.png']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#W_T = \"./ckpt/VMQA_UMSI/T-question/UMSI_weight1:1/Fold0/VMQA_UMSI_ep10_valloss0.6198.hdf5\"\n",
+    "W_T = \"./ckpt/RecallNet_xception/T-question/Fold4/RecallNet_xception_ep10.hdf5\"\n",
+    "\n",
+    "model.load_weights(W_T)\n",
+    "\n",
+    "image_path = \"/your/path/to/src\"\n",
+    "list_img_targets = glob.glob(image_path + '/*.png')\n",
+    "print(list_img_targets)\n",
+    "images = preprocess_images(list_img_targets, model_inp_size[0], model_inp_size[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds_recallNet = model.predict(images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(12, 1) [array([[0.6729137 ],\n",
+      "       [0.62224036],\n",
+      "       [0.6644782 ],\n",
+      "       [0.63536835],\n",
+      "       [0.6595377 ],\n",
+      "       [0.6628296 ],\n",
+      "       [0.600067  ],\n",
+      "       [0.64756936],\n",
+      "       [0.600191  ],\n",
+      "       [0.6459451 ],\n",
+      "       [0.6242438 ],\n",
+      "       [0.60450727]], dtype=float32), array([[0.7803583 ],\n",
+      "       [0.67262685],\n",
+      "       [0.75816834],\n",
+      "       [0.718869  ],\n",
+      "       [0.7442306 ],\n",
+      "       [0.76404536],\n",
+      "       [0.8003189 ],\n",
+      "       [0.7172563 ],\n",
+      "       [0.7985541 ],\n",
+      "       [0.81216127],\n",
+      "       [0.6953638 ],\n",
+      "       [0.6564585 ]], dtype=float32)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(preds_recallNet[0].shape, preds_recallNet)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/RecallNet/notebooks/massvis_recall.json b/RecallNet/notebooks/massvis_recall.json
new file mode 100644
index 0000000..0145c8c
--- /dev/null
+++ b/RecallNet/notebooks/massvis_recall.json
@@ -0,0 +1,1747 @@
+{
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+        "type0_prediction": "0.7693369"
+    },
+    "economist_daily_chart_106.png": {
+        "expert_score": "2.0",
+        "mean_prediction": "0.59069777",
+        "type0_prediction": "0.6385198"
+    },
+    "economist_daily_chart_110.png": {
+        "expert_score": "1.5",
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+        "type0_prediction": "0.7389466"
+    },
+    "economist_daily_chart_116.png": {
+        "expert_score": "2.125",
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+        "type0_prediction": "0.7434572"
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+        "expert_score": "0.8",
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+    },
+    "economist_daily_chart_18.png": {
+        "expert_score": "0.5",
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+    },
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+        "type0_prediction": "0.63441557"
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+        "type0_prediction": "0.76749367"
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+    "economist_daily_chart_439.png": {
+        "expert_score": "1.8",
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+        "type0_prediction": "0.63726085"
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+    "treasuryB04.png": {
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+        "expert_score": "1.0",
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+        "type0_prediction": "0.6811003"
+    },
+    "treasuryG02_2.png": {
+        "expert_score": "0.3333333333333333",
+        "mean_prediction": "0.59302926",
+        "type0_prediction": "0.6767729"
+    },
+    "treasuryG05_1.png": {
+        "expert_score": "1.0",
+        "mean_prediction": "0.6114187",
+        "type0_prediction": "0.68985176"
+    },
+    "treasuryG06_2.png": {
+        "expert_score": "0.5714285714285714",
+        "mean_prediction": "0.6068216",
+        "type0_prediction": "0.69132614"
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+    "treasuryG07_2.png": {
+        "expert_score": "0.5",
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+        "type0_prediction": "0.6862509"
+    },
+    "treasuryI01_2.png": {
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+        "type0_prediction": "0.68543863"
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+        "expert_score": "1.5",
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+        "type0_prediction": "0.6854466"
+    },
+    "treasuryK01.png": {
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+        "type0_prediction": "0.617098"
+    },
+    "treasuryK03_2.png": {
+        "expert_score": "1.0",
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+        "type0_prediction": "0.6506131"
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+    "treasuryK05_1.png": {
+        "expert_score": "0.5",
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+        "type0_prediction": "0.66523165"
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+    "treasuryK06.png": {
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+        "type0_prediction": "0.64748055"
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+    "treasuryL04_1.png": {
+        "expert_score": "0.5",
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+        "type0_prediction": "0.69232345"
+    },
+    "treasuryL10_1.png": {
+        "expert_score": "0.0",
+        "mean_prediction": "0.6104216",
+        "type0_prediction": "0.74653834"
+    },
+    "treasuryL11_2.png": {
+        "expert_score": "0.3333333333333333",
+        "mean_prediction": "0.58738047",
+        "type0_prediction": "0.656926"
+    },
+    "treasuryL13.png": {
+        "expert_score": "0.0",
+        "mean_prediction": "0.600685",
+        "type0_prediction": "0.60607654"
+    },
+    "treasuryL16.png": {
+        "expert_score": "1.5",
+        "mean_prediction": "0.58622843",
+        "type0_prediction": "0.656503"
+    },
+    "v482_n7386_5_f5.png": {
+        "expert_score": "1.3846153846153846",
+        "mean_prediction": "0.606468",
+        "type0_prediction": "0.648476"
+    },
+    "v482_n7386_7_f2.png": {
+        "expert_score": "1.125",
+        "mean_prediction": "0.62070495",
+        "type0_prediction": "0.68547374"
+    },
+    "v483_n7387_20_f2.png": {
+        "expert_score": "1.875",
+        "mean_prediction": "0.6065424",
+        "type0_prediction": "0.68959403"
+    },
+    "v483_n7387_5_f1.png": {
+        "expert_score": "0.8",
+        "mean_prediction": "0.6137462",
+        "type0_prediction": "0.6786304"
+    },
+    "v483_n7387_7_f4.png": {
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+        "expert_score": "2.25",
+        "mean_prediction": "0.6201169",
+        "type0_prediction": "0.6848236"
+    },
+    "wsj21.png": {
+        "expert_score": "2.2",
+        "mean_prediction": "0.580458",
+        "type0_prediction": "0.6775363"
+    },
+    "wsj214.png": {
+        "expert_score": "2.0",
+        "mean_prediction": "0.62013805",
+        "type0_prediction": "0.6190731"
+    },
+    "wsj220.png": {
+        "expert_score": "1.5",
+        "mean_prediction": "0.61162263",
+        "type0_prediction": "0.61598086"
+    },
+    "wsj226.png": {
+        "expert_score": "1.8888888888888888",
+        "mean_prediction": "0.6100644",
+        "type0_prediction": "0.67237806"
+    },
+    "wsj233.png": {
+        "expert_score": "2.3846153846153846",
+        "mean_prediction": "0.6182009",
+        "type0_prediction": "0.6094952"
+    },
+    "wsj240.png": {
+        "expert_score": "1.4285714285714286",
+        "mean_prediction": "0.62312",
+        "type0_prediction": "0.6201723"
+    },
+    "wsj259.png": {
+        "expert_score": "2.4285714285714284",
+        "mean_prediction": "0.6079014",
+        "type0_prediction": "0.6855644"
+    },
+    "wsj262.png": {
+        "expert_score": "2.230769230769231",
+        "mean_prediction": "0.58680624",
+        "type0_prediction": "0.6027097"
+    },
+    "wsj264.png": {
+        "expert_score": "1.4615384615384615",
+        "mean_prediction": "0.60944563",
+        "type0_prediction": "0.6723376"
+    },
+    "wsj270.png": {
+        "expert_score": "1.7777777777777777",
+        "mean_prediction": "0.62300086",
+        "type0_prediction": "0.60853285"
+    },
+    "wsj271.png": {
+        "expert_score": "2.2",
+        "mean_prediction": "0.6027496",
+        "type0_prediction": "0.61026865"
+    },
+    "wsj28.png": {
+        "expert_score": "1.0",
+        "mean_prediction": "0.6005725",
+        "type0_prediction": "0.6955757"
+    },
+    "wsj286.png": {
+        "expert_score": "1.5",
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+        "type0_prediction": "0.6588508"
+    },
+    "wsj292.png": {
+        "expert_score": "2.2",
+        "mean_prediction": "0.6121757",
+        "type0_prediction": "0.6092938"
+    },
+    "wsj294.png": {
+        "expert_score": "2.375",
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+    },
+    "wsj297.png": {
+        "expert_score": "1.8571428571428572",
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+        "type0_prediction": "0.6649289"
+    },
+    "wsj299.png": {
+        "expert_score": "2.1818181818181817",
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+    },
+    "wsj308.png": {
+        "expert_score": "1.4285714285714286",
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+    },
+    "wsj316.png": {
+        "expert_score": "1.375",
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+    },
+    "wsj340.png": {
+        "expert_score": "1.375",
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+        "type0_prediction": "0.60803014"
+    },
+    "wsj359.png": {
+        "expert_score": "1.8571428571428572",
+        "mean_prediction": "0.59416246",
+        "type0_prediction": "0.63225895"
+    },
+    "wsj360.png": {
+        "expert_score": "0.0",
+        "mean_prediction": "0.6171618",
+        "type0_prediction": "0.6207669"
+    },
+    "wsj392.png": {
+        "expert_score": "2.0",
+        "mean_prediction": "0.60224444",
+        "type0_prediction": "0.60939544"
+    },
+    "wsj395.png": {
+        "expert_score": "1.0",
+        "mean_prediction": "0.61262494",
+        "type0_prediction": "0.68072736"
+    },
+    "wsj405.png": {
+        "expert_score": "2.0",
+        "mean_prediction": "0.6039667",
+        "type0_prediction": "0.666097"
+    },
+    "wsj441.png": {
+        "expert_score": "2.3",
+        "mean_prediction": "0.6222627",
+        "type0_prediction": "0.6152519"
+    },
+    "wsj459.png": {
+        "expert_score": "2.3333333333333335",
+        "mean_prediction": "0.6130302",
+        "type0_prediction": "0.63472694"
+    },
+    "wsj462.png": {
+        "expert_score": "1.625",
+        "mean_prediction": "0.61105555",
+        "type0_prediction": "0.65729415"
+    },
+    "wsj474.png": {
+        "expert_score": "1.875",
+        "mean_prediction": "0.5946794",
+        "type0_prediction": "0.6762944"
+    },
+    "wsj481.png": {
+        "expert_score": "2.125",
+        "mean_prediction": "0.59432137",
+        "type0_prediction": "0.60720557"
+    },
+    "wsj505.png": {
+        "expert_score": "2.7142857142857144",
+        "mean_prediction": "0.6189315",
+        "type0_prediction": "0.61387897"
+    },
+    "wsj508.png": {
+        "expert_score": "1.25",
+        "mean_prediction": "0.6108691",
+        "type0_prediction": "0.6140786"
+    },
+    "wsj511.png": {
+        "expert_score": "1.9230769230769231",
+        "mean_prediction": "0.61884344",
+        "type0_prediction": "0.6120235"
+    },
+    "wsj52.png": {
+        "expert_score": "2.1875",
+        "mean_prediction": "0.62004066",
+        "type0_prediction": "0.6926921"
+    },
+    "wsj521.png": {
+        "expert_score": "2.0",
+        "mean_prediction": "0.60643303",
+        "type0_prediction": "0.62309235"
+    },
+    "wsj529.png": {
+        "expert_score": "1.6666666666666667",
+        "mean_prediction": "0.5830598",
+        "type0_prediction": "0.6055927"
+    },
+    "wsj533.png": {
+        "expert_score": "1.8333333333333333",
+        "mean_prediction": "0.59500945",
+        "type0_prediction": "0.6247512"
+    },
+    "wsj536.png": {
+        "expert_score": "2.0",
+        "mean_prediction": "0.60739934",
+        "type0_prediction": "0.6004783"
+    },
+    "wsj54.png": {
+        "expert_score": "2.4",
+        "mean_prediction": "0.59857804",
+        "type0_prediction": "0.61043245"
+    },
+    "wsj540.png": {
+        "expert_score": "1.625",
+        "mean_prediction": "0.6172153",
+        "type0_prediction": "0.61701214"
+    },
+    "wsj55.png": {
+        "expert_score": "1.5",
+        "mean_prediction": "0.5929787",
+        "type0_prediction": "0.62133396"
+    },
+    "wsj557.png": {
+        "expert_score": "2.0",
+        "mean_prediction": "0.61045426",
+        "type0_prediction": "0.6700942"
+    },
+    "wsj561.png": {
+        "expert_score": "2.1818181818181817",
+        "mean_prediction": "0.5914847",
+        "type0_prediction": "0.60710984"
+    },
+    "wsj612.png": {
+        "expert_score": "2.125",
+        "mean_prediction": "0.5982393",
+        "type0_prediction": "0.61593604"
+    },
+    "wsj79.png": {
+        "expert_score": "2.4",
+        "mean_prediction": "0.60665363",
+        "type0_prediction": "0.6006525"
+    },
+    "wsj9.png": {
+        "expert_score": "2.5",
+        "mean_prediction": "0.59658015",
+        "type0_prediction": "0.6401547"
+    },
+    "wsj99.png": {
+        "expert_score": "2.0833333333333335",
+        "mean_prediction": "0.6135405",
+        "type0_prediction": "0.6242973"
+    }
+}
\ No newline at end of file
diff --git a/RecallNet/notebooks/train_RecallNet.ipynb b/RecallNet/notebooks/train_RecallNet.ipynb
new file mode 100644
index 0000000..03ad027
--- /dev/null
+++ b/RecallNet/notebooks/train_RecallNet.ipynb
@@ -0,0 +1,984 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import keras\n",
+    "import sys\n",
+    "import os, glob\n",
+    "from keras.models import Model\n",
+    "import tensorflow as tf\n",
+    "from keras.utils import Sequence\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
+    "#from IPython.display import clear_output\n",
+    "import tqdm \n",
+    "import math\n",
+    "\n",
+    "sys.path.append('../src')\n",
+    "\n",
+    "from util import get_model_by_name\n",
+    "\n",
+    "from sal_imp_utilities import *\n",
+    "from cb import InteractivePlot\n",
+    "\n",
+    "config = tf.ConfigProto()\n",
+    "config.gpu_options.allow_growth = True\n",
+    "sess = tf.Session(config=config)\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mon May  9 14:27:53 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 470.103.01   Driver Version: 470.103.01   CUDA Version: 11.4     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  NVIDIA GeForce ...  Off  | 00000000:29:00.0  On |                  N/A |\n",
+      "| 30%   41C    P2    38W / 184W |   1027MiB /  7959MiB |      2%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|    0   N/A  N/A      1377      G   /usr/lib/xorg/Xorg                 95MiB |\n",
+      "|    0   N/A  N/A      2243      G   /usr/lib/xorg/Xorg                432MiB |\n",
+      "|    0   N/A  N/A      2378      G   /usr/bin/gnome-shell               72MiB |\n",
+      "|    0   N/A  N/A      3521      G   ...AAAAAAAAA= --shared-files       16MiB |\n",
+      "|    0   N/A  N/A      5912      G   ...RendererForSitePerProcess       87MiB |\n",
+      "|    0   N/A  N/A     18975      G   ...sktop/bin/mendeleydesktop       42MiB |\n",
+      "|    0   N/A  N/A    168057      G   ...178058406726361824,131072      146MiB |\n",
+      "|    0   N/A  N/A    179935      C   .../envs/tf-cuda9/bin/python       87MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ENV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = '/your/path/VisRecall/'\n",
+    "# 0: T, 1: FE, 2: F, 3: RV, 4: U\n",
+    "TYPE = 0\n",
+    "split = 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TYPE_Q = ['T','FE','F','RV','U']\n",
+    "bp_imp = data_path + 'merged/src/'\n",
+    "training_set = np.load(data_path + 'training_data/%s-question/train_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)\n",
+    "val_set = np.load(data_path + 'training_data/%s-question/val_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_filename = []\n",
+    "train_label = []\n",
+    "train_mean = []\n",
+    "train_type = []\n",
+    "\n",
+    "for data in training_set:\n",
+    "    one_hot = [0,0,0,0,0,0]\n",
+    "    one_hot[data['vistype']] = 1\n",
+    "    train_filename.append(bp_imp+data['name'])\n",
+    "    train_label.append(one_hot)\n",
+    "    train_mean.append(data['norm_mean_acc_withD'])\n",
+    "    train_type.append(data['norm_%d_withD'%(TYPE)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_filename = []\n",
+    "val_label = []\n",
+    "val_mean = []\n",
+    "val_type = []\n",
+    "for data in val_set:\n",
+    "    one_hot = [0,0,0,0,0,0]\n",
+    "    one_hot[data['vistype']] = 1\n",
+    "    val_filename.append(bp_imp+data['name'])\n",
+    "    val_label.append(one_hot)\n",
+    "    val_mean.append(data['norm_mean_acc_withD'])\n",
+    "    val_type.append(data['norm_%d_withD'%(TYPE)])\n",
+    "#val_filename"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model and training params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# FILL THESE IN: set training parameters \n",
+    "ckpt_savedir = \"ckpt\"\n",
+    "\n",
+    "load_weights = False\n",
+    "weightspath = \"\"\n",
+    "\n",
+    "batch_size = 4\n",
+    "init_lr = 0.002\n",
+    "lr_reduce_by = .1\n",
+    "reduce_at_epoch = 3\n",
+    "n_epochs = 15\n",
+    "\n",
+    "opt = Adam(lr=init_lr) \n",
+    "\n",
+    "\n",
+    "model_name = \"RecallNet_xception\"\n",
+    "\n",
+    "model_inp_size = (240, 320)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_shape = model_inp_size + (3,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
+      "\n",
+      "xception output shapes: (?, 30, 40, 2048)\n",
+      "Model: \"model_1\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            (None, 240, 320, 3)  0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1 (Conv2D)           (None, 119, 159, 32) 864         input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_bn (BatchNormaliza (None, 119, 159, 32) 128         block1_conv1[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv1_act (Activation)   (None, 119, 159, 32) 0           block1_conv1_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2 (Conv2D)           (None, 117, 157, 64) 18432       block1_conv1_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_bn (BatchNormaliza (None, 117, 157, 64) 256         block1_conv2[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "block1_conv2_act (Activation)   (None, 117, 157, 64) 0           block1_conv2_bn[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1 (SeparableConv2 (None, 117, 157, 128 8768        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv1_bn (BatchNormal (None, 117, 157, 128 512         block2_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_act (Activation (None, 117, 157, 128 0           block2_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2 (SeparableConv2 (None, 117, 157, 128 17536       block2_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_sepconv2_bn (BatchNormal (None, 117, 157, 128 512         block2_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_1 (Conv2D)               (None, 59, 79, 128)  8192        block1_conv2_act[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block2_pool (MaxPooling2D)      (None, 59, 79, 128)  0           block2_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_1 (BatchNor (None, 59, 79, 128)  512         conv2d_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_1 (Add)                     (None, 59, 79, 128)  0           block2_pool[0][0]                \n",
+      "                                                                 batch_normalization_1[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_act (Activation (None, 59, 79, 128)  0           add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1 (SeparableConv2 (None, 59, 79, 256)  33920       block3_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv1_bn (BatchNormal (None, 59, 79, 256)  1024        block3_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_act (Activation (None, 59, 79, 256)  0           block3_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2 (SeparableConv2 (None, 59, 79, 256)  67840       block3_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_sepconv2_bn (BatchNormal (None, 59, 79, 256)  1024        block3_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_2 (Conv2D)               (None, 30, 40, 256)  32768       add_1[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block3_pool (MaxPooling2D)      (None, 30, 40, 256)  0           block3_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_2 (BatchNor (None, 30, 40, 256)  1024        conv2d_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_2 (Add)                     (None, 30, 40, 256)  0           block3_pool[0][0]                \n",
+      "                                                                 batch_normalization_2[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_act (Activation (None, 30, 40, 256)  0           add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1 (SeparableConv2 (None, 30, 40, 728)  188672      block4_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block4_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_act (Activation (None, 30, 40, 728)  0           block4_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block4_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block4_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_3 (Conv2D)               (None, 30, 40, 728)  186368      add_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block4_pool (MaxPooling2D)      (None, 30, 40, 728)  0           block4_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_3 (BatchNor (None, 30, 40, 728)  2912        conv2d_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_3 (Add)                     (None, 30, 40, 728)  0           block4_pool[0][0]                \n",
+      "                                                                 batch_normalization_3[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_act (Activation (None, 30, 40, 728)  0           add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block5_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block5_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_act (Activation (None, 30, 40, 728)  0           block5_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block5_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block5_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_act (Activation (None, 30, 40, 728)  0           block5_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block5_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block5_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_4 (Add)                     (None, 30, 40, 728)  0           block5_sepconv3_bn[0][0]         \n",
+      "                                                                 add_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_act (Activation (None, 30, 40, 728)  0           add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block6_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block6_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_act (Activation (None, 30, 40, 728)  0           block6_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block6_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block6_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_act (Activation (None, 30, 40, 728)  0           block6_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block6_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block6_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_5 (Add)                     (None, 30, 40, 728)  0           block6_sepconv3_bn[0][0]         \n",
+      "                                                                 add_4[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_act (Activation (None, 30, 40, 728)  0           add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block7_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block7_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_act (Activation (None, 30, 40, 728)  0           block7_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block7_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block7_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_act (Activation (None, 30, 40, 728)  0           block7_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block7_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block7_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_6 (Add)                     (None, 30, 40, 728)  0           block7_sepconv3_bn[0][0]         \n",
+      "                                                                 add_5[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_act (Activation (None, 30, 40, 728)  0           add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block8_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block8_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_act (Activation (None, 30, 40, 728)  0           block8_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block8_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block8_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_act (Activation (None, 30, 40, 728)  0           block8_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block8_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block8_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block8_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_7 (Add)                     (None, 30, 40, 728)  0           block8_sepconv3_bn[0][0]         \n",
+      "                                                                 add_6[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_act (Activation (None, 30, 40, 728)  0           add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1 (SeparableConv2 (None, 30, 40, 728)  536536      block9_sepconv1_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv1_bn (BatchNormal (None, 30, 40, 728)  2912        block9_sepconv1[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_act (Activation (None, 30, 40, 728)  0           block9_sepconv1_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2 (SeparableConv2 (None, 30, 40, 728)  536536      block9_sepconv2_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv2_bn (BatchNormal (None, 30, 40, 728)  2912        block9_sepconv2[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_act (Activation (None, 30, 40, 728)  0           block9_sepconv2_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3 (SeparableConv2 (None, 30, 40, 728)  536536      block9_sepconv3_act[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block9_sepconv3_bn (BatchNormal (None, 30, 40, 728)  2912        block9_sepconv3[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "add_8 (Add)                     (None, 30, 40, 728)  0           block9_sepconv3_bn[0][0]         \n",
+      "                                                                 add_7[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block10_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block10_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_act (Activatio (None, 30, 40, 728)  0           block10_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2 (SeparableConv (None, 30, 40, 728)  536536      block10_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv2_bn (BatchNorma (None, 30, 40, 728)  2912        block10_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_act (Activatio (None, 30, 40, 728)  0           block10_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3 (SeparableConv (None, 30, 40, 728)  536536      block10_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block10_sepconv3_bn (BatchNorma (None, 30, 40, 728)  2912        block10_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_9 (Add)                     (None, 30, 40, 728)  0           block10_sepconv3_bn[0][0]        \n",
+      "                                                                 add_8[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block11_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block11_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_act (Activatio (None, 30, 40, 728)  0           block11_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2 (SeparableConv (None, 30, 40, 728)  536536      block11_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv2_bn (BatchNorma (None, 30, 40, 728)  2912        block11_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_act (Activatio (None, 30, 40, 728)  0           block11_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3 (SeparableConv (None, 30, 40, 728)  536536      block11_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block11_sepconv3_bn (BatchNorma (None, 30, 40, 728)  2912        block11_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_10 (Add)                    (None, 30, 40, 728)  0           block11_sepconv3_bn[0][0]        \n",
+      "                                                                 add_9[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block12_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block12_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_act (Activatio (None, 30, 40, 728)  0           block12_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2 (SeparableConv (None, 30, 40, 728)  536536      block12_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv2_bn (BatchNorma (None, 30, 40, 728)  2912        block12_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_act (Activatio (None, 30, 40, 728)  0           block12_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3 (SeparableConv (None, 30, 40, 728)  536536      block12_sepconv3_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block12_sepconv3_bn (BatchNorma (None, 30, 40, 728)  2912        block12_sepconv3[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "add_11 (Add)                    (None, 30, 40, 728)  0           block12_sepconv3_bn[0][0]        \n",
+      "                                                                 add_10[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_act (Activatio (None, 30, 40, 728)  0           add_11[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1 (SeparableConv (None, 30, 40, 728)  536536      block13_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv1_bn (BatchNorma (None, 30, 40, 728)  2912        block13_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_act (Activatio (None, 30, 40, 728)  0           block13_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2 (SeparableConv (None, 30, 40, 1024) 752024      block13_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_sepconv2_bn (BatchNorma (None, 30, 40, 1024) 4096        block13_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2d_4 (Conv2D)               (None, 30, 40, 1024) 745472      add_11[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block13_pool (MaxPooling2D)     (None, 30, 40, 1024) 0           block13_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_4 (BatchNor (None, 30, 40, 1024) 4096        conv2d_4[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "add_12 (Add)                    (None, 30, 40, 1024) 0           block13_pool[0][0]               \n",
+      "                                                                 batch_normalization_4[0][0]      \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1 (SeparableConv (None, 30, 40, 1536) 1582080     add_12[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_bn (BatchNorma (None, 30, 40, 1536) 6144        block14_sepconv1[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv1_act (Activatio (None, 30, 40, 1536) 0           block14_sepconv1_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2 (SeparableConv (None, 30, 40, 2048) 3159552     block14_sepconv1_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_bn (BatchNorma (None, 30, 40, 2048) 8192        block14_sepconv2[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "block14_sepconv2_act (Activatio (None, 30, 40, 2048) 0           block14_sepconv2_bn[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "global_conv (Conv2D)            (None, 10, 14, 256)  4718592     block14_sepconv2_act[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "global_BN (BatchNormalization)  (None, 10, 14, 256)  1024        global_conv[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "activation_1 (Activation)       (None, 10, 14, 256)  0           global_BN[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_1 (Dropout)             (None, 10, 14, 256)  0           activation_1[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "global_average_pooling2d_1 (Glo (None, 256)          0           dropout_1[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "global_dense (Dense)            (None, 256)          65792       global_average_pooling2d_1[0][0] \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_2 (Dropout)             (None, 256)          0           global_dense[0][0]               \n",
+      "__________________________________________________________________________________________________\n",
+      "out_mean_acc (Dense)            (None, 1)            257         dropout_2[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "out_type0_acc (Dense)           (None, 1)            257         dropout_2[0][0]                  \n",
+      "==================================================================================================\n",
+      "Total params: 25,647,402\n",
+      "Trainable params: 25,592,362\n",
+      "Non-trainable params: 55,040\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get model \n",
+    "model_params = {\n",
+    "    'input_shape': input_shape,\n",
+    "    #'n_outs': len(losses),\n",
+    "    'n_outs': 2\n",
+    "}\n",
+    "model_func, mode = get_model_by_name(model_name)\n",
+    "#assert mode == \"simple\", \"%s is a multi-duration model! Please use the multi-duration notebook to train.\" % model_name\n",
+    "model = model_func(**model_params)\n",
+    "\n",
+    "if load_weights: \n",
+    "    model.load_weights(weightspath)\n",
+    "    print(\"load\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checkpoints will be saved with format ckpt/RecallNet_xception/RecallNet_xception_ep{epoch:02d}_valloss{val_loss:.4f}.hdf5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set up data generation and checkpoints\n",
+    "if not os.path.exists(ckpt_savedir): \n",
+    "    os.makedirs(ckpt_savedir)\n",
+    "\n",
+    "\n",
+    "# Generators\n",
+    "gen_train = RecallNet_Generator(train_filename,\n",
+    "                           train_label,\n",
+    "                           train_mean,\n",
+    "                           train_type,\n",
+    "                           batch_size = 4)\n",
+    "\n",
+    "gen_val = RecallNet_Generator(val_filename,\n",
+    "                           val_label,\n",
+    "                           val_mean,\n",
+    "                           val_type,\n",
+    "                           1)\n",
+    "\n",
+    "# Callbacks\n",
+    "\n",
+    "# where to save checkpoints\n",
+    "#filepath = os.path.join(ckpt_savedir, dataset_sal + \"_\" + l_str + '_ep{epoch:02d}_valloss{val_loss:.4f}.hdf5')\n",
+    "filepath = os.path.join(ckpt_savedir, model_name, model_name+'_ep{epoch:02d}_valloss{val_loss:.4f}.hdf5')\n",
+    "\n",
+    "print(\"Checkpoints will be saved with format %s\" % filepath)\n",
+    "\n",
+    "cb_chk = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_weights_only=False, period=1)\n",
+    "cb_plot = InteractivePlot()\n",
+    "\n",
+    "def step_decay(epoch):\n",
+    "    lrate = init_lr * math.pow(lr_reduce_by, math.floor((1+epoch)/reduce_at_epoch))\n",
+    "    if epoch%reduce_at_epoch:\n",
+    "        print('Reducing lr. New lr is:', lrate)\n",
+    "    return lrate\n",
+    "cb_sched = LearningRateScheduler(step_decay)\n",
+    "\n",
+    "cbs = [cb_chk, cb_sched, cb_plot]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([0.74688797, 0.84647303, 0.93360996, 0.8340249 ]), array([0.7826087 , 0.95652174, 1.        , 0.86956522])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#test the generator \n",
+    "batch_img, out= gen_train.__getitem__(1)\n",
+    "print(out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.callbacks.History at 0x7ff9ed6f3950>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#model.compile(optimizer=opt, loss={'out_mean_acc': 'mean_squared_error', 'out_type0_acc':'mean_squared_error','out_classif':'binary_crossentropy'}, loss_weights={'out_mean_acc': 3, 'out_type0_acc': 3, 'out_classif': 1})\n",
+    "model.compile(optimizer=opt, loss={'out_mean_acc': 'mean_squared_error', 'out_type0_acc':'mean_squared_error'}, loss_weights={'out_mean_acc': 1, 'out_type0_acc': 1})\n",
+    "\n",
+    "print('Ready to train')\n",
+    "model.fit_generator(gen_train, epochs=n_epochs, verbose=1, callbacks=cbs, validation_data=gen_val, max_queue_size=10, workers=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_path = '/your/path/VisRecall/'\n",
+    "\n",
+    "if True:\n",
+    "    W = \"./ckpt/RecallNet/U-question/Fold4/RecallNet_xception_ep10_valloss0.0313.hdf5\"\n",
+    "    model.load_weights(W)\n",
+    "    print('load')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "avg, type:  0.02553353417062307 0.02617591625785007\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "mean_list = []\n",
+    "type_list = []\n",
+    "\n",
+    "num_t = 0.0\n",
+    "\n",
+    "for i in range(len(val_filename)):\n",
+    "    img = preprocess_images([val_filename[i]], 240, 320)\n",
+    "    preds = model.predict(img)\n",
+    "    #if(np.argmax(preds[2][0]) == np.argmax(val_label[i])):\n",
+    "    #    num_t += 1\n",
+    "    mean_acc = preds[0][0]\n",
+    "    type_acc = preds[1][0]\n",
+    "    mean_list.append(mean_squared_error([val_mean[i]], mean_acc))\n",
+    "    type_list.append(mean_squared_error([val_type[i]], type_acc))\n",
+    "    #print(val_label[i])\n",
+    "\n",
+    "#print('classificaion: ', num_t/len(val_filename))\n",
+    "print('avg, type: ', np.mean(mean_list),np.mean(type_list))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781), (0.5932855723937102, 0.38129302771926993)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781), (0.5932855723937102, 0.38129302771926993), (0.015003968232081454, 0.06507362662601399)]\n",
+      "load\n",
+      "[(0.1154690275375791, 0.07620564377135046), (0.10878741427577963, 0.2667425108749781), (0.5932855723937102, 0.38129302771926993), (0.015003968232081454, 0.06507362662601399), (0.02553353417062307, 0.02617591625785007)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 0: T, 1: FE, 2: F, 3: RV, 4: U\n",
+    "TYPE = 4\n",
+    "final = []\n",
+    "for split in range(5):\n",
+    "    TYPE_Q = ['T','FE','F','RV','U']\n",
+    "    bp_imp = data_path + 'merged/src/'\n",
+    "    training_set = np.load(data_path + 'training_data/%s-question/train_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)\n",
+    "    val_set = np.load(data_path + 'training_data/%s-question/val_split%d.npy'%(TYPE_Q[TYPE], split),allow_pickle=True)\n",
+    "    train_filename = []\n",
+    "    train_label = []\n",
+    "    train_mean = []\n",
+    "    train_type = []\n",
+    "\n",
+    "    for data in training_set:\n",
+    "        one_hot = [0,0,0,0,0,0]\n",
+    "        one_hot[data['vistype']] = 1\n",
+    "        train_filename.append(bp_imp+data['name'])\n",
+    "        train_label.append(one_hot)\n",
+    "        train_mean.append(data['norm_mean_acc_withD'])\n",
+    "        train_type.append(data['norm_%d_withD'%(TYPE)])\n",
+    "    \n",
+    "    val_filename = []\n",
+    "    val_label = []\n",
+    "    val_mean = []\n",
+    "    val_type = []\n",
+    "    for data in val_set:\n",
+    "        one_hot = [0,0,0,0,0,0]\n",
+    "        one_hot[data['vistype']] = 1\n",
+    "        val_filename.append(bp_imp+data['name'])\n",
+    "        val_label.append(one_hot)\n",
+    "        val_mean.append(data['norm_mean_acc_withD'])\n",
+    "        val_type.append(data['norm_%d_withD'%(TYPE)])\n",
+    "\n",
+    "    mean_list = []\n",
+    "    type_list = []\n",
+    "\n",
+    "    num_t = 0.0\n",
+    "    if True:\n",
+    "        W = \"./ckpt/RecallNet_xception/U-question/Fold\"+str(split)+\"/RecallNet_xception_ep10.hdf5\"\n",
+    "        model.load_weights(W)\n",
+    "        print('load')\n",
+    "\n",
+    "    for i in range(len(val_filename)):\n",
+    "        img = preprocess_images([val_filename[i]], 240, 320)\n",
+    "        preds = model.predict(img)\n",
+    "        #if(np.argmax(preds[2][0]) == np.argmax(val_label[i])):\n",
+    "        #    num_t += 1\n",
+    "        mean_acc = preds[0][0]\n",
+    "        type_acc = preds[1][0]\n",
+    "        mean_list.append(mean_squared_error([val_mean[i]], mean_acc))\n",
+    "        type_list.append(mean_squared_error([val_type[i]], type_acc))\n",
+    "    final.append((np.mean(mean_list),np.mean(type_list)))\n",
+    "    print(final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0.1716159 , 0.16309815]), array([0.21483601, 0.13730111]))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(final ,axis=0),np.std(final ,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar4.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie4.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar1.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie2.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar3.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line1.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/bar2.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie1.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line2.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line4.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/line3.png', '/netpool/homes/wangyo/Projects/2020_luwei_VisQA/Dataset/application/src/pie3.png']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#W_T = \"./ckpt/VMQA_UMSI/T-question/UMSI_weight1:1/Fold0/VMQA_UMSI_ep10_valloss0.6198.hdf5\"\n",
+    "W_T = \"./ckpt/RecallNet_xception/T-question/Fold4/RecallNet_xception_ep10.hdf5\"\n",
+    "\n",
+    "model.load_weights(W_T)\n",
+    "\n",
+    "image_path = \"/your/path/to/src\"\n",
+    "list_img_targets = glob.glob(image_path + '/*.png')\n",
+    "print(list_img_targets)\n",
+    "images = preprocess_images(list_img_targets, model_inp_size[0], model_inp_size[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds_recallNet = model.predict(images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(12, 1) [array([[0.6729137 ],\n",
+      "       [0.62224036],\n",
+      "       [0.6644782 ],\n",
+      "       [0.63536835],\n",
+      "       [0.6595377 ],\n",
+      "       [0.6628296 ],\n",
+      "       [0.600067  ],\n",
+      "       [0.64756936],\n",
+      "       [0.600191  ],\n",
+      "       [0.6459451 ],\n",
+      "       [0.6242438 ],\n",
+      "       [0.60450727]], dtype=float32), array([[0.7803583 ],\n",
+      "       [0.67262685],\n",
+      "       [0.75816834],\n",
+      "       [0.718869  ],\n",
+      "       [0.7442306 ],\n",
+      "       [0.76404536],\n",
+      "       [0.8003189 ],\n",
+      "       [0.7172563 ],\n",
+      "       [0.7985541 ],\n",
+      "       [0.81216127],\n",
+      "       [0.6953638 ],\n",
+      "       [0.6564585 ]], dtype=float32)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(preds_recallNet[0].shape, preds_recallNet)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/RecallNet/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/RecallNet/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..8a71010
--- /dev/null
+++ b/RecallNet/src/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+      "/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "from xception_custom import Xception_wrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from keras.layers import Input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[155 225  83]\n",
+      "  [174  33  86]\n",
+      "  [ 24 223  10]\n",
+      "  ...\n",
+      "  [147 233  79]\n",
+      "  [232 187 173]\n",
+      "  [ 69 126  85]]\n",
+      "\n",
+      " [[166 203  47]\n",
+      "  [111  65  37]\n",
+      "  [210 182 244]\n",
+      "  ...\n",
+      "  [154  62  70]\n",
+      "  [ 62  93 101]\n",
+      "  [132 231 126]]\n",
+      "\n",
+      " [[ 30 110 125]\n",
+      "  [242  45  71]\n",
+      "  [150  10 217]\n",
+      "  ...\n",
+      "  [ 38 165 128]\n",
+      "  [ 64  58 127]\n",
+      "  [179 174  72]]\n",
+      "\n",
+      " ...\n",
+      "\n",
+      " [[159   2  99]\n",
+      "  [201 220 158]\n",
+      "  [170 172  13]\n",
+      "  ...\n",
+      "  [ 79  72  65]\n",
+      "  [ 10 228   7]\n",
+      "  [ 99  60 129]]\n",
+      "\n",
+      " [[187 249   6]\n",
+      "  [ 57 166  83]\n",
+      "  [187 243  66]\n",
+      "  ...\n",
+      "  [109 184 147]\n",
+      "  [142 158  83]\n",
+      "  [190  61  30]]\n",
+      "\n",
+      " [[146 238  74]\n",
+      "  [156  20  43]\n",
+      "  [ 55 217  43]\n",
+      "  ...\n",
+      "  [208 181 141]\n",
+      "  [196  88  15]\n",
+      "  [132 225  63]]]\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "Error converting shape to a TensorShape: only size-1 arrays can be converted to Python scalars.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mmake_shape\u001b[0;34m(v, arg_name)\u001b[0m\n\u001b[1;32m    145\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m     \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mas_shape\u001b[0;34m(shape)\u001b[0m\n\u001b[1;32m   1203\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1204\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mTensorShape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dims)\u001b[0m\n\u001b[1;32m    773\u001b[0m         \u001b[0;31m# Got a list of dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mas_dimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdims_iter\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    773\u001b[0m         \u001b[0;31m# Got a list of dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mas_dimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdims_iter\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mas_dimension\u001b[0;34m(value)\u001b[0m\n\u001b[1;32m    715\u001b[0m   \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mDimension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m       if (not isinstance(value, compat.bytes_or_text_types) and\n",
+      "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-8f06c869009e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mxception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXception_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagenet'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_tensor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpooling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xception:'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtest_xception_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-8-8f06c869009e>\u001b[0m in \u001b[0;36mtest_xception_shape\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0minput_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m240\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m320\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0minp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mxception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mXception_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagenet'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_tensor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpooling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xception:'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mxception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/keras/engine/input_layer.py\u001b[0m in \u001b[0;36mInput\u001b[0;34m(shape, batch_shape, name, dtype, sparse, tensor)\u001b[0m\n\u001b[1;32m    176\u001b[0m                              \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m                              \u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m                              input_tensor=tensor)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0;31m# Return tensor including _keras_shape and _keras_history.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;31m# Note that in this case train_output and test_output are the same pointer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/keras/engine/input_layer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_shape, batch_size, batch_input_shape, dtype, input_tensor, sparse, name)\u001b[0m\n\u001b[1;32m     85\u001b[0m                                          \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m                                          \u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m                                          name=self.name)\n\u001b[0m\u001b[1;32m     88\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_placeholder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36mplaceholder\u001b[0;34m(shape, ndim, dtype, sparse, name)\u001b[0m\n\u001b[1;32m    734\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloatx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    735\u001b[0m     x = tf_keras_backend.placeholder(\n\u001b[0;32m--> 736\u001b[0;31m         shape=shape, ndim=ndim, dtype=dtype, sparse=sparse, name=name)\n\u001b[0m\u001b[1;32m    737\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    738\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mndim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36mplaceholder\u001b[0;34m(shape, ndim, dtype, sparse, name)\u001b[0m\n\u001b[1;32m    996\u001b[0m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse_placeholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    997\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 998\u001b[0;31m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    999\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py\u001b[0m in \u001b[0;36mplaceholder\u001b[0;34m(dtype, shape, name)\u001b[0m\n\u001b[1;32m   2141\u001b[0m                        \"eager execution.\")\n\u001b[1;32m   2142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2143\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py\u001b[0m in \u001b[0;36mplaceholder\u001b[0;34m(dtype, shape, name)\u001b[0m\n\u001b[1;32m   6258\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6259\u001b[0m     \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6260\u001b[0;31m   \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_execute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"shape\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6261\u001b[0m   _, _, _op = _op_def_lib._apply_op_helper(\n\u001b[1;32m   6262\u001b[0m         \"Placeholder\", dtype=dtype, shape=shape, name=name)\n",
+      "\u001b[0;32m/netpool/homes/wangyo/.conda/envs/tf-cuda9/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mmake_shape\u001b[0;34m(v, arg_name)\u001b[0m\n\u001b[1;32m    146\u001b[0m     \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Error converting %s to a TensorShape: %s.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0marg_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    149\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     raise ValueError(\"Error converting %s to a TensorShape: %s.\" % (arg_name,\n",
+      "\u001b[0;31mTypeError\u001b[0m: Error converting shape to a TensorShape: only size-1 arrays can be converted to Python scalars."
+     ]
+    }
+   ],
+   "source": [
+    "def test_xception_shape():\n",
+    "    input_ = np.random.randint(0,256, (240,320,3))\n",
+    "    print(input_)\n",
+    "    inp = Input(input_)\n",
+    "    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)\n",
+    "    print('xception:',xception.output.shape)\n",
+    "test_xception_shape()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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diff --git a/RecallNet/src/attentive_convlstm_new.py b/RecallNet/src/attentive_convlstm_new.py
new file mode 100644
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--- /dev/null
+++ b/RecallNet/src/attentive_convlstm_new.py
@@ -0,0 +1,717 @@
+# -*- coding: utf-8 -*-
+"""Convolutional-recurrent layers.
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from keras import backend as K
+from keras import activations
+from keras import initializers
+from keras import regularizers
+from keras import constraints
+#from keras.layers.recurrent import _generate_dropout_mask
+#from keras.layers.recurrent import _standardize_args
+
+import numpy as np
+import warnings
+from keras.engine.base_layer import InputSpec, Layer
+from keras.utils import conv_utils
+#from keras.legacy import interfaces
+#from keras.legacy.layers import Recurrent, ConvRecurrent2D
+from keras.layers.recurrent import RNN
+from keras.utils.generic_utils import has_arg
+from keras.utils.generic_utils import to_list
+from keras.utils.generic_utils import transpose_shape
+from tensorflow.python.keras.layers.convolutional_recurrent import ConvRNN2D
+
+
+def _generate_dropout_mask(ones, rate, training=None, count=1):
+    def dropped_inputs():
+        return K.dropout(ones, rate)
+
+class AttentiveConvLSTM2DCell(Layer):
+
+      def __init__(self,
+                   filters,
+                   attentive_filters,
+                   kernel_size,
+                   attentive_kernel_size,
+                   strides=(1, 1),
+                   padding='valid',
+                   data_format=None,
+                   dilation_rate=(1, 1),
+                   activation='tanh',
+                   recurrent_activation='hard_sigmoid',
+                   attentive_activation='tanh',
+                   use_bias=True,
+                   kernel_initializer='glorot_uniform',
+                   recurrent_initializer='orthogonal',
+                   attentive_initializer='zeros',
+                   bias_initializer='zeros',
+                   unit_forget_bias=True,
+                   kernel_regularizer=None,
+                   recurrent_regularizer=None,
+                   attentive_regularizer=None,
+                   bias_regularizer=None,
+                   kernel_constraint=None,
+                   recurrent_constraint=None,
+                   attentive_constraint=None,
+                   bias_constraint=None,
+                   dropout=0.,
+                   recurrent_dropout=0.,
+                   attentive_dropout=0.,
+                   **kwargs):
+        super(AttentiveConvLSTM2DCell, self).__init__(**kwargs)
+        self.filters = filters
+        self.attentive_filters = attentive_filters
+        self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size')
+        self.attentive_kernel_size = conv_utils.normalize_tuple(attentive_kernel_size, 2, 'attentive_kernel_size')
+        self.strides = conv_utils.normalize_tuple(strides, 2, 'strides')
+        self.padding = conv_utils.normalize_padding(padding)
+        self.data_format = K.normalize_data_format(data_format)
+        self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2,
+                                                        'dilation_rate')
+        self.activation = activations.get(activation)
+        self.recurrent_activation = activations.get(recurrent_activation)
+        self.attentive_activation = activations.get(attentive_activation)
+        self.use_bias = use_bias
+
+        self.kernel_initializer = initializers.get(kernel_initializer)
+        self.recurrent_initializer = initializers.get(recurrent_initializer)
+        self.attentive_initializer = initializers.get(attentive_initializer)
+        self.bias_initializer = initializers.get(bias_initializer)
+        self.unit_forget_bias = unit_forget_bias
+
+        self.kernel_regularizer = regularizers.get(kernel_regularizer)
+        self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
+        self.attentive_regularizer = regularizers.get(attentive_regularizer)
+        self.bias_regularizer = regularizers.get(bias_regularizer)
+
+        self.kernel_constraint = constraints.get(kernel_constraint)
+        self.recurrent_constraint = constraints.get(recurrent_constraint)
+        self.attentive_constraint = constraints.get(attentive_constraint)
+        self.bias_constraint = constraints.get(bias_constraint)
+
+
+        if K.backend() == 'theano' and (dropout or recurrent_dropout):
+            warnings.warn(
+                'RNN dropout is no longer supported with the Theano backend '
+                'due to technical limitations. '
+                'You can either set `dropout` and `recurrent_dropout` to 0, '
+                'or use the TensorFlow backend.')
+            dropout = 0.
+            recurrent_dropout = 0.
+        self.dropout = min(1., max(0., dropout))
+        self.recurrent_dropout = min(1., max(0., recurrent_dropout))
+        self.attentive_dropout = min(1., max(0., attentive_dropout))
+        self.state_size = (self.filters, self.filters)
+        self._dropout_mask = None
+        self._recurrent_dropout_mask = None
+        self._attentive_dropout_mask = None
+
+      def build(self, input_shape):
+
+        if self.data_format == 'channels_first':
+          channel_axis = 1
+        else:
+          channel_axis = -1
+        if input_shape[channel_axis] is None:
+          raise ValueError('The channel dimension of the inputs '
+                           'should be defined. Found `None`.')
+        input_dim = input_shape[channel_axis]
+        kernel_shape = self.kernel_size + (input_dim, self.filters * 4)
+
+        self.kernel_shape = kernel_shape
+        print('kernel_shape', kernel_shape)
+        recurrent_kernel_shape = self.kernel_size + (self.filters, self.filters * 4)
+        input_attentive_kernel_shape = self.attentive_kernel_size + (input_dim, self.attentive_filters)
+        hidden_attentive_kernel_shape = self.attentive_kernel_size + (self.filters, self.attentive_filters)
+        squeeze_attentive_kernel_shape =  self.attentive_kernel_size + (self.attentive_filters, 1)
+
+        self.kernel = self.add_weight(shape=kernel_shape,
+                                      initializer=self.kernel_initializer,
+                                      name='kernel',
+                                      regularizer=self.kernel_regularizer,
+                                      constraint=self.kernel_constraint)
+
+        self.recurrent_kernel = self.add_weight(
+            shape=recurrent_kernel_shape,
+            initializer=self.recurrent_initializer,
+            name='recurrent_kernel',
+            regularizer=self.recurrent_regularizer,
+            constraint=self.recurrent_constraint)
+
+        self.input_attentive_kernel = self.add_weight(
+            shape=input_attentive_kernel_shape,
+            initializer=self.attentive_initializer,
+            name='input_attentive_kernel',
+            regularizer=self.attentive_regularizer,
+            constraint=self.attentive_constraint)
+
+        self.hidden_attentive_kernel = self.add_weight(
+            shape=hidden_attentive_kernel_shape,
+            initializer=self.attentive_initializer,
+            name='hidden_attentive_kernel',
+            regularizer=self.attentive_regularizer,
+            constraint=self.attentive_constraint)
+
+        self.squeeze_attentive_kernel = self.add_weight(
+            shape=squeeze_attentive_kernel_shape,
+            initializer=self.attentive_initializer,
+            name='squeeze_attentive_kernel',
+            regularizer=self.attentive_regularizer,
+            constraint=self.attentive_constraint)
+
+
+        if self.use_bias:
+          if self.unit_forget_bias:
+
+            def bias_initializer(_, *args, **kwargs):
+              return K.concatenate([
+                  self.bias_initializer((self.filters,), *args, **kwargs),
+                  initializers.Ones()((self.filters,), *args, **kwargs),
+                  self.bias_initializer((self.filters * 2,), *args, **kwargs),
+              ])
+          else:
+            bias_initializer = self.bias_initializer
+          self.bias = self.add_weight(
+              shape=(self.filters * 4,),
+              name='bias',
+              initializer=bias_initializer,
+              regularizer=self.bias_regularizer,
+              constraint=self.bias_constraint)
+
+          self.attentive_bias = self.add_weight(
+              shape=(self.attentive_filters * 2,),
+              name='attentive_bias',
+              initializer=bias_initializer,
+              regularizer=self.bias_regularizer,
+              constraint=self.bias_constraint)
+
+        else:
+          self.bias = None
+
+        self.kernel_i = self.kernel[:, :, :, :self.filters]
+        self.recurrent_kernel_i = self.recurrent_kernel[:, :, :, :self.filters]
+        self.kernel_f = self.kernel[:, :, :, self.filters: self.filters * 2]
+        self.recurrent_kernel_f = (self.recurrent_kernel[:, :, :, self.filters:
+                                                        self.filters * 2])
+        self.kernel_c = self.kernel[:, :, :, self.filters * 2: self.filters * 3]
+        self.recurrent_kernel_c = (self.recurrent_kernel[:, :, :, self.filters * 2:
+                                                        self.filters * 3])
+        self.kernel_o = self.kernel[:, :, :, self.filters * 3:]
+        self.recurrent_kernel_o = self.recurrent_kernel[:, :, :, self.filters * 3:]
+
+        if self.use_bias:
+          self.bias_i = self.bias[:self.filters]
+          self.bias_f = self.bias[self.filters: self.filters * 2]
+          self.bias_c = self.bias[self.filters * 2: self.filters * 3]
+          self.bias_o = self.bias[self.filters * 3:]
+          self.bias_wa = self.attentive_bias[:self.attentive_filters ]
+          self.bias_ua = self.attentive_bias[self.attentive_filters : self.attentive_filters * 2]
+        else:
+          self.bias_i = None
+          self.bias_f = None
+          self.bias_c = None
+          self.bias_o = None
+
+        self.built = True
+
+      def call(self, inputs, states, training=None):
+        if 0 < self.dropout < 1 and self._dropout_mask is None:
+          self._dropout_mask = _generate_dropout_mask(
+              K.ones_like(inputs),
+              self.dropout,
+              training=training,
+              count=4)
+        if (0 < self.recurrent_dropout < 1 and
+            self._recurrent_dropout_mask is None):
+          self._recurrent_dropout_mask = _generate_dropout_mask(
+              K.ones_like(states[1]),
+              self.recurrent_dropout,
+              training=training,
+              count=4)
+        # if (0 < self.attentive_dropout < 1 and self._attentive_dropout_mask is None):
+        #   self._attentive_dropout_mask = _generate_dropout_mask(
+        #       K.ones_like(inputs),
+        #       self.attentive_dropout,
+        #       training=training,
+        #       count=4)
+
+        # dropout matrices for input units
+        dp_mask = self._dropout_mask
+        # dropout matrices for recurrent units
+        rec_dp_mask = self._recurrent_dropout_mask
+        # dropout matrices for attentive units
+        # att_dp_mask = self._attentive_dropout_mask
+
+        h_tm1 = states[0]  # previous memory state
+        c_tm1 = states[1]  # previous carry state
+
+
+        ##### ATTENTION MECHANISM
+
+        h_and_x = self.input_conv(h_tm1, self.hidden_attentive_kernel, self.bias_ua, padding='same') + self.input_conv(inputs, self.input_attentive_kernel, self.bias_wa, padding='same')
+
+        e = self.recurrent_conv(self.attentive_activation(h_and_x), self.squeeze_attentive_kernel)
+
+        a = K.reshape(K.softmax(K.batch_flatten(e)), K.shape(e))
+        inputs = inputs * K.repeat_elements(a, inputs.shape[-1], -1)
+
+
+        ##### END OF ATTENTION MECHANISM
+
+
+        if 0 < self.dropout < 1.:
+          inputs_i = inputs * dp_mask[0]
+          inputs_f = inputs * dp_mask[1]
+          inputs_c = inputs * dp_mask[2]
+          inputs_o = inputs * dp_mask[3]
+        else:
+          inputs_i = inputs
+          inputs_f = inputs
+          inputs_c = inputs
+          inputs_o = inputs
+
+        if 0 < self.recurrent_dropout < 1.:
+          h_tm1_i = h_tm1 * rec_dp_mask[0]
+          h_tm1_f = h_tm1 * rec_dp_mask[1]
+          h_tm1_c = h_tm1 * rec_dp_mask[2]
+          h_tm1_o = h_tm1 * rec_dp_mask[3]
+        else:
+          h_tm1_i = h_tm1
+          h_tm1_f = h_tm1
+          h_tm1_c = h_tm1
+          h_tm1_o = h_tm1
+
+
+        x_i = self.input_conv(inputs_i, self.kernel_i, self.bias_i,
+                              padding=self.padding)
+        x_f = self.input_conv(inputs_f, self.kernel_f, self.bias_f,
+                              padding=self.padding)
+        x_c = self.input_conv(inputs_c, self.kernel_c, self.bias_c,
+                              padding=self.padding)
+        x_o = self.input_conv(inputs_o, self.kernel_o, self.bias_o,
+                              padding=self.padding)
+        h_i = self.recurrent_conv(h_tm1_i,
+                                  self.recurrent_kernel_i)
+        h_f = self.recurrent_conv(h_tm1_f,
+                                  self.recurrent_kernel_f)
+        h_c = self.recurrent_conv(h_tm1_c,
+                                  self.recurrent_kernel_c)
+        h_o = self.recurrent_conv(h_tm1_o,
+                                  self.recurrent_kernel_o)
+
+        i = self.recurrent_activation(x_i + h_i)
+        f = self.recurrent_activation(x_f + h_f)
+        c = f * c_tm1 + i * self.activation(x_c + h_c)
+        o = self.recurrent_activation(x_o + h_o)
+        h = o * self.activation(c)
+
+        if 0 < self.dropout + self.recurrent_dropout:
+          if training is None:
+            h._uses_learning_phase = True
+
+        return h, [h, c]
+
+      def input_conv(self, x, w, b=None, padding='valid'):
+        conv_out = K.conv2d(x, w, strides=self.strides,
+                            padding=padding,
+                            data_format=self.data_format,
+                            dilation_rate=self.dilation_rate)
+        if b is not None:
+          conv_out = K.bias_add(conv_out, b,
+                                data_format=self.data_format)
+        return conv_out
+
+      def recurrent_conv(self, x, w):
+        conv_out = K.conv2d(x, w, strides=(1, 1),
+                            padding='same',
+                            data_format=self.data_format)
+        return conv_out
+
+      def get_config(self):
+        config = {'filters': self.filters,
+                  'attentive_filters': self.attentive_filters,
+                  'kernel_size': self.kernel_size,
+                  'attentive_kernel_size': self.attentive_kernel_size,
+                  'strides': self.strides,
+                  'padding': self.padding,
+                  'data_format': self.data_format,
+                  'dilation_rate': self.dilation_rate,
+                  'activation': activations.serialize(self.activation),
+                  'recurrent_activation': activations.serialize(
+                      self.recurrent_activation),
+                  'attentive_activation': activations.serialize(
+                      self.attentive_activation),
+                  'use_bias': self.use_bias,
+                  'kernel_initializer': initializers.serialize(
+                      self.kernel_initializer),
+                  'recurrent_initializer': initializers.serialize(
+                      self.recurrent_initializer),
+                  'attentive_initializer': initializers.serialize(
+                      self.attentive_initializer),
+                  'bias_initializer': initializers.serialize(self.bias_initializer),
+                  'unit_forget_bias': self.unit_forget_bias,
+                  'kernel_regularizer': regularizers.serialize(
+                      self.kernel_regularizer),
+                  'recurrent_regularizer': regularizers.serialize(
+                      self.recurrent_regularizer),
+                  'attentive_regularizer': regularizers.serialize(
+                      self.attentive_regularizer),
+                  'bias_regularizer': regularizers.serialize(self.bias_regularizer),
+                  'kernel_constraint': constraints.serialize(
+                      self.kernel_constraint),
+                  'recurrent_constraint': constraints.serialize(
+                      self.recurrent_constraint),
+                  'attentive_constraint': constraints.serialize(
+                      self.attentive_constraint),
+                  'bias_constraint': constraints.serialize(self.bias_constraint),
+                  'dropout': self.dropout,
+                  'recurrent_dropout': self.recurrent_dropout,
+                  'attentive_dropout': self.attentive_dropout}
+        base_config = super(AttentiveConvLSTM2DCell, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+
+class AttentiveConvLSTM2D(ConvRNN2D):
+  """Convolutional LSTM.
+  It is similar to an LSTM layer, but the input transformations
+  and recurrent transformations are both convolutional.
+  Arguments:
+    filters: Integer, the dimensionality of the output space
+        (i.e. the number of output filters in the convolution).
+    kernel_size: An integer or tuple/list of n integers, specifying the
+        dimensions of the convolution window.
+    strides: An integer or tuple/list of n integers,
+        specifying the strides of the convolution.
+        Specifying any stride value != 1 is incompatible with specifying
+        any `dilation_rate` value != 1.
+    padding: One of `"valid"` or `"same"` (case-insensitive).
+    data_format: A string,
+        one of `channels_last` (default) or `channels_first`.
+        The ordering of the dimensions in the inputs.
+        `channels_last` corresponds to inputs with shape
+        `(batch, time, ..., channels)`
+        while `channels_first` corresponds to
+        inputs with shape `(batch, time, channels, ...)`.
+        It defaults to the `image_data_format` value found in your
+        Keras config file at `~/.keras/keras.json`.
+        If you never set it, then it will be "channels_last".
+    dilation_rate: An integer or tuple/list of n integers, specifying
+        the dilation rate to use for dilated convolution.
+        Currently, specifying any `dilation_rate` value != 1 is
+        incompatible with specifying any `strides` value != 1.
+    activation: Activation function to use.
+        If you don't specify anything, no activation is applied
+        (ie. "linear" activation: `a(x) = x`).
+    recurrent_activation: Activation function to use
+        for the recurrent step.
+    use_bias: Boolean, whether the layer uses a bias vector.
+    kernel_initializer: Initializer for the `kernel` weights matrix,
+        used for the linear transformation of the inputs.
+    recurrent_initializer: Initializer for the `recurrent_kernel`
+        weights matrix,
+        used for the linear transformation of the recurrent state.
+    bias_initializer: Initializer for the bias vector.
+    unit_forget_bias: Boolean.
+        If True, add 1 to the bias of the forget gate at initialization.
+        Use in combination with `bias_initializer="zeros"`.
+        This is recommended in [Jozefowicz et al.]
+        (http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
+    kernel_regularizer: Regularizer function applied to
+        the `kernel` weights matrix.
+    recurrent_regularizer: Regularizer function applied to
+        the `recurrent_kernel` weights matrix.
+    bias_regularizer: Regularizer function applied to the bias vector.
+    activity_regularizer: Regularizer function applied to.
+    kernel_constraint: Constraint function applied to
+        the `kernel` weights matrix.
+    recurrent_constraint: Constraint function applied to
+        the `recurrent_kernel` weights matrix.
+    bias_constraint: Constraint function applied to the bias vector.
+    return_sequences: Boolean. Whether to return the last output
+        in the output sequence, or the full sequence.
+    go_backwards: Boolean (default False).
+        If True, process the input sequence backwards.
+    stateful: Boolean (default False). If True, the last state
+        for each sample at index i in a batch will be used as initial
+        state for the sample of index i in the following batch.
+    dropout: Float between 0 and 1.
+        Fraction of the units to drop for
+        the linear transformation of the inputs.
+    recurrent_dropout: Float between 0 and 1.
+        Fraction of the units to drop for
+        the linear transformation of the recurrent state.
+  Input shape:
+    - if data_format='channels_first'
+        5D tensor with shape:
+        `(samples, time, channels, rows, cols)`
+    - if data_format='channels_last'
+        5D tensor with shape:
+        `(samples, time, rows, cols, channels)`
+  Output shape:
+    - if `return_sequences`
+         - if data_format='channels_first'
+            5D tensor with shape:
+            `(samples, time, filters, output_row, output_col)`
+         - if data_format='channels_last'
+            5D tensor with shape:
+            `(samples, time, output_row, output_col, filters)`
+    - else
+        - if data_format ='channels_first'
+            4D tensor with shape:
+            `(samples, filters, output_row, output_col)`
+        - if data_format='channels_last'
+            4D tensor with shape:
+            `(samples, output_row, output_col, filters)`
+        where o_row and o_col depend on the shape of the filter and
+        the padding
+  Raises:
+    ValueError: in case of invalid constructor arguments.
+  References:
+    - [Convolutional LSTM Network: A Machine Learning Approach for
+    Precipitation Nowcasting](http://arxiv.org/abs/1506.04214v1)
+    The current implementation does not include the feedback loop on the
+    cells output.
+  """
+
+  def __init__(self,
+               filters,
+               attentive_filters,
+               kernel_size,
+               attentive_kernel_size,
+               strides=(1, 1),
+               padding='valid',
+               data_format=None,
+               dilation_rate=(1, 1),
+               activation='tanh',
+               recurrent_activation='hard_sigmoid',
+               attentive_activation='tanh',
+               use_bias=True,
+               kernel_initializer='glorot_uniform',
+               recurrent_initializer='orthogonal',
+               attentive_initializer='zeros',
+               bias_initializer='zeros',
+               unit_forget_bias=True,
+               kernel_regularizer=None,
+               recurrent_regularizer=None,
+               attentive_regularizer=None,
+               bias_regularizer=None,
+               activity_regularizer=None,
+               kernel_constraint=None,
+               recurrent_constraint=None,
+               attentive_constraint=None,
+               bias_constraint=None,
+               return_sequences=False,
+               go_backwards=False,
+               stateful=False,
+               dropout=0.,
+               recurrent_dropout=0.,
+               attentive_dropout=0.,
+               **kwargs):
+    cell = AttentiveConvLSTM2DCell(filters=filters,
+                          attentive_filters=attentive_filters,
+                          kernel_size=kernel_size,
+                          attentive_kernel_size=attentive_kernel_size,
+                          strides=strides,
+                          padding=padding,
+                          data_format=data_format,
+                          dilation_rate=dilation_rate,
+                          activation=activation,
+                          recurrent_activation=recurrent_activation,
+                          attentive_activation=attentive_activation,
+                          use_bias=use_bias,
+                          kernel_initializer=kernel_initializer,
+                          recurrent_initializer=recurrent_initializer,
+                          attentive_initializer=attentive_initializer,
+                          bias_initializer=bias_initializer,
+                          unit_forget_bias=unit_forget_bias,
+                          kernel_regularizer=kernel_regularizer,
+                          recurrent_regularizer=recurrent_regularizer,
+                          attentive_regularizer=attentive_regularizer,
+                          bias_regularizer=bias_regularizer,
+                          kernel_constraint=kernel_constraint,
+                          recurrent_constraint=recurrent_constraint,
+                          attentive_constraint=attentive_constraint,
+                          bias_constraint=bias_constraint,
+                          dropout=dropout,
+                          recurrent_dropout=recurrent_dropout,
+                          attentive_dropout=attentive_dropout)
+
+    super(AttentiveConvLSTM2D, self).__init__(cell,
+                                     return_sequences=return_sequences,
+                                     go_backwards=go_backwards,
+                                     stateful=stateful,
+                                     **kwargs)
+    self.activity_regularizer = regularizers.get(activity_regularizer)
+
+  def call(self, inputs, mask=None, training=None, initial_state=None):
+    return super(AttentiveConvLSTM2D, self).call(inputs,
+                                        mask=mask,
+                                        training=training,
+                                        initial_state=initial_state)
+
+  @property
+  def filters(self):
+    return self.cell.filters
+
+  @property
+  def attentive_filters(self):
+    return self.cell.attentive_filters
+
+  @property
+  def kernel_size(self):
+    return self.cell.kernel_size
+
+  @property
+  def attentive_kernel_size(self):
+    return self.cell.attentive_kernel_size
+
+  @property
+  def strides(self):
+    return self.cell.strides
+
+  @property
+  def padding(self):
+    return self.cell.padding
+
+  @property
+  def data_format(self):
+    return self.cell.data_format
+
+  @property
+  def dilation_rate(self):
+    return self.cell.dilation_rate
+
+  @property
+  def activation(self):
+    return self.cell.activation
+
+  @property
+  def recurrent_activation(self):
+    return self.cell.recurrent_activation
+
+  @property
+  def attentive_activation(self):
+    return self.cell.attentive_activation
+
+  @property
+  def use_bias(self):
+    return self.cell.use_bias
+
+  @property
+  def kernel_initializer(self):
+    return self.cell.kernel_initializer
+
+  @property
+  def recurrent_initializer(self):
+    return self.cell.recurrent_initializer
+
+  @property
+  def attentive_initializer(self):
+    return self.cell.attentive_initializer
+
+  @property
+  def bias_initializer(self):
+    return self.cell.bias_initializer
+
+  @property
+  def unit_forget_bias(self):
+    return self.cell.unit_forget_bias
+
+  @property
+  def kernel_regularizer(self):
+    return self.cell.kernel_regularizer
+
+  @property
+  def recurrent_regularizer(self):
+    return self.cell.recurrent_regularizer
+
+  @property
+  def attentive_regularizer(self):
+    return self.cell.attentive_regularizer
+
+  @property
+  def bias_regularizer(self):
+    return self.cell.bias_regularizer
+
+  @property
+  def kernel_constraint(self):
+    return self.cell.kernel_constraint
+
+  @property
+  def recurrent_constraint(self):
+    return self.cell.recurrent_constraint
+
+  @property
+  def attentive_constraint(self):
+    return self.cell.attentive_constraint
+
+  @property
+  def bias_constraint(self):
+    return self.cell.bias_constraint
+
+  @property
+  def dropout(self):
+    return self.cell.dropout
+
+  @property
+  def recurrent_dropout(self):
+    return self.cell.recurrent_dropout
+
+  @property
+  def attentive_dropout(self):
+    return self.cell.attentive_dropout
+
+  def get_config(self):
+    config = {'filters': self.filters,
+              'attentive_filters': self.attentive_filters,
+              'kernel_size': self.kernel_size,
+              'attentive_kernel_size': self.attentive_kernel_size,
+              'strides': self.strides,
+              'padding': self.padding,
+              'data_format': self.data_format,
+              'dilation_rate': self.dilation_rate,
+              'activation': activations.serialize(self.activation),
+              'recurrent_activation': activations.serialize(
+                  self.recurrent_activation),
+              'attentive_activation': activations.serialize(
+                  self.attentive_activation),
+              'use_bias': self.use_bias,
+              'kernel_initializer': initializers.serialize(
+                  self.kernel_initializer),
+              'recurrent_initializer': initializers.serialize(
+                  self.recurrent_initializer),
+              'attentive_initializer': initializers.serialize(
+                  self.attentive_initializer),
+              'bias_initializer': initializers.serialize(self.bias_initializer),
+              'unit_forget_bias': self.unit_forget_bias,
+              'kernel_regularizer': regularizers.serialize(
+                  self.kernel_regularizer),
+              'recurrent_regularizer': regularizers.serialize(
+                  self.recurrent_regularizer),
+              'attentive_regularizer': regularizers.serialize(
+                  self.attentive_regularizer),
+              'bias_regularizer': regularizers.serialize(self.bias_regularizer),
+              'activity_regularizer': regularizers.serialize(
+                  self.activity_regularizer),
+              'kernel_constraint': constraints.serialize(
+                  self.kernel_constraint),
+              'recurrent_constraint': constraints.serialize(
+                  self.recurrent_constraint),
+              'attentive_constraint': constraints.serialize(
+                  self.attentive_constraint),
+              'bias_constraint': constraints.serialize(self.bias_constraint),
+              'dropout': self.dropout,
+              'recurrent_dropout': self.recurrent_dropout,
+              'attentive_dropout': self.attentive_dropout}
+    base_config = super(AttentiveConvLSTM2D, self).get_config()
+    del base_config['cell']
+    return dict(list(base_config.items()) + list(config.items()))
+
+  @classmethod
+  def from_config(cls, config):
+    return cls(**config)
diff --git a/RecallNet/src/cb.py b/RecallNet/src/cb.py
new file mode 100644
index 0000000..a5054c1
--- /dev/null
+++ b/RecallNet/src/cb.py
@@ -0,0 +1,80 @@
+import keras
+import matplotlib.pyplot as plt
+from IPython.display import clear_output
+import os
+import keras
+from keras.callbacks import ModelCheckpoint
+import numpy as np
+import math
+
+
+class Unfreeze(keras.callbacks.Callback):
+  def __init__(self,it_to_unfreeze):
+      self.it_to_unfreeze = it_to_unfreeze
+      self.c=0
+      self.frozen=True
+
+  def on_batch_end(self, batch, logs=None):
+      self.c+=1
+      if not self.c > self.it_to_unfreeze and self.frozen:
+          print('Iteration %d reached: UNFREEZING ENCODER' % self.c)
+          self.frozen=False
+          for layer in self.model.layers:
+              layer.trainable=True
+
+class InteractivePlot(keras.callbacks.Callback):
+  def __init__(self):
+      pass
+
+  def on_train_begin(self, logs={}):
+      self.losses = []
+      self.logs = []
+      self.batchnr = 0
+      self.icount = 0
+
+  def on_train_end(self, logs={}):
+      pass
+
+  def on_epoch_end(self, epoch, logs={}):
+      self.batchnr = 0
+      loss_train = logs.get('loss')
+
+      self.losses.append(loss_train)
+
+      self.icount+=1
+      clear_output(wait=True)
+      plt.figure(figsize=(14,10))
+      train_vals = [self.losses]
+      desc = ['loss']
+      for i in range(len(train_vals)):
+          #plt.subplot(2, 3, i+1)
+          plt.plot(range(self.icount), train_vals[i], label=desc[i])
+          plt.legend()
+      #plt.savefig(self.logfile.replace('.txt', '.png'), bbox_inches='tight', format='png')
+      plt.show()
+
+  def on_batch_end(self, batch, logs=None):
+      self.batchnr+=1
+      if self.batchnr % 10 == 0:
+          self.on_epoch_end(epoch=0, logs=logs)
+
+
+
+def ckpt_callback(model_name, dataset, l_str, bs, extra_str='',
+                period=1, save_weights_only=True,
+                ckpt_folder_path = '../../predimportance_shared/models/ckpt/'):
+    path = os.path.join(ckpt_folder_path, model_name)
+    if not os.path.exists(path):
+        os.makedirs(path)
+    filepath = os.path.join(path, model_name+'_'+dataset+'_'+l_str+'_bs'+str(bs)+extra_str+'_ep{epoch:02d}_valloss{val_loss:.4f}.hdf5')
+    cb_chk = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_weights_only=True, period=1)
+    return cb_chk
+
+
+def step_decay(init_lr = 0.0001, drop = 0.1, epochs_drop = 3.0):
+    def inner(epoch):
+        lrate = init_lr * math.pow(drop, math.floor((1+epoch)/epochs_drop))
+        if not (epoch+1)%epochs_drop:
+            print('Reducing lr. New lr is:', lrate)
+        return lrate
+    return inner
diff --git a/RecallNet/src/classif_capable_models.py b/RecallNet/src/classif_capable_models.py
new file mode 100644
index 0000000..1b024f5
--- /dev/null
+++ b/RecallNet/src/classif_capable_models.py
@@ -0,0 +1,566 @@
+import numpy as np
+import keras
+import sys
+import os
+from keras.layers import Layer, Input, Multiply, Dropout,DepthwiseConv2D, TimeDistributed, LSTM, Activation, Lambda, Conv2D, Dense, GlobalAveragePooling2D, MaxPooling2D, ZeroPadding2D, UpSampling2D, BatchNormalization, Concatenate
+import keras.backend as K
+from keras.models import Model
+import tensorflow as tf
+from keras.utils import Sequence
+import cv2
+import scipy.io
+import math
+from attentive_convlstm_new import AttentiveConvLSTM2D
+from dcn_resnet_new import dcn_resnet
+from gaussian_prior_new import LearningPrior
+from sal_imp_utilities import *
+from multiduration_models import decoder_block_timedist
+from xception_custom import Xception_wrapper
+from keras.applications import keras_modules_injection
+
+
+
+def xception_cl(input_shape = (None, None, 3),
+                 verbose=True,
+                 print_shapes=True,
+                 n_outs=1,
+                 ups=8,
+                 freeze_enc=False,
+                 dil_rate = (2,2),
+                 freeze_cl=True,
+                 append_classif=True,
+                 num_classes=5):
+    """Xception with classification capabilities"""
+    inp = Input(shape=input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+	        layer.trainable = False
+
+    ### CLASSIFIER ###
+    cl = GlobalAveragePooling2D(name='gap_cl')(xception.output)
+    cl = Dense(512,name='dense_cl')(cl)
+    cl = Dropout(0.3, name='dropout_cl')(cl)
+    cl = Dense(num_classes, activation='softmax', name='dense_cl_out')(cl)
+
+    ## DECODER ##
+    outs_dec = decoder_block(xception.output, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=512, prefix='decoder')
+
+    outs_final = [outs_dec]*n_outs
+
+    if append_classif:
+        outs_final.append(cl)
+
+    # Building model
+    m = Model(inp, outs_final) # Last element of outs_final is classification vector
+    if verbose:
+        m.summary()
+
+    if freeze_cl:
+        print('Freezing classification dense layers')
+        m.get_layer('dense_cl').trainable = False
+        m.get_layer('dense_cl_out').trainable = False
+
+    return m
+
+def xception_cl_fus(input_shape=(None, None, 3),
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     dil_rate=(2,2),
+                     freeze_enc=False,
+                     freeze_cl=True,
+                     internal_filts=256,
+                     num_classes=5,
+                     dp=0.3):
+    """Xception with classification capabilities that fuses representations from both tasks"""
+    inp = Input(shape=input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+            layer.trainable = False
+
+    ### GLOBAL FEATURES ###
+    g_n = global_net(xception.output, nfilts=internal_filts, dp=dp)
+    if print_shapes: print('g_n shapes:', g_n.shape)
+
+    ### CLASSIFIER ###
+    # We potentially need another layer here
+    out_classif = Dense(num_classes, activation='softmax', name='out_classif')(g_n)
+
+    ### ASPP (MID LEVEL FEATURES) ###
+    aspp_out = app(xception.output, internal_filts)
+    if print_shapes: print('aspp out shapes:', aspp_out.shape)
+
+    ### FUSION ###
+    dense_f = Dense(internal_filts, name = 'dense_fusion')(g_n)
+    if print_shapes: print('dense_f shapes:', dense_f.shape)
+    reshap = Lambda(lambda x: K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(x, axis=1), K.int_shape(aspp_out)[2], axis=1), axis=1), K.int_shape(aspp_out)[1], axis=1),
+                lambda s: (s[0], K.int_shape(aspp_out)[1], K.int_shape(aspp_out)[2], s[1]))(dense_f)
+    if print_shapes: print('after lambda shapes:', reshap.shape)
+
+    conc = Concatenate()([aspp_out,reshap])
+
+    ### Projection ###
+    x = Conv2D(internal_filts, (1, 1), padding='same', use_bias=False, name='concat_projection')(conc)
+    x = BatchNormalization(name='concat_projection_BN', epsilon=1e-5)(x)
+    x = Activation('relu')(x)
+    x = Dropout(dp)(x)
+
+
+    ### DECODER ###
+    outs_dec = decoder_block(x, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=internal_filts, dp=dp)
+
+    outs_final = [outs_dec]*n_outs
+    outs_final.append(out_classif)
+
+    # Building model
+    m = Model(inp, outs_final) # Last element of outs_final is classification vector
+
+    if freeze_cl:
+        m.get_layer('out_classif').trainable = False
+        # for l in g_n.layers:
+        #     l.trainable=False
+
+    if verbose:
+        m.summary()
+
+    return m
+
+
+
+def xception_cl_fus_aspp(input_shape=(None, None, 3),
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     dil_rate=(2,2),
+                     freeze_enc=False,
+                     freeze_cl=True,
+                     internal_filts=256,
+                     num_classes=6,
+                     dp=0.3,
+                     lambda_layer_for_save=False):
+
+    inp = Input(shape=input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+            layer.trainable = False
+
+    ### GLOBAL FEATURES ###
+    g_n = global_net(xception.output, nfilts=internal_filts, dp=dp)
+    if print_shapes: print('g_n shapes:', g_n.shape)
+
+    ### CLASSIFIER ###
+    # We potentially need another layer here
+    out_classif = Dense(num_classes, activation='softmax', name='out_classif')(g_n)
+
+    ### ASPP (MID LEVEL FEATURES) ###
+    aspp_out = aspp(xception.output, internal_filts)
+    if print_shapes: print('aspp out shapes:', aspp_out.shape)
+
+    ### FUSION ###
+    dense_f = Dense(internal_filts, name = 'dense_fusion')(g_n)
+    if print_shapes: print('dense_f shapes:', dense_f.shape)
+
+    if not lambda_layer_for_save:
+        reshap = Lambda(lambda x: K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(x, axis=1), K.int_shape(aspp_out)[2], axis=1), axis=1), K.int_shape(aspp_out)[1], axis=1),
+                    lambda s: (s[0], K.int_shape(aspp_out)[1], K.int_shape(aspp_out)[2], s[1]))(dense_f)
+    else: # Use this lambda layer if you want to be able to use model.save() (set lambda_layer_for_save to True)
+        print("Using lambda layer adapted to model.save()")
+        reshap = Lambda(lambda x: K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(x, axis=1), 40, axis=1), axis=1), 30, axis=1),
+                    lambda s: (s[0], 30, 40, s[1]))(dense_f)
+        # reshap = FusionReshape()(dense_f)
+
+    if print_shapes: print('after lambda shapes:', reshap.shape)
+
+    conc = Concatenate()([aspp_out,reshap])
+
+    ### Projection ###
+    x = Conv2D(internal_filts, (1, 1), padding='same', use_bias=False, name='concat_projection')(conc)
+    x = BatchNormalization(name='concat_projection_BN', epsilon=1e-5)(x)
+    x = Activation('relu')(x)
+    x = Dropout(dp)(x)
+
+
+    ### DECODER ###
+    outs_dec = decoder_block(x, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=internal_filts, dp=dp)
+
+    outs_final = [outs_dec]*n_outs
+    outs_final.append(out_classif)
+
+    # Building model
+    m = Model(inp, outs_final,name = 'xception_cl_fus_aspp') # Last element of outs_final is classification vector
+
+    if freeze_cl:
+        m.get_layer('out_classif').trainable = False
+        # for l in g_n.layers:
+        #     l.trainable=False
+
+    if verbose:
+        m.summary()
+
+    return m
+
+
+
+
+def umsi(input_shape=(None, None, 3),
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     dil_rate=(2,2),
+                     freeze_enc=False,
+                     freeze_cl=True,
+                     internal_filts=256,
+                     num_classes=6,
+                     dp=0.3,
+                     lambda_layer_for_save=False):
+
+    inp = Input(shape=input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+            layer.trainable = False
+            
+#     xception.summary()
+    
+    skip_layers = ['block3_sepconv2_bn','block1_conv1_act']
+    # sizes: 119x159x32, 59x79x256
+    skip_feature_maps = [xception.get_layer(n).output for n in skip_layers]
+
+    ### GLOBAL FEATURES ###
+    g_n = global_net(xception.output, nfilts=internal_filts, dp=dp)
+    if print_shapes: print('g_n shapes:', g_n.shape)
+
+    ### CLASSIFIER ###
+    # We potentially need another layer here
+    out_classif = Dense(num_classes, activation='softmax', name='out_classif')(g_n)
+
+    ### ASPP (MID LEVEL FEATURES) ###
+    aspp_out = aspp(xception.output, internal_filts)
+    if print_shapes: print('aspp out shapes:', aspp_out.shape)
+
+    ### FUSION ###
+    dense_f = Dense(internal_filts, name = 'dense_fusion')(g_n)
+    if print_shapes: print('dense_f shapes:', dense_f.shape)
+
+    if not lambda_layer_for_save:
+        reshap = Lambda(lambda x: K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(x, axis=1), K.int_shape(aspp_out)[2], axis=1), axis=1), K.int_shape(aspp_out)[1], axis=1),
+                    lambda s: (s[0], K.int_shape(aspp_out)[1], K.int_shape(aspp_out)[2], s[1]))(dense_f)
+    else: # Use this lambda layer if you want to be able to use model.save() (set lambda_layer_for_save to True)
+        print("Using lambda layer adapted to model.save()")
+        reshap = Lambda(lambda x: K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(x, axis=1), 40, axis=1), axis=1), 30, axis=1),
+                    lambda s: (s[0], 30, 40, s[1]))(dense_f)
+        # reshap = FusionReshape()(dense_f)
+
+    if print_shapes: print('after lambda shapes:', reshap.shape)
+
+    conc = Concatenate()([aspp_out,reshap])
+
+    ### Projection ###
+    x = Conv2D(internal_filts, (1, 1), padding='same', use_bias=False, name='concat_projection')(conc)
+    x = BatchNormalization(name='concat_projection_BN', epsilon=1e-5)(x)
+    x = Activation('relu')(x)
+    x = Dropout(dp)(x)
+
+    ### DECODER ###
+#     outs_dec = decoder_block(x, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=internal_filts, dp=dp)
+    
+    outs_dec = decoder_with_skip(x, 
+                                 skip_feature_maps,  
+                                 print_shapes=print_shapes, 
+                                 dec_filt=internal_filts, 
+                                 dp=dp)
+
+    outs_final = [outs_dec]*n_outs
+    outs_final.append(out_classif)
+    
+
+    # Building model
+    m = Model(inp, outs_final, name = 'umsi') # Last element of outs_final is classification vector
+
+    if freeze_cl:
+        m.get_layer('out_classif').trainable = False
+        # for l in g_n.layers:
+        #     l.trainable=False
+
+    if verbose:
+        m.summary()
+
+    return m
+
+def xception_cl_fus_skipdec(input_shape=(None, None, 3),
+                             verbose=True,
+                             print_shapes=True,
+                             n_outs=1,
+                             ups=8,
+                             dil_rate=(2,2),
+                             freeze_enc=False,
+                             freeze_cl=True,
+                             internal_filts=256,
+                             num_classes=5,
+                             dp=0.3):
+
+    inp = Input(shape=input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+        
+    xception.summary()    
+    
+    if freeze_enc:
+        for layer in xception.layers:
+            layer.trainable = False
+
+    ### GLOBAL FEATURES ###
+    g_n = global_net(xception.output, nfilts=internal_filts, dp=dp)
+    if print_shapes: print('g_n shapes:', g_n.shape)
+
+    ### CLASSIFIER ###
+    # We potentially need another layer here
+    out_classif = Dense(num_classes, activation='softmax', name='out_classif')(g_n)
+
+    ### ASPP (MID LEVEL FEATURES) ###
+    aspp_out = aspp(xception.output, internal_filts)
+    if print_shapes: print('aspp out shapes:', aspp_out.shape)
+
+    ### FUSION ###
+    dense_f = Dense(internal_filts, name = 'dense_fusion')(g_n)
+    if print_shapes: print('dense_f shapes:', dense_f.shape)
+    reshap = Lambda(lambda x: K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(x, axis=1), K.int_shape(aspp_out)[2], axis=1), axis=1), K.int_shape(aspp_out)[1], axis=1),
+                lambda s: (s[0], K.int_shape(aspp_out)[1], K.int_shape(aspp_out)[2], s[1]))(dense_f)
+    if print_shapes: print('after lambda shapes:', reshap.shape)
+
+    conc = Concatenate()([aspp_out,reshap])
+
+    ### Projection ###
+    x = Conv2D(internal_filts, (1, 1), padding='same', use_bias=False, name='concat_projection')(conc)
+    x = BatchNormalization(name='concat_projection_BN', epsilon=1e-5)(x)
+    x = Activation('relu')(x)
+    x = Dropout(dp)(x)
+
+    ### DECODER ###
+    outs_dec = decoder_block(x, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=internal_filts, dp=dp)
+    
+#     outs_dec = decoder_with_skip(x, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=internal_filts, dp=dp)
+
+    outs_final = [outs_dec]*n_outs
+    outs_final.append(out_classif)
+
+    # Building model
+    m = Model(inp, outs_final) # Last element of outs_final is classification vector
+
+    if freeze_cl:
+        m.get_layer('out_classif').trainable = False
+        # for l in g_n.layers:
+        #     l.trainable=False
+
+    if verbose:
+        m.summary()
+
+    return m
+
+
+def global_net(x, nfilts=512, dp=0.1, print_shapes = True):
+
+    x = Conv2D(nfilts, (3, 3), strides=3, padding='same', use_bias=False, name='global_conv')(x)
+    if print_shapes: print('Shape after global net conv:', x.shape)
+    x = BatchNormalization(name='global_BN',epsilon=1e-5)(x)
+    x = Activation('relu')(x)
+    x = Dropout(dp)(x)
+    x = GlobalAveragePooling2D()(x)
+    x = Dense(nfilts, name='global_dense')(x)
+    x = Dropout(dp)(x)
+
+    return x
+
+
+def app(x, nfilts=256, prefix='app', dils=[6,12,18]):
+
+    x1 = Conv2D(nfilts, 1, padding='same', activation='relu', dilation_rate=(1,1), name=prefix+'_c1x1')(x)
+    x2 = Conv2D(nfilts, 3, padding='same', activation='relu', dilation_rate=(dils[0],dils[0]), name=prefix+'_c3x3d'+str(dils[0]))(x)
+    x3 = Conv2D(nfilts, 3, padding='same', activation='relu', dilation_rate=(dils[1],dils[1]), name=prefix+'_c3x3d'+str(dils[1]))(x)
+    x4 = Conv2D(nfilts, 3, padding='same', activation='relu', dilation_rate=(dils[2],dils[2]), name=prefix+'_c3x3d'+str(dils[2]))(x)
+
+    x = Concatenate()([x1,x2,x3,x4])
+
+    return x
+
+def aspp(x, nfilts=256, prefix='aspp', dils=[6,12,18]):
+
+    x1 = Conv2D(nfilts, (1, 1), padding='same', use_bias=False, name=prefix+'_csep0')(x)
+    x1 = BatchNormalization(name='aspp0_BN', epsilon=1e-5)(x1)
+    x1 = Activation('relu', name='aspp0_activation')(x1)
+
+    # rate = 6
+    x2 = SepConv_BN(x, nfilts, prefix+'_csepd'+str(dils[0]), rate=dils[0], depth_activation=True, epsilon=1e-5)
+    # rate = 12 (24)
+    x3 = SepConv_BN(x, nfilts, prefix+'_csepd'+str(dils[1]),rate=dils[1], depth_activation=True, epsilon=1e-5)
+    # rate = 18 (36)
+    x4 = SepConv_BN(x, nfilts, prefix+'_csepd'+str(dils[2]),rate=dils[2], depth_activation=True, epsilon=1e-5)
+
+    x = Concatenate()([x1,x2,x3,x4])
+
+    return x
+
+
+def decoder_with_skip(x, skip_tensors, dil_rate=1, print_shapes=True, dec_filt=1024, dp=0.2, ups=16, prefix='decskip'):
+
+        # sizes of input skip connections from Xception: 119x159x32, 117x157x128, 59x79x256
+        
+        for i, sk in enumerate(skip_tensors, start=1):
+            # Upsample
+            x = UpSampling2D((2,2), interpolation='bilinear', name=prefix+'_ups%d'%i)(x)
+            if x.shape[1] != sk.shape[1] or x.shape[2] != sk.shape[2]:
+                x = Lambda(lambda t: tf.image.resize(t, (K.int_shape(sk)[1], K.int_shape(sk)[2])))(x)
+
+            # Concatenate
+            x = Concatenate()([x, sk])
+            
+            # Convolve to reduce feature dimensionality
+            x = Conv2D(dec_filt//2**i, (1, 1), padding='same', use_bias=False, name=prefix+'_proj_%d'%i)(x) 
+            x = BatchNormalization(name=prefix+'_bn_%d'%i, epsilon=1e-5)(x)
+            x = Activation('relu', name=prefix+'_act_%d'%i)(x)            
+            
+            # Convolve with depth sep convs
+            x = SepConv_BN(x, 
+                           dec_filt//2**i, 
+                           kernel_size=3,
+                           depth_activation=True, 
+                           epsilon=1e-5, 
+                           rate=dil_rate, 
+                           prefix=prefix+'_sepconvA_%d'%i)
+            x = SepConv_BN(x, 
+                           dec_filt//2**i, 
+                           kernel_size=3,
+                           depth_activation=True, 
+                           epsilon=1e-5, 
+                           rate=dil_rate, 
+                           prefix=prefix+'_sepconvB_%d'%i)
+            x = Dropout(dp, name=prefix+'_dp%d'%i)(x)
+    
+            
+            print("shape after block %d of dec:"%i, x.shape)
+        
+        
+        # Upsampling and normal conv
+#         i+=1
+#         x = UpSampling2D((2,2), interpolation='bilinear', name=prefix+'_ups_prefinal')(x)
+#         x = Conv2D(dec_filt//2**i, (3, 3), padding='same', use_bias=True, name=prefix+'_conv_%d'%i)(x) 
+#         x = BatchNormalization(name=prefix+'_bn_%d'%i, epsilon=1e-5)(x)
+#         x = Activation('relu', name=prefix+'_act_%d'%i)(x)          
+        
+        # Final upsample to get to desired output size (480x640)
+        x = UpSampling2D((4,4), interpolation='bilinear', name=prefix+'_ups_final')(x)
+        if x.shape[1] != shape_r_out or x.shape[2] != shape_c_out:
+            x = Lambda(lambda t: tf.image.resize(t, (shape_r_out, shape_c_out)))(x)
+
+        if print_shapes: print('Shape after last ups and resize:',x.shape)
+            
+        # Final conv to get to a heatmap
+        x = Conv2D(1, kernel_size=1, padding='same', activation='relu', name=prefix+'_c_out')(x)
+        if print_shapes: print('Shape after 1x1 conv:',x.shape)
+
+        return x
+
+def decoder_block(x, dil_rate=(2,2), print_shapes=True, dec_filt=1024, dp=0.2, ups=16, prefix='dec'):
+
+    # Dilated convolutions
+    x = Conv2D(dec_filt, 3, padding='same', activation='relu', dilation_rate=dil_rate, name=prefix+'_c1')(x)
+    x = Conv2D(dec_filt, 3, padding='same', activation='relu', dilation_rate=dil_rate, name=prefix+'_c2')(x)
+    x = Dropout(dp, name=prefix+'_dp1')(x)
+    x = UpSampling2D((2,2), interpolation='bilinear', name=prefix+'_ups1')(x)
+
+    x = Conv2D(dec_filt//2, 3, padding='same', activation='relu', dilation_rate=dil_rate, name=prefix+'_c3')(x)
+    x = Conv2D(dec_filt//2, 3, padding='same', activation='relu', dilation_rate=dil_rate, name=prefix+'_c4')(x)
+    x = Dropout(dp, name=prefix+'_dp2')(x)
+    x = UpSampling2D((2,2), interpolation='bilinear', name=prefix+'_ups2')(x)
+
+    x = Conv2D(dec_filt//4, 3, padding='same', activation='relu', dilation_rate=dil_rate, name=prefix+'_c5')(x)
+    x = Dropout(dp, name=prefix+'_dp3')(x)
+    x = UpSampling2D((4,4), interpolation='bilinear', name=prefix+'_ups3')(x)
+
+    if print_shapes: print('Shape after last ups:',x.shape)
+
+    # Final conv to get to a heatmap
+    x = Conv2D(1, kernel_size=1, padding='same', activation='relu', name=prefix+'_c_out')(x)
+    if print_shapes: print('Shape after 1x1 conv:',x.shape)
+
+    return x
+
+
+
+
+class FusionReshape(Layer):
+
+    def __init__(self, **kwargs):
+        super(FusionReshape, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        super(FusionReshape, self).build(input_shape) 
+
+    def call(self, x):
+        return K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(x, axis=1), 40, axis=1), axis=1), 30, axis=1)
+
+    def compute_output_shape(self, input_shape):
+        return (input_shape[0], 30, 40, input_shape[1])
+
+
+##### DEEPLAB V3 CODE #####
+
+def SepConv_BN(x, filters, prefix='scb', stride=1, kernel_size=3, rate=1,
+                depth_activation=False, epsilon=1e-3):
+    """ SepConv with BN between depthwise & pointwise. Optionally add activation after BN
+        Implements right "same" padding for even kernel sizes
+        Args:
+            x: input tensor
+            filters: num of filters in pointwise convolution
+            prefix: prefix before name
+            stride: stride at depthwise conv
+            kernel_size: kernel size for depthwise convolution
+            rate: atrous rate for depthwise convolution
+            depth_activation: flag to use activation between depthwise & poinwise convs
+            epsilon: epsilon to use in BN layer
+    """
+
+    if stride == 1:
+        depth_padding = 'same'
+    else:
+        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
+        pad_total = kernel_size_effective - 1
+        pad_beg = pad_total // 2
+        pad_end = pad_total - pad_beg
+        x = ZeroPadding2D((pad_beg, pad_end))(x)
+        depth_padding = 'valid'
+
+    if not depth_activation:
+        x = Activation('relu')(x)
+    x = DepthwiseConv2D((kernel_size, kernel_size), strides=(stride, stride), dilation_rate=(rate, rate),
+                        padding=depth_padding, use_bias=False, name=prefix + '_depthwise')(x)
+    x = BatchNormalization(name=prefix + '_depthwise_BN', epsilon=epsilon)(x)
+    if depth_activation:
+        x = Activation('relu')(x)
+    x = Conv2D(filters, (1, 1), padding='same',
+               use_bias=False, name=prefix + '_pointwise')(x)
+    x = BatchNormalization(name=prefix + '_pointwise_BN', epsilon=epsilon)(x)
+    if depth_activation:
+        x = Activation('relu')(x)
+
+    return x
diff --git a/RecallNet/src/dcn_resnet_new.py b/RecallNet/src/dcn_resnet_new.py
new file mode 100644
index 0000000..849dfd3
--- /dev/null
+++ b/RecallNet/src/dcn_resnet_new.py
@@ -0,0 +1,239 @@
+from __future__ import print_function
+from __future__ import absolute_import
+
+from keras.layers import Input, Activation, Add
+from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
+from keras.layers import BatchNormalization
+from keras. models import Model
+from keras import backend as K
+from keras.utils.data_utils import get_file
+
+# TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels_notop.h5'
+
+WEIGHTS_PATH_NO_TOP = ('https://github.com/fchollet/deep-learning-models/'
+                       'releases/download/v0.2/'
+                       'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
+
+
+def identity_block(input_tensor, kernel_size, filters, stage, block):
+    """The identity block is the block that has no conv layer at shortcut.
+    # Arguments
+        input_tensor: input tensor
+        kernel_size: default 3, the kernel size of
+            middle conv layer at main path
+        filters: list of integers, the filters of 3 conv layer at main path
+        stage: integer, current stage label, used for generating layer names
+        block: 'a','b'..., current block label, used for generating layer names
+    # Returns
+        Output tensor for the block.
+    """
+    filters1, filters2, filters3 = filters
+    if K.image_data_format() == 'channels_last':
+        bn_axis = 3
+    else:
+        bn_axis = 1
+    conv_name_base = 'res' + str(stage) + block + '_branch'
+    bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+    x = Conv2D(filters1, (1, 1),
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2a')(input_tensor)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(filters2, kernel_size,
+                      padding='same',
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2b')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(filters3, (1, 1),
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2c')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
+
+    x = Add()([x, input_tensor])
+    x = Activation('relu')(x)
+    return x
+
+
+
+def conv_block(input_tensor,
+               kernel_size,
+               filters,
+               stage,
+               block,
+               strides=(2, 2)):
+    """A block that has a conv layer at shortcut.
+    # Arguments
+        input_tensor: input tensor
+        kernel_size: default 3, the kernel size of
+            middle conv layer at main path
+        filters: list of integers, the filters of 3 conv layer at main path
+        stage: integer, current stage label, used for generating layer names
+        block: 'a','b'..., current block label, used for generating layer names
+        strides: Strides for the first conv layer in the block.
+    # Returns
+        Output tensor for the block.
+    Note that from stage 3,
+    the first conv layer at main path is with strides=(2, 2)
+    And the shortcut should have strides=(2, 2) as well
+    """
+    filters1, filters2, filters3 = filters
+    if K.image_data_format() == 'channels_last':
+        bn_axis = 3
+    else:
+        bn_axis = 1
+    conv_name_base = 'res' + str(stage) + block + '_branch'
+    bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+    x = Conv2D(filters1, (1, 1), strides=strides,
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2a')(input_tensor)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(filters2, kernel_size, padding='same',
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2b')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(filters3, (1, 1),
+                      kernel_initializer='he_normal',
+                      name=conv_name_base + '2c')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
+
+    shortcut = Conv2D(filters3, (1, 1), strides=strides,
+                             kernel_initializer='he_normal',
+                             name=conv_name_base + '1')(input_tensor)
+    shortcut = BatchNormalization(
+        axis=bn_axis, name=bn_name_base + '1')(shortcut)
+
+    x = Add()([x, shortcut])
+    x = Activation('relu')(x)
+    return x
+
+
+def conv_block_atrous(input_tensor, kernel_size, filters, stage, block, atrous_rate=(2, 2)):
+    nb_filter1, nb_filter2, nb_filter3 = filters
+    if K.image_data_format() == 'channels_last':
+        bn_axis = 3
+    else:
+        bn_axis = 1
+
+    conv_name_base = 'res' + str(stage) + block + '_branch'
+    bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+    x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a')(input_tensor)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(nb_filter2, kernel_size, padding='same',dilation_rate=atrous_rate,name=conv_name_base + '2b')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
+
+    shortcut = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '1')(input_tensor)
+    shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
+
+    x = Add()([x, shortcut])
+    x = Activation('relu')(x)
+    return x
+
+
+def identity_block_atrous(input_tensor, kernel_size, filters, stage, block, atrous_rate=(2, 2)):
+    nb_filter1, nb_filter2, nb_filter3 = filters
+    if K.image_data_format() == 'channels_last':
+        bn_axis = 3
+    else:
+        bn_axis = 1
+
+    conv_name_base = 'res' + str(stage) + block + '_branch'
+    bn_name_base = 'bn' + str(stage) + block + '_branch'
+
+    x = Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a')(input_tensor)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(nb_filter2, kernel_size, dilation_rate=atrous_rate,
+                            padding='same', name=conv_name_base + '2b')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
+    x = Activation('relu')(x)
+
+    x = Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c')(x)
+    x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
+
+    x = Add()([x, input_tensor])
+    x = Activation('relu')(x)
+    return x
+
+
+def dcn_resnet(input_tensor=None):
+    input_shape = (None, None, 3)
+
+    if input_tensor is None:
+        img_input = Input(shape=input_shape)
+    else:
+        if not K.is_keras_tensor(input_tensor):
+            img_input = Input(tensor=input_tensor)
+        else:
+            img_input = input_tensor
+
+    if K.image_data_format() == 'channels_last':
+        bn_axis = 3
+    else:
+        bn_axis = 1
+
+    # conv_1
+    x = ZeroPadding2D(padding=(3, 3), name='conv1_pad')(img_input)
+    x = Conv2D(64, (7, 7),
+                      strides=(2, 2),
+                      padding='valid',
+                      kernel_initializer='he_normal',
+                      name='conv1')(x)
+    x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
+    x = Activation('relu')(x)
+    x = ZeroPadding2D(padding=(1, 1), name='pool1_pad')(x)
+    x = MaxPooling2D((3, 3), strides=(2, 2))(x)
+
+    # conv_2
+    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
+    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
+    x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
+
+    # conv_3
+    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', strides=(2, 2))
+    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
+    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
+    x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
+
+    # conv_4
+    x = conv_block_atrous(x, 3, [256, 256, 1024], stage=4, block='a', atrous_rate=(2, 2))
+    x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='b', atrous_rate=(2, 2))
+    x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='c', atrous_rate=(2, 2))
+    x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='d', atrous_rate=(2, 2))
+    x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='e', atrous_rate=(2, 2))
+    x = identity_block_atrous(x, 3, [256, 256, 1024], stage=4, block='f', atrous_rate=(2, 2))
+
+    # conv_5
+    x = conv_block_atrous(x, 3, [512, 512, 2048], stage=5, block='a', atrous_rate=(4, 4))
+    x = identity_block_atrous(x, 3, [512, 512, 2048], stage=5, block='b', atrous_rate=(4, 4))
+    x = identity_block_atrous(x, 3, [512, 512, 2048], stage=5, block='c', atrous_rate=(4, 4))
+
+    # Create model
+    model = Model(img_input, x)
+
+    # Load weights
+
+    weights_path = get_file(
+                'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                WEIGHTS_PATH_NO_TOP,
+                cache_subdir='models',
+                md5_hash='a268eb855778b3df3c7506639542a6af')
+    model.load_weights(weights_path)
+
+    return model
diff --git a/RecallNet/src/gaussian_prior_new.py b/RecallNet/src/gaussian_prior_new.py
new file mode 100644
index 0000000..8ff691e
--- /dev/null
+++ b/RecallNet/src/gaussian_prior_new.py
@@ -0,0 +1,241 @@
+from __future__ import division
+
+from keras.engine.base_layer import Layer
+from keras import backend as K
+from keras import activations
+from keras import initializers
+from keras import regularizers
+from keras import constraints
+import numpy as np
+import tensorflow as tf
+
+def gaussian_priors_init(shape, name=None, dtype=None):
+    means = np.random.uniform(low=0.3, high=0.7, size=shape[0] // 2)
+    covars = np.random.uniform(low=0.05, high=0.3, size=shape[0] // 2)
+    return K.variable(np.concatenate((means, covars), axis=0), name=name)
+
+class LearningPrior(Layer):
+    def __init__(self, nb_gaussian, init=None, weights=None,
+                 W_regularizer=None, activity_regularizer=None,
+                 W_constraint=None, **kwargs):
+        self.nb_gaussian = nb_gaussian
+
+        if not init:
+            self.init = tf.initializers.random_uniform() #replaced from gaussian_priors_init
+        else:
+            self.init = initializers.get(init)
+        
+
+        self.W_regularizer = regularizers.get(W_regularizer)
+        self.activity_regularizer = regularizers.get(activity_regularizer)
+
+        self.W_constraint = constraints.get(W_constraint)
+
+        self.initial_weights = weights
+        super(LearningPrior, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        self.W_shape = (self.nb_gaussian*4, )
+
+        self.W = self.add_weight(shape=self.W_shape,
+                        initializer= self.init,
+                        name='{}_W'.format(self.name),
+                        regularizer=self.W_regularizer,
+                        constraint=self.W_constraint )
+
+        # Possibly unnecessary
+        self.regularizers = []
+        if self.W_regularizer:
+            self.W_regularizer.set_param(self.W)
+            self.regularizers.append(self.W_regularizer)
+
+        # Possibly unnecessary
+        self.constraints = {}
+        if self.W_constraint:
+            self.constraints[self.W] = self.W_constraint
+
+        # Possibly unnecessary
+        if self.activity_regularizer:
+            self.activity_regularizer.set_layer(self)
+            self.regularizers.append(self.activity_regularizer)
+
+
+        #  Not changed because same syntax in Keras 2
+        if self.initial_weights is not None:
+            self.set_weights(self.initial_weights)
+            del self.initial_weights
+
+        self.built = True
+
+        super(LearningPrior, self).build(input_shape)
+
+    def compute_output_shape(self, input_shape):
+        return (input_shape[0], input_shape[1], input_shape[2], self.nb_gaussian)
+
+    def call(self, x):
+        mu_x = self.W[:self.nb_gaussian]
+        mu_y = self.W[self.nb_gaussian:self.nb_gaussian*2]
+        sigma_x = self.W[self.nb_gaussian*2:self.nb_gaussian*3]
+        sigma_y = self.W[self.nb_gaussian*3:]
+
+        self.b_s = x.shape[0].value
+        self.height = x.shape[1].value
+        self.width = x.shape[2].value
+
+        e = self.height / self.width
+        e1 = (1 - e) / 2
+        e2 = e1 + e
+
+        mu_x = K.clip(mu_x, 0.25, 0.75)
+        mu_y = K.clip(mu_y, 0.35, 0.65)
+
+        sigma_x = K.clip(sigma_x, 0.1, 0.9)
+        sigma_y = K.clip(sigma_y, 0.2, 0.8)
+
+
+        x_t = K.dot(K.ones((self.height, 1)), K.expand_dims(self._linspace(0, 1.0, self.width), axis=0))
+        y_t = K.dot(K.expand_dims(self._linspace(e1, e2, self.height), axis=1), K.ones((1, self.width)))
+
+        x_t = K.repeat_elements(K.expand_dims(x_t, axis=-1), self.nb_gaussian, axis=-1)
+        y_t = K.repeat_elements(K.expand_dims(y_t, axis=-1), self.nb_gaussian, axis=-1)
+
+        gaussian = 1 / (2 * np.pi * sigma_x * sigma_y + K.epsilon()) * \
+                   K.exp(-((x_t - mu_x) ** 2 / (2 * sigma_x ** 2 + K.epsilon()) +
+                           (y_t - mu_y) ** 2 / (2 * sigma_y ** 2 + K.epsilon())))
+
+        max_gauss = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(gaussian, axis=0), axis=0), axis=0), self.width, axis=0), axis=0), self.height, axis=0)
+        gaussian = gaussian / max_gauss
+
+        output = K.ones_like(K.expand_dims(x[...,0]))*gaussian
+
+        return output
+
+    @staticmethod
+    def _linspace(start, stop, num):
+        lin = np.linspace(start, stop, num)
+        range = tf.convert_to_tensor(lin, dtype='float32')
+
+        return range
+
+    def get_config(self):
+        config = {'nb_gaussian': self.nb_gaussian,
+#                   'init': self.init.__name__,
+                  'W_regularizer': self.W_regularizer.get_config() if self.W_regularizer else None,
+                  'activity_regularizer': self.activity_regularizer.get_config() if self.activity_regularizer else None,
+                  'W_constraint': self.W_constraint.get_config() if self.W_constraint else None,
+                  }
+        base_config = super(LearningPrior, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+
+
+
+class OldLearningPrior(Layer):
+    def __init__(self, nb_gaussian, init='normal', weights=None,
+                 W_regularizer=None, activity_regularizer=None,
+                 W_constraint=None, **kwargs):
+        self.nb_gaussian = nb_gaussian
+        self.init = initializations.get(init, dim_ordering='th')
+
+        self.W_regularizer = regularizers.get(W_regularizer)
+        self.activity_regularizer = regularizers.get(activity_regularizer)
+
+        self.W_constraint = constraints.get(W_constraint)
+
+        self.input_spec = [InputSpec(ndim=4)]
+        self.initial_weights = weights
+        super(LearningPrior, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        self.W_shape = (self.nb_gaussian*4, )
+        # Might need change
+        self.W = self.init(self.W_shape, name='{}_W'.format(self.name))
+
+        # Might need change - to self.add_weight
+        self.trainable_weights = [self.W]
+
+        # Might need change - could be absorbed by add_weight
+        self.regularizers = []
+        if self.W_regularizer:
+            self.W_regularizer.set_param(self.W)
+            self.regularizers.append(self.W_regularizer)
+
+        if self.activity_regularizer:
+            self.activity_regularizer.set_layer(self)
+            self.regularizers.append(self.activity_regularizer)
+
+        # Might need change
+        if self.initial_weights is not None:
+            self.set_weights(self.initial_weights)
+            del self.initial_weights
+
+        # Might need change
+        self.constraints = {}
+        if self.W_constraint:
+            self.constraints[self.W] = self.W_constraint
+
+    def get_output_shape_for(self, input_shape):
+        self.b_s = input_shape[0]
+        self.height = input_shape[2]
+        self.width = input_shape[3]
+
+        return self.b_s, self.nb_gaussian, self.height, self.width
+
+    def call(self, x, mask=None):
+        mu_x = self.W[:self.nb_gaussian]
+        mu_y = self.W[self.nb_gaussian:self.nb_gaussian*2]
+        sigma_x = self.W[self.nb_gaussian*2:self.nb_gaussian*3]
+        sigma_y = self.W[self.nb_gaussian*3:]
+
+        # Needs change
+        self.b_s = x.shape[0]
+        self.height = x.shape[2]
+        self.width = x.shape[3]
+
+        e = self.height / self.width
+        e1 = (1 - e) / 2
+        e2 = e1 + e
+
+        mu_x = K.clip(mu_x, 0.25, 0.75)
+        mu_y = K.clip(mu_y, 0.35, 0.65)
+
+        sigma_x = K.clip(sigma_x, 0.1, 0.9)
+        sigma_y = K.clip(sigma_y, 0.2, 0.8)
+
+        x_t = T.dot(T.ones((self.height, 1)), self._linspace(0, 1.0, self.width).dimshuffle('x', 0))
+        y_t = T.dot(self._linspace(e1, e2, self.height).dimshuffle(0, 'x'), T.ones((1, self.width)))
+
+        x_t = K.repeat_elements(K.expand_dims(x_t, dim=-1), self.nb_gaussian, axis=-1)
+        y_t = K.repeat_elements(K.expand_dims(y_t, dim=-1), self.nb_gaussian, axis=-1)
+
+        gaussian = 1 / (2 * np.pi * sigma_x * sigma_y + K.epsilon()) * \
+                   T.exp(-((x_t - mu_x) ** 2 / (2 * sigma_x ** 2 + K.epsilon()) +
+                           (y_t - mu_y) ** 2 / (2 * sigma_y ** 2 + K.epsilon())))
+
+        gaussian = K.permute_dimensions(gaussian, (2, 0, 1))
+        max_gauss = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(gaussian, axis=1), axis=1)), self.height, axis=-1)), self.width, axis=-1)
+        gaussian = gaussian / max_gauss
+
+        output = K.repeat_elements(K.expand_dims(gaussian, dim=0), self.b_s, axis=0)
+
+        return output
+
+    @staticmethod
+    def _linspace(start, stop, num):
+        # produces results identical to:
+        # np.linspace(start, stop, num)
+        start = T.cast(start, floatX)
+        stop = T.cast(stop, floatX)
+        num = T.cast(num, floatX)
+        step = (stop - start) / (num - 1)
+        return T.arange(num, dtype=floatX) * step + start
+
+    def get_config(self):
+        config = {'nb_gaussian': self.nb_gaussian,
+                  'init': self.init.__name__,
+                  'W_regularizer': self.W_regularizer.get_config() if self.W_regularizer else None,
+                  'activity_regularizer': self.activity_regularizer.get_config() if self.activity_regularizer else None,
+                  'W_constraint': self.W_constraint.get_config() if self.W_constraint else None,
+                  }
+        base_config = super(LearningPrior, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
diff --git a/RecallNet/src/losses_keras2.py b/RecallNet/src/losses_keras2.py
new file mode 100644
index 0000000..8c37631
--- /dev/null
+++ b/RecallNet/src/losses_keras2.py
@@ -0,0 +1,256 @@
+import keras.backend as K
+import numpy as np
+from sal_imp_utilities import *
+from tensorflow.keras.losses import KLDivergence
+
+# KL-Divergence Loss
+def kl_divergence(y_true, y_pred):
+
+    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+
+    y_pred /= max_y_pred
+
+    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    y_true /= (sum_y_true + K.epsilon())
+    y_pred /= (sum_y_pred + K.epsilon())
+
+
+    # This constant was defined by Cornia et al. and is a bit arbitrary
+    return K.sum(K.sum(y_true * K.log((y_true / (y_pred + K.epsilon())) + K.epsilon()), axis=1), axis=1)
+
+def kl_time(y_true, y_pred):
+    if len(y_true.shape) == 5:
+        ax = 2
+    else:
+        ax = 1
+    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=ax), axis=ax), axis=ax),
+                                                                   shape_r_out, axis=ax), axis=ax+1), shape_c_out, axis=ax+1)
+
+    y_pred /= max_y_pred
+
+    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=ax), axis=ax), axis=ax),
+                                                                   shape_r_out, axis=ax), axis=ax+1), shape_c_out, axis=ax+1)
+    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=ax), axis=ax), axis=ax),
+                                                                   shape_r_out, axis=ax), axis=ax+1), shape_c_out, axis=ax+1)
+    y_true /= (sum_y_true + K.epsilon())
+    y_pred /= (sum_y_pred + K.epsilon())
+
+    kl_out = K.sum(K.sum(y_true * K.log((y_true / (y_pred + K.epsilon())) + K.epsilon()), axis=ax), axis=ax)
+
+    if len(y_true.shape) == 5:
+        kl_out = K.mean(kl_out, axis = 1)
+
+    return kl_out
+
+# Correlation Coefficient Loss
+def correlation_coefficient(y_true, y_pred):
+    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    y_pred /= max_y_pred
+
+    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    y_true /= (sum_y_true + K.epsilon())
+    y_pred /= (sum_y_pred + K.epsilon())
+
+    N = shape_r_out * shape_c_out
+    sum_prod = K.sum(K.sum(y_true * y_pred, axis=1), axis=1)
+    sum_x = K.sum(K.sum(y_true, axis=1), axis=1)
+    sum_y = K.sum(K.sum(y_pred, axis=1), axis=1)
+    sum_x_square = K.sum(K.sum(K.square(y_true), axis=1), axis=1)
+    sum_y_square = K.sum(K.sum(K.square(y_pred), axis=1), axis=1)
+
+    num = sum_prod - ((sum_x * sum_y) / N)
+    den = K.sqrt((sum_x_square - K.square(sum_x) / N) * (sum_y_square - K.square(sum_y) / N))
+
+    return num / den
+
+def cc_time(y_true, y_pred):
+    if len(y_true.shape) == 5:
+        ax = 2
+    else:
+        ax = 1
+    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=ax), axis=ax), axis=ax),
+                                                                   shape_r_out, axis=ax), axis=ax+1), shape_c_out, axis=ax+1)
+    y_pred /= max_y_pred
+
+    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=ax), axis=ax), axis=ax),
+                                                                   shape_r_out, axis=ax), axis=ax+1), shape_c_out, axis=ax+1)
+    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=ax), axis=ax), axis=ax),
+                                                                   shape_r_out, axis=ax), axis=ax+1), shape_c_out, axis=ax+1)
+    y_true /= (sum_y_true + K.epsilon())
+    y_pred /= (sum_y_pred + K.epsilon())
+
+    N = shape_r_out * shape_c_out
+    sum_prod = K.sum(K.sum(y_true * y_pred, axis=ax), axis=ax)
+    sum_x = K.sum(K.sum(y_true, axis=ax), axis=ax)
+    sum_y = K.sum(K.sum(y_pred, axis=ax), axis=ax)
+    sum_x_square = K.sum(K.sum(K.square(y_true), axis=ax), axis=ax)
+    sum_y_square = K.sum(K.sum(K.square(y_pred), axis=ax), axis=ax)
+
+
+    num = sum_prod - ((sum_x * sum_y) / N)
+    den = K.sqrt((sum_x_square - K.square(sum_x) / N) * (sum_y_square - K.square(sum_y) / N))
+
+    if len(y_true.shape) == 5:
+        cc_out = K.mean(num / den, axis = 1)
+    else:
+        cc_out = num / den
+
+    return cc_out
+
+# Normalized Scanpath Saliency Loss
+def nss_time(y_true, y_pred):
+    if len(y_true.shape) == 5:
+        ax = 2
+    else:
+        ax = 1
+
+    maxi = K.max(K.max(y_pred, axis=ax), axis=ax)
+    first_rep = K.repeat_elements(K.expand_dims(maxi, axis=ax),shape_r_out, axis=ax)
+    max_y_pred = K.repeat_elements(K.expand_dims(first_rep, axis=ax+1), shape_c_out, axis=ax+1)
+    y_pred /= max_y_pred
+
+    if len(y_true.shape) == 5:
+        y_pred_flatten = K.reshape(y_pred, (K.shape(y_pred)[0],K.shape(y_pred)[1],K.shape(y_pred)[2]*K.shape(y_pred)[3]*K.shape(y_pred)[4])) #K.batch_flatten(y_pred)
+    else:
+        y_pred_flatten = K.batch_flatten(y_pred)
+
+    y_mean = K.mean(y_pred_flatten, axis=-1)
+    y_mean = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.expand_dims(y_mean)),
+                                                               shape_r_out, axis=ax)), shape_c_out, axis=ax+1)
+
+    y_std = K.std(y_pred_flatten, axis=-1)
+    y_std = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.expand_dims(y_std)),
+                                                              shape_r_out, axis=ax)), shape_c_out, axis=ax+1)
+
+    y_pred = (y_pred - y_mean) / (y_std + K.epsilon())
+
+    num = K.sum(K.sum(y_true * y_pred, axis=ax), axis=ax)
+    den = K.sum(K.sum(y_true, axis=ax), axis=ax) + K.epsilon()
+
+    if len(y_true.shape) == 5:
+        nss_out = K.mean(num/den, axis = 1)
+    else:
+        nss_out = num/den
+
+    return nss_out
+
+
+def nss(y_true, y_pred):
+
+    ax = 1
+
+    if K.sum(K.sum(y_true, axis=ax), axis=ax) == 0:
+        return 0
+
+    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=ax), axis=ax), axis=ax+1),
+                                                                   shape_r_out, axis=ax), axis=ax+1), shape_c_out, axis=ax+1)
+
+    y_pred /= max_y_pred
+
+
+    y_pred_flatten = K.batch_flatten(y_pred)
+
+    y_mean = K.mean(y_pred_flatten, axis=-1)
+    y_mean = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.expand_dims(y_mean)),
+                                                               shape_r_out, axis=ax)), shape_c_out, axis=ax+1)
+
+    y_std = K.std(y_pred_flatten, axis=-1)
+    y_std = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.expand_dims(y_std)),
+                                                              shape_r_out, axis=ax)), shape_c_out, axis=ax+1)
+
+    y_pred = (y_pred - y_mean) / (y_std + K.epsilon())
+
+    den = K.sum(K.sum(y_true * y_pred, axis=ax), axis=ax)
+    nom = K.sum(K.sum(y_true, axis=ax), axis=ax) + K.epsilon()
+
+    nss_out = den/nom
+
+    return nss_out
+
+def cc_match(y_true, y_pred):
+    '''Calculates CC between initial, mid and final timestep from both y_true and y_pred
+    and calculates the mean absolute error between the CCs from y_true and from y_pred.
+    Requires a y_true and y_pred to be tensors of shape (bs, t, r, c, 1)'''
+
+    mid = 1 # y_true.shape[1].value//2
+    ccim_true = cc_time(y_true[:,0,...], y_true[:,mid,...])
+    ccmf_true = cc_time(y_true[:,mid,...], y_true[:,-1,...])
+
+    ccim_pred = cc_time(y_pred[:,0,...], y_pred[:,mid,...])
+    ccmf_pred = cc_time(y_pred[:,mid,...], y_pred[:,-1,...])
+
+    return  (K.abs(ccim_true-ccim_pred) + K.abs(ccmf_true-ccmf_pred) )/2 
+
+def kl_cc_nss_combined(lw=[10,-2,-1]):
+    # DEPRECATED
+    '''Loss function that combines cc, nss and kl. Beacuse nss receives a different ground truth than kl and cc (maps),
+        the function requires y_true to contains both maps. It has to be a tensor with dimensions [bs, 2, r, c, 1]. y_pred also
+        has to be a tensor of the same dim, so the model should add a 5th dimension between bs and r and repeat the predict map
+        twice along that dim.
+    '''
+    def loss(y_true, y_pred):
+
+        map_true = y_true[:,0,...]
+        fix_true = y_true[:,1,...]
+        pred = y_pred[:,0,...]
+
+
+        k = kl_divergence(map_true, pred)
+        c = correlation_coefficient(map_true, pred)
+        n = nss(fix_true, pred)
+
+
+        return lw[0]*k+lw[1]*c+lw[2]*n
+
+    return loss
+
+
+def loss_wrapper(loss, input_shape):
+    shape_r_out, shape_c_out = input_shape
+    print("shape r out, shape c out", shape_r_out, shape_c_out)
+    def _wrapper(y_true, y_pred):
+        return loss(y_true, y_pred)
+    return _wrapper
+
+
+def kl_new(y_true, y_pred):
+    '''
+    This function is for singleduration model. The old kl_divergence() may cause nan in training.
+    '''
+    max_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.max(K.max(y_pred, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    y_pred /= max_y_pred
+
+    sum_y_true = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_true, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    sum_y_pred = K.repeat_elements(K.expand_dims(K.repeat_elements(K.expand_dims(K.sum(K.sum(y_pred, axis=1), axis=1), axis=1),
+                                                                   shape_r_out, axis=1), axis=2), shape_c_out, axis=2)
+    y_true /= (sum_y_true + K.epsilon())
+    y_pred /= (sum_y_pred + K.epsilon())
+    kl = tf.keras.losses.KLDivergence()
+    return kl(y_true,y_pred)
+
+def kl_cc_combined(y_true, y_pred):
+    # For Singleduration
+    '''Loss function that combines cc, nss and kl. Beacuse nss receives a different ground truth than kl and cc (maps),
+        the function requires y_true to contains both maps. It has to be a tensor with dimensions [bs, 2, r, c, 1]. y_pred also
+        has to be a tensor of the same dim, so the model should add a 5th dimension between bs and r and repeat the predict map
+        twice along that dim.
+    '''
+
+    #k = kl_time(y_true, y_pred)
+    k = kl_new(y_true, y_pred)
+    print('k=',k)
+    #c = cc_time(y_true, y_pred)
+    c = correlation_coefficient(y_true, y_pred)
+    print('c=', c)
+    return 10*k-3*c
+
diff --git a/RecallNet/src/sal_imp_utilities.py b/RecallNet/src/sal_imp_utilities.py
new file mode 100644
index 0000000..d948bb7
--- /dev/null
+++ b/RecallNet/src/sal_imp_utilities.py
@@ -0,0 +1,944 @@
+import numpy as np
+import keras
+import matplotlib.pyplot as plt
+import sys
+import os
+from keras.layers import Input, TimeDistributed, Lambda, Conv2D, MaxPooling2D, UpSampling2D, Concatenate
+import keras.backend as K
+from keras.models import Model
+import tensorflow as tf
+from keras.utils import Sequence
+from keras.optimizers import Adam, RMSprop, SGD
+import cv2
+from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
+from PIL import Image
+from IPython.display import clear_output
+import scipy.io
+from copy import deepcopy
+import re
+
+# DEBUG
+DEBUG = False
+
+# number of rows of input images
+cat2000_c = 1920
+cat2000_r = 1080
+#cat2000_r_out = 1088 # this is divisible by 16
+cat2000_r_out = 1104 # divible by 48
+cat2000_c_out = cat2000_c # already divisible by 16
+
+cc_c = 300
+cc_r = 225
+cc_c_out = 1776
+cc_r_out = 1344
+
+#shape_r = int(cat2000_r/6)
+shape_r = 240
+#shape_r = cc_r
+# number of cols of input images
+#shape_c = int(cat2000_c/6)
+shape_c = 320
+#shape_c = cc_c
+# number of rows of downsampled maps
+shape_r_gt = 30
+# number of cols of downsampled maps
+shape_c_gt = 40
+# number of rows of model outputs
+#shape_r_out = cat2000_r_out
+shape_r_out = 480
+#shape_r_out = cc_r_out
+# number of cols of model outputs
+#shape_c_out = cat2000_c_out
+shape_c_out = 640
+#shape_c_out = cc_c_out
+# final upsampling factor
+upsampling_factor = 16
+# number of epochs
+nb_epoch = 50
+# number of timesteps
+nb_timestep = 3
+# number of learned priors
+nb_gaussian = 16
+
+def repeat(x):
+    return K.repeat_elements(K.expand_dims(x,axis=1), nb_timestep, axis=1)
+#     return K.reshape(K.repeat(K.batch_flatten(x), nb_timestep), (1, nb_timestep, shape_r_gt, shape_c_gt, 512))
+
+def repeat_shape(s):
+    return (s[0], nb_timestep) + s[1:]
+
+
+def padding(img, shape_r, shape_c, channels=3):
+    img_padded = np.zeros((shape_r, shape_c, channels), dtype=np.uint8)
+    if channels == 1:
+        img_padded = np.zeros((shape_r, shape_c), dtype=np.uint8)
+
+    original_shape = img.shape
+    rows_rate = original_shape[0]/shape_r
+    cols_rate = original_shape[1]/shape_c
+
+    if rows_rate > cols_rate:
+        new_cols = (original_shape[1] * shape_r) // original_shape[0]
+        img = cv2.resize(img, (new_cols, shape_r))
+        if new_cols > shape_c:
+            new_cols = shape_c
+        img_padded[:, ((img_padded.shape[1] - new_cols) // 2):((img_padded.shape[1] - new_cols) // 2 + new_cols)] = img
+    else:
+        new_rows = (original_shape[0] * shape_c) // original_shape[1]
+        img = cv2.resize(img, (shape_c, new_rows))
+        if new_rows > shape_r:
+            new_rows = shape_r
+        img_padded[((img_padded.shape[0] - new_rows) // 2):((img_padded.shape[0] - new_rows) // 2 + new_rows), :] = img
+
+    return img_padded
+
+def resize_fixation(img, rows=480, cols=640):
+    out = np.zeros((rows, cols))
+    factor_scale_r = rows / img.shape[0]
+    factor_scale_c = cols / img.shape[1]
+
+    coords = np.argwhere(img)
+    for coord in coords:
+        r = int(np.round(coord[0]*factor_scale_r))
+        c = int(np.round(coord[1]*factor_scale_c))
+        if r == rows:
+            r -= 1
+        if c == cols:
+            c -= 1
+        out[r, c] = 1
+
+    return out
+
+def padding_fixation(img, shape_r, shape_c):
+    img_padded = np.zeros((shape_r, shape_c))
+
+    original_shape = img.shape
+    rows_rate = original_shape[0]/shape_r
+    cols_rate = original_shape[1]/shape_c
+
+    if rows_rate > cols_rate:
+        new_cols = (original_shape[1] * shape_r) // original_shape[0]
+        img = resize_fixation(img, rows=shape_r, cols=new_cols)
+        if new_cols > shape_c:
+            new_cols = shape_c
+        img_padded[:, ((img_padded.shape[1] - new_cols) // 2):((img_padded.shape[1] - new_cols) // 2 + new_cols)] = img
+    else:
+        new_rows = (original_shape[0] * shape_c) // original_shape[1]
+        img = resize_fixation(img, rows=new_rows, cols=shape_c)
+        if new_rows > shape_r:
+            new_rows = shape_r
+        img_padded[((img_padded.shape[0] - new_rows) // 2):((img_padded.shape[0] - new_rows) // 2 + new_rows), :] = img
+
+    return img_padded
+
+def preprocess_fixmaps(paths, shape_r, shape_c, fix_as_mat=False, fix_key="", pad=True):
+
+    if pad:
+        ims = np.zeros((len(paths), shape_r, shape_c, 1))
+    else:
+        ims = []
+
+#     print('ims.shape:',ims.shape)
+    for i, path in enumerate(paths):
+        if path == 'dummy':
+            fix_map = np.zeros((480,640))
+        elif fix_as_mat:
+            mat = scipy.io.loadmat(path)
+            if DEBUG:
+                print('mat',mat)
+            fix_map = mat[fix_key]
+        else:
+            fix_map = cv2.imread(path, 0)
+
+        if DEBUG:
+            print('fix_map shape, np.max(fix_map),np.min(fix_map),np.mean(fix_map)',fix_map.shape,np.max(fix_map),np.min(fix_map),np.mean(fix_map))
+        if pad:
+            ims[i, :, :, 0] = padding_fixation(fix_map, shape_r=shape_r, shape_c=shape_c)
+        else:
+            ims.append(fix_map)
+#             ims = np.array(ims)
+        # print('ims[-1].shape:',ims[-1].shape)
+    return ims
+
+def load_maps(paths):
+    ims = []
+
+    for i, path in enumerate(paths):
+        original_map = np.load(path, allow_pickle=True)
+        # TODO: chect for /255.0
+        ims.append(original_map.astype(np.float32))
+    ims = np.array(ims)
+    # print('load_maps: ims[-1].shape',ims[-1].shape)
+    return ims
+
+def preprocess_maps(paths, shape_r, shape_c, pad=True):
+    if pad:
+        ims = np.zeros((len(paths), shape_r, shape_c, 1))
+    else:
+        ims = []
+    for i, path in enumerate(paths):
+        original_map = cv2.imread(path, 0)
+        if pad:
+            padded_map = padding(original_map, shape_r, shape_c, 1)
+            ims[i,:,:, 0] = padded_map.astype(np.float32)
+            ims[i,:,:, 0] /= 255.0
+        else:
+            ims.append(original_map.astype(np.float32)/255.0)
+#             ims = np.array(ims)
+            # print('ims.shape in preprocess_maps',ims.shape)
+    # print('prep_maps: ims[-1].shape',ims[-1].shape)
+    return ims
+
+def load_images(paths):
+    ims =[]
+
+    for i, path in enumerate(paths):
+        img = np.load(path, allow_pickle=True)
+        ims.append(img)
+    
+    ims = np.array(ims)
+    # print('load_images: ims.shape',np.array(ims).shape)
+    return ims
+
+def preprocess_images(paths, shape_r, shape_c, pad=True):
+    if pad:
+        ims = np.zeros((len(paths), shape_r, shape_c, 3))
+    else:
+        ims =[]
+
+    for i, path in enumerate(paths):
+        original_image = cv2.imread(path)
+        if original_image is None:
+            raise ValueError('Path unreadable: %s' % path)
+        if pad:
+            padded_image = padding(original_image, shape_r, shape_c, 3)
+            ims[i] = padded_image
+        else:
+            original_image = original_image.astype(np.float32)
+            original_image[..., 0] -= 103.939
+            original_image[..., 1] -= 116.779
+            original_image[..., 2] -= 123.68
+            ims.append(original_image)
+#             ims = np.array(ims)
+            print('ims.shape in preprocess_imgs',ims.shape)
+
+        # DEBUG
+#     plt.figure()
+#     plt.subplot(1,2,1)
+#     plt.imshow(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
+#     plt.subplot(1,2,2)
+#     plt.imshow(cv2.cvtColor(padded_image, cv2.COLOR_BGR2RGB))
+#     plt.suptitle(path)
+
+    if pad:
+        ims[:, :, :, 0] -= 103.939
+        ims[:, :, :, 1] -= 116.779
+        ims[:, :, :, 2] -= 123.68
+
+    return ims
+
+def reverse_preprocess(img):
+    im = deepcopy(img)
+
+    im[:, :, 0] += 103.939
+    im[:, :, 1] += 116.779
+    im[:, :, 2] += 123.68
+
+#     print(np.max(im), np.min(im), type(im[0][0][0]))
+
+    im = im[...,::-1]
+
+
+    im = np.array(im, dtype=np.uint8)
+
+    return im
+
+
+
+def postprocess_predictions(pred, shape_r, shape_c, blur=False, normalize=False, zero_to_255 = False):
+    predictions_shape = pred.shape
+    rows_rate = shape_r / predictions_shape[0]
+    cols_rate = shape_c / predictions_shape[1]
+
+    # pred = pred / np.max(pred) * 255
+
+#    print('Preparing to resize...')
+    if blur:
+        sigma=blur
+        pred = scipy.ndimage.filters.gaussian_filter(pred, sigma=sigma)
+
+    if rows_rate > cols_rate:
+        new_cols = (predictions_shape[1] * shape_r) // predictions_shape[0]
+        pred = cv2.resize(pred, (new_cols, shape_r))
+        img = pred[:, ((pred.shape[1] - shape_c) // 2):((pred.shape[1] - shape_c) // 2 + shape_c)]
+    else:
+        new_rows = (predictions_shape[0] * shape_c) // predictions_shape[1]
+        pred = cv2.resize(pred, (shape_c, new_rows))
+        img = pred[((pred.shape[0] - shape_r) // 2):((pred.shape[0] - shape_r) // 2 + shape_r), :]
+
+
+#    print('Resized')
+
+    if normalize:
+        img = img / np.max(img) * 255
+    if zero_to_255:
+        img = np.abs(img - 255)
+    return img
+
+
+
+class MultidurationGenerator(Sequence):
+    def __init__(self, 
+                 img_filenames, 
+                 map_filenames=None, 
+                 fix_filenames=None, 
+                 batch_size=1, 
+                 img_size=(shape_r,shape_c), 
+                 map_size=(shape_r_out, shape_c_out),
+                 shuffle=True, 
+                 augment=False, 
+                 n_output_maps=1, 
+                 n_output_fixs=1, 
+                 mode = 'multistream_concat', 
+                 fix_key='',
+                 return_names=False, 
+                 fix_as_mat=False, 
+                 pad_gt_maps=True,
+                 read_npy=False
+                 ):
+        '''
+        Generator for multi-duration saliency data. Receives lists of images, and t lists of heatmaps and fixations, where t
+        is the number of saliency time steps to yield. The generator will automatically infer t from the length of map_filenames.
+
+        This generator has 3 different modes:
+        1. multistream_concat: concatenates fix and maps for a given timestep into one tensor of shape (bs, 2, r, c, 1). Then appends
+        all these tensors in a list of size t, and yields that tensor as y_true. This mode is made to work with losses that recuperate the
+        map and fixation by slicing the y_true tensor internally.
+
+        2. multistream_full: doesn't concatenate the fix and maps; instead, yields all fixations and maps needed for each timestep as a
+        different element in the final output list. For example, if we are training with 3 losses and 2 timesteps, this generator will
+        yield a list of length 6 as y_true output: 3 maps/fis for timestep1, and 3 maps/fixs for timestep2.
+
+        3. singlestream: concatenates all timesteps in one tensor. for each loss, the generator will yield a tensor of shape
+        (bs, time, r, c, 1). If we are working with kl, cc and nss, for example, the generator will output a list of length 3,
+        where each element is a tensor of the mentioned shape. This mode should be used with losses that are adapted to tensors with
+        a time dimension.
+
+        '''
+
+        print('Instantiating MultidurationGenerator. \
+        Number of files received: %d. Batch size: %d. \
+        Image size: %s. Augmentation: %d. Mode: %s' \
+        % (len(img_filenames), batch_size, str(img_size), augment,mode ))
+
+        if (mode == 'multistream_concat') and (map_filenames is None or fix_filenames is None):
+            print('Multistream concat can only be used when both fixations and maps are provided. \
+            If only one is enough, use `multistream_full`.')
+
+
+        self.n_output_maps = n_output_maps
+        self.n_output_fixs = n_output_fixs
+        self.fix_as_mat = fix_as_mat
+        self.fix_key = fix_key
+        self.pad_gt_maps = pad_gt_maps
+
+        self.img_filenames = np.array(img_filenames)
+        self.read_npy = read_npy
+
+        # check that maps make sense
+        if map_filenames is not None:
+            self.map_filenames = np.array(map_filenames)
+            assert all([len(self.img_filenames) == len(elt) for elt in self.map_filenames]), "Mismatch between images and maps. Images size: " + self.img_filenames.shape.__str__() + " Maps size: " + self.map_filenames.shape.__str__()
+            self.timesteps = len(map_filenames)
+        else:
+            self.n_output_maps = 0
+            self.map_filenames = None
+            print('Warning: No maps filenames provided, no outputs of that kind will be generated')
+
+
+        # check that fixs make sense
+        if fix_filenames is not None:
+            self.fix_filenames = np.array(fix_filenames)
+            assert all([len(self.img_filenames) == len(elt) for elt in self.fix_filenames]), "Mismatch between images and fixations. Images size: " + self.img_filenames.shape.__str__() + " Fix size: " + self.fix_filenames.shape.__str__()
+            self.timesteps = len(fix_filenames)
+        else:
+            self.n_output_fixs = 0
+            self.fix_filenames = None
+            print('Warning: No fix filenames provided, no outputs of that kind will be generated')
+
+        self.batch_size = batch_size
+        self.img_size = img_size
+        self.map_size = map_size
+        self.shuffle = shuffle
+        self.augment = augment
+        self.mode = mode
+        self.return_names = return_names
+
+        # Defining augmentation sequence
+        if augment:
+            sometimes = lambda aug: iaa.Sometimes(0.4, aug)
+            self.seq = iaa.Sequential([
+                    sometimes(iaa.CropAndPad(px=(0, 20))), # crop images from each side by 0 to 16px (randomly chosen)
+                    iaa.Fliplr(0.5), # horizontally flip 50% of the images
+                    sometimes(iaa.CoarseDropout(p=0.1, size_percent=0.05)),
+                    sometimes(iaa.Affine(rotate=(-15, 15)))
+                ], random_order=True)
+
+
+        if shuffle:
+            self.on_epoch_end()
+
+    def __len__(self):
+        return int(np.ceil(len(self.img_filenames) / float(self.batch_size)))
+
+    def __getitem__(self, idx):
+
+        # Get input images
+        batch_imgs = self.img_filenames[idx * self.batch_size : (idx + 1) * self.batch_size]
+        if self.read_npy:
+            images = load_images(batch_imgs)
+        else:
+            images = preprocess_images(batch_imgs, self.img_size[0], self.img_size[1])
+
+        # Get ground truth maps for all times
+        if self.n_output_maps>=1:
+            maps = []
+            for t in range(self.timesteps):
+                maps_names_t = self.map_filenames[t][idx * self.batch_size : (idx + 1) * self.batch_size]
+                
+                if self.read_npy:
+                    maps_t = load_maps(maps_names_t)
+                else:
+                    maps_t = preprocess_maps(maps_names_t, self.map_size[0], self.map_size[1], pad=self.pad_gt_maps)
+
+                maps.append(maps_t)
+
+        # Get fix maps for all times
+        if self.n_output_fixs>=1:
+            fixs = []
+            for t in range(self.timesteps):
+                fix_names_t = self.fix_filenames[t][idx * self.batch_size : (idx + 1) * self.batch_size]
+                
+                if self.read_npy:
+                    fix_t = load_images(fix_names_t)
+                else:
+                    fix_t = preprocess_fixmaps(fix_names_t, self.map_size[0], self.map_size[1], fix_as_mat=self.fix_as_mat, fix_key=self.fix_key, pad=self.pad_gt_maps)
+
+                fixs.append(fix_t)
+
+        if self.augment:
+            seq_det = self.seq.to_deterministic()
+            images = seq_det.augment_images(images)
+            for ta in range(len(maps)):
+                if self.n_output_maps>=1:
+                    maps[ta] = seq_det.augment_heatmaps(maps[ta])
+                if self.n_output_fixs>=1:
+                    fixs[ta] = seq_det.augment_heatmaps(fixs[ta])
+
+        if self.mode == 'singlestream':
+            # Returns a list of n_output_maps+n_output_fixs elements. Each element is a 5D tensor: (bs, timesteps, r, c, 1)
+            outs = []
+            if self.n_output_maps>=1:
+                maps_with_time = np.zeros((len(batch_imgs),self.timesteps,self.map_size[0],self.map_size[1],1))
+                for i in range(self.timesteps):
+                    maps_with_time[:,i,...] = maps[i]
+
+                # new version of block above that handles images of varying size
+#                 maps_with_time = []
+#                 for bidx in range(self.batch_size):
+#                     # maps_with_time is list of len batch_size with 3D tensors of shape t,w,h
+#                     maps_with_time.append( [maps[ti][bidx] for ti in range(self.timesteps)] )
+                
+                
+                outs.extend([maps_with_time]*self.n_output_maps)
+
+
+            if self.n_output_fixs>=1:
+                fixs_with_time = np.zeros((len(batch_imgs),self.timesteps,self.map_size[0],self.map_size[1],1))
+                for i in range(self.timesteps):
+                    fixs_with_time[:,i,...] = fixs[i]
+
+                # new version of block above that handles images of varying size
+#                 fixs_with_time = []
+#                 for bidx in range(self.batch_size):
+#                     # fixs_with_time is list of len batch_size with 3D tensors of shape t,w,h
+#                     fixs_with_time.append( np.array([fixs[ti][bidx] for ti in range(self.timesteps)]) )                    
+                    
+                outs.extend([fixs_with_time]*self.n_output_fixs)
+
+        elif self.mode == 'multistream_concat':
+            # returns a list of t elements: [ [maps_t1,fix_t1], [maps_t2,fix_t2] , [maps_t3,fix_t3], ...]
+            outs=[]
+            for i in range(self.timesteps):
+                outs.append(np.concatenate([np.expand_dims(maps[i],axis=1),np.expand_dims(fixs[i],axis=1)], axis=1))
+                # print('len(outs) multistream concat:',len(outs))
+
+        elif self.mode == 'multistream_full':
+            # returns a list of size timestep*losses. If 2 losses maps, 1 loss fix, 2 timesteps, we have: [m1, m1, m2, m2, fix1, fix2]
+            outs = []
+            if self.n_output_maps >= 1:
+                for i in range(self.timesteps):
+                    outs.extend([maps[i]]*self.n_output_maps)
+            if self.n_output_fixs >= 1:
+                for i in range(self.timesteps):
+                    outs.extend([fixs[i]]*self.n_output_fixs)
+
+        if self.return_names:
+            return images, outs, batch_imgs
+        return images, outs
+
+
+    def on_epoch_end(self):
+        if self.shuffle:
+            idxs = list(range(len(self.img_filenames)))
+            np.random.shuffle(idxs)
+            self.img_filenames = self.img_filenames[idxs]
+            for i in range(len(self.map_filenames)):
+                self.map_filenames[i] = self.map_filenames[i][idxs]
+                if self.fix_filenames is not None:
+                    self.fix_filenames[i] = self.fix_filenames[i][idxs]
+
+class SalImpGenerator(Sequence):
+
+    def __init__(
+        self,
+        img_filenames,
+        imp_filenames,
+        fix_filenames=None,
+        batch_size=1,
+        img_size=(shape_r,shape_c),
+        map_size=(shape_r_out, shape_c_out),
+        shuffle=True,
+        augment=False,
+        n_output_maps=1,
+        concat_fix_and_maps=True,
+        fix_as_mat=False,
+        fix_key="",
+        pad_maps=True,
+        pad_imgs=True,
+        read_npy=False,
+        return_names=False):
+
+        print('Instantiating SalImpGenerator. Number of files received: %d. Batch size: %d. Image size: %s. Map size: %s. Augmentation: %d, Pad_imgs: %s. Pad_maps: %s.' %
+              (len(img_filenames), batch_size, str(img_size), str(map_size), augment, pad_imgs, pad_maps ))
+
+        self.img_filenames = np.array(img_filenames)
+        self.imp_filenames = np.array(imp_filenames)
+        self.batch_size = batch_size
+        self.img_size = img_size
+        self.map_size = map_size
+        self.shuffle = shuffle
+        self.augment = augment
+        self.n_output_maps = n_output_maps
+        self.concat_fix_and_maps = concat_fix_and_maps
+        self.fix_as_mat=fix_as_mat
+        self.fix_key = fix_key
+        self.pad_imgs = pad_imgs
+        self.pad_maps = pad_maps
+        self.return_names=return_names
+        self.read_npy = read_npy
+
+        if fix_filenames is not None:
+            self.fix_filenames = np.array(fix_filenames)
+        else:
+            self.fix_filenames = None
+
+        if augment:
+            sometimes = lambda aug: iaa.Sometimes(0.4, aug)
+            self.seq = iaa.Sequential([
+                    sometimes(iaa.CropAndPad(px=(0, 20))), # crop images from each side by 0 to 16px (randomly chosen)
+                    iaa.Fliplr(0.5), # horizontally flip 50% of the images
+                    sometimes(iaa.CoarseDropout(p=0.1, size_percent=0.05)),
+                    sometimes(iaa.Affine(rotate=(-15, 15)))
+                ], random_order=True)
+
+
+        if shuffle:
+            self.on_epoch_end()
+
+    def __len__(self):
+        return int(np.ceil(len(self.img_filenames) / float(self.batch_size)))
+
+    def __getitem__(self, idx):
+        batch_x = self.img_filenames[idx * self.batch_size : (idx + 1) * self.batch_size]
+        batch_y = self.imp_filenames[idx * self.batch_size : (idx + 1) * self.batch_size]
+
+#         print('img names in this batch:', batch_x)
+#         print('imp names in this batch:', batch_y)
+
+        
+        if self.read_npy:
+            images = load_images(batch_x)
+            maps = load_maps(batch_y)
+        else:
+            images = preprocess_images(batch_x, self.img_size[0], self.img_size[1], pad =self.pad_imgs)
+            maps = preprocess_maps(batch_y, self.map_size[0], self.map_size[1], pad =self.pad_maps)
+        
+        if self.fix_filenames is not None:
+            if self.read_npy:
+                fixs = load_images(self.fix_filenames[idx * self.batch_size : (idx + 1) * self.batch_size])
+            else:
+                fixs = preprocess_fixmaps(
+                    self.fix_filenames[idx * self.batch_size : (idx + 1) * self.batch_size],
+                    self.map_size[0],
+                    self.map_size[1],
+                    fix_as_mat=self.fix_as_mat,
+                    fix_key=self.fix_key)
+            
+        if self.augment:
+            seq_det = self.seq.to_deterministic()
+            images = seq_det.augment_images(images)
+            maps = seq_det.augment_heatmaps(maps)
+            if self.fix_filenames is not None:
+                fixs = seq_det.augment_heatmaps(fixs)
+
+        if self.fix_filenames is not None and self.concat_fix_and_maps:
+            outs = np.concatenate([np.expand_dims(maps,axis=1),np.expand_dims(fixs,axis=1)], axis=1)
+            if self.n_output_maps >1:
+                outs = [outs]*self.n_output_maps
+        else:
+            if self.n_output_maps ==1:
+                if self.fix_filenames is not None:
+                    outs=[maps,fixs]
+                else:
+                    outs=maps
+            else:
+                outs = [maps]*self.n_output_maps
+                if self.fix_filenames is not None:
+                    outs.append(fixs)
+
+#         print('generator: len(outs) should be 3:', len(outs))
+#         print('generator: outs[0].shape (should be bs,2,r,c,1):', outs[0].shape)
+#         print('generator: outs[0][0][0].shape (should be first map of batch)',outs[0][0][0].shape)
+        if self.return_names:
+            return images, outs, batch_x
+        return images, outs
+
+
+    def on_epoch_end(self):
+        if self.shuffle:
+            idxs = list(range(len(self.img_filenames)))
+            np.random.shuffle(idxs)
+            self.img_filenames = self.img_filenames[idxs]
+            self.imp_filenames = self.imp_filenames[idxs]
+            if self.fix_filenames is not None:
+                self.fix_filenames = self.fix_filenames[idxs]
+
+def eval_generator(
+    img_filenames,
+    map_filenames,
+    fixmap_filenames,
+    fixcoord_filenames,
+    inp_size,
+    fix_as_mat=False,
+    fix_key="",
+    fixcoord_filetype='mat',
+    ):
+    """
+        Returns tuples img, heatmap, fixmap, fix_coords to be used for data eval
+
+        img_filenames, map_filesnames, fixmap_filenames should a length-n list where
+        n is the number of timestamps
+
+        heatmap, fixmap, fixcoords are all also length-n
+
+    """
+    assert len(map_filenames) == len(fixmap_filenames)
+    n_times = len(map_filenames)
+    n_img = len(map_filenames[0])
+    for i in range(n_img):
+        imgs = []
+        maps = []
+        fixmaps = []
+        fixcoords = []
+        #img = load_images([img_filenames[i]])
+        img = preprocess_images([img_filenames[i]], inp_size[0], inp_size[1])
+
+        for t in range(n_times):
+            # load the image
+            #img = cv2.imread(img_filenames[i])
+            map_ = cv2.imread(map_filenames[t][i], cv2.IMREAD_GRAYSCALE)
+#             print("map max min", map_.max(), map_.min())
+            mapshape = map_.shape
+            if fix_as_mat:
+                # fixmap = load_images([fixmap_filenames[t][i]],)
+                fixmap = preprocess_fixmaps(
+                    [fixmap_filenames[t][i]],
+                    mapshape[0],
+                    mapshape[1],
+                    fix_as_mat=fix_as_mat,
+                    fix_key=fix_key)
+                fixmap = np.squeeze(fixmap)
+            else:
+                fixmap = cv2.imread(fixmap_filenames[t][i], 0)
+            if fixcoord_filenames:
+                assert len(fixcoord_filenames) == n_times
+                if fixcoord_filetype == 'mat':
+                    fixdata = scipy.io.loadmat(fixcoord_filenames[t][i])
+                    resolution = fixdata["resolution"][0]
+                    #assert resolution[0] == img.shape[1] and resolution[1] == img.shape[2]
+                    fix_coords_all_participants = fixdata["gaze"]
+                    all_fixations = []
+                    for participant in fix_coords_all_participants:
+                        all_fixations.extend(participant[0][2])
+                else:
+                    raise RuntimeError("fixcoord filetype %s is unsupported" % fixcoord_filetype)
+            else:
+                all_fixations = None
+            imgs.append(img)
+            maps.append(map_)
+            fixmaps.append(fixmap)
+            fixcoords.append(all_fixations)
+        yield (imgs, maps, fixmaps, fixcoords, img_filenames[i])
+
+
+
+
+
+
+def get_str2label(dataset_path, label_mapping_file=None):
+    str2label={}
+    if label_mapping_file:
+        with open(label_mapping_file, "r") as f:
+            lines = [l.strip() for l in f.readlines()]
+            for l in lines:
+                cl = l.split()[0]
+                i = l.split()[-1]
+                str2label[cl] = int(i)
+    else:
+        for i,cl in enumerate([d for d in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, d))]):
+            str2label[cl] = i
+    return str2label
+
+def get_labels(filenames, str2label=None):
+
+    if not str2label:
+        str2label = get_str2label(dataset_path = '/netpool/homes/wangyo/Dataset/imp1k/imgs', label_mapping_file = "/netpool/homes/wangyo/Dataset/imp1k/imp1k_with_nat_images_label_map.txt")
+
+    onehot_arr = np.zeros((len(filenames), len(str2label)))
+#     print('filenames in get labels',filenames)
+
+    for i,f in enumerate(filenames):
+        split = re.split('/|\\\\',f)
+        class_name = split[-2]
+        if split[-4] == 'Salicon':
+            label = str2label['natural_images']
+            onehot_arr[i, label] = 1
+        else:
+    #         print('CLASS NAME IN GET_LABELS', class_name)
+            label = str2label[class_name]
+            onehot_arr[i, label] = 1
+    return onehot_arr
+
+
+class ImpAndClassifGenerator(Sequence):
+
+    def __init__(
+        self,
+        img_filenames,
+        imp_filenames,
+        fix_filenames=None,
+        extra_imgs=None, # For feeding a much larger dataset, e.g. salicon, that the generator will subsample to maintain class balance
+        extra_imps=None,
+        extra_fixs=None,
+        extras_per_epoch=160,
+        batch_size=1,
+        img_size=(shape_r,shape_c),
+        map_size=(shape_r_out, shape_c_out),
+        shuffle=True,
+        augment=False,
+        n_output_maps=1,
+        concat_fix_and_maps=True,
+        fix_as_mat=False,
+        fix_key="",
+        str2label=None,
+        dummy_labels=False,
+        num_classes=6,
+        pad_imgs=True,
+        pad_maps=True,
+        return_names=False,
+        return_labels=True,
+        read_npy=False):
+
+        print('Instantiating ImpAndClassifGenerator. Number of files received: %d. Extras: %s. Batch size: %d. Image size: %s. Map size: %s. Augmentation: %d, Pad_imgs: %s. Pad_maps: %s. Num classes: %d.' %
+              (len(img_filenames), len(extra_imgs) if extra_imgs is not None else None, batch_size, str(img_size), str(map_size), augment, pad_imgs, pad_maps, num_classes ))
+
+        self.img_filenames = np.array(img_filenames)
+        self.imp_filenames = np.array(imp_filenames)
+        self.batch_size = batch_size
+        self.img_size = img_size
+        self.map_size = map_size
+        self.shuffle = shuffle
+        self.augment = augment
+        self.n_output_maps = n_output_maps
+        self.concat_fix_and_maps = concat_fix_and_maps
+        self.fix_as_mat = fix_as_mat
+        self.fix_key = fix_key
+        self.str2label = str2label
+        self.num_classes = num_classes
+        self.dummy_labels = dummy_labels
+        self.pad_imgs = pad_imgs
+        self.pad_maps = pad_maps
+        self.extra_idx = 0
+        self.extra_imgs = np.array(extra_imgs) if extra_imgs is not None else None
+        self.extra_imps = np.array(extra_imps) if extra_imps is not None else None
+        self.extra_fixs = np.array(extra_fixs) if extra_fixs is not None else None
+        self.extras_per_epoch = extras_per_epoch
+        self.return_names = return_names
+        self.return_labels=return_labels
+        self.read_npy=read_npy
+
+        if fix_filenames is not None:
+            self.fix_filenames = np.array(fix_filenames)
+        else:
+            self.fix_filenames = None
+
+        if augment:
+            sometimes = lambda aug: iaa.Sometimes(0.4, aug)
+            self.seq = iaa.Sequential([
+                    sometimes(iaa.CropAndPad(px=(0, 20))), # crop images from each side by 0 to 16px (randomly chosen)
+                    iaa.Fliplr(0.5), # horizontally flip 50% of the images
+                    sometimes(iaa.CoarseDropout(p=0.1, size_percent=0.05)),
+                    sometimes(iaa.Affine(rotate=(-15, 15)))
+                ], random_order=True)
+
+
+        self.on_epoch_end()
+
+    def __len__(self):
+        return int(np.ceil(len(self.imgs_this_epoch) / float(self.batch_size)))
+
+    def __getitem__(self, idx):
+        batch_x = self.imgs_this_epoch[idx * self.batch_size : (idx + 1) * self.batch_size]
+        batch_y = self.imps_this_epoch[idx * self.batch_size : (idx + 1) * self.batch_size]
+
+#         print('img names in this batch:', batch_x)
+#         print('imp names in this batch:', batch_y)
+
+        if self.read_npy:
+            images = load_images(batch_x)
+            maps = load_maps(batch_y)
+        else:
+            images = preprocess_images(batch_x, self.img_size[0], self.img_size[1], pad= self.pad_imgs)
+            maps = preprocess_maps(batch_y, self.map_size[0], self.map_size[1], pad=self.pad_maps)
+        
+
+        if not self.dummy_labels:
+            labels = get_labels(batch_x, self.str2label) # Returns a numpy array of shape (bs, num_classes)
+        else:
+            labels = np.zeros((len(images),self.num_classes))
+        if self.fix_filenames is not None:
+            if read_npy:
+                fixs = load_images(self.fixs_this_epoch[idx * self.batch_size : (idx + 1) * self.batch_size])
+            else:
+                fixs = preprocess_fixmaps(
+                    self.fixs_this_epoch[idx * self.batch_size : (idx + 1) * self.batch_size],
+                    self.map_size[0],
+                    self.map_size[1],
+                    fix_as_mat=self.fix_as_mat,
+                    fix_key=self.fix_key)
+            
+        if self.augment:
+            seq_det = self.seq.to_deterministic()
+            images = seq_det.augment_images(images)
+            maps = seq_det.augment_heatmaps(maps)
+            if self.fixs_this_epoch is not None:
+                fixs = seq_det.augment_heatmaps(fixs)
+
+        if self.fix_filenames is not None and self.concat_fix_and_maps:
+#             outs = np.concatenate([np.expand_dims(maps,axis=1),np.expand_dims(fixs,axis=1)], axis=1)
+            if self.n_output_maps >1:
+                outs = [outs]*self.n_output_maps
+            if self.return_labels: outs.append(labels)
+        else:
+            if self.n_output_maps ==1:
+                if self.fix_filenames is not None:
+                    outs=[maps,fixs]
+                    if self.return_labels: outs.append(labels)
+                else:
+                    outs=[maps]
+                    if self.return_labels: outs.append(labels)
+            else:
+                outs = [maps]*self.n_output_maps
+                if self.fix_filenames is not None:
+                    outs.append(fixs)
+                if self.return_labels: outs.append(labels)
+        if self.return_names:
+           outs.append(batch_x)
+        
+        return images, outs
+
+
+    def on_epoch_end(self):
+
+        if self.extra_imgs is not None:
+            # Sample a new set of extra images
+            extra_imgs_this_epoch = self.extra_imgs[self.extra_idx * self.extras_per_epoch : (self.extra_idx+1) * self.extras_per_epoch]
+            extra_imps_this_epoch = self.extra_imps[self.extra_idx * self.extras_per_epoch : (self.extra_idx+1) * self.extras_per_epoch]
+            if self.extra_fixs is not None:
+                extra_fixs_this_epoch = self.extra_fixs[self.extra_idx * self.extras_per_epoch : (self.extra_idx+1) * self.extras_per_epoch]
+            else:
+                extra_fixs_this_epoch = []
+            self.extra_idx +=1
+
+        else:
+            extra_imgs_this_epoch = []
+            extra_imps_this_epoch = []
+            extra_fixs_this_epoch = []
+
+        self.imgs_this_epoch = np.concatenate([self.img_filenames, extra_imgs_this_epoch])
+        self.imps_this_epoch = np.concatenate([self.imp_filenames,  extra_imps_this_epoch])
+        if self.fix_filenames is not None:
+            self.fixs_this_epoch = np.concatenate([self.fix_filenames, extra_fixs_this_epoch])
+
+        idxs = np.array(range(len(self.imgs_this_epoch)))
+        if self.shuffle:
+            np.random.shuffle(idxs)
+            self.imgs_this_epoch = self.imgs_this_epoch[idxs]
+            self.imps_this_epoch = self.imps_this_epoch[idxs]
+            if self.fix_filenames is not None:
+                self.fixs_this_epoch = self.fixs_this_epoch[idxs]
+
+def UMSI_eval_generator(
+    img_filenames,
+    map_filenames,
+    inp_size,
+
+    ):
+    """
+        Returns tuples img, heatmap to be used for data eval
+
+    """
+    n_img = len(map_filenames[0])
+    for i in range(n_img):
+        imgs = []
+        maps = []
+        img = preprocess_images([img_filenames[i]], inp_size[0], inp_size[1])
+        map_ = cv2.imread(map_filenames[i], cv2.IMREAD_GRAYSCALE)
+       
+        mapshape = map_.shape
+        imgs.append(img)
+        maps.append(map_)
+            
+        yield (imgs, maps, img_filenames[i])
+
+
+class RecallNet_Generator(Sequence) :
+    def __init__(self, image_filenames, labels, mean_accs, type0_accs, batch_size=8) :
+        self.image_filenames = np.array(image_filenames)
+        self.labels = np.array(labels)
+        self.mean_accs = np.array(mean_accs)
+        self.type0_accs = np.array(type0_accs)
+        self.batch_size = batch_size
+    def __len__(self) :
+        return (np.ceil(len(self.image_filenames) / float(self.batch_size))).astype(np.int)
+
+    def __getitem__(self, idx) :
+        batch_label = self.labels[idx * self.batch_size : (idx+1) * self.batch_size]
+        batch_img = self.image_filenames[idx * self.batch_size : (idx+1) * self.batch_size]
+        batch_img = preprocess_images(batch_img,240,320)
+        batch_mean = self.mean_accs[idx * self.batch_size : (idx+1) * self.batch_size]
+        batch_type0 = self.type0_accs[idx * self.batch_size : (idx+1) * self.batch_size]
+        #out = [batch_mean,batch_type0,batch_label]
+        out = [batch_mean,batch_type0]
+        return batch_img, out
\ No newline at end of file
diff --git a/RecallNet/src/singleduration_models.py b/RecallNet/src/singleduration_models.py
new file mode 100644
index 0000000..b5c2645
--- /dev/null
+++ b/RecallNet/src/singleduration_models.py
@@ -0,0 +1,822 @@
+import numpy as np
+import keras
+import sys
+import os
+from keras.layers import Layer, Input, Multiply, Dropout, TimeDistributed, LSTM, Activation, Lambda, Conv2D, Dense, GlobalAveragePooling2D, MaxPooling2D, ZeroPadding2D, UpSampling2D, BatchNormalization, Concatenate, Add, DepthwiseConv2D
+import keras.backend as K
+from keras.models import Model
+import tensorflow as tf
+from keras.utils import Sequence
+import cv2
+import scipy.io
+import math
+from attentive_convlstm_new import AttentiveConvLSTM2D
+from dcn_resnet_new import dcn_resnet
+from gaussian_prior_new import LearningPrior
+from sal_imp_utilities import *
+from xception_custom import Xception_wrapper
+#from keras.applications import keras_modules_injection
+from keras.regularizers import l2
+
+
+
+def decoder_block(x, dil_rate=(2,2), print_shapes=True, dec_filt=1024):
+    # Dilated convolutions
+    x = Conv2D(dec_filt, 3, padding='same', activation='relu', dilation_rate=(2, 2))(x)
+    x = Conv2D(dec_filt, 3, padding='same', activation='relu', dilation_rate=(2, 2))(x)
+    x = UpSampling2D((2,2), interpolation='bilinear')(x)
+
+    x = Conv2D(dec_filt//2, 3, padding='same', activation='relu', dilation_rate=(2, 2))(x)
+    x = Conv2D(dec_filt//2, 3, padding='same', activation='relu', dilation_rate=(2, 2))(x)
+    x = UpSampling2D((2,2), interpolation='bilinear')(x)
+
+    x = Conv2D(dec_filt//4, 3, padding='same', activation='relu', dilation_rate=(2, 2))(x)
+    x = Conv2D(dec_filt//4, 3, padding='same', activation='relu', dilation_rate=(2, 2))(x)
+    x = UpSampling2D((4,4), interpolation='bilinear')(x)
+    if print_shapes: print('Shape after last ups:',x.shape)
+
+    # Final conv to get to a heatmap
+    x = Conv2D(1, kernel_size=1, padding='same', activation='relu')(x)
+    if print_shapes: print('Shape after 1x1 conv:',x.shape)
+
+    return x
+
+def decoder_block_simple(x, dil_rate=(2,2), print_shapes=True, dec_filt=1024):
+
+    x = Conv2D(dec_filt, 3, padding='same', activation='relu')(x)
+    x = UpSampling2D((2,2), interpolation='bilinear')(x)
+
+    x = Conv2D(dec_filt//2, 3, padding='same', activation='relu')(x)
+    x = UpSampling2D((2,2), interpolation='bilinear')(x)
+
+    x = Conv2D(dec_filt//4, 3, padding='same', activation='relu')(x)
+    x = UpSampling2D((4,4), interpolation='bilinear')(x)
+    if print_shapes: print('Shape after last ups:',x.shape)
+
+    # Final conv to get to a heatmap
+    x = Conv2D(1, kernel_size=1, padding='same', activation='relu')(x)
+    if print_shapes: print('Shape after 1x1 conv:',x.shape)
+
+    return x
+
+
+def decoder_block_dp(x, dil_rate=(2,2), print_shapes=True, dec_filt=1024, dp=0.3):
+    # Dilated convolutions
+    x = Conv2D(dec_filt, 3, padding='same', activation='relu', dilation_rate=dil_rate)(x)
+    x = Conv2D(dec_filt, 3, padding='same', activation='relu', dilation_rate=dil_rate)(x)
+    x = Dropout(dp)(x)
+    x = UpSampling2D((2,2), interpolation='bilinear')(x)
+
+    x = Conv2D(dec_filt//2, 3, padding='same', activation='relu', dilation_rate=dil_rate)(x)
+    x = Conv2D(dec_filt//2, 3, padding='same', activation='relu', dilation_rate=dil_rate)(x)
+    x = Dropout(dp)(x)
+    x = UpSampling2D((2,2), interpolation='bilinear')(x)
+
+    x = Conv2D(dec_filt//4, 3, padding='same', activation='relu', dilation_rate=dil_rate)(x)
+    x = Dropout(dp)(x)
+    x = UpSampling2D((4,4), interpolation='bilinear')(x)
+
+    x = Conv2D(dec_filt//4, 3, padding='same', activation='relu', dilation_rate=dil_rate)(x)
+    x = Dropout(dp)(x)
+    x = UpSampling2D((4,4), interpolation='bilinear')(x)
+    if print_shapes: print('Shape after last ups:',x.shape)
+
+    # Final conv to get to a heatmap
+    x = Conv2D(1, kernel_size=1, padding='same', activation='relu')(x)
+    if print_shapes: print('Shape after 1x1 conv:',x.shape)
+
+    return x
+
+
+
+######### ENCODER DECODER MODELS #############
+
+def xception_decoder(input_shape = (shape_r, shape_c, 3),
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                    dil_rate = (2,2)):
+
+    inp = Input(shape=input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception:',xception.output.shape)
+
+    ## DECODER ##
+    outs_dec = decoder_block(xception.output, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=512)
+
+    outs_final = [outs_dec]*n_outs
+
+    # Building model
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+    return m
+
+
+def resnet_decoder(input_shape = (shape_r, shape_c, 3),
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                    dil_rate = (2,2)):
+    inp = Input(shape=input_shape)
+
+    ### ENCODER ###
+    dcn = dcn_resnet(input_tensor=inp)
+    if print_shapes: print('resnet output shape:',dcn.output.shape)
+
+    ## DECODER ##
+    outs_dec = decoder_block(dcn.output, dil_rate=dil_rate, print_shapes=print_shapes, dec_filt=512)
+
+    outs_final = [outs_dec]*n_outs
+
+    # Building model
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+    return m
+
+
+def fcn_vgg16(input_shape=(shape_r, shape_c, 3),
+                 verbose=True,
+                 print_shapes=True,
+                 n_outs=1,
+                 ups=8,
+                 dil_rate=(2,2),
+                 freeze_enc=False,
+                 freeze_cl=True,
+                 internal_filts=256,
+                 num_classes=4,
+                 dp=0.3,
+                 weight_decay=0.,
+                 batch_shape=None):
+
+    if batch_shape:
+        img_input = Input(batch_shape=batch_shape)
+        image_size = batch_shape[1:3]
+    else:
+        img_input = Input(shape=input_shape)
+        image_size = input_shape[0:2]
+    # Block 1
+    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', kernel_regularizer=l2(weight_decay))(img_input)
+    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', kernel_regularizer=l2(weight_decay))(x)
+    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
+
+    # Block 2
+    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', kernel_regularizer=l2(weight_decay))(x)
+    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', kernel_regularizer=l2(weight_decay))(x)
+    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
+
+    # Block 3
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1', kernel_regularizer=l2(weight_decay))(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2', kernel_regularizer=l2(weight_decay))(x)
+    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3', kernel_regularizer=l2(weight_decay))(x)
+    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
+
+    # Block 4
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1', kernel_regularizer=l2(weight_decay))(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', kernel_regularizer=l2(weight_decay))(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', kernel_regularizer=l2(weight_decay))(x)
+    pool4 = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
+
+    # Block 5
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', kernel_regularizer=l2(weight_decay))(pool4)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', kernel_regularizer=l2(weight_decay))(x)
+    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', kernel_regularizer=l2(weight_decay))(x)
+    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
+    print("pool5 shape", x.shape)
+
+    # Convolutional layers transfered from fully-connected layers
+    x = Conv2D(4096, (7, 7), activation='relu', padding='same', name='fc1', kernel_regularizer=l2(weight_decay))(x)
+    x = Dropout(0.5)(x)
+    x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2', kernel_regularizer=l2(weight_decay))(x)
+    x = Dropout(0.5)(x)
+
+    # classification layer from fc7
+    classif_layer_fc7 = Conv2D(1, (1, 1), kernel_initializer='he_normal', activation='linear',
+                padding='valid', strides=(1, 1))(x)
+    print("classif_layer_fc7 shape", classif_layer_fc7.shape)
+
+    # Upsampling fc7 classif layer to sum with pool4 classif layer
+    classif_layer_fc7_ups = UpSampling2D(size=(2,2), interpolation="bilinear")(classif_layer_fc7)
+    print("classif_layer_fc7_ups shape", classif_layer_fc7_ups.shape)
+
+    # Lambda layer to match shape of pool4
+    def concat_one(fc7):
+        shape_fc7 = K.shape(fc7)
+        shape_zeros = (shape_fc7[0], 1, shape_fc7[2],  shape_fc7[3] )
+        return K.concatenate([K.zeros(shape=shape_zeros), classif_layer_fc7_ups], axis=1)
+    classif_layer_fc7_ups = Lambda(concat_one)(classif_layer_fc7_ups)
+    print("classif_layer_fc7_ups shape after lambda:", classif_layer_fc7_ups.shape)
+
+    # Classification layer from pool4
+    classif_layer_pool4 = Conv2D(1, (1, 1), kernel_initializer='he_normal', activation='linear',
+                padding='valid', strides=(1, 1))(pool4)
+
+    x = Add()([classif_layer_pool4, classif_layer_fc7_ups])
+
+    outs_up = UpSampling2D(size=(32, 32), interpolation="bilinear")(x)
+
+    outs_final = [outs_up]*n_outs
+
+    model = Model(img_input, outs_final)
+
+    weights_path = '../../predimportance_shared/models/ckpt/fcn_vgg16/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5'
+    model.load_weights(weights_path, by_name=True)
+
+    if verbose:
+        model.summary()
+
+    return model
+
+############# SAM BASED MODELS ###############
+
+def sam_simple(input_shape = (224, 224, 3), in_conv_filters=512,
+               verbose=True, print_shapes=True, n_outs=1, ups=8):
+    '''Simple network that uses an attentive convlstm and a few convolutions.'''
+
+    inp = Input(shape=input_shape)
+
+    x = Conv2D(filters=in_conv_filters, kernel_size=(3,3), strides=(2, 2), padding='same', data_format=None, dilation_rate=(1,1))(inp)
+    if print_shapes:
+        print('after first conv')
+
+    x = MaxPooling2D(pool_size=(4,4))(x)
+    if print_shapes:
+        print('after maxpool',x.shape)
+
+    x = Lambda(repeat, repeat_shape)(x)
+    if print_shapes:
+        print('after repeat',x.shape)
+
+    x = AttentiveConvLSTM2D(filters=512, attentive_filters=512, kernel_size=(3,3),
+                            attentive_kernel_size=(3,3), padding='same', return_sequences=False)(x)
+    if print_shapes:
+        print('after ACLSTM',x.shape)
+
+
+    x = UpSampling2D(size=(ups,ups), interpolation='bilinear')(x)
+
+    outs_up = Conv2D(filters=1, kernel_size=(3,3), strides=(1, 1), padding='same', data_format=None, dilation_rate=(1,1))(x)
+    if print_shapes:
+        print('output shape',outs_up.shape)
+
+
+    outs_final = [outs_up]*n_outs
+
+    att_convlstm = Model(inputs=inp, outputs=outs_final)
+    if verbose:
+        att_convlstm.summary()
+
+    return att_convlstm
+
+
+def sam_resnet_nopriors(input_shape = (224, 224, 3), conv_filters=128, lstm_filters=512,
+                        att_filters=512, verbose=True, print_shapes=True, n_outs=1, ups=8):
+    '''Sam ResNet with no priors.'''
+
+    inp = Input(shape=input_shape)
+
+    dcn = dcn_resnet(input_tensor=inp)
+    conv_feat = Conv2D(conv_filters, 3, padding='same', activation='relu')(dcn.output)
+    if print_shapes:
+        print('Shape after first conv after dcn_resnet:',conv_feat.shape)
+
+    # Attentive ConvLSTM
+    att_convlstm = Lambda(repeat, repeat_shape)(conv_feat)
+    att_convlstm = AttentiveConvLSTM2D(filters=lstm_filters, attentive_filters=att_filters, kernel_size=(3,3),
+                            attentive_kernel_size=(3,3), padding='same', return_sequences=False)(att_convlstm)
+
+    # Dilated convolutions (priors would go here)
+    dil_conv1 = Conv2D(conv_filters, 5, padding='same', activation='relu', dilation_rate=(4, 4))(att_convlstm)
+    dil_conv2 = Conv2D(conv_filters, 5, padding='same', activation='relu', dilation_rate=(4, 4))(dil_conv1)
+
+    # Final conv to get to a heatmap
+    outs = Conv2D(1, kernel_size=1, padding='same', activation='relu')(dil_conv2)
+    if print_shapes:
+        print('Shape after 1x1 conv:',outs.shape)
+
+    # Upsampling back to input shape
+    outs_up = UpSampling2D(size=(ups,ups), interpolation='bilinear')(outs)
+    if print_shapes:
+        print('shape after upsampling',outs_up.shape)
+
+
+    outs_final = [outs_up]*n_outs
+
+
+    # Building model
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+
+    return m
+
+
+def sam_resnet_new(input_shape = (shape_r, shape_c, 3),
+                   conv_filters=512, 
+                   lstm_filters=512, 
+                   att_filters=512,
+                   verbose=True, 
+                   print_shapes=True, 
+                   n_outs=1, 
+                   ups=8, 
+                   nb_gaussian=nb_gaussian):
+    '''SAM-ResNet ported from the original code.'''
+
+    inp = Input(shape=input_shape)
+
+    dcn = dcn_resnet(input_tensor=inp)
+    conv_feat = Conv2D(conv_filters, 3, padding='same', activation='relu')(dcn.output)
+    if print_shapes:
+        print('Shape after first conv after dcn_resnet:',conv_feat.shape)
+
+    # Attentive ConvLSTM
+    att_convlstm = Lambda(repeat, repeat_shape)(conv_feat)
+    att_convlstm = AttentiveConvLSTM2D(filters=lstm_filters, 
+                                       attentive_filters=att_filters, 
+                                       kernel_size=(3,3),
+                                       attentive_kernel_size=(3,3), 
+                                       padding='same', 
+                                       return_sequences=False)(att_convlstm)
+
+    # Learned Prior (1)
+    priors1 = LearningPrior(nb_gaussian=nb_gaussian)(att_convlstm)
+    concat1 = Concatenate(axis=-1)([att_convlstm, priors1])
+    dil_conv1 = Conv2D(conv_filters, 5, padding='same', activation='relu', dilation_rate=(4, 4))(concat1)
+
+    # Learned Prior (2)
+    priors2 = LearningPrior(nb_gaussian=nb_gaussian)(att_convlstm)
+    concat2 = Concatenate(axis=-1)([dil_conv1, priors2])
+    dil_conv2 = Conv2D(conv_filters, 5, padding='same', activation='relu', dilation_rate=(4, 4))(concat2)
+
+    # Final conv to get to a heatmap
+    outs = Conv2D(1, kernel_size=1, padding='same', activation='relu')(dil_conv2)
+    if print_shapes:
+        print('Shape after 1x1 conv:',outs.shape)
+
+    # Upsampling back to input shape
+    outs_up = UpSampling2D(size=(ups,ups), interpolation='bilinear')(outs)
+    if print_shapes:
+        print('shape after upsampling',outs_up.shape)
+
+
+    outs_final = [outs_up]*n_outs
+
+
+    # Building model
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+
+    return m
+
+def sam_xception_new(input_shape = (shape_r, shape_c, 3), conv_filters=512, lstm_filters=512, att_filters=512,
+                   verbose=True, print_shapes=True, n_outs=1, ups=8, nb_gaussian=nb_gaussian):
+    '''SAM with a custom Xception as encoder.'''
+
+    inp = Input(shape=input_shape)
+
+    from xception_custom import Xception
+    from keras.applications import keras_modules_injection
+    #@keras_modules_injection
+    def Xception_wrapper(*args, **kwargs):
+        return Xception(*args, **kwargs)
+
+    inp = Input(shape = input_shape)
+    dcn = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception:',dcn.output.shape)
+
+    conv_feat = Conv2D(conv_filters, 3, padding='same', activation='relu')(dcn.output)
+    if print_shapes:
+        print('Shape after first conv after dcn_resnet:',conv_feat.shape)
+
+    # Attentive ConvLSTM
+    att_convlstm = Lambda(repeat, repeat_shape)(conv_feat)
+    att_convlstm = AttentiveConvLSTM2D(filters=lstm_filters, attentive_filters=att_filters, kernel_size=(3,3),
+                            attentive_kernel_size=(3,3), padding='same', return_sequences=False)(att_convlstm)
+
+    # Learned Prior (1)
+    priors1 = LearningPrior(nb_gaussian=nb_gaussian)(att_convlstm)
+    concat1 = Concatenate(axis=-1)([att_convlstm, priors1])
+    dil_conv1 = Conv2D(conv_filters, 5, padding='same', activation='relu', dilation_rate=(4, 4))(concat1)
+
+    # Learned Prior (2)
+    priors2 = LearningPrior(nb_gaussian=nb_gaussian)(att_convlstm)
+    concat2 = Concatenate(axis=-1)([dil_conv1, priors2])
+    dil_conv2 = Conv2D(conv_filters, 5, padding='same', activation='relu', dilation_rate=(4, 4))(concat2)
+
+    # Final conv to get to a heatmap
+    outs = Conv2D(1, kernel_size=1, padding='same', activation='relu')(dil_conv2)
+    if print_shapes:
+        print('Shape after 1x1 conv:',outs.shape)
+
+    # Upsampling back to input shape
+    outs_up = UpSampling2D(size=(ups,ups), interpolation='bilinear')(outs)
+    if print_shapes:
+        print('shape after upsampling',outs_up.shape)
+
+
+    outs_final = [outs_up]*n_outs
+
+
+    # Building model
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+
+    return m
+
+
+
+def xception_se_lstm_singledur(input_shape = (shape_r, shape_c, 3),
+                     conv_filters=256,
+                     lstm_filters=512,
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     freeze_enc=False,
+                     return_sequences=False):
+    inp = Input(shape = input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+	        layer.trainable = False
+
+    ### LSTM over SE representation ###
+    x = se_lstm_block(xception.output, nb_timestep, lstm_filters=lstm_filters, return_sequences=return_sequences)
+
+    ### DECODER ###
+    outs_dec = decoder_block(x, dil_rate=(2,2), print_shapes=print_shapes, dec_filt=conv_filters)
+
+    outs_final = [outs_dec]*n_outs
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+    return m
+
+def se_lstm_block(inp, nb_timestep, units=512, print_shapes=True, lstm_filters=512, return_sequences=False):
+
+    inp_rep = Lambda(lambda y: K.repeat_elements(K.expand_dims(y, axis=1), nb_timestep, axis=1),
+                     lambda s: (s[0], nb_timestep) + s[1:])(inp)
+    x = TimeDistributed(GlobalAveragePooling2D())(inp_rep)
+    if print_shapes: print('shape after AvgPool',x.shape)
+    x = TimeDistributed(Dense(units, activation='relu'))(x)
+    if print_shapes: print('shape after first dense',x.shape)
+
+    # Normally se block would feed into another fully connected. Instead, we feed it to an LSTM.
+    x = LSTM(lstm_filters, return_sequences=return_sequences, unroll=True, activation='relu')(x)
+    if print_shapes: print('shape after lstm',x.shape)
+
+    x = Dense(inp.shape[-1].value, activation='sigmoid')(x)
+    if print_shapes: print('shape after second dense:', x.shape)
+
+    x = Lambda(lambda y: K.expand_dims(K.expand_dims(y, axis=1),axis=1),
+                lambda s: (s[0], 1, 1, s[-1]))(x)
+    if print_shapes: print('shape before mult',x.shape)
+
+    out = Multiply()([x,inp])
+
+    print('shape out',out.shape)
+    # out is (bs, r, c, 2048)
+
+    return out
+
+
+def xception_aspp(input_shape = (shape_r, shape_c, 3),
+                 conv_filters=256,
+                 lstm_filters=512,
+                 verbose=True,
+                 print_shapes=True,
+                 n_outs=1,
+                 ups=8,
+                 freeze_enc=False,
+                 return_sequences=False):
+
+    # Xception
+
+    # Conv1,2,ASPP
+    dil_conv1 = Conv2D(conv_filters, 3, padding='same', activation='relu', dilation_rate=(2, 2))(x)
+    dil_conv2 = Conv2D(conv_filters, 3, padding='same', activation='relu', dilation_rate=(4, 4))(x)
+    dil_conv3 = Conv2D(conv_filters, 3, padding='same', activation='relu', dilation_rate=(8, 8))(x)
+    pass
+
+############# UMSI MODELS ###############
+
+def UMSI(input_shape = (shape_r, shape_c, 3),
+                     conv_filters=256,
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     freeze_enc=False,
+                     return_sequences=False):
+    inp = Input(shape = input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+	        layer.trainable = False
+        #ASPP
+    c0 = Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csep0")(xception.output)
+    c6 = DepthwiseConv2D((3,3),dilation_rate=(6,6),padding="same",use_bias=False,name="aspp_csepd6_depthwise")(xception.output)
+    c12 = DepthwiseConv2D((3,3),dilation_rate=(12,12),padding="same",use_bias=False,name="aspp_csepd12_depthwise")(xception.output)
+    c18 = DepthwiseConv2D((3,3),dilation_rate=(18,18),padding="same",use_bias=False,name="aspp_csepd18_depthwise")(xception.output)
+
+    
+    c6 = BatchNormalization(name="aspp_csepd6_depthwise_BN")(c6)
+    c12 = BatchNormalization(name="aspp_csepd12_depthwise_BN")(c12)
+    c18 = BatchNormalization(name="aspp_csepd18_depthwise_BN")(c18)
+    c6 = Activation("relu", name = "activation_2")(c6)
+    c12 = Activation("relu", name = "activation_4")(c12)
+    c18 = Activation("relu", name = "activation_6")(c18)
+    c6 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd6_pointwise")(c6)
+    c12 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd12_pointwise")(c12)
+    c18 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd18_pointwise")(c18)
+
+    c0 = BatchNormalization(name='aspp0_BN')(c0)
+    c6 = BatchNormalization(name='aspp_csepd6_pointwise_BN')(c6)
+    c12 = BatchNormalization(name='aspp_csepd12_pointwise_BN')(c12)
+    c18 = BatchNormalization(name='aspp_csepd18_pointwise_BN')(c18)
+
+    c0 = Activation("relu", name = "aspp0_activation")(c0)
+    c6 = Activation("relu", name = "activation_3")(c6)
+    c12 = Activation("relu", name = "activation_5")(c12)
+    c18 = Activation("relu", name = "activation_7")(c18)
+
+    concat1 = Concatenate(name="concatenate_1")([c0,c6,c12,c18])
+
+        ### classification module ###
+    x = Conv2D(256, (3,3), strides = (3,3), padding="same",use_bias=False,name = "global_conv")(xception.output)
+    x = BatchNormalization(name="global_BN")(x)
+    x = Activation("relu", name = "activation_1")(x)
+    x = Dropout(.3, name="dropout_1")(x)
+    x = GlobalAveragePooling2D(name = "global_average_pooling2d_1")(x)
+    x = Dense(256, name="global_dense")(x)
+    classif = Dropout(.3, name="dropout_2")(x)
+    out_classif = Dense(6, activation="softmax", name="out_classif")(classif)
+
+
+    x = Dense(256, name="dense_fusion")(classif)
+    def lambda_layer_function(x):
+        x = tf.reshape(x,(tf.shape(x)[0],1,1,256))
+        con = [x for i in range(30)]
+        con = tf.concat(con,axis=1)
+        con = tf.concat([con for i in range(40)],axis=2)
+        return con
+    
+    
+    x = Lambda(lambda_layer_function, name = "lambda_1")(x)
+
+    concat2 = Concatenate(name="concatenate_2")([concat1, x])
+    ### DECODER ###
+    x = Conv2D(256,(1,1),padding="same",use_bias=False,name = "concat_projection")(concat2)
+    x = BatchNormalization(name="concat_projection_BN")(x)
+    x = Activation("relu", name="activation_8")(x)
+    x = Dropout(.3, name="dropout_3")(x)
+    x = Conv2D(256,(3,3),padding="same",use_bias=False,name = "dec_c1")(x)
+    x = Conv2D(256,(3,3),padding="same",use_bias=False,name = "dec_c2")(x)
+    x = Dropout(.3, name="dec_dp1")(x)
+    x = UpSampling2D(size=(2,2), interpolation='bilinear', name="dec_ups1")(x)
+    x = Conv2D(128,(3,3),padding="same",use_bias=False,name = "dec_c3")(x)
+    x = Conv2D(128,(3,3),padding="same",use_bias=False,name = "dec_c4")(x)
+    x = Dropout(.3, name="dec_dp2")(x)
+    x = UpSampling2D(size=(2,2), interpolation='bilinear', name="dec_ups2")(x)
+    x = Conv2D(64,(3,3),padding="same",use_bias=False,name = "dec_c5")(x)
+    x = Dropout(.3, name="dec_dp3")(x)
+    x = UpSampling2D(size=(4,4), interpolation='bilinear', name="dec_ups3")(x)
+    out_heatmap = Conv2D(1,(1,1),padding="same",use_bias=False,name = "dec_c_cout")(x)
+    
+    # Building model
+    outs_final = [out_heatmap, out_classif]
+    print(out_heatmap.shape)
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+
+    return m
+
+
+############# MODELS FOR RecallNet ###############
+
+def RecallNet_UMSI(input_shape = (shape_r, shape_c, 3),
+                     conv_filters=256,
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     freeze_enc=False,
+                     return_sequences=False):
+    inp = Input(shape = input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+	        layer.trainable = False
+    #ASPP
+    c0 = Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csep0")(xception.output)
+    c6 = DepthwiseConv2D((3,3),dilation_rate=(6,6),padding="same",use_bias=False,name="aspp_csepd6_depthwise")(xception.output)
+    c12 = DepthwiseConv2D((3,3),dilation_rate=(12,12),padding="same",use_bias=False,name="aspp_csepd12_depthwise")(xception.output)
+    c18 = DepthwiseConv2D((3,3),dilation_rate=(18,18),padding="same",use_bias=False,name="aspp_csepd18_depthwise")(xception.output)
+
+    
+    c6 = BatchNormalization(name="aspp_csepd6_depthwise_BN")(c6)
+    c12 = BatchNormalization(name="aspp_csepd12_depthwise_BN")(c12)
+    c18 = BatchNormalization(name="aspp_csepd18_depthwise_BN")(c18)
+    c6 = Activation("relu", name = "activation_2")(c6)
+    c12 = Activation("relu", name = "activation_4")(c12)
+    c18 = Activation("relu", name = "activation_6")(c18)
+    c6 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd6_pointwise")(c6)
+    c12 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd12_pointwise")(c12)
+    c18 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd18_pointwise")(c18)
+
+    c0 = BatchNormalization(name='aspp0_BN')(c0)
+    c6 = BatchNormalization(name='aspp_csepd6_pointwise_BN')(c6)
+    c12 = BatchNormalization(name='aspp_csepd12_pointwise_BN')(c12)
+    c18 = BatchNormalization(name='aspp_csepd18_pointwise_BN')(c18)
+
+    c0 = Activation("relu", name = "aspp0_activation")(c0)
+    c6 = Activation("relu", name = "activation_3")(c6)
+    c12 = Activation("relu", name = "activation_5")(c12)
+    c18 = Activation("relu", name = "activation_7")(c18)
+
+    concat1 = Concatenate(name="concatenate_1")([c0,c6,c12,c18])
+
+        ### classification module ###
+    x = Conv2D(256, (3,3), strides = (3,3), padding="same",use_bias=False,name = "global_conv")(xception.output)
+    x = BatchNormalization(name="global_BN")(x)
+    x = Activation("relu", name = "activation_1")(x)
+    x = Dropout(.3, name="dropout_1")(x)
+    x = GlobalAveragePooling2D(name = "global_average_pooling2d_1")(x)
+    x = Dense(256, name="global_dense")(x)
+    classif = Dropout(.3, name="dropout_2")(x)
+    out_classif = Dense(6, activation="softmax", name="out_classif")(classif)
+
+
+    x = Dense(256, name="dense_fusion")(classif)
+    def lambda_layer_function(x):
+        x = tf.reshape(x,(tf.shape(x)[0],1,1,256))
+        con = [x for i in range(30)]
+        con = tf.concat(con,axis=1)
+        con = tf.concat([con for i in range(40)],axis=2)
+        return con
+    
+    
+    x = Lambda(lambda_layer_function, name = "lambda_1")(x)
+
+    concat2 = Concatenate(name="concatenate_2")([concat1, x])
+    ### DECODER ###
+    flatten = GlobalAveragePooling2D(name = "global_average_pooling2d_2")(concat2)
+    mean_acc = Dense(256, name="mean_dense")(flatten)
+    mean_acc = Dense(1, name='out_mean_acc')(mean_acc)
+
+    type0_acc = Dense(256, name="type0_dense")(flatten)
+    type0_acc = Dense(1, name='out_type0_acc')(type0_acc)
+
+    # Building model
+    outs_final = [type0_acc, mean_acc, out_classif]
+
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+
+    return m
+
+#Model for RecallNet
+def RecallNet_xception_aspp(input_shape = (shape_r, shape_c, 3),
+                     conv_filters=256,
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     freeze_enc=False,
+                     return_sequences=False):
+    inp = Input(shape = input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+	        layer.trainable = False
+        #ASPP
+    c0 = Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csep0")(xception.output)
+    c6 = DepthwiseConv2D((3,3),dilation_rate=(6,6),padding="same",use_bias=False,name="aspp_csepd6_depthwise")(xception.output)
+    c12 = DepthwiseConv2D((3,3),dilation_rate=(12,12),padding="same",use_bias=False,name="aspp_csepd12_depthwise")(xception.output)
+    c18 = DepthwiseConv2D((3,3),dilation_rate=(18,18),padding="same",use_bias=False,name="aspp_csepd18_depthwise")(xception.output)
+
+    
+    c6 = BatchNormalization(name="aspp_csepd6_depthwise_BN")(c6)
+    c12 = BatchNormalization(name="aspp_csepd12_depthwise_BN")(c12)
+    c18 = BatchNormalization(name="aspp_csepd18_depthwise_BN")(c18)
+    c6 = Activation("relu", name = "activation_2")(c6)
+    c12 = Activation("relu", name = "activation_4")(c12)
+    c18 = Activation("relu", name = "activation_6")(c18)
+    c6 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd6_pointwise")(c6)
+    c12 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd12_pointwise")(c12)
+    c18 =  Conv2D(256,(1,1),padding="same",use_bias=False,name = "aspp_csepd18_pointwise")(c18)
+
+    c0 = BatchNormalization(name='aspp0_BN')(c0)
+    c6 = BatchNormalization(name='aspp_csepd6_pointwise_BN')(c6)
+    c12 = BatchNormalization(name='aspp_csepd12_pointwise_BN')(c12)
+    c18 = BatchNormalization(name='aspp_csepd18_pointwise_BN')(c18)
+
+    c0 = Activation("relu", name = "aspp0_activation")(c0)
+    c6 = Activation("relu", name = "activation_3")(c6)
+    c12 = Activation("relu", name = "activation_5")(c12)
+    c18 = Activation("relu", name = "activation_7")(c18)
+
+    concat1 = Concatenate(name="concatenate_1")([c0,c6,c12,c18])
+
+    ### classification module ###
+    x = Conv2D(256, (3,3), strides = (3,3), padding="same",use_bias=False,name = "global_conv")(xception.output)
+    x = BatchNormalization(name="global_BN")(x)
+    x = Activation("relu", name = "activation_1")(x)
+    x = Dropout(.3, name="dropout_1")(x)
+    x = GlobalAveragePooling2D(name = "global_average_pooling2d_1")(x)
+    x = Dense(256, name="global_dense")(x)
+    classif = Dropout(.3, name="dropout_2")(x)
+    #out_classif = Dense(6, activation="softmax", name="out_classif")(classif)
+
+
+    x = Dense(256, name="dense_fusion")(classif)
+    def lambda_layer_function(x):
+        x = tf.reshape(x,(tf.shape(x)[0],1,1,256))
+        con = [x for i in range(30)]
+        con = tf.concat(con,axis=1)
+        con = tf.concat([con for i in range(40)],axis=2)
+        return con
+    
+    
+    x = Lambda(lambda_layer_function, name = "lambda_1")(x)
+
+    concat2 = Concatenate(name="concatenate_2")([concat1, x])
+    ### DECODER ###
+    flatten = GlobalAveragePooling2D(name = "global_average_pooling2d_2")(concat2)
+    mean_acc = Dense(256, name="mean_dense")(flatten)
+    mean_acc = Dense(1, name='out_mean_acc')(mean_acc)
+
+    type0_acc = Dense(256, name="type0_dense")(flatten)
+    type0_acc = Dense(1, name='out_type0_acc')(type0_acc)
+    
+    # Building model
+    outs_final = [mean_acc, type0_acc]
+
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+
+    return m
+
+
+
+#Model for VMQA 
+def RecallNet_xception(input_shape = (shape_r, shape_c, 3),
+                     conv_filters=256,
+                     verbose=True,
+                     print_shapes=True,
+                     n_outs=1,
+                     ups=8,
+                     freeze_enc=False,
+                     return_sequences=False):
+    inp = Input(shape = input_shape)
+
+    ### ENCODER ###
+    xception = Xception_wrapper(include_top=False, weights='imagenet', input_tensor=inp, pooling=None)
+    if print_shapes: print('xception output shapes:',xception.output.shape)
+    if freeze_enc:
+        for layer in xception.layers:
+	        layer.trainable = False
+
+    x = Conv2D(256, (3,3), strides = (3,3), padding="same",use_bias=False,name = "global_conv")(xception.output)
+    x = BatchNormalization(name="global_BN")(x)
+    x = Activation("relu", name = "activation_1")(x)
+    x = Dropout(.3, name="dropout_1")(x)
+    x = GlobalAveragePooling2D(name = "global_average_pooling2d_1")(x)
+    x = Dense(256, name="global_dense")(x)
+    classif = Dropout(.3, name="dropout_2")(x)
+#    out_classif = Dense(6, activation="softmax", name="out_classif")(classif)
+
+    mean_acc = Dense(1, name='out_mean_acc')(classif)
+    type_0 = Dense(1, name='out_type0_acc')(classif)
+    
+    # Building model
+    outs_final = [ mean_acc, type_0]
+
+    m = Model(inp, outs_final)
+    if verbose:
+        m.summary()
+
+    return m
+
diff --git a/RecallNet/src/util.py b/RecallNet/src/util.py
new file mode 100644
index 0000000..a96f9e3
--- /dev/null
+++ b/RecallNet/src/util.py
@@ -0,0 +1,67 @@
+from singleduration_models import sam_resnet_new, UMSI, RecallNet_UMSI, RecallNet_xception, RecallNet_xception_aspp
+from losses_keras2 import loss_wrapper, kl_time, cc_time, nss_time, cc_match, kl_cc_combined
+
+MODELS = {
+    'sam-resnet': (sam_resnet_new, 'simple'),
+    "UMSI": (UMSI, "simple"),
+    'RecallNet_xception':(RecallNet_xception,'simple'),
+    'RecallNet_xception_aspp':(RecallNet_xception_aspp,'simple'),
+    'RecallNet_UMSI':(RecallNet_UMSI,'simple')
+}
+
+LOSSES = {
+    'kl': (kl_time, 'heatmap'),
+    'cc': (cc_time, 'heatmap'),
+    'nss': (nss_time, 'fixmap'),
+    'ccmatch': (cc_match, 'heatmap'),
+    "kl+cc": (kl_cc_combined, "heatmap")
+}
+
+def get_model_by_name(name): 
+    """ Returns a model and a string indicating its mode of use."""
+    if name not in MODELS: 
+        allowed_models = list(MODELS.keys())
+        raise RuntimeError("Model %s is not recognized. Please choose one of: %s" % (name, ",".join(allowed_models)))
+    else: 
+        return MODELS[name]
+
+def get_loss_by_name(name, out_size): 
+    """Gets the loss associated with a certain name. 
+
+    If there is no custom loss associated with name `name`, returns the string
+    `name` so that keras can interpret it as a keras loss.
+    """
+    if name not in LOSSES: 
+        print("WARNING: found no custom loss with name %s, defaulting to a string." % name)
+        return name, 'heatmap'
+    else: 
+        loss, out_type = LOSSES[name]
+        loss = loss_wrapper(loss, out_size)
+        return loss, out_type
+
+def create_losses(loss_dict, out_size): 
+    """Given a dictionary that maps loss names to weights, returns loss functions and weights in the correct order. 
+
+    By convention, losses that take in a heatmap (as opposed to a fixmap) come first in the array of losses. This function enforces that convention.
+
+    This function looks up the correct loss function by name and outputs the correct functions, ordering, and weights to pass to the model/generator.
+    """
+    l_hm = []
+    l_hm_w = [] 
+    l_fm = []
+    l_fm_w = []
+    lstr = ""
+    for lname, wt in loss_dict.items(): 
+        loss, out_type = get_loss_by_name(lname, out_size)    
+        if out_type == 'heatmap': 
+            l_hm.append(loss)
+            l_hm_w.append(wt)
+        else: 
+            l_fm.append(loss)
+            l_fm_w.append(wt)
+        lstr += lname + str(wt)
+
+    l = l_hm + l_fm
+    lw = l_hm_w + l_fm_w
+    n_heatmaps = len(l_hm)
+    return l, lw, lstr, n_heatmaps    
diff --git a/RecallNet/src/xception_custom.py b/RecallNet/src/xception_custom.py
new file mode 100644
index 0000000..cb00726
--- /dev/null
+++ b/RecallNet/src/xception_custom.py
@@ -0,0 +1,325 @@
+"""Xception V1 model for Keras.
+On ImageNet, this model gets to a top-1 validation accuracy of 0.790
+and a top-5 validation accuracy of 0.945.
+Do note that the input image format for this model is different than for
+the VGG16 and ResNet models (299x299 instead of 224x224),
+and that the input preprocessing function
+is also different (same as Inception V3).
+# Reference
+- [Xception: Deep Learning with Depthwise Separable Convolutions](
+    https://arxiv.org/abs/1610.02357)
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import warnings
+
+from keras_applications import get_submodules_from_kwargs
+from keras.applications import imagenet_utils
+from keras.applications.imagenet_utils import decode_predictions
+from keras_applications.imagenet_utils import _obtain_input_shape
+from keras.applications import keras_modules_injection
+
+TF_WEIGHTS_PATH = (
+    'https://github.com/fchollet/deep-learning-models/'
+    'releases/download/v0.4/'
+    'xception_weights_tf_dim_ordering_tf_kernels.h5')
+TF_WEIGHTS_PATH_NO_TOP = (
+    'https://github.com/fchollet/deep-learning-models/'
+    'releases/download/v0.4/'
+    'xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
+
+@keras_modules_injection
+def Xception_wrapper(*args, **kwargs):
+    return Xception(*args, **kwargs)
+
+def Xception(include_top=True,
+             weights='imagenet',
+             input_tensor=None,
+             input_shape=None,
+             pooling=None,
+             classes=1000,
+             **kwargs):
+    """Instantiates the Xception architecture.
+    Optionally loads weights pre-trained on ImageNet. This model can
+    only be used with the data format `(width, height, channels)`.
+    You should set `image_data_format='channels_last'` in your Keras config
+    located at ~/.keras/keras.json.
+    Note that the default input image size for this model is 299x299.
+    # Arguments
+        include_top: whether to include the fully-connected
+            layer at the top of the network.
+        weights: one of `None` (random initialization),
+              'imagenet' (pre-training on ImageNet),
+              or the path to the weights file to be loaded.
+        input_tensor: optional Keras tensor
+            (i.e. output of `layers.Input()`)
+            to use as image input for the model.
+        input_shape: optional shape tuple, only to be specified
+            if `include_top` is False (otherwise the input shape
+            has to be `(299, 299, 3)`.
+            It should have exactly 3 inputs channels,
+            and width and height should be no smaller than 71.
+            E.g. `(150, 150, 3)` would be one valid value.
+        pooling: Optional pooling mode for feature extraction
+            when `include_top` is `False`.
+            - `None` means that the output of the model will be
+                the 4D tensor output of the
+                last convolutional block.
+            - `avg` means that global average pooling
+                will be applied to the output of the
+                last convolutional block, and thus
+                the output of the model will be a 2D tensor.
+            - `max` means that global max pooling will
+                be applied.
+        classes: optional number of classes to classify images
+            into, only to be specified if `include_top` is True,
+            and if no `weights` argument is specified.
+    # Returns
+        A Keras model instance.
+    # Raises
+        ValueError: in case of invalid argument for `weights`,
+            or invalid input shape.
+        RuntimeError: If attempting to run this model with a
+            backend that does not support separable convolutions.
+    """
+    backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
+
+    if not (weights in {'imagenet', None} or os.path.exists(weights)):
+        raise ValueError('The `weights` argument should be either '
+                         '`None` (random initialization), `imagenet` '
+                         '(pre-training on ImageNet), '
+                         'or the path to the weights file to be loaded.')
+
+    if weights == 'imagenet' and include_top and classes != 1000:
+        raise ValueError('If using `weights` as `"imagenet"` with `include_top`'
+                         ' as true, `classes` should be 1000')
+
+    if backend.image_data_format() != 'channels_last':
+        warnings.warn('The Xception model is only available for the '
+                      'input data format "channels_last" '
+                      '(width, height, channels). '
+                      'However your settings specify the default '
+                      'data format "channels_first" '
+                      '(channels, width, height). '
+                      'You should set `image_data_format="channels_last"` '
+                      'in your Keras '
+                      'config located at ~/.keras/keras.json. '
+                      'The model being returned right now will expect inputs '
+                      'to follow the "channels_last" data format.')
+        backend.set_image_data_format('channels_last')
+        old_data_format = 'channels_first'
+    else:
+        old_data_format = None
+
+    # Determine proper input shape
+    input_shape = _obtain_input_shape(input_shape,
+                                      default_size=299,
+                                      min_size=71,
+                                      data_format=backend.image_data_format(),
+                                      require_flatten=include_top,
+                                      weights=weights)
+
+    if input_tensor is None:
+        img_input = layers.Input(shape=input_shape)
+    else:
+        if not backend.is_keras_tensor(input_tensor):
+            img_input = layers.Input(tensor=input_tensor, shape=input_shape)
+        else:
+            img_input = input_tensor
+
+    x = layers.Conv2D(32, (3, 3),
+                      strides=(2, 2),
+                      use_bias=False,
+                      name='block1_conv1')(img_input)
+    x = layers.BatchNormalization(name='block1_conv1_bn')(x)
+    x = layers.Activation('relu', name='block1_conv1_act')(x)
+    x = layers.Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
+    x = layers.BatchNormalization(name='block1_conv2_bn')(x)
+    x = layers.Activation('relu', name='block1_conv2_act')(x)
+
+    residual = layers.Conv2D(128, (1, 1),
+                             strides=(2, 2),
+                             padding='same',
+                             use_bias=False)(x)
+    residual = layers.BatchNormalization()(residual)
+
+    x = layers.SeparableConv2D(128, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block2_sepconv1')(x)
+    x = layers.BatchNormalization(name='block2_sepconv1_bn')(x)
+    x = layers.Activation('relu', name='block2_sepconv2_act')(x)
+    x = layers.SeparableConv2D(128, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block2_sepconv2')(x)
+    x = layers.BatchNormalization(name='block2_sepconv2_bn')(x)
+
+    x = layers.MaxPooling2D((3, 3),
+                            strides=(2, 2),
+                            padding='same',
+                            name='block2_pool')(x)
+    x = layers.add([x, residual])
+
+    residual = layers.Conv2D(256, (1, 1), strides=(2, 2),
+                             padding='same', use_bias=False)(x)
+    residual = layers.BatchNormalization()(residual)
+
+    x = layers.Activation('relu', name='block3_sepconv1_act')(x)
+    x = layers.SeparableConv2D(256, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block3_sepconv1')(x)
+    x = layers.BatchNormalization(name='block3_sepconv1_bn')(x)
+    x = layers.Activation('relu', name='block3_sepconv2_act')(x)
+    x = layers.SeparableConv2D(256, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block3_sepconv2')(x)
+    x = layers.BatchNormalization(name='block3_sepconv2_bn')(x)
+
+    x = layers.MaxPooling2D((3, 3), strides=(2, 2),
+                            padding='same',
+                            name='block3_pool')(x)
+    x = layers.add([x, residual])
+
+    residual = layers.Conv2D(728, (1, 1),
+                             strides=(1, 1),# ORIGINAL (2,2)
+                             padding='same',
+                             use_bias=False)(x)
+    residual = layers.BatchNormalization()(residual)
+
+    x = layers.Activation('relu', name='block4_sepconv1_act')(x)
+    x = layers.SeparableConv2D(728, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block4_sepconv1')(x)
+    x = layers.BatchNormalization(name='block4_sepconv1_bn')(x)
+    x = layers.Activation('relu', name='block4_sepconv2_act')(x)
+    x = layers.SeparableConv2D(728, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block4_sepconv2')(x)
+    x = layers.BatchNormalization(name='block4_sepconv2_bn')(x)
+
+    x = layers.MaxPooling2D((3, 3), strides=(1, 1),# ORIGINAL (2,2)
+                            padding='same',
+                            name='block4_pool')(x)
+    x = layers.add([x, residual])
+
+    for i in range(8):
+        residual = x
+        prefix = 'block' + str(i + 5)
+
+        x = layers.Activation('relu', name=prefix + '_sepconv1_act')(x)
+        x = layers.SeparableConv2D(728, (3, 3),
+                                   padding='same',
+                                   use_bias=False,
+                                   name=prefix + '_sepconv1')(x)
+        x = layers.BatchNormalization(name=prefix + '_sepconv1_bn')(x)
+        x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x)
+        x = layers.SeparableConv2D(728, (3, 3),
+                                   padding='same',
+                                   use_bias=False,
+                                   name=prefix + '_sepconv2')(x)
+        x = layers.BatchNormalization(name=prefix + '_sepconv2_bn')(x)
+        x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x)
+        x = layers.SeparableConv2D(728, (3, 3),
+                                   padding='same',
+                                   use_bias=False,
+                                   name=prefix + '_sepconv3')(x)
+        x = layers.BatchNormalization(name=prefix + '_sepconv3_bn')(x)
+
+        x = layers.add([x, residual])
+
+    residual = layers.Conv2D(1024, (1, 1), strides=(1, 1),# ORIGINAL (2,2)
+                             padding='same', use_bias=False)(x)
+    residual = layers.BatchNormalization()(residual)
+
+    x = layers.Activation('relu', name='block13_sepconv1_act')(x)
+    x = layers.SeparableConv2D(728, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block13_sepconv1')(x)
+    x = layers.BatchNormalization(name='block13_sepconv1_bn')(x)
+    x = layers.Activation('relu', name='block13_sepconv2_act')(x)
+    x = layers.SeparableConv2D(1024, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block13_sepconv2')(x)
+    x = layers.BatchNormalization(name='block13_sepconv2_bn')(x)
+
+    x = layers.MaxPooling2D((3, 3),
+                            strides=(1, 1), # ORIGINAL (2,2)
+                            padding='same',
+                            name='block13_pool')(x)
+    x = layers.add([x, residual])
+
+    x = layers.SeparableConv2D(1536, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block14_sepconv1')(x)
+    x = layers.BatchNormalization(name='block14_sepconv1_bn')(x)
+    x = layers.Activation('relu', name='block14_sepconv1_act')(x)
+
+    x = layers.SeparableConv2D(2048, (3, 3),
+                               padding='same',
+                               use_bias=False,
+                               name='block14_sepconv2')(x)
+    x = layers.BatchNormalization(name='block14_sepconv2_bn')(x)
+    x = layers.Activation('relu', name='block14_sepconv2_act')(x)
+
+    if include_top:
+        x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
+        x = layers.Dense(classes, activation='softmax', name='predictions')(x)
+    else:
+        if pooling == 'avg':
+            x = layers.GlobalAveragePooling2D()(x)
+        elif pooling == 'max':
+            x = layers.GlobalMaxPooling2D()(x)
+
+    # Ensure that the model takes into account
+    # any potential predecessors of `input_tensor`.
+    if input_tensor is not None:
+        inputs = keras_utils.get_source_inputs(input_tensor)
+    else:
+        inputs = img_input
+    # Create model.
+    model = models.Model(inputs, x, name='xception')
+
+    # Load weights.
+    if weights == 'imagenet':
+        if include_top:
+            weights_path = keras_utils.get_file(
+                'xception_weights_tf_dim_ordering_tf_kernels.h5',
+                TF_WEIGHTS_PATH,
+                cache_subdir='models',
+                file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
+        else:
+            weights_path = keras_utils.get_file(
+                'xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
+                TF_WEIGHTS_PATH_NO_TOP,
+                cache_subdir='models',
+                file_hash='b0042744bf5b25fce3cb969f33bebb97')
+        model.load_weights(weights_path)
+        if backend.backend() == 'theano':
+            keras_utils.convert_all_kernels_in_model(model)
+    elif weights is not None:
+        model.load_weights(weights)
+
+    if old_data_format:
+        backend.set_image_data_format(old_data_format)
+    return model
+
+
+def preprocess_input(x, **kwargs):
+    """Preprocesses a numpy array encoding a batch of images.
+    # Arguments
+        x: a 4D numpy array consists of RGB values within [0, 255].
+    # Returns
+        Preprocessed array.
+    """
+    return imagenet_utils.preprocess_input(x, mode='tf', **kwargs)
diff --git a/VisRecall/answer_raw/group1.csv b/VisRecall/answer_raw/group1.csv
new file mode 100755
index 0000000..809106b
--- /dev/null
+++ b/VisRecall/answer_raw/group1.csv
@@ -0,0 +1,78 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+1,"['A', 'D', 'D', 'D', 'D', 'A', 'D', 'A', 'D', 'D', 'C', 'C', 'A', 'D', 'D', 'B', 'D', 'B', 'B', 'A', 'A', 'D', 'B', 'D', 'D', 'C', 'B', 'C', 'C', 'D', 'B', 'A', 'A', 'C', 'D', 'B', 'A', 'C', 'D', 'C', 'C', 'C', 'B', 'A', 'D', 'C', 'B', 'D', 'D', 'D', 'A', 'C', 'D', 'A', 'C', 'C', 'B', 'A', 'B', 'B', 'A', 'C', 'C', 'B', 'C', 'B', 'D', 'A', 'C', 'D', 'C', 'B', 'A', 'A', 'C', 'B', 'B', 'A', 'A', 'B', 'A', 'C', 'A', 'B', 'B', 'B', 'C', 'B', 'D', 'D', 'A', 'D', 'C', 'B', 'C', 'C', 'A', 'D', 'A', 'A']","['2', '1', '1', '2', '2', '1', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '1', '2', '1']","{'openTime': '2021-08-11T14:37:58.505Z', 'loadTime': '2021-08-11T14:38:06.986Z', 'startTime': '2021-08-11T15:12:30.541Z', 'endTime': '2021-08-11T15:34:37.940Z', 'timeToCompleteFromOpenMsec': 3399435, 'timeToLoadMsec': 8481, 'timeToCompleteFromStartMsec': 1327399}","{'gender': 'male', 'ageGroup': 'ageGroup18', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': '', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'W38', 'isValidCode': True, 'coordinate': [601, 56]}, '2': {'rememberedCode': 'W38', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W38', 'isValidCode': True, 'coordinate': [601, 56]}, '4': {'rememberedCode': 'Z76', 'isValidCode': True, 'coordinate': [468, 128]}, '5': {'rememberedCode': 'T61', 'isValidCode': True, 'coordinate': [649, 219]}, '6': {'rememberedCode': 'T61', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'T61', 'isValidCode': True, 'coordinate': [649, 219]}, '8': {'rememberedCode': 'K92', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'W78', 'isValidCode': True, 'coordinate': [1163, 692]}, '10': {'rememberedCode': 'W78', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'W78', 'isValidCode': True, 'coordinate': [1163, 692]}, '12': {'rememberedCode': 'T32', 'isValidCode': True, 'coordinate': [789, 430]}, '13': {'rememberedCode': 'P33', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'P33', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'P33', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'B68', 'isValidCode': True, 'coordinate': [730, 1095]}, '17': {'rememberedCode': 'V62', 'isValidCode': True, 'coordinate': [1460, 892]}, '18': {'rememberedCode': 'V62', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V62', 'isValidCode': True, 'coordinate': [1460, 892]}, '20': {'rememberedCode': 'C63', 'isValidCode': True, 'coordinate': [563, 418]}, '21': {'rememberedCode': 'F38', 'isValidCode': True, 'coordinate': [879, 744]}, '22': {'rememberedCode': 'F38', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'F38', 'isValidCode': True, 'coordinate': [879, 744]}, '24': {'rememberedCode': 'H64', 'isValidCode': True, 'coordinate': [473, 308]}, '25': {'rememberedCode': 'H67', 'isValidCode': True, 'coordinate': [1469, 611]}, '26': {'rememberedCode': 'H67', 'isValidCode': True, 'coordinate': [559, 325]}, '27': {'rememberedCode': 'H67', 'isValidCode': True, 'coordinate': [1469, 611]}, '28': {'rememberedCode': '', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'Z53', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'Z53', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Z53', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'E38', 'isValidCode': True, 'coordinate': [164, 729]}, '33': {'rememberedCode': 'T39', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'T39', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'T39', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'L87', 'isValidCode': True, 'coordinate': [285, 230]}, '37': {'rememberedCode': 'D59', 'isValidCode': True, 'coordinate': [863, 531]}, '38': {'rememberedCode': 'D59', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'D59', 'isValidCode': True, 'coordinate': [863, 531]}}",598,31
+2,"['A', 'C', 'C', 'C', 'A', 'B', 'C', 'C', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'B', 'D', 'C', 'D', 'D', 'A', 'B', 'B', 'D', 'C', 'C', 'D', 'C', 'D', 'C', 'B', 'D', 'A', 'D', 'D', 'B', 'A', 'C', 'D', 'C', 'C', 'C', 'B', 'A', 'A', 'D', 'B', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'C', 'D', 'A', 'B', 'D', 'A', 'D', 'C', 'B', 'D', 'B', 'A', 'D', 'D', 'D', 'D', 'D', 'A', 'A', 'C', 'B', 'B', 'D', 'D', 'D', 'A', 'C', 'B', 'C', 'D', 'B', 'B', 'C', 'D', 'D', 'A', 'D', 'C', 'B', 'C', 'C', 'A', 'D', 'A', 'A']","['2', '1', '1', '2', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1']","{'openTime': '2021-08-11T16:32:27.930Z', 'loadTime': '2021-08-11T16:32:38.908Z', 'startTime': '2021-08-11T16:37:54.080Z', 'endTime': '2021-08-11T16:55:31.533Z', 'timeToCompleteFromOpenMsec': 1383603, 'timeToLoadMsec': 10978, 'timeToCompleteFromStartMsec': 1057453}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'M63', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'S28', 'isValidCode': True, 'coordinate': [857, 331]}, '2': {'rememberedCode': 'S28', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'S28', 'isValidCode': True, 'coordinate': [857, 331]}, '4': {'rememberedCode': 'H18', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'N41', 'isValidCode': True, 'coordinate': [234, 533]}, '6': {'rememberedCode': 'N41', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N41', 'isValidCode': True, 'coordinate': [234, 533]}, '8': {'rememberedCode': 'H68', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'F23', 'isValidCode': True, 'coordinate': [1435, 294]}, '10': {'rememberedCode': 'F23', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'F23', 'isValidCode': True, 'coordinate': [1435, 294]}, '12': {'rememberedCode': 'D69', 'isValidCode': True, 'coordinate': [1597, 697]}, '13': {'rememberedCode': 'C78', 'isValidCode': True, 'coordinate': [598, 998]}, '14': {'rememberedCode': 'C78', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C78', 'isValidCode': True, 'coordinate': [598, 998]}, '16': {'rememberedCode': 'B91', 'isValidCode': True, 'coordinate': [743, 644]}, '17': {'rememberedCode': 'Q18', 'isValidCode': True, 'coordinate': [1074, 436]}, '18': {'rememberedCode': 'Q18', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Q18', 'isValidCode': True, 'coordinate': [1074, 436]}, '20': {'rememberedCode': 'M18', 'isValidCode': True, 'coordinate': [1159, 206]}, '21': {'rememberedCode': 'L28', 'isValidCode': True, 'coordinate': [754, 1222]}, '22': {'rememberedCode': 'L28', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L28', 'isValidCode': True, 'coordinate': [754, 1222]}, '24': {'rememberedCode': 'J97', 'isValidCode': True, 'coordinate': [994, 607]}, '25': {'rememberedCode': 'E95', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'E95', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'E95', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'P47', 'isValidCode': True, 'coordinate': [834, 218]}, '29': {'rememberedCode': 'H71', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'H71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'H71', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X41', 'isValidCode': True, 'coordinate': [432, 1112]}, '33': {'rememberedCode': 'K89', 'isValidCode': True, 'coordinate': [1070, 312]}, '34': {'rememberedCode': 'K89', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'K89', 'isValidCode': True, 'coordinate': [1070, 312]}, '36': {'rememberedCode': 'G61', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'C62', 'isValidCode': True, 'coordinate': [1307, 355]}, '38': {'rememberedCode': 'C62', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'C62', 'isValidCode': True, 'coordinate': [1307, 355]}}",987,787
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+4,"['A', 'A', 'D', 'C', 'D', 'B', 'D', 'A', 'D', 'B', 'A', 'D', 'D', 'D', 'C', 'C', 'A', 'A', 'B', 'A', 'A', 'A', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'A', 'C', 'C', 'B', 'A', 'C', 'C', 'D', 'C', 'C', 'D', 'D', 'D', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'D', 'B', 'D', 'C', 'C', 'C', 'D', 'C', 'A', 'A', 'C', 'D', 'C', 'B', 'A', 'B', 'D', 'D', 'A', 'C', 'C', 'A', 'D', 'B', 'C', 'B', 'B', 'A', 'A', 'A', 'A', 'A', 'D', 'B', 'A', 'B', 'B', 'D', 'D', 'D', 'D', 'B', 'B', 'B', 'D', 'D', 'D', 'D']","['2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '1', '1', '2', '2', '1', '2', '2', '1', '1', '1']","{'openTime': '2021-08-11T16:55:58.407Z', 'loadTime': '2021-08-11T16:56:20.466Z', 'startTime': '2021-08-11T16:56:30.864Z', 'endTime': '2021-08-11T17:17:22.934Z', 'timeToCompleteFromOpenMsec': 1284527, 'timeToLoadMsec': 22059, 'timeToCompleteFromStartMsec': 1252070}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'S29', 'isValidCode': True, 'coordinate': [1037, 23]}, '1': {'rememberedCode': 'A23', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'A23', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'A23', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'K19', 'isValidCode': True, 'coordinate': [1317, 122]}, '5': {'rememberedCode': 'U58', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'U58', 'isValidCode': True, 'coordinate': [1141, 143]}, '7': {'rememberedCode': 'U58', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'E19', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'K75', 'isValidCode': True, 'coordinate': [213, 105]}, '10': {'rememberedCode': 'K75', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K75', 'isValidCode': True, 'coordinate': [213, 105]}, '12': {'rememberedCode': 'N81', 'isValidCode': True, 'coordinate': [610, 399]}, '13': {'rememberedCode': 'T19', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'T19', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T19', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'U95', 'isValidCode': True, 'coordinate': [290, 520]}, '17': {'rememberedCode': 'H98', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'H98', 'isValidCode': True, 'coordinate': [1285, 924]}, '19': {'rememberedCode': 'H98', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'G63', 'isValidCode': True, 'coordinate': [842, 781]}, '21': {'rememberedCode': 'V59', 'isValidCode': True, 'coordinate': [897, 261]}, '22': {'rememberedCode': 'V59', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'V59', 'isValidCode': True, 'coordinate': [897, 261]}, '24': {'rememberedCode': 'V43', 'isValidCode': True, 'coordinate': [753, 498]}, '25': {'rememberedCode': 'E75', 'isValidCode': True, 'coordinate': [1039, 380]}, '26': {'rememberedCode': 'E75', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'E75', 'isValidCode': True, 'coordinate': [1039, 380]}, '28': {'rememberedCode': 'V32', 'isValidCode': True, 'coordinate': [737, 511]}, '29': {'rememberedCode': 'L38', 'isValidCode': True, 'coordinate': [1157, 348]}, '30': {'rememberedCode': 'L38', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L38', 'isValidCode': True, 'coordinate': [1157, 348]}, '32': {'rememberedCode': 'W95', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'R95', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'R95', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'R95', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'W37', 'isValidCode': True, 'coordinate': [448, 976]}, '37': {'rememberedCode': 'L62', 'isValidCode': True, 'coordinate': [285, 256]}, '38': {'rememberedCode': 'L62', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L62', 'isValidCode': True, 'coordinate': [285, 256]}}",455,408
+5,"['C', 'D', 'D', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'A', 'D', 'B', 'C', 'D', 'C', 'D', 'C', 'D', 'D', 'B', 'A', 'A', 'C', 'D', 'B', 'A', 'C', 'D', 'D', 'C', 'D', 'B', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'C', 'B', 'A', 'C', 'D', 'A', 'D', 'C', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'C', 'D', 'A', 'C', 'C', 'B', 'B', 'A', 'B', 'D', 'A', 'D', 'A', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'C', 'A', 'D', 'A', 'A']","['2', '1', '1', '2', '2', '2', '2', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '1', '1', '2', '1']","{'openTime': '2021-08-11T17:43:11.603Z', 'loadTime': '2021-08-11T17:43:28.195Z', 'startTime': '2021-08-11T17:45:09.959Z', 'endTime': '2021-08-11T18:03:27.112Z', 'timeToCompleteFromOpenMsec': 1215509, 'timeToLoadMsec': 16592, 'timeToCompleteFromStartMsec': 1097153}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'someCollege', 'vizExperience': 'littleExposure', 'feedback': 'You need to provide more time on the screen that shows the 3 digit character codes. It flashed by so fast it made it difficult to remember one. I know i messed up a few.'}","{'0': {'rememberedCode': 'AJ4', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'X98', 'isValidCode': True, 'coordinate': [1756, 823]}, '2': {'rememberedCode': 'X98', 'isValidCode': True, 'coordinate': [1462, 969]}, '3': {'rememberedCode': 'X98', 'isValidCode': True, 'coordinate': [1756, 823]}, '4': {'rememberedCode': 'Y41', 'isValidCode': True, 'coordinate': [874, 790]}, '5': {'rememberedCode': 'Z68', 'isValidCode': True, 'coordinate': [751, 320]}, '6': {'rememberedCode': 'Z68', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Z68', 'isValidCode': True, 'coordinate': [751, 320]}, '8': {'rememberedCode': 'J12', 'isValidCode': True, 'coordinate': [744, 113]}, '9': {'rememberedCode': 'M78', 'isValidCode': True, 'coordinate': [875, 1162]}, '10': {'rememberedCode': 'M78', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'M78', 'isValidCode': True, 'coordinate': [875, 1162]}, '12': {'rememberedCode': 'Y14', 'isValidCode': True, 'coordinate': [350, 299]}, '13': {'rememberedCode': 'Z49', 'isValidCode': True, 'coordinate': [844, 1074]}, '14': {'rememberedCode': 'Z49', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Z49', 'isValidCode': True, 'coordinate': [844, 1074]}, '16': {'rememberedCode': 'D54', 'isValidCode': True, 'coordinate': [466, 357]}, '17': {'rememberedCode': 'L73', 'isValidCode': True, 'coordinate': [468, 403]}, '18': {'rememberedCode': 'L73', 'isValidCode': True, 'coordinate': [755, 415]}, '19': {'rememberedCode': 'L73', 'isValidCode': True, 'coordinate': [468, 403]}, '20': {'rememberedCode': 'E43', 'isValidCode': True, 'coordinate': [1690, 979]}, '21': {'rememberedCode': 'H79', 'isValidCode': True, 'coordinate': [918, 348]}, '22': {'rememberedCode': 'H79', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'H79', 'isValidCode': True, 'coordinate': [918, 348]}, '24': {'rememberedCode': 'X71', 'isValidCode': True, 'coordinate': [1001, 1187]}, '25': {'rememberedCode': 'G57', 'isValidCode': True, 'coordinate': [507, 1067]}, '26': {'rememberedCode': 'G57', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G57', 'isValidCode': True, 'coordinate': [507, 1067]}, '28': {'rememberedCode': 'U37', 'isValidCode': True, 'coordinate': [1131, 703]}, '29': {'rememberedCode': 'Z74', 'isValidCode': True, 'coordinate': [1744, 1104]}, '30': {'rememberedCode': 'Z74', 'isValidCode': True, 'coordinate': [609, 111]}, '31': {'rememberedCode': 'Z74', 'isValidCode': True, 'coordinate': [1744, 1104]}, '32': {'rememberedCode': 'J28', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'B39', 'isValidCode': True, 'coordinate': [1497, 881]}, '34': {'rememberedCode': 'B39', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'B39', 'isValidCode': True, 'coordinate': [1497, 881]}, '36': {'rememberedCode': 'Y87', 'isValidCode': True, 'coordinate': [1425, 321]}, '37': {'rememberedCode': 'N71', 'isValidCode': True, 'coordinate': [1749, 129]}, '38': {'rememberedCode': 'N71', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'N71', 'isValidCode': True, 'coordinate': [1749, 129]}}",687,239
+6,"['A', 'B', 'C', 'B', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'C', 'A', 'D', 'A', 'B', 'B', 'C', 'A', 'B', 'C', 'B', 'B', 'D', 'C', 'B', 'B', 'C', 'A', 'D', 'C', 'B', 'A', 'C', 'B', 'C', 'D', 'C', 'A', 'D', 'A', 'B', 'B', 'B', 'D', 'A', 'B', 'C', 'A', 'C', 'A', 'B', 'A', 'D', 'C', 'C', 'B', 'C', 'B', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'A', 'B', 'B', 'B', 'C', 'B', 'A', 'A', 'C', 'A', 'A', 'B', 'B', 'A', 'A', 'A', 'B', 'B', 'C', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'A']","['2', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '2', '2', '1']","{'openTime': '2021-08-11T17:52:27.188Z', 'loadTime': '2021-08-11T17:52:27.596Z', 'startTime': '2021-08-11T17:52:28.833Z', 'endTime': '2021-08-11T18:07:43.397Z', 'timeToCompleteFromOpenMsec': 916209, 'timeToLoadMsec': 408, 'timeToCompleteFromStartMsec': 914564}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good Study'}","{'0': {'rememberedCode': 'K73', 'isValidCode': True, 'coordinate': [739, 381]}, '1': {'rememberedCode': 'F54', 'isValidCode': True, 'coordinate': [887, 636]}, '2': {'rememberedCode': 'F54', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'F54', 'isValidCode': True, 'coordinate': [887, 636]}, '4': {'rememberedCode': 'K19', 'isValidCode': True, 'coordinate': [1317, 122]}, '5': {'rememberedCode': 'K29', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'K29', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'K29', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'H86', 'isValidCode': True, 'coordinate': [446, 37]}, '9': {'rememberedCode': 'E37', 'isValidCode': True, 'coordinate': [485, 595]}, '10': {'rememberedCode': 'E37', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'E37', 'isValidCode': True, 'coordinate': [485, 595]}, '12': {'rememberedCode': 'N59', 'isValidCode': True, 'coordinate': [466, 700]}, '13': {'rememberedCode': 'J32', 'isValidCode': True, 'coordinate': [283, 282]}, '14': {'rememberedCode': 'J32', 'isValidCode': True, 'coordinate': [1174, 227]}, '15': {'rememberedCode': 'J32', 'isValidCode': True, 'coordinate': [283, 282]}, '16': {'rememberedCode': 'Q98', 'isValidCode': True, 'coordinate': [342, 342]}, '17': {'rememberedCode': 'T61', 'isValidCode': True, 'coordinate': [490, 530]}, '18': {'rememberedCode': 'T61', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'T61', 'isValidCode': True, 'coordinate': [490, 530]}, '20': {'rememberedCode': 'F64', 'isValidCode': True, 'coordinate': [883, 223]}, '21': {'rememberedCode': 'H79', 'isValidCode': True, 'coordinate': [918, 348]}, '22': {'rememberedCode': 'H79', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'H79', 'isValidCode': True, 'coordinate': [918, 348]}, '24': {'rememberedCode': 'V83', 'isValidCode': True, 'coordinate': [1012, 684]}, '25': {'rememberedCode': 'N69', 'isValidCode': True, 'coordinate': [617, 1082]}, '26': {'rememberedCode': 'N69', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'N69', 'isValidCode': True, 'coordinate': [617, 1082]}, '28': {'rememberedCode': 'V32', 'isValidCode': True, 'coordinate': [737, 511]}, '29': {'rememberedCode': 'K27', 'isValidCode': True, 'coordinate': [989, 427]}, '30': {'rememberedCode': 'K27', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'K27', 'isValidCode': True, 'coordinate': [989, 427]}, '32': {'rememberedCode': 'Z17', 'isValidCode': True, 'coordinate': [756, 251]}, '33': {'rememberedCode': 'D64', 'isValidCode': True, 'coordinate': [488, 698]}, '34': {'rememberedCode': 'D64', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D64', 'isValidCode': True, 'coordinate': [488, 698]}, '36': {'rememberedCode': 'B83', 'isValidCode': True, 'coordinate': [735, 425]}, '37': {'rememberedCode': 'R12', 'isValidCode': True, 'coordinate': [1726, 241]}, '38': {'rememberedCode': 'R12', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'R12', 'isValidCode': True, 'coordinate': [1726, 241]}}",798,621
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+14,"['C', 'B', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'B', 'A', 'A', 'B', 'A', 'B', 'A', 'A', 'C', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'B', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'B', 'A', 'B', 'B', 'B', 'A', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'B', 'B', 'A', 'B', 'A', 'B', 'C', 'A']","['2', '1', '1', '2', '1', '1', '1', '2', '1', '2', '1', '1', '2', '1', '2', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1']","{'openTime': '2021-08-11T21:46:58.079Z', 'loadTime': '2021-08-11T21:46:59.562Z', 'startTime': '2021-08-11T21:47:01.689Z', 'endTime': '2021-08-11T22:03:50.628Z', 'timeToCompleteFromOpenMsec': 1012549, 'timeToLoadMsec': 1483, 'timeToCompleteFromStartMsec': 1008939}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good task to work'}","{'0': {'rememberedCode': 'R46', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'E56', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'E56', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'E56', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'G62', 'isValidCode': True, 'coordinate': [865, 512]}, '5': {'rememberedCode': 'H84', 'isValidCode': True, 'coordinate': [623, 506]}, '6': {'rememberedCode': 'H84', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H84', 'isValidCode': True, 'coordinate': [623, 506]}, '8': {'rememberedCode': 'M15', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'X82', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'X82', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'X82', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'V58', 'isValidCode': True, 'coordinate': [912, 688]}, '13': {'rememberedCode': 'P53', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'P53', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'P53', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'B67', 'isValidCode': True, 'coordinate': [1001, 339]}, '17': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [508, 623]}, '18': {'rememberedCode': 'H89', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [508, 623]}, '20': {'rememberedCode': 'Q36', 'isValidCode': True, 'coordinate': [871, 907]}, '21': {'rememberedCode': 'L73', 'isValidCode': True, 'coordinate': [745, 435]}, '22': {'rememberedCode': 'L73', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L73', 'isValidCode': True, 'coordinate': [745, 435]}, '24': {'rememberedCode': 'Y12', 'isValidCode': True, 'coordinate': [842, 604]}, '25': {'rememberedCode': 'Y46', 'isValidCode': True, 'coordinate': [1189, 484]}, '26': {'rememberedCode': 'Y46', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Y46', 'isValidCode': True, 'coordinate': [1189, 484]}, '28': {'rememberedCode': 'H84', 'isValidCode': True, 'coordinate': [575, 706]}, '29': {'rememberedCode': 'F82', 'isValidCode': True, 'coordinate': [734, 432]}, '30': {'rememberedCode': 'F82', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'F82', 'isValidCode': True, 'coordinate': [734, 432]}, '32': {'rememberedCode': 'R74', 'isValidCode': True, 'coordinate': [732, 614]}, '33': {'rememberedCode': 'W17', 'isValidCode': True, 'coordinate': [1350, 784]}, '34': {'rememberedCode': 'W17', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'W17', 'isValidCode': True, 'coordinate': [1350, 784]}, '36': {'rememberedCode': 'L37', 'isValidCode': True, 'coordinate': [601, 516]}, '37': {'rememberedCode': 'X32', 'isValidCode': True, 'coordinate': [574, 431]}, '38': {'rememberedCode': 'X32', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'X32', 'isValidCode': True, 'coordinate': [574, 431]}}",446,908
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+20,"['A', 'C', 'C', 'C', 'A', 'A', 'B', 'A', 'B', 'B', 'C', 'D', 'D', 'D', 'D', 'B', 'B', 'D', 'D', 'D', 'A', 'B', 'A', 'D', 'D', 'C', 'B', 'C', 'D', 'D', 'B', 'A', 'A', 'C', 'D', 'B', 'D', 'D', 'D', 'D', 'C', 'C', 'A', 'D', 'A', 'D', 'D', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'C', 'B', 'A', 'B', 'D', 'A', 'C', 'C', 'D', 'D', 'B', 'A', 'D', 'D', 'D', 'A', 'D', 'A', 'A', 'C', 'B', 'B', 'D', 'D', 'D', 'A', 'C', 'B', 'C', 'D', 'B', 'A', 'A', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'C', 'A', 'C', 'D', 'D']","['2', '1', '1', '2', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '1', '2', '1']","{'openTime': '2021-08-11T22:00:45.655Z', 'loadTime': '2021-08-11T22:00:55.192Z', 'startTime': '2021-08-11T22:01:15.746Z', 'endTime': '2021-08-11T22:24:44.733Z', 'timeToCompleteFromOpenMsec': 1439078, 'timeToLoadMsec': 9537, 'timeToCompleteFromStartMsec': 1408987}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'thank you'}","{'0': {'rememberedCode': 'H95', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'M52', 'isValidCode': True, 'coordinate': [1326, 49]}, '2': {'rememberedCode': 'M52', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'M52', 'isValidCode': True, 'coordinate': [1326, 49]}, '4': {'rememberedCode': 'K97', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': '785', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': '785', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': '785', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'C63', 'isValidCode': True, 'coordinate': [868, 123]}, '9': {'rememberedCode': 'L69', 'isValidCode': True, 'coordinate': [1336, 700]}, '10': {'rememberedCode': 'L69', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'L69', 'isValidCode': True, 'coordinate': [1336, 700]}, '12': {'rememberedCode': 'C24', 'isValidCode': True, 'coordinate': [908, 618]}, '13': {'rememberedCode': 'V95', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'V95', 'isValidCode': True, 'coordinate': [50, 206]}, '15': {'rememberedCode': 'V95', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'S87', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'B82', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'B82', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'B82', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'F63', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'V75', 'isValidCode': True, 'coordinate': [731, 243]}, '22': {'rememberedCode': 'V75', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'V75', 'isValidCode': True, 'coordinate': [731, 243]}, '24': {'rememberedCode': 'A54', 'isValidCode': True, 'coordinate': [1430, 984]}, '25': {'rememberedCode': 'C75', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'C75', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C75', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'U75', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'X81', 'isValidCode': True, 'coordinate': [1434, 505]}, '30': {'rememberedCode': 'X81', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'X81', 'isValidCode': True, 'coordinate': [1434, 505]}, '32': {'rememberedCode': 'X78', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'B59', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'B59', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'B59', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'B76', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'B95', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'B95', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'B95', 'isValidCode': False, 'coordinate': False}}",805,735
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+24,"['A', 'C', 'A', 'D', 'A', 'C', 'D', 'D', 'B', 'A', 'C', 'D', 'A', 'D', 'D', 'B', 'B', 'A', 'C', 'C', 'A', 'B', 'B', 'D', 'D', 'C', 'B', 'C', 'D', 'D', 'B', 'D', 'A', 'C', 'B', 'A', 'A', 'C', 'D', 'D', 'C', 'C', 'B', 'C', 'D', 'B', 'D', 'D', 'D', 'D', 'A', 'B', 'A', 'A', 'D', 'A', 'B', 'A', 'D', 'D', 'A', 'C', 'C', 'B', 'C', 'B', 'A', 'A', 'B', 'D', 'C', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'B', 'A', 'A', 'B', 'C', 'C', 'B', 'C', 'C', 'B', 'D', 'A', 'D', 'C', 'B', 'C', 'C', 'A', 'D', 'A', 'A']","['2', '1', '1', '2', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '2']","{'openTime': '2021-08-11T22:45:51.447Z', 'loadTime': '2021-08-11T22:46:08.092Z', 'startTime': '2021-08-11T22:46:38.203Z', 'endTime': '2021-08-11T23:04:30.534Z', 'timeToCompleteFromOpenMsec': 1119087, 'timeToLoadMsec': 16645, 'timeToCompleteFromStartMsec': 1072331}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': '0GE', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'A63', 'isValidCode': True, 'coordinate': [1187, 923]}, '2': {'rememberedCode': 'A63', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'A63', 'isValidCode': True, 'coordinate': [1187, 923]}, '4': {'rememberedCode': 'F73', 'isValidCode': True, 'coordinate': [1030, 337]}, '5': {'rememberedCode': 'H21', 'isValidCode': True, 'coordinate': [1079, 728]}, '6': {'rememberedCode': 'H21', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H21', 'isValidCode': True, 'coordinate': [1079, 728]}, '8': {'rememberedCode': 'L31', 'isValidCode': True, 'coordinate': [894, 809]}, '9': {'rememberedCode': 'F23', 'isValidCode': True, 'coordinate': [1435, 294]}, '10': {'rememberedCode': 'F23', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'F23', 'isValidCode': True, 'coordinate': [1435, 294]}, '12': {'rememberedCode': 'Z38', 'isValidCode': True, 'coordinate': [1332, 989]}, '13': {'rememberedCode': 'Q39', 'isValidCode': True, 'coordinate': [559, 95]}, '14': {'rememberedCode': 'Q39', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Q39', 'isValidCode': True, 'coordinate': [559, 95]}, '16': {'rememberedCode': 'Z15', 'isValidCode': True, 'coordinate': [760, 712]}, '17': {'rememberedCode': 'J69', 'isValidCode': True, 'coordinate': [1186, 902]}, '18': {'rememberedCode': 'J69', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'J69', 'isValidCode': True, 'coordinate': [1186, 902]}, '20': {'rememberedCode': 'T76', 'isValidCode': True, 'coordinate': [1142, 323]}, '21': {'rememberedCode': 'S18', 'isValidCode': True, 'coordinate': [776, 134]}, '22': {'rememberedCode': 'S18', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'S18', 'isValidCode': True, 'coordinate': [776, 134]}, '24': {'rememberedCode': 'N32', 'isValidCode': True, 'coordinate': [864, 1202]}, '25': {'rememberedCode': 'K97', 'isValidCode': True, 'coordinate': [748, 571]}, '26': {'rememberedCode': 'K97', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'K97', 'isValidCode': True, 'coordinate': [748, 571]}, '28': {'rememberedCode': 'H43', 'isValidCode': True, 'coordinate': [852, 301]}, '29': {'rememberedCode': 'U13', 'isValidCode': True, 'coordinate': [1291, 418]}, '30': {'rememberedCode': 'U13', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'U13', 'isValidCode': True, 'coordinate': [1291, 418]}, '32': {'rememberedCode': 'J21', 'isValidCode': True, 'coordinate': [1176, 528]}, '33': {'rememberedCode': 'T76', 'isValidCode': True, 'coordinate': [1071, 1006]}, '34': {'rememberedCode': 'T76', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'T76', 'isValidCode': True, 'coordinate': [1071, 1006]}, '36': {'rememberedCode': 'B39', 'isValidCode': True, 'coordinate': [333, 483]}, '37': {'rememberedCode': 'M43', 'isValidCode': True, 'coordinate': [1428, 338]}, '38': {'rememberedCode': 'M43', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'M43', 'isValidCode': True, 'coordinate': [1428, 338]}}",120,174
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+65,"['A', 'C', 'C', 'A', 'A', 'A', 'B', 'A', 'D', 'B', 'C', 'C', 'A', 'D', 'D', 'B', 'B', 'C', 'C', 'A', 'A', 'A', 'B', 'D', 'D', 'C', 'B', 'C', 'B', 'D', 'B', 'A', 'A', 'C', 'C', 'B', 'A', 'C', 'D', 'D', 'C', 'C', 'B', 'A', 'D', 'A', 'B', 'A', 'D', 'D', 'A', 'C', 'B', 'A', 'D', 'C', 'B', 'A', 'B', 'D', 'A', 'C', 'D', 'D', 'D', 'B', 'A', 'D', 'C', 'D', 'D', 'A', 'A', 'A', 'C', 'B', 'B', 'D', 'D', 'C', 'A', 'C', 'A', 'B', 'D', 'B', 'A', 'A', 'C', 'D', 'A', 'B', 'C', 'B', 'C', 'C', 'D', 'D', 'D', 'D']","['2', '1', '1', '2', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '1', '1', '2', '1']","{'openTime': '2021-08-13T10:15:29.931Z', 'loadTime': '2021-08-13T10:15:31.904Z', 'startTime': '2021-08-13T10:15:35.866Z', 'endTime': '2021-08-13T11:48:08.861Z', 'timeToCompleteFromOpenMsec': 5558930, 'timeToLoadMsec': 1973, 'timeToCompleteFromStartMsec': 5552995}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'someCollege', 'vizExperience': 'someExposure', 'feedback': 'I am not sure about the accuracy of my answers, but I tried my best. Hope my participation helps. Thanks!'}","{'0': {'rememberedCode': 'T73', 'isValidCode': True, 'coordinate': [761, 118]}, '1': {'rememberedCode': 'K5A', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'K5A', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'K5A', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'U15', 'isValidCode': True, 'coordinate': [318, 115]}, '5': {'rememberedCode': 'H91', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'H91', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H91', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'K72', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'Y21', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'Y21', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Y21', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'R72', 'isValidCode': True, 'coordinate': [1332, 922]}, '13': {'rememberedCode': 'D79', 'isValidCode': True, 'coordinate': [270, 184]}, '14': {'rememberedCode': 'D79', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'D79', 'isValidCode': True, 'coordinate': [270, 184]}, '16': {'rememberedCode': 'Y15', 'isValidCode': True, 'coordinate': [324, 158]}, '17': {'rememberedCode': 'B62', 'isValidCode': True, 'coordinate': [904, 424]}, '18': {'rememberedCode': 'B62', 'isValidCode': True, 'coordinate': [1014, 726]}, '19': {'rememberedCode': 'B62', 'isValidCode': True, 'coordinate': [904, 424]}, '20': {'rememberedCode': 'K46', 'isValidCode': True, 'coordinate': [718, 306]}, '21': {'rememberedCode': 'H79', 'isValidCode': True, 'coordinate': [918, 348]}, '22': {'rememberedCode': 'H79', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'H79', 'isValidCode': True, 'coordinate': [918, 348]}, '24': {'rememberedCode': 'Y65', 'isValidCode': True, 'coordinate': [1018, 412]}, '25': {'rememberedCode': 'U84', 'isValidCode': True, 'coordinate': [622, 303]}, '26': {'rememberedCode': 'U84', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U84', 'isValidCode': True, 'coordinate': [622, 303]}, '28': {'rememberedCode': 'V71', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'Z76', 'isValidCode': True, 'coordinate': [752, 151]}, '30': {'rememberedCode': 'Z76', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Z76', 'isValidCode': True, 'coordinate': [752, 151]}, '32': {'rememberedCode': 'M59', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'N59', 'isValidCode': True, 'coordinate': [931, 415]}, '34': {'rememberedCode': 'N59', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'N59', 'isValidCode': True, 'coordinate': [931, 415]}, '36': {'rememberedCode': 'M83', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'K23', 'isValidCode': True, 'coordinate': [865, 345]}, '38': {'rememberedCode': 'K23', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K23', 'isValidCode': True, 'coordinate': [865, 345]}}",798,775
+66,"['A', 'D', 'C', 'C', 'D', 'D', 'D', 'A', 'D', 'B', 'C', 'D', 'A', 'D', 'D', 'B', 'C', 'D', 'D', 'D', 'A', 'A', 'A', 'C', 'D', 'C', 'A', 'C', 'D', 'D', 'B', 'A', 'A', 'D', 'B', 'B', 'B', 'B', 'B', 'D', 'C', 'C', 'B', 'A', 'D', 'B', 'B', 'A', 'D', 'D', 'A', 'C', 'D', 'B', 'A', 'C', 'B', 'A', 'B', 'B', 'A', 'A', 'C', 'B', 'D', 'B', 'D', 'A', 'D', 'D', 'C', 'C', 'A', 'A', 'B', 'B', 'D', 'D', 'D', 'D', 'A', 'B', 'A', 'B', 'D', 'B', 'C', 'A', 'C', 'D', 'A', 'A', 'A', 'C', 'D', 'B', 'A', 'D', 'A', 'A']","['2', '1', '1', '2', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '2', '2', '1', '1', '2', '2', '2', '2', '2', '1', '1', '2', '2', '2', '2', '1', '1', '2', '1']","{'openTime': '2021-08-13T11:23:55.210Z', 'loadTime': '2021-08-13T11:29:48.034Z', 'startTime': '2021-08-13T11:30:09.905Z', 'endTime': '2021-08-13T11:59:42.121Z', 'timeToCompleteFromOpenMsec': 2146911, 'timeToLoadMsec': 352824, 'timeToCompleteFromStartMsec': 1772216}","{'gender': 'female', 'ageGroup': 'ageGroup65', 'ethnicity': 'Asian', 'education': 'professionalDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'Y35', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'L57', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'L57', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'L57', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'L47', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'LPX', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'LPX', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'LPX', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'H80', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'D47', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'D47', 'isValidCode': True, 'coordinate': [755, 1213]}, '11': {'rememberedCode': 'D47', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'H53', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'X53', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'X53', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X53', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'M81', 'isValidCode': True, 'coordinate': [475, 425]}, '17': {'rememberedCode': 'C26', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'C26', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'C26', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'E32', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'Z39', 'isValidCode': True, 'coordinate': [1005, 634]}, '22': {'rememberedCode': 'Z39', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Z39', 'isValidCode': True, 'coordinate': [1005, 634]}, '24': {'rememberedCode': 'M43', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'D57', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'D57', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'D57', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'B37', 'isValidCode': True, 'coordinate': [158, 430]}, '29': {'rememberedCode': 'H95', 'isValidCode': True, 'coordinate': [704, 618]}, '30': {'rememberedCode': 'H95', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'H95', 'isValidCode': True, 'coordinate': [704, 618]}, '32': {'rememberedCode': 'X49', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'G17', 'isValidCode': True, 'coordinate': [769, 722]}, '34': {'rememberedCode': 'G17', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'G17', 'isValidCode': True, 'coordinate': [769, 722]}, '36': {'rememberedCode': 'L57', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}}",551,535
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+77,"['A', 'B', 'C', 'B', 'C', 'B', 'B', 'D', 'D', 'D', 'C', 'C', 'B', 'B', 'C', 'B', 'B', 'C', 'B', 'A', 'A', 'B', 'B', 'A', 'C', 'C', 'B', 'C', 'C', 'C', 'B', 'A', 'A', 'C', 'C', 'B', 'A', 'C', 'A', 'C', 'C', 'C', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'C', 'C', 'B', 'A', 'B', 'B', 'A', 'C', 'C', 'B', 'C', 'B', 'A', 'A', 'B', 'C', 'A', 'A', 'A', 'A', 'C', 'B', 'B', 'B', 'C', 'A', 'A', 'C', 'B', 'C', 'B', 'B', 'A', 'C', 'A', 'D', 'A', 'B', 'C', 'B', 'C', 'C', 'A', 'C', 'A', 'A']","['2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '2', '1']","{'openTime': '2021-08-14T03:25:15.568Z', 'loadTime': '2021-08-14T03:25:17.400Z', 'startTime': '2021-08-14T03:25:18.678Z', 'endTime': '2021-08-14T03:50:59.066Z', 'timeToCompleteFromOpenMsec': 1543498, 'timeToLoadMsec': 1832, 'timeToCompleteFromStartMsec': 1540388}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Nice study.'}","{'0': {'rememberedCode': 'SAT', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'J82', 'isValidCode': True, 'coordinate': [587, 220]}, '2': {'rememberedCode': 'J82', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J82', 'isValidCode': True, 'coordinate': [587, 220]}, '4': {'rememberedCode': 'B82', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'H76', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'H76', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H76', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'J38', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'E87', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'E87', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'E87', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'M36', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'H74', 'isValidCode': True, 'coordinate': [1171, 891]}, '15': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'H86', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'V46', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'V46', 'isValidCode': True, 'coordinate': [152, 704]}, '19': {'rememberedCode': 'V46', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'C85', 'isValidCode': True, 'coordinate': [713, 603]}, '21': {'rememberedCode': 'V74', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'V74', 'isValidCode': True, 'coordinate': [14, 1089]}, '23': {'rememberedCode': 'V74', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'R75', 'isValidCode': True, 'coordinate': [1453, 415]}, '25': {'rememberedCode': 'V26', 'isValidCode': True, 'coordinate': [750, 204]}, '26': {'rememberedCode': 'V26', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'V26', 'isValidCode': True, 'coordinate': [750, 204]}, '28': {'rememberedCode': 'N17', 'isValidCode': True, 'coordinate': [841, 498]}, '29': {'rememberedCode': 'Y48', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'Y48', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Y48', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'U37', 'isValidCode': True, 'coordinate': [720, 352]}, '33': {'rememberedCode': 'F85', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'F85', 'isValidCode': True, 'coordinate': [885, 617]}, '35': {'rememberedCode': 'F85', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'Q41', 'isValidCode': True, 'coordinate': [896, 398]}, '37': {'rememberedCode': 'V47', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'V47', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'V47', 'isValidCode': False, 'coordinate': False}}",208,731
diff --git a/VisRecall/answer_raw/group10.csv b/VisRecall/answer_raw/group10.csv
new file mode 100755
index 0000000..884ade4
--- /dev/null
+++ b/VisRecall/answer_raw/group10.csv
@@ -0,0 +1,61 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+0,"['A', 'C', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B']","['2', '1', '2', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '2', '1', '1', '1', '2', '2', '2', '2', '2']","{'openTime': '2021-08-18T16:01:38.299Z', 'loadTime': '2021-08-18T16:01:40.824Z', 'startTime': '2021-08-18T16:01:43.438Z', 'endTime': '2021-08-18T16:17:51.743Z', 'timeToCompleteFromOpenMsec': 973444, 'timeToLoadMsec': 2525, 'timeToCompleteFromStartMsec': 968305}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'good'}","{'0': {'rememberedCode': '137', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '444', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '444', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '444', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': '122', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'C22', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'K12', 'isValidCode': True, 'coordinate': [345, 350]}, '10': {'rememberedCode': 'K12', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K12', 'isValidCode': True, 'coordinate': [345, 350]}, '12': {'rememberedCode': 'K89', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'U53', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'U53', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'U53', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'V98', 'isValidCode': True, 'coordinate': [691, 109]}, '17': {'rememberedCode': 'W97', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'W97', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'W97', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'J14', 'isValidCode': True, 'coordinate': [697, 240]}, '21': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'J14', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'K19', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'K19', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'K19', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'S97', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'V56', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'V56', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'V56', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'M73', 'isValidCode': True, 'coordinate': [630, 240]}, '33': {'rememberedCode': 'D74', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'D74', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D74', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'U61', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'G56', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'G56', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G56', 'isValidCode': False, 'coordinate': False}}",397,871
+1,"['C', 'A', 'B', 'A', 'D', 'C', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'C', 'D', 'A', 'D', 'B', 'D', 'D', 'B', 'D', 'A', 'D', 'D', 'A', 'D', 'A', 'B', 'D', 'A', 'C', 'C', 'D', 'A', 'C', 'B', 'C', 'C', 'B', 'A', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'B', 'D', 'A', 'D', 'D', 'B', 'A', 'B', 'B', 'B', 'C', 'D', 'B', 'D', 'D', 'C', 'D', 'C', 'D', 'A', 'B', 'C', 'A', 'C', 'D', 'C', 'D', 'D', 'D', 'D', 'B', 'C', 'B', 'B', 'D', 'B', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D']","['2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '2', '2']","{'openTime': '2021-08-18T15:59:38.485Z', 'loadTime': '2021-08-18T15:59:58.284Z', 'startTime': '2021-08-18T16:01:11.700Z', 'endTime': '2021-08-18T16:19:55.190Z', 'timeToCompleteFromOpenMsec': 1216705, 'timeToLoadMsec': 19799, 'timeToCompleteFromStartMsec': 1123490}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'D24', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'D17', 'isValidCode': True, 'coordinate': [776, 319]}, '2': {'rememberedCode': 'D17', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'D17', 'isValidCode': True, 'coordinate': [776, 319]}, '4': {'rememberedCode': 'V74', 'isValidCode': True, 'coordinate': [1602, 244]}, '5': {'rememberedCode': 'B24', 'isValidCode': True, 'coordinate': [34, 927]}, '6': {'rememberedCode': 'B24', 'isValidCode': True, 'coordinate': [225, 1233]}, '7': {'rememberedCode': 'B24', 'isValidCode': True, 'coordinate': [34, 927]}, '8': {'rememberedCode': 'B94', 'isValidCode': True, 'coordinate': [941, 546]}, '9': {'rememberedCode': 'R63', 'isValidCode': True, 'coordinate': [1272, 438]}, '10': {'rememberedCode': 'R63', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'R63', 'isValidCode': True, 'coordinate': [1272, 438]}, '12': {'rememberedCode': 'L56', 'isValidCode': True, 'coordinate': [766, 563]}, '13': {'rememberedCode': 'M53', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'M53', 'isValidCode': True, 'coordinate': [515, 829]}, '15': {'rememberedCode': 'M53', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'M77', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'V69', 'isValidCode': True, 'coordinate': [661, 750]}, '18': {'rememberedCode': 'V69', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V69', 'isValidCode': True, 'coordinate': [661, 750]}, '20': {'rememberedCode': 'Z53', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'B91', 'isValidCode': True, 'coordinate': [656, 316]}, '22': {'rememberedCode': 'B91', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'B91', 'isValidCode': True, 'coordinate': [656, 316]}, '24': {'rememberedCode': 'P99', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'G40', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'G40', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G40', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'F44', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'C57', 'isValidCode': True, 'coordinate': [1289, 388]}, '30': {'rememberedCode': 'C57', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C57', 'isValidCode': True, 'coordinate': [1289, 388]}, '32': {'rememberedCode': 'L63', 'isValidCode': True, 'coordinate': [787, 659]}, '33': {'rememberedCode': 'A47', 'isValidCode': True, 'coordinate': [39, 597]}, '34': {'rememberedCode': 'A47', 'isValidCode': True, 'coordinate': [1287, 659]}, '35': {'rememberedCode': 'A47', 'isValidCode': True, 'coordinate': [39, 597]}, '36': {'rememberedCode': 'B49', 'isValidCode': True, 'coordinate': [1018, 748]}, '37': {'rememberedCode': 'Y59', 'isValidCode': True, 'coordinate': [1264, 1099]}, '38': {'rememberedCode': 'Y59', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Y59', 'isValidCode': True, 'coordinate': [1264, 1099]}}",2,818
+2,"['A', 'A', 'D', 'D', 'B', 'C', 'C', 'B', 'D', 'D', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'D', 'B', 'C', 'D', 'B', 'D', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'B', 'D', 'D', 'D', 'C', 'B', 'C', 'A', 'B', 'C', 'B', 'C', 'C', 'B', 'D', 'B', 'C', 'A', 'D', 'A', 'C', 'C', 'B', 'A', 'D', 'A', 'B', 'B', 'A', 'B', 'A', 'D', 'A', 'B', 'A']","['2', '1', '2', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '2', '1', '2', '1', '1', '1', '1', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1']","{'openTime': '2021-08-18T16:00:09.333Z', 'loadTime': '2021-08-18T16:01:01.950Z', 'startTime': '2021-08-18T16:01:25.395Z', 'endTime': '2021-08-18T16:26:30.193Z', 'timeToCompleteFromOpenMsec': 1580860, 'timeToLoadMsec': 52617, 'timeToCompleteFromStartMsec': 1504798}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'professional', 'feedback': 'Interesting task '}","{'0': {'rememberedCode': '201', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '123', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '123', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '123', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'Z36', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'N18', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'N18', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N18', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'A14', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'V98', 'isValidCode': True, 'coordinate': [376, 139]}, '10': {'rememberedCode': 'V98', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'V98', 'isValidCode': True, 'coordinate': [376, 139]}, '12': {'rememberedCode': 'R39', 'isValidCode': True, 'coordinate': [963, 529]}, '13': {'rememberedCode': 'A57', 'isValidCode': True, 'coordinate': [771, 833]}, '14': {'rememberedCode': 'A57', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'A57', 'isValidCode': True, 'coordinate': [771, 833]}, '16': {'rememberedCode': 'C87', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'E91', 'isValidCode': True, 'coordinate': [1078, 350]}, '18': {'rememberedCode': 'E91', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'E91', 'isValidCode': True, 'coordinate': [1078, 350]}, '20': {'rememberedCode': 'G39', 'isValidCode': True, 'coordinate': [664, 626]}, '21': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'Z38', 'isValidCode': True, 'coordinate': [161, 146]}, '25': {'rememberedCode': 'C28', 'isValidCode': True, 'coordinate': [1015, 633]}, '26': {'rememberedCode': 'C28', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C28', 'isValidCode': True, 'coordinate': [1015, 633]}, '28': {'rememberedCode': 'C29', 'isValidCode': True, 'coordinate': [693, 191]}, '29': {'rememberedCode': 'B63', 'isValidCode': True, 'coordinate': [927, 209]}, '30': {'rememberedCode': 'B63', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'B63', 'isValidCode': True, 'coordinate': [927, 209]}, '32': {'rememberedCode': 'E94', 'isValidCode': True, 'coordinate': [637, 423]}, '33': {'rememberedCode': 'A48', 'isValidCode': True, 'coordinate': [612, 608]}, '34': {'rememberedCode': 'A48', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'A48', 'isValidCode': True, 'coordinate': [612, 608]}, '36': {'rememberedCode': 'N15', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'K45', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'K45', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K45', 'isValidCode': False, 'coordinate': False}}",968,542
+3,"['C', 'C', 'D', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'A', 'A', 'C', 'C', 'C', 'B', 'B', 'B', 'A', 'A', 'A', 'A', 'C', 'C', 'B', 'C', 'B', 'C', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'B', 'B', 'B', 'C', 'A', 'C', 'C', 'B', 'B', 'B', 'C', 'C', 'B', 'C', 'B', 'B', 'B', 'D', 'C', 'C', 'C', 'A', 'B', 'C', 'C', 'A', 'C', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'B', 'B', 'C', 'C', 'C', 'C', 'A', 'A', 'A', 'A', 'A']","['2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1']","{'openTime': '2021-08-18T13:02:01.692Z', 'loadTime': '2021-08-18T13:02:03.604Z', 'startTime': '2021-08-18T13:02:27.715Z', 'endTime': '2021-08-18T13:25:12.275Z', 'timeToCompleteFromOpenMsec': 1390583, 'timeToLoadMsec': 1912, 'timeToCompleteFromStartMsec': 1364560}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'mastersDegree', 'vizExperience': 'professional', 'feedback': 'NICE'}","{'0': {'rememberedCode': '201', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': '24', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': '40', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': '10', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': '44B', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': '44B', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': '44B', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': '03', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': '199', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': '199', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': '199', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': '221', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': '221', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': '221', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': '201', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'RBC', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'RBC', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'RBC', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': '412', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': '700', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': '700', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': '700', 'isValidCode': False, 'coordinate': False}}",890,336
+4,"['B', 'A', 'B', 'B', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'B', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'B', 'A', 'C', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'B']","['2', '1', '1', '1', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '2', '2', '2', '1', '1', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '2', '2', '2', '2']","{'openTime': '2021-08-18T16:04:09.127Z', 'loadTime': '2021-08-18T16:04:12.329Z', 'startTime': '2021-08-18T16:04:30.364Z', 'endTime': '2021-08-18T16:28:08.086Z', 'timeToCompleteFromOpenMsec': 1438959, 'timeToLoadMsec': 3202, 'timeToCompleteFromStartMsec': 1417722}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'professional', 'feedback': 'Good study'}","{'0': {'rememberedCode': 'R35', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'F28', 'isValidCode': True, 'coordinate': [936, 610]}, '2': {'rememberedCode': 'F28', 'isValidCode': True, 'coordinate': [8, 1019]}, '3': {'rememberedCode': 'F28', 'isValidCode': True, 'coordinate': [936, 610]}, '4': {'rememberedCode': 'L46', 'isValidCode': True, 'coordinate': [205, 435]}, '5': {'rememberedCode': 'U23', 'isValidCode': True, 'coordinate': [223, 333]}, '6': {'rememberedCode': 'U23', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U23', 'isValidCode': True, 'coordinate': [223, 333]}, '8': {'rememberedCode': 'H56', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'H26', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'H26', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H26', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'H58', 'isValidCode': True, 'coordinate': [953, 332]}, '13': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [440, 263]}, '14': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [807, 422]}, '15': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [440, 263]}, '16': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [641, 202]}, '17': {'rememberedCode': 'V74', 'isValidCode': True, 'coordinate': [792, 551]}, '18': {'rememberedCode': 'V74', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V74', 'isValidCode': True, 'coordinate': [792, 551]}, '20': {'rememberedCode': 'A92', 'isValidCode': True, 'coordinate': [518, 328]}, '21': {'rememberedCode': 'G65', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'G65', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G65', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'Q23', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'T19', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'T19', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'T19', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'V53', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'K58', 'isValidCode': True, 'coordinate': [521, 921]}, '30': {'rememberedCode': 'K58', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'K58', 'isValidCode': True, 'coordinate': [521, 921]}, '32': {'rememberedCode': 'N81', 'isValidCode': True, 'coordinate': [649, 662]}, '33': {'rememberedCode': 'G74', 'isValidCode': True, 'coordinate': [1108, 523]}, '34': {'rememberedCode': 'G74', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'G74', 'isValidCode': True, 'coordinate': [1108, 523]}, '36': {'rememberedCode': 'W14', 'isValidCode': True, 'coordinate': [856, 465]}, '37': {'rememberedCode': 'M51', 'isValidCode': True, 'coordinate': [906, 732]}, '38': {'rememberedCode': 'M51', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'M51', 'isValidCode': True, 'coordinate': [906, 732]}}",13,647
+5,"['A', 'B', 'A', 'C', 'B', 'B', 'A', 'B', 'D', 'A', 'B', 'C', 'C', 'A', 'B', 'B', 'D', 'A', 'B', 'B', 'B', 'A', 'C', 'B', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'A', 'C', 'B', 'B', 'B', 'B', 'A', 'B', 'A', 'A', 'A', 'A', 'C', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'C', 'A', 'B', 'C', 'C', 'C', 'B', 'A', 'C', 'A', 'B', 'B', 'A', 'C', 'C', 'B', 'B', 'A', 'B', 'B', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'D', 'C', 'A', 'A', 'A', 'A', 'C', 'B', 'B', 'D', 'D', 'D', 'D', 'D', 'C']","['2', '2', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '2']","{'openTime': '2021-08-18T16:03:49.623Z', 'loadTime': '2021-08-18T16:03:49.977Z', 'startTime': '2021-08-18T16:03:59.812Z', 'endTime': '2021-08-18T16:28:12.987Z', 'timeToCompleteFromOpenMsec': 1463364, 'timeToLoadMsec': 354, 'timeToCompleteFromStartMsec': 1453175}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'n/a but I really hope to see a bonus with this for my time because that was awfully repetitive and underpaid too'}","{'0': {'rememberedCode': 'D15', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'X29', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'X29', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'X29', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'D82', 'isValidCode': True, 'coordinate': [403, 427]}, '5': {'rememberedCode': 'F59', 'isValidCode': True, 'coordinate': [791, 636]}, '6': {'rememberedCode': 'F59', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'F59', 'isValidCode': True, 'coordinate': [791, 636]}, '8': {'rememberedCode': 'U34', 'isValidCode': True, 'coordinate': [1268, 551]}, '9': {'rememberedCode': 'M62', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'M62', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'M62', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'R39', 'isValidCode': True, 'coordinate': [963, 529]}, '13': {'rememberedCode': 'D42', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'D42', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'D42', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'U54', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'J27', 'isValidCode': True, 'coordinate': [1599, 939]}, '18': {'rememberedCode': 'J27', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'J27', 'isValidCode': True, 'coordinate': [1599, 939]}, '20': {'rememberedCode': 'T43', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'F43', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'F43', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'F43', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'F58', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'C28', 'isValidCode': True, 'coordinate': [1015, 633]}, '26': {'rememberedCode': 'C28', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C28', 'isValidCode': True, 'coordinate': [1015, 633]}, '28': {'rememberedCode': 'W92', 'isValidCode': True, 'coordinate': [818, 404]}, '29': {'rememberedCode': 'L59', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'L59', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L59', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'J42', 'isValidCode': True, 'coordinate': [807, 746]}, '33': {'rememberedCode': 'X14', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'X14', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'X14', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'X94', 'isValidCode': True, 'coordinate': [992, 1057]}, '37': {'rememberedCode': 'D42', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'D42', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'D42', 'isValidCode': False, 'coordinate': False}}",267,560
+6,"['C', 'C', 'D', 'C', 'B', 'C', 'A', 'D', 'A', 'B', 'C', 'C', 'A', 'B', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'B', 'A', 'A', 'C', 'C', 'B', 'B', 'C', 'C', 'A', 'D', 'C', 'A', 'A', 'C', 'C', 'B', 'C', 'D', 'A', 'B', 'B', 'A', 'B', 'C', 'B', 'B', 'D', 'B', 'A', 'C', 'D', 'A', 'B', 'A', 'D', 'C', 'A', 'D', 'C', 'B', 'B', 'B', 'A', 'A', 'A', 'C', 'C', 'C', 'B', 'A', 'A', 'C', 'A', 'C', 'D', 'D', 'C', 'D', 'D', 'C', 'B', 'C', 'C', 'A', 'D', 'C', 'D', 'A', 'C', 'C', 'A', 'C', 'A', 'B', 'A', 'D', 'A', 'D']","['2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '2', '2']","{'openTime': '2021-08-18T16:07:06.857Z', 'loadTime': '2021-08-18T16:07:19.927Z', 'startTime': '2021-08-18T16:09:46.371Z', 'endTime': '2021-08-18T16:31:23.166Z', 'timeToCompleteFromOpenMsec': 1456309, 'timeToLoadMsec': 13070, 'timeToCompleteFromStartMsec': 1296795}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'A58', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [522, 499]}, '2': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [167, 108]}, '3': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [522, 499]}, '4': {'rememberedCode': 'U61', 'isValidCode': True, 'coordinate': [838, 710]}, '5': {'rememberedCode': 'S19', 'isValidCode': True, 'coordinate': [1151, 248]}, '6': {'rememberedCode': 'S19', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'S19', 'isValidCode': True, 'coordinate': [1151, 248]}, '8': {'rememberedCode': 'Y89', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'J13', 'isValidCode': True, 'coordinate': [1099, 750]}, '10': {'rememberedCode': 'J13', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'J13', 'isValidCode': True, 'coordinate': [1099, 750]}, '12': {'rememberedCode': 'T32', 'isValidCode': True, 'coordinate': [496, 254]}, '13': {'rememberedCode': 'X54', 'isValidCode': True, 'coordinate': [779, 741]}, '14': {'rememberedCode': 'X54', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X54', 'isValidCode': True, 'coordinate': [779, 741]}, '16': {'rememberedCode': 'S85', 'isValidCode': True, 'coordinate': [1292, 641]}, '17': {'rememberedCode': 'P25', 'isValidCode': True, 'coordinate': [352, 1137]}, '18': {'rememberedCode': 'P25', 'isValidCode': True, 'coordinate': [491, 825]}, '19': {'rememberedCode': 'P25', 'isValidCode': True, 'coordinate': [352, 1137]}, '20': {'rememberedCode': 'T42', 'isValidCode': True, 'coordinate': [216, 1055]}, '21': {'rememberedCode': 'P62', 'isValidCode': True, 'coordinate': [1133, 997]}, '22': {'rememberedCode': 'P62', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'P62', 'isValidCode': True, 'coordinate': [1133, 997]}, '24': {'rememberedCode': 'G83', 'isValidCode': True, 'coordinate': [1275, 331]}, '25': {'rememberedCode': 'S54', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'S54', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'S54', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'F19', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'N58', 'isValidCode': True, 'coordinate': [1396, 212]}, '30': {'rememberedCode': 'N58', 'isValidCode': True, 'coordinate': [550, 24]}, '31': {'rememberedCode': 'N58', 'isValidCode': True, 'coordinate': [1396, 212]}, '32': {'rememberedCode': 'E52', 'isValidCode': True, 'coordinate': [1100, 248]}, '33': {'rememberedCode': 'K61', 'isValidCode': True, 'coordinate': [964, 314]}, '34': {'rememberedCode': 'K61', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'K61', 'isValidCode': True, 'coordinate': [964, 314]}, '36': {'rememberedCode': 'D84', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'Z61', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'Z61', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Z61', 'isValidCode': False, 'coordinate': False}}",538,442
+7,"['D', 'C', 'A', 'C', 'D', 'C', 'D', 'D', 'D', 'B', 'C', 'C', 'B', 'A', 'D', 'A', 'B', 'A', 'B', 'D', 'B', 'D', 'A', 'D', 'D', 'A', 'D', 'A', 'A', 'A', 'A', 'B', 'D', 'C', 'A', 'B', 'B', 'C', 'A', 'A', 'A', 'A', 'D', 'D', 'B', 'B', 'B', 'C', 'C', 'C', 'B', 'B', 'B', 'B', 'A', 'B', 'A', 'A', 'A', 'D', 'C', 'D', 'B', 'B', 'A', 'A', 'A', 'C', 'A', 'A', 'B', 'C', 'A', 'A', 'C', 'D', 'D', 'B', 'D', 'D', 'B', 'C', 'B', 'C', 'A', 'B', 'B', 'C', 'D', 'B', 'C', 'D', 'D', 'D', 'D', 'C', 'A', 'A', 'D', 'A']","['2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '2', '2']","{'openTime': '2021-08-18T16:01:59.863Z', 'loadTime': '2021-08-18T16:02:46.170Z', 'startTime': '2021-08-18T16:06:37.073Z', 'endTime': '2021-08-18T16:33:29.996Z', 'timeToCompleteFromOpenMsec': 1890133, 'timeToLoadMsec': 46307, 'timeToCompleteFromStartMsec': 1612923}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'littleExposure', 'feedback': 'Thank you, and have a nice day!'}","{'0': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [167, 108]}, '1': {'rememberedCode': 'D64', 'isValidCode': True, 'coordinate': [508, 924]}, '2': {'rememberedCode': 'D64', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'D64', 'isValidCode': True, 'coordinate': [508, 924]}, '4': {'rememberedCode': 'H58', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'M67', 'isValidCode': True, 'coordinate': [630, 831]}, '6': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'M67', 'isValidCode': True, 'coordinate': [630, 831]}, '8': {'rememberedCode': 'F43', 'isValidCode': True, 'coordinate': [373, 733]}, '9': {'rememberedCode': 'M63', 'isValidCode': True, 'coordinate': [501, 422]}, '10': {'rememberedCode': 'M63', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'M63', 'isValidCode': True, 'coordinate': [501, 422]}, '12': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [807, 422]}, '13': {'rememberedCode': 'F16', 'isValidCode': True, 'coordinate': [598, 137]}, '14': {'rememberedCode': 'F16', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F16', 'isValidCode': True, 'coordinate': [598, 137]}, '16': {'rememberedCode': 'Z15', 'isValidCode': True, 'coordinate': [373, 129]}, '17': {'rememberedCode': 'X83', 'isValidCode': True, 'coordinate': [767, 429]}, '18': {'rememberedCode': 'X83', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'X83', 'isValidCode': True, 'coordinate': [767, 429]}, '20': {'rememberedCode': 'X61', 'isValidCode': True, 'coordinate': [842, 729]}, '21': {'rememberedCode': 'V54', 'isValidCode': True, 'coordinate': [345, 417]}, '22': {'rememberedCode': 'V54', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'V54', 'isValidCode': True, 'coordinate': [345, 417]}, '24': {'rememberedCode': 'J47', 'isValidCode': True, 'coordinate': [458, 37]}, '25': {'rememberedCode': 'T36', 'isValidCode': True, 'coordinate': [543, 208]}, '26': {'rememberedCode': 'T36', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'T36', 'isValidCode': True, 'coordinate': [543, 208]}, '28': {'rememberedCode': 'C29', 'isValidCode': True, 'coordinate': [693, 191]}, '29': {'rememberedCode': 'V78', 'isValidCode': True, 'coordinate': [526, 988]}, '30': {'rememberedCode': 'V78', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'V78', 'isValidCode': True, 'coordinate': [526, 988]}, '32': {'rememberedCode': 'B73', 'isValidCode': True, 'coordinate': [774, 142]}, '33': {'rememberedCode': 'K61', 'isValidCode': True, 'coordinate': [964, 314]}, '34': {'rememberedCode': 'K61', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'K61', 'isValidCode': True, 'coordinate': [964, 314]}, '36': {'rememberedCode': 'T91', 'isValidCode': True, 'coordinate': [223, 1252]}, '37': {'rememberedCode': 'S46', 'isValidCode': True, 'coordinate': [962, 906]}, '38': {'rememberedCode': 'S46', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'S46', 'isValidCode': True, 'coordinate': [962, 906]}}",573,803
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+9,"['D', 'C', 'B', 'D', 'B', 'C', 'C', 'C', 'B', 'B', 'C', 'C', 'A', 'A', 'A', 'A', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'A', 'D', 'B', 'C', 'C', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'A', 'C', 'B', 'B', 'C', 'C', 'A', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'C']","['2', '1', '2', '1', '2', '1', '2', '2', '1', '2', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '2', '2', '1', '1', '2']","{'openTime': '2021-08-18T16:21:51.717Z', 'loadTime': '2021-08-18T16:22:32.178Z', 'startTime': '2021-08-18T16:22:52.899Z', 'endTime': '2021-08-18T16:41:57.480Z', 'timeToCompleteFromOpenMsec': 1205763, 'timeToLoadMsec': 40461, 'timeToCompleteFromStartMsec': 1144581}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Black_African', 'education': 'fourYearDegree', 'vizExperience': 'professional', 'feedback': 'nice survey'}","{'0': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '25', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '25', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '25', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': '60', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': '10', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': '10', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': '10', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': '23', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': '20', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': '20', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': '20', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': '12', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': '30', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': '30', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': '30', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': '56', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': '13', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': '13', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': '13', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': '24', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': '65', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': '65', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': '65', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': '4', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': '29', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': '29', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': '29', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': '17', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': '14', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': '14', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': '14', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': '12', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': '40', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': '40', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': '40', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': '45', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': '45', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': '45', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': '45', 'isValidCode': False, 'coordinate': False}}",507,374
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+11,"['A', 'A', 'B', 'C', 'B', 'C', 'C', 'A', 'C', 'A', 'C', 'B', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'B', 'B', 'A', 'A', 'A', 'A', 'A', 'B', 'A', 'C', 'B', 'A', 'A', 'C', 'B', 'A', 'B', 'B', 'A', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'C', 'B', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'C', 'C', 'B', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'B', 'A', 'A', 'C', 'C', 'C', 'A', 'C', 'C', 'C', 'B', 'B', 'B', 'B', 'B', 'C', 'B', 'C', 'B', 'C', 'C', 'C', 'B', 'B', 'B', 'C', 'C', 'C', 'A', 'A']","['2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '1', '1', '1', '1', '2', '2', '2', '1', '2', '1', '2', '2', '2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '1', '2', '2', '2']","{'openTime': '2021-08-18T16:22:15.227Z', 'loadTime': '2021-08-18T16:22:34.608Z', 'startTime': '2021-08-18T16:23:03.153Z', 'endTime': '2021-08-18T16:48:54.383Z', 'timeToCompleteFromOpenMsec': 1599156, 'timeToLoadMsec': 19381, 'timeToCompleteFromStartMsec': 1551230}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': '347', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'MG3', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'MG3', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'MG3', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'V34', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'W78', 'isValidCode': True, 'coordinate': [1588, 936]}, '6': {'rememberedCode': 'W78', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'W78', 'isValidCode': True, 'coordinate': [1588, 936]}, '8': {'rememberedCode': 'H37', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'M37', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'M37', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'M37', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'M98', 'isValidCode': True, 'coordinate': [505, 746]}, '13': {'rememberedCode': 'M85', 'isValidCode': True, 'coordinate': [791, 1035]}, '14': {'rememberedCode': 'M85', 'isValidCode': True, 'coordinate': [1428, 1244]}, '15': {'rememberedCode': 'M85', 'isValidCode': True, 'coordinate': [791, 1035]}, '16': {'rememberedCode': 'Z37', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'M59', 'isValidCode': True, 'coordinate': [1081, 542]}, '18': {'rememberedCode': 'M59', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M59', 'isValidCode': True, 'coordinate': [1081, 542]}, '20': {'rememberedCode': 'J14', 'isValidCode': True, 'coordinate': [697, 240]}, '21': {'rememberedCode': 'A39', 'isValidCode': True, 'coordinate': [1262, 1288]}, '22': {'rememberedCode': 'A39', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'A39', 'isValidCode': True, 'coordinate': [1262, 1288]}, '24': {'rememberedCode': 'V64', 'isValidCode': True, 'coordinate': [165, 940]}, '25': {'rememberedCode': 'X19', 'isValidCode': True, 'coordinate': [1116, 523]}, '26': {'rememberedCode': 'X19', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'X19', 'isValidCode': True, 'coordinate': [1116, 523]}, '28': {'rememberedCode': 'E49', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [970, 22]}, '30': {'rememberedCode': 'U79', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [970, 22]}, '32': {'rememberedCode': 'Z89', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'V79', 'isValidCode': True, 'coordinate': [325, 813]}, '34': {'rememberedCode': 'V79', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'V79', 'isValidCode': True, 'coordinate': [325, 813]}, '36': {'rememberedCode': 'H24', 'isValidCode': True, 'coordinate': [397, 131]}, '37': {'rememberedCode': 'N23', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'N23', 'isValidCode': True, 'coordinate': [349, 751]}, '39': {'rememberedCode': 'N23', 'isValidCode': False, 'coordinate': False}}",715,55
+12,"['A', 'D', 'D', 'D', 'B', 'A', 'D', 'C', 'B', 'C', 'C', 'C', 'B', 'C', 'C', 'A', 'B', 'B', 'C', 'C', 'B', 'B', 'A', 'B', 'D', 'C', 'B', 'A', 'B', 'D', 'A', 'B', 'C', 'B', 'A', 'B', 'B', 'B', 'B', 'B', 'A', 'B', 'A', 'B', 'D', 'C', 'A', 'B', 'A', 'C', 'C', 'D', 'B', 'B', 'D', 'B', 'A', 'C', 'B', 'B', 'B', 'D', 'D', 'D', 'D', 'C', 'C', 'D', 'D', 'D', 'D', 'A', 'A', 'A', 'D', 'B', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D']","['2', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '1', '1', '1', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2']","{'openTime': '2021-08-18T16:12:34.199Z', 'loadTime': '2021-08-18T16:12:35.030Z', 'startTime': '2021-08-18T16:12:38.859Z', 'endTime': '2021-08-18T16:48:44.304Z', 'timeToCompleteFromOpenMsec': 2170105, 'timeToLoadMsec': 831, 'timeToCompleteFromStartMsec': 2165445}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'unspecified', 'feedback': ''}","{'0': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [651, 218]}, '1': {'rememberedCode': 'K65', 'isValidCode': True, 'coordinate': [659, 302]}, '2': {'rememberedCode': 'K65', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'K65', 'isValidCode': True, 'coordinate': [659, 302]}, '4': {'rememberedCode': 'A45', 'isValidCode': True, 'coordinate': [715, 426]}, '5': {'rememberedCode': 'F29', 'isValidCode': True, 'coordinate': [976, 725]}, '6': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'F29', 'isValidCode': True, 'coordinate': [976, 725]}, '8': {'rememberedCode': 'M67', 'isValidCode': True, 'coordinate': [661, 851]}, '9': {'rememberedCode': 'J29', 'isValidCode': True, 'coordinate': [1131, 622]}, '10': {'rememberedCode': 'J29', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'J29', 'isValidCode': True, 'coordinate': [1131, 622]}, '12': {'rememberedCode': 'F92', 'isValidCode': True, 'coordinate': [339, 841]}, '13': {'rememberedCode': 'A71', 'isValidCode': True, 'coordinate': [1515, 868]}, '14': {'rememberedCode': 'A71', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'A71', 'isValidCode': True, 'coordinate': [1515, 868]}, '16': {'rememberedCode': 'F27', 'isValidCode': True, 'coordinate': [959, 326]}, '17': {'rememberedCode': 'C35', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'C35', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'C35', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'M17', 'isValidCode': True, 'coordinate': [968, 164]}, '21': {'rememberedCode': 'K12', 'isValidCode': True, 'coordinate': [933, 217]}, '22': {'rememberedCode': 'K12', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'K12', 'isValidCode': True, 'coordinate': [933, 217]}, '24': {'rememberedCode': 'W74', 'isValidCode': True, 'coordinate': [604, 308]}, '25': {'rememberedCode': 'R17', 'isValidCode': True, 'coordinate': [1025, 1226]}, '26': {'rememberedCode': 'R17', 'isValidCode': True, 'coordinate': [611, 150]}, '27': {'rememberedCode': 'R17', 'isValidCode': True, 'coordinate': [1025, 1226]}, '28': {'rememberedCode': 'W74', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'G52', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'G52', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'G52', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'V43', 'isValidCode': True, 'coordinate': [1109, 538]}, '33': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [1418, 289]}, '34': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [956, 841]}, '35': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [1418, 289]}, '36': {'rememberedCode': 'M64', 'isValidCode': True, 'coordinate': [528, 366]}, '37': {'rememberedCode': 'K18', 'isValidCode': True, 'coordinate': [500, 136]}, '38': {'rememberedCode': 'K18', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K18', 'isValidCode': True, 'coordinate': [500, 136]}}",693,421
+13,"['D', 'C', 'D', 'B', 'B', 'D', 'D', 'A', 'D', 'B', 'C', 'C', 'C', 'D', 'A', 'A', 'C', 'D', 'D', 'D', 'D', 'C', 'C', 'D', 'D', 'A', 'A', 'A', 'A', 'B', 'A', 'A', 'A', 'C', 'C', 'D', 'C', 'C', 'C', 'C', 'C', 'A', 'B', 'C', 'D', 'D', 'D', 'B', 'B', 'B', 'B', 'C', 'C', 'D', 'D', 'A', 'A', 'D', 'A', 'A', 'C', 'A', 'B', 'C', 'C', 'C', 'D', 'A', 'D', 'A', 'D', 'C', 'C', 'C', 'A', 'C', 'A', 'B', 'B', 'D', 'B', 'C', 'B', 'A', 'C', 'B', 'B', 'C', 'D', 'B', 'C', 'A', 'B', 'D', 'D', 'C', 'A', 'A', 'A', 'A']","['2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '2', '2']","{'openTime': '2021-08-18T16:35:10.307Z', 'loadTime': '2021-08-18T16:35:22.228Z', 'startTime': '2021-08-18T16:35:24.055Z', 'endTime': '2021-08-18T16:57:33.229Z', 'timeToCompleteFromOpenMsec': 1342922, 'timeToLoadMsec': 11921, 'timeToCompleteFromStartMsec': 1329174}","{'gender': 'male', 'ageGroup': 'ageGroup18', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': '218', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'W17', 'isValidCode': True, 'coordinate': [808, 406]}, '2': {'rememberedCode': 'W17', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W17', 'isValidCode': True, 'coordinate': [808, 406]}, '4': {'rememberedCode': 'V51', 'isValidCode': True, 'coordinate': [384, 710]}, '5': {'rememberedCode': 'H53', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'H53', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H53', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'C13', 'isValidCode': True, 'coordinate': [672, 728]}, '9': {'rememberedCode': 'H52', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'H52', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H52', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'D54', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'C59', 'isValidCode': True, 'coordinate': [489, 347]}, '14': {'rememberedCode': 'C59', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C59', 'isValidCode': True, 'coordinate': [489, 347]}, '16': {'rememberedCode': 'V58', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'B48', 'isValidCode': True, 'coordinate': [634, 456]}, '18': {'rememberedCode': 'B48', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'B48', 'isValidCode': True, 'coordinate': [634, 456]}, '20': {'rememberedCode': 'J14', 'isValidCode': True, 'coordinate': [697, 240]}, '21': {'rememberedCode': 'R49', 'isValidCode': True, 'coordinate': [798, 405]}, '22': {'rememberedCode': 'R49', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'R49', 'isValidCode': True, 'coordinate': [798, 405]}, '24': {'rememberedCode': 'B56', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'P29', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'P29', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'P29', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'T28', 'isValidCode': True, 'coordinate': [396, 600]}, '29': {'rememberedCode': 'W49', 'isValidCode': True, 'coordinate': [781, 1214]}, '30': {'rememberedCode': 'W49', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'W49', 'isValidCode': True, 'coordinate': [781, 1214]}, '32': {'rememberedCode': 'R94', 'isValidCode': True, 'coordinate': [821, 1038]}, '33': {'rememberedCode': 'Q86', 'isValidCode': True, 'coordinate': [785, 890]}, '34': {'rememberedCode': 'Q86', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Q86', 'isValidCode': True, 'coordinate': [785, 890]}, '36': {'rememberedCode': 'W19', 'isValidCode': True, 'coordinate': [684, 765]}, '37': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [622, 818]}, '38': {'rememberedCode': 'C54', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [622, 818]}}",351,588
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+15,"['A', 'C', 'A', 'C', 'B', 'C', 'D', 'C', 'B', 'C', 'C', 'C', 'A', 'C', 'D', 'A', 'B', 'B', 'C', 'C', 'B', 'B', 'B', 'D', 'C', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'D', 'A', 'C', 'B', 'D', 'B', 'B', 'A', 'B', 'B', 'B', 'A', 'A', 'A', 'A', 'C', 'C', 'B', 'D', 'A', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'C', 'C', 'A', 'C', 'A', 'A', 'B', 'C', 'A', 'C', 'A', 'C', 'A', 'B', 'A', 'D', 'B', 'C', 'B', 'C', 'C', 'B', 'B', 'C', 'B', 'B', 'C', 'A', 'B', 'A', 'B', 'B', 'A', 'A', 'A', 'A']","['2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '2', '2']","{'openTime': '2021-08-18T16:48:47.324Z', 'loadTime': '2021-08-18T16:48:47.817Z', 'startTime': '2021-08-18T16:49:16.598Z', 'endTime': '2021-08-18T17:16:03.564Z', 'timeToCompleteFromOpenMsec': 1636240, 'timeToLoadMsec': 493, 'timeToCompleteFromStartMsec': 1606966}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'highSchool', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'HB1', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'D31', 'isValidCode': True, 'coordinate': [649, 487]}, '2': {'rememberedCode': 'D31', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'D31', 'isValidCode': True, 'coordinate': [649, 487]}, '4': {'rememberedCode': 'A45', 'isValidCode': True, 'coordinate': [715, 426]}, '5': {'rememberedCode': 'W51', 'isValidCode': True, 'coordinate': [806, 522]}, '6': {'rememberedCode': 'W51', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'W51', 'isValidCode': True, 'coordinate': [806, 522]}, '8': {'rememberedCode': 'A42', 'isValidCode': True, 'coordinate': [775, 524]}, '9': {'rememberedCode': 'V98', 'isValidCode': True, 'coordinate': [376, 139]}, '10': {'rememberedCode': 'V98', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'V98', 'isValidCode': True, 'coordinate': [376, 139]}, '12': {'rememberedCode': 'K69', 'isValidCode': True, 'coordinate': [645, 359]}, '13': {'rememberedCode': 'Z98', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'Z98', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Z98', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'S27', 'isValidCode': True, 'coordinate': [528, 132]}, '17': {'rememberedCode': 'J69', 'isValidCode': True, 'coordinate': [364, 242]}, '18': {'rememberedCode': 'J69', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'J69', 'isValidCode': True, 'coordinate': [364, 242]}, '20': {'rememberedCode': 'Z43', 'isValidCode': True, 'coordinate': [503, 236]}, '21': {'rememberedCode': 'Q32', 'isValidCode': True, 'coordinate': [190, 1199]}, '22': {'rememberedCode': 'Q32', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Q32', 'isValidCode': True, 'coordinate': [190, 1199]}, '24': {'rememberedCode': 'M25', 'isValidCode': True, 'coordinate': [1221, 515]}, '25': {'rememberedCode': 'X19', 'isValidCode': True, 'coordinate': [1116, 523]}, '26': {'rememberedCode': 'X19', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'X19', 'isValidCode': True, 'coordinate': [1116, 523]}, '28': {'rememberedCode': 'E84', 'isValidCode': True, 'coordinate': [980, 494]}, '29': {'rememberedCode': 'V78', 'isValidCode': True, 'coordinate': [526, 988]}, '30': {'rememberedCode': 'V78', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'V78', 'isValidCode': True, 'coordinate': [526, 988]}, '32': {'rememberedCode': 'Z83', 'isValidCode': True, 'coordinate': [1417, 1043]}, '33': {'rememberedCode': 'F94', 'isValidCode': True, 'coordinate': [337, 429]}, '34': {'rememberedCode': 'F94', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'F94', 'isValidCode': True, 'coordinate': [337, 429]}, '36': {'rememberedCode': 'B49', 'isValidCode': True, 'coordinate': [1018, 748]}, '37': {'rememberedCode': 'A19', 'isValidCode': True, 'coordinate': [600, 1038]}, '38': {'rememberedCode': 'A19', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'A19', 'isValidCode': True, 'coordinate': [600, 1038]}}",115,533
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+17,"['C', 'C', 'C', 'C', 'D', 'D', 'B', 'B', 'B', 'D', 'D', 'C', 'C', 'C', 'C', 'A', 'A', 'C', 'B', 'C', 'D', 'B', 'B', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'B', 'C', 'C', 'A', 'A', 'C', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'A', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'A', 'A', 'A', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'B', 'B', 'D', 'D', 'B', 'B', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'B', 'B', 'B', 'B', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D']","['2', '2', '1', '1', '2', '2', '1', '2', '1', '2', '2', '1', '2', '1', '2', '2', '2', '2', '2', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2']","{'openTime': '2021-08-18T16:51:29.640Z', 'loadTime': '2021-08-18T16:51:53.331Z', 'startTime': '2021-08-18T16:53:11.529Z', 'endTime': '2021-08-18T17:19:10.337Z', 'timeToCompleteFromOpenMsec': 1660697, 'timeToLoadMsec': 23691, 'timeToCompleteFromStartMsec': 1558808}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'Black_African', 'education': 'mastersDegree', 'vizExperience': 'professional', 'feedback': 'I Like this Survey'}","{'0': {'rememberedCode': '3', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'E23', 'isValidCode': True, 'coordinate': [1559, 414]}, '2': {'rememberedCode': 'E23', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'E23', 'isValidCode': True, 'coordinate': [1559, 414]}, '4': {'rememberedCode': 'C23', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'U48', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'U48', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U48', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'S17', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'K12', 'isValidCode': True, 'coordinate': [345, 350]}, '10': {'rememberedCode': 'K12', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K12', 'isValidCode': True, 'coordinate': [345, 350]}, '12': {'rememberedCode': 'N75', 'isValidCode': True, 'coordinate': [1401, 1032]}, '13': {'rememberedCode': 'E73', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'E73', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'E73', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'Y71', 'isValidCode': True, 'coordinate': [1572, 1203]}, '17': {'rememberedCode': 'L95', 'isValidCode': True, 'coordinate': [1557, 1260]}, '18': {'rememberedCode': 'L95', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'L95', 'isValidCode': True, 'coordinate': [1557, 1260]}, '20': {'rememberedCode': 'Y88', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'P91', 'isValidCode': True, 'coordinate': [1604, 1185]}, '22': {'rememberedCode': 'P91', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'P91', 'isValidCode': True, 'coordinate': [1604, 1185]}, '24': {'rememberedCode': 'V91', 'isValidCode': True, 'coordinate': [1572, 1219]}, '25': {'rememberedCode': 'G41', 'isValidCode': True, 'coordinate': [718, 622]}, '26': {'rememberedCode': 'G41', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G41', 'isValidCode': True, 'coordinate': [718, 622]}, '28': {'rememberedCode': 'V91', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'S35', 'isValidCode': True, 'coordinate': [1556, 1288]}, '30': {'rememberedCode': 'S35', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'S35', 'isValidCode': True, 'coordinate': [1556, 1288]}, '32': {'rememberedCode': 'S35', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'V91', 'isValidCode': True, 'coordinate': [1400, 589]}, '34': {'rememberedCode': 'V91', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'V91', 'isValidCode': True, 'coordinate': [1400, 589]}, '36': {'rememberedCode': 'Z46', 'isValidCode': True, 'coordinate': [1620, 1268]}, '37': {'rememberedCode': 'N52', 'isValidCode': True, 'coordinate': [1692, 1208]}, '38': {'rememberedCode': 'N52', 'isValidCode': True, 'coordinate': [702, 969]}, '39': {'rememberedCode': 'N52', 'isValidCode': True, 'coordinate': [1692, 1208]}}",758,355
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+21,"['D', 'D', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'A', 'C', 'C', 'A', 'A', 'B', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B']","['2', '1', '1', '1', '1', '1', '1', '1', '1', '2', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '1', '1', '1', '1', '1']","{'openTime': '2021-08-18T17:07:17.528Z', 'loadTime': '2021-08-18T17:07:18.842Z', 'startTime': '2021-08-18T17:07:25.511Z', 'endTime': '2021-08-18T17:22:26.199Z', 'timeToCompleteFromOpenMsec': 908671, 'timeToLoadMsec': 1314, 'timeToCompleteFromStartMsec': 900688}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Black_African', 'education': 'mastersDegree', 'vizExperience': 'professional', 'feedback': 'goodsurvey'}","{'0': {'rememberedCode': '5TE', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '55T', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '55T', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '55T', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': '131', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': '282', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': '282', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': '282', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': '565', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': '564', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': '564', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': '564', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'Q74', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'G56', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'G56', 'isValidCode': True, 'coordinate': [806, 151]}, '15': {'rememberedCode': 'G56', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': '564', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': '453', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': '453', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': '453', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': '564', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': '34T', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': '34T', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': '34T', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'RT4', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'RRR', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'RRR', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'RRR', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': '4RW', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'E69', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'E69', 'isValidCode': True, 'coordinate': [682, 11]}, '31': {'rememberedCode': 'E69', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': '54W', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': '866', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': '866', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': '866', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'R23', 'isValidCode': True, 'coordinate': [851, 740]}, '37': {'rememberedCode': 'E52', 'isValidCode': True, 'coordinate': [902, 1098]}, '38': {'rememberedCode': 'E52', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E52', 'isValidCode': True, 'coordinate': [902, 1098]}}",120,216
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+39,"['A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'B', 'B', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'A', 'B', 'B', 'B', 'A', 'A', 'B', 'B', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'C', 'B', 'B', 'A', 'A', 'A', 'C', 'C', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'B', 'B', 'B', 'A', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B']","['2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '2', '2', '1', '1', '1', '2', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2']","{'openTime': '2021-08-18T19:50:59.711Z', 'loadTime': '2021-08-18T19:51:00.325Z', 'startTime': '2021-08-18T19:51:19.118Z', 'endTime': '2021-08-18T20:10:54.656Z', 'timeToCompleteFromOpenMsec': 1194945, 'timeToLoadMsec': 614, 'timeToCompleteFromStartMsec': 1175538}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'this task is too hard'}","{'0': {'rememberedCode': 'H81', 'isValidCode': True, 'coordinate': [1534, 1233]}, '1': {'rememberedCode': 'V89', 'isValidCode': True, 'coordinate': [1590, 1289]}, '2': {'rememberedCode': 'V89', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'V89', 'isValidCode': True, 'coordinate': [1590, 1289]}, '4': {'rememberedCode': 'N35', 'isValidCode': True, 'coordinate': [1604, 1225]}, '5': {'rememberedCode': 'V48', 'isValidCode': True, 'coordinate': [1559, 1226]}, '6': {'rememberedCode': 'V48', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'V48', 'isValidCode': True, 'coordinate': [1559, 1226]}, '8': {'rememberedCode': 'A23', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'N76', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'N76', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'N76', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'H89', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'Y71', 'isValidCode': True, 'coordinate': [1572, 1203]}, '17': {'rememberedCode': 'L95', 'isValidCode': True, 'coordinate': [1557, 1260]}, '18': {'rememberedCode': 'L95', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'L95', 'isValidCode': True, 'coordinate': [1557, 1260]}, '20': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'G76', 'isValidCode': True, 'coordinate': [621, 1018]}, '22': {'rememberedCode': 'G76', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G76', 'isValidCode': True, 'coordinate': [621, 1018]}, '24': {'rememberedCode': 'U78', 'isValidCode': True, 'coordinate': [1072, 239]}, '25': {'rememberedCode': 'U21', 'isValidCode': True, 'coordinate': [1633, 1213]}, '26': {'rememberedCode': 'U21', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U21', 'isValidCode': True, 'coordinate': [1633, 1213]}, '28': {'rememberedCode': 'J71', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'S35', 'isValidCode': True, 'coordinate': [1556, 1288]}, '30': {'rememberedCode': 'S35', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'S35', 'isValidCode': True, 'coordinate': [1556, 1288]}, '32': {'rememberedCode': 'L21', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'Y78', 'isValidCode': True, 'coordinate': [1544, 1305]}, '34': {'rememberedCode': 'Y78', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Y78', 'isValidCode': True, 'coordinate': [1544, 1305]}, '36': {'rememberedCode': 'U76', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'N82', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'N82', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'N82', 'isValidCode': False, 'coordinate': False}}",459,184
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+58,"['A', 'B', 'C', 'B', 'B', 'C', 'B', 'B', 'D', 'B', 'C', 'B', 'A', 'D', 'A', 'A', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'D', 'A', 'B', 'C', 'A', 'D', 'D', 'A', 'C', 'C', 'D', 'A', 'C', 'C', 'D', 'D', 'C', 'C', 'B', 'C', 'D', 'C', 'C', 'C', 'C', 'B', 'B', 'B', 'B', 'C', 'C', 'B', 'C', 'C', 'C', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'C', 'B', 'B', 'B', 'C', 'C', 'C', 'B', 'B', 'B', 'D', 'D', 'D', 'D', 'D', 'C', 'C', 'C', 'D', 'D', 'C', 'C', 'B', 'B', 'D', 'B', 'B', 'C', 'C', 'B', 'B', 'B', 'C', 'C', 'D']","['2', '1', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '2', '2', '2', '1', '1', '1', '1', '1', '1', '2', '2', '2', '1', '1', '1', '1', '1']","{'openTime': '2021-08-19T13:38:09.831Z', 'loadTime': '2021-08-19T13:38:20.927Z', 'startTime': '2021-08-19T13:38:24.489Z', 'endTime': '2021-08-19T13:56:26.043Z', 'timeToCompleteFromOpenMsec': 1096212, 'timeToLoadMsec': 11096, 'timeToCompleteFromStartMsec': 1081554}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'nice'}","{'0': {'rememberedCode': 'CAC', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'PC', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'PC', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'PC', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'B41', 'isValidCode': True, 'coordinate': [107, 514]}, '5': {'rememberedCode': 'B43', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'B43', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'B43', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'F39', 'isValidCode': True, 'coordinate': [317, 343]}, '9': {'rememberedCode': 'E82', 'isValidCode': True, 'coordinate': [334, 438]}, '10': {'rememberedCode': 'E82', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'E82', 'isValidCode': True, 'coordinate': [334, 438]}, '12': {'rememberedCode': 'U67', 'isValidCode': True, 'coordinate': [973, 264]}, '13': {'rememberedCode': 'W46', 'isValidCode': True, 'coordinate': [646, 66]}, '14': {'rememberedCode': 'W46', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'W46', 'isValidCode': True, 'coordinate': [646, 66]}, '16': {'rememberedCode': 'X89', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'W37', 'isValidCode': True, 'coordinate': [619, 40]}, '18': {'rememberedCode': 'W37', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'W37', 'isValidCode': True, 'coordinate': [619, 40]}, '20': {'rememberedCode': 'W37', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'T29', 'isValidCode': True, 'coordinate': [651, 208]}, '22': {'rememberedCode': 'T29', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'T29', 'isValidCode': True, 'coordinate': [651, 208]}, '24': {'rememberedCode': 'A59', 'isValidCode': True, 'coordinate': [345, 243]}, '25': {'rememberedCode': 'S13', 'isValidCode': True, 'coordinate': [513, 128]}, '26': {'rememberedCode': 'S13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'S13', 'isValidCode': True, 'coordinate': [513, 128]}, '28': {'rememberedCode': 'B31', 'isValidCode': True, 'coordinate': [720, 323]}, '29': {'rememberedCode': 'T18', 'isValidCode': True, 'coordinate': [627, 94]}, '30': {'rememberedCode': 'T18', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'T18', 'isValidCode': True, 'coordinate': [627, 94]}, '32': {'rememberedCode': 'E25', 'isValidCode': True, 'coordinate': [497, 244]}, '33': {'rememberedCode': 'T89', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'T89', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'T89', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'UT2', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'S62', 'isValidCode': True, 'coordinate': [777, 420]}, '38': {'rememberedCode': 'S62', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'S62', 'isValidCode': True, 'coordinate': [777, 420]}}",526,709
+59,"['D', 'C', 'C', 'B', 'B', 'B', 'B', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'C', 'C', 'A', 'C', 'A', 'D', 'D', 'A', 'D', 'B', 'A', 'B', 'A', 'C', 'D', 'B', 'B', 'A', 'A', 'D', 'C', 'D', 'B', 'A', 'A', 'B', 'B', 'D', 'B', 'D', 'B', 'B', 'B', 'C', 'A', 'D', 'B', 'B', 'B', 'D', 'B', 'B', 'D', 'B', 'A', 'B', 'B', 'B', 'B', 'C', 'B', 'B', 'B', 'C', 'B', 'A', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'D', 'A', 'B', 'B', 'B', 'B', 'A', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B']","['2', '2', '1', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '2', '2', '2', '1', '2', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '2']","{'openTime': '2021-08-19T12:28:03.558Z', 'loadTime': '2021-08-19T12:28:48.232Z', 'startTime': '2021-08-19T12:29:28.177Z', 'endTime': '2021-08-19T14:13:26.646Z', 'timeToCompleteFromOpenMsec': 6323088, 'timeToLoadMsec': 44674, 'timeToCompleteFromStartMsec': 6238469}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'professionalDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': '223', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': '233', 'isValidCode': False, 'coordinate': False}}",72,39
\ No newline at end of file
diff --git a/VisRecall/answer_raw/group2.csv b/VisRecall/answer_raw/group2.csv
new file mode 100755
index 0000000..ec906f8
--- /dev/null
+++ b/VisRecall/answer_raw/group2.csv
@@ -0,0 +1,34 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+1,"['B', 'A', 'A', 'B', 'C', 'C', 'B', 'A', 'B', 'B', 'C', 'C', 'C', 'B', 'B', 'A', 'B', 'C', 'B', 'C', 'C', 'C', 'A', 'A', 'B', 'A', 'B', 'C', 'C', 'A', 'B', 'A', 'A', 'C', 'B', 'A', 'A', 'C', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'A', 'C', 'C', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'A', 'C', 'B', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'C', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'B']","['2', '1', '2', '2', '1', '1', '2', '1', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '1', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2', '2']","{'openTime': '2021-08-20T08:06:32.666Z', 'loadTime': '2021-08-20T08:07:02.491Z', 'startTime': '2021-08-20T08:07:19.896Z', 'endTime': '2021-08-20T08:49:33.667Z', 'timeToCompleteFromOpenMsec': 2581001, 'timeToLoadMsec': 29825, 'timeToCompleteFromStartMsec': 2533771}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'F29', 'isValidCode': True, 'coordinate': [848, 65]}, '1': {'rememberedCode': 'J21', 'isValidCode': True, 'coordinate': [1080, 217]}, '2': {'rememberedCode': 'J21', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J21', 'isValidCode': True, 'coordinate': [1080, 217]}, '4': {'rememberedCode': 'J52', 'isValidCode': True, 'coordinate': [548, 137]}, '5': {'rememberedCode': 'M49', 'isValidCode': True, 'coordinate': [744, 283]}, '6': {'rememberedCode': 'M49', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'M49', 'isValidCode': True, 'coordinate': [744, 283]}, '8': {'rememberedCode': 'R97', 'isValidCode': True, 'coordinate': [574, 306]}, '9': {'rememberedCode': 'C27', 'isValidCode': True, 'coordinate': [875, 368]}, '10': {'rememberedCode': 'C27', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'C27', 'isValidCode': True, 'coordinate': [875, 368]}, '12': {'rememberedCode': 'E28', 'isValidCode': True, 'coordinate': [508, 70]}, '13': {'rememberedCode': 'U16', 'isValidCode': True, 'coordinate': [762, 97]}, '14': {'rememberedCode': 'U16', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'U16', 'isValidCode': True, 'coordinate': [762, 97]}, '16': {'rememberedCode': 'Q92', 'isValidCode': True, 'coordinate': [636, 431]}, '17': {'rememberedCode': 'T12', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'T12', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'T12', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'A85', 'isValidCode': True, 'coordinate': [677, 143]}, '21': {'rememberedCode': 'J49', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'J49', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'J49', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'A38', 'isValidCode': True, 'coordinate': [849, 278]}, '25': {'rememberedCode': 'H24', 'isValidCode': True, 'coordinate': [125, 713]}, '26': {'rememberedCode': 'H24', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'H24', 'isValidCode': True, 'coordinate': [125, 713]}, '28': {'rememberedCode': 'Q32', 'isValidCode': True, 'coordinate': [854, 293]}, '29': {'rememberedCode': 'L57', 'isValidCode': True, 'coordinate': [859, 173]}, '30': {'rememberedCode': 'L57', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L57', 'isValidCode': True, 'coordinate': [859, 173]}, '32': {'rememberedCode': 'R84', 'isValidCode': True, 'coordinate': [852, 217]}, '33': {'rememberedCode': 'R21', 'isValidCode': True, 'coordinate': [927, 460]}, '34': {'rememberedCode': 'R21', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'R21', 'isValidCode': True, 'coordinate': [927, 460]}, '36': {'rememberedCode': 'B12', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'S23', 'isValidCode': True, 'coordinate': [854, 351]}, '38': {'rememberedCode': 'S23', 'isValidCode': True, 'coordinate': [1457, 711]}, '39': {'rememberedCode': 'S23', 'isValidCode': True, 'coordinate': [854, 351]}}",196,524
+2,"['C', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'B', 'A', 'A', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'C', 'A', 'A', 'A', 'A', 'B', 'A', 'A', 'C', 'D', 'A', 'C', 'D', 'C', 'C', 'C', 'A', 'D', 'D', 'D', 'C', 'B', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'C', 'B', 'B', 'B', 'C', 'C', 'B', 'D', 'D', 'C', 'C', 'B', 'A', 'D', 'D', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'C', 'D', 'D', 'B', 'C', 'B', 'C', 'D', 'C', 'B', 'C', 'B', 'C']","['2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '2']","{'openTime': '2021-08-20T09:25:47.367Z', 'loadTime': '2021-08-20T09:26:13.091Z', 'startTime': '2021-08-20T09:26:24.593Z', 'endTime': '2021-08-20T09:51:09.951Z', 'timeToCompleteFromOpenMsec': 1522584, 'timeToLoadMsec': 25724, 'timeToCompleteFromStartMsec': 1485358}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'unspecified', 'feedback': 'Happy to participate on this Memorability Game on Visualizations.'}","{'0': {'rememberedCode': 'P59', 'isValidCode': True, 'coordinate': [992, 507]}, '1': {'rememberedCode': 'E24', 'isValidCode': True, 'coordinate': [674, 588]}, '2': {'rememberedCode': 'E24', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'E24', 'isValidCode': True, 'coordinate': [674, 588]}, '4': {'rememberedCode': 'X29', 'isValidCode': True, 'coordinate': [654, 434]}, '5': {'rememberedCode': 'W23', 'isValidCode': True, 'coordinate': [1047, 346]}, '6': {'rememberedCode': 'W23', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'W23', 'isValidCode': True, 'coordinate': [1047, 346]}, '8': {'rememberedCode': 'M78', 'isValidCode': True, 'coordinate': [870, 113]}, '9': {'rememberedCode': 'N29', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'N29', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'N29', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'P17', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'S16', 'isValidCode': True, 'coordinate': [856, 250]}, '14': {'rememberedCode': 'S16', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'S16', 'isValidCode': True, 'coordinate': [856, 250]}, '16': {'rememberedCode': 'E71', 'isValidCode': True, 'coordinate': [819, 557]}, '17': {'rememberedCode': 'M31', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'M31', 'isValidCode': True, 'coordinate': [1354, 550]}, '19': {'rememberedCode': 'M31', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'S18', 'isValidCode': True, 'coordinate': [1064, 499]}, '21': {'rememberedCode': 'Y89', 'isValidCode': True, 'coordinate': [637, 382]}, '22': {'rememberedCode': 'Y89', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Y89', 'isValidCode': True, 'coordinate': [637, 382]}, '24': {'rememberedCode': 'V51', 'isValidCode': True, 'coordinate': [831, 561]}, '25': {'rememberedCode': 'B64', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'B64', 'isValidCode': True, 'coordinate': [838, 100]}, '27': {'rememberedCode': 'B64', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'K91', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'H35', 'isValidCode': True, 'coordinate': [795, 212]}, '30': {'rememberedCode': 'H35', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'H35', 'isValidCode': True, 'coordinate': [795, 212]}, '32': {'rememberedCode': 'L63', 'isValidCode': True, 'coordinate': [1573, 363]}, '33': {'rememberedCode': 'S58', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'S58', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'S58', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'V82', 'isValidCode': True, 'coordinate': [835, 457]}, '37': {'rememberedCode': 'S94', 'isValidCode': True, 'coordinate': [1026, 462]}, '38': {'rememberedCode': 'S94', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'S94', 'isValidCode': True, 'coordinate': [1026, 462]}}",324,401
+3,"['B', 'C', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'B', 'C', 'A', 'A', 'A', 'D', 'C', 'B', 'A', 'C', 'C', 'A', 'A', 'C', 'B', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'A', 'B', 'A', 'C', 'A', 'C', 'C', 'A', 'B', 'A', 'A', 'C', 'A', 'B', 'C', 'B', 'A', 'A', 'B', 'C', 'B', 'B', 'C', 'A', 'A', 'C', 'B', 'B', 'C', 'A', 'B', 'A', 'A', 'C', 'A', 'C', 'A', 'B', 'A', 'C', 'B', 'C', 'C', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'C', 'A', 'A', 'C', 'A', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'C']","['2', '2', '1', '2', '2', '2', '1', '1', '2', '1', '1', '2', '2', '1', '1', '2', '1', '1', '2', '2', '1', '1', '2', '1', '1', '2', '2', '2', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T09:33:50.502Z', 'loadTime': '2021-08-20T09:34:29.637Z', 'startTime': '2021-08-20T09:34:31.466Z', 'endTime': '2021-08-20T09:57:37.650Z', 'timeToCompleteFromOpenMsec': 1427148, 'timeToLoadMsec': 39135, 'timeToCompleteFromStartMsec': 1386184}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good study.'}","{'0': {'rememberedCode': 'P81', 'isValidCode': True, 'coordinate': [860, 862]}, '1': {'rememberedCode': 'Q58', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'Q58', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Q58', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'B43', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'Z21', 'isValidCode': True, 'coordinate': [221, 697]}, '6': {'rememberedCode': 'Z21', 'isValidCode': True, 'coordinate': [257, 312]}, '7': {'rememberedCode': 'Z21', 'isValidCode': True, 'coordinate': [221, 697]}, '8': {'rememberedCode': 'R63', 'isValidCode': True, 'coordinate': [960, 588]}, '9': {'rememberedCode': 'B78', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'B78', 'isValidCode': True, 'coordinate': [647, 47]}, '11': {'rememberedCode': 'B78', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'B72', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'S75', 'isValidCode': True, 'coordinate': [763, 311]}, '14': {'rememberedCode': 'S75', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'S75', 'isValidCode': True, 'coordinate': [763, 311]}, '16': {'rememberedCode': 'C53', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'B74', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'B74', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'B74', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'R15', 'isValidCode': True, 'coordinate': [837, 215]}, '21': {'rememberedCode': 'P23', 'isValidCode': True, 'coordinate': [827, 172]}, '22': {'rememberedCode': 'P23', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'P23', 'isValidCode': True, 'coordinate': [827, 172]}, '24': {'rememberedCode': 'U53', 'isValidCode': True, 'coordinate': [949, 101]}, '25': {'rememberedCode': 'Q46', 'isValidCode': True, 'coordinate': [861, 169]}, '26': {'rememberedCode': 'Q46', 'isValidCode': True, 'coordinate': [1356, 6]}, '27': {'rememberedCode': 'Q46', 'isValidCode': True, 'coordinate': [861, 169]}, '28': {'rememberedCode': 'R67', 'isValidCode': True, 'coordinate': [950, 284]}, '29': {'rememberedCode': 'B95', 'isValidCode': True, 'coordinate': [1066, 213]}, '30': {'rememberedCode': 'B95', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'B95', 'isValidCode': True, 'coordinate': [1066, 213]}, '32': {'rememberedCode': 'W68', 'isValidCode': True, 'coordinate': [323, 153]}, '33': {'rememberedCode': 'M14', 'isValidCode': True, 'coordinate': [1061, 232]}, '34': {'rememberedCode': 'M14', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'M14', 'isValidCode': True, 'coordinate': [1061, 232]}, '36': {'rememberedCode': 'C72', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'P95', 'isValidCode': True, 'coordinate': [1160, 394]}, '38': {'rememberedCode': 'P95', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'P95', 'isValidCode': True, 'coordinate': [1160, 394]}}",532,50
+4,"['C', 'C', 'A', 'B', 'C', 'A', 'B', 'D', 'A', 'D', 'B', 'C', 'A', 'B', 'C', 'C', 'B', 'B', 'B', 'D', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'B', 'B', 'A', 'A', 'B', 'C', 'D', 'A', 'A', 'C', 'C', 'A', 'A', 'C', 'C', 'D', 'B', 'C', 'A', 'A', 'D', 'C', 'C', 'B', 'A', 'A', 'B', 'A', 'B', 'D', 'D', 'A', 'B', 'B', 'C', 'B', 'A', 'C', 'B', 'C', 'D', 'A', 'C', 'B', 'A', 'C', 'C', 'C', 'A', 'B', 'C', 'B', 'C', 'C', 'B', 'B', 'A', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'C', 'B', 'B', 'C', 'C', 'B', 'D', 'A']","['2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T09:28:17.430Z', 'loadTime': '2021-08-20T09:28:38.899Z', 'startTime': '2021-08-20T09:30:18.718Z', 'endTime': '2021-08-20T09:59:02.006Z', 'timeToCompleteFromOpenMsec': 1844576, 'timeToLoadMsec': 21469, 'timeToCompleteFromStartMsec': 1723288}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'Y42', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'A13', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'A13', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'A13', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'P51', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'T18', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'J21', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'W14', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'W14', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'W14', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'B18', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'M12', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'M12', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M12', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'M32', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'N12', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'N12', 'isValidCode': True, 'coordinate': [1475, 339]}, '23': {'rememberedCode': 'N12', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'N52', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'H23', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'H23', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'H23', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'V32', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'M36', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'M36', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'M36', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'G21', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'J52', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'J52', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'J52', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'D82', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'M16', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'M16', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'M16', 'isValidCode': False, 'coordinate': False}}",445,741
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+13,"['C', 'C', 'A', 'B', 'D', 'A', 'B', 'D', 'D', 'D', 'B', 'A', 'A', 'C', 'B', 'C', 'B', 'B', 'D', 'D', 'A', 'B', 'A', 'D', 'B', 'C', 'D', 'D', 'D', 'D', 'B', 'A', 'A', 'C', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'A', 'C', 'D', 'D', 'C', 'B', 'D', 'D', 'D', 'C', 'B', 'D', 'D', 'A', 'A', 'C', 'C', 'D', 'C', 'A', 'B', 'A', 'D', 'D', 'D', 'B', 'D', 'D', 'D', 'C', 'B', 'D', 'D', 'D', 'C', 'B', 'A', 'B', 'D', 'C', 'A', 'D', 'D', 'C', 'A', 'B', 'C', 'A', 'D', 'A', 'A', 'A', 'B', 'D', 'C', 'B', 'D', 'D', 'D']","['2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T12:40:01.585Z', 'loadTime': '2021-08-20T12:40:24.616Z', 'startTime': '2021-08-20T12:40:29.935Z', 'endTime': '2021-08-20T13:06:37.816Z', 'timeToCompleteFromOpenMsec': 1596231, 'timeToLoadMsec': 23031, 'timeToCompleteFromStartMsec': 1567881}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'X38', 'isValidCode': True, 'coordinate': [1180, 803]}, '1': {'rememberedCode': 'Q28', 'isValidCode': True, 'coordinate': [887, 864]}, '2': {'rememberedCode': 'Q28', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Q28', 'isValidCode': True, 'coordinate': [887, 864]}, '4': {'rememberedCode': 'M41', 'isValidCode': True, 'coordinate': [966, 520]}, '5': {'rememberedCode': 'N57', 'isValidCode': True, 'coordinate': [1050, 544]}, '6': {'rememberedCode': 'N57', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N57', 'isValidCode': True, 'coordinate': [1050, 544]}, '8': {'rememberedCode': 'M45', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'B62', 'isValidCode': True, 'coordinate': [129, 502]}, '10': {'rememberedCode': 'B62', 'isValidCode': True, 'coordinate': [1765, 583]}, '11': {'rememberedCode': 'B62', 'isValidCode': True, 'coordinate': [129, 502]}, '12': {'rememberedCode': 'K85', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'C53', 'isValidCode': True, 'coordinate': [1293, 866]}, '14': {'rememberedCode': 'C53', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C53', 'isValidCode': True, 'coordinate': [1293, 866]}, '16': {'rememberedCode': 'S53', 'isValidCode': True, 'coordinate': [1023, 500]}, '17': {'rememberedCode': 'J58', 'isValidCode': True, 'coordinate': [653, 372]}, '18': {'rememberedCode': 'J58', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'J58', 'isValidCode': True, 'coordinate': [653, 372]}, '20': {'rememberedCode': 'Z73', 'isValidCode': True, 'coordinate': [1144, 692]}, '21': {'rememberedCode': 'Z73', 'isValidCode': True, 'coordinate': [1857, 388]}, '22': {'rememberedCode': 'Z73', 'isValidCode': True, 'coordinate': [1144, 692]}, '23': {'rememberedCode': 'Z73', 'isValidCode': True, 'coordinate': [1857, 388]}, '24': {'rememberedCode': 'L82', 'isValidCode': True, 'coordinate': [539, 643]}, '25': {'rememberedCode': 'G64', 'isValidCode': True, 'coordinate': [11, 846]}, '26': {'rememberedCode': 'G64', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G64', 'isValidCode': True, 'coordinate': [11, 846]}, '28': {'rememberedCode': 'D69', 'isValidCode': True, 'coordinate': [626, 829]}, '29': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [1096, 575]}, '30': {'rememberedCode': 'K71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [1096, 575]}, '32': {'rememberedCode': 'Z85', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'H71', 'isValidCode': True, 'coordinate': [1372, 844]}, '34': {'rememberedCode': 'H71', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'H71', 'isValidCode': True, 'coordinate': [1372, 844]}, '36': {'rememberedCode': 'Q36', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'Y64', 'isValidCode': True, 'coordinate': [1251, 644]}, '38': {'rememberedCode': 'Y64', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Y64', 'isValidCode': True, 'coordinate': [1251, 644]}}",233,74
+14,"['C', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'A', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'A', 'C', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'A', 'C', 'B', 'B', 'C', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'D', 'B', 'A', 'C', 'C', 'B', 'C', 'A', 'B', 'B', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'C', 'B', 'A', 'C', 'C', 'C', 'B', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'A', 'B', 'C', 'C', 'B', 'D', 'B', 'C']","['2', '2', '1', '2', '2', '1', '2', '1', '2', '1', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T12:28:27.903Z', 'loadTime': '2021-08-20T12:29:10.004Z', 'startTime': '2021-08-20T12:29:19.380Z', 'endTime': '2021-08-20T13:11:06.352Z', 'timeToCompleteFromOpenMsec': 2558449, 'timeToLoadMsec': 42101, 'timeToCompleteFromStartMsec': 2506972}","{'gender': 'female', 'ageGroup': 'unspecified', 'ethnicity': 'unspecified', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'B36', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'Z56', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'Z56', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Z56', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'U43', 'isValidCode': True, 'coordinate': [857, 587]}, '5': {'rememberedCode': 'S25', 'isValidCode': True, 'coordinate': [1066, 499]}, '6': {'rememberedCode': 'S25', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'S25', 'isValidCode': True, 'coordinate': [1066, 499]}, '8': {'rememberedCode': 'B46', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'Y30', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'Y30', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Y30', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'P47', 'isValidCode': True, 'coordinate': [775, 546]}, '13': {'rememberedCode': 'V65', 'isValidCode': True, 'coordinate': [547, 731]}, '14': {'rememberedCode': 'V65', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'V65', 'isValidCode': True, 'coordinate': [547, 731]}, '16': {'rememberedCode': 'F84', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'E54', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'E54', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'E54', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'Y26', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'L96', 'isValidCode': True, 'coordinate': [926, 596]}, '22': {'rememberedCode': 'L96', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L96', 'isValidCode': True, 'coordinate': [926, 596]}, '24': {'rememberedCode': 'T17', 'isValidCode': True, 'coordinate': [1158, 570]}, '25': {'rememberedCode': 'L27', 'isValidCode': True, 'coordinate': [1067, 524]}, '26': {'rememberedCode': 'L27', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'L27', 'isValidCode': True, 'coordinate': [1067, 524]}, '28': {'rememberedCode': 'N53', 'isValidCode': True, 'coordinate': [1170, 412]}, '29': {'rememberedCode': 'K29', 'isValidCode': True, 'coordinate': [869, 503]}, '30': {'rememberedCode': 'K29', 'isValidCode': True, 'coordinate': [1674, 17]}, '31': {'rememberedCode': 'K29', 'isValidCode': True, 'coordinate': [869, 503]}, '32': {'rememberedCode': 'L67', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'Y15', 'isValidCode': True, 'coordinate': [981, 442]}, '34': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Y15', 'isValidCode': True, 'coordinate': [981, 442]}, '36': {'rememberedCode': 'W63', 'isValidCode': True, 'coordinate': [765, 520]}, '37': {'rememberedCode': 'W26', 'isValidCode': True, 'coordinate': [832, 457]}, '38': {'rememberedCode': 'W26', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'W26', 'isValidCode': True, 'coordinate': [832, 457]}}",795,174
+15,"['C', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'A', 'A', 'B', 'A', 'A', 'C', 'D', 'C', 'B', 'B', 'B', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'C', 'A', 'A', 'A', 'B', 'A', 'A', 'C', 'C', 'A', 'C', 'A', 'C', 'C', 'A', 'A', 'B', 'B', 'B', 'C', 'B', 'A', 'C', 'B', 'C', 'B', 'B', 'C', 'A', 'A', 'C', 'C', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'C', 'C', 'B', 'A', 'C', 'C', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'A', 'A', 'D', 'C', 'B', 'B', 'B', 'B']","['2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T12:39:51.093Z', 'loadTime': '2021-08-20T12:40:34.313Z', 'startTime': '2021-08-20T12:40:41.561Z', 'endTime': '2021-08-20T13:23:03.045Z', 'timeToCompleteFromOpenMsec': 2591952, 'timeToLoadMsec': 43220, 'timeToCompleteFromStartMsec': 2541484}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'T38', 'isValidCode': True, 'coordinate': [789, 305]}, '1': {'rememberedCode': 'Y43', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'Y43', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Y43', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'Y56', 'isValidCode': True, 'coordinate': [885, 439]}, '5': {'rememberedCode': 'H47', 'isValidCode': True, 'coordinate': [1137, 279]}, '6': {'rememberedCode': 'H47', 'isValidCode': True, 'coordinate': [1187, 644]}, '7': {'rememberedCode': 'H47', 'isValidCode': True, 'coordinate': [1137, 279]}, '8': {'rememberedCode': 'G82', 'isValidCode': True, 'coordinate': [861, 465]}, '9': {'rememberedCode': 'W71', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'W71', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'W71', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'N56', 'isValidCode': True, 'coordinate': [872, 482]}, '13': {'rememberedCode': 'Q57', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'Q57', 'isValidCode': True, 'coordinate': [338, 284]}, '15': {'rememberedCode': 'Q57', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'G34', 'isValidCode': True, 'coordinate': [713, 289]}, '17': {'rememberedCode': 'X42', 'isValidCode': True, 'coordinate': [877, 296]}, '18': {'rememberedCode': 'X42', 'isValidCode': True, 'coordinate': [1438, 645]}, '19': {'rememberedCode': 'X42', 'isValidCode': True, 'coordinate': [877, 296]}, '20': {'rememberedCode': 'J96', 'isValidCode': True, 'coordinate': [932, 494]}, '21': {'rememberedCode': 'W49', 'isValidCode': True, 'coordinate': [726, 457]}, '22': {'rememberedCode': 'W49', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'W49', 'isValidCode': True, 'coordinate': [726, 457]}, '24': {'rememberedCode': 'N25', 'isValidCode': True, 'coordinate': [926, 426]}, '25': {'rememberedCode': 'J92', 'isValidCode': True, 'coordinate': [741, 373]}, '26': {'rememberedCode': 'J92', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'J92', 'isValidCode': True, 'coordinate': [741, 373]}, '28': {'rememberedCode': 'T78', 'isValidCode': True, 'coordinate': [835, 490]}, '29': {'rememberedCode': 'K29', 'isValidCode': True, 'coordinate': [869, 503]}, '30': {'rememberedCode': 'K29', 'isValidCode': True, 'coordinate': [1674, 17]}, '31': {'rememberedCode': 'K29', 'isValidCode': True, 'coordinate': [869, 503]}, '32': {'rememberedCode': 'T59', 'isValidCode': True, 'coordinate': [861, 426]}, '33': {'rememberedCode': 'T67', 'isValidCode': True, 'coordinate': [855, 430]}, '34': {'rememberedCode': 'T67', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'T67', 'isValidCode': True, 'coordinate': [855, 430]}, '36': {'rememberedCode': 'B95', 'isValidCode': True, 'coordinate': [945, 385]}, '37': {'rememberedCode': 'S85', 'isValidCode': True, 'coordinate': [1060, 506]}, '38': {'rememberedCode': 'S85', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'S85', 'isValidCode': True, 'coordinate': [1060, 506]}}",624,156
+16,"['A', 'C', 'C', 'B', 'A', 'A', 'C', 'B', 'A', 'B', 'A', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'C', 'A', 'A', 'C', 'C', 'A', 'A', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'A', 'A', 'C', 'B', 'A', 'B', 'C', 'C', 'C', 'B', 'C', 'A', 'B', 'C', 'A', 'C', 'C', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'A', 'C', 'C', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'B', 'A', 'C', 'A', 'B', 'C', 'C', 'C', 'A', 'B', 'C', 'B', 'C', 'C', 'B', 'C', 'A', 'C']","['2', '2', '2', '2', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '2', '2', '1', '1', '2', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '2']","{'openTime': '2021-08-20T12:54:27.521Z', 'loadTime': '2021-08-20T12:55:05.346Z', 'startTime': '2021-08-20T13:11:20.267Z', 'endTime': '2021-08-20T13:28:53.712Z', 'timeToCompleteFromOpenMsec': 2066191, 'timeToLoadMsec': 37825, 'timeToCompleteFromStartMsec': 1053445}","{'gender': 'male', 'ageGroup': 'ageGroup18', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'Z68', 'isValidCode': True, 'coordinate': [1586, 521]}, '1': {'rememberedCode': 'D43', 'isValidCode': True, 'coordinate': [870, 372]}, '2': {'rememberedCode': 'D43', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'D43', 'isValidCode': True, 'coordinate': [870, 372]}, '4': {'rememberedCode': 'K42', 'isValidCode': True, 'coordinate': [570, 164]}, '5': {'rememberedCode': 'H51', 'isValidCode': True, 'coordinate': [1657, 414]}, '6': {'rememberedCode': 'H51', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H51', 'isValidCode': True, 'coordinate': [1657, 414]}, '8': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'L92', 'isValidCode': True, 'coordinate': [733, 859]}, '10': {'rememberedCode': 'L92', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'L92', 'isValidCode': True, 'coordinate': [733, 859]}, '12': {'rememberedCode': 'X72', 'isValidCode': True, 'coordinate': [748, 827]}, '13': {'rememberedCode': 'C49', 'isValidCode': True, 'coordinate': [340, 649]}, '14': {'rememberedCode': 'C49', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C49', 'isValidCode': True, 'coordinate': [340, 649]}, '16': {'rememberedCode': 'Z97', 'isValidCode': True, 'coordinate': [1230, 67]}, '17': {'rememberedCode': 'W43', 'isValidCode': True, 'coordinate': [1355, 647]}, '18': {'rememberedCode': 'W43', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'W43', 'isValidCode': True, 'coordinate': [1355, 647]}, '20': {'rememberedCode': 'M26', 'isValidCode': True, 'coordinate': [1245, 633]}, '21': {'rememberedCode': 'L81', 'isValidCode': True, 'coordinate': [1542, 229]}, '22': {'rememberedCode': 'L81', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L81', 'isValidCode': True, 'coordinate': [1542, 229]}, '24': {'rememberedCode': 'R87', 'isValidCode': True, 'coordinate': [551, 496]}, '25': {'rememberedCode': 'B27', 'isValidCode': True, 'coordinate': [1662, 795]}, '26': {'rememberedCode': 'B27', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'B27', 'isValidCode': True, 'coordinate': [1662, 795]}, '28': {'rememberedCode': 'X45', 'isValidCode': True, 'coordinate': [536, 759]}, '29': {'rememberedCode': 'J83', 'isValidCode': True, 'coordinate': [156, 145]}, '30': {'rememberedCode': 'J83', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'J83', 'isValidCode': True, 'coordinate': [156, 145]}, '32': {'rememberedCode': 'N25', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'J47', 'isValidCode': True, 'coordinate': [255, 158]}, '34': {'rememberedCode': 'J47', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'J47', 'isValidCode': True, 'coordinate': [255, 158]}, '36': {'rememberedCode': 'J43', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'B56', 'isValidCode': True, 'coordinate': [1338, 662]}, '38': {'rememberedCode': 'B56', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'B56', 'isValidCode': True, 'coordinate': [1338, 662]}}",20,72
+17,"['C', 'C', 'A', 'B', 'C', 'A', 'A', 'C', 'D', 'A', 'B', 'A', 'A', 'B', 'B', 'C', 'D', 'B', 'A', 'B', 'A', 'B', 'A', 'D', 'B', 'C', 'D', 'A', 'D', 'D', 'C', 'D', 'B', 'D', 'D', 'A', 'D', 'D', 'C', 'A', 'A', 'D', 'C', 'D', 'D', 'C', 'D', 'D', 'C', 'D', 'C', 'B', 'B', 'C', 'A', 'B', 'C', 'C', 'D', 'C', 'A', 'D', 'D', 'D', 'D', 'C', 'B', 'D', 'D', 'C', 'C', 'D', 'A', 'C', 'D', 'C', 'B', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'A', 'A', 'B', 'C', 'D', 'D', 'B', 'A', 'A', 'B', 'D', 'C', 'D', 'A', 'B', 'C']","['2', '2', '1', '2', '2', '1', '2', '1', '2', '1', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '2', '1', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T13:09:48.842Z', 'loadTime': '2021-08-20T13:10:10.812Z', 'startTime': '2021-08-20T13:11:29.665Z', 'endTime': '2021-08-20T13:41:28.385Z', 'timeToCompleteFromOpenMsec': 1899543, 'timeToLoadMsec': 21970, 'timeToCompleteFromStartMsec': 1798720}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'the screen with the letter-number codes was smaller/ smaller font made it hard for me to identify/recall the codes; I also forget the codes because I&#39;m trying to hold number data in my head!'}","{'0': {'rememberedCode': 'R71', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'Y73', 'isValidCode': True, 'coordinate': [670, 380]}, '2': {'rememberedCode': 'Y73', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Y73', 'isValidCode': True, 'coordinate': [670, 380]}, '4': {'rememberedCode': 'K62', 'isValidCode': True, 'coordinate': [958, 305]}, '5': {'rememberedCode': 'F51', 'isValidCode': True, 'coordinate': [417, 203]}, '6': {'rememberedCode': 'F51', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'F51', 'isValidCode': True, 'coordinate': [417, 203]}, '8': {'rememberedCode': 'E35', 'isValidCode': True, 'coordinate': [1490, 808]}, '9': {'rememberedCode': 'E41', 'isValidCode': True, 'coordinate': [836, 231]}, '10': {'rememberedCode': 'E41', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'E41', 'isValidCode': True, 'coordinate': [836, 231]}, '12': {'rememberedCode': 'V69', 'isValidCode': True, 'coordinate': [876, 558]}, '13': {'rememberedCode': 'L82', 'isValidCode': True, 'coordinate': [855, 590]}, '14': {'rememberedCode': 'L82', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L82', 'isValidCode': True, 'coordinate': [855, 590]}, '16': {'rememberedCode': 'L35', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'M17', 'isValidCode': True, 'coordinate': [963, 701]}, '18': {'rememberedCode': 'M17', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M17', 'isValidCode': True, 'coordinate': [963, 701]}, '20': {'rememberedCode': 'B34', 'isValidCode': True, 'coordinate': [628, 496]}, '21': {'rememberedCode': 'E41', 'isValidCode': True, 'coordinate': [1027, 255]}, '22': {'rememberedCode': 'E41', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'E41', 'isValidCode': True, 'coordinate': [1027, 255]}, '24': {'rememberedCode': 'P35', 'isValidCode': True, 'coordinate': [828, 214]}, '25': {'rememberedCode': 'F72', 'isValidCode': True, 'coordinate': [1173, 733]}, '26': {'rememberedCode': 'F72', 'isValidCode': True, 'coordinate': [339, 238]}, '27': {'rememberedCode': 'F72', 'isValidCode': True, 'coordinate': [1173, 733]}, '28': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [836, 548]}, '29': {'rememberedCode': 'A14', 'isValidCode': True, 'coordinate': [878, 693]}, '30': {'rememberedCode': 'A14', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'A14', 'isValidCode': True, 'coordinate': [878, 693]}, '32': {'rememberedCode': 'Q34', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'Q58', 'isValidCode': True, 'coordinate': [871, 497]}, '34': {'rememberedCode': 'Q58', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Q58', 'isValidCode': True, 'coordinate': [871, 497]}, '36': {'rememberedCode': 'W42', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'G42', 'isValidCode': True, 'coordinate': [939, 647]}, '38': {'rememberedCode': 'G42', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G42', 'isValidCode': True, 'coordinate': [939, 647]}}",275,574
+18,"['C', 'C', 'A', 'B', 'C', 'A', 'B', 'D', 'D', 'D', 'B', 'A', 'D', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'C', 'A', 'D', 'D', 'D', 'B', 'A', 'A', 'C', 'D', 'A', 'C', 'D', 'C', 'D', 'B', 'A', 'C', 'D', 'D', 'C', 'B', 'D', 'D', 'D', 'C', 'B', 'D', 'D', 'A', 'A', 'C', 'C', 'B', 'C', 'A', 'D', 'A', 'B', 'C', 'D', 'B', 'D', 'D', 'D', 'C', 'B', 'D', 'D', 'D', 'C', 'B', 'A', 'B', 'D', 'C', 'A', 'A', 'B', 'C', 'A', 'B', 'C', 'D', 'D', 'B', 'A', 'A', 'D', 'D', 'C', 'B', 'D', 'D', 'D']","['2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T13:20:50.700Z', 'loadTime': '2021-08-20T13:20:59.462Z', 'startTime': '2021-08-20T13:21:17.605Z', 'endTime': '2021-08-20T13:46:37.490Z', 'timeToCompleteFromOpenMsec': 1546790, 'timeToLoadMsec': 8762, 'timeToCompleteFromStartMsec': 1519885}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'T47', 'isValidCode': True, 'coordinate': [979, 596]}, '1': {'rememberedCode': 'B67', 'isValidCode': True, 'coordinate': [1060, 524]}, '2': {'rememberedCode': 'B67', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'B67', 'isValidCode': True, 'coordinate': [1060, 524]}, '4': {'rememberedCode': 'Z37', 'isValidCode': True, 'coordinate': [251, 640]}, '5': {'rememberedCode': 'N57', 'isValidCode': True, 'coordinate': [1050, 544]}, '6': {'rememberedCode': 'N57', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N57', 'isValidCode': True, 'coordinate': [1050, 544]}, '8': {'rememberedCode': 'G63', 'isValidCode': True, 'coordinate': [338, 719]}, '9': {'rememberedCode': 'L38', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'L38', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'L38', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'N18', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'F64', 'isValidCode': True, 'coordinate': [578, 858]}, '14': {'rememberedCode': 'F64', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F64', 'isValidCode': True, 'coordinate': [578, 858]}, '16': {'rememberedCode': 'U85', 'isValidCode': True, 'coordinate': [313, 847]}, '17': {'rememberedCode': 'S87', 'isValidCode': True, 'coordinate': [745, 775]}, '18': {'rememberedCode': 'S87', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'S87', 'isValidCode': True, 'coordinate': [745, 775]}, '20': {'rememberedCode': 'R95', 'isValidCode': True, 'coordinate': [648, 270]}, '21': {'rememberedCode': 'Y32', 'isValidCode': True, 'coordinate': [1039, 736]}, '22': {'rememberedCode': 'Y32', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Y32', 'isValidCode': True, 'coordinate': [1039, 736]}, '24': {'rememberedCode': 'T17', 'isValidCode': True, 'coordinate': [1158, 570]}, '25': {'rememberedCode': 'S27', 'isValidCode': True, 'coordinate': [535, 856]}, '26': {'rememberedCode': 'S27', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'S27', 'isValidCode': True, 'coordinate': [535, 856]}, '28': {'rememberedCode': 'O98', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [1096, 575]}, '30': {'rememberedCode': 'K71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [1096, 575]}, '32': {'rememberedCode': 'B24', 'isValidCode': True, 'coordinate': [1586, 622]}, '33': {'rememberedCode': 'K85', 'isValidCode': True, 'coordinate': [1372, 573]}, '34': {'rememberedCode': 'K85', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'K85', 'isValidCode': True, 'coordinate': [1372, 573]}, '36': {'rememberedCode': 'G29', 'isValidCode': True, 'coordinate': [753, 869]}, '37': {'rememberedCode': 'J16', 'isValidCode': True, 'coordinate': [1263, 588]}, '38': {'rememberedCode': 'J16', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'J16', 'isValidCode': True, 'coordinate': [1263, 588]}}",213,859
+19,"['C', 'C', 'A', 'B', 'D', 'A', 'B', 'D', 'D', 'D', 'B', 'A', 'A', 'C', 'B', 'C', 'A', 'B', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'C', 'C', 'A', 'C', 'B', 'B', 'A', 'A', 'C', 'C', 'A', 'C', 'A', 'C', 'A', 'A', 'D', 'C', 'B', 'B', 'C', 'B', 'D', 'D', 'D', 'C', 'B', 'D', 'C', 'B', 'A', 'C', 'C', 'B', 'C', 'A', 'B', 'A', 'D', 'C', 'D', 'B', 'D', 'D', 'C', 'C', 'B', 'A', 'C', 'C', 'C', 'B', 'D', 'D', 'A', 'B', 'A', 'A', 'B', 'C', 'A', 'D', 'D', 'D', 'D', 'B', 'A', 'A', 'B', 'C', 'C', 'B', 'D', 'B', 'D']","['2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T13:27:53.763Z', 'loadTime': '2021-08-20T13:28:10.514Z', 'startTime': '2021-08-20T13:28:20.638Z', 'endTime': '2021-08-20T13:56:27.219Z', 'timeToCompleteFromOpenMsec': 1713456, 'timeToLoadMsec': 16751, 'timeToCompleteFromStartMsec': 1686581}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'R83', 'isValidCode': True, 'coordinate': [859, 254]}, '1': {'rememberedCode': 'W46', 'isValidCode': True, 'coordinate': [373, 102]}, '2': {'rememberedCode': 'W46', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W46', 'isValidCode': True, 'coordinate': [373, 102]}, '4': {'rememberedCode': 'M53', 'isValidCode': True, 'coordinate': [1478, 158]}, '5': {'rememberedCode': 'W23', 'isValidCode': True, 'coordinate': [1047, 346]}, '6': {'rememberedCode': 'W23', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'W23', 'isValidCode': True, 'coordinate': [1047, 346]}, '8': {'rememberedCode': 'H45', 'isValidCode': True, 'coordinate': [838, 25]}, '9': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [720, 15]}, '10': {'rememberedCode': 'F45', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [720, 15]}, '12': {'rememberedCode': 'X17', 'isValidCode': True, 'coordinate': [1178, 153]}, '13': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [1398, 458]}, '14': {'rememberedCode': 'K13', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [1398, 458]}, '16': {'rememberedCode': 'U19', 'isValidCode': True, 'coordinate': [436, 148]}, '17': {'rememberedCode': 'Y56', 'isValidCode': True, 'coordinate': [649, 288]}, '18': {'rememberedCode': 'Y56', 'isValidCode': True, 'coordinate': [312, 83]}, '19': {'rememberedCode': 'Y56', 'isValidCode': True, 'coordinate': [649, 288]}, '20': {'rememberedCode': 'N75', 'isValidCode': True, 'coordinate': [415, 75]}, '21': {'rememberedCode': 'N87', 'isValidCode': True, 'coordinate': [713, 180]}, '22': {'rememberedCode': 'N87', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'N87', 'isValidCode': True, 'coordinate': [713, 180]}, '24': {'rememberedCode': 'R47', 'isValidCode': True, 'coordinate': [1052, 366]}, '25': {'rememberedCode': 'R97', 'isValidCode': True, 'coordinate': [943, 179]}, '26': {'rememberedCode': 'R97', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'R97', 'isValidCode': True, 'coordinate': [943, 179]}, '28': {'rememberedCode': 'D74', 'isValidCode': True, 'coordinate': [1372, 157]}, '29': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [459, 78]}, '30': {'rememberedCode': 'X12', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [459, 78]}, '32': {'rememberedCode': 'F25', 'isValidCode': True, 'coordinate': [346, 5]}, '33': {'rememberedCode': 'Q62', 'isValidCode': True, 'coordinate': [1595, 16]}, '34': {'rememberedCode': 'Q62', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Q62', 'isValidCode': True, 'coordinate': [1595, 16]}, '36': {'rememberedCode': 'R36', 'isValidCode': True, 'coordinate': [1067, 38]}, '37': {'rememberedCode': 'D38', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'D38', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'D38', 'isValidCode': False, 'coordinate': False}}",904,161
+20,"['C', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'B', 'C', 'B', 'B', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'A', 'C', 'C', 'A', 'C', 'A', 'D', 'B', 'A', 'A', 'C', 'B', 'B', 'C', 'B', 'A', 'C', 'A', 'C', 'B', 'B', 'B', 'B', 'A', 'C', 'C', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'D', 'C', 'B', 'A', 'C', 'C', 'C', 'B', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'C', 'A', 'B', 'C', 'D', 'A', 'B', 'A', 'A', 'B', 'C', 'C', 'B', 'B', 'D', 'C']","['2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '2', '2', '1', '1', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T13:54:14.585Z', 'loadTime': '2021-08-20T13:55:08.468Z', 'startTime': '2021-08-20T13:55:16.426Z', 'endTime': '2021-08-20T14:24:23.735Z', 'timeToCompleteFromOpenMsec': 1809150, 'timeToLoadMsec': 53883, 'timeToCompleteFromStartMsec': 1747309}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'someCollege', 'vizExperience': 'someExposure', 'feedback': 'thanx!'}","{'0': {'rememberedCode': 'S59', 'isValidCode': True, 'coordinate': [986, 384]}, '1': {'rememberedCode': 'E93', 'isValidCode': True, 'coordinate': [960, 441]}, '2': {'rememberedCode': 'E93', 'isValidCode': True, 'coordinate': [777, 720]}, '3': {'rememberedCode': 'E93', 'isValidCode': True, 'coordinate': [960, 441]}, '4': {'rememberedCode': 'H17', 'isValidCode': True, 'coordinate': [439, 104]}, '5': {'rememberedCode': 'X65', 'isValidCode': True, 'coordinate': [758, 280]}, '6': {'rememberedCode': 'X65', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'X65', 'isValidCode': True, 'coordinate': [758, 280]}, '8': {'rememberedCode': 'L69', 'isValidCode': True, 'coordinate': [1078, 444]}, '9': {'rememberedCode': 'L39', 'isValidCode': True, 'coordinate': [1149, 433]}, '10': {'rememberedCode': 'L39', 'isValidCode': True, 'coordinate': [1270, 729]}, '11': {'rememberedCode': 'L39', 'isValidCode': True, 'coordinate': [1149, 433]}, '12': {'rememberedCode': 'J86', 'isValidCode': True, 'coordinate': [974, 430]}, '13': {'rememberedCode': 'D43', 'isValidCode': True, 'coordinate': [982, 640]}, '14': {'rememberedCode': 'D43', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'D43', 'isValidCode': True, 'coordinate': [982, 640]}, '16': {'rememberedCode': 'L72', 'isValidCode': True, 'coordinate': [948, 365]}, '17': {'rememberedCode': 'N29', 'isValidCode': True, 'coordinate': [971, 505]}, '18': {'rememberedCode': 'N29', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'N29', 'isValidCode': True, 'coordinate': [971, 505]}, '20': {'rememberedCode': 'N71', 'isValidCode': True, 'coordinate': [438, 220]}, '21': {'rememberedCode': 'Z56', 'isValidCode': True, 'coordinate': [729, 512]}, '22': {'rememberedCode': 'Z56', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Z56', 'isValidCode': True, 'coordinate': [729, 512]}, '24': {'rememberedCode': 'V13', 'isValidCode': True, 'coordinate': [1373, 161]}, '25': {'rememberedCode': 'L27', 'isValidCode': True, 'coordinate': [1067, 524]}, '26': {'rememberedCode': 'L27', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'L27', 'isValidCode': True, 'coordinate': [1067, 524]}, '28': {'rememberedCode': 'T78', 'isValidCode': True, 'coordinate': [835, 490]}, '29': {'rememberedCode': 'M38', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'M38', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'M38', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'G85', 'isValidCode': True, 'coordinate': [1069, 499]}, '33': {'rememberedCode': 'T67', 'isValidCode': True, 'coordinate': [855, 430]}, '34': {'rememberedCode': 'T67', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'T67', 'isValidCode': True, 'coordinate': [855, 430]}, '36': {'rememberedCode': 'T38', 'isValidCode': True, 'coordinate': [963, 592]}, '37': {'rememberedCode': 'H59', 'isValidCode': True, 'coordinate': [935, 531]}, '38': {'rememberedCode': 'H59', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'H59', 'isValidCode': True, 'coordinate': [935, 531]}}",698,315
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+30,"['C', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'A', 'A', 'B', 'A', 'A', 'C', 'B', 'C', 'B', 'B', 'B', 'D', 'A', 'B', 'A', 'A', 'D', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'A', 'C', 'C', 'A', 'C', 'A', 'C', 'A', 'A', 'A', 'C', 'B', 'D', 'C', 'B', 'A', 'D', 'D', 'C', 'B', 'B', 'B', 'B', 'A', 'C', 'C', 'B', 'C', 'A', 'B', 'D', 'B', 'C', 'A', 'B', 'C', 'D', 'C', 'C', 'B', 'A', 'C', 'C', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'A', 'B', 'B', 'C', 'B', 'D', 'C', 'C']","['2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2']","{'openTime': '2021-08-21T01:55:15.167Z', 'loadTime': '2021-08-21T01:55:42.963Z', 'startTime': '2021-08-21T01:55:44.806Z', 'endTime': '2021-08-21T02:26:31.914Z', 'timeToCompleteFromOpenMsec': 1876747, 'timeToLoadMsec': 27796, 'timeToCompleteFromStartMsec': 1847108}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Nice task, I really enjoyed it.'}","{'0': {'rememberedCode': 'N54', 'isValidCode': True, 'coordinate': [893, 307]}, '1': {'rememberedCode': 'M73', 'isValidCode': True, 'coordinate': [768, 502]}, '2': {'rememberedCode': 'M73', 'isValidCode': True, 'coordinate': [45, 313]}, '3': {'rememberedCode': 'M73', 'isValidCode': True, 'coordinate': [768, 502]}, '4': {'rememberedCode': 'W63', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'U17', 'isValidCode': True, 'coordinate': [854, 487]}, '6': {'rememberedCode': 'U17', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U17', 'isValidCode': True, 'coordinate': [854, 487]}, '8': {'rememberedCode': 'W87', 'isValidCode': True, 'coordinate': [978, 527]}, '9': {'rememberedCode': 'Y53', 'isValidCode': True, 'coordinate': [961, 500]}, '10': {'rememberedCode': 'Y53', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Y53', 'isValidCode': True, 'coordinate': [961, 500]}, '12': {'rememberedCode': 'L48', 'isValidCode': True, 'coordinate': [980, 498]}, '13': {'rememberedCode': 'C95', 'isValidCode': True, 'coordinate': [783, 792]}, '14': {'rememberedCode': 'C95', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C95', 'isValidCode': True, 'coordinate': [783, 792]}, '16': {'rememberedCode': 'P82', 'isValidCode': True, 'coordinate': [1022, 413]}, '17': {'rememberedCode': 'R32', 'isValidCode': True, 'coordinate': [872, 493]}, '18': {'rememberedCode': 'R32', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'R32', 'isValidCode': True, 'coordinate': [872, 493]}, '20': {'rememberedCode': 'X31', 'isValidCode': True, 'coordinate': [952, 424]}, '21': {'rememberedCode': 'C52', 'isValidCode': True, 'coordinate': [845, 324]}, '22': {'rememberedCode': 'C52', 'isValidCode': True, 'coordinate': [1063, 768]}, '23': {'rememberedCode': 'C52', 'isValidCode': True, 'coordinate': [845, 324]}, '24': {'rememberedCode': 'C61', 'isValidCode': True, 'coordinate': [1033, 640]}, '25': {'rememberedCode': 'D16', 'isValidCode': True, 'coordinate': [943, 456]}, '26': {'rememberedCode': 'D16', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'D16', 'isValidCode': True, 'coordinate': [943, 456]}, '28': {'rememberedCode': 'A94', 'isValidCode': True, 'coordinate': [934, 348]}, '29': {'rememberedCode': 'B53', 'isValidCode': True, 'coordinate': [997, 228]}, '30': {'rememberedCode': 'B53', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'B53', 'isValidCode': True, 'coordinate': [997, 228]}, '32': {'rememberedCode': 'F57', 'isValidCode': True, 'coordinate': [1157, 409]}, '33': {'rememberedCode': 'E36', 'isValidCode': True, 'coordinate': [460, 367]}, '34': {'rememberedCode': 'E36', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E36', 'isValidCode': True, 'coordinate': [460, 367]}, '36': {'rememberedCode': 'B95', 'isValidCode': True, 'coordinate': [945, 385]}, '37': {'rememberedCode': 'E71', 'isValidCode': True, 'coordinate': [846, 392]}, '38': {'rememberedCode': 'E71', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E71', 'isValidCode': True, 'coordinate': [846, 392]}}",22,492
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+32,"['D', 'B', 'C', 'C', 'A', 'A', 'D', 'A', 'A', 'A', 'B', 'A', 'A', 'A', 'A', 'C', 'B', 'B', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'B', 'A', 'C', 'D', 'C', 'D', 'A', 'B', 'A', 'A', 'A', 'C', 'C', 'C', 'C', 'A', 'B', 'A', 'B', 'C', 'B', 'D', 'C', 'A', 'A', 'C', 'C', 'B', 'C', 'C', 'B', 'B', 'B', 'C', 'C', 'D', 'A', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'A', 'C', 'A', 'B', 'C', 'B', 'D', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'D', 'D', 'D']","['2', '2', '1', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-21T21:41:11.194Z', 'loadTime': '2021-08-21T21:41:31.263Z', 'startTime': '2021-08-21T21:41:36.537Z', 'endTime': '2021-08-21T22:04:42.246Z', 'timeToCompleteFromOpenMsec': 1411052, 'timeToLoadMsec': 20069, 'timeToCompleteFromStartMsec': 1385709}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'highSchool', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'P23', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'B93', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'B93', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'B93', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'M41', 'isValidCode': True, 'coordinate': [966, 520]}, '5': {'rememberedCode': 'K94', 'isValidCode': True, 'coordinate': [852, 416]}, '6': {'rememberedCode': 'K94', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'K94', 'isValidCode': True, 'coordinate': [852, 416]}, '8': {'rememberedCode': 'P61', 'isValidCode': True, 'coordinate': [454, 596]}, '9': {'rememberedCode': 'E35', 'isValidCode': True, 'coordinate': [939, 637]}, '10': {'rememberedCode': 'E35', 'isValidCode': True, 'coordinate': [1490, 808]}, '11': {'rememberedCode': 'E35', 'isValidCode': True, 'coordinate': [939, 637]}, '12': {'rememberedCode': 'N86', 'isValidCode': True, 'coordinate': [564, 705]}, '13': {'rememberedCode': 'C91', 'isValidCode': True, 'coordinate': [1292, 734]}, '14': {'rememberedCode': 'C91', 'isValidCode': True, 'coordinate': [569, 632]}, '15': {'rememberedCode': 'C91', 'isValidCode': True, 'coordinate': [1292, 734]}, '16': {'rememberedCode': 'L19', 'isValidCode': True, 'coordinate': [197, 622]}, '17': {'rememberedCode': 'Y65', 'isValidCode': True, 'coordinate': [737, 441]}, '18': {'rememberedCode': 'Y65', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Y65', 'isValidCode': True, 'coordinate': [737, 441]}, '20': {'rememberedCode': 'B34', 'isValidCode': True, 'coordinate': [628, 496]}, '21': {'rememberedCode': 'U14', 'isValidCode': True, 'coordinate': [826, 667]}, '22': {'rememberedCode': 'U14', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U14', 'isValidCode': True, 'coordinate': [826, 667]}, '24': {'rememberedCode': 'M35', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'T13', 'isValidCode': True, 'coordinate': [639, 785]}, '26': {'rememberedCode': 'T13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'T13', 'isValidCode': True, 'coordinate': [639, 785]}, '28': {'rememberedCode': 'J34', 'isValidCode': True, 'coordinate': [936, 686]}, '29': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [1096, 575]}, '30': {'rememberedCode': 'K71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [1096, 575]}, '32': {'rememberedCode': 'Y32', 'isValidCode': True, 'coordinate': [864, 764]}, '33': {'rememberedCode': 'J15', 'isValidCode': True, 'coordinate': [863, 841]}, '34': {'rememberedCode': 'J15', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'J15', 'isValidCode': True, 'coordinate': [863, 841]}, '36': {'rememberedCode': 'B81', 'isValidCode': True, 'coordinate': [752, 783]}, '37': {'rememberedCode': 'X63', 'isValidCode': True, 'coordinate': [830, 577]}, '38': {'rememberedCode': 'X63', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'X63', 'isValidCode': True, 'coordinate': [830, 577]}}",104,424
+33,"['A', 'C', 'A', 'B', 'D', 'A', 'A', 'D', 'B', 'D', 'B', 'A', 'D', 'D', 'B', 'C', 'B', 'B', 'D', 'D', 'A', 'B', 'D', 'B', 'D', 'C', 'D', 'B', 'B', 'D', 'B', 'D', 'A', 'C', 'D', 'A', 'B', 'D', 'C', 'D', 'A', 'A', 'C', 'D', 'D', 'C', 'D', 'A', 'A', 'D', 'C', 'B', 'D', 'C', 'A', 'A', 'D', 'C', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'C', 'C', 'B', 'A', 'D', 'D', 'C', 'D', 'B', 'D', 'D', 'A', 'A', 'A', 'B', 'A', 'A', 'D', 'D', 'D', 'D', 'B', 'A', 'D', 'D', 'D', 'C', 'A', 'A', 'D', 'D']","['2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-22T05:18:25.951Z', 'loadTime': '2021-08-22T05:18:48.701Z', 'startTime': '2021-08-22T05:19:13.932Z', 'endTime': '2021-08-22T05:39:11.814Z', 'timeToCompleteFromOpenMsec': 1245863, 'timeToLoadMsec': 22750, 'timeToCompleteFromStartMsec': 1197882}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'L48', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'F11', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'F11', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'F11', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'F71', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'V38', 'isValidCode': True, 'coordinate': [1774, 343]}, '6': {'rememberedCode': 'V38', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'V38', 'isValidCode': True, 'coordinate': [1774, 343]}, '8': {'rememberedCode': 'L78', 'isValidCode': True, 'coordinate': [742, 240]}, '9': {'rememberedCode': 'K19', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'K19', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K19', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'V97', 'isValidCode': True, 'coordinate': [540, 843]}, '13': {'rememberedCode': 'E19', 'isValidCode': True, 'coordinate': [766, 852]}, '14': {'rememberedCode': 'E19', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'E19', 'isValidCode': True, 'coordinate': [766, 852]}, '16': {'rememberedCode': 'F19', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'Y71', 'isValidCode': True, 'coordinate': [637, 144]}, '18': {'rememberedCode': 'Y71', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Y71', 'isValidCode': True, 'coordinate': [637, 144]}, '20': {'rememberedCode': 'R23', 'isValidCode': True, 'coordinate': [851, 641]}, '21': {'rememberedCode': 'E18', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'E18', 'isValidCode': True, 'coordinate': [1565, 203]}, '23': {'rememberedCode': 'E18', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'Y47', 'isValidCode': True, 'coordinate': [1038, 234]}, '25': {'rememberedCode': 'E61', 'isValidCode': True, 'coordinate': [954, 786]}, '26': {'rememberedCode': 'E61', 'isValidCode': True, 'coordinate': [539, 167]}, '27': {'rememberedCode': 'E61', 'isValidCode': True, 'coordinate': [954, 786]}, '28': {'rememberedCode': 'F73', 'isValidCode': True, 'coordinate': [635, 556]}, '29': {'rememberedCode': 'Y67', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'Y67', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Y67', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'U14', 'isValidCode': True, 'coordinate': [331, 755]}, '33': {'rememberedCode': 'F45', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'F45', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'F45', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'N15', 'isValidCode': True, 'coordinate': [863, 296]}, '37': {'rememberedCode': 'L68', 'isValidCode': True, 'coordinate': [1255, 878]}, '38': {'rememberedCode': 'L68', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L68', 'isValidCode': True, 'coordinate': [1255, 878]}}",254,235
diff --git a/VisRecall/answer_raw/group3.csv b/VisRecall/answer_raw/group3.csv
new file mode 100755
index 0000000..f4af82f
--- /dev/null
+++ b/VisRecall/answer_raw/group3.csv
@@ -0,0 +1,25 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+109,"['C', 'D', 'A', 'D', 'D', 'C', 'A', 'A', 'C', 'D', 'C', 'C', 'C', 'D', 'B', 'A', 'A', 'D', 'B', 'B', 'C', 'D', 'A', 'D', 'A', 'A', 'B', 'D', 'C', 'D', 'B', 'A', 'B', 'B', 'B', 'D', 'A', 'D', 'B', 'D', 'C', 'D', 'C', 'A', 'B', 'C', 'B', 'D', 'D', 'B', 'A', 'D', 'A', 'D', 'A', 'A', 'D', 'D', 'C', 'A', 'A', 'C', 'D', 'D', 'D', 'C', 'A', 'D', 'B', 'B', 'C', 'B', 'D', 'D', 'D', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'D', 'A', 'D', 'A', 'C', 'C', 'A', 'B', 'B', 'B', 'C', 'A', 'D', 'A', 'A', 'B', 'A', 'D']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '2', '1', '2', '1', '1', '1', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T15:55:23.060Z', 'loadTime': '2021-08-19T15:55:38.343Z', 'startTime': '2021-08-19T15:55:39.357Z', 'endTime': '2021-08-19T16:15:19.161Z', 'timeToCompleteFromOpenMsec': 1196101, 'timeToLoadMsec': 15283, 'timeToCompleteFromStartMsec': 1179804}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'N81', 'isValidCode': True, 'coordinate': [1153, 65]}, '1': {'rememberedCode': 'R25', 'isValidCode': True, 'coordinate': [877, 694]}, '2': {'rememberedCode': 'R25', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'R25', 'isValidCode': True, 'coordinate': [877, 694]}, '4': {'rememberedCode': 'K43', 'isValidCode': True, 'coordinate': [327, 202]}, '5': {'rememberedCode': 'Y57', 'isValidCode': True, 'coordinate': [324, 836]}, '6': {'rememberedCode': 'Y57', 'isValidCode': True, 'coordinate': [1669, 224]}, '7': {'rememberedCode': 'Y57', 'isValidCode': True, 'coordinate': [324, 836]}, '8': {'rememberedCode': 'B63', 'isValidCode': True, 'coordinate': [1595, 229]}, '9': {'rememberedCode': 'H23', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'H23', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H23', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'T95', 'isValidCode': True, 'coordinate': [745, 13]}, '13': {'rememberedCode': 'W32', 'isValidCode': True, 'coordinate': [239, 33]}, '14': {'rememberedCode': 'W32', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'W32', 'isValidCode': True, 'coordinate': [239, 33]}, '16': {'rememberedCode': 'V49', 'isValidCode': True, 'coordinate': [449, 28]}, '17': {'rememberedCode': 'J31', 'isValidCode': True, 'coordinate': [1269, 356]}, '18': {'rememberedCode': 'J31', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'J31', 'isValidCode': True, 'coordinate': [1269, 356]}, '20': {'rememberedCode': 'D89', 'isValidCode': True, 'coordinate': [867, 712]}, '21': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [1444, 723]}, '22': {'rememberedCode': 'U79', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [1444, 723]}, '24': {'rememberedCode': 'B98', 'isValidCode': True, 'coordinate': [1348, 842]}, '25': {'rememberedCode': 'C81', 'isValidCode': True, 'coordinate': [837, 370]}, '26': {'rememberedCode': 'C81', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C81', 'isValidCode': True, 'coordinate': [837, 370]}, '28': {'rememberedCode': 'U78', 'isValidCode': True, 'coordinate': [225, 423]}, '29': {'rememberedCode': 'L56', 'isValidCode': True, 'coordinate': [356, 639]}, '30': {'rememberedCode': 'L56', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L56', 'isValidCode': True, 'coordinate': [356, 639]}, '32': {'rememberedCode': 'K19', 'isValidCode': True, 'coordinate': [765, 6]}, '33': {'rememberedCode': 'E64', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'E64', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E64', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [937, 498]}, '37': {'rememberedCode': 'S23', 'isValidCode': True, 'coordinate': [854, 351]}, '38': {'rememberedCode': 'S23', 'isValidCode': True, 'coordinate': [1457, 711]}, '39': {'rememberedCode': 'S23', 'isValidCode': True, 'coordinate': [854, 351]}}",314,383
+110,"['D', 'B', 'A', 'D', 'C', 'C', 'B', 'A', 'D', 'D', 'B', 'D', 'B', 'D', 'A', 'A', 'A', 'D', 'D', 'B', 'C', 'D', 'D', 'A', 'A', 'A', 'B', 'A', 'D', 'D', 'B', 'B', 'B', 'C', 'A', 'A', 'C', 'C', 'B', 'B', 'C', 'D', 'D', 'D', 'D', 'C', 'B', 'C', 'D', 'D', 'A', 'C', 'A', 'D', 'A', 'A', 'A', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'D', 'C', 'D', 'C', 'B', 'D', 'A', 'D', 'D', 'D', 'D', 'C', 'D', 'A', 'C', 'B', 'B', 'A', 'D', 'B', 'D', 'A', 'C', 'C', 'D', 'B', 'B', 'B', 'D', 'A', 'A', 'A', 'C', 'D', 'A', 'D']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T15:57:00.440Z', 'loadTime': '2021-08-19T15:57:26.275Z', 'startTime': '2021-08-19T15:57:39.159Z', 'endTime': '2021-08-19T16:15:27.723Z', 'timeToCompleteFromOpenMsec': 1107283, 'timeToLoadMsec': 25835, 'timeToCompleteFromStartMsec': 1068564}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'N84', 'isValidCode': True, 'coordinate': [827, 20]}, '1': {'rememberedCode': 'W26', 'isValidCode': True, 'coordinate': [650, 142]}, '2': {'rememberedCode': 'W26', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W26', 'isValidCode': True, 'coordinate': [650, 142]}, '4': {'rememberedCode': 'L86', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'V54', 'isValidCode': True, 'coordinate': [568, 484]}, '6': {'rememberedCode': 'V54', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'V54', 'isValidCode': True, 'coordinate': [568, 484]}, '8': {'rememberedCode': 'V56', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'U52', 'isValidCode': True, 'coordinate': [363, 83]}, '10': {'rememberedCode': 'U52', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'U52', 'isValidCode': True, 'coordinate': [363, 83]}, '12': {'rememberedCode': 'K79', 'isValidCode': True, 'coordinate': [626, 283]}, '13': {'rememberedCode': 'D28', 'isValidCode': True, 'coordinate': [537, 94]}, '14': {'rememberedCode': 'D28', 'isValidCode': True, 'coordinate': [1153, 17]}, '15': {'rememberedCode': 'D28', 'isValidCode': True, 'coordinate': [537, 94]}, '16': {'rememberedCode': 'U25', 'isValidCode': True, 'coordinate': [429, 170]}, '17': {'rememberedCode': 'T28', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'T28', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'T28', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'Z13', 'isValidCode': True, 'coordinate': [977, 485]}, '21': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1459, 520]}, '22': {'rememberedCode': 'X93', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1459, 520]}, '24': {'rememberedCode': 'U65', 'isValidCode': True, 'coordinate': [1564, 425]}, '25': {'rememberedCode': 'U62', 'isValidCode': True, 'coordinate': [1250, 795]}, '26': {'rememberedCode': 'U62', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U62', 'isValidCode': True, 'coordinate': [1250, 795]}, '28': {'rememberedCode': 'Q32', 'isValidCode': True, 'coordinate': [854, 293]}, '29': {'rememberedCode': 'Q62', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'Q62', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Q62', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'V64', 'isValidCode': True, 'coordinate': [1162, 419]}, '33': {'rememberedCode': 'S63', 'isValidCode': True, 'coordinate': [828, 513]}, '34': {'rememberedCode': 'S63', 'isValidCode': True, 'coordinate': [1152, 76]}, '35': {'rememberedCode': 'S63', 'isValidCode': True, 'coordinate': [828, 513]}, '36': {'rememberedCode': 'F72', 'isValidCode': True, 'coordinate': [856, 222]}, '37': {'rememberedCode': 'L23', 'isValidCode': True, 'coordinate': [860, 426]}, '38': {'rememberedCode': 'L23', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L23', 'isValidCode': True, 'coordinate': [860, 426]}}",672,989
+113,"['B', 'A', 'A', 'D', 'C', 'C', 'B', 'A', 'A', 'D', 'A', 'D', 'A', 'D', 'B', 'D', 'D', 'D', 'D', 'B', 'C', 'B', 'D', 'A', 'A', 'A', 'B', 'C', 'C', 'B', 'C', 'A', 'D', 'C', 'B', 'A', 'A', 'A', 'D', 'D', 'C', 'B', 'C', 'A', 'A', 'C', 'B', 'C', 'D', 'A', 'A', 'C', 'B', 'D', 'A', 'B', 'D', 'B', 'C', 'A', 'B', 'B', 'D', 'C', 'D', 'C', 'C', 'C', 'C', 'D', 'A', 'D', 'B', 'C', 'D', 'D', 'A', 'A', 'C', 'D', 'B', 'A', 'D', 'B', 'D', 'C', 'D', 'B', 'D', 'B', 'B', 'A', 'A', 'A', 'D', 'A', 'B', 'D', 'A', 'D']","['2', '1', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '2', '1', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '2']","{'openTime': '2021-08-19T15:53:56.055Z', 'loadTime': '2021-08-19T15:54:10.498Z', 'startTime': '2021-08-19T15:54:12.656Z', 'endTime': '2021-08-19T16:18:11.993Z', 'timeToCompleteFromOpenMsec': 1455938, 'timeToLoadMsec': 14443, 'timeToCompleteFromStartMsec': 1439337}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'P41', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'M57', 'isValidCode': True, 'coordinate': [1160, 26]}, '2': {'rememberedCode': 'M57', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'M57', 'isValidCode': True, 'coordinate': [1160, 26]}, '4': {'rememberedCode': 'L86', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'D87', 'isValidCode': True, 'coordinate': [340, 773]}, '6': {'rememberedCode': 'D87', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'D87', 'isValidCode': True, 'coordinate': [340, 773]}, '8': {'rememberedCode': 'V47', 'isValidCode': True, 'coordinate': [781, 304]}, '9': {'rememberedCode': 'G96', 'isValidCode': True, 'coordinate': [563, 17]}, '10': {'rememberedCode': 'G96', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'G96', 'isValidCode': True, 'coordinate': [563, 17]}, '12': {'rememberedCode': 'S16', 'isValidCode': True, 'coordinate': [820, 498]}, '13': {'rememberedCode': 'T88', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'T88', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T88', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'V23', 'isValidCode': True, 'coordinate': [644, 661]}, '17': {'rememberedCode': 'Z94', 'isValidCode': True, 'coordinate': [732, 505]}, '18': {'rememberedCode': 'Z94', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Z94', 'isValidCode': True, 'coordinate': [732, 505]}, '20': {'rememberedCode': 'Q41', 'isValidCode': True, 'coordinate': [577, 777]}, '21': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [721, 162]}, '22': {'rememberedCode': 'F45', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [721, 162]}, '24': {'rememberedCode': 'L63', 'isValidCode': True, 'coordinate': [939, 627]}, '25': {'rememberedCode': 'G53', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'G53', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G53', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'L67', 'isValidCode': True, 'coordinate': [437, 579]}, '29': {'rememberedCode': 'S75', 'isValidCode': True, 'coordinate': [366, 427]}, '30': {'rememberedCode': 'S75', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'S75', 'isValidCode': True, 'coordinate': [366, 427]}, '32': {'rememberedCode': 'L84', 'isValidCode': True, 'coordinate': [546, 219]}, '33': {'rememberedCode': 'W98', 'isValidCode': True, 'coordinate': [957, 571]}, '34': {'rememberedCode': 'W98', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'W98', 'isValidCode': True, 'coordinate': [957, 571]}, '36': {'rememberedCode': 'S29', 'isValidCode': True, 'coordinate': [750, 649]}, '37': {'rememberedCode': 'B34', 'isValidCode': True, 'coordinate': [551, 90]}, '38': {'rememberedCode': 'B34', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'B34', 'isValidCode': True, 'coordinate': [551, 90]}}",662,279
+116,"['A', 'A', 'A', 'B', 'D', 'C', 'B', 'A', 'C', 'B', 'A', 'C', 'D', 'D', 'A', 'A', 'A', 'D', 'A', 'B', 'C', 'C', 'A', 'B', 'D', 'A', 'B', 'C', 'C', 'A', 'B', 'A', 'B', 'C', 'D', 'A', 'C', 'A', 'D', 'C', 'C', 'B', 'B', 'A', 'D', 'C', 'B', 'A', 'B', 'A', 'A', 'C', 'C', 'B', 'A', 'A', 'A', 'C', 'A', 'A', 'C', 'A', 'C', 'B', 'D', 'C', 'A', 'C', 'B', 'D', 'C', 'B', 'D', 'C', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'C', 'B', 'C', 'A', 'C', 'C', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'A', 'B', 'D', 'A', 'B']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:00:20.528Z', 'loadTime': '2021-08-19T16:02:06.551Z', 'startTime': '2021-08-19T16:02:51.406Z', 'endTime': '2021-08-19T16:22:28.959Z', 'timeToCompleteFromOpenMsec': 1328431, 'timeToLoadMsec': 106023, 'timeToCompleteFromStartMsec': 1177553}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'A89', 'isValidCode': True, 'coordinate': [410, 553]}, '1': {'rememberedCode': 'S41', 'isValidCode': True, 'coordinate': [1273, 693]}, '2': {'rememberedCode': 'S41', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'S41', 'isValidCode': True, 'coordinate': [1273, 693]}, '4': {'rememberedCode': 'H37', 'isValidCode': True, 'coordinate': [1484, 828]}, '5': {'rememberedCode': 'Y83', 'isValidCode': True, 'coordinate': [1464, 276]}, '6': {'rememberedCode': 'Y83', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Y83', 'isValidCode': True, 'coordinate': [1464, 276]}, '8': {'rememberedCode': 'V49', 'isValidCode': True, 'coordinate': [1373, 515]}, '9': {'rememberedCode': 'K31', 'isValidCode': True, 'coordinate': [1185, 502]}, '10': {'rememberedCode': 'K31', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K31', 'isValidCode': True, 'coordinate': [1185, 502]}, '12': {'rememberedCode': 'A91', 'isValidCode': True, 'coordinate': [522, 299]}, '13': {'rememberedCode': 'V69', 'isValidCode': True, 'coordinate': [468, 23]}, '14': {'rememberedCode': 'V69', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'V69', 'isValidCode': True, 'coordinate': [468, 23]}, '16': {'rememberedCode': 'B79', 'isValidCode': True, 'coordinate': [340, 110]}, '17': {'rememberedCode': 'F28', 'isValidCode': True, 'coordinate': [1454, 343]}, '18': {'rememberedCode': 'F28', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F28', 'isValidCode': True, 'coordinate': [1454, 343]}, '20': {'rememberedCode': 'Y82', 'isValidCode': True, 'coordinate': [679, 764]}, '21': {'rememberedCode': 'G26', 'isValidCode': True, 'coordinate': [1153, 845]}, '22': {'rememberedCode': 'G26', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G26', 'isValidCode': True, 'coordinate': [1153, 845]}, '24': {'rememberedCode': 'K25', 'isValidCode': True, 'coordinate': [1033, 831]}, '25': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [651, 18]}, '26': {'rememberedCode': 'K13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [651, 18]}, '28': {'rememberedCode': 'H72', 'isValidCode': True, 'coordinate': [204, 630]}, '29': {'rememberedCode': 'E28', 'isValidCode': True, 'coordinate': [370, 227]}, '30': {'rememberedCode': 'E28', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'E28', 'isValidCode': True, 'coordinate': [370, 227]}, '32': {'rememberedCode': 'D82', 'isValidCode': True, 'coordinate': [456, 418]}, '33': {'rememberedCode': 'Z12', 'isValidCode': True, 'coordinate': [746, 734]}, '34': {'rememberedCode': 'Z12', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Z12', 'isValidCode': True, 'coordinate': [746, 734]}, '36': {'rememberedCode': 'G87', 'isValidCode': True, 'coordinate': [853, 721]}, '37': {'rememberedCode': 'Z26', 'isValidCode': True, 'coordinate': [223, 361]}, '38': {'rememberedCode': 'Z26', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Z26', 'isValidCode': True, 'coordinate': [223, 361]}}",270,460
+118,"['A', 'B', 'A', 'C', 'A', 'B', 'B', 'C', 'A', 'B', 'B', 'C', 'A', 'C', 'A', 'A', 'B', 'C', 'A', 'A', 'C', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'B', 'B', 'B', 'C', 'A', 'C', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'B', 'A', 'C', 'B', 'C', 'B', 'A', 'D', 'C', 'B', 'A', 'A', 'B', 'C', 'A', 'A', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'B', 'C', 'C', 'B', 'C', 'A', 'C', 'A', 'C', 'B', 'C', 'A', 'A', 'A', 'C', 'B', 'B', 'C', 'C', 'B', 'A', 'C', 'C', 'B', 'C', 'B', 'A', 'A', 'B', 'C', 'B', 'C', 'C', 'A', 'A', 'B']","['2', '1', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '2', '1', '2', '1', '2', '2', '2', '2', '1', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:06:32.720Z', 'loadTime': '2021-08-19T16:06:36.856Z', 'startTime': '2021-08-19T16:06:39.684Z', 'endTime': '2021-08-19T16:30:51.640Z', 'timeToCompleteFromOpenMsec': 1458920, 'timeToLoadMsec': 4136, 'timeToCompleteFromStartMsec': 1451956}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good study.'}","{'0': {'rememberedCode': 'B87', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'W91', 'isValidCode': True, 'coordinate': [1396, 767]}, '2': {'rememberedCode': 'W91', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W91', 'isValidCode': True, 'coordinate': [1396, 767]}, '4': {'rememberedCode': 'V31', 'isValidCode': True, 'coordinate': [750, 276]}, '5': {'rememberedCode': 'S54', 'isValidCode': True, 'coordinate': [1286, 848]}, '6': {'rememberedCode': 'S54', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'S54', 'isValidCode': True, 'coordinate': [1286, 848]}, '8': {'rememberedCode': 'K49', 'isValidCode': True, 'coordinate': [1089, 425]}, '9': {'rememberedCode': 'A94', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'A94', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'A94', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'E28', 'isValidCode': True, 'coordinate': [508, 70]}, '13': {'rememberedCode': 'R42', 'isValidCode': True, 'coordinate': [670, 96]}, '14': {'rememberedCode': 'R42', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'R42', 'isValidCode': True, 'coordinate': [670, 96]}, '16': {'rememberedCode': 'O67', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'M51', 'isValidCode': True, 'coordinate': [343, 416]}, '18': {'rememberedCode': 'M51', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M51', 'isValidCode': True, 'coordinate': [343, 416]}, '20': {'rememberedCode': 'Y98', 'isValidCode': True, 'coordinate': [748, 161]}, '21': {'rememberedCode': 'N64', 'isValidCode': True, 'coordinate': [1151, 713]}, '22': {'rememberedCode': 'N64', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'N64', 'isValidCode': True, 'coordinate': [1151, 713]}, '24': {'rememberedCode': 'E56', 'isValidCode': True, 'coordinate': [414, 237]}, '25': {'rememberedCode': 'C12', 'isValidCode': True, 'coordinate': [332, 301]}, '26': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C12', 'isValidCode': True, 'coordinate': [332, 301]}, '28': {'rememberedCode': 'W84', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'L97', 'isValidCode': True, 'coordinate': [983, 308]}, '30': {'rememberedCode': 'L97', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L97', 'isValidCode': True, 'coordinate': [983, 308]}, '32': {'rememberedCode': 'C75', 'isValidCode': True, 'coordinate': [560, 762]}, '33': {'rememberedCode': 'V94', 'isValidCode': True, 'coordinate': [826, 642]}, '34': {'rememberedCode': 'V94', 'isValidCode': True, 'coordinate': [742, 268]}, '35': {'rememberedCode': 'V94', 'isValidCode': True, 'coordinate': [826, 642]}, '36': {'rememberedCode': 'N38', 'isValidCode': True, 'coordinate': [758, 779]}, '37': {'rememberedCode': 'B74', 'isValidCode': True, 'coordinate': [416, 559]}, '38': {'rememberedCode': 'B74', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'B74', 'isValidCode': True, 'coordinate': [416, 559]}}",933,555
+119,"['B', 'D', 'A', 'D', 'C', 'C', 'B', 'D', 'C', 'B', 'C', 'C', 'C', 'D', 'B', 'A', 'C', 'C', 'A', 'B', 'C', 'C', 'D', 'D', 'A', 'A', 'B', 'C', 'C', 'B', 'B', 'C', 'D', 'C', 'B', 'A', 'A', 'C', 'A', 'B', 'C', 'D', 'D', 'A', 'D', 'C', 'B', 'D', 'D', 'D', 'C', 'C', 'D', 'D', 'A', 'A', 'A', 'D', 'C', 'A', 'D', 'B', 'C', 'D', 'A', 'C', 'B', 'C', 'B', 'B', 'C', 'D', 'D', 'D', 'D', 'A', 'C', 'A', 'C', 'B', 'B', 'D', 'B', 'D', 'D', 'A', 'D', 'C', 'A', 'B', 'B', 'B', 'C', 'A', 'D', 'A', 'A', 'D', 'B', 'D']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:09:57.772Z', 'loadTime': '2021-08-19T16:11:10.041Z', 'startTime': '2021-08-19T16:11:14.692Z', 'endTime': '2021-08-19T16:35:27.492Z', 'timeToCompleteFromOpenMsec': 1529720, 'timeToLoadMsec': 72269, 'timeToCompleteFromStartMsec': 1452800}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Okay, this grouping of charts was new to me. Seemed like one set of questions was mismatched with the chart though.'}","{'0': {'rememberedCode': 'R38', 'isValidCode': True, 'coordinate': [1158, 414]}, '1': {'rememberedCode': 'Z37', 'isValidCode': True, 'coordinate': [1296, 783]}, '2': {'rememberedCode': 'Z37', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Z37', 'isValidCode': True, 'coordinate': [1296, 783]}, '4': {'rememberedCode': 'V21', 'isValidCode': True, 'coordinate': [970, 502]}, '5': {'rememberedCode': 'T71', 'isValidCode': True, 'coordinate': [643, 282]}, '6': {'rememberedCode': 'T71', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'T71', 'isValidCode': True, 'coordinate': [643, 282]}, '8': {'rememberedCode': 'L79', 'isValidCode': True, 'coordinate': [1304, 299]}, '9': {'rememberedCode': 'Q45', 'isValidCode': True, 'coordinate': [951, 292]}, '10': {'rememberedCode': 'Q45', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Q45', 'isValidCode': True, 'coordinate': [951, 292]}, '12': {'rememberedCode': 'P95', 'isValidCode': True, 'coordinate': [609, 840]}, '13': {'rememberedCode': 'C69', 'isValidCode': True, 'coordinate': [863, 368]}, '14': {'rememberedCode': 'C69', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C69', 'isValidCode': True, 'coordinate': [863, 368]}, '16': {'rememberedCode': 'J83', 'isValidCode': True, 'coordinate': [636, 306]}, '17': {'rememberedCode': 'C83', 'isValidCode': True, 'coordinate': [1168, 346]}, '18': {'rememberedCode': 'C83', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'C83', 'isValidCode': True, 'coordinate': [1168, 346]}, '20': {'rememberedCode': 'M97', 'isValidCode': True, 'coordinate': [563, 573]}, '21': {'rememberedCode': 'L78', 'isValidCode': True, 'coordinate': [1023, 367]}, '22': {'rememberedCode': 'L78', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L78', 'isValidCode': True, 'coordinate': [1023, 367]}, '24': {'rememberedCode': 'Y18', 'isValidCode': True, 'coordinate': [1041, 494]}, '25': {'rememberedCode': 'C27', 'isValidCode': True, 'coordinate': [944, 844]}, '26': {'rememberedCode': 'C27', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C27', 'isValidCode': True, 'coordinate': [944, 844]}, '28': {'rememberedCode': 'N35', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'L28', 'isValidCode': True, 'coordinate': [766, 294]}, '30': {'rememberedCode': 'L28', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L28', 'isValidCode': True, 'coordinate': [766, 294]}, '32': {'rememberedCode': 'D73', 'isValidCode': True, 'coordinate': [561, 140]}, '33': {'rememberedCode': 'A63', 'isValidCode': True, 'coordinate': [831, 457]}, '34': {'rememberedCode': 'A63', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'A63', 'isValidCode': True, 'coordinate': [831, 457]}, '36': {'rememberedCode': 'N38', 'isValidCode': True, 'coordinate': [758, 779]}, '37': {'rememberedCode': 'Z59', 'isValidCode': True, 'coordinate': [859, 300]}, '38': {'rememberedCode': 'Z59', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Z59', 'isValidCode': True, 'coordinate': [859, 300]}}",587,71
+120,"['B', 'C', 'B', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'B', 'C', 'C', 'B', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'A']","['2', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '2', '1', '2', '2', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:18:21.352Z', 'loadTime': '2021-08-19T16:18:37.231Z', 'startTime': '2021-08-19T16:18:55.555Z', 'endTime': '2021-08-19T16:35:34.523Z', 'timeToCompleteFromOpenMsec': 1033171, 'timeToLoadMsec': 15879, 'timeToCompleteFromStartMsec': 998968}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good task'}","{'0': {'rememberedCode': 'M24', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'D47', 'isValidCode': True, 'coordinate': [762, 223]}, '2': {'rememberedCode': 'D47', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'D47', 'isValidCode': True, 'coordinate': [762, 223]}, '4': {'rememberedCode': 'Y85', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'M49', 'isValidCode': True, 'coordinate': [744, 283]}, '6': {'rememberedCode': 'M49', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'M49', 'isValidCode': True, 'coordinate': [744, 283]}, '8': {'rememberedCode': 'M65', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'D67', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'D67', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'D67', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'S16', 'isValidCode': True, 'coordinate': [820, 498]}, '13': {'rememberedCode': 'C87', 'isValidCode': True, 'coordinate': [565, 640]}, '14': {'rememberedCode': 'C87', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C87', 'isValidCode': True, 'coordinate': [565, 640]}, '16': {'rememberedCode': 'R18', 'isValidCode': True, 'coordinate': [854, 516]}, '17': {'rememberedCode': 'K93', 'isValidCode': True, 'coordinate': [1057, 274]}, '18': {'rememberedCode': 'K93', 'isValidCode': True, 'coordinate': [147, 237]}, '19': {'rememberedCode': 'K93', 'isValidCode': True, 'coordinate': [1057, 274]}, '20': {'rememberedCode': 'H34', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'K41', 'isValidCode': True, 'coordinate': [1358, 795]}, '22': {'rememberedCode': 'K41', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'K41', 'isValidCode': True, 'coordinate': [1358, 795]}, '24': {'rememberedCode': 'R13', 'isValidCode': True, 'coordinate': [402, 146]}, '25': {'rememberedCode': 'Q13', 'isValidCode': True, 'coordinate': [1486, 844]}, '26': {'rememberedCode': 'Q13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Q13', 'isValidCode': True, 'coordinate': [1486, 844]}, '28': {'rememberedCode': 'S74', 'isValidCode': True, 'coordinate': [930, 305]}, '29': {'rememberedCode': 'H41', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'H41', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'H41', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [1798, 153]}, '33': {'rememberedCode': 'E54', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'E54', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E54', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'G56', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'L76', 'isValidCode': True, 'coordinate': [622, 92]}, '38': {'rememberedCode': 'L76', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L76', 'isValidCode': True, 'coordinate': [622, 92]}}",434,376
+123,"['A', 'B', 'B', 'B', 'A', 'C', 'B', 'C', 'C', 'B', 'A', 'C', 'D', 'D', 'A', 'A', 'A', 'C', 'C', 'B', 'C', 'C', 'A', 'C', 'A', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'B', 'A', 'A', 'A', 'C', 'C', 'B', 'A', 'B', 'B', 'C', 'B', 'C', 'D', 'D', 'A', 'C', 'A', 'A', 'D', 'A', 'A', 'A', 'C', 'A', 'A', 'D', 'D', 'D', 'B', 'C', 'C', 'C', 'B', 'B', 'C', 'A', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'D', 'B', 'A', 'C', 'D', 'D', 'A', 'C', 'C', 'A', 'B', 'B', 'B', 'C', 'A', 'D', 'A', 'C', 'D', 'A', 'A']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:25:20.186Z', 'loadTime': '2021-08-19T16:25:47.090Z', 'startTime': '2021-08-19T16:25:49.964Z', 'endTime': '2021-08-19T16:44:41.959Z', 'timeToCompleteFromOpenMsec': 1161773, 'timeToLoadMsec': 26904, 'timeToCompleteFromStartMsec': 1131995}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'highSchool', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'A03', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [958, 356]}, '2': {'rememberedCode': 'M62', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [958, 356]}, '4': {'rememberedCode': 'V31', 'isValidCode': True, 'coordinate': [750, 276]}, '5': {'rememberedCode': '038', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': '038', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': '038', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': '055', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'P39', 'isValidCode': True, 'coordinate': [549, 103]}, '10': {'rememberedCode': 'P39', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'P39', 'isValidCode': True, 'coordinate': [549, 103]}, '12': {'rememberedCode': 'G12', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'F89', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'F89', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F89', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'N21', 'isValidCode': True, 'coordinate': [735, 225]}, '17': {'rememberedCode': 'B17', 'isValidCode': True, 'coordinate': [950, 283]}, '18': {'rememberedCode': 'B17', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'B17', 'isValidCode': True, 'coordinate': [950, 283]}, '20': {'rememberedCode': 'K67', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'V31', 'isValidCode': True, 'coordinate': [944, 448]}, '22': {'rememberedCode': 'V31', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'V31', 'isValidCode': True, 'coordinate': [944, 448]}, '24': {'rememberedCode': 'L63', 'isValidCode': True, 'coordinate': [939, 627]}, '25': {'rememberedCode': 'P63', 'isValidCode': True, 'coordinate': [1388, 364]}, '26': {'rememberedCode': 'P63', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'P63', 'isValidCode': True, 'coordinate': [1388, 364]}, '28': {'rememberedCode': 'Q32', 'isValidCode': True, 'coordinate': [854, 293]}, '29': {'rememberedCode': '037', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': '037', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': '037', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'E34', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'G78', 'isValidCode': True, 'coordinate': [959, 252]}, '34': {'rememberedCode': 'G78', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'G78', 'isValidCode': True, 'coordinate': [959, 252]}, '36': {'rememberedCode': 'D23', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'E97', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'E97', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E97', 'isValidCode': False, 'coordinate': False}}",83,94
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+127,"['B', 'A', 'A', 'B', 'C', 'C', 'B', 'A', 'C', 'B', 'C', 'C', 'C', 'C', 'B', 'A', 'C', 'C', 'B', 'B', 'C', 'C', 'A', 'A', 'B', 'A', 'B', 'C', 'C', 'A', 'B', 'A', 'A', 'D', 'B', 'A', 'A', 'C', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'C', 'B', 'C', 'D', 'A', 'A', 'D', 'C', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'D', 'B', 'D', 'D', 'D', 'C', 'A', 'C', 'B', 'B', 'C', 'B', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'C', 'C', 'B', 'B', 'B', 'C', 'C', 'B', 'A', 'A', 'B', 'A', 'C']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:27:30.609Z', 'loadTime': '2021-08-19T16:28:11.885Z', 'startTime': '2021-08-19T16:28:13.115Z', 'endTime': '2021-08-19T17:05:57.453Z', 'timeToCompleteFromOpenMsec': 2306844, 'timeToLoadMsec': 41276, 'timeToCompleteFromStartMsec': 2264338}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'W71', 'isValidCode': True, 'coordinate': [1042, 15]}, '1': {'rememberedCode': 'J21', 'isValidCode': True, 'coordinate': [1080, 217]}, '2': {'rememberedCode': 'J21', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J21', 'isValidCode': True, 'coordinate': [1080, 217]}, '4': {'rememberedCode': 'N15', 'isValidCode': True, 'coordinate': [1064, 617]}, '5': {'rememberedCode': 'M49', 'isValidCode': True, 'coordinate': [744, 283]}, '6': {'rememberedCode': 'M49', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'M49', 'isValidCode': True, 'coordinate': [744, 283]}, '8': {'rememberedCode': 'K54', 'isValidCode': True, 'coordinate': [557, 556]}, '9': {'rememberedCode': 'M27', 'isValidCode': True, 'coordinate': [1277, 428]}, '10': {'rememberedCode': 'M27', 'isValidCode': True, 'coordinate': [790, 784]}, '11': {'rememberedCode': 'M27', 'isValidCode': True, 'coordinate': [1277, 428]}, '12': {'rememberedCode': 'C26', 'isValidCode': True, 'coordinate': [1352, 630]}, '13': {'rememberedCode': 'B95', 'isValidCode': True, 'coordinate': [869, 639]}, '14': {'rememberedCode': 'B95', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'B95', 'isValidCode': True, 'coordinate': [869, 639]}, '16': {'rememberedCode': 'M18', 'isValidCode': True, 'coordinate': [938, 307]}, '17': {'rememberedCode': 'K98', 'isValidCode': True, 'coordinate': [966, 358]}, '18': {'rememberedCode': 'K98', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K98', 'isValidCode': True, 'coordinate': [966, 358]}, '20': {'rememberedCode': 'G15', 'isValidCode': True, 'coordinate': [980, 428]}, '21': {'rememberedCode': 'Z75', 'isValidCode': True, 'coordinate': [847, 237]}, '22': {'rememberedCode': 'Z75', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Z75', 'isValidCode': True, 'coordinate': [847, 237]}, '24': {'rememberedCode': 'R36', 'isValidCode': True, 'coordinate': [839, 373]}, '25': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [651, 18]}, '26': {'rememberedCode': 'K13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [651, 18]}, '28': {'rememberedCode': 'S68', 'isValidCode': True, 'coordinate': [1662, 309]}, '29': {'rememberedCode': 'D56', 'isValidCode': True, 'coordinate': [882, 433]}, '30': {'rememberedCode': 'D56', 'isValidCode': True, 'coordinate': [1168, 713]}, '31': {'rememberedCode': 'D56', 'isValidCode': True, 'coordinate': [882, 433]}, '32': {'rememberedCode': 'V51', 'isValidCode': True, 'coordinate': [968, 271]}, '33': {'rememberedCode': 'D69', 'isValidCode': True, 'coordinate': [1045, 310]}, '34': {'rememberedCode': 'D69', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D69', 'isValidCode': True, 'coordinate': [1045, 310]}, '36': {'rememberedCode': 'G97', 'isValidCode': True, 'coordinate': [951, 432]}, '37': {'rememberedCode': 'G86', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'G86', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G86', 'isValidCode': False, 'coordinate': False}}",456,250
+128,"['B', 'D', 'D', 'D', 'C', 'C', 'D', 'A', 'C', 'B', 'C', 'C', 'C', 'D', 'B', 'A', 'C', 'C', 'D', 'D', 'C', 'C', 'A', 'A', 'D', 'A', 'B', 'C', 'C', 'D', 'B', 'A', 'D', 'D', 'B', 'A', 'A', 'D', 'D', 'D', 'C', 'D', 'C', 'D', 'D', 'C', 'B', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'A', 'A', 'A', 'D', 'C', 'D', 'D', 'B', 'D', 'D', 'D', 'C', 'A', 'C', 'B', 'D', 'C', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'C', 'D', 'B', 'D', 'D', 'D', 'D', 'A', 'C', 'C', 'D', 'B', 'B', 'B', 'D', 'A', 'D', 'A', 'C', 'D', 'A', 'D']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '2', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:39:29.898Z', 'loadTime': '2021-08-19T16:39:54.339Z', 'startTime': '2021-08-19T16:39:55.810Z', 'endTime': '2021-08-19T17:08:37.261Z', 'timeToCompleteFromOpenMsec': 1747363, 'timeToLoadMsec': 24441, 'timeToCompleteFromStartMsec': 1721451}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'V19', 'isValidCode': True, 'coordinate': [626, 498]}, '1': {'rememberedCode': 'Z19', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'Z19', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Z19', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'N47', 'isValidCode': True, 'coordinate': [1279, 690]}, '5': {'rememberedCode': 'U73', 'isValidCode': True, 'coordinate': [670, 712]}, '6': {'rememberedCode': 'U73', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U73', 'isValidCode': True, 'coordinate': [670, 712]}, '8': {'rememberedCode': 'P31', 'isValidCode': True, 'coordinate': [985, 627]}, '9': {'rememberedCode': 'W78', 'isValidCode': True, 'coordinate': [1180, 628]}, '10': {'rememberedCode': 'W78', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'W78', 'isValidCode': True, 'coordinate': [1180, 628]}, '12': {'rememberedCode': 'D65', 'isValidCode': True, 'coordinate': [505, 435]}, '13': {'rememberedCode': 'E16', 'isValidCode': True, 'coordinate': [639, 226]}, '14': {'rememberedCode': 'E16', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'E16', 'isValidCode': True, 'coordinate': [639, 226]}, '16': {'rememberedCode': 'M54', 'isValidCode': True, 'coordinate': [1154, 646]}, '17': {'rememberedCode': 'N63', 'isValidCode': True, 'coordinate': [1269, 764]}, '18': {'rememberedCode': 'N63', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'N63', 'isValidCode': True, 'coordinate': [1269, 764]}, '20': {'rememberedCode': 'X58', 'isValidCode': True, 'coordinate': [1187, 352]}, '21': {'rememberedCode': 'R32', 'isValidCode': True, 'coordinate': [729, 631]}, '22': {'rememberedCode': 'R32', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'R32', 'isValidCode': True, 'coordinate': [729, 631]}, '24': {'rememberedCode': 'A33', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'X68', 'isValidCode': True, 'coordinate': [716, 81]}, '27': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'C58', 'isValidCode': True, 'coordinate': [735, 859]}, '29': {'rememberedCode': 'L94', 'isValidCode': True, 'coordinate': [1177, 586]}, '30': {'rememberedCode': 'L94', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L94', 'isValidCode': True, 'coordinate': [1177, 586]}, '32': {'rememberedCode': 'Q25', 'isValidCode': True, 'coordinate': [877, 432]}, '33': {'rememberedCode': 'D46', 'isValidCode': True, 'coordinate': [450, 296]}, '34': {'rememberedCode': 'D46', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D46', 'isValidCode': True, 'coordinate': [450, 296]}, '36': {'rememberedCode': 'N22', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'X68', 'isValidCode': True, 'coordinate': [625, 639]}, '38': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'X68', 'isValidCode': True, 'coordinate': [625, 639]}}",199,439
+129,"['B', 'B', 'A', 'C', 'A', 'B', 'B', 'B', 'A', 'B', 'C', 'C', 'A', 'C', 'B', 'A', 'C', 'C', 'B', 'B', 'C', 'C', 'A', 'A', 'B', 'A', 'B', 'C', 'C', 'C', 'B', 'A', 'A', 'A', 'B', 'A', 'A', 'B', 'B', 'C', 'C', 'B', 'C', 'A', 'B', 'C', 'B', 'C', 'D', 'A', 'A', 'C', 'A', 'C', 'A', 'B', 'D', 'A', 'C', 'A', 'A', 'B', 'C', 'A', 'C', 'C', 'A', 'A', 'C', 'B', 'C', 'C', 'B', 'B', 'C', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'B', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'B', 'C', 'B', 'B', 'C', 'B', 'A', 'A', 'C']","['2', '1', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:34:57.221Z', 'loadTime': '2021-08-19T16:35:15.898Z', 'startTime': '2021-08-19T16:35:38.328Z', 'endTime': '2021-08-19T17:10:09.018Z', 'timeToCompleteFromOpenMsec': 2111797, 'timeToLoadMsec': 18677, 'timeToCompleteFromStartMsec': 2070690}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Other', 'education': 'fourYearDegree', 'vizExperience': 'professional', 'feedback': 'This study is good for learning.'}","{'0': {'rememberedCode': 'N95', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'S87', 'isValidCode': True, 'coordinate': [1086, 151]}, '2': {'rememberedCode': 'S87', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'S87', 'isValidCode': True, 'coordinate': [1086, 151]}, '4': {'rememberedCode': 'C16', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'U26', 'isValidCode': True, 'coordinate': [749, 346]}, '6': {'rememberedCode': 'U26', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U26', 'isValidCode': True, 'coordinate': [749, 346]}, '8': {'rememberedCode': 'T62', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'F79', 'isValidCode': True, 'coordinate': [952, 426]}, '10': {'rememberedCode': 'F79', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'F79', 'isValidCode': True, 'coordinate': [952, 426]}, '12': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [1034, 356]}, '13': {'rememberedCode': 'V23', 'isValidCode': True, 'coordinate': [1049, 442]}, '14': {'rememberedCode': 'V23', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'V23', 'isValidCode': True, 'coordinate': [1049, 442]}, '16': {'rememberedCode': 'Z14', 'isValidCode': True, 'coordinate': [629, 240]}, '17': {'rememberedCode': 'B28', 'isValidCode': True, 'coordinate': [1248, 420]}, '18': {'rememberedCode': 'B28', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'B28', 'isValidCode': True, 'coordinate': [1248, 420]}, '20': {'rememberedCode': 'U29', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'U23', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'U23', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U23', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'V73', 'isValidCode': True, 'coordinate': [219, 24]}, '25': {'rememberedCode': 'L23', 'isValidCode': True, 'coordinate': [864, 16]}, '26': {'rememberedCode': 'L23', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'L23', 'isValidCode': True, 'coordinate': [864, 16]}, '28': {'rememberedCode': 'A31', 'isValidCode': True, 'coordinate': [1134, 214]}, '29': {'rememberedCode': 'V42', 'isValidCode': True, 'coordinate': [1406, 845]}, '30': {'rememberedCode': 'V42', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'V42', 'isValidCode': True, 'coordinate': [1406, 845]}, '32': {'rememberedCode': 'H26', 'isValidCode': True, 'coordinate': [1489, 847]}, '33': {'rememberedCode': 'H53', 'isValidCode': True, 'coordinate': [1030, 449]}, '34': {'rememberedCode': 'H53', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'H53', 'isValidCode': True, 'coordinate': [1030, 449]}, '36': {'rememberedCode': 'G67', 'isValidCode': True, 'coordinate': [145, 767]}, '37': {'rememberedCode': 'T55', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'T55', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'T55', 'isValidCode': False, 'coordinate': False}}",427,648
+130,"['B', 'A', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'C', 'C', 'A', 'B', 'B', 'A', 'C', 'C', 'B', 'B', 'C', 'C', 'A', 'A', 'B', 'A', 'B', 'C', 'C', 'D', 'B', 'A', 'C', 'C', 'B', 'A', 'A', 'C', 'A', 'B', 'C', 'B', 'C', 'D', 'B', 'C', 'B', 'C', 'D', 'A', 'A', 'C', 'C', 'A', 'A', 'A', 'A', 'B', 'C', 'C', 'A', 'B', 'D', 'D', 'B', 'C', 'A', 'C', 'B', 'B', 'C', 'B', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'B', 'D', 'A', 'C', 'C', 'C', 'B', 'B', 'B', 'C', 'A', 'D', 'A', 'C', 'B', 'A', 'C']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T16:28:09.123Z', 'loadTime': '2021-08-19T16:29:02.467Z', 'startTime': '2021-08-19T16:29:21.917Z', 'endTime': '2021-08-19T17:08:54.853Z', 'timeToCompleteFromOpenMsec': 2445730, 'timeToLoadMsec': 53344, 'timeToCompleteFromStartMsec': 2372936}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'someCollege', 'vizExperience': 'someExposure', 'feedback': 'The chart of 3 character codes ought to be shown for a bit long and with bigger text.'}","{'0': {'rememberedCode': 'J57', 'isValidCode': True, 'coordinate': [1140, 621]}, '1': {'rememberedCode': 'F79', 'isValidCode': True, 'coordinate': [1082, 503]}, '2': {'rememberedCode': 'F79', 'isValidCode': True, 'coordinate': [334, 835]}, '3': {'rememberedCode': 'F79', 'isValidCode': True, 'coordinate': [1082, 503]}, '4': {'rememberedCode': 'G46', 'isValidCode': True, 'coordinate': [863, 549]}, '5': {'rememberedCode': 'M74', 'isValidCode': True, 'coordinate': [960, 560]}, '6': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'M74', 'isValidCode': True, 'coordinate': [960, 560]}, '8': {'rememberedCode': 'P53', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'L49', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'L49', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'L49', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'S16', 'isValidCode': True, 'coordinate': [820, 498]}, '13': {'rememberedCode': 'M73', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'M73', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'M73', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'M32', 'isValidCode': True, 'coordinate': [661, 727]}, '17': {'rememberedCode': 'K98', 'isValidCode': True, 'coordinate': [966, 358]}, '18': {'rememberedCode': 'K98', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K98', 'isValidCode': True, 'coordinate': [966, 358]}, '20': {'rememberedCode': 'D16', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'T18', 'isValidCode': True, 'coordinate': [1663, 95]}, '22': {'rememberedCode': 'T18', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'T18', 'isValidCode': True, 'coordinate': [1663, 95]}, '24': {'rememberedCode': 'S97', 'isValidCode': True, 'coordinate': [945, 500]}, '25': {'rememberedCode': 'L83', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'L83', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'L83', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'E17', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'V13', 'isValidCode': True, 'coordinate': [784, 228]}, '30': {'rememberedCode': 'V13', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'V13', 'isValidCode': True, 'coordinate': [784, 228]}, '32': {'rememberedCode': 'G12', 'isValidCode': True, 'coordinate': [468, 545]}, '33': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1057, 236]}, '34': {'rememberedCode': 'E14', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1057, 236]}, '36': {'rememberedCode': 'K73', 'isValidCode': True, 'coordinate': [838, 509]}, '37': {'rememberedCode': 'Z12', 'isValidCode': True, 'coordinate': [835, 573]}, '38': {'rememberedCode': 'Z12', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Z12', 'isValidCode': True, 'coordinate': [835, 573]}}",173,453
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+137,"['C', 'B', 'A', 'B', 'A', 'C', 'B', 'C', 'C', 'D', 'B', 'A', 'A', 'C', 'A', 'A', 'B', 'B', 'B', 'B', 'C', 'D', 'A', 'B', 'A', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'A', 'D', 'C', 'B', 'A', 'D', 'C', 'A', 'D', 'B', 'B', 'A', 'A', 'A', 'A', 'C', 'A', 'C', 'B', 'D', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'C', 'D', 'A', 'B', 'A', 'C', 'B', 'C', 'A', 'C', 'A', 'C', 'A', 'C', 'C', 'B', 'B', 'B', 'A', 'C', 'A', 'B', 'D', 'C', 'C', 'B', 'A']","['2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '2', '2', '1', '2', '1', '1', '1', '1', '2', '2', '2', '1', '1', '2', '2', '1', '1', '1', '2', '1']","{'openTime': '2021-08-20T00:53:01.385Z', 'loadTime': '2021-08-20T00:53:01.677Z', 'startTime': '2021-08-20T00:53:03.719Z', 'endTime': '2021-08-20T01:12:32.963Z', 'timeToCompleteFromOpenMsec': 1171578, 'timeToLoadMsec': 292, 'timeToCompleteFromStartMsec': 1169244}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'highSchool', 'vizExperience': 'frequentInteraction', 'feedback': 'Not sure why I was told I could not advance during one of the surveys and had to restart the task.'}","{'0': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [1054, 435]}, '1': {'rememberedCode': 'T95', 'isValidCode': True, 'coordinate': [1260, 362]}, '2': {'rememberedCode': 'T95', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'T95', 'isValidCode': True, 'coordinate': [1260, 362]}, '4': {'rememberedCode': 'C67', 'isValidCode': True, 'coordinate': [1057, 569]}, '5': {'rememberedCode': 'N36', 'isValidCode': True, 'coordinate': [868, 355]}, '6': {'rememberedCode': 'N36', 'isValidCode': True, 'coordinate': [48, 135]}, '7': {'rememberedCode': 'N36', 'isValidCode': True, 'coordinate': [868, 355]}, '8': {'rememberedCode': 'L61', 'isValidCode': True, 'coordinate': [1066, 714]}, '9': {'rememberedCode': 'W31', 'isValidCode': True, 'coordinate': [1281, 232]}, '10': {'rememberedCode': 'W31', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'W31', 'isValidCode': True, 'coordinate': [1281, 232]}, '12': {'rememberedCode': 'N46', 'isValidCode': True, 'coordinate': [937, 717]}, '13': {'rememberedCode': 'E13', 'isValidCode': True, 'coordinate': [1153, 426]}, '14': {'rememberedCode': 'E13', 'isValidCode': True, 'coordinate': [5, 82]}, '15': {'rememberedCode': 'E13', 'isValidCode': True, 'coordinate': [1153, 426]}, '16': {'rememberedCode': 'K12', 'isValidCode': True, 'coordinate': [1150, 506]}, '17': {'rememberedCode': 'M58', 'isValidCode': True, 'coordinate': [1057, 570]}, '18': {'rememberedCode': 'M58', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M58', 'isValidCode': True, 'coordinate': [1057, 570]}, '20': {'rememberedCode': 'H93', 'isValidCode': True, 'coordinate': [1267, 574]}, '21': {'rememberedCode': 'U35', 'isValidCode': True, 'coordinate': [1332, 585]}, '22': {'rememberedCode': 'U35', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U35', 'isValidCode': True, 'coordinate': [1332, 585]}, '24': {'rememberedCode': 'M83', 'isValidCode': True, 'coordinate': [1366, 565]}, '25': {'rememberedCode': 'D45', 'isValidCode': True, 'coordinate': [1255, 377]}, '26': {'rememberedCode': 'D45', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'D45', 'isValidCode': True, 'coordinate': [1255, 377]}, '28': {'rememberedCode': 'C71', 'isValidCode': True, 'coordinate': [831, 429]}, '29': {'rememberedCode': 'U68', 'isValidCode': True, 'coordinate': [1176, 434]}, '30': {'rememberedCode': 'U68', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'U68', 'isValidCode': True, 'coordinate': [1176, 434]}, '32': {'rememberedCode': 'J41', 'isValidCode': True, 'coordinate': [1159, 619]}, '33': {'rememberedCode': 'D89', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'D89', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D89', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [937, 498]}, '37': {'rememberedCode': 'F94', 'isValidCode': True, 'coordinate': [1135, 490]}, '38': {'rememberedCode': 'F94', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'F94', 'isValidCode': True, 'coordinate': [1135, 490]}}",928,977
+138,"['D', 'A', 'B', 'A', 'D', 'C', 'B', 'C', 'A', 'D', 'A', 'C', 'C', 'C', 'D', 'A', 'A', 'C', 'C', 'B', 'C', 'D', 'A', 'C', 'B', 'A', 'B', 'D', 'A', 'D', 'C', 'B', 'A', 'B', 'A', 'A', 'A', 'D', 'A', 'D', 'C', 'B', 'B', 'A', 'D', 'C', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'D', 'A', 'A', 'A', 'D', 'C', 'A', 'C', 'B', 'A', 'B', 'D', 'C', 'A', 'C', 'B', 'D', 'C', 'A', 'D', 'B', 'D', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'D', 'B', 'D', 'C', 'C', 'C', 'A', 'B', 'D', 'B', 'A', 'A', 'D', 'A', 'B', 'D', 'B', 'D']","['2', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-20T01:26:47.199Z', 'loadTime': '2021-08-20T01:27:01.003Z', 'startTime': '2021-08-20T01:27:04.221Z', 'endTime': '2021-08-20T01:46:00.489Z', 'timeToCompleteFromOpenMsec': 1153290, 'timeToLoadMsec': 13804, 'timeToCompleteFromStartMsec': 1136268}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'V19', 'isValidCode': True, 'coordinate': [626, 498]}, '1': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [660, 512]}, '2': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [1054, 435]}, '3': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [660, 512]}, '4': {'rememberedCode': 'Q18', 'isValidCode': True, 'coordinate': [667, 633]}, '5': {'rememberedCode': 'C46', 'isValidCode': True, 'coordinate': [332, 152]}, '6': {'rememberedCode': 'C46', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'C46', 'isValidCode': True, 'coordinate': [332, 152]}, '8': {'rememberedCode': 'U34', 'isValidCode': True, 'coordinate': [1507, 431]}, '9': {'rememberedCode': 'H64', 'isValidCode': True, 'coordinate': [342, 714]}, '10': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H64', 'isValidCode': True, 'coordinate': [342, 714]}, '12': {'rememberedCode': 'C43', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'F21', 'isValidCode': True, 'coordinate': [770, 234]}, '14': {'rememberedCode': 'F21', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F21', 'isValidCode': True, 'coordinate': [770, 234]}, '16': {'rememberedCode': 'S12', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'N52', 'isValidCode': True, 'coordinate': [747, 558]}, '18': {'rememberedCode': 'N52', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'N52', 'isValidCode': True, 'coordinate': [747, 558]}, '20': {'rememberedCode': 'C92', 'isValidCode': True, 'coordinate': [1193, 421]}, '21': {'rememberedCode': 'B82', 'isValidCode': True, 'coordinate': [318, 439]}, '22': {'rememberedCode': 'B82', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'B82', 'isValidCode': True, 'coordinate': [318, 439]}, '24': {'rememberedCode': 'Q41', 'isValidCode': True, 'coordinate': [1235, 849]}, '25': {'rememberedCode': 'N39', 'isValidCode': True, 'coordinate': [345, 438]}, '26': {'rememberedCode': 'N39', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'N39', 'isValidCode': True, 'coordinate': [345, 438]}, '28': {'rememberedCode': 'R12', 'isValidCode': True, 'coordinate': [1272, 627]}, '29': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Y15', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'K52', 'isValidCode': True, 'coordinate': [669, 639]}, '33': {'rememberedCode': 'R21', 'isValidCode': True, 'coordinate': [927, 460]}, '34': {'rememberedCode': 'R21', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'R21', 'isValidCode': True, 'coordinate': [927, 460]}, '36': {'rememberedCode': 'Y52', 'isValidCode': True, 'coordinate': [863, 640]}, '37': {'rememberedCode': 'E42', 'isValidCode': True, 'coordinate': [832, 646]}, '38': {'rememberedCode': 'E42', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E42', 'isValidCode': True, 'coordinate': [832, 646]}}",778,357
+139,"['B', 'D', 'A', 'D', 'C', 'C', 'B', 'A', 'C', 'D', 'B', 'A', 'D', 'D', 'B', 'B', 'D', 'B', 'B', 'D', 'A', 'D', 'A', 'A', 'D', 'A', 'B', 'C', 'C', 'B', 'B', 'A', 'D', 'C', 'D', 'A', 'A', 'B', 'D', 'B', 'C', 'B', 'C', 'B', 'D', 'C', 'B', 'C', 'D', 'D', 'B', 'C', 'D', 'D', 'A', 'A', 'A', 'D', 'A', 'A', 'C', 'D', 'D', 'D', 'B', 'C', 'D', 'B', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'B', 'B', 'A', 'C', 'B', 'B', 'A', 'D', 'D', 'D', 'A', 'D', 'C', 'C', 'D', 'B', 'B', 'D', 'C', 'D', 'D', 'A', 'D', 'B', 'D']","['2', '1', '2', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '2', '2', '2']","{'openTime': '2021-08-20T03:25:01.561Z', 'loadTime': '2021-08-20T03:25:03.046Z', 'startTime': '2021-08-20T03:25:52.254Z', 'endTime': '2021-08-20T03:46:35.031Z', 'timeToCompleteFromOpenMsec': 1293470, 'timeToLoadMsec': 1485, 'timeToCompleteFromStartMsec': 1242777}","{'gender': 'male', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'someCollege', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'P71', 'isValidCode': True, 'coordinate': [646, 366]}, '1': {'rememberedCode': 'F46', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'F46', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'F46', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'A43', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'B11', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'B11', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'B11', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'M27', 'isValidCode': True, 'coordinate': [790, 784]}, '9': {'rememberedCode': 'I18', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'I18', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'I18', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'H39', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'V38', 'isValidCode': True, 'coordinate': [977, 641]}, '14': {'rememberedCode': 'V38', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'V38', 'isValidCode': True, 'coordinate': [977, 641]}, '16': {'rememberedCode': 'B19', 'isValidCode': True, 'coordinate': [1182, 155]}, '17': {'rememberedCode': 'Q12', 'isValidCode': True, 'coordinate': [447, 359]}, '18': {'rememberedCode': 'Q12', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Q12', 'isValidCode': True, 'coordinate': [447, 359]}, '20': {'rememberedCode': 'X59', 'isValidCode': True, 'coordinate': [550, 497]}, '21': {'rememberedCode': 'C63', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'C63', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C63', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'B56', 'isValidCode': True, 'coordinate': [1746, 646]}, '25': {'rememberedCode': 'N64', 'isValidCode': True, 'coordinate': [633, 789]}, '26': {'rememberedCode': 'N64', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'N64', 'isValidCode': True, 'coordinate': [633, 789]}, '28': {'rememberedCode': 'Y38', 'isValidCode': True, 'coordinate': [756, 497]}, '29': {'rememberedCode': 'B75', 'isValidCode': True, 'coordinate': [891, 295]}, '30': {'rememberedCode': 'B75', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'B75', 'isValidCode': True, 'coordinate': [891, 295]}, '32': {'rememberedCode': 'M54', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'E62', 'isValidCode': True, 'coordinate': [744, 655]}, '34': {'rememberedCode': 'E62', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E62', 'isValidCode': True, 'coordinate': [744, 655]}, '36': {'rememberedCode': 'W54', 'isValidCode': True, 'coordinate': [749, 432]}, '37': {'rememberedCode': 'K42', 'isValidCode': True, 'coordinate': [1144, 417]}, '38': {'rememberedCode': 'K42', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K42', 'isValidCode': True, 'coordinate': [1144, 417]}}",621,218
+140,"['A', 'D', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'C', 'C', 'C', 'D', 'B', 'A', 'C', 'C', 'D', 'D', 'C', 'D', 'A', 'D', 'D', 'A', 'B', 'C', 'D', 'D', 'B', 'A', 'B', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'C', 'B', 'A', 'D', 'D', 'A', 'A', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'B', 'C', 'B', 'A', 'B', 'D', 'A', 'A', 'D', 'D', 'D', 'A', 'D', 'A', 'D', 'B', 'C', 'A', 'B', 'D', 'D', 'C', 'D', 'C', 'D', 'A', 'B', 'D', 'D', 'C', 'D', 'A', 'D', 'D', 'A', 'D']","['2', '1', '2', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '2', '1', '1', '1']","{'openTime': '2021-08-20T05:09:36.428Z', 'loadTime': '2021-08-20T05:09:44.089Z', 'startTime': '2021-08-20T05:09:46.012Z', 'endTime': '2021-08-20T05:27:28.186Z', 'timeToCompleteFromOpenMsec': 1071758, 'timeToLoadMsec': 7661, 'timeToCompleteFromStartMsec': 1062174}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'none'}","{'0': {'rememberedCode': 'R93', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'U37', 'isValidCode': True, 'coordinate': [1165, 510]}, '2': {'rememberedCode': 'U37', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'U37', 'isValidCode': True, 'coordinate': [1165, 510]}, '4': {'rememberedCode': 'N15', 'isValidCode': True, 'coordinate': [1064, 617]}, '5': {'rememberedCode': 'V79', 'isValidCode': True, 'coordinate': [558, 218]}, '6': {'rememberedCode': 'V79', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'V79', 'isValidCode': True, 'coordinate': [558, 218]}, '8': {'rememberedCode': 'J25', 'isValidCode': True, 'coordinate': [549, 358]}, '9': {'rememberedCode': 'Q85', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'Q85', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Q85', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'G35', 'isValidCode': True, 'coordinate': [1261, 648]}, '13': {'rememberedCode': 'T72', 'isValidCode': True, 'coordinate': [539, 851]}, '14': {'rememberedCode': 'T72', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T72', 'isValidCode': True, 'coordinate': [539, 851]}, '16': {'rememberedCode': 'B79', 'isValidCode': True, 'coordinate': [340, 110]}, '17': {'rememberedCode': 'F58', 'isValidCode': True, 'coordinate': [1242, 572]}, '18': {'rememberedCode': 'F58', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F58', 'isValidCode': True, 'coordinate': [1242, 572]}, '20': {'rememberedCode': 'Z13', 'isValidCode': True, 'coordinate': [977, 485]}, '21': {'rememberedCode': 'T28', 'isValidCode': True, 'coordinate': [306, 245]}, '22': {'rememberedCode': 'T28', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'T28', 'isValidCode': True, 'coordinate': [306, 245]}, '24': {'rememberedCode': 'F12', 'isValidCode': True, 'coordinate': [1541, 290]}, '25': {'rememberedCode': 'U45', 'isValidCode': True, 'coordinate': [1362, 844]}, '26': {'rememberedCode': 'U45', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U45', 'isValidCode': True, 'coordinate': [1362, 844]}, '28': {'rememberedCode': 'Y69', 'isValidCode': True, 'coordinate': [318, 442]}, '29': {'rememberedCode': 'L78', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'L78', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L78', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'W14', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'A59', 'isValidCode': True, 'coordinate': [735, 588]}, '34': {'rememberedCode': 'A59', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'A59', 'isValidCode': True, 'coordinate': [735, 588]}, '36': {'rememberedCode': 'K73', 'isValidCode': True, 'coordinate': [838, 509]}, '37': {'rememberedCode': 'Z82', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'Z82', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Z82', 'isValidCode': False, 'coordinate': False}}",594,856
diff --git a/VisRecall/answer_raw/group4.csv b/VisRecall/answer_raw/group4.csv
new file mode 100755
index 0000000..772f2e3
--- /dev/null
+++ b/VisRecall/answer_raw/group4.csv
@@ -0,0 +1,51 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+2,"['C', 'B', 'A', 'C', 'C', 'A', 'D', 'D', 'D', 'D', 'A', 'C', 'B', 'A', 'A', 'A', 'A', 'A', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'C', 'D', 'A', 'C', 'A', 'D', 'D', 'C', 'C', 'A', 'C', 'C', 'C', 'C', 'A', 'B', 'B', 'A', 'B', 'A', 'B', 'B', 'D', 'B', 'B', 'B', 'D', 'D', 'D', 'A', 'A', 'B', 'C', 'D', 'A', 'B', 'A', 'D', 'D', 'C', 'C', 'C', 'D', 'D', 'B', 'B', 'D', 'D', 'D', 'C', 'A', 'A', 'A', 'A', 'B', 'B', 'A', 'C', 'C', 'C', 'C', 'C', 'A', 'C', 'A', 'D', 'B', 'C', 'C', 'C', 'B', 'A', 'D', 'D']","['2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '1', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2']","{'openTime': '2021-08-16T20:52:41.095Z', 'loadTime': '2021-08-16T20:52:41.580Z', 'startTime': '2021-08-16T20:53:03.250Z', 'endTime': '2021-08-16T21:08:14.416Z', 'timeToCompleteFromOpenMsec': 933321, 'timeToLoadMsec': 485, 'timeToCompleteFromStartMsec': 911166}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Latino_Hispanic', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'F16', 'isValidCode': True, 'coordinate': [135, 1012]}, '1': {'rememberedCode': 'W93', 'isValidCode': True, 'coordinate': [664, 46]}, '2': {'rememberedCode': 'W93', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W93', 'isValidCode': True, 'coordinate': [664, 46]}, '4': {'rememberedCode': 'G79', 'isValidCode': True, 'coordinate': [852, 136]}, '5': {'rememberedCode': 'N13', 'isValidCode': True, 'coordinate': [665, 230]}, '6': {'rememberedCode': 'N13', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N13', 'isValidCode': True, 'coordinate': [665, 230]}, '8': {'rememberedCode': 'N34', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'G92', 'isValidCode': True, 'coordinate': [835, 116]}, '10': {'rememberedCode': 'G92', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'G92', 'isValidCode': True, 'coordinate': [835, 116]}, '12': {'rememberedCode': 'M65', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1140, 622]}, '14': {'rememberedCode': 'X93', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1140, 622]}, '16': {'rememberedCode': 'D85', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'H61', 'isValidCode': True, 'coordinate': [344, 521]}, '18': {'rememberedCode': 'H61', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'H61', 'isValidCode': True, 'coordinate': [344, 521]}, '20': {'rememberedCode': 'G15', 'isValidCode': True, 'coordinate': [1076, 818]}, '21': {'rememberedCode': 'C18', 'isValidCode': True, 'coordinate': [680, 226]}, '22': {'rememberedCode': 'C18', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C18', 'isValidCode': True, 'coordinate': [680, 226]}, '24': {'rememberedCode': 'B42', 'isValidCode': True, 'coordinate': [791, 469]}, '25': {'rememberedCode': 'H72', 'isValidCode': True, 'coordinate': [701, 317]}, '26': {'rememberedCode': 'H72', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'H72', 'isValidCode': True, 'coordinate': [701, 317]}, '28': {'rememberedCode': 'M15', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'U45', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'U45', 'isValidCode': True, 'coordinate': [684, 310]}, '31': {'rememberedCode': 'U45', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X97', 'isValidCode': True, 'coordinate': [497, 258]}, '33': {'rememberedCode': 'D37', 'isValidCode': True, 'coordinate': [716, 237]}, '34': {'rememberedCode': 'D37', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D37', 'isValidCode': True, 'coordinate': [716, 237]}, '36': {'rememberedCode': 'M19', 'isValidCode': True, 'coordinate': [500, 115]}, '37': {'rememberedCode': 'Y97', 'isValidCode': True, 'coordinate': [852, 327]}, '38': {'rememberedCode': 'Y97', 'isValidCode': True, 'coordinate': [1306, 993]}, '39': {'rememberedCode': 'Y97', 'isValidCode': True, 'coordinate': [852, 327]}}",525,933
+4,"['D', 'B', 'A', 'C', 'C', 'D', 'A', 'C', 'B', 'D', 'A', 'D', 'A', 'A', 'B', 'A', 'A', 'D', 'A', 'A', 'A', 'A', 'A', 'C', 'C', 'A', 'B', 'D', 'A', 'C', 'A', 'D', 'A', 'A', 'A', 'A', 'A', 'D', 'D', 'C', 'A', 'B', 'A', 'A', 'D', 'A', 'D', 'A', 'B', 'D', 'C', 'A', 'A', 'D', 'C', 'A', 'A', 'B', 'C', 'D', 'A', 'B', 'A', 'D', 'A', 'A', 'D', 'B', 'D', 'D', 'D', 'C', 'C', 'B', 'D', 'B', 'D', 'B', 'D', 'D', 'B', 'B', 'A', 'C', 'D', 'C', 'A', 'D', 'D', 'D', 'B', 'B', 'A', 'C', 'B', 'C', 'D', 'D', 'D', 'D']","['2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '1', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2']","{'openTime': '2021-08-16T20:57:21.418Z', 'loadTime': '2021-08-16T20:57:44.661Z', 'startTime': '2021-08-16T20:57:48.841Z', 'endTime': '2021-08-16T21:15:16.326Z', 'timeToCompleteFromOpenMsec': 1074908, 'timeToLoadMsec': 23243, 'timeToCompleteFromStartMsec': 1047485}","{'gender': 'male', 'ageGroup': 'ageGroup18', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': '154', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'R78', 'isValidCode': True, 'coordinate': [652, 354]}, '2': {'rememberedCode': 'R78', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'R78', 'isValidCode': True, 'coordinate': [652, 354]}, '4': {'rememberedCode': 'G78', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'P72', 'isValidCode': True, 'coordinate': [386, 137]}, '6': {'rememberedCode': 'P72', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'P72', 'isValidCode': True, 'coordinate': [386, 137]}, '8': {'rememberedCode': 'M68', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'D31', 'isValidCode': True, 'coordinate': [667, 38]}, '10': {'rememberedCode': 'D31', 'isValidCode': True, 'coordinate': [1455, 1229]}, '11': {'rememberedCode': 'D31', 'isValidCode': True, 'coordinate': [667, 38]}, '12': {'rememberedCode': 'G62', 'isValidCode': True, 'coordinate': [1137, 1197]}, '13': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [394, 114]}, '14': {'rememberedCode': 'H89', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [394, 114]}, '16': {'rememberedCode': 'W68', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'H47', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'H47', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'H47', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'S58', 'isValidCode': True, 'coordinate': [511, 345]}, '21': {'rememberedCode': 'L91', 'isValidCode': True, 'coordinate': [509, 637]}, '22': {'rememberedCode': 'L91', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L91', 'isValidCode': True, 'coordinate': [509, 637]}, '24': {'rememberedCode': 'Y87', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'W71', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'W71', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'W71', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'G54', 'isValidCode': True, 'coordinate': [262, 704]}, '29': {'rememberedCode': 'H98', 'isValidCode': True, 'coordinate': [627, 450]}, '30': {'rememberedCode': 'H98', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'H98', 'isValidCode': True, 'coordinate': [627, 450]}, '32': {'rememberedCode': 'Q84', 'isValidCode': True, 'coordinate': [1381, 25]}, '33': {'rememberedCode': 'E76', 'isValidCode': True, 'coordinate': [677, 26]}, '34': {'rememberedCode': 'E76', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E76', 'isValidCode': True, 'coordinate': [677, 26]}, '36': {'rememberedCode': 'G57', 'isValidCode': True, 'coordinate': [1166, 110]}, '37': {'rememberedCode': 'G58', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'G58', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G58', 'isValidCode': False, 'coordinate': False}}",333,482
+5,"['B', 'B', 'B', 'C', 'A', 'B', 'B', 'C', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'B', 'B', 'C', 'B', 'C', 'C', 'B', 'B', 'B', 'B', 'B', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'C', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'C', 'B', 'A']","['2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '2', '1', '2', '2', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '2', '2', '2', '1', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-16T20:54:49.018Z', 'loadTime': '2021-08-16T20:55:15.949Z', 'startTime': '2021-08-16T20:56:04.371Z', 'endTime': '2021-08-16T21:18:37.127Z', 'timeToCompleteFromOpenMsec': 1428109, 'timeToLoadMsec': 26931, 'timeToCompleteFromStartMsec': 1352756}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'nice'}","{'0': {'rememberedCode': 'GRA', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'A76', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'A76', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'A76', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'V91', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'D93', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'D93', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'D93', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'X15', 'isValidCode': True, 'coordinate': [837, 126]}, '9': {'rememberedCode': 'X15', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'X15', 'isValidCode': True, 'coordinate': [837, 126]}, '11': {'rememberedCode': 'X15', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'U17', 'isValidCode': True, 'coordinate': [403, 596]}, '13': {'rememberedCode': 'L26', 'isValidCode': True, 'coordinate': [966, 733]}, '14': {'rememberedCode': 'L26', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L26', 'isValidCode': True, 'coordinate': [966, 733]}, '16': {'rememberedCode': 'G18', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'V29', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'V29', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V29', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'X27', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'E28', 'isValidCode': True, 'coordinate': [365, 652]}, '22': {'rememberedCode': 'E28', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'E28', 'isValidCode': True, 'coordinate': [365, 652]}, '24': {'rememberedCode': 'E81', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'C42', 'isValidCode': True, 'coordinate': [1333, 317]}, '26': {'rememberedCode': 'C42', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C42', 'isValidCode': True, 'coordinate': [1333, 317]}, '28': {'rememberedCode': 'C42', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'L98', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'L98', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L98', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X18', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'V47', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'V47', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'V47', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'F48', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'T41', 'isValidCode': True, 'coordinate': [1172, 319]}, '38': {'rememberedCode': 'T41', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'T41', 'isValidCode': True, 'coordinate': [1172, 319]}}",171,344
+6,"['B', 'C', 'D', 'D', 'C', 'B', 'A', 'B', 'A', 'D', 'A', 'D', 'B', 'A', 'A', 'B', 'D', 'A', 'C', 'A', 'C', 'A', 'A', 'C', 'C', 'D', 'B', 'C', 'A', 'D', 'A', 'D', 'C', 'B', 'D', 'A', 'D', 'B', 'D', 'A', 'A', 'B', 'B', 'B', 'D', 'A', 'B', 'A', 'D', 'D', 'A', 'A', 'A', 'D', 'D', 'B', 'D', 'B', 'B', 'D', 'B', 'C', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'B', 'B', 'B', 'D', 'D', 'D', 'D', 'A', 'D', 'A', 'A', 'D', 'B', 'B', 'D', 'D', 'D', 'B', 'A', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D']","['2', '1', '2', '2', '1', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '1', '2', '2', '2', '2', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2']","{'openTime': '2021-08-16T20:56:40.368Z', 'loadTime': '2021-08-16T20:56:43.684Z', 'startTime': '2021-08-16T20:56:54.901Z', 'endTime': '2021-08-16T21:21:59.400Z', 'timeToCompleteFromOpenMsec': 1519032, 'timeToLoadMsec': 3316, 'timeToCompleteFromStartMsec': 1504499}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'East_Indian', 'education': 'mastersDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'L2Z', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'Y46', 'isValidCode': True, 'coordinate': [797, 839]}, '2': {'rememberedCode': 'Y46', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Y46', 'isValidCode': True, 'coordinate': [797, 839]}, '4': {'rememberedCode': 'PK9', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'U6M', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'U6M', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U6M', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'YR6', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'PN5', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'PN5', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'PN5', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'K6Z', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'T7X', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'T7X', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T7X', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'Y8H', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [199, 293]}, '18': {'rememberedCode': 'H89', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [199, 293]}, '20': {'rememberedCode': 'Y39', 'isValidCode': True, 'coordinate': [952, 715]}, '21': {'rememberedCode': 'U76', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'U76', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U76', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'F78', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'G65', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'G65', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G65', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'G9Z', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'K97', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'K97', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'K97', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'W38', 'isValidCode': True, 'coordinate': [784, 330]}, '33': {'rememberedCode': 'X65', 'isValidCode': True, 'coordinate': [517, 823]}, '34': {'rememberedCode': 'X65', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'X65', 'isValidCode': True, 'coordinate': [517, 823]}, '36': {'rememberedCode': 'P2Z', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'E78', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'E78', 'isValidCode': True, 'coordinate': [1577, 390]}, '39': {'rememberedCode': 'E78', 'isValidCode': False, 'coordinate': False}}",97,580
+7,"['C', 'A', 'D', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'C', 'C', 'B', 'A', 'C', 'B', 'D', 'C', 'D', 'A', 'A', 'B', 'A', 'C', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'C', 'D', 'A', 'D', 'A', 'C', 'B', 'B', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'C', 'A', 'D', 'D', 'C', 'A', 'B', 'B', 'C', 'A', 'A', 'A', 'C', 'C', 'A', 'B', 'A', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'C', 'C', 'D', 'D', 'B', 'B', 'C', 'A', 'D', 'D', 'C', 'D', 'C', 'A', 'D', 'A', 'C', 'A', 'C', 'C', 'C', 'D', 'A', 'B', 'C']","['2', '1', '2', '2', '1', '2', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2']","{'openTime': '2021-08-16T20:46:45.440Z', 'loadTime': '2021-08-16T20:46:59.287Z', 'startTime': '2021-08-16T21:04:17.999Z', 'endTime': '2021-08-16T21:23:15.508Z', 'timeToCompleteFromOpenMsec': 2190068, 'timeToLoadMsec': 13847, 'timeToCompleteFromStartMsec': 1137509}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'someCollege', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'T85', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'Y41', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'Y41', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Y41', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'D72', 'isValidCode': True, 'coordinate': [833, 456]}, '5': {'rememberedCode': 'C76', 'isValidCode': True, 'coordinate': [697, 136]}, '6': {'rememberedCode': 'C76', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'C76', 'isValidCode': True, 'coordinate': [697, 136]}, '8': {'rememberedCode': 'R34', 'isValidCode': True, 'coordinate': [1006, 455]}, '9': {'rememberedCode': 'G92', 'isValidCode': True, 'coordinate': [835, 116]}, '10': {'rememberedCode': 'G92', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'G92', 'isValidCode': True, 'coordinate': [835, 116]}, '12': {'rememberedCode': 'R28', 'isValidCode': True, 'coordinate': [388, 700]}, '13': {'rememberedCode': 'Y39', 'isValidCode': True, 'coordinate': [802, 213]}, '14': {'rememberedCode': 'Y39', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Y39', 'isValidCode': True, 'coordinate': [802, 213]}, '16': {'rememberedCode': 'U32', 'isValidCode': True, 'coordinate': [684, 526]}, '17': {'rememberedCode': 'V54', 'isValidCode': True, 'coordinate': [531, 316]}, '18': {'rememberedCode': 'V54', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V54', 'isValidCode': True, 'coordinate': [531, 316]}, '20': {'rememberedCode': 'B86', 'isValidCode': True, 'coordinate': [478, 235]}, '21': {'rememberedCode': 'C39', 'isValidCode': True, 'coordinate': [675, 155]}, '22': {'rememberedCode': 'C39', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C39', 'isValidCode': True, 'coordinate': [675, 155]}, '24': {'rememberedCode': 'B38', 'isValidCode': True, 'coordinate': [748, 528]}, '25': {'rememberedCode': 'Z21', 'isValidCode': True, 'coordinate': [534, 415]}, '26': {'rememberedCode': 'Z21', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Z21', 'isValidCode': True, 'coordinate': [534, 415]}, '28': {'rememberedCode': 'Z96', 'isValidCode': True, 'coordinate': [515, 195]}, '29': {'rememberedCode': 'F18', 'isValidCode': True, 'coordinate': [643, 366]}, '30': {'rememberedCode': 'F18', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'F18', 'isValidCode': True, 'coordinate': [643, 366]}, '32': {'rememberedCode': 'S78', 'isValidCode': True, 'coordinate': [494, 627]}, '33': {'rememberedCode': 'L34', 'isValidCode': True, 'coordinate': [700, 762]}, '34': {'rememberedCode': 'L34', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'L34', 'isValidCode': True, 'coordinate': [700, 762]}, '36': {'rememberedCode': 'W12', 'isValidCode': True, 'coordinate': [833, 300]}, '37': {'rememberedCode': 'W46', 'isValidCode': True, 'coordinate': [992, 99]}, '38': {'rememberedCode': 'W46', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'W46', 'isValidCode': True, 'coordinate': [992, 99]}}",98,387
+9,"['B', 'B', 'D', 'D', 'B', 'B', 'C', 'D', 'B', 'B', 'A', 'C', 'D', 'B', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'B', 'A', 'A', 'C', 'B', 'D', 'D', 'D', 'D', 'B', 'B', 'D', 'D', 'C', 'B', 'C', 'C', 'D', 'D', 'D', 'C', 'C', 'C', 'B', 'C', 'D', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'A', 'B', 'C', 'C', 'B', 'D', 'B', 'B', 'D', 'B', 'D', 'C', 'B', 'C', 'C', 'A', 'C', 'C', 'A', 'C', 'A', 'D', 'B', 'A', 'B', 'B', 'B', 'A', 'C', 'A', 'C', 'D', 'B', 'C', 'C', 'C', 'C', 'C', 'B', 'D', 'A', 'B', 'A', 'D', 'A', 'C']","['2', '2', '2', '2', '1', '1', '2', '2', '1', '1', '1', '1', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '1', '1', '2', '1', '1', '2', '2', '1', '1', '1', '2', '2', '1', '2', '1', '1', '1', '1']","{'openTime': '2021-08-16T21:08:34.653Z', 'loadTime': '2021-08-16T21:08:39.472Z', 'startTime': '2021-08-16T21:08:56.250Z', 'endTime': '2021-08-16T21:25:09.403Z', 'timeToCompleteFromOpenMsec': 994750, 'timeToLoadMsec': 4819, 'timeToCompleteFromStartMsec': 973153}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'CSZ', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'P29', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'P29', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'P29', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'BS4', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'D98', 'isValidCode': True, 'coordinate': [191, 55]}, '6': {'rememberedCode': 'D98', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'D98', 'isValidCode': True, 'coordinate': [191, 55]}, '8': {'rememberedCode': 'C37', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'K86', 'isValidCode': True, 'coordinate': [405, 39]}, '10': {'rememberedCode': 'K86', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K86', 'isValidCode': True, 'coordinate': [405, 39]}, '12': {'rememberedCode': 'M64', 'isValidCode': True, 'coordinate': [987, 18]}, '13': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'T81', 'isValidCode': True, 'coordinate': [345, 13]}, '17': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'A75', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'C72', 'isValidCode': True, 'coordinate': [773, 52]}, '25': {'rememberedCode': 'U23', 'isValidCode': True, 'coordinate': [532, 30]}, '26': {'rememberedCode': 'U23', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U23', 'isValidCode': True, 'coordinate': [532, 30]}, '28': {'rememberedCode': 'X28', 'isValidCode': True, 'coordinate': [387, 34]}, '29': {'rememberedCode': 'D46', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'D46', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'D46', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'V91', 'isValidCode': True, 'coordinate': [609, 154]}, '33': {'rememberedCode': 'U27', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'U27', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'U27', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'U27', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'C23', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'C23', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'C23', 'isValidCode': False, 'coordinate': False}}",877,285
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+13,"['A', 'A', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'C', 'B', 'C', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'C']","['2', '1', '2', '2', '1', '1', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1', '1', '2', '2', '2', '1', '2', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '1', '2', '1', '2', '1', '1']","{'openTime': '2021-08-16T21:07:16.134Z', 'loadTime': '2021-08-16T21:07:46.549Z', 'startTime': '2021-08-16T21:09:15.822Z', 'endTime': '2021-08-16T21:32:38.929Z', 'timeToCompleteFromOpenMsec': 1522795, 'timeToLoadMsec': 30415, 'timeToCompleteFromStartMsec': 1403107}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Nothing'}","{'0': {'rememberedCode': 'C15', 'isValidCode': True, 'coordinate': [787, 402]}, '1': {'rememberedCode': 'V14', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'V14', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'V14', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'H27', 'isValidCode': True, 'coordinate': [543, 858]}, '5': {'rememberedCode': 'V52', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'V52', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'V52', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'F78', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'V52', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'V52', 'isValidCode': True, 'coordinate': [802, 635]}, '11': {'rememberedCode': 'V52', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'A76', 'isValidCode': True, 'coordinate': [820, 519]}, '13': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [394, 114]}, '14': {'rememberedCode': 'H89', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'H89', 'isValidCode': True, 'coordinate': [394, 114]}, '16': {'rememberedCode': 'H89', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'V89', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'V89', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V89', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'V39', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'V81', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'V81', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'V81', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'R86', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'L38', 'isValidCode': True, 'coordinate': [1000, 433]}, '26': {'rememberedCode': 'L38', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'L38', 'isValidCode': True, 'coordinate': [1000, 433]}, '28': {'rememberedCode': 'V89', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'D86', 'isValidCode': True, 'coordinate': [625, 864]}, '30': {'rememberedCode': 'D86', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'D86', 'isValidCode': True, 'coordinate': [625, 864]}, '32': {'rememberedCode': 'U71', 'isValidCode': True, 'coordinate': [799, 855]}, '33': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [681, 622]}, '34': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [809, 1158]}, '35': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [681, 622]}, '36': {'rememberedCode': 'R12', 'isValidCode': True, 'coordinate': [819, 522]}, '37': {'rememberedCode': 'R12', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'R12', 'isValidCode': True, 'coordinate': [819, 522]}, '39': {'rememberedCode': 'R12', 'isValidCode': False, 'coordinate': False}}",614,210
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+35,"['D', 'D', 'D', 'D', 'D', 'D', 'B', 'D', 'D', 'D', 'A', 'D', 'B', 'D', 'D', 'A', 'D', 'A', 'D', 'D', 'D', 'B', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'C', 'B', 'C', 'C', 'A', 'C', 'C', 'D', 'C', 'A', 'D', 'D', 'D', 'D', 'A', 'B', 'A', 'A', 'A', 'C', 'A', 'A', 'D', 'C', 'A', 'D', 'A', 'C', 'C', 'A', 'D', 'C', 'D', 'D', 'A', 'C', 'B', 'D', 'D', 'B', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'B', 'C', 'A', 'D', 'D', 'C', 'D', 'D', 'A', 'D', 'A', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D']","['2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '2', '2', '1', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '2', '2', '1', '1', '2', '2', '2', '2']","{'openTime': '2021-08-16T22:07:51.061Z', 'loadTime': '2021-08-16T22:07:53.498Z', 'startTime': '2021-08-16T22:08:11.235Z', 'endTime': '2021-08-16T22:34:27.706Z', 'timeToCompleteFromOpenMsec': 1596645, 'timeToLoadMsec': 2437, 'timeToCompleteFromStartMsec': 1576471}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good Survey.'}","{'0': {'rememberedCode': 'D67', 'isValidCode': True, 'coordinate': [1261, 419]}, '1': {'rememberedCode': 'A97', 'isValidCode': True, 'coordinate': [318, 240]}, '2': {'rememberedCode': 'A97', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'A97', 'isValidCode': True, 'coordinate': [318, 240]}, '4': {'rememberedCode': 'Y63', 'isValidCode': True, 'coordinate': [1323, 659]}, '5': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [652, 49]}, '6': {'rememberedCode': 'M62', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [652, 49]}, '8': {'rememberedCode': 'G73', 'isValidCode': True, 'coordinate': [976, 236]}, '9': {'rememberedCode': 'F72', 'isValidCode': True, 'coordinate': [677, 228]}, '10': {'rememberedCode': 'F72', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'F72', 'isValidCode': True, 'coordinate': [677, 228]}, '12': {'rememberedCode': 'P26', 'isValidCode': True, 'coordinate': [704, 285]}, '13': {'rememberedCode': 'L86', 'isValidCode': True, 'coordinate': [1132, 1216]}, '14': {'rememberedCode': 'L86', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L86', 'isValidCode': True, 'coordinate': [1132, 1216]}, '16': {'rememberedCode': 'K34', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'Z13', 'isValidCode': True, 'coordinate': [636, 324]}, '18': {'rememberedCode': 'Z13', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Z13', 'isValidCode': True, 'coordinate': [636, 324]}, '20': {'rememberedCode': 'K28', 'isValidCode': True, 'coordinate': [1258, 452]}, '21': {'rememberedCode': 'J16', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'J16', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'J16', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'P15', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'U19', 'isValidCode': True, 'coordinate': [688, 1112]}, '26': {'rememberedCode': 'U19', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U19', 'isValidCode': True, 'coordinate': [688, 1112]}, '28': {'rememberedCode': 'M47', 'isValidCode': True, 'coordinate': [843, 321]}, '29': {'rememberedCode': 'T46', 'isValidCode': True, 'coordinate': [1128, 1048]}, '30': {'rememberedCode': 'T46', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'T46', 'isValidCode': True, 'coordinate': [1128, 1048]}, '32': {'rememberedCode': 'B18', 'isValidCode': True, 'coordinate': [940, 254]}, '33': {'rememberedCode': 'D65', 'isValidCode': True, 'coordinate': [1144, 363]}, '34': {'rememberedCode': 'D65', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D65', 'isValidCode': True, 'coordinate': [1144, 363]}, '36': {'rememberedCode': 'Z15', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'F64', 'isValidCode': True, 'coordinate': [1160, 1206]}, '38': {'rememberedCode': 'F64', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'F64', 'isValidCode': True, 'coordinate': [1160, 1206]}}",835,998
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+100,"['B', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'A', 'B', 'D', 'D', 'A', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'C', 'A', 'B', 'D', 'B', 'D', 'A', 'B', 'D', 'D', 'D', 'D', 'D', 'A', 'C', 'C', 'D', 'D', 'B', 'C', 'C', 'A', 'D', 'A', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'C', 'D', 'B', 'D', 'D', 'B', 'D', 'D', 'D', 'B', 'B', 'D', 'A', 'C', 'B', 'D', 'B', 'C', 'A', 'D', 'A', 'D', 'D', 'C', 'C', 'C', 'A', 'D', 'D', 'A']","['2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '1', '2', '2', '2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2']","{'openTime': '2021-08-18T20:41:00.759Z', 'loadTime': '2021-08-18T20:41:01.880Z', 'startTime': '2021-08-18T20:41:04.571Z', 'endTime': '2021-08-18T21:06:59.599Z', 'timeToCompleteFromOpenMsec': 1558840, 'timeToLoadMsec': 1121, 'timeToCompleteFromStartMsec': 1555028}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'W42', 'isValidCode': True, 'coordinate': [1228, 710]}, '1': {'rememberedCode': 'T26', 'isValidCode': True, 'coordinate': [816, 341]}, '2': {'rememberedCode': 'T26', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'T26', 'isValidCode': True, 'coordinate': [816, 341]}, '4': {'rememberedCode': 'P89', 'isValidCode': True, 'coordinate': [851, 325]}, '5': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [652, 49]}, '6': {'rememberedCode': 'M62', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [652, 49]}, '8': {'rememberedCode': 'P57', 'isValidCode': True, 'coordinate': [517, 160]}, '9': {'rememberedCode': 'L53', 'isValidCode': True, 'coordinate': [1153, 413]}, '10': {'rememberedCode': 'L53', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'L53', 'isValidCode': True, 'coordinate': [1153, 413]}, '12': {'rememberedCode': 'L14', 'isValidCode': True, 'coordinate': [867, 211]}, '13': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1140, 622]}, '14': {'rememberedCode': 'X93', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1140, 622]}, '16': {'rememberedCode': 'M63', 'isValidCode': True, 'coordinate': [808, 630]}, '17': {'rememberedCode': 'M47', 'isValidCode': True, 'coordinate': [680, 193]}, '18': {'rememberedCode': 'M47', 'isValidCode': True, 'coordinate': [72, 1134]}, '19': {'rememberedCode': 'M47', 'isValidCode': True, 'coordinate': [680, 193]}, '20': {'rememberedCode': 'Y39', 'isValidCode': True, 'coordinate': [952, 715]}, '21': {'rememberedCode': 'Y27', 'isValidCode': True, 'coordinate': [215, 455]}, '22': {'rememberedCode': 'Y27', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Y27', 'isValidCode': True, 'coordinate': [215, 455]}, '24': {'rememberedCode': 'F18', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'M37', 'isValidCode': True, 'coordinate': [861, 136]}, '26': {'rememberedCode': 'M37', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'M37', 'isValidCode': True, 'coordinate': [861, 136]}, '28': {'rememberedCode': 'C71', 'isValidCode': True, 'coordinate': [415, 706]}, '29': {'rememberedCode': 'H93', 'isValidCode': True, 'coordinate': [483, 161]}, '30': {'rememberedCode': 'H93', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'H93', 'isValidCode': True, 'coordinate': [483, 161]}, '32': {'rememberedCode': 'P18', 'isValidCode': True, 'coordinate': [813, 42]}, '33': {'rememberedCode': 'X29', 'isValidCode': True, 'coordinate': [547, 50]}, '34': {'rememberedCode': 'X29', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'X29', 'isValidCode': True, 'coordinate': [547, 50]}, '36': {'rememberedCode': 'T25', 'isValidCode': True, 'coordinate': [496, 193]}, '37': {'rememberedCode': 'X57', 'isValidCode': True, 'coordinate': [1476, 694]}, '38': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'X57', 'isValidCode': True, 'coordinate': [1476, 694]}}",257,832
+102,"['C', 'A', 'C', 'C', 'B', 'C', 'A', 'A', 'B', 'C', 'A', 'A', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'C', 'A', 'A', 'C', 'B', 'C', 'A', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'A', 'A', 'C', 'C', 'C', 'B', 'A', 'B', 'B', 'B', 'A', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'A', 'A', 'C', 'A', 'B', 'C', 'A', 'C', 'C', 'C', 'B', 'C', 'B', 'A', 'C', 'B', 'B', 'B', 'B', 'A', 'B', 'B', 'A', 'B', 'C', 'C', 'C', 'C', 'A', 'B', 'C', 'C', 'C', 'C', 'C', 'B', 'B', 'B']","['2', '1', '2', '2', '1', '2', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2']","{'openTime': '2021-08-18T21:58:15.226Z', 'loadTime': '2021-08-18T21:58:29.157Z', 'startTime': '2021-08-18T21:58:41.088Z', 'endTime': '2021-08-18T22:19:41.655Z', 'timeToCompleteFromOpenMsec': 1286429, 'timeToLoadMsec': 13931, 'timeToCompleteFromStartMsec': 1260567}","{'gender': 'male', 'ageGroup': 'ageGroup18', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'professional', 'feedback': ''}","{'0': {'rememberedCode': '200', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '100', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'V18', 'isValidCode': True, 'coordinate': [1161, 256]}, '5': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'V18', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'M64', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'M64', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'M64', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'F52', 'isValidCode': True, 'coordinate': [852, 324]}, '13': {'rememberedCode': 'Z18', 'isValidCode': True, 'coordinate': [1300, 398]}, '14': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Z18', 'isValidCode': True, 'coordinate': [1300, 398]}, '16': {'rememberedCode': 'F38', 'isValidCode': True, 'coordinate': [659, 332]}, '17': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'W21', 'isValidCode': True, 'coordinate': [939, 319]}, '21': {'rememberedCode': 'M21', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'M21', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'M21', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'F27', 'isValidCode': True, 'coordinate': [914, 263]}, '25': {'rememberedCode': 'M37', 'isValidCode': True, 'coordinate': [861, 136]}, '26': {'rememberedCode': 'M37', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'M37', 'isValidCode': True, 'coordinate': [861, 136]}, '28': {'rememberedCode': 'M37', 'isValidCode': True, 'coordinate': [1127, 293]}, '29': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'E72', 'isValidCode': True, 'coordinate': [307, 348]}, '33': {'rememberedCode': 'J35', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'J35', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'J35', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'A12', 'isValidCode': True, 'coordinate': [504, 329]}, '37': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'Z18', 'isValidCode': True, 'coordinate': [862, 631]}, '39': {'rememberedCode': 'Z18', 'isValidCode': False, 'coordinate': False}}",103,430
+103,"['B', 'C', 'C', 'C', 'C', 'A', 'B', 'C', 'C', 'A', 'C', 'C', 'A', 'A', 'B', 'A', 'B', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'C', 'A', 'A', 'B', 'A', 'C', 'A', 'B', 'B', 'C', 'C', 'A', 'C', 'A', 'B', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'A', 'C', 'A', 'B', 'B', 'D', 'A', 'A', 'A', 'C', 'C', 'A', 'B', 'A', 'C', 'A', 'A', 'A', 'B', 'C', 'A', 'C', 'C', 'A', 'C', 'C', 'C', 'A', 'B', 'C', 'B', 'B', 'C', 'A', 'C', 'B', 'C', 'B', 'C', 'A', 'C', 'A', 'C', 'B', 'C', 'C', 'A', 'A', 'A', 'B', 'A']","['2', '1', '2', '2', '1', '1', '2', '1', '1', '1', '1', '1', '1', '2', '1', '2', '1', '1', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '2']","{'openTime': '2021-08-18T22:40:31.023Z', 'loadTime': '2021-08-18T22:40:58.499Z', 'startTime': '2021-08-18T22:41:11.796Z', 'endTime': '2021-08-18T23:06:47.320Z', 'timeToCompleteFromOpenMsec': 1576297, 'timeToLoadMsec': 27476, 'timeToCompleteFromStartMsec': 1535524}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'It was a very challenging task, I confess that it was very difficult to keep some data. I referenced the major and minor data and the chart name to base my answers on.'}","{'0': {'rememberedCode': 'E47', 'isValidCode': True, 'coordinate': [1112, 136]}, '1': {'rememberedCode': 'B53', 'isValidCode': True, 'coordinate': [926, 134]}, '2': {'rememberedCode': 'B53', 'isValidCode': True, 'coordinate': [1255, 200]}, '3': {'rememberedCode': 'B53', 'isValidCode': True, 'coordinate': [926, 134]}, '4': {'rememberedCode': 'J18', 'isValidCode': True, 'coordinate': [669, 137]}, '5': {'rememberedCode': 'E71', 'isValidCode': True, 'coordinate': [702, 465]}, '6': {'rememberedCode': 'E71', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E71', 'isValidCode': True, 'coordinate': [702, 465]}, '8': {'rememberedCode': 'Z36', 'isValidCode': True, 'coordinate': [666, 231]}, '9': {'rememberedCode': 'R34', 'isValidCode': True, 'coordinate': [867, 324]}, '10': {'rememberedCode': 'R34', 'isValidCode': True, 'coordinate': [1006, 455]}, '11': {'rememberedCode': 'R34', 'isValidCode': True, 'coordinate': [867, 324]}, '12': {'rememberedCode': 'L14', 'isValidCode': True, 'coordinate': [867, 211]}, '13': {'rememberedCode': 'F97', 'isValidCode': True, 'coordinate': [1439, 495]}, '14': {'rememberedCode': 'F97', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F97', 'isValidCode': True, 'coordinate': [1439, 495]}, '16': {'rememberedCode': 'X14', 'isValidCode': True, 'coordinate': [1428, 323]}, '17': {'rememberedCode': 'W51', 'isValidCode': True, 'coordinate': [361, 405]}, '18': {'rememberedCode': 'W51', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'W51', 'isValidCode': True, 'coordinate': [361, 405]}, '20': {'rememberedCode': 'W21', 'isValidCode': True, 'coordinate': [939, 319]}, '21': {'rememberedCode': 'G89', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'G89', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G89', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'M71', 'isValidCode': True, 'coordinate': [633, 155]}, '25': {'rememberedCode': 'B48', 'isValidCode': True, 'coordinate': [824, 728]}, '26': {'rememberedCode': 'B48', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'B48', 'isValidCode': True, 'coordinate': [824, 728]}, '28': {'rememberedCode': 'M47', 'isValidCode': True, 'coordinate': [843, 321]}, '29': {'rememberedCode': 'P35', 'isValidCode': True, 'coordinate': [1128, 359]}, '30': {'rememberedCode': 'P35', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'P35', 'isValidCode': True, 'coordinate': [1128, 359]}, '32': {'rememberedCode': 'M48', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'S72', 'isValidCode': True, 'coordinate': [510, 350]}, '34': {'rememberedCode': 'S72', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'S72', 'isValidCode': True, 'coordinate': [510, 350]}, '36': {'rememberedCode': 'Q15', 'isValidCode': True, 'coordinate': [1274, 191]}, '37': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1141, 202]}, '38': {'rememberedCode': 'X93', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'X93', 'isValidCode': True, 'coordinate': [1141, 202]}}",17,312
+105,"['A', 'B', 'B', 'B', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'C', 'C', 'B', 'A', 'B', 'C', 'A', 'C', 'A', 'A', 'B', 'A', 'A', 'B', 'B', 'B', 'C', 'B', 'A', 'B', 'B', 'C', 'B', 'A', 'B', 'A', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'A', 'B', 'C', 'A', 'B', 'B', 'B', 'B', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'B', 'C', 'B', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'B', 'C', 'C', 'B', 'B', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B']","['2', '1', '2', '2', '1', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '2', '2', '1', '2', '2']","{'openTime': '2021-08-19T13:35:31.178Z', 'loadTime': '2021-08-19T13:36:13.499Z', 'startTime': '2021-08-19T13:36:18.091Z', 'endTime': '2021-08-19T13:51:41.815Z', 'timeToCompleteFromOpenMsec': 970637, 'timeToLoadMsec': 42321, 'timeToCompleteFromStartMsec': 923724}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'mastersDegree', 'vizExperience': 'professional', 'feedback': 'none'}","{'0': {'rememberedCode': 'L14', 'isValidCode': True, 'coordinate': [790, 22]}, '1': {'rememberedCode': 'Q82', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'Q82', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Q82', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'E46', 'isValidCode': True, 'coordinate': [960, 238]}, '5': {'rememberedCode': 'R69', 'isValidCode': True, 'coordinate': [665, 330]}, '6': {'rememberedCode': 'R69', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'R69', 'isValidCode': True, 'coordinate': [665, 330]}, '8': {'rememberedCode': 'Y12', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'M84', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'M84', 'isValidCode': True, 'coordinate': [807, 1064]}, '11': {'rememberedCode': 'M84', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'E16', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'C29', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'C29', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C29', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'F71', 'isValidCode': True, 'coordinate': [507, 724]}, '17': {'rememberedCode': 'V75', 'isValidCode': True, 'coordinate': [471, 409]}, '18': {'rememberedCode': 'V75', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V75', 'isValidCode': True, 'coordinate': [471, 409]}, '20': {'rememberedCode': 'H12', 'isValidCode': True, 'coordinate': [334, 320]}, '21': {'rememberedCode': 'C63', 'isValidCode': True, 'coordinate': [1305, 440]}, '22': {'rememberedCode': 'C63', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C63', 'isValidCode': True, 'coordinate': [1305, 440]}, '24': {'rememberedCode': 'F27', 'isValidCode': True, 'coordinate': [914, 263]}, '25': {'rememberedCode': 'F31', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'F31', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'F31', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'X24', 'isValidCode': True, 'coordinate': [679, 393]}, '29': {'rememberedCode': 'A31', 'isValidCode': True, 'coordinate': [809, 735]}, '30': {'rememberedCode': 'A31', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'A31', 'isValidCode': True, 'coordinate': [809, 735]}, '32': {'rememberedCode': 'G51', 'isValidCode': True, 'coordinate': [787, 541]}, '33': {'rememberedCode': 'X21', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'X21', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'X21', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'S17', 'isValidCode': True, 'coordinate': [684, 297]}, '37': {'rememberedCode': 'F52', 'isValidCode': True, 'coordinate': [1131, 629]}, '38': {'rememberedCode': 'F52', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'F52', 'isValidCode': True, 'coordinate': [1131, 629]}}",531,262
diff --git a/VisRecall/answer_raw/group5.csv b/VisRecall/answer_raw/group5.csv
new file mode 100755
index 0000000..8e9cb03
--- /dev/null
+++ b/VisRecall/answer_raw/group5.csv
@@ -0,0 +1,34 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+0,"['D', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'A', 'C', 'D', 'D', 'B', 'D', 'A', 'D', 'D', 'A', 'A', 'A', 'C', 'D', 'B', 'C', 'D', 'D', 'D', 'A', 'D', 'D', 'B', 'D', 'A', 'A', 'A', 'D', 'D', 'A', 'A', 'A', 'A', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'A', 'A', 'C', 'A', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'C', 'A', 'D', 'D', 'D', 'A', 'A', 'A', 'D', 'A', 'A', 'D', 'A', 'C', 'D', 'A', 'B', 'A', 'A', 'A']","['2', '2', '2', '1', '1', '2', '2', '2', '1', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '1', '2']","{'openTime': '2021-08-17T10:27:48.349Z', 'loadTime': '2021-08-17T10:27:50.646Z', 'startTime': '2021-08-17T10:27:54.380Z', 'endTime': '2021-08-17T10:48:05.802Z', 'timeToCompleteFromOpenMsec': 1217453, 'timeToLoadMsec': 2297, 'timeToCompleteFromStartMsec': 1211422}","{'gender': 'male', 'ageGroup': 'ageGroup65', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'i&#39;ve been doing work on m-turk for 7 years and i have to tell you that this hit, and especially the first half, was one of the most difficult i&#39;ve ever done.  this was wild.  but i promise you that i tried my hardest.  after a while my strategy was just to remember one or two facts, especially the heading.  WOW'}","{'0': {'rememberedCode': 'F23', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'K39', 'isValidCode': True, 'coordinate': [662, 740]}, '2': {'rememberedCode': 'K39', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'K39', 'isValidCode': True, 'coordinate': [662, 740]}, '4': {'rememberedCode': 'C17', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'X17', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'X17', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'X17', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'W15', 'isValidCode': True, 'coordinate': [637, 129]}, '9': {'rememberedCode': 'B15', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'B15', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'B15', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'R16', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'R16', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'R16', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'W15', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [653, 447]}, '18': {'rememberedCode': 'A28', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [653, 447]}, '20': {'rememberedCode': 'A17', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'A17', 'isValidCode': True, 'coordinate': [483, 148]}, '22': {'rememberedCode': 'A17', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'A17', 'isValidCode': True, 'coordinate': [483, 148]}, '24': {'rememberedCode': 'M18', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'C28', 'isValidCode': True, 'coordinate': [867, 624]}, '26': {'rememberedCode': 'C28', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'C28', 'isValidCode': True, 'coordinate': [867, 624]}, '28': {'rememberedCode': 'Y29', 'isValidCode': True, 'coordinate': [1070, 744]}, '29': {'rememberedCode': 'P27', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'P27', 'isValidCode': True, 'coordinate': [773, 339]}, '31': {'rememberedCode': 'P27', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [689, 633]}, '33': {'rememberedCode': 'X38', 'isValidCode': True, 'coordinate': [501, 600]}, '34': {'rememberedCode': 'X38', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'X38', 'isValidCode': True, 'coordinate': [501, 600]}, '36': {'rememberedCode': 'J17', 'isValidCode': True, 'coordinate': [618, 429]}, '37': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}}",368,834
+1,"['B', 'C', 'C', 'A', 'C', 'A', 'D', 'C', 'A', 'D', 'A', 'B', 'A', 'D', 'C', 'A', 'C', 'C', 'B', 'D', 'A', 'B', 'A', 'D', 'D', 'A', 'A', 'A', 'B', 'D', 'B', 'B', 'A', 'B', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'B', 'A', 'B', 'D', 'A', 'C', 'A', 'D', 'D', 'A', 'A', 'B', 'C', 'B', 'A', 'B', 'D', 'D', 'D', 'A', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'D', 'B', 'A', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'A', 'B', 'A', 'D', 'C', 'B', 'A', 'A', 'B', 'C', 'C', 'B', 'A', 'A', 'D', 'C', 'D']","['2', '1', '2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '2', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '2', '2']","{'openTime': '2021-08-17T14:01:15.988Z', 'loadTime': '2021-08-17T14:01:35.569Z', 'startTime': '2021-08-17T14:01:37.869Z', 'endTime': '2021-08-17T14:26:27.524Z', 'timeToCompleteFromOpenMsec': 1511536, 'timeToLoadMsec': 19581, 'timeToCompleteFromStartMsec': 1489655}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'sometimes interact...'}","{'0': {'rememberedCode': 'P53', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'E94', 'isValidCode': True, 'coordinate': [1024, 1126]}, '2': {'rememberedCode': 'E94', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'E94', 'isValidCode': True, 'coordinate': [1024, 1126]}, '4': {'rememberedCode': 'Y51', 'isValidCode': True, 'coordinate': [1098, 1109]}, '5': {'rememberedCode': 'B89', 'isValidCode': True, 'coordinate': [199, 1230]}, '6': {'rememberedCode': 'B89', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'B89', 'isValidCode': True, 'coordinate': [199, 1230]}, '8': {'rememberedCode': 'D27', 'isValidCode': True, 'coordinate': [312, 768]}, '9': {'rememberedCode': 'Y64', 'isValidCode': True, 'coordinate': [1299, 920]}, '10': {'rememberedCode': 'Y64', 'isValidCode': True, 'coordinate': [1679, 663]}, '11': {'rememberedCode': 'Y64', 'isValidCode': True, 'coordinate': [1299, 920]}, '12': {'rememberedCode': 'H76', 'isValidCode': True, 'coordinate': [792, 1070]}, '13': {'rememberedCode': 'W67', 'isValidCode': True, 'coordinate': [820, 715]}, '14': {'rememberedCode': 'W67', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'W67', 'isValidCode': True, 'coordinate': [820, 715]}, '16': {'rememberedCode': 'Z28', 'isValidCode': True, 'coordinate': [992, 1048]}, '17': {'rememberedCode': 'G81', 'isValidCode': True, 'coordinate': [940, 323]}, '18': {'rememberedCode': 'G81', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'G81', 'isValidCode': True, 'coordinate': [940, 323]}, '20': {'rememberedCode': 'R18', 'isValidCode': True, 'coordinate': [481, 729]}, '21': {'rememberedCode': 'T41', 'isValidCode': True, 'coordinate': [778, 342]}, '22': {'rememberedCode': 'T41', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'T41', 'isValidCode': True, 'coordinate': [778, 342]}, '24': {'rememberedCode': 'R24', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'T65', 'isValidCode': True, 'coordinate': [1273, 465]}, '26': {'rememberedCode': 'T65', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'T65', 'isValidCode': True, 'coordinate': [1273, 465]}, '28': {'rememberedCode': 'G52', 'isValidCode': True, 'coordinate': [1086, 670]}, '29': {'rememberedCode': 'C21', 'isValidCode': True, 'coordinate': [861, 822]}, '30': {'rememberedCode': 'C21', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C21', 'isValidCode': True, 'coordinate': [861, 822]}, '32': {'rememberedCode': 'N95', 'isValidCode': True, 'coordinate': [1308, 319]}, '33': {'rememberedCode': 'D23', 'isValidCode': True, 'coordinate': [1000, 521]}, '34': {'rememberedCode': 'D23', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D23', 'isValidCode': True, 'coordinate': [1000, 521]}, '36': {'rememberedCode': 'H46', 'isValidCode': True, 'coordinate': [1128, 446]}, '37': {'rememberedCode': 'L21', 'isValidCode': True, 'coordinate': [702, 126]}, '38': {'rememberedCode': 'L21', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L21', 'isValidCode': True, 'coordinate': [702, 126]}}",457,422
+2,"['B', 'C', 'C', 'B', 'A', 'A', 'B', 'C', 'D', 'D', 'A', 'B', 'D', 'D', 'C', 'A', 'D', 'C', 'B', 'D', 'A', 'D', 'A', 'B', 'D', 'A', 'D', 'A', 'B', 'D', 'B', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'A', 'D', 'D', 'C', 'D', 'A', 'A', 'C', 'D', 'D', 'A', 'D', 'A', 'B', 'D', 'A', 'B', 'C', 'B', 'A', 'A', 'D', 'D', 'C', 'D', 'A', 'A', 'D', 'D', 'A', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'C', 'B', 'A', 'C', 'B', 'C', 'A', 'A', 'A', 'B', 'A', 'D', 'D', 'A', 'A', 'D', 'D', 'D']","['2', '1', '1', '1', '2', '1', '2', '1', '1', '2', '2', '2', '1', '2', '2', '2', '2', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '2']","{'openTime': '2021-08-19T14:29:01.984Z', 'loadTime': '2021-08-19T14:29:22.004Z', 'startTime': '2021-08-19T14:29:22.582Z', 'endTime': '2021-08-19T14:45:16.357Z', 'timeToCompleteFromOpenMsec': 974373, 'timeToLoadMsec': 20020, 'timeToCompleteFromStartMsec': 953775}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'F26', 'isValidCode': True, 'coordinate': [1145, 50]}, '1': {'rememberedCode': 'J42', 'isValidCode': True, 'coordinate': [520, 440]}, '2': {'rememberedCode': 'J42', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J42', 'isValidCode': True, 'coordinate': [520, 440]}, '4': {'rememberedCode': 'Y97', 'isValidCode': True, 'coordinate': [644, 604]}, '5': {'rememberedCode': 'A86', 'isValidCode': True, 'coordinate': [1127, 127]}, '6': {'rememberedCode': 'A86', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'A86', 'isValidCode': True, 'coordinate': [1127, 127]}, '8': {'rememberedCode': 'Y48', 'isValidCode': True, 'coordinate': [936, 455]}, '9': {'rememberedCode': 'K95', 'isValidCode': True, 'coordinate': [829, 826]}, '10': {'rememberedCode': 'K95', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K95', 'isValidCode': True, 'coordinate': [829, 826]}, '12': {'rememberedCode': 'G13', 'isValidCode': True, 'coordinate': [791, 253]}, '13': {'rememberedCode': 'F31', 'isValidCode': True, 'coordinate': [480, 330]}, '14': {'rememberedCode': 'F31', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F31', 'isValidCode': True, 'coordinate': [480, 330]}, '16': {'rememberedCode': 'C31', 'isValidCode': True, 'coordinate': [1472, 434]}, '17': {'rememberedCode': 'M38', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'M38', 'isValidCode': True, 'coordinate': [205, 238]}, '19': {'rememberedCode': 'M38', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'V23', 'isValidCode': True, 'coordinate': [302, 722]}, '21': {'rememberedCode': 'R74', 'isValidCode': True, 'coordinate': [524, 545]}, '22': {'rememberedCode': 'R74', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'R74', 'isValidCode': True, 'coordinate': [524, 545]}, '24': {'rememberedCode': 'A93', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'Y28', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'Y28', 'isValidCode': True, 'coordinate': [996, 1248]}, '27': {'rememberedCode': 'Y28', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'Y95', 'isValidCode': True, 'coordinate': [1283, 564]}, '29': {'rememberedCode': 'R45', 'isValidCode': True, 'coordinate': [1157, 731]}, '30': {'rememberedCode': 'R45', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'R45', 'isValidCode': True, 'coordinate': [1157, 731]}, '32': {'rememberedCode': 'A91', 'isValidCode': True, 'coordinate': [1310, 524]}, '33': {'rememberedCode': 'N26', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'N26', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'N26', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'M56', 'isValidCode': True, 'coordinate': [350, 1107]}, '37': {'rememberedCode': 'G79', 'isValidCode': True, 'coordinate': [822, 846]}, '38': {'rememberedCode': 'G79', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G79', 'isValidCode': True, 'coordinate': [822, 846]}}",900,769
+3,"['B', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'C', 'A', 'C', 'A', 'B', 'A', 'A', 'C', 'C', 'B', 'A', 'A', 'A', 'C', 'B', 'C', 'A', 'A', 'A', 'B', 'C', 'B', 'B', 'C', 'B', 'A', 'A', 'C', 'B', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'A', 'B', 'C', 'A', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'A', 'B', 'C', 'A', 'B', 'A', 'A', 'C', 'C', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'A', 'C', 'B', 'B', 'C', 'A', 'C', 'A', 'C', 'A', 'B', 'C', 'C', 'C', 'A', 'A', 'B', 'C', 'B']","['2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '1', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '2']","{'openTime': '2021-08-19T14:29:12.584Z', 'loadTime': '2021-08-19T14:29:37.420Z', 'startTime': '2021-08-19T14:29:38.953Z', 'endTime': '2021-08-19T14:55:38.789Z', 'timeToCompleteFromOpenMsec': 1586205, 'timeToLoadMsec': 24836, 'timeToCompleteFromStartMsec': 1559836}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'A57', 'isValidCode': True, 'coordinate': [1128, 723]}, '1': {'rememberedCode': 'B61', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'B61', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'B61', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'W54', 'isValidCode': True, 'coordinate': [766, 1030]}, '5': {'rememberedCode': 'Z39', 'isValidCode': True, 'coordinate': [188, 128]}, '6': {'rememberedCode': 'Z39', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Z39', 'isValidCode': True, 'coordinate': [188, 128]}, '8': {'rememberedCode': 'K93', 'isValidCode': True, 'coordinate': [303, 55]}, '9': {'rememberedCode': 'J29', 'isValidCode': True, 'coordinate': [201, 245]}, '10': {'rememberedCode': 'J29', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'J29', 'isValidCode': True, 'coordinate': [201, 245]}, '12': {'rememberedCode': 'S17', 'isValidCode': True, 'coordinate': [478, 1058]}, '13': {'rememberedCode': 'R65', 'isValidCode': True, 'coordinate': [361, 143]}, '14': {'rememberedCode': 'R65', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'R65', 'isValidCode': True, 'coordinate': [361, 143]}, '16': {'rememberedCode': 'M38', 'isValidCode': True, 'coordinate': [205, 238]}, '17': {'rememberedCode': 'F24', 'isValidCode': True, 'coordinate': [974, 250]}, '18': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F24', 'isValidCode': True, 'coordinate': [974, 250]}, '20': {'rememberedCode': 'R39', 'isValidCode': True, 'coordinate': [1429, 740]}, '21': {'rememberedCode': 'A17', 'isValidCode': True, 'coordinate': [483, 148]}, '22': {'rememberedCode': 'A17', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'A17', 'isValidCode': True, 'coordinate': [483, 148]}, '24': {'rememberedCode': 'Y93', 'isValidCode': True, 'coordinate': [368, 668]}, '25': {'rememberedCode': 'B57', 'isValidCode': True, 'coordinate': [656, 165]}, '26': {'rememberedCode': 'B57', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'B57', 'isValidCode': True, 'coordinate': [656, 165]}, '28': {'rememberedCode': 'C43', 'isValidCode': True, 'coordinate': [1116, 446]}, '29': {'rememberedCode': 'D85', 'isValidCode': True, 'coordinate': [980, 662]}, '30': {'rememberedCode': 'D85', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'D85', 'isValidCode': True, 'coordinate': [980, 662]}, '32': {'rememberedCode': 'P53', 'isValidCode': True, 'coordinate': [1325, 917]}, '33': {'rememberedCode': 'X17', 'isValidCode': True, 'coordinate': [1451, 389]}, '34': {'rememberedCode': 'X17', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'X17', 'isValidCode': True, 'coordinate': [1451, 389]}, '36': {'rememberedCode': 'X89', 'isValidCode': True, 'coordinate': [465, 129]}, '37': {'rememberedCode': 'E75', 'isValidCode': True, 'coordinate': [360, 120]}, '38': {'rememberedCode': 'E75', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E75', 'isValidCode': True, 'coordinate': [360, 120]}}",722,992
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+5,"['B', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'C', 'C', 'B', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'A', 'A', 'C', 'C', 'B', 'B', 'A', 'B', 'C', 'A', 'A', 'B', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'C', 'A', 'B', 'A', 'A', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'A', 'C', 'B', 'C', 'A', 'A', 'C', 'A', 'A', 'A', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'B', 'C', 'A', 'B', 'C', 'C', 'B', 'A', 'A', 'C', 'B', 'A']","['2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '2', '2', '1', '1', '2', '1', '2', '1', '1', '2', '2', '1', '1']","{'openTime': '2021-08-19T14:31:39.083Z', 'loadTime': '2021-08-19T14:31:55.496Z', 'startTime': '2021-08-19T14:31:56.697Z', 'endTime': '2021-08-19T14:59:34.877Z', 'timeToCompleteFromOpenMsec': 1675794, 'timeToLoadMsec': 16413, 'timeToCompleteFromStartMsec': 1658180}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'V47', 'isValidCode': True, 'coordinate': [802, 421]}, '1': {'rememberedCode': 'C83', 'isValidCode': True, 'coordinate': [699, 433]}, '2': {'rememberedCode': 'C83', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'C83', 'isValidCode': True, 'coordinate': [699, 433]}, '4': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [657, 301]}, '5': {'rememberedCode': 'B65', 'isValidCode': True, 'coordinate': [970, 554]}, '6': {'rememberedCode': 'B65', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'B65', 'isValidCode': True, 'coordinate': [970, 554]}, '8': {'rememberedCode': 'P28', 'isValidCode': True, 'coordinate': [767, 428]}, '9': {'rememberedCode': 'Z16', 'isValidCode': True, 'coordinate': [836, 623]}, '10': {'rememberedCode': 'Z16', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Z16', 'isValidCode': True, 'coordinate': [836, 623]}, '12': {'rememberedCode': 'G13', 'isValidCode': True, 'coordinate': [791, 253]}, '13': {'rememberedCode': 'H19', 'isValidCode': True, 'coordinate': [682, 544]}, '14': {'rememberedCode': 'H19', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'H19', 'isValidCode': True, 'coordinate': [682, 544]}, '16': {'rememberedCode': 'F63', 'isValidCode': True, 'coordinate': [967, 437]}, '17': {'rememberedCode': 'F29', 'isValidCode': True, 'coordinate': [795, 528]}, '18': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F29', 'isValidCode': True, 'coordinate': [795, 528]}, '20': {'rememberedCode': 'P74', 'isValidCode': True, 'coordinate': [645, 441]}, '21': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [795, 544]}, '22': {'rememberedCode': 'F45', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [795, 544]}, '24': {'rememberedCode': 'F32', 'isValidCode': True, 'coordinate': [1147, 833]}, '25': {'rememberedCode': 'G45', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'G45', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G45', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'Y29', 'isValidCode': True, 'coordinate': [1070, 744]}, '29': {'rememberedCode': 'D23', 'isValidCode': True, 'coordinate': [724, 451]}, '30': {'rememberedCode': 'D23', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'D23', 'isValidCode': True, 'coordinate': [724, 451]}, '32': {'rememberedCode': 'K43', 'isValidCode': True, 'coordinate': [856, 339]}, '33': {'rememberedCode': 'V92', 'isValidCode': True, 'coordinate': [845, 515]}, '34': {'rememberedCode': 'V92', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'V92', 'isValidCode': True, 'coordinate': [845, 515]}, '36': {'rememberedCode': 'Q89', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'D41', 'isValidCode': True, 'coordinate': [723, 514]}, '38': {'rememberedCode': 'D41', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'D41', 'isValidCode': True, 'coordinate': [723, 514]}}",757,636
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+11,"['B', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'B', 'D', 'A', 'B', 'D', 'D', 'A', 'A', 'C', 'C', 'B', 'D', 'A', 'C', 'A', 'D', 'D', 'A', 'A', 'A', 'D', 'C', 'B', 'B', 'D', 'D', 'D', 'A', 'A', 'B', 'D', 'D', 'A', 'B', 'A', 'B', 'D', 'A', 'C', 'A', 'B', 'D', 'A', 'A', 'C', 'A', 'B', 'A', 'B', 'D', 'B', 'D', 'A', 'B', 'C', 'D', 'A', 'A', 'A', 'A', 'C', 'B', 'A', 'A', 'C', 'A', 'D', 'A', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'C', 'D', 'B', 'A', 'A', 'D', 'B', 'C']","['2', '1', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1']","{'openTime': '2021-08-19T14:41:40.402Z', 'loadTime': '2021-08-19T14:42:16.226Z', 'startTime': '2021-08-19T14:42:24.691Z', 'endTime': '2021-08-19T15:20:42.913Z', 'timeToCompleteFromOpenMsec': 2342511, 'timeToLoadMsec': 35824, 'timeToCompleteFromStartMsec': 2298222}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'It was a great study about memory and concentration power and who is are all well known about graph reader. '}","{'0': {'rememberedCode': 'H92', 'isValidCode': True, 'coordinate': [1564, 1232]}, '1': {'rememberedCode': 'U76', 'isValidCode': True, 'coordinate': [1622, 1039]}, '2': {'rememberedCode': 'U76', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'U76', 'isValidCode': True, 'coordinate': [1622, 1039]}, '4': {'rememberedCode': 'V79', 'isValidCode': True, 'coordinate': [1570, 1238]}, '5': {'rememberedCode': 'R89', 'isValidCode': True, 'coordinate': [1582, 1225]}, '6': {'rememberedCode': 'R89', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'R89', 'isValidCode': True, 'coordinate': [1582, 1225]}, '8': {'rememberedCode': 'E53', 'isValidCode': True, 'coordinate': [1684, 1235]}, '9': {'rememberedCode': 'J94', 'isValidCode': True, 'coordinate': [1562, 1214]}, '10': {'rememberedCode': 'J94', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'J94', 'isValidCode': True, 'coordinate': [1562, 1214]}, '12': {'rememberedCode': 'F41', 'isValidCode': True, 'coordinate': [1608, 1159]}, '13': {'rememberedCode': 'G95', 'isValidCode': True, 'coordinate': [1574, 1228]}, '14': {'rememberedCode': 'G95', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'G95', 'isValidCode': True, 'coordinate': [1574, 1228]}, '16': {'rememberedCode': 'V49', 'isValidCode': True, 'coordinate': [1571, 1207]}, '17': {'rememberedCode': 'A43', 'isValidCode': True, 'coordinate': [1588, 1125]}, '18': {'rememberedCode': 'A43', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'A43', 'isValidCode': True, 'coordinate': [1588, 1125]}, '20': {'rememberedCode': 'L84', 'isValidCode': True, 'coordinate': [1530, 1222]}, '21': {'rememberedCode': 'F21', 'isValidCode': True, 'coordinate': [1599, 1234]}, '22': {'rememberedCode': 'F21', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'F21', 'isValidCode': True, 'coordinate': [1599, 1234]}, '24': {'rememberedCode': 'M58', 'isValidCode': True, 'coordinate': [1608, 1169]}, '25': {'rememberedCode': 'N56', 'isValidCode': True, 'coordinate': [1621, 1165]}, '26': {'rememberedCode': 'N56', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'N56', 'isValidCode': True, 'coordinate': [1621, 1165]}, '28': {'rememberedCode': 'L21', 'isValidCode': True, 'coordinate': [1544, 1267]}, '29': {'rememberedCode': 'N94', 'isValidCode': True, 'coordinate': [1614, 1259]}, '30': {'rememberedCode': 'N94', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'N94', 'isValidCode': True, 'coordinate': [1614, 1259]}, '32': {'rememberedCode': 'D48', 'isValidCode': True, 'coordinate': [1616, 1230]}, '33': {'rememberedCode': 'L59', 'isValidCode': True, 'coordinate': [1591, 1222]}, '34': {'rememberedCode': 'L59', 'isValidCode': True, 'coordinate': [988, 443]}, '35': {'rememberedCode': 'L59', 'isValidCode': True, 'coordinate': [1591, 1222]}, '36': {'rememberedCode': 'Q75', 'isValidCode': True, 'coordinate': [1578, 1247]}, '37': {'rememberedCode': 'V32', 'isValidCode': True, 'coordinate': [1606, 1244]}, '38': {'rememberedCode': 'V32', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'V32', 'isValidCode': True, 'coordinate': [1606, 1244]}}",611,652
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+13,"['B', 'C', 'A', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'B', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'C', 'B', 'C', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'C', 'B', 'C', 'A', 'C', 'B', 'A', 'C', 'B', 'C', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B']","['2', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']","{'openTime': '2021-08-19T15:06:30.933Z', 'loadTime': '2021-08-19T15:06:36.408Z', 'startTime': '2021-08-19T15:06:53.096Z', 'endTime': '2021-08-19T15:26:40.240Z', 'timeToCompleteFromOpenMsec': 1209307, 'timeToLoadMsec': 5475, 'timeToCompleteFromStartMsec': 1187144}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'nice'}","{'0': {'rememberedCode': 'K93', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': '', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': '', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': '', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'T63', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'N49', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'N49', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N49', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'L38', 'isValidCode': True, 'coordinate': [1405, 457]}, '9': {'rememberedCode': 'A74', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'A74', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'A74', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'C78', 'isValidCode': True, 'coordinate': [956, 628]}, '13': {'rememberedCode': 'P92', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'P92', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'P92', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'H15', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'M67', 'isValidCode': True, 'coordinate': [811, 535]}, '19': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'K34', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'U69', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'U69', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U69', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'P58', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'M47', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'M47', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'M47', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'N48', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'K73', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'K73', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'K73', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'B78', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'W65', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'W65', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'W65', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'E13', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'R48', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'R48', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'R48', 'isValidCode': False, 'coordinate': False}}",849,819
+14,"['B', 'C', 'C', 'A', 'C', 'A', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'C', 'A', 'A', 'C', 'C', 'B', 'D', 'A', 'C', 'A', 'D', 'A', 'A', 'A', 'A', 'C', 'C', 'B', 'B', 'A', 'B', 'D', 'A', 'A', 'B', 'D', 'C', 'A', 'B', 'A', 'B', 'D', 'A', 'C', 'A', 'B', 'A', 'A', 'D', 'D', 'D', 'D', 'A', 'B', 'C', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'A', 'C', 'A', 'A', 'C', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'B', 'A', 'A', 'D', 'C', 'B', 'C', 'A', 'B', 'D', 'D', 'D', 'A', 'A', 'D', 'B', 'C']","['2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '1', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '2', '2']","{'openTime': '2021-08-19T14:45:24.026Z', 'loadTime': '2021-08-19T14:45:39.244Z', 'startTime': '2021-08-19T14:53:04.911Z', 'endTime': '2021-08-19T15:27:06.088Z', 'timeToCompleteFromOpenMsec': 2502062, 'timeToLoadMsec': 15218, 'timeToCompleteFromStartMsec': 2041177}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'L96', 'isValidCode': True, 'coordinate': [812, 331]}, '1': {'rememberedCode': 'Q34', 'isValidCode': True, 'coordinate': [820, 421]}, '2': {'rememberedCode': 'Q34', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Q34', 'isValidCode': True, 'coordinate': [820, 421]}, '4': {'rememberedCode': 'J12', 'isValidCode': True, 'coordinate': [931, 438]}, '5': {'rememberedCode': 'T61', 'isValidCode': True, 'coordinate': [835, 234]}, '6': {'rememberedCode': 'T61', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'T61', 'isValidCode': True, 'coordinate': [835, 234]}, '8': {'rememberedCode': 'F87', 'isValidCode': True, 'coordinate': [913, 245]}, '9': {'rememberedCode': 'Y46', 'isValidCode': True, 'coordinate': [661, 316]}, '10': {'rememberedCode': 'Y46', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Y46', 'isValidCode': True, 'coordinate': [661, 316]}, '12': {'rememberedCode': 'G36', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'U61', 'isValidCode': True, 'coordinate': [796, 345]}, '14': {'rememberedCode': 'U61', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'U61', 'isValidCode': True, 'coordinate': [796, 345]}, '16': {'rememberedCode': 'F35', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'Z47', 'isValidCode': True, 'coordinate': [798, 431]}, '18': {'rememberedCode': 'Z47', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Z47', 'isValidCode': True, 'coordinate': [798, 431]}, '20': {'rememberedCode': 'T89', 'isValidCode': True, 'coordinate': [809, 502]}, '21': {'rememberedCode': 'W14', 'isValidCode': True, 'coordinate': [967, 336]}, '22': {'rememberedCode': 'W14', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'W14', 'isValidCode': True, 'coordinate': [967, 336]}, '24': {'rememberedCode': 'D31', 'isValidCode': True, 'coordinate': [840, 470]}, '25': {'rememberedCode': 'A51', 'isValidCode': True, 'coordinate': [848, 447]}, '26': {'rememberedCode': 'A51', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'A51', 'isValidCode': True, 'coordinate': [848, 447]}, '28': {'rememberedCode': 'H57', 'isValidCode': True, 'coordinate': [764, 551]}, '29': {'rememberedCode': 'C48', 'isValidCode': True, 'coordinate': [719, 853]}, '30': {'rememberedCode': 'C48', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C48', 'isValidCode': True, 'coordinate': [719, 853]}, '32': {'rememberedCode': 'R54', 'isValidCode': True, 'coordinate': [969, 632]}, '33': {'rememberedCode': 'H36', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'H36', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'H36', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'D54', 'isValidCode': True, 'coordinate': [973, 235]}, '37': {'rememberedCode': 'V34', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'V34', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'V34', 'isValidCode': False, 'coordinate': False}}",792,948
+15,"['B', 'C', 'C', 'A', 'B', 'A', 'D', 'B', 'D', 'B', 'A', 'B', 'D', 'D', 'C', 'A', 'D', 'C', 'B', 'D', 'A', 'B', 'A', 'B', 'D', 'A', 'D', 'A', 'B', 'C', 'B', 'B', 'A', 'B', 'D', 'A', 'A', 'D', 'B', 'C', 'A', 'D', 'A', 'B', 'C', 'A', 'C', 'A', 'D', 'D', 'A', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'D', 'D', 'A', 'B', 'C', 'C', 'B', 'A', 'D', 'D', 'A', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'A', 'C', 'D', 'B', 'A', 'C', 'C', 'B', 'C', 'A', 'C', 'C', 'C', 'B', 'A', 'A', 'C', 'B', 'D']","['2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '2']","{'openTime': '2021-08-19T15:01:06.110Z', 'loadTime': '2021-08-19T15:02:33.535Z', 'startTime': '2021-08-19T15:02:36.066Z', 'endTime': '2021-08-19T15:28:17.525Z', 'timeToCompleteFromOpenMsec': 1631415, 'timeToLoadMsec': 87425, 'timeToCompleteFromStartMsec': 1541459}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'I apologize if this is the same group of charts from a previous hit, but I accepted and worked on the hit because the pay is much better and because I suspect this was the chart group that had many errors that I pointed out in feedback. Email me for details if you like.'}","{'0': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [826, 639]}, '1': {'rememberedCode': 'S41', 'isValidCode': True, 'coordinate': [66, 619]}, '2': {'rememberedCode': 'S41', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'S41', 'isValidCode': True, 'coordinate': [66, 619]}, '4': {'rememberedCode': 'R34', 'isValidCode': True, 'coordinate': [673, 1200]}, '5': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [681, 235]}, '6': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [657, 301]}, '7': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [681, 235]}, '8': {'rememberedCode': 'U83', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'Z39', 'isValidCode': True, 'coordinate': [1275, 714]}, '10': {'rememberedCode': 'Z39', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Z39', 'isValidCode': True, 'coordinate': [1275, 714]}, '12': {'rememberedCode': 'E57', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'H88', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'H88', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'H88', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'M38', 'isValidCode': True, 'coordinate': [205, 238]}, '17': {'rememberedCode': 'F39', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'F39', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F39', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'Y92', 'isValidCode': True, 'coordinate': [1068, 712]}, '21': {'rememberedCode': 'W54', 'isValidCode': True, 'coordinate': [982, 267]}, '22': {'rememberedCode': 'W54', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'W54', 'isValidCode': True, 'coordinate': [982, 267]}, '24': {'rememberedCode': 'A84', 'isValidCode': True, 'coordinate': [658, 566]}, '25': {'rememberedCode': 'T65', 'isValidCode': True, 'coordinate': [1273, 465]}, '26': {'rememberedCode': 'T65', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'T65', 'isValidCode': True, 'coordinate': [1273, 465]}, '28': {'rememberedCode': 'L13', 'isValidCode': True, 'coordinate': [974, 943]}, '29': {'rememberedCode': 'B63', 'isValidCode': True, 'coordinate': [854, 632]}, '30': {'rememberedCode': 'B63', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'B63', 'isValidCode': True, 'coordinate': [854, 632]}, '32': {'rememberedCode': 'G94', 'isValidCode': True, 'coordinate': [1178, 348]}, '33': {'rememberedCode': 'W29', 'isValidCode': True, 'coordinate': [802, 602]}, '34': {'rememberedCode': 'W29', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'W29', 'isValidCode': True, 'coordinate': [802, 602]}, '36': {'rememberedCode': 'X17', 'isValidCode': True, 'coordinate': [1138, 248]}, '37': {'rememberedCode': 'K63', 'isValidCode': True, 'coordinate': [876, 642]}, '38': {'rememberedCode': 'K63', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K63', 'isValidCode': True, 'coordinate': [876, 642]}}",248,728
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+17,"['A', 'C', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'B', 'C', 'A', 'C', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'C', 'B', 'B']","['2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']","{'openTime': '2021-08-19T15:16:09.574Z', 'loadTime': '2021-08-19T15:16:47.847Z', 'startTime': '2021-08-19T15:16:49.301Z', 'endTime': '2021-08-19T15:30:21.868Z', 'timeToCompleteFromOpenMsec': 852294, 'timeToLoadMsec': 38273, 'timeToCompleteFromStartMsec': 812567}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'professional', 'feedback': 'good task'}","{'0': {'rememberedCode': 'Z42', 'isValidCode': True, 'coordinate': [1088, 433]}, '1': {'rememberedCode': 'X41', 'isValidCode': True, 'coordinate': [356, 136]}, '2': {'rememberedCode': 'X41', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'X41', 'isValidCode': True, 'coordinate': [356, 136]}, '4': {'rememberedCode': 'J12', 'isValidCode': True, 'coordinate': [931, 438]}, '5': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'C12', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'X31', 'isValidCode': True, 'coordinate': [291, 446]}, '9': {'rememberedCode': 'K47', 'isValidCode': True, 'coordinate': [672, 744]}, '10': {'rememberedCode': 'K47', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K47', 'isValidCode': True, 'coordinate': [672, 744]}, '12': {'rememberedCode': 'E67', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'N24', 'isValidCode': True, 'coordinate': [1096, 216]}, '14': {'rememberedCode': 'N24', 'isValidCode': True, 'coordinate': [476, 362]}, '15': {'rememberedCode': 'N24', 'isValidCode': True, 'coordinate': [1096, 216]}, '16': {'rememberedCode': 'W13', 'isValidCode': True, 'coordinate': [806, 311]}, '17': {'rememberedCode': 'V75', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'V75', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'V75', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'Y92', 'isValidCode': True, 'coordinate': [1068, 712]}, '21': {'rememberedCode': 'G85', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'G85', 'isValidCode': True, 'coordinate': [619, 307]}, '23': {'rememberedCode': 'G85', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'W87', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'A51', 'isValidCode': True, 'coordinate': [848, 447]}, '26': {'rememberedCode': 'A51', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'A51', 'isValidCode': True, 'coordinate': [848, 447]}, '28': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [788, 468]}, '29': {'rememberedCode': 'Q78', 'isValidCode': True, 'coordinate': [875, 361]}, '30': {'rememberedCode': 'Q78', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Q78', 'isValidCode': True, 'coordinate': [875, 361]}, '32': {'rememberedCode': 'J15', 'isValidCode': True, 'coordinate': [971, 542]}, '33': {'rememberedCode': 'K46', 'isValidCode': True, 'coordinate': [1003, 325]}, '34': {'rememberedCode': 'K46', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'K46', 'isValidCode': True, 'coordinate': [1003, 325]}, '36': {'rememberedCode': 'J92', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'H12', 'isValidCode': True, 'coordinate': [986, 213]}, '38': {'rememberedCode': 'H12', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'H12', 'isValidCode': True, 'coordinate': [986, 213]}}",288,30
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+30,"['B', 'A', 'D', 'D', 'B', 'A', 'B', 'B', 'B', 'B', 'A', 'B', 'C', 'C', 'D', 'A', 'C', 'C', 'C', 'D', 'A', 'C', 'A', 'D', 'D', 'A', 'A', 'A', 'D', 'A', 'B', 'B', 'A', 'B', 'D', 'A', 'A', 'B', 'D', 'D', 'A', 'B', 'A', 'B', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'A', 'C', 'A', 'B', 'A', 'D', 'D', 'B', 'D', 'A', 'B', 'C', 'D', 'D', 'A', 'D', 'D', 'D', 'C', 'A', 'A', 'B', 'A', 'D', 'A', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'A', 'A', 'A', 'A', 'D', 'D', 'C', 'A', 'B', 'C', 'C', 'D', 'A', 'A', 'D', 'B', 'D']","['2', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '2']","{'openTime': '2021-08-19T17:20:34.512Z', 'loadTime': '2021-08-19T17:20:50.766Z', 'startTime': '2021-08-19T17:20:52.652Z', 'endTime': '2021-08-19T17:46:39.503Z', 'timeToCompleteFromOpenMsec': 1564991, 'timeToLoadMsec': 16254, 'timeToCompleteFromStartMsec': 1546851}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'X63', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'C83', 'isValidCode': True, 'coordinate': [699, 433]}, '2': {'rememberedCode': 'C83', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'C83', 'isValidCode': True, 'coordinate': [699, 433]}, '4': {'rememberedCode': 'Y84', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [681, 235]}, '6': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [657, 301]}, '7': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [681, 235]}, '8': {'rememberedCode': 'U71', 'isValidCode': True, 'coordinate': [338, 544]}, '9': {'rememberedCode': 'U71', 'isValidCode': True, 'coordinate': [802, 428]}, '10': {'rememberedCode': 'U71', 'isValidCode': True, 'coordinate': [338, 544]}, '11': {'rememberedCode': 'U71', 'isValidCode': True, 'coordinate': [802, 428]}, '12': {'rememberedCode': 'A91', 'isValidCode': True, 'coordinate': [825, 357]}, '13': {'rememberedCode': 'R47', 'isValidCode': True, 'coordinate': [945, 328]}, '14': {'rememberedCode': 'R47', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'R47', 'isValidCode': True, 'coordinate': [945, 328]}, '16': {'rememberedCode': 'P84', 'isValidCode': True, 'coordinate': [210, 44]}, '17': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [653, 447]}, '18': {'rememberedCode': 'A28', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [653, 447]}, '20': {'rememberedCode': 'B32', 'isValidCode': True, 'coordinate': [469, 628]}, '21': {'rememberedCode': 'C15', 'isValidCode': True, 'coordinate': [1400, 455]}, '22': {'rememberedCode': 'C15', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C15', 'isValidCode': True, 'coordinate': [1400, 455]}, '24': {'rememberedCode': 'R71', 'isValidCode': True, 'coordinate': [692, 361]}, '25': {'rememberedCode': 'R14', 'isValidCode': True, 'coordinate': [373, 330]}, '26': {'rememberedCode': 'R14', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'R14', 'isValidCode': True, 'coordinate': [373, 330]}, '28': {'rememberedCode': 'E37', 'isValidCode': True, 'coordinate': [455, 557]}, '29': {'rememberedCode': 'C23', 'isValidCode': True, 'coordinate': [1184, 560]}, '30': {'rememberedCode': 'C23', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C23', 'isValidCode': True, 'coordinate': [1184, 560]}, '32': {'rememberedCode': 'X59', 'isValidCode': True, 'coordinate': [1126, 238]}, '33': {'rememberedCode': 'S57', 'isValidCode': True, 'coordinate': [642, 285]}, '34': {'rememberedCode': 'S57', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'S57', 'isValidCode': True, 'coordinate': [642, 285]}, '36': {'rememberedCode': 'J45', 'isValidCode': True, 'coordinate': [946, 131]}, '37': {'rememberedCode': 'P27', 'isValidCode': True, 'coordinate': [407, 336]}, '38': {'rememberedCode': 'P27', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'P27', 'isValidCode': True, 'coordinate': [407, 336]}}",759,281
+31,"['A', 'B', 'C', 'A', 'C', 'A', 'D', 'B', 'B', 'D', 'C', 'C', 'C', 'A', 'A', 'A', 'A', 'C', 'C', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'A', 'B', 'A', 'A', 'B', 'C', 'D', 'B', 'D', 'B', 'D', 'D', 'D', 'D', 'A', 'A', 'A', 'B', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'A', 'A', 'A', 'D', 'C', 'A', 'A', 'C', 'D', 'A', 'B', 'B', 'C', 'D', 'D', 'D', 'B', 'C', 'D', 'D', 'D', 'C', 'A', 'C', 'A', 'C', 'D', 'A', 'A', 'D', 'C', 'D']","['2', '1', '2', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '1', '2', '1', '1', '2', '1', '1', '2', '1', '1', '2']","{'openTime': '2021-08-20T02:40:05.977Z', 'loadTime': '2021-08-20T02:40:25.078Z', 'startTime': '2021-08-20T02:40:26.175Z', 'endTime': '2021-08-20T03:01:36.287Z', 'timeToCompleteFromOpenMsec': 1290310, 'timeToLoadMsec': 19101, 'timeToCompleteFromStartMsec': 1270112}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'T74', 'isValidCode': True, 'coordinate': [991, 930]}, '1': {'rememberedCode': 'Y29', 'isValidCode': True, 'coordinate': [816, 925]}, '2': {'rememberedCode': 'Y29', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Y29', 'isValidCode': True, 'coordinate': [816, 925]}, '4': {'rememberedCode': 'W31', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'Z21', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'Z21', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Z21', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'H38', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'Z78', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'Z78', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Z78', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'Q38', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'X38', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'X38', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X38', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'U95', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'M14', 'isValidCode': True, 'coordinate': [1401, 445]}, '18': {'rememberedCode': 'M14', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M14', 'isValidCode': True, 'coordinate': [1401, 445]}, '20': {'rememberedCode': 'J67', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'C19', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'C19', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C19', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'M79', 'isValidCode': True, 'coordinate': [703, 234]}, '25': {'rememberedCode': 'V83', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'V83', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'V83', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'S65', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'X83', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'X83', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'X83', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'H38', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'U57', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'U57', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'U57', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'H29', 'isValidCode': True, 'coordinate': [620, 1006]}, '37': {'rememberedCode': 'H32', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'H32', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'H32', 'isValidCode': False, 'coordinate': False}}",78,538
+32,"['B', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'C', 'C', 'B', 'D', 'A', 'C', 'A', 'B', 'D', 'A', 'A', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'D', 'A', 'A', 'B', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'C', 'A', 'B', 'A', 'A', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'A', 'C', 'B', 'C', 'A', 'A', 'B', 'A', 'A', 'A', 'B', 'A', 'C', 'A', 'A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'B', 'C', 'A', 'B', 'C', 'C', 'B', 'A', 'A', 'C', 'B', 'D']","['2', '1', '2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '2', '2', '1', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1']","{'openTime': '2021-08-20T04:33:36.787Z', 'loadTime': '2021-08-20T04:34:02.897Z', 'startTime': '2021-08-20T04:35:09.463Z', 'endTime': '2021-08-20T05:16:19.170Z', 'timeToCompleteFromOpenMsec': 2562383, 'timeToLoadMsec': 26110, 'timeToCompleteFromStartMsec': 2469707}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'F87', 'isValidCode': True, 'coordinate': [956, 427]}, '1': {'rememberedCode': 'Q34', 'isValidCode': True, 'coordinate': [820, 421]}, '2': {'rememberedCode': 'Q34', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Q34', 'isValidCode': True, 'coordinate': [820, 421]}, '4': {'rememberedCode': 'R43', 'isValidCode': True, 'coordinate': [952, 643]}, '5': {'rememberedCode': 'B65', 'isValidCode': True, 'coordinate': [970, 554]}, '6': {'rememberedCode': 'B65', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'B65', 'isValidCode': True, 'coordinate': [970, 554]}, '8': {'rememberedCode': 'V82', 'isValidCode': True, 'coordinate': [1262, 627]}, '9': {'rememberedCode': 'B75', 'isValidCode': True, 'coordinate': [838, 542]}, '10': {'rememberedCode': 'B75', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'B75', 'isValidCode': True, 'coordinate': [838, 542]}, '12': {'rememberedCode': 'D92', 'isValidCode': True, 'coordinate': [514, 737]}, '13': {'rememberedCode': 'A49', 'isValidCode': True, 'coordinate': [961, 521]}, '14': {'rememberedCode': 'A49', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'A49', 'isValidCode': True, 'coordinate': [961, 521]}, '16': {'rememberedCode': 'F63', 'isValidCode': True, 'coordinate': [967, 437]}, '17': {'rememberedCode': 'G81', 'isValidCode': True, 'coordinate': [940, 323]}, '18': {'rememberedCode': 'G81', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'G81', 'isValidCode': True, 'coordinate': [940, 323]}, '20': {'rememberedCode': 'S23', 'isValidCode': True, 'coordinate': [934, 509]}, '21': {'rememberedCode': 'V69', 'isValidCode': True, 'coordinate': [784, 455]}, '22': {'rememberedCode': 'V69', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'V69', 'isValidCode': True, 'coordinate': [784, 455]}, '24': {'rememberedCode': 'X19', 'isValidCode': True, 'coordinate': [1129, 263]}, '25': {'rememberedCode': 'P65', 'isValidCode': True, 'coordinate': [990, 642]}, '26': {'rememberedCode': 'P65', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'P65', 'isValidCode': True, 'coordinate': [990, 642]}, '28': {'rememberedCode': 'C43', 'isValidCode': True, 'coordinate': [1116, 446]}, '29': {'rememberedCode': 'C23', 'isValidCode': True, 'coordinate': [1184, 560]}, '30': {'rememberedCode': 'C23', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C23', 'isValidCode': True, 'coordinate': [1184, 560]}, '32': {'rememberedCode': 'K75', 'isValidCode': True, 'coordinate': [1140, 517]}, '33': {'rememberedCode': 'D49', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'D49', 'isValidCode': True, 'coordinate': [680, 1015]}, '35': {'rememberedCode': 'D49', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'A15', 'isValidCode': True, 'coordinate': [830, 1043]}, '37': {'rememberedCode': 'K72', 'isValidCode': True, 'coordinate': [1170, 1041]}, '38': {'rememberedCode': 'K72', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K72', 'isValidCode': True, 'coordinate': [1170, 1041]}}",862,597
\ No newline at end of file
diff --git a/VisRecall/answer_raw/group6.csv b/VisRecall/answer_raw/group6.csv
new file mode 100755
index 0000000..85056fa
--- /dev/null
+++ b/VisRecall/answer_raw/group6.csv
@@ -0,0 +1,43 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+1,"['A', 'B', 'D', 'D', 'D', 'A', 'A', 'C', 'A', 'D', 'D', 'A', 'A', 'A', 'C', 'C', 'A', 'B', 'D', 'D', 'A', 'A', 'D', 'B', 'A', 'A', 'D', 'A', 'C', 'D', 'C', 'A', 'A', 'D', 'D', 'A', 'D', 'C', 'D', 'B', 'A', 'A', 'C', 'A', 'D', 'A', 'D', 'C', 'D', 'D', 'D', 'C', 'C', 'A', 'D', 'C', 'A', 'D', 'D', 'D', 'C', 'D', 'A', 'A', 'C', 'A', 'B', 'A', 'D', 'A', 'B', 'C', 'D', 'D', 'A', 'A', 'A', 'B', 'B', 'C', 'C', 'B', 'B', 'A', 'D', 'A', 'A', 'A', 'B', 'D', 'A', 'D', 'B', 'C', 'C', 'A', 'A', 'B', 'B', 'C']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-17T14:46:05.900Z', 'loadTime': '2021-08-17T14:46:25.218Z', 'startTime': '2021-08-17T14:46:55.641Z', 'endTime': '2021-08-17T15:11:55.108Z', 'timeToCompleteFromOpenMsec': 1549208, 'timeToLoadMsec': 19318, 'timeToCompleteFromStartMsec': 1499467}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'lots of issues with wrong words in follow-up questions [male color asked twice for icon chart, country instead of other value, etc]'}","{'0': {'rememberedCode': 'P63', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'R67', 'isValidCode': True, 'coordinate': [976, 498]}, '2': {'rememberedCode': 'R67', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'R67', 'isValidCode': True, 'coordinate': [976, 498]}, '4': {'rememberedCode': 'Z94', 'isValidCode': True, 'coordinate': [508, 914]}, '5': {'rememberedCode': 'Y72', 'isValidCode': True, 'coordinate': [818, 412]}, '6': {'rememberedCode': 'Y72', 'isValidCode': True, 'coordinate': [1080, 441]}, '7': {'rememberedCode': 'Y72', 'isValidCode': True, 'coordinate': [818, 412]}, '8': {'rememberedCode': 'D37', 'isValidCode': True, 'coordinate': [741, 1027]}, '9': {'rememberedCode': 'G47', 'isValidCode': True, 'coordinate': [951, 119]}, '10': {'rememberedCode': 'G47', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'G47', 'isValidCode': True, 'coordinate': [951, 119]}, '12': {'rememberedCode': 'K64', 'isValidCode': True, 'coordinate': [596, 492]}, '13': {'rememberedCode': 'M63', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'M63', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'M63', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'H39', 'isValidCode': True, 'coordinate': [899, 364]}, '17': {'rememberedCode': 'B43', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'B43', 'isValidCode': True, 'coordinate': [1203, 1064]}, '19': {'rememberedCode': 'B43', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'E15', 'isValidCode': True, 'coordinate': [816, 466]}, '21': {'rememberedCode': 'D63', 'isValidCode': True, 'coordinate': [790, 211]}, '22': {'rememberedCode': 'D63', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'D63', 'isValidCode': True, 'coordinate': [790, 211]}, '24': {'rememberedCode': 'Q61', 'isValidCode': True, 'coordinate': [914, 631]}, '25': {'rememberedCode': 'W16', 'isValidCode': True, 'coordinate': [813, 1165]}, '26': {'rememberedCode': 'W16', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'W16', 'isValidCode': True, 'coordinate': [813, 1165]}, '28': {'rememberedCode': 'X13', 'isValidCode': True, 'coordinate': [643, 833]}, '29': {'rememberedCode': 'M26', 'isValidCode': True, 'coordinate': [771, 506]}, '30': {'rememberedCode': 'M26', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'M26', 'isValidCode': True, 'coordinate': [771, 506]}, '32': {'rememberedCode': 'J82', 'isValidCode': True, 'coordinate': [670, 207]}, '33': {'rememberedCode': 'H63', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'H63', 'isValidCode': True, 'coordinate': [242, 419]}, '35': {'rememberedCode': 'H63', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'H59', 'isValidCode': True, 'coordinate': [829, 1134]}, '37': {'rememberedCode': 'L89', 'isValidCode': True, 'coordinate': [40, 219]}, '38': {'rememberedCode': 'L89', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L89', 'isValidCode': True, 'coordinate': [40, 219]}}",897,681
+2,"['A', 'B', 'D', 'B', 'C', 'A', 'A', 'C', 'B', 'B', 'A', 'A', 'A', 'A', 'C', 'C', 'B', 'B', 'B', 'D', 'A', 'B', 'C', 'B', 'A', 'A', 'A', 'A', 'C', 'B', 'C', 'B', 'C', 'A', 'B', 'A', 'A', 'B', 'A', 'A', 'A', 'C', 'C', 'C', 'A', 'A', 'A', 'C', 'B', 'B', 'B', 'C', 'C', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'A', 'C', 'A', 'B', 'B', 'A', 'C', 'A', 'B', 'B', 'B', 'C', 'A', 'A', 'A', 'A', 'A', 'A', 'B', 'B', 'A', 'B', 'B', 'A', 'D', 'A', 'C', 'A', 'B', 'C', 'A', 'A', 'B', 'C', 'C', 'A', 'A', 'B', 'B', 'A']","['2', '1', '1', '2', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-17T15:22:27.442Z', 'loadTime': '2021-08-17T15:22:36.251Z', 'startTime': '2021-08-17T15:26:28.634Z', 'endTime': '2021-08-17T15:45:35.612Z', 'timeToCompleteFromOpenMsec': 1388170, 'timeToLoadMsec': 8809, 'timeToCompleteFromStartMsec': 1146978}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'D24', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'J57', 'isValidCode': True, 'coordinate': [518, 285]}, '2': {'rememberedCode': 'J57', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J57', 'isValidCode': True, 'coordinate': [518, 285]}, '4': {'rememberedCode': 'A53', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'Q43', 'isValidCode': True, 'coordinate': [370, 402]}, '6': {'rememberedCode': 'Q43', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Q43', 'isValidCode': True, 'coordinate': [370, 402]}, '8': {'rememberedCode': 'N17', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'H65', 'isValidCode': True, 'coordinate': [330, 223]}, '10': {'rememberedCode': 'H65', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H65', 'isValidCode': True, 'coordinate': [330, 223]}, '12': {'rememberedCode': 'W28', 'isValidCode': True, 'coordinate': [284, 591]}, '13': {'rememberedCode': 'C65', 'isValidCode': True, 'coordinate': [685, 1055]}, '14': {'rememberedCode': 'C65', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'C65', 'isValidCode': True, 'coordinate': [685, 1055]}, '16': {'rememberedCode': 'V81', 'isValidCode': True, 'coordinate': [163, 136]}, '17': {'rememberedCode': 'Q71', 'isValidCode': True, 'coordinate': [1209, 688]}, '18': {'rememberedCode': 'Q71', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Q71', 'isValidCode': True, 'coordinate': [1209, 688]}, '20': {'rememberedCode': 'C54', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'U76', 'isValidCode': True, 'coordinate': [515, 1011]}, '22': {'rememberedCode': 'U76', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U76', 'isValidCode': True, 'coordinate': [515, 1011]}, '24': {'rememberedCode': 'J27', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [647, 263]}, '26': {'rememberedCode': 'E89', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [647, 263]}, '28': {'rememberedCode': 'E89', 'isValidCode': True, 'coordinate': [374, 929]}, '29': {'rememberedCode': 'W45', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'W45', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'W45', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X89', 'isValidCode': True, 'coordinate': [241, 105]}, '33': {'rememberedCode': 'Y72', 'isValidCode': True, 'coordinate': [1524, 512]}, '34': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Y72', 'isValidCode': True, 'coordinate': [1524, 512]}, '36': {'rememberedCode': 'N34', 'isValidCode': True, 'coordinate': [537, 638]}, '37': {'rememberedCode': 'L87', 'isValidCode': True, 'coordinate': [755, 810]}, '38': {'rememberedCode': 'L87', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L87', 'isValidCode': True, 'coordinate': [755, 810]}}",16,384
+3,"['A', 'B', 'B', 'C', 'B', 'A', 'A', 'C', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'C', 'A', 'B', 'B', 'C', 'A', 'A', 'C', 'B', 'A', 'A', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'C', 'B', 'A', 'B', 'C', 'B', 'B', 'A', 'A', 'B', 'B', 'B', 'B', 'B', 'A', 'A', 'C', 'A', 'A', 'C', 'B', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'B', 'C', 'A', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'A', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'A', 'A', 'A', 'A', 'B', 'A', 'A', 'A', 'B', 'B', 'A', 'A', 'A', 'B', 'B', 'C']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-17T16:01:07.569Z', 'loadTime': '2021-08-17T16:01:27.162Z', 'startTime': '2021-08-17T16:01:29.390Z', 'endTime': '2021-08-17T16:24:28.046Z', 'timeToCompleteFromOpenMsec': 1400477, 'timeToLoadMsec': 19593, 'timeToCompleteFromStartMsec': 1378656}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'interesting survey and it really did help me look for certain things in the graphs to attempt to remember - not sure how well I did but I did my best. thanks. '}","{'0': {'rememberedCode': 'V46', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'G32', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'G32', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'G32', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'Z24', 'isValidCode': True, 'coordinate': [484, 115]}, '5': {'rememberedCode': 'R43', 'isValidCode': True, 'coordinate': [170, 317]}, '6': {'rememberedCode': 'R43', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'R43', 'isValidCode': True, 'coordinate': [170, 317]}, '8': {'rememberedCode': 'G95', 'isValidCode': True, 'coordinate': [754, 396]}, '9': {'rememberedCode': 'M64', 'isValidCode': True, 'coordinate': [337, 403]}, '10': {'rememberedCode': 'M64', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'M64', 'isValidCode': True, 'coordinate': [337, 403]}, '12': {'rememberedCode': 'F38', 'isValidCode': True, 'coordinate': [1196, 127]}, '13': {'rememberedCode': 'N35', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'N35', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'N35', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'X36', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'Y56', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'Y56', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Y56', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'B63', 'isValidCode': True, 'coordinate': [405, 255]}, '21': {'rememberedCode': 'J38', 'isValidCode': True, 'coordinate': [781, 128]}, '22': {'rememberedCode': 'J38', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'J38', 'isValidCode': True, 'coordinate': [781, 128]}, '24': {'rememberedCode': 'N72', 'isValidCode': True, 'coordinate': [621, 119]}, '25': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [796, 129]}, '26': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [625, 842]}, '27': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [796, 129]}, '28': {'rememberedCode': 'F71', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [804, 392]}, '30': {'rememberedCode': 'R73', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'R73', 'isValidCode': True, 'coordinate': [804, 392]}, '32': {'rememberedCode': 'D65', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'J34', 'isValidCode': True, 'coordinate': [769, 126]}, '34': {'rememberedCode': 'J34', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'J34', 'isValidCode': True, 'coordinate': [769, 126]}, '36': {'rememberedCode': 'X26', 'isValidCode': True, 'coordinate': [627, 234]}, '37': {'rememberedCode': 'G12', 'isValidCode': True, 'coordinate': [764, 703]}, '38': {'rememberedCode': 'G12', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G12', 'isValidCode': True, 'coordinate': [764, 703]}}",289,615
+4,"['A', 'B', 'C', 'D', 'D', 'A', 'A', 'A', 'D', 'D', 'A', 'A', 'D', 'D', 'C', 'C', 'A', 'A', 'D', 'D', 'A', 'D', 'D', 'B', 'A', 'A', 'D', 'A', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'B', 'C', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'C', 'B', 'A', 'A', 'A', 'B', 'B', 'A', 'D', 'D', 'B', 'D', 'D', 'D', 'A', 'A', 'A', 'A', 'D', 'C', 'A', 'B', 'B', 'A', 'A', 'A', 'A', 'D', 'D', 'A', 'A', 'D', 'B', 'D', 'D', 'A', 'A', 'D', 'B', 'C']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '1', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-18T00:36:21.544Z', 'loadTime': '2021-08-18T00:36:56.688Z', 'startTime': '2021-08-18T00:37:20.805Z', 'endTime': '2021-08-18T01:01:44.614Z', 'timeToCompleteFromOpenMsec': 1523070, 'timeToLoadMsec': 35144, 'timeToCompleteFromStartMsec': 1463809}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'XMR', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'C86', 'isValidCode': True, 'coordinate': [771, 405]}, '2': {'rememberedCode': 'C86', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'C86', 'isValidCode': True, 'coordinate': [771, 405]}, '4': {'rememberedCode': 'G34', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'Z13', 'isValidCode': True, 'coordinate': [634, 505]}, '6': {'rememberedCode': 'Z13', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Z13', 'isValidCode': True, 'coordinate': [634, 505]}, '8': {'rememberedCode': 'J57', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'H48', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'H48', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H48', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'J90', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'B15', 'isValidCode': True, 'coordinate': [1250, 345]}, '14': {'rememberedCode': 'B15', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'B15', 'isValidCode': True, 'coordinate': [1250, 345]}, '16': {'rememberedCode': 'A48', 'isValidCode': True, 'coordinate': [744, 958]}, '17': {'rememberedCode': 'D63', 'isValidCode': True, 'coordinate': [1068, 1004]}, '18': {'rememberedCode': 'D63', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'D63', 'isValidCode': True, 'coordinate': [1068, 1004]}, '20': {'rememberedCode': 'S84', 'isValidCode': True, 'coordinate': [992, 444]}, '21': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [798, 498]}, '22': {'rememberedCode': 'B29', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [798, 498]}, '24': {'rememberedCode': 'L84', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'B65', 'isValidCode': True, 'coordinate': [659, 526]}, '26': {'rememberedCode': 'B65', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'B65', 'isValidCode': True, 'coordinate': [659, 526]}, '28': {'rememberedCode': 'A58', 'isValidCode': True, 'coordinate': [1407, 636]}, '29': {'rememberedCode': 'X81', 'isValidCode': True, 'coordinate': [940, 707]}, '30': {'rememberedCode': 'X81', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'X81', 'isValidCode': True, 'coordinate': [940, 707]}, '32': {'rememberedCode': 'Z84', 'isValidCode': True, 'coordinate': [1086, 610]}, '33': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [966, 729]}, '34': {'rememberedCode': 'U79', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [966, 729]}, '36': {'rememberedCode': 'N84', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'H34', 'isValidCode': True, 'coordinate': [476, 612]}, '38': {'rememberedCode': 'H34', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'H34', 'isValidCode': True, 'coordinate': [476, 612]}}",715,318
+5,"['B', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'B', 'A', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'B', 'A']","['2', '1', '2', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1']","{'openTime': '2021-08-19T13:53:08.595Z', 'loadTime': '2021-08-19T13:53:24.431Z', 'startTime': '2021-08-19T13:53:29.416Z', 'endTime': '2021-08-19T14:16:19.599Z', 'timeToCompleteFromOpenMsec': 1391004, 'timeToLoadMsec': 15836, 'timeToCompleteFromStartMsec': 1370183}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good task'}","{'0': {'rememberedCode': 'B65', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'G57', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'G57', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'G57', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'R12', 'isValidCode': True, 'coordinate': [189, 134]}, '5': {'rememberedCode': 'G50', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'G50', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'G50', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'J65', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'B89', 'isValidCode': True, 'coordinate': [1529, 1226]}, '10': {'rememberedCode': 'B89', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'B89', 'isValidCode': True, 'coordinate': [1529, 1226]}, '12': {'rememberedCode': 'H75', 'isValidCode': True, 'coordinate': [1480, 1305]}, '13': {'rememberedCode': 'T81', 'isValidCode': True, 'coordinate': [1573, 1219]}, '14': {'rememberedCode': 'T81', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T81', 'isValidCode': True, 'coordinate': [1573, 1219]}, '16': {'rememberedCode': 'J84', 'isValidCode': True, 'coordinate': [1475, 1230]}, '17': {'rememberedCode': 'M64', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'M64', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M64', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'V82', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'P32', 'isValidCode': True, 'coordinate': [1542, 1212]}, '22': {'rememberedCode': 'P32', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'P32', 'isValidCode': True, 'coordinate': [1542, 1212]}, '24': {'rememberedCode': 'H67', 'isValidCode': True, 'coordinate': [1530, 1204]}, '25': {'rememberedCode': 'J39', 'isValidCode': True, 'coordinate': [1503, 1256]}, '26': {'rememberedCode': 'J39', 'isValidCode': True, 'coordinate': [514, 14]}, '27': {'rememberedCode': 'J39', 'isValidCode': True, 'coordinate': [1503, 1256]}, '28': {'rememberedCode': 'D92', 'isValidCode': True, 'coordinate': [1556, 1243]}, '29': {'rememberedCode': 'G57', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'G57', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'G57', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X57', 'isValidCode': True, 'coordinate': [1541, 1295]}, '33': {'rememberedCode': 'D45', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'D45', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D45', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'U67', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'Y86', 'isValidCode': True, 'coordinate': [1547, 1228]}, '38': {'rememberedCode': 'Y86', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Y86', 'isValidCode': True, 'coordinate': [1547, 1228]}}",809,793
+6,"['C', 'A', 'C', 'C', 'C', 'A', 'B', 'C', 'C', 'C', 'A', 'A', 'A', 'A', 'C', 'A', 'B', 'B', 'B', 'A', 'A', 'B', 'A', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'C', 'C', 'B', 'B', 'B', 'B', 'A', 'A', 'A', 'C', 'C', 'A', 'A', 'C', 'C', 'C', 'C', 'B', 'A', 'A', 'B', 'A', 'B', 'C', 'C', 'A', 'A', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'C', 'C', 'B', 'A', 'A', 'C', 'B', 'B', 'A', 'A', 'B', 'A', 'A', 'A', 'B', 'C', 'C', 'A', 'A', 'A', 'A', 'B', 'B', 'A', 'A', 'C', 'B', 'B', 'C']","['2', '1', '2', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '2', '2', '1', '2', '1', '2', '2', '2', '1']","{'openTime': '2021-08-19T14:01:52.203Z', 'loadTime': '2021-08-19T14:02:10.746Z', 'startTime': '2021-08-19T14:02:12.070Z', 'endTime': '2021-08-19T14:22:34.408Z', 'timeToCompleteFromOpenMsec': 1242205, 'timeToLoadMsec': 18543, 'timeToCompleteFromStartMsec': 1222338}","{'gender': 'male', 'ageGroup': 'ageGroup18', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'B57', 'isValidCode': True, 'coordinate': [1344, 442]}, '1': {'rememberedCode': 'G17', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'G17', 'isValidCode': True, 'coordinate': [895, 1056]}, '3': {'rememberedCode': 'G17', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'M32', 'isValidCode': True, 'coordinate': [1413, 1015]}, '5': {'rememberedCode': 'M97', 'isValidCode': True, 'coordinate': [377, 821]}, '6': {'rememberedCode': 'M97', 'isValidCode': True, 'coordinate': [45, 33]}, '7': {'rememberedCode': 'M97', 'isValidCode': True, 'coordinate': [377, 821]}, '8': {'rememberedCode': 'V89', 'isValidCode': True, 'coordinate': [327, 1106]}, '9': {'rememberedCode': 'G47', 'isValidCode': True, 'coordinate': [951, 119]}, '10': {'rememberedCode': 'G47', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'G47', 'isValidCode': True, 'coordinate': [951, 119]}, '12': {'rememberedCode': 'J72', 'isValidCode': True, 'coordinate': [291, 690]}, '13': {'rememberedCode': 'L65', 'isValidCode': True, 'coordinate': [1235, 134]}, '14': {'rememberedCode': 'L65', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L65', 'isValidCode': True, 'coordinate': [1235, 134]}, '16': {'rememberedCode': 'B27', 'isValidCode': True, 'coordinate': [1378, 1121]}, '17': {'rememberedCode': 'K17', 'isValidCode': True, 'coordinate': [634, 916]}, '18': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K17', 'isValidCode': True, 'coordinate': [634, 916]}, '20': {'rememberedCode': 'S28', 'isValidCode': True, 'coordinate': [105, 651]}, '21': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [1505, 830]}, '22': {'rememberedCode': 'M62', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'M62', 'isValidCode': True, 'coordinate': [1505, 830]}, '24': {'rememberedCode': 'Z84', 'isValidCode': True, 'coordinate': [212, 836]}, '25': {'rememberedCode': 'E37', 'isValidCode': True, 'coordinate': [168, 1154]}, '26': {'rememberedCode': 'E37', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'E37', 'isValidCode': True, 'coordinate': [168, 1154]}, '28': {'rememberedCode': 'X85', 'isValidCode': True, 'coordinate': [1228, 624]}, '29': {'rememberedCode': 'T85', 'isValidCode': True, 'coordinate': [1091, 929]}, '30': {'rememberedCode': 'T85', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'T85', 'isValidCode': True, 'coordinate': [1091, 929]}, '32': {'rememberedCode': 'K41', 'isValidCode': True, 'coordinate': [796, 1001]}, '33': {'rememberedCode': 'F89', 'isValidCode': True, 'coordinate': [1094, 132]}, '34': {'rememberedCode': 'F89', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'F89', 'isValidCode': True, 'coordinate': [1094, 132]}, '36': {'rememberedCode': 'S38', 'isValidCode': True, 'coordinate': [1070, 1125]}, '37': {'rememberedCode': 'Q53', 'isValidCode': True, 'coordinate': [1377, 1030]}, '38': {'rememberedCode': 'Q53', 'isValidCode': True, 'coordinate': [66, 1151]}, '39': {'rememberedCode': 'Q53', 'isValidCode': True, 'coordinate': [1377, 1030]}}",18,473
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+15,"['A', 'B', 'A', 'A', 'D', 'A', 'B', 'C', 'D', 'B', 'B', 'A', 'D', 'A', 'D', 'C', 'A', 'B', 'B', 'D', 'A', 'B', 'C', 'D', 'A', 'A', 'B', 'A', 'C', 'D', 'A', 'B', 'A', 'C', 'A', 'A', 'D', 'D', 'A', 'A', 'A', 'B', 'C', 'A', 'B', 'D', 'B', 'A', 'A', 'C', 'C', 'C', 'C', 'B', 'B', 'B', 'A', 'A', 'A', 'D', 'A', 'C', 'A', 'B', 'B', 'A', 'B', 'A', 'D', 'D', 'C', 'B', 'C', 'A', 'C', 'A', 'A', 'C', 'D', 'B', 'B', 'B', 'B', 'A', 'A', 'A', 'B', 'A', 'B', 'D', 'A', 'A', 'B', 'B', 'B', 'A', 'B', 'A', 'A', 'C']","['2', '1', '1', '2', '1', '1', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '2', '1', '1', '1', '2', '2', '2', '2', '1']","{'openTime': '2021-08-19T14:09:25.275Z', 'loadTime': '2021-08-19T14:09:26.441Z', 'startTime': '2021-08-19T14:09:32.108Z', 'endTime': '2021-08-19T14:35:01.137Z', 'timeToCompleteFromOpenMsec': 1535862, 'timeToLoadMsec': 1166, 'timeToCompleteFromStartMsec': 1529029}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'professional', 'feedback': 'good'}","{'0': {'rememberedCode': 'K59', 'isValidCode': True, 'coordinate': [1075, 332]}, '1': {'rememberedCode': 'J19', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'J19', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J19', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'X32', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'G34', 'isValidCode': True, 'coordinate': [951, 28]}, '6': {'rememberedCode': 'G34', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'G34', 'isValidCode': True, 'coordinate': [951, 28]}, '8': {'rememberedCode': 'K53', 'isValidCode': True, 'coordinate': [475, 236]}, '9': {'rememberedCode': 'K39', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'K39', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'K39', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'M98', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'E48', 'isValidCode': True, 'coordinate': [784, 336]}, '14': {'rememberedCode': 'E48', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'E48', 'isValidCode': True, 'coordinate': [784, 336]}, '16': {'rememberedCode': 'G95', 'isValidCode': True, 'coordinate': [925, 639]}, '17': {'rememberedCode': 'T32', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'T32', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'T32', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'X32', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'G49', 'isValidCode': True, 'coordinate': [916, 493]}, '22': {'rememberedCode': 'G49', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G49', 'isValidCode': True, 'coordinate': [916, 493]}, '24': {'rememberedCode': 'J19', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'U35', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'U35', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U35', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'J19', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'P69', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'P69', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'P69', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'A12', 'isValidCode': True, 'coordinate': [962, 524]}, '33': {'rememberedCode': 'H29', 'isValidCode': True, 'coordinate': [1068, 441]}, '34': {'rememberedCode': 'H29', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'H29', 'isValidCode': True, 'coordinate': [1068, 441]}, '36': {'rememberedCode': 'V78', 'isValidCode': True, 'coordinate': [957, 339]}, '37': {'rememberedCode': 'M24', 'isValidCode': True, 'coordinate': [1196, 629]}, '38': {'rememberedCode': 'M24', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'M24', 'isValidCode': True, 'coordinate': [1196, 629]}}",687,451
+16,"['B', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'B', 'C', 'A', 'C', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'B']","['2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1']","{'openTime': '2021-08-19T14:05:20.415Z', 'loadTime': '2021-08-19T14:05:22.140Z', 'startTime': '2021-08-19T14:05:31.462Z', 'endTime': '2021-08-19T14:34:59.232Z', 'timeToCompleteFromOpenMsec': 1778817, 'timeToLoadMsec': 1725, 'timeToCompleteFromStartMsec': 1767770}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'someCollege', 'vizExperience': 'frequentInteraction', 'feedback': 'nice'}","{'0': {'rememberedCode': 'G17', 'isValidCode': True, 'coordinate': [895, 1056]}, '1': {'rememberedCode': 'L50', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'L50', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'L50', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'M37', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'E84', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'E84', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E84', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'C13', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'E75', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'L83', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'L83', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L83', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'S57', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'M28', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'M28', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'M28', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'L54', 'isValidCode': True, 'coordinate': [1130, 1160]}, '21': {'rememberedCode': 'Y83', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'Y83', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Y83', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'G17', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'Z45', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'Z45', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Z45', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'M37', 'isValidCode': True, 'coordinate': [216, 815]}, '29': {'rememberedCode': 'R25', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'R25', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'R25', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'U30', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'U12', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'U12', 'isValidCode': True, 'coordinate': [979, 1292]}, '35': {'rememberedCode': 'U12', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'L95', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'M53', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'M53', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'M53', 'isValidCode': False, 'coordinate': False}}",732,697
+17,"['C', 'B', 'B', 'B', 'C', 'C', 'B', 'C', 'C', 'B', 'C', 'C', 'C', 'D', 'A', 'C', 'A', 'A', 'D', 'B', 'B', 'C', 'C', 'D', 'D', 'A', 'A', 'B', 'C', 'B', 'D', 'A', 'A', 'C', 'C', 'C', 'B', 'C', 'D', 'B', 'A', 'A', 'C', 'B', 'D', 'A', 'A', 'A', 'A', 'B', 'C', 'C', 'B', 'B', 'B', 'A', 'B', 'B', 'D', 'C', 'A', 'C', 'C', 'A', 'C', 'A', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'D', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'B', 'B', 'B', 'C', 'B', 'A', 'B', 'B', 'B', 'B', 'C', 'B', 'B', 'D', 'A', 'B', 'B', 'B', 'D']","['2', '1', '2', '1', '1', '1', '2', '1', '2', '1', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '2', '2', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '1']","{'openTime': '2021-08-19T13:53:29.200Z', 'loadTime': '2021-08-19T13:54:10.293Z', 'startTime': '2021-08-19T13:56:32.694Z', 'endTime': '2021-08-19T14:32:12.852Z', 'timeToCompleteFromOpenMsec': 2323652, 'timeToLoadMsec': 41093, 'timeToCompleteFromStartMsec': 2140158}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'unspecified', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'I am directly taken to the test without being shown the proper visualizations. I did the best according to my understanding. I&#39;m not sure if it was intentional or technical error, but it apparently was not my fault. Thank you.'}","{'0': {'rememberedCode': 'B62', 'isValidCode': True, 'coordinate': [892, 452]}, '1': {'rememberedCode': 'W51', 'isValidCode': True, 'coordinate': [798, 491]}, '2': {'rememberedCode': 'W51', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W51', 'isValidCode': True, 'coordinate': [798, 491]}, '4': {'rememberedCode': 'Q71', 'isValidCode': True, 'coordinate': [774, 436]}, '5': {'rememberedCode': 'H83', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'H83', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H83', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'K76', 'isValidCode': True, 'coordinate': [1045, 431]}, '9': {'rememberedCode': 'T36', 'isValidCode': True, 'coordinate': [955, 332]}, '10': {'rememberedCode': 'T36', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'T36', 'isValidCode': True, 'coordinate': [955, 332]}, '12': {'rememberedCode': 'R61', 'isValidCode': True, 'coordinate': [932, 486]}, '13': {'rememberedCode': 'S26', 'isValidCode': True, 'coordinate': [946, 449]}, '14': {'rememberedCode': 'S26', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'S26', 'isValidCode': True, 'coordinate': [946, 449]}, '16': {'rememberedCode': 'L62', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'D78', 'isValidCode': True, 'coordinate': [771, 516]}, '18': {'rememberedCode': 'D78', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'D78', 'isValidCode': True, 'coordinate': [771, 516]}, '20': {'rememberedCode': 'G51', 'isValidCode': True, 'coordinate': [992, 570]}, '21': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [798, 498]}, '22': {'rememberedCode': 'B29', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [798, 498]}, '24': {'rememberedCode': 'P12', 'isValidCode': True, 'coordinate': [921, 508]}, '25': {'rememberedCode': 'M13', 'isValidCode': True, 'coordinate': [919, 523]}, '26': {'rememberedCode': 'M13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'M13', 'isValidCode': True, 'coordinate': [919, 523]}, '28': {'rememberedCode': 'Q31', 'isValidCode': True, 'coordinate': [984, 546]}, '29': {'rememberedCode': 'L35', 'isValidCode': True, 'coordinate': [908, 500]}, '30': {'rememberedCode': 'L35', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L35', 'isValidCode': True, 'coordinate': [908, 500]}, '32': {'rememberedCode': 'E25', 'isValidCode': True, 'coordinate': [775, 507]}, '33': {'rememberedCode': 'R98', 'isValidCode': True, 'coordinate': [822, 532]}, '34': {'rememberedCode': 'R98', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'R98', 'isValidCode': True, 'coordinate': [822, 532]}, '36': {'rememberedCode': 'A41', 'isValidCode': True, 'coordinate': [922, 450]}, '37': {'rememberedCode': 'S63', 'isValidCode': True, 'coordinate': [768, 398]}, '38': {'rememberedCode': 'S63', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'S63', 'isValidCode': True, 'coordinate': [768, 398]}}",79,847
+18,"['A', 'B', 'D', 'D', 'A', 'A', 'B', 'C', 'D', 'B', 'A', 'A', 'D', 'A', 'C', 'C', 'B', 'B', 'B', 'D', 'A', 'A', 'D', 'D', 'A', 'A', 'B', 'A', 'C', 'D', 'A', 'B', 'D', 'D', 'A', 'A', 'B', 'A', 'D', 'A', 'A', 'D', 'C', 'A', 'B', 'A', 'C', 'B', 'D', 'A', 'A', 'A', 'D', 'D', 'B', 'C', 'A', 'A', 'D', 'D', 'A', 'C', 'A', 'D', 'A', 'A', 'B', 'A', 'C', 'A', 'B', 'C', 'D', 'A', 'A', 'A', 'A', 'B', 'C', 'C', 'A', 'B', 'B', 'A', 'C', 'A', 'A', 'A', 'B', 'A', 'A', 'A', 'B', 'D', 'C', 'A', 'A', 'B', 'D', 'C']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '1', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '1', '1']","{'openTime': '2021-08-19T13:59:50.081Z', 'loadTime': '2021-08-19T14:00:09.051Z', 'startTime': '2021-08-19T14:02:00.958Z', 'endTime': '2021-08-19T14:36:23.054Z', 'timeToCompleteFromOpenMsec': 2192973, 'timeToLoadMsec': 18970, 'timeToCompleteFromStartMsec': 2062096}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'It was a great study. It is very useful to analyse the concentration and memory power. '}","{'0': {'rememberedCode': 'D53', 'isValidCode': True, 'coordinate': [1531, 1059]}, '1': {'rememberedCode': 'J87', 'isValidCode': True, 'coordinate': [1564, 1206]}, '2': {'rememberedCode': 'J87', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J87', 'isValidCode': True, 'coordinate': [1564, 1206]}, '4': {'rememberedCode': 'M25', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'X87', 'isValidCode': True, 'coordinate': [1555, 1140]}, '6': {'rememberedCode': 'X87', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'X87', 'isValidCode': True, 'coordinate': [1555, 1140]}, '8': {'rememberedCode': 'G87', 'isValidCode': True, 'coordinate': [1616, 1010]}, '9': {'rememberedCode': 'B89', 'isValidCode': True, 'coordinate': [1529, 1226]}, '10': {'rememberedCode': 'B89', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'B89', 'isValidCode': True, 'coordinate': [1529, 1226]}, '12': {'rememberedCode': 'D16', 'isValidCode': True, 'coordinate': [1467, 1193]}, '13': {'rememberedCode': 'T81', 'isValidCode': True, 'coordinate': [1573, 1219]}, '14': {'rememberedCode': 'T81', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T81', 'isValidCode': True, 'coordinate': [1573, 1219]}, '16': {'rememberedCode': 'T25', 'isValidCode': True, 'coordinate': [1476, 1164]}, '17': {'rememberedCode': 'R41', 'isValidCode': True, 'coordinate': [1516, 1288]}, '18': {'rememberedCode': 'R41', 'isValidCode': True, 'coordinate': [1029, 952]}, '19': {'rememberedCode': 'R41', 'isValidCode': True, 'coordinate': [1516, 1288]}, '20': {'rememberedCode': 'H19', 'isValidCode': True, 'coordinate': [1542, 1053]}, '21': {'rememberedCode': 'P32', 'isValidCode': True, 'coordinate': [1542, 1212]}, '22': {'rememberedCode': 'P32', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'P32', 'isValidCode': True, 'coordinate': [1542, 1212]}, '24': {'rememberedCode': 'W18', 'isValidCode': True, 'coordinate': [1547, 1109]}, '25': {'rememberedCode': 'U14', 'isValidCode': True, 'coordinate': [1538, 1121]}, '26': {'rememberedCode': 'U14', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U14', 'isValidCode': True, 'coordinate': [1538, 1121]}, '28': {'rememberedCode': 'D92', 'isValidCode': True, 'coordinate': [1556, 1243]}, '29': {'rememberedCode': 'Y18', 'isValidCode': True, 'coordinate': [1500, 1096]}, '30': {'rememberedCode': 'Y18', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Y18', 'isValidCode': True, 'coordinate': [1500, 1096]}, '32': {'rememberedCode': 'G25', 'isValidCode': True, 'coordinate': [1561, 1215]}, '33': {'rememberedCode': 'T89', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'T89', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'T89', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'X98', 'isValidCode': True, 'coordinate': [1535, 1051]}, '37': {'rememberedCode': 'Y86', 'isValidCode': True, 'coordinate': [1547, 1228]}, '38': {'rememberedCode': 'Y86', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Y86', 'isValidCode': True, 'coordinate': [1547, 1228]}}",690,326
+19,"['A', 'A', 'A', 'B', 'C', 'A', 'A', 'A', 'C', 'A', 'A', 'B', 'A', 'C', 'D', 'A', 'A', 'B', 'D', 'C', 'A', 'A', 'C', 'B', 'A', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'D', 'B', 'B', 'A', 'A', 'C', 'B', 'D', 'A', 'C', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'B', 'A', 'B', 'C', 'D', 'C', 'A', 'C', 'A', 'A', 'B', 'A', 'A', 'A', 'B', 'B', 'C', 'B', 'B', 'D', 'A', 'A', 'A', 'B', 'B', 'A', 'C', 'B', 'B', 'A', 'A', 'A', 'C', 'B', 'B', 'C', 'A', 'C', 'B', 'B', 'A', 'A', 'A', 'B', 'B', 'B']","['2', '1', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '2', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '2', '1']","{'openTime': '2021-08-19T14:17:21.918Z', 'loadTime': '2021-08-19T14:17:39.993Z', 'startTime': '2021-08-19T14:19:07.855Z', 'endTime': '2021-08-19T14:54:53.667Z', 'timeToCompleteFromOpenMsec': 2251749, 'timeToLoadMsec': 18075, 'timeToCompleteFromStartMsec': 2145812}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': 'V82', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'M27', 'isValidCode': True, 'coordinate': [87, 215]}, '2': {'rememberedCode': 'M27', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'M27', 'isValidCode': True, 'coordinate': [87, 215]}, '4': {'rememberedCode': 'L48', 'isValidCode': True, 'coordinate': [204, 255]}, '5': {'rememberedCode': 'W49', 'isValidCode': True, 'coordinate': [823, 315]}, '6': {'rememberedCode': 'W49', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'W49', 'isValidCode': True, 'coordinate': [823, 315]}, '8': {'rememberedCode': 'D58', 'isValidCode': True, 'coordinate': [1054, 303]}, '9': {'rememberedCode': 'N78', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'N78', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'N78', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'M92', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'W34', 'isValidCode': True, 'coordinate': [336, 1255]}, '14': {'rememberedCode': 'W34', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'W34', 'isValidCode': True, 'coordinate': [336, 1255]}, '16': {'rememberedCode': 'P18', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'D79', 'isValidCode': True, 'coordinate': [482, 520]}, '18': {'rememberedCode': 'D79', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'D79', 'isValidCode': True, 'coordinate': [482, 520]}, '20': {'rememberedCode': 'Q43', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'N24', 'isValidCode': True, 'coordinate': [50, 496]}, '22': {'rememberedCode': 'N24', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'N24', 'isValidCode': True, 'coordinate': [50, 496]}, '24': {'rememberedCode': 'P58', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'U98', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'U98', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U98', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'E34', 'isValidCode': True, 'coordinate': [80, 737]}, '29': {'rememberedCode': 'C37', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'C37', 'isValidCode': True, 'coordinate': [795, 333]}, '31': {'rememberedCode': 'C37', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X89', 'isValidCode': True, 'coordinate': [241, 105]}, '33': {'rememberedCode': 'F89', 'isValidCode': True, 'coordinate': [1094, 132]}, '34': {'rememberedCode': 'F89', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'F89', 'isValidCode': True, 'coordinate': [1094, 132]}, '36': {'rememberedCode': 'D82', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'Z84', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'Z84', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Z84', 'isValidCode': False, 'coordinate': False}}",135,343
+20,"['A', 'C', 'A', 'B', 'C', 'C', 'A', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'D', 'C', 'A', 'B', 'C', 'D', 'A', 'B', 'C', 'B', 'A', 'C', 'C', 'A', 'C', 'A', 'B', 'A', 'D', 'D', 'B', 'A', 'A', 'C', 'B', 'C', 'A', 'C', 'C', 'D', 'A', 'B', 'C', 'B', 'C', 'A', 'A', 'C', 'B', 'A', 'A', 'A', 'B', 'A', 'A', 'B', 'A', 'B', 'C', 'B', 'B', 'A', 'A', 'B', 'A', 'A', 'B', 'A', 'C', 'B', 'A', 'A', 'A', 'A', 'C', 'A', 'C', 'C', 'B', 'A', 'B', 'B', 'C', 'B', 'A', 'D', 'A', 'A', 'B', 'A', 'C', 'A', 'A', 'B', 'B', 'C']","['2', '2', '1', '2', '1', '2', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-19T14:24:51.358Z', 'loadTime': '2021-08-19T14:25:31.305Z', 'startTime': '2021-08-19T14:25:41.675Z', 'endTime': '2021-08-19T14:55:13.367Z', 'timeToCompleteFromOpenMsec': 1822009, 'timeToLoadMsec': 39947, 'timeToCompleteFromStartMsec': 1771692}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good study.'}","{'0': {'rememberedCode': '1AZ', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'V97', 'isValidCode': True, 'coordinate': [964, 205]}, '2': {'rememberedCode': 'V97', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'V97', 'isValidCode': True, 'coordinate': [964, 205]}, '4': {'rememberedCode': 'E57', 'isValidCode': True, 'coordinate': [359, 1029]}, '5': {'rememberedCode': 'V24', 'isValidCode': True, 'coordinate': [629, 428]}, '6': {'rememberedCode': 'V24', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'V24', 'isValidCode': True, 'coordinate': [629, 428]}, '8': {'rememberedCode': 'X98', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'L43', 'isValidCode': True, 'coordinate': [776, 305]}, '10': {'rememberedCode': 'L43', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'L43', 'isValidCode': True, 'coordinate': [776, 305]}, '12': {'rememberedCode': 'R61', 'isValidCode': True, 'coordinate': [932, 486]}, '13': {'rememberedCode': 'H93', 'isValidCode': True, 'coordinate': [788, 124]}, '14': {'rememberedCode': 'H93', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'H93', 'isValidCode': True, 'coordinate': [788, 124]}, '16': {'rememberedCode': 'A87', 'isValidCode': True, 'coordinate': [468, 1256]}, '17': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [587, 631]}, '18': {'rememberedCode': 'F67', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [587, 631]}, '20': {'rememberedCode': 'B38', 'isValidCode': True, 'coordinate': [534, 637]}, '21': {'rememberedCode': 'X18', 'isValidCode': True, 'coordinate': [756, 334]}, '22': {'rememberedCode': 'X18', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'X18', 'isValidCode': True, 'coordinate': [756, 334]}, '24': {'rememberedCode': 'B12', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [796, 129]}, '26': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [625, 842]}, '27': {'rememberedCode': 'E65', 'isValidCode': True, 'coordinate': [796, 129]}, '28': {'rememberedCode': 'U16', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'C65', 'isValidCode': True, 'coordinate': [625, 428]}, '30': {'rememberedCode': 'C65', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C65', 'isValidCode': True, 'coordinate': [625, 428]}, '32': {'rememberedCode': 'J64', 'isValidCode': True, 'coordinate': [483, 410]}, '33': {'rememberedCode': 'X68', 'isValidCode': True, 'coordinate': [664, 520]}, '34': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'X68', 'isValidCode': True, 'coordinate': [664, 520]}, '36': {'rememberedCode': 'Z79', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'C17', 'isValidCode': True, 'coordinate': [511, 432]}, '38': {'rememberedCode': 'C17', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'C17', 'isValidCode': True, 'coordinate': [511, 432]}}",323,970
+21,"['D', 'C', 'B', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'B', 'A', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'D', 'B', 'B', 'C', 'B', 'C']","['2', '1', '1', '1', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '2', '2', '2', '1', '2', '2', '1', '2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-19T14:53:48.187Z', 'loadTime': '2021-08-19T14:53:49.826Z', 'startTime': '2021-08-19T14:53:53.190Z', 'endTime': '2021-08-19T15:05:27.707Z', 'timeToCompleteFromOpenMsec': 699520, 'timeToLoadMsec': 1639, 'timeToCompleteFromStartMsec': 694517}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'unspecified', 'vizExperience': 'unspecified', 'feedback': 'Happy to participate.'}","{'0': {'rememberedCode': 'S65', 'isValidCode': True, 'coordinate': [765, 959]}, '1': {'rememberedCode': 'G79', 'isValidCode': True, 'coordinate': [931, 623]}, '2': {'rememberedCode': 'G79', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'G79', 'isValidCode': True, 'coordinate': [931, 623]}, '4': {'rememberedCode': 'S26', 'isValidCode': True, 'coordinate': [928, 719]}, '5': {'rememberedCode': 'H36', 'isValidCode': True, 'coordinate': [818, 516]}, '6': {'rememberedCode': 'H36', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H36', 'isValidCode': True, 'coordinate': [818, 516]}, '8': {'rememberedCode': 'X37', 'isValidCode': True, 'coordinate': [1352, 506]}, '9': {'rememberedCode': 'L73', 'isValidCode': True, 'coordinate': [1117, 529]}, '10': {'rememberedCode': 'L73', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'L73', 'isValidCode': True, 'coordinate': [1117, 529]}, '12': {'rememberedCode': 'E17', 'isValidCode': True, 'coordinate': [918, 693]}, '13': {'rememberedCode': 'D35', 'isValidCode': True, 'coordinate': [799, 534]}, '14': {'rememberedCode': 'D35', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'D35', 'isValidCode': True, 'coordinate': [799, 534]}, '16': {'rememberedCode': 'F65', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'D78', 'isValidCode': True, 'coordinate': [771, 516]}, '18': {'rememberedCode': 'D78', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'D78', 'isValidCode': True, 'coordinate': [771, 516]}, '20': {'rememberedCode': 'E26', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'Y32', 'isValidCode': True, 'coordinate': [1074, 610]}, '22': {'rememberedCode': 'Y32', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Y32', 'isValidCode': True, 'coordinate': [1074, 610]}, '24': {'rememberedCode': 'F17', 'isValidCode': True, 'coordinate': [934, 828]}, '25': {'rememberedCode': 'D80', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'D80', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'D80', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'F36', 'isValidCode': True, 'coordinate': [836, 536]}, '29': {'rememberedCode': 'S71', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'S71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'S71', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'A41', 'isValidCode': True, 'coordinate': [1533, 991]}, '33': {'rememberedCode': 'M74', 'isValidCode': True, 'coordinate': [815, 710]}, '34': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'M74', 'isValidCode': True, 'coordinate': [815, 710]}, '36': {'rememberedCode': 'M07', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'T97', 'isValidCode': True, 'coordinate': [952, 836]}, '38': {'rememberedCode': 'T97', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'T97', 'isValidCode': True, 'coordinate': [952, 836]}}",768,705
+22,"['A', 'B', 'D', 'C', 'D', 'A', 'B', 'C', 'D', 'D', 'A', 'A', 'A', 'A', 'C', 'C', 'A', 'C', 'D', 'C', 'A', 'B', 'C', 'B', 'A', 'A', 'A', 'A', 'C', 'D', 'C', 'B', 'A', 'D', 'A', 'A', 'B', 'C', 'D', 'A', 'A', 'B', 'C', 'A', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'C', 'C', 'A', 'D', 'C', 'A', 'D', 'D', 'D', 'A', 'B', 'A', 'D', 'D', 'A', 'B', 'A', 'A', 'A', 'B', 'D', 'D', 'D', 'C', 'A', 'A', 'A', 'B', 'A', 'C', 'B', 'D', 'A', 'D', 'A', 'A', 'A', 'B', 'D', 'A', 'A', 'B', 'D', 'C', 'A', 'A', 'B', 'B', 'C']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-19T15:43:29.413Z', 'loadTime': '2021-08-19T15:44:59.275Z', 'startTime': '2021-08-19T15:45:03.662Z', 'endTime': '2021-08-19T16:08:13.351Z', 'timeToCompleteFromOpenMsec': 1483938, 'timeToLoadMsec': 89862, 'timeToCompleteFromStartMsec': 1389689}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'I&#39;m kind of confused because this appears to be the same grouping as a previous hit, but with higher pay. Hit description says its okay to do this hit if I&#39;ve participated before. It seems this contains questions with errors I pointed out before -- &#39;countries&#39; for &#39;commodities&#39;, cabo verde question connected with wrong chart, etc. - email me if you need more details'}","{'0': {'rememberedCode': 'T76', 'isValidCode': True, 'coordinate': [903, 933]}, '1': {'rememberedCode': 'T19', 'isValidCode': True, 'coordinate': [935, 93]}, '2': {'rememberedCode': 'T19', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'T19', 'isValidCode': True, 'coordinate': [935, 93]}, '4': {'rememberedCode': 'A39', 'isValidCode': True, 'coordinate': [620, 233]}, '5': {'rememberedCode': 'A92', 'isValidCode': True, 'coordinate': [1067, 935]}, '6': {'rememberedCode': 'A92', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'A92', 'isValidCode': True, 'coordinate': [1067, 935]}, '8': {'rememberedCode': 'N72', 'isValidCode': True, 'coordinate': [1017, 537]}, '9': {'rememberedCode': 'N73', 'isValidCode': True, 'coordinate': [512, 347]}, '10': {'rememberedCode': 'N73', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'N73', 'isValidCode': True, 'coordinate': [512, 347]}, '12': {'rememberedCode': 'H26', 'isValidCode': True, 'coordinate': [744, 689]}, '13': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [832, 628]}, '14': {'rememberedCode': 'X12', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [832, 628]}, '16': {'rememberedCode': 'X52', 'isValidCode': True, 'coordinate': [626, 1254]}, '17': {'rememberedCode': 'K17', 'isValidCode': True, 'coordinate': [634, 916]}, '18': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K17', 'isValidCode': True, 'coordinate': [634, 916]}, '20': {'rememberedCode': 'L13', 'isValidCode': True, 'coordinate': [497, 753]}, '21': {'rememberedCode': 'Y76', 'isValidCode': True, 'coordinate': [776, 733]}, '22': {'rememberedCode': 'Y76', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Y76', 'isValidCode': True, 'coordinate': [776, 733]}, '24': {'rememberedCode': 'F82', 'isValidCode': True, 'coordinate': [647, 505]}, '25': {'rememberedCode': 'M95', 'isValidCode': True, 'coordinate': [1393, 1064]}, '26': {'rememberedCode': 'M95', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'M95', 'isValidCode': True, 'coordinate': [1393, 1064]}, '28': {'rememberedCode': 'S93', 'isValidCode': True, 'coordinate': [685, 347]}, '29': {'rememberedCode': 'F58', 'isValidCode': True, 'coordinate': [494, 993]}, '30': {'rememberedCode': 'F58', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'F58', 'isValidCode': True, 'coordinate': [494, 993]}, '32': {'rememberedCode': 'A49', 'isValidCode': True, 'coordinate': [494, 114]}, '33': {'rememberedCode': 'Q51', 'isValidCode': True, 'coordinate': [939, 422]}, '34': {'rememberedCode': 'Q51', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Q51', 'isValidCode': True, 'coordinate': [939, 422]}, '36': {'rememberedCode': 'B19', 'isValidCode': True, 'coordinate': [643, 347]}, '37': {'rememberedCode': 'S93', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'S93', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'S93', 'isValidCode': False, 'coordinate': False}}",106,133
+23,"['A', 'B', 'C', 'C', 'D', 'A', 'B', 'C', 'C', 'D', 'A', 'B', 'C', 'D', 'C', 'C', 'C', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'A', 'C', 'A', 'A', 'B', 'D', 'C', 'C', 'C', 'B', 'C', 'A', 'A', 'A', 'B', 'C', 'A', 'D', 'A', 'D', 'C', 'A', 'B', 'A', 'C', 'D', 'A', 'D', 'C', 'A', 'A', 'D', 'B', 'A', 'A', 'D', 'D', 'D', 'B', 'B', 'A', 'C', 'A', 'A', 'C', 'D', 'A', 'D', 'A', 'A', 'A', 'C', 'D', 'A', 'B', 'D', 'A', 'D', 'A', 'C', 'D', 'A', 'D', 'A', 'D', 'B', 'C', 'D', 'A', 'A', 'B', 'B', 'D']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-19T15:57:00.418Z', 'loadTime': '2021-08-19T15:57:28.205Z', 'startTime': '2021-08-19T15:57:33.869Z', 'endTime': '2021-08-19T16:21:31.292Z', 'timeToCompleteFromOpenMsec': 1470874, 'timeToLoadMsec': 27787, 'timeToCompleteFromStartMsec': 1437423}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'none'}","{'0': {'rememberedCode': 'D59', 'isValidCode': True, 'coordinate': [288, 546]}, '1': {'rememberedCode': 'X56', 'isValidCode': True, 'coordinate': [665, 1298]}, '2': {'rememberedCode': 'X56', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'X56', 'isValidCode': True, 'coordinate': [665, 1298]}, '4': {'rememberedCode': 'C43', 'isValidCode': True, 'coordinate': [676, 716]}, '5': {'rememberedCode': 'A23', 'isValidCode': True, 'coordinate': [673, 621]}, '6': {'rememberedCode': 'A23', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'A23', 'isValidCode': True, 'coordinate': [673, 621]}, '8': {'rememberedCode': 'L25', 'isValidCode': True, 'coordinate': [1368, 1034]}, '9': {'rememberedCode': 'H19', 'isValidCode': True, 'coordinate': [657, 1112]}, '10': {'rememberedCode': 'H19', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H19', 'isValidCode': True, 'coordinate': [657, 1112]}, '12': {'rememberedCode': 'F47', 'isValidCode': True, 'coordinate': [141, 609]}, '13': {'rememberedCode': 'T97', 'isValidCode': True, 'coordinate': [184, 1257]}, '14': {'rememberedCode': 'T97', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T97', 'isValidCode': True, 'coordinate': [184, 1257]}, '16': {'rememberedCode': 'X52', 'isValidCode': True, 'coordinate': [626, 1254]}, '17': {'rememberedCode': 'X72', 'isValidCode': True, 'coordinate': [1078, 714]}, '18': {'rememberedCode': 'X72', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'X72', 'isValidCode': True, 'coordinate': [1078, 714]}, '20': {'rememberedCode': 'G53', 'isValidCode': True, 'coordinate': [405, 1140]}, '21': {'rememberedCode': 'M72', 'isValidCode': True, 'coordinate': [795, 1102]}, '22': {'rememberedCode': 'M72', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'M72', 'isValidCode': True, 'coordinate': [795, 1102]}, '24': {'rememberedCode': 'U31', 'isValidCode': True, 'coordinate': [1530, 836]}, '25': {'rememberedCode': 'D53', 'isValidCode': True, 'coordinate': [1104, 1022]}, '26': {'rememberedCode': 'D53', 'isValidCode': True, 'coordinate': [907, 42]}, '27': {'rememberedCode': 'D53', 'isValidCode': True, 'coordinate': [1104, 1022]}, '28': {'rememberedCode': 'N59', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'B23', 'isValidCode': True, 'coordinate': [49, 1308]}, '30': {'rememberedCode': 'B23', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'B23', 'isValidCode': True, 'coordinate': [49, 1308]}, '32': {'rememberedCode': 'S38', 'isValidCode': True, 'coordinate': [335, 1091]}, '33': {'rememberedCode': 'N84', 'isValidCode': True, 'coordinate': [1231, 925]}, '34': {'rememberedCode': 'N84', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'N84', 'isValidCode': True, 'coordinate': [1231, 925]}, '36': {'rememberedCode': 'L82', 'isValidCode': True, 'coordinate': [1371, 622]}, '37': {'rememberedCode': 'K84', 'isValidCode': True, 'coordinate': [41, 1096]}, '38': {'rememberedCode': 'K84', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K84', 'isValidCode': True, 'coordinate': [41, 1096]}}",767,248
+24,"['A', 'B', 'D', 'D', 'D', 'D', 'B', 'C', 'D', 'D', 'B', 'D', 'D', 'D', 'C', 'C', 'D', 'D', 'D', 'C', 'A', 'D', 'D', 'D', 'D', 'B', 'D', 'A', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'A', 'C', 'C', 'A', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'A', 'D', 'A', 'D', 'D', 'A', 'B', 'D', 'B', 'D', 'B', 'C', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'C', 'C', 'B', 'B', 'A', 'D', 'A', 'D', 'D', 'B', 'D', 'B', 'D', 'B', 'D', 'C', 'A', 'A', 'D', 'D', 'A']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-19T16:21:41.330Z', 'loadTime': '2021-08-19T16:22:11.074Z', 'startTime': '2021-08-19T16:22:33.149Z', 'endTime': '2021-08-19T16:45:42.130Z', 'timeToCompleteFromOpenMsec': 1440800, 'timeToLoadMsec': 29744, 'timeToCompleteFromStartMsec': 1388981}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'E85', 'isValidCode': True, 'coordinate': [767, 328]}, '1': {'rememberedCode': 'C86', 'isValidCode': True, 'coordinate': [771, 405]}, '2': {'rememberedCode': 'C86', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'C86', 'isValidCode': True, 'coordinate': [771, 405]}, '4': {'rememberedCode': 'D96', 'isValidCode': True, 'coordinate': [826, 649]}, '5': {'rememberedCode': 'W15', 'isValidCode': True, 'coordinate': [821, 216]}, '6': {'rememberedCode': 'W15', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'W15', 'isValidCode': True, 'coordinate': [821, 216]}, '8': {'rememberedCode': 'E8Q', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'Z85', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'Z85', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'Z85', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'C71', 'isValidCode': True, 'coordinate': [481, 227]}, '13': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [832, 628]}, '14': {'rememberedCode': 'X12', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X12', 'isValidCode': True, 'coordinate': [832, 628]}, '16': {'rememberedCode': 'X94', 'isValidCode': True, 'coordinate': [586, 157]}, '17': {'rememberedCode': 'Y86', 'isValidCode': True, 'coordinate': [579, 289]}, '18': {'rememberedCode': 'Y86', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Y86', 'isValidCode': True, 'coordinate': [579, 289]}, '20': {'rememberedCode': 'A48', 'isValidCode': True, 'coordinate': [683, 462]}, '21': {'rememberedCode': 'P83', 'isValidCode': True, 'coordinate': [478, 110]}, '22': {'rememberedCode': 'P83', 'isValidCode': True, 'coordinate': [221, 262]}, '23': {'rememberedCode': 'P83', 'isValidCode': True, 'coordinate': [478, 110]}, '24': {'rememberedCode': 'S27', 'isValidCode': True, 'coordinate': [750, 309]}, '25': {'rememberedCode': 'R52', 'isValidCode': True, 'coordinate': [361, 558]}, '26': {'rememberedCode': 'R52', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'R52', 'isValidCode': True, 'coordinate': [361, 558]}, '28': {'rememberedCode': 'F24', 'isValidCode': True, 'coordinate': [794, 912]}, '29': {'rememberedCode': 'A53', 'isValidCode': True, 'coordinate': [333, 705]}, '30': {'rememberedCode': 'A53', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'A53', 'isValidCode': True, 'coordinate': [333, 705]}, '32': {'rememberedCode': 'K95', 'isValidCode': True, 'coordinate': [487, 189]}, '33': {'rememberedCode': 'V67', 'isValidCode': True, 'coordinate': [682, 633]}, '34': {'rememberedCode': 'V67', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'V67', 'isValidCode': True, 'coordinate': [682, 633]}, '36': {'rememberedCode': 'Y82', 'isValidCode': True, 'coordinate': [368, 425]}, '37': {'rememberedCode': 'L62', 'isValidCode': True, 'coordinate': [1396, 204]}, '38': {'rememberedCode': 'L62', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L62', 'isValidCode': True, 'coordinate': [1396, 204]}}",53,988
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+42,"['A', 'B', 'D', 'D', 'D', 'A', 'B', 'C', 'D', 'D', 'A', 'A', 'A', 'D', 'C', 'C', 'A', 'A', 'D', 'D', 'A', 'D', 'D', 'B', 'A', 'A', 'B', 'A', 'C', 'D', 'C', 'D', 'C', 'D', 'A', 'A', 'B', 'D', 'D', 'A', 'A', 'D', 'D', 'B', 'D', 'A', 'C', 'B', 'D', 'D', 'A', 'C', 'D', 'A', 'D', 'B', 'A', 'D', 'C', 'C', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'A', 'D', 'D', 'B', 'C', 'D', 'D', 'A', 'A', 'A', 'D', 'B', 'C', 'C', 'B', 'D', 'A', 'D', 'A', 'B', 'A', 'B', 'D', 'A', 'D', 'B', 'D', 'D', 'A', 'A', 'D', 'B', 'D']","['2', '1', '1', '2', '1', '1', '2', '1', '2', '2', '2', '1', '2', '1', '1', '2', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '1', '1', '1', '2', '2', '2', '1']","{'openTime': '2021-08-22T08:17:30.336Z', 'loadTime': '2021-08-22T08:17:55.576Z', 'startTime': '2021-08-22T08:17:58.406Z', 'endTime': '2021-08-22T08:35:42.726Z', 'timeToCompleteFromOpenMsec': 1092390, 'timeToLoadMsec': 25240, 'timeToCompleteFromStartMsec': 1064320}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'Q59', 'isValidCode': True, 'coordinate': [1347, 758]}, '1': {'rememberedCode': 'P35', 'isValidCode': True, 'coordinate': [631, 89]}, '2': {'rememberedCode': 'P35', 'isValidCode': True, 'coordinate': [173, 329]}, '3': {'rememberedCode': 'P35', 'isValidCode': True, 'coordinate': [631, 89]}, '4': {'rememberedCode': 'Y41', 'isValidCode': True, 'coordinate': [36, 212]}, '5': {'rememberedCode': 'F64', 'isValidCode': True, 'coordinate': [955, 402]}, '6': {'rememberedCode': 'F64', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'F64', 'isValidCode': True, 'coordinate': [955, 402]}, '8': {'rememberedCode': 'N64', 'isValidCode': True, 'coordinate': [294, 195]}, '9': {'rememberedCode': 'H65', 'isValidCode': True, 'coordinate': [330, 223]}, '10': {'rememberedCode': 'H65', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'H65', 'isValidCode': True, 'coordinate': [330, 223]}, '12': {'rememberedCode': 'H26', 'isValidCode': True, 'coordinate': [744, 689]}, '13': {'rememberedCode': 'B97', 'isValidCode': True, 'coordinate': [389, 315]}, '14': {'rememberedCode': 'B97', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'B97', 'isValidCode': True, 'coordinate': [389, 315]}, '16': {'rememberedCode': 'N51', 'isValidCode': True, 'coordinate': [1026, 156]}, '17': {'rememberedCode': 'X58', 'isValidCode': True, 'coordinate': [1343, 531]}, '18': {'rememberedCode': 'X58', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'X58', 'isValidCode': True, 'coordinate': [1343, 531]}, '20': {'rememberedCode': 'G43', 'isValidCode': True, 'coordinate': [505, 546]}, '21': {'rememberedCode': 'U83', 'isValidCode': True, 'coordinate': [361, 527]}, '22': {'rememberedCode': 'U83', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U83', 'isValidCode': True, 'coordinate': [361, 527]}, '24': {'rememberedCode': 'F54', 'isValidCode': True, 'coordinate': [1077, 1031]}, '25': {'rememberedCode': 'L97', 'isValidCode': True, 'coordinate': [1095, 133]}, '26': {'rememberedCode': 'L97', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'L97', 'isValidCode': True, 'coordinate': [1095, 133]}, '28': {'rememberedCode': 'F13', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'J39', 'isValidCode': True, 'coordinate': [494, 720]}, '30': {'rememberedCode': 'J39', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'J39', 'isValidCode': True, 'coordinate': [494, 720]}, '32': {'rememberedCode': 'Z84', 'isValidCode': True, 'coordinate': [1086, 610]}, '33': {'rememberedCode': 'N64', 'isValidCode': True, 'coordinate': [507, 924]}, '34': {'rememberedCode': 'N64', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'N64', 'isValidCode': True, 'coordinate': [507, 924]}, '36': {'rememberedCode': 'H59', 'isValidCode': True, 'coordinate': [829, 1134]}, '37': {'rememberedCode': 'C82', 'isValidCode': True, 'coordinate': [944, 734]}, '38': {'rememberedCode': 'C82', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'C82', 'isValidCode': True, 'coordinate': [944, 734]}}",981,958
diff --git a/VisRecall/answer_raw/group7.csv b/VisRecall/answer_raw/group7.csv
new file mode 100755
index 0000000..1f6667c
--- /dev/null
+++ b/VisRecall/answer_raw/group7.csv
@@ -0,0 +1,19 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+1,"['B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'A', 'C', 'B', 'A', 'B']","['2', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1']","{'openTime': '2021-08-20T22:22:54.461Z', 'loadTime': '2021-08-20T22:23:01.990Z', 'startTime': '2021-08-20T22:23:09.183Z', 'endTime': '2021-08-20T22:44:08.576Z', 'timeToCompleteFromOpenMsec': 1274115, 'timeToLoadMsec': 7529, 'timeToCompleteFromStartMsec': 1259393}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'good stuty'}","{'0': {'rememberedCode': 'T63', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'K73', 'isValidCode': True, 'coordinate': [353, 823]}, '5': {'rememberedCode': 'U92', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'U92', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U92', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'W19', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'V96', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'V96', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'V96', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'A21', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'T13', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'T13', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'T13', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'A93', 'isValidCode': True, 'coordinate': [364, 1300]}, '17': {'rememberedCode': 'K93', 'isValidCode': True, 'coordinate': [1593, 216]}, '18': {'rememberedCode': 'K93', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K93', 'isValidCode': True, 'coordinate': [1593, 216]}, '20': {'rememberedCode': 'R68', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'B78', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'B78', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'B78', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'K76', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'U28', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'U28', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U28', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'Z62', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'E27', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'E27', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'E27', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'A36', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'P52', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'P52', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'P52', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'N49', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'A37', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'A37', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'A37', 'isValidCode': False, 'coordinate': False}}",549,662
+2,"['A', 'C', 'D', 'D', 'C', 'B', 'A', 'A', 'A', 'A', 'C', 'C', 'C', 'D', 'D', 'C', 'C', 'D', 'D', 'B', 'A', 'C', 'C', 'D', 'D', 'B', 'A', 'B', 'B', 'D', 'C', 'B', 'D', 'B', 'D', 'B', 'B', 'C', 'B', 'A', 'C', 'A', 'B', 'C', 'C', 'A', 'C', 'A', 'D', 'A', 'A', 'A', 'C', 'C', 'D', 'A', 'B', 'B', 'A', 'B', 'B', 'A', 'D', 'D', 'D', 'C', 'A', 'D', 'D', 'D', 'C', 'B', 'B', 'B', 'B', 'A', 'B', 'B', 'D', 'A', 'B', 'B', 'B', 'B', 'C', 'A', 'D', 'C', 'C', 'D', 'B', 'A', 'C', 'C', 'D', 'C', 'C', 'B', 'D', 'C']","['2', '1', '1', '2', '2', '2', '1', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '1', '1', '1', '1', '2', '1', '1', '2', '2', '2', '2', '1', '2', '2', '1', '1', '2', '2']","{'openTime': '2021-08-20T22:38:15.710Z', 'loadTime': '2021-08-20T22:38:32.225Z', 'startTime': '2021-08-20T22:38:43.520Z', 'endTime': '2021-08-20T22:58:19.879Z', 'timeToCompleteFromOpenMsec': 1204169, 'timeToLoadMsec': 16515, 'timeToCompleteFromStartMsec': 1176359}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'A57', 'isValidCode': True, 'coordinate': [206, 425]}, '1': {'rememberedCode': 'Y31', 'isValidCode': True, 'coordinate': [448, 834]}, '2': {'rememberedCode': 'Y31', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Y31', 'isValidCode': True, 'coordinate': [448, 834]}, '4': {'rememberedCode': 'T79', 'isValidCode': True, 'coordinate': [680, 310]}, '5': {'rememberedCode': 'N31', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'N31', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N31', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'N66', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'X82', 'isValidCode': True, 'coordinate': [672, 111]}, '10': {'rememberedCode': 'X82', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'X82', 'isValidCode': True, 'coordinate': [672, 111]}, '12': {'rememberedCode': 'D86', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'J48', 'isValidCode': True, 'coordinate': [813, 1007]}, '14': {'rememberedCode': 'J48', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'J48', 'isValidCode': True, 'coordinate': [813, 1007]}, '16': {'rememberedCode': 'Q74', 'isValidCode': True, 'coordinate': [319, 1229]}, '17': {'rememberedCode': 'N39', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'N39', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'N39', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'F86', 'isValidCode': True, 'coordinate': [877, 369]}, '21': {'rememberedCode': 'C13', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'C13', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'C13', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'A31', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'Y72', 'isValidCode': True, 'coordinate': [785, 652]}, '26': {'rememberedCode': 'Y72', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Y72', 'isValidCode': True, 'coordinate': [785, 652]}, '28': {'rememberedCode': 'A28', 'isValidCode': True, 'coordinate': [682, 643]}, '29': {'rememberedCode': 'X54', 'isValidCode': True, 'coordinate': [495, 854]}, '30': {'rememberedCode': 'X54', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'X54', 'isValidCode': True, 'coordinate': [495, 854]}, '32': {'rememberedCode': 'A29', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'Y64', 'isValidCode': True, 'coordinate': [991, 507]}, '34': {'rememberedCode': 'Y64', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Y64', 'isValidCode': True, 'coordinate': [991, 507]}, '36': {'rememberedCode': 'P41', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'K26', 'isValidCode': True, 'coordinate': [379, 327]}, '38': {'rememberedCode': 'K26', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K26', 'isValidCode': True, 'coordinate': [379, 327]}}",443,581
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+4,"['A', 'B', 'B', 'A', 'C', 'B', 'B', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'B', 'C', 'B', 'A', 'B', 'B', 'B', 'C', 'B', 'A', 'C', 'A', 'A', 'C', 'C', 'B', 'B', 'B', 'B', 'A', 'A', 'B', 'A', 'B', 'B', 'A', 'A', 'B', 'B', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'C', 'C', 'B', 'B', 'A', 'A', 'B', 'B', 'C', 'B', 'B', 'B', 'A', 'C', 'C', 'A', 'B', 'B', 'A', 'B', 'B', 'C', 'C', 'C', 'B', 'C', 'C', 'B', 'A', 'B']","['2', '2', '1', '2', '2', '2', '2', '2', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '2', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1']","{'openTime': '2021-08-20T23:24:16.638Z', 'loadTime': '2021-08-20T23:24:33.095Z', 'startTime': '2021-08-20T23:24:38.805Z', 'endTime': '2021-08-20T23:40:42.724Z', 'timeToCompleteFromOpenMsec': 986086, 'timeToLoadMsec': 16457, 'timeToCompleteFromStartMsec': 963919}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good Study'}","{'0': {'rememberedCode': 'Z37', 'isValidCode': True, 'coordinate': [234, 193]}, '1': {'rememberedCode': 'M52', 'isValidCode': True, 'coordinate': [508, 449]}, '2': {'rememberedCode': 'M52', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'M52', 'isValidCode': True, 'coordinate': [508, 449]}, '4': {'rememberedCode': 'N12', 'isValidCode': True, 'coordinate': [678, 723]}, '5': {'rememberedCode': 'W73', 'isValidCode': True, 'coordinate': [857, 928]}, '6': {'rememberedCode': 'W73', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'W73', 'isValidCode': True, 'coordinate': [857, 928]}, '8': {'rememberedCode': 'J84', 'isValidCode': True, 'coordinate': [1289, 345]}, '9': {'rememberedCode': 'P91', 'isValidCode': True, 'coordinate': [1126, 639]}, '10': {'rememberedCode': 'P91', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'P91', 'isValidCode': True, 'coordinate': [1126, 639]}, '12': {'rememberedCode': 'A26', 'isValidCode': True, 'coordinate': [1442, 737]}, '13': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [311, 500]}, '14': {'rememberedCode': 'B29', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'B29', 'isValidCode': True, 'coordinate': [311, 500]}, '16': {'rememberedCode': 'Z53', 'isValidCode': True, 'coordinate': [940, 1191]}, '17': {'rememberedCode': 'C53', 'isValidCode': True, 'coordinate': [640, 228]}, '18': {'rememberedCode': 'C53', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'C53', 'isValidCode': True, 'coordinate': [640, 228]}, '20': {'rememberedCode': 'Y41', 'isValidCode': True, 'coordinate': [358, 932]}, '21': {'rememberedCode': 'L92', 'isValidCode': True, 'coordinate': [827, 1139]}, '22': {'rememberedCode': 'L92', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L92', 'isValidCode': True, 'coordinate': [827, 1139]}, '24': {'rememberedCode': 'D81', 'isValidCode': True, 'coordinate': [654, 106]}, '25': {'rememberedCode': 'K39', 'isValidCode': True, 'coordinate': [500, 346]}, '26': {'rememberedCode': 'K39', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'K39', 'isValidCode': True, 'coordinate': [500, 346]}, '28': {'rememberedCode': 'P38', 'isValidCode': True, 'coordinate': [353, 431]}, '29': {'rememberedCode': 'D19', 'isValidCode': True, 'coordinate': [618, 220]}, '30': {'rememberedCode': 'D19', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'D19', 'isValidCode': True, 'coordinate': [618, 220]}, '32': {'rememberedCode': 'X47', 'isValidCode': True, 'coordinate': [1407, 1145]}, '33': {'rememberedCode': 'Y58', 'isValidCode': True, 'coordinate': [853, 199]}, '34': {'rememberedCode': 'Y58', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Y58', 'isValidCode': True, 'coordinate': [853, 199]}, '36': {'rememberedCode': 'P28', 'isValidCode': True, 'coordinate': [1090, 122]}, '37': {'rememberedCode': 'A74', 'isValidCode': True, 'coordinate': [390, 909]}, '38': {'rememberedCode': 'A74', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'A74', 'isValidCode': True, 'coordinate': [390, 909]}}",584,668
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+17,"['A', 'A', 'C', 'B', 'B', 'B', 'B', 'C', 'A', 'A', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'C', 'C', 'C', 'A', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'C', 'D', 'C', 'C', 'B', 'B', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'C', 'A', 'B', 'C', 'B', 'C', 'A', 'C', 'C', 'B', 'D', 'A', 'B', 'B', 'B', 'B', 'A', 'B', 'C', 'A', 'A', 'B', 'A', 'B', 'B', 'C', 'C', 'A', 'B', 'C', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'C', 'C', 'B', 'B', 'C', 'C', 'C', 'C', 'D', 'D', 'D', 'D', 'D']","['2', '2', '1', '1', '2', '2', '2', '2', '2', '1', '2', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1']","{'openTime': '2021-08-22T02:42:23.989Z', 'loadTime': '2021-08-22T02:42:25.957Z', 'startTime': '2021-08-22T02:42:50.036Z', 'endTime': '2021-08-22T03:39:02.276Z', 'timeToCompleteFromOpenMsec': 3398287, 'timeToLoadMsec': 1968, 'timeToCompleteFromStartMsec': 3372240}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'K18', 'isValidCode': True, 'coordinate': [1131, 716]}, '1': {'rememberedCode': 'D38', 'isValidCode': True, 'coordinate': [326, 41]}, '2': {'rememberedCode': 'D38', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'D38', 'isValidCode': True, 'coordinate': [326, 41]}, '4': {'rememberedCode': 'R12', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'H94', 'isValidCode': True, 'coordinate': [1117, 738]}, '6': {'rememberedCode': 'H94', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H94', 'isValidCode': True, 'coordinate': [1117, 738]}, '8': {'rememberedCode': 'S38', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'T48', 'isValidCode': True, 'coordinate': [497, 229]}, '10': {'rememberedCode': 'T48', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'T48', 'isValidCode': True, 'coordinate': [497, 229]}, '12': {'rememberedCode': 'A26', 'isValidCode': True, 'coordinate': [1442, 737]}, '13': {'rememberedCode': 'U13', 'isValidCode': True, 'coordinate': [453, 311]}, '14': {'rememberedCode': 'U13', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'U13', 'isValidCode': True, 'coordinate': [453, 311]}, '16': {'rememberedCode': 'E94', 'isValidCode': True, 'coordinate': [786, 297]}, '17': {'rememberedCode': 'N51', 'isValidCode': True, 'coordinate': [483, 215]}, '18': {'rememberedCode': 'N51', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'N51', 'isValidCode': True, 'coordinate': [483, 215]}, '20': {'rememberedCode': 'U18', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'B76', 'isValidCode': True, 'coordinate': [504, 320]}, '22': {'rememberedCode': 'B76', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'B76', 'isValidCode': True, 'coordinate': [504, 320]}, '24': {'rememberedCode': 'V95', 'isValidCode': True, 'coordinate': [1435, 320]}, '25': {'rememberedCode': 'W82', 'isValidCode': True, 'coordinate': [952, 664]}, '26': {'rememberedCode': 'W82', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'W82', 'isValidCode': True, 'coordinate': [952, 664]}, '28': {'rememberedCode': 'R96', 'isValidCode': True, 'coordinate': [964, 532]}, '29': {'rememberedCode': 'U67', 'isValidCode': True, 'coordinate': [1122, 431]}, '30': {'rememberedCode': 'U67', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'U67', 'isValidCode': True, 'coordinate': [1122, 431]}, '32': {'rememberedCode': 'R76', 'isValidCode': True, 'coordinate': [1144, 634]}, '33': {'rememberedCode': 'E18', 'isValidCode': True, 'coordinate': [1280, 730]}, '34': {'rememberedCode': 'E18', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E18', 'isValidCode': True, 'coordinate': [1280, 730]}, '36': {'rememberedCode': 'B39', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'E18', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'E18', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E18', 'isValidCode': False, 'coordinate': False}}",931,893
+18,"['A', 'A', 'C', 'C', 'B', 'B', 'B', 'A', 'A', 'A', 'C', 'C', 'D', 'B', 'A', 'A', 'A', 'A', 'D', 'C', 'A', 'A', 'C', 'B', 'D', 'B', 'A', 'B', 'B', 'A', 'C', 'B', 'C', 'B', 'B', 'B', 'D', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'C', 'A', 'B', 'C', 'D', 'C', 'A', 'C', 'C', 'B', 'C', 'A', 'B', 'B', 'B', 'B', 'C', 'C', 'C', 'A', 'D', 'B', 'A', 'B', 'B', 'B', 'C', 'B', 'B', 'C', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'C', 'C', 'B', 'B', 'C', 'C', 'C', 'C', 'C', 'C', 'A', 'B', 'C']","['2', '2', '2', '2', '2', '2', '2', '2', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '1', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '1', '1']","{'openTime': '2021-08-22T03:42:19.202Z', 'loadTime': '2021-08-22T03:42:20.931Z', 'startTime': '2021-08-22T03:42:47.339Z', 'endTime': '2021-08-22T04:12:29.622Z', 'timeToCompleteFromOpenMsec': 1810420, 'timeToLoadMsec': 1729, 'timeToCompleteFromStartMsec': 1782283}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Interesting task.'}","{'0': {'rememberedCode': 'Y92', 'isValidCode': True, 'coordinate': [846, 586]}, '1': {'rememberedCode': 'J57', 'isValidCode': True, 'coordinate': [779, 661]}, '2': {'rememberedCode': 'J57', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'J57', 'isValidCode': True, 'coordinate': [779, 661]}, '4': {'rememberedCode': 'V87', 'isValidCode': True, 'coordinate': [836, 535]}, '5': {'rememberedCode': 'E53', 'isValidCode': True, 'coordinate': [870, 559]}, '6': {'rememberedCode': 'E53', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E53', 'isValidCode': True, 'coordinate': [870, 559]}, '8': {'rememberedCode': 'S62', 'isValidCode': True, 'coordinate': [979, 648]}, '9': {'rememberedCode': 'A25', 'isValidCode': True, 'coordinate': [936, 539]}, '10': {'rememberedCode': 'A25', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'A25', 'isValidCode': True, 'coordinate': [936, 539]}, '12': {'rememberedCode': 'R48', 'isValidCode': True, 'coordinate': [841, 633]}, '13': {'rememberedCode': 'M13', 'isValidCode': True, 'coordinate': [961, 612]}, '14': {'rememberedCode': 'M13', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'M13', 'isValidCode': True, 'coordinate': [961, 612]}, '16': {'rememberedCode': 'T97', 'isValidCode': True, 'coordinate': [787, 697]}, '17': {'rememberedCode': 'G16', 'isValidCode': True, 'coordinate': [676, 443]}, '18': {'rememberedCode': 'G16', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'G16', 'isValidCode': True, 'coordinate': [676, 443]}, '20': {'rememberedCode': 'Y63', 'isValidCode': True, 'coordinate': [825, 636]}, '21': {'rememberedCode': 'S62', 'isValidCode': True, 'coordinate': [798, 532]}, '22': {'rememberedCode': 'S62', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'S62', 'isValidCode': True, 'coordinate': [798, 532]}, '24': {'rememberedCode': 'Q82', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'P78', 'isValidCode': True, 'coordinate': [655, 551]}, '26': {'rememberedCode': 'P78', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'P78', 'isValidCode': True, 'coordinate': [655, 551]}, '28': {'rememberedCode': 'G91', 'isValidCode': True, 'coordinate': [1151, 672]}, '29': {'rememberedCode': 'Q25', 'isValidCode': True, 'coordinate': [930, 646]}, '30': {'rememberedCode': 'Q25', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Q25', 'isValidCode': True, 'coordinate': [930, 646]}, '32': {'rememberedCode': 'P75', 'isValidCode': True, 'coordinate': [818, 623]}, '33': {'rememberedCode': 'T96', 'isValidCode': True, 'coordinate': [844, 529]}, '34': {'rememberedCode': 'T96', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'T96', 'isValidCode': True, 'coordinate': [844, 529]}, '36': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'Y21', 'isValidCode': True, 'coordinate': [705, 519]}, '38': {'rememberedCode': 'Y21', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'Y21', 'isValidCode': True, 'coordinate': [705, 519]}}",805,779
diff --git a/VisRecall/answer_raw/group8.csv b/VisRecall/answer_raw/group8.csv
new file mode 100755
index 0000000..895dbba
--- /dev/null
+++ b/VisRecall/answer_raw/group8.csv
@@ -0,0 +1,40 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+0,"['A', 'A', 'A', 'B', 'B', 'C', 'B', 'D', 'C', 'A', 'C', 'A', 'A', 'B', 'B', 'A', 'C', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'A', 'A', 'C', 'D', 'B', 'C', 'A', 'B', 'A', 'D', 'D', 'A', 'B', 'B', 'C', 'C', 'B', 'D', 'D', 'D', 'C', 'A', 'A', 'C', 'B', 'B', 'C', 'A', 'A', 'A', 'C', 'A', 'A', 'D', 'D', 'C', 'A', 'C', 'A', 'D', 'C', 'B', 'C', 'B', 'A', 'A', 'A', 'B', 'C', 'D', 'D', 'A', 'B', 'C', 'D', 'D', 'A', 'B', 'D', 'B', 'A', 'C', 'D', 'A', 'B', 'B', 'A', 'B', 'D', 'D', 'D', 'A', 'A', 'B', 'C', 'B']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '1', '1', '2']","{'openTime': '2021-08-14T13:47:27.118Z', 'loadTime': '2021-08-14T13:48:11.399Z', 'startTime': '2021-08-14T13:48:23.629Z', 'endTime': '2021-08-14T14:19:35.511Z', 'timeToCompleteFromOpenMsec': 1928393, 'timeToLoadMsec': 44281, 'timeToCompleteFromStartMsec': 1871882}","{'gender': 'unspecified', 'ageGroup': 'unspecified', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'It was longer than anticipated.'}","{'0': {'rememberedCode': 'B21', 'isValidCode': True, 'coordinate': [612, 730]}, '1': {'rememberedCode': 'P41', 'isValidCode': True, 'coordinate': [693, 368]}, '2': {'rememberedCode': 'P41', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'P41', 'isValidCode': True, 'coordinate': [693, 368]}, '4': {'rememberedCode': 'R45', 'isValidCode': True, 'coordinate': [682, 318]}, '5': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1102, 413]}, '6': {'rememberedCode': 'E14', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1102, 413]}, '8': {'rememberedCode': 'K23', 'isValidCode': True, 'coordinate': [194, 33]}, '9': {'rememberedCode': 'R81', 'isValidCode': True, 'coordinate': [946, 468]}, '10': {'rememberedCode': 'R81', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'R81', 'isValidCode': True, 'coordinate': [946, 468]}, '12': {'rememberedCode': 'E19', 'isValidCode': True, 'coordinate': [665, 519]}, '13': {'rememberedCode': 'P41', 'isValidCode': True, 'coordinate': [1271, 412]}, '14': {'rememberedCode': 'P41', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'P41', 'isValidCode': True, 'coordinate': [1271, 412]}, '16': {'rememberedCode': 'B13', 'isValidCode': True, 'coordinate': [782, 320]}, '17': {'rememberedCode': 'K87', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'K87', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K87', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'K46', 'isValidCode': True, 'coordinate': [976, 359]}, '21': {'rememberedCode': 'E47', 'isValidCode': True, 'coordinate': [521, 343]}, '22': {'rememberedCode': 'E47', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'E47', 'isValidCode': True, 'coordinate': [521, 343]}, '24': {'rememberedCode': 'K14', 'isValidCode': True, 'coordinate': [830, 740]}, '25': {'rememberedCode': 'A36', 'isValidCode': True, 'coordinate': [548, 418]}, '26': {'rememberedCode': 'A36', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'A36', 'isValidCode': True, 'coordinate': [548, 418]}, '28': {'rememberedCode': 'K81', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'L51', 'isValidCode': True, 'coordinate': [1024, 445]}, '30': {'rememberedCode': 'L51', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L51', 'isValidCode': True, 'coordinate': [1024, 445]}, '32': {'rememberedCode': 'V41', 'isValidCode': True, 'coordinate': [940, 523]}, '33': {'rememberedCode': 'K92', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'K92', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'K92', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'G14', 'isValidCode': True, 'coordinate': [975, 727]}, '37': {'rememberedCode': 'R69', 'isValidCode': True, 'coordinate': [513, 231]}, '38': {'rememberedCode': 'R69', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'R69', 'isValidCode': True, 'coordinate': [513, 231]}}",485,987
+1,"['A', 'A', 'A', 'B', 'B', 'A', 'B', 'B', 'A', 'A', 'C', 'C', 'A', 'A', 'A', 'A', 'C', 'B', 'B', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'A', 'A', 'A', 'C', 'A', 'A', 'A', 'B', 'B', 'A', 'A', 'A', 'A', 'C', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'C', 'C', 'A', 'C', 'C', 'B', 'B', 'C', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'C', 'A', 'C', 'B', 'C', 'C', 'B', 'A', 'A', 'A', 'C', 'A', 'A', 'A', 'B', 'C', 'A', 'D', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'A', 'A', 'A', 'A', 'A', 'C', 'A', 'C', 'A', 'A', 'B', 'B', 'B']","['2', '2', '1', '2', '2', '2', '1', '2', '2', '1', '1', '2', '2', '1', '1', '1', '1', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '1', '2', '1', '1', '2']","{'openTime': '2021-08-14T13:57:53.400Z', 'loadTime': '2021-08-14T13:58:48.223Z', 'startTime': '2021-08-14T13:58:49.515Z', 'endTime': '2021-08-14T14:27:44.011Z', 'timeToCompleteFromOpenMsec': 1790611, 'timeToLoadMsec': 54823, 'timeToCompleteFromStartMsec': 1734496}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'n/a'}","{'0': {'rememberedCode': 'V56', 'isValidCode': True, 'coordinate': [186, 528]}, '1': {'rememberedCode': 'W28', 'isValidCode': True, 'coordinate': [856, 673]}, '2': {'rememberedCode': 'W28', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'W28', 'isValidCode': True, 'coordinate': [856, 673]}, '4': {'rememberedCode': 'M41', 'isValidCode': True, 'coordinate': [840, 439]}, '5': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1102, 413]}, '6': {'rememberedCode': 'E14', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1102, 413]}, '8': {'rememberedCode': 'H25', 'isValidCode': True, 'coordinate': [1103, 539]}, '9': {'rememberedCode': 'U74', 'isValidCode': True, 'coordinate': [667, 548]}, '10': {'rememberedCode': 'U74', 'isValidCode': True, 'coordinate': [939, 1233]}, '11': {'rememberedCode': 'U74', 'isValidCode': True, 'coordinate': [667, 548]}, '12': {'rememberedCode': 'L43', 'isValidCode': True, 'coordinate': [852, 555]}, '13': {'rememberedCode': 'W34', 'isValidCode': True, 'coordinate': [964, 402]}, '14': {'rememberedCode': 'W34', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'W34', 'isValidCode': True, 'coordinate': [964, 402]}, '16': {'rememberedCode': 'C39', 'isValidCode': True, 'coordinate': [785, 420]}, '17': {'rememberedCode': 'K75', 'isValidCode': True, 'coordinate': [704, 339]}, '18': {'rememberedCode': 'K75', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K75', 'isValidCode': True, 'coordinate': [704, 339]}, '20': {'rememberedCode': 'S49', 'isValidCode': True, 'coordinate': [872, 552]}, '21': {'rememberedCode': 'A39', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'A39', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'A39', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'Y62', 'isValidCode': True, 'coordinate': [979, 414]}, '25': {'rememberedCode': 'Y41', 'isValidCode': True, 'coordinate': [1012, 492]}, '26': {'rememberedCode': 'Y41', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Y41', 'isValidCode': True, 'coordinate': [1012, 492]}, '28': {'rememberedCode': 'K61', 'isValidCode': True, 'coordinate': [533, 792]}, '29': {'rememberedCode': 'U84', 'isValidCode': True, 'coordinate': [1016, 638]}, '30': {'rememberedCode': 'U84', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'U84', 'isValidCode': True, 'coordinate': [1016, 638]}, '32': {'rememberedCode': 'N95', 'isValidCode': True, 'coordinate': [1290, 531]}, '33': {'rememberedCode': 'G28', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'G28', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'G28', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'J71', 'isValidCode': True, 'coordinate': [1006, 458]}, '37': {'rememberedCode': 'F49', 'isValidCode': True, 'coordinate': [974, 527]}, '38': {'rememberedCode': 'F49', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'F49', 'isValidCode': True, 'coordinate': [974, 527]}}",914,65
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+3,"['A', 'A', 'B', 'A', 'B', 'C', 'B', 'C', 'C', 'A', 'C', 'B', 'A', 'D', 'D', 'A', 'D', 'D', 'A', 'B', 'C', 'B', 'D', 'D', 'D', 'A', 'D', 'C', 'D', 'D', 'D', 'B', 'C', 'A', 'D', 'A', 'B', 'B', 'C', 'C', 'A', 'D', 'D', 'D', 'C', 'C', 'A', 'D', 'D', 'D', 'C', 'D', 'B', 'D', 'C', 'C', 'B', 'B', 'D', 'D', 'A', 'C', 'C', 'C', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'B', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'B', 'C', 'B', 'A', 'C', 'D', 'D', 'C', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'A', 'B', 'C', 'D']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '2', '1', '1', '2']","{'openTime': '2021-08-14T14:14:20.816Z', 'loadTime': '2021-08-14T14:14:42.385Z', 'startTime': '2021-08-14T14:14:56.185Z', 'endTime': '2021-08-14T14:33:57.354Z', 'timeToCompleteFromOpenMsec': 1176538, 'timeToLoadMsec': 21569, 'timeToCompleteFromStartMsec': 1141169}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'Q34', 'isValidCode': True, 'coordinate': [1576, 628]}, '1': {'rememberedCode': 'S48', 'isValidCode': True, 'coordinate': [1276, 335]}, '2': {'rememberedCode': 'S48', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'S48', 'isValidCode': True, 'coordinate': [1276, 335]}, '4': {'rememberedCode': 'N93', 'isValidCode': True, 'coordinate': [720, 608]}, '5': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H64', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'E32', 'isValidCode': True, 'coordinate': [1602, 228]}, '9': {'rememberedCode': 'P21', 'isValidCode': True, 'coordinate': [665, 648]}, '10': {'rememberedCode': 'P21', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'P21', 'isValidCode': True, 'coordinate': [665, 648]}, '12': {'rememberedCode': 'Y19', 'isValidCode': True, 'coordinate': [1304, 843]}, '13': {'rememberedCode': 'M14', 'isValidCode': True, 'coordinate': [669, 603]}, '14': {'rememberedCode': 'M14', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'M14', 'isValidCode': True, 'coordinate': [669, 603]}, '16': {'rememberedCode': 'B13', 'isValidCode': True, 'coordinate': [782, 320]}, '17': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'G24', 'isValidCode': True, 'coordinate': [724, 524]}, '21': {'rememberedCode': 'W39', 'isValidCode': True, 'coordinate': [831, 732]}, '22': {'rememberedCode': 'W39', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'W39', 'isValidCode': True, 'coordinate': [831, 732]}, '24': {'rememberedCode': 'J97', 'isValidCode': True, 'coordinate': [789, 421]}, '25': {'rememberedCode': 'G37', 'isValidCode': True, 'coordinate': [708, 407]}, '26': {'rememberedCode': 'G37', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'G37', 'isValidCode': True, 'coordinate': [708, 407]}, '28': {'rememberedCode': 'H32', 'isValidCode': True, 'coordinate': [661, 612]}, '29': {'rememberedCode': 'F18', 'isValidCode': True, 'coordinate': [1185, 831]}, '30': {'rememberedCode': 'F18', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'F18', 'isValidCode': True, 'coordinate': [1185, 831]}, '32': {'rememberedCode': 'H58', 'isValidCode': True, 'coordinate': [828, 740]}, '33': {'rememberedCode': 'F72', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'F72', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'F72', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'P63', 'isValidCode': True, 'coordinate': [847, 662]}, '37': {'rememberedCode': 'L87', 'isValidCode': True, 'coordinate': [1287, 1026]}, '38': {'rememberedCode': 'L87', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L87', 'isValidCode': True, 'coordinate': [1287, 1026]}}",752,858
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+5,"['A', 'A', 'A', 'B', 'B', 'C', 'B', 'B', 'C', 'D', 'C', 'D', 'A', 'D', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'D', 'B', 'A', 'A', 'C', 'C', 'B', 'D', 'D', 'B', 'A', 'D', 'C', 'D', 'B', 'B', 'D', 'C', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'B', 'D', 'C', 'B', 'B', 'D', 'C', 'A', 'A', 'D', 'D', 'D', 'A', 'C', 'C', 'D', 'C', 'B', 'C', 'D', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'C', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'D']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '2', '1', '1', '2']","{'openTime': '2021-08-14T14:04:06.874Z', 'loadTime': '2021-08-14T14:04:33.205Z', 'startTime': '2021-08-14T14:09:21.145Z', 'endTime': '2021-08-14T14:38:27.053Z', 'timeToCompleteFromOpenMsec': 2060179, 'timeToLoadMsec': 26331, 'timeToCompleteFromStartMsec': 1745908}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': '3DG', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'P41', 'isValidCode': True, 'coordinate': [693, 368]}, '2': {'rememberedCode': 'P41', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'P41', 'isValidCode': True, 'coordinate': [693, 368]}, '4': {'rememberedCode': 'C47', 'isValidCode': True, 'coordinate': [667, 399]}, '5': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1102, 413]}, '6': {'rememberedCode': 'E14', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E14', 'isValidCode': True, 'coordinate': [1102, 413]}, '8': {'rememberedCode': 'A27', 'isValidCode': True, 'coordinate': [787, 530]}, '9': {'rememberedCode': 'A41', 'isValidCode': True, 'coordinate': [626, 473]}, '10': {'rememberedCode': 'A41', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'A41', 'isValidCode': True, 'coordinate': [626, 473]}, '12': {'rememberedCode': 'V96', 'isValidCode': True, 'coordinate': [513, 632]}, '13': {'rememberedCode': 'N89', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'N89', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'N89', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'Y61', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'R51', 'isValidCode': True, 'coordinate': [807, 942]}, '18': {'rememberedCode': 'R51', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'R51', 'isValidCode': True, 'coordinate': [807, 942]}, '20': {'rememberedCode': 'W45', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'Z84', 'isValidCode': True, 'coordinate': [353, 455]}, '22': {'rememberedCode': 'Z84', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Z84', 'isValidCode': True, 'coordinate': [353, 455]}, '24': {'rememberedCode': 'S63', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'T45', 'isValidCode': True, 'coordinate': [1158, 431]}, '26': {'rememberedCode': 'T45', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'T45', 'isValidCode': True, 'coordinate': [1158, 431]}, '28': {'rememberedCode': 'U28', 'isValidCode': True, 'coordinate': [1300, 594]}, '29': {'rememberedCode': 'S71', 'isValidCode': True, 'coordinate': [690, 658]}, '30': {'rememberedCode': 'S71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'S71', 'isValidCode': True, 'coordinate': [690, 658]}, '32': {'rememberedCode': 'D98', 'isValidCode': True, 'coordinate': [823, 432]}, '33': {'rememberedCode': 'E92', 'isValidCode': True, 'coordinate': [243, 710]}, '34': {'rememberedCode': 'E92', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E92', 'isValidCode': True, 'coordinate': [243, 710]}, '36': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [560, 160]}, '37': {'rememberedCode': 'M85', 'isValidCode': True, 'coordinate': [486, 1239]}, '38': {'rememberedCode': 'M85', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'M85', 'isValidCode': True, 'coordinate': [486, 1239]}}",865,605
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+11,"['A', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'C', 'C', 'C', 'C', 'D', 'A', 'D', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'B', 'D', 'D', 'A', 'C', 'C', 'B', 'C', 'A', 'C', 'C', 'A', 'D', 'A', 'B', 'A', 'C', 'B', 'D', 'B', 'D', 'D', 'D', 'A', 'C', 'C', 'A', 'D', 'A', 'D', 'B', 'C', 'B', 'D', 'D', 'B', 'D', 'D', 'A', 'D', 'A', 'A', 'C', 'A', 'D', 'B', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'D', 'C', 'C', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'B', 'C', 'D']","['2', '1', '1', '2', '2', '2', '1', '2', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '2', '1', '2', '1', '2', '1', '1', '1', '1', '1', '1', '2', '1', '2', '1', '2', '2', '1', '1', '2']","{'openTime': '2021-08-14T16:33:35.790Z', 'loadTime': '2021-08-14T16:33:53.611Z', 'startTime': '2021-08-14T16:35:59.831Z', 'endTime': '2021-08-14T16:54:01.254Z', 'timeToCompleteFromOpenMsec': 1225464, 'timeToLoadMsec': 17821, 'timeToCompleteFromStartMsec': 1081423}","{'gender': 'male', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'someCollege', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'CAR', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'NP9', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'NP9', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'NP9', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'P28', 'isValidCode': True, 'coordinate': [864, 720]}, '5': {'rememberedCode': 'I71', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'I71', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'I71', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'C54', 'isValidCode': True, 'coordinate': [310, 462]}, '9': {'rememberedCode': 'D58', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'D58', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'D58', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'D17', 'isValidCode': True, 'coordinate': [658, 641]}, '13': {'rememberedCode': 'K58', 'isValidCode': True, 'coordinate': [930, 624]}, '14': {'rememberedCode': 'K58', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'K58', 'isValidCode': True, 'coordinate': [930, 624]}, '16': {'rememberedCode': 'D58', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'W12', 'isValidCode': True, 'coordinate': [687, 529]}, '18': {'rememberedCode': 'W12', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'W12', 'isValidCode': True, 'coordinate': [687, 529]}, '20': {'rememberedCode': 'Q31', 'isValidCode': True, 'coordinate': [554, 744]}, '21': {'rememberedCode': 'P58', 'isValidCode': True, 'coordinate': [655, 560]}, '22': {'rememberedCode': 'P58', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'P58', 'isValidCode': True, 'coordinate': [655, 560]}, '24': {'rememberedCode': 'J31', 'isValidCode': True, 'coordinate': [645, 521]}, '25': {'rememberedCode': 'U31', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'U31', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U31', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'F81', 'isValidCode': True, 'coordinate': [525, 709]}, '29': {'rememberedCode': 'Y28', 'isValidCode': True, 'coordinate': [727, 533]}, '30': {'rememberedCode': 'Y28', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Y28', 'isValidCode': True, 'coordinate': [727, 533]}, '32': {'rememberedCode': 'Y58', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'Z32', 'isValidCode': True, 'coordinate': [848, 611]}, '34': {'rememberedCode': 'Z32', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Z32', 'isValidCode': True, 'coordinate': [848, 611]}, '36': {'rememberedCode': 'P57', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'F47', 'isValidCode': True, 'coordinate': [1127, 1221]}, '38': {'rememberedCode': 'F47', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'F47', 'isValidCode': True, 'coordinate': [1127, 1221]}}",438,635
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+13,"['A', 'A', 'A', 'B', 'B', 'C', 'B', 'B', 'D', 'D', 'C', 'A', 'A', 'B', 'D', 'D', 'C', 'B', 'A', 'B', 'A', 'D', 'D', 'B', 'A', 'A', 'C', 'D', 'B', 'C', 'A', 'B', 'A', 'D', 'C', 'A', 'B', 'B', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'C', 'A', 'D', 'C', 'B', 'B', 'B', 'C', 'A', 'A', 'D', 'D', 'D', 'A', 'C', 'C', 'A', 'C', 'B', 'C', 'A', 'B', 'D', 'A', 'C', 'C', 'D', 'B', 'A', 'D', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'C', 'A', 'B', 'D', 'A', 'D', 'D', 'A', 'B', 'A', 'A', 'B', 'C', 'D']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '1', '1', '2', '1', '1', '2', '2', '2', '1', '1', '2', '1', '1', '2']","{'openTime': '2021-08-14T17:32:22.198Z', 'loadTime': '2021-08-14T17:32:41.024Z', 'startTime': '2021-08-14T17:33:12.736Z', 'endTime': '2021-08-14T18:14:49.824Z', 'timeToCompleteFromOpenMsec': 2547626, 'timeToLoadMsec': 18826, 'timeToCompleteFromStartMsec': 2497088}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': 'It is a very good analyze memory power. It was a a great study. Amazing experience. '}","{'0': {'rememberedCode': 'Y35', 'isValidCode': True, 'coordinate': [1277, 1116]}, '1': {'rememberedCode': 'T32', 'isValidCode': True, 'coordinate': [1616, 1150]}, '2': {'rememberedCode': 'T32', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'T32', 'isValidCode': True, 'coordinate': [1616, 1150]}, '4': {'rememberedCode': 'D37', 'isValidCode': True, 'coordinate': [1627, 1214]}, '5': {'rememberedCode': 'Q17', 'isValidCode': True, 'coordinate': [1606, 1113]}, '6': {'rememberedCode': 'Q17', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Q17', 'isValidCode': True, 'coordinate': [1606, 1113]}, '8': {'rememberedCode': 'K49', 'isValidCode': True, 'coordinate': [1577, 1143]}, '9': {'rememberedCode': 'C97', 'isValidCode': True, 'coordinate': [1578, 1146]}, '10': {'rememberedCode': 'C97', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'C97', 'isValidCode': True, 'coordinate': [1578, 1146]}, '12': {'rememberedCode': 'Z24', 'isValidCode': True, 'coordinate': [1588, 1148]}, '13': {'rememberedCode': 'L59', 'isValidCode': True, 'coordinate': [1549, 1202]}, '14': {'rememberedCode': 'L59', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L59', 'isValidCode': True, 'coordinate': [1549, 1202]}, '16': {'rememberedCode': 'M87', 'isValidCode': True, 'coordinate': [1570, 1216]}, '17': {'rememberedCode': 'V91', 'isValidCode': True, 'coordinate': [1616, 1245]}, '18': {'rememberedCode': 'V91', 'isValidCode': True, 'coordinate': [831, 800]}, '19': {'rememberedCode': 'V91', 'isValidCode': True, 'coordinate': [1616, 1245]}, '20': {'rememberedCode': 'B41', 'isValidCode': True, 'coordinate': [1641, 1125]}, '21': {'rememberedCode': 'U19', 'isValidCode': True, 'coordinate': [1555, 1042]}, '22': {'rememberedCode': 'U19', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'U19', 'isValidCode': True, 'coordinate': [1555, 1042]}, '24': {'rememberedCode': 'D36', 'isValidCode': True, 'coordinate': [1584, 1236]}, '25': {'rememberedCode': 'W14', 'isValidCode': True, 'coordinate': [1647, 1090]}, '26': {'rememberedCode': 'W14', 'isValidCode': True, 'coordinate': [1258, 1134]}, '27': {'rememberedCode': 'W14', 'isValidCode': True, 'coordinate': [1647, 1090]}, '28': {'rememberedCode': 'S51', 'isValidCode': True, 'coordinate': [1576, 1206]}, '29': {'rememberedCode': 'C23', 'isValidCode': True, 'coordinate': [1648, 1246]}, '30': {'rememberedCode': 'C23', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C23', 'isValidCode': True, 'coordinate': [1648, 1246]}, '32': {'rememberedCode': 'B89', 'isValidCode': True, 'coordinate': [1575, 1228]}, '33': {'rememberedCode': 'M82', 'isValidCode': True, 'coordinate': [1616, 1222]}, '34': {'rememberedCode': 'M82', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'M82', 'isValidCode': True, 'coordinate': [1616, 1222]}, '36': {'rememberedCode': 'X17', 'isValidCode': True, 'coordinate': [1636, 1263]}, '37': {'rememberedCode': 'G75', 'isValidCode': True, 'coordinate': [1622, 1218]}, '38': {'rememberedCode': 'G75', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G75', 'isValidCode': True, 'coordinate': [1622, 1218]}}",994,708
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+19,"['A', 'A', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'D', 'D', 'A', 'D', 'B', 'D', 'D', 'A', 'B', 'A', 'D', 'B', 'D', 'C', 'D', 'D', 'A', 'A', 'C', 'B', 'D', 'A', 'B', 'C', 'A', 'A', 'A', 'C', 'D', 'C', 'C', 'D', 'C', 'D', 'B', 'B', 'D', 'B', 'D', 'D', 'D', 'C', 'B', 'A', 'A', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'C', 'B', 'D', 'C', 'B', 'B', 'A', 'B', 'C', 'C', 'A', 'B', 'A', 'D', 'A', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'B', 'D', 'A', 'B', 'A', 'A', 'B', 'A', 'A', 'D', 'D', 'D', 'A', 'A', 'B', 'C', 'D']","['2', '1', '1', '2', '2', '2', '1', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '1', '2', '1', '1', '1', '2', '1', '1', '1', '1', '2', '1', '1', '2', '1', '2', '2']","{'openTime': '2021-08-15T05:25:28.721Z', 'loadTime': '2021-08-15T05:25:30.295Z', 'startTime': '2021-08-15T05:25:37.504Z', 'endTime': '2021-08-15T05:44:35.369Z', 'timeToCompleteFromOpenMsec': 1146648, 'timeToLoadMsec': 1574, 'timeToCompleteFromStartMsec': 1137865}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': 'none'}","{'0': {'rememberedCode': 'C29', 'isValidCode': True, 'coordinate': [775, 923]}, '1': {'rememberedCode': 'R18', 'isValidCode': True, 'coordinate': [502, 638]}, '2': {'rememberedCode': 'R18', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'R18', 'isValidCode': True, 'coordinate': [502, 638]}, '4': {'rememberedCode': 'N89', 'isValidCode': True, 'coordinate': [216, 130]}, '5': {'rememberedCode': 'A49', 'isValidCode': True, 'coordinate': [1565, 425]}, '6': {'rememberedCode': 'A49', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'A49', 'isValidCode': True, 'coordinate': [1565, 425]}, '8': {'rememberedCode': 'K54', 'isValidCode': True, 'coordinate': [986, 370]}, '9': {'rememberedCode': 'D43', 'isValidCode': True, 'coordinate': [496, 531]}, '10': {'rememberedCode': 'D43', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'D43', 'isValidCode': True, 'coordinate': [496, 531]}, '12': {'rememberedCode': 'V24', 'isValidCode': True, 'coordinate': [238, 249]}, '13': {'rememberedCode': 'G96', 'isValidCode': True, 'coordinate': [1265, 217]}, '14': {'rememberedCode': 'G96', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'G96', 'isValidCode': True, 'coordinate': [1265, 217]}, '16': {'rememberedCode': 'A45', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'N84', 'isValidCode': True, 'coordinate': [826, 840]}, '18': {'rememberedCode': 'N84', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'N84', 'isValidCode': True, 'coordinate': [826, 840]}, '20': {'rememberedCode': 'K46', 'isValidCode': True, 'coordinate': [976, 359]}, '21': {'rememberedCode': 'G94', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'G94', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G94', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'N48', 'isValidCode': True, 'coordinate': [678, 1236]}, '25': {'rememberedCode': 'N13', 'isValidCode': True, 'coordinate': [246, 607]}, '26': {'rememberedCode': 'N13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'N13', 'isValidCode': True, 'coordinate': [246, 607]}, '28': {'rememberedCode': 'L16', 'isValidCode': True, 'coordinate': [786, 1028]}, '29': {'rememberedCode': 'L12', 'isValidCode': True, 'coordinate': [1294, 25]}, '30': {'rememberedCode': 'L12', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'L12', 'isValidCode': True, 'coordinate': [1294, 25]}, '32': {'rememberedCode': 'N95', 'isValidCode': True, 'coordinate': [1290, 531]}, '33': {'rememberedCode': 'D93', 'isValidCode': True, 'coordinate': [491, 838]}, '34': {'rememberedCode': 'D93', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'D93', 'isValidCode': True, 'coordinate': [491, 838]}, '36': {'rememberedCode': 'Q61', 'isValidCode': True, 'coordinate': [65, 457]}, '37': {'rememberedCode': 'J69', 'isValidCode': True, 'coordinate': [649, 1146]}, '38': {'rememberedCode': 'J69', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'J69', 'isValidCode': True, 'coordinate': [649, 1146]}}",122,860
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+25,"['A', 'A', 'B', 'A', 'B', 'C', 'C', 'B', 'C', 'D', 'C', 'C', 'B', 'A', 'D', 'A', 'A', 'C', 'A', 'B', 'A', 'D', 'B', 'D', 'C', 'A', 'C', 'A', 'B', 'D', 'A', 'A', 'C', 'C', 'B', 'A', 'A', 'D', 'C', 'B', 'A', 'C', 'D', 'D', 'A', 'A', 'A', 'C', 'A', 'B', 'A', 'C', 'D', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'C', 'B', 'B', 'A', 'B', 'B', 'B', 'A', 'B', 'D', 'D', 'B', 'A', 'D', 'D', 'A', 'A', 'A', 'B', 'A', 'B', 'C', 'C', 'A', 'B', 'A', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'B', 'C', 'D']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '1', '2', '1', '2', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '1', '1', '2']","{'openTime': '2021-08-16T05:16:40.169Z', 'loadTime': '2021-08-16T05:17:05.951Z', 'startTime': '2021-08-16T05:17:30.655Z', 'endTime': '2021-08-16T05:39:19.933Z', 'timeToCompleteFromOpenMsec': 1359764, 'timeToLoadMsec': 25782, 'timeToCompleteFromStartMsec': 1309278}","{'gender': 'male', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'Z86', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'S97', 'isValidCode': True, 'coordinate': [683, 538]}, '2': {'rememberedCode': 'S97', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'S97', 'isValidCode': True, 'coordinate': [683, 538]}, '4': {'rememberedCode': 'C86', 'isValidCode': True, 'coordinate': [1144, 831]}, '5': {'rememberedCode': 'S86', 'isValidCode': True, 'coordinate': [802, 215]}, '6': {'rememberedCode': 'S86', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'S86', 'isValidCode': True, 'coordinate': [802, 215]}, '8': {'rememberedCode': 'D47', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'X52', 'isValidCode': True, 'coordinate': [826, 873]}, '10': {'rememberedCode': 'X52', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'X52', 'isValidCode': True, 'coordinate': [826, 873]}, '12': {'rememberedCode': 'M35', 'isValidCode': True, 'coordinate': [847, 622]}, '13': {'rememberedCode': 'R78', 'isValidCode': True, 'coordinate': [650, 818]}, '14': {'rememberedCode': 'R78', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'R78', 'isValidCode': True, 'coordinate': [650, 818]}, '16': {'rememberedCode': 'M54', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'W12', 'isValidCode': True, 'coordinate': [687, 529]}, '18': {'rememberedCode': 'W12', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'W12', 'isValidCode': True, 'coordinate': [687, 529]}, '20': {'rememberedCode': 'Y17', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'W39', 'isValidCode': True, 'coordinate': [831, 732]}, '22': {'rememberedCode': 'W39', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'W39', 'isValidCode': True, 'coordinate': [831, 732]}, '24': {'rememberedCode': 'J63', 'isValidCode': True, 'coordinate': [991, 516]}, '25': {'rememberedCode': 'E64', 'isValidCode': True, 'coordinate': [538, 731]}, '26': {'rememberedCode': 'E64', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'E64', 'isValidCode': True, 'coordinate': [538, 731]}, '28': {'rememberedCode': 'Z49', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'S71', 'isValidCode': True, 'coordinate': [690, 658]}, '30': {'rememberedCode': 'S71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'S71', 'isValidCode': True, 'coordinate': [690, 658]}, '32': {'rememberedCode': 'X73', 'isValidCode': True, 'coordinate': [664, 532]}, '33': {'rememberedCode': 'Q91', 'isValidCode': True, 'coordinate': [548, 726]}, '34': {'rememberedCode': 'Q91', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Q91', 'isValidCode': True, 'coordinate': [548, 726]}, '36': {'rememberedCode': 'A21', 'isValidCode': True, 'coordinate': [567, 654]}, '37': {'rememberedCode': 'T18', 'isValidCode': True, 'coordinate': [698, 649]}, '38': {'rememberedCode': 'T18', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'T18', 'isValidCode': True, 'coordinate': [698, 649]}}",988,934
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+34,"['A', 'C', 'A', 'B', 'D', 'C', 'B', 'D', 'C', 'D', 'C', 'C', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'D', 'B', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'D', 'A', 'A', 'B', 'D', 'C', 'A', 'D', 'D', 'D', 'D', 'C', 'A', 'D', 'D', 'D', 'C', 'B', 'C', 'D', 'D', 'B', 'A', 'D', 'D', 'D', 'A', 'D', 'C', 'C', 'C', 'B', 'C', 'B', 'D', 'B', 'D', 'B', 'D', 'D', 'D', 'A', 'B', 'D', 'D', 'B', 'A', 'B', 'D', 'D', 'D', 'C', 'C', 'A', 'D', 'D', 'A', 'B', 'C', 'D', 'D', 'B', 'A', 'B', 'C', 'D']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '2', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '1', '2', '1', '2', '2']","{'openTime': '2021-08-16T22:33:42.001Z', 'loadTime': '2021-08-16T22:33:46.308Z', 'startTime': '2021-08-16T22:33:49.984Z', 'endTime': '2021-08-16T22:50:43.760Z', 'timeToCompleteFromOpenMsec': 1021759, 'timeToLoadMsec': 4307, 'timeToCompleteFromStartMsec': 1013776}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'G96', 'isValidCode': True, 'coordinate': [640, 412]}, '1': {'rememberedCode': 'S69', 'isValidCode': True, 'coordinate': [375, 1069]}, '2': {'rememberedCode': 'S69', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'S69', 'isValidCode': True, 'coordinate': [375, 1069]}, '4': {'rememberedCode': 'M58', 'isValidCode': True, 'coordinate': [554, 516]}, '5': {'rememberedCode': 'E41', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'E41', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E41', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'N39', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'N54', 'isValidCode': True, 'coordinate': [478, 462]}, '10': {'rememberedCode': 'N54', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'N54', 'isValidCode': True, 'coordinate': [478, 462]}, '12': {'rememberedCode': 'V24', 'isValidCode': True, 'coordinate': [238, 249]}, '13': {'rememberedCode': 'U29', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'U29', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'U29', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'U35', 'isValidCode': True, 'coordinate': [801, 209]}, '17': {'rememberedCode': 'U82', 'isValidCode': True, 'coordinate': [1135, 452]}, '18': {'rememberedCode': 'U82', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'U82', 'isValidCode': True, 'coordinate': [1135, 452]}, '20': {'rememberedCode': 'P62', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [787, 469]}, '22': {'rememberedCode': 'K71', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'K71', 'isValidCode': True, 'coordinate': [787, 469]}, '24': {'rememberedCode': 'S94', 'isValidCode': True, 'coordinate': [786, 136]}, '25': {'rememberedCode': 'Z81', 'isValidCode': True, 'coordinate': [973, 628]}, '26': {'rememberedCode': 'Z81', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Z81', 'isValidCode': True, 'coordinate': [973, 628]}, '28': {'rememberedCode': 'L56', 'isValidCode': True, 'coordinate': [807, 604]}, '29': {'rememberedCode': 'T25', 'isValidCode': True, 'coordinate': [96, 343]}, '30': {'rememberedCode': 'T25', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'T25', 'isValidCode': True, 'coordinate': [96, 343]}, '32': {'rememberedCode': 'W54', 'isValidCode': True, 'coordinate': [937, 319]}, '33': {'rememberedCode': 'G78', 'isValidCode': True, 'coordinate': [365, 535]}, '34': {'rememberedCode': 'G78', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'G78', 'isValidCode': True, 'coordinate': [365, 535]}, '36': {'rememberedCode': 'E87', 'isValidCode': True, 'coordinate': [412, 750]}, '37': {'rememberedCode': 'G59', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'G59', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G59', 'isValidCode': False, 'coordinate': False}}",668,356
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+36,"['A', 'A', 'B', 'B', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'D', 'A', 'B', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'B', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'C', 'C', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'C', 'B', 'C', 'D', 'D', 'D', 'D', 'D', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'A', 'B', 'A', 'C', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'C', 'D']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '2', '2', '2', '2', '1', '1', '2', '1', '1', '2']","{'openTime': '2021-08-18T03:42:38.104Z', 'loadTime': '2021-08-18T03:44:25.749Z', 'startTime': '2021-08-18T03:49:58.823Z', 'endTime': '2021-08-18T04:24:14.238Z', 'timeToCompleteFromOpenMsec': 2496134, 'timeToLoadMsec': 107645, 'timeToCompleteFromStartMsec': 2055415}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'littleExposure', 'feedback': ''}","{'0': {'rememberedCode': '734', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'D23', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'D23', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'D23', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'B27', 'isValidCode': True, 'coordinate': [226, 12]}, '5': {'rememberedCode': 'C24', 'isValidCode': True, 'coordinate': [190, 147]}, '6': {'rememberedCode': 'C24', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'C24', 'isValidCode': True, 'coordinate': [190, 147]}, '8': {'rememberedCode': 'R84', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'U35', 'isValidCode': True, 'coordinate': [355, 169]}, '10': {'rememberedCode': 'U35', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'U35', 'isValidCode': True, 'coordinate': [355, 169]}, '12': {'rememberedCode': 'V24', 'isValidCode': True, 'coordinate': [238, 249]}, '13': {'rememberedCode': 'X51', 'isValidCode': True, 'coordinate': [1088, 110]}, '14': {'rememberedCode': 'X51', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'X51', 'isValidCode': True, 'coordinate': [1088, 110]}, '16': {'rememberedCode': 'A81', 'isValidCode': True, 'coordinate': [487, 711]}, '17': {'rememberedCode': 'E17', 'isValidCode': True, 'coordinate': [533, 464]}, '18': {'rememberedCode': 'E17', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'E17', 'isValidCode': True, 'coordinate': [533, 464]}, '20': {'rememberedCode': 'G52', 'isValidCode': True, 'coordinate': [82, 521]}, '21': {'rememberedCode': 'Y51', 'isValidCode': True, 'coordinate': [511, 934]}, '22': {'rememberedCode': 'Y51', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'Y51', 'isValidCode': True, 'coordinate': [511, 934]}, '24': {'rememberedCode': 'J61', 'isValidCode': True, 'coordinate': [481, 451]}, '25': {'rememberedCode': 'J42', 'isValidCode': True, 'coordinate': [557, 524]}, '26': {'rememberedCode': 'J42', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'J42', 'isValidCode': True, 'coordinate': [557, 524]}, '28': {'rememberedCode': 'Z97', 'isValidCode': True, 'coordinate': [170, 115]}, '29': {'rememberedCode': 'D36', 'isValidCode': True, 'coordinate': [989, 119]}, '30': {'rememberedCode': 'D36', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'D36', 'isValidCode': True, 'coordinate': [989, 119]}, '32': {'rememberedCode': 'M58', 'isValidCode': True, 'coordinate': [682, 112]}, '33': {'rememberedCode': 'Y51', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'Y51', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Y51', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'S81', 'isValidCode': True, 'coordinate': [370, 447]}, '37': {'rememberedCode': 'B43', 'isValidCode': True, 'coordinate': [221, 336]}, '38': {'rememberedCode': 'B43', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'B43', 'isValidCode': True, 'coordinate': [221, 336]}}",761,371
+37,"['A', 'A', 'A', 'A', 'D', 'C', 'D', 'B', 'C', 'D', 'C', 'D', 'A', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'C', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'C', 'C', 'D', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'C', 'B', 'B', 'B', 'D', 'A', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'D', 'C', 'B', 'B', 'B', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'D', 'A', 'A', 'D', 'D', 'D', 'C', 'D', 'A', 'D', 'D', 'A', 'D', 'D', 'D', 'D', 'B', 'A', 'A', 'C', 'D']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '1', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '1', '1', '1', '2', '1', '1', '2', '2', '2', '1', '1', '2', '1', '1', '2']","{'openTime': '2021-08-18T11:14:21.175Z', 'loadTime': '2021-08-18T11:14:49.971Z', 'startTime': '2021-08-18T11:15:02.479Z', 'endTime': '2021-08-18T11:38:28.538Z', 'timeToCompleteFromOpenMsec': 1447363, 'timeToLoadMsec': 28796, 'timeToCompleteFromStartMsec': 1406059}","{'gender': 'female', 'ageGroup': 'ageGroup45', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': 'there was hardly time to see anything on the charts, and one of them was in font so small I couldn&#39;t read it at all.  This was also scandalously underpaid!'}","{'0': {'rememberedCode': 'Q17', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'F32', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'F32', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'F32', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'B24', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'Y12', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'Y12', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Y12', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'N69', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'P64', 'isValidCode': True, 'coordinate': [816, 463]}, '10': {'rememberedCode': 'P64', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'P64', 'isValidCode': True, 'coordinate': [816, 463]}, '12': {'rememberedCode': 'F45', 'isValidCode': True, 'coordinate': [822, 151]}, '13': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'E43', 'isValidCode': True, 'coordinate': [639, 716]}, '17': {'rememberedCode': 'W72', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'W72', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'W72', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'F25', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'N65', 'isValidCode': True, 'coordinate': [184, 453]}, '22': {'rememberedCode': 'N65', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'N65', 'isValidCode': True, 'coordinate': [184, 453]}, '24': {'rememberedCode': 'W54', 'isValidCode': True, 'coordinate': [671, 754]}, '25': {'rememberedCode': 'A13', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'A13', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'A13', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'K67', 'isValidCode': True, 'coordinate': [673, 906]}, '29': {'rememberedCode': 'M35', 'isValidCode': True, 'coordinate': [1443, 1048]}, '30': {'rememberedCode': 'M35', 'isValidCode': True, 'coordinate': [1446, 485]}, '31': {'rememberedCode': 'M35', 'isValidCode': True, 'coordinate': [1443, 1048]}, '32': {'rememberedCode': 'T48', 'isValidCode': True, 'coordinate': [200, 515]}, '33': {'rememberedCode': 'K75', 'isValidCode': True, 'coordinate': [832, 544]}, '34': {'rememberedCode': 'K75', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'K75', 'isValidCode': True, 'coordinate': [832, 544]}, '36': {'rememberedCode': 'U61', 'isValidCode': True, 'coordinate': [706, 858]}, '37': {'rememberedCode': 'L39', 'isValidCode': True, 'coordinate': [702, 736]}, '38': {'rememberedCode': 'L39', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'L39', 'isValidCode': True, 'coordinate': [702, 736]}}",355,541
+38,"['A', 'A', 'A', 'B', 'B', 'C', 'D', 'B', 'C', 'A', 'C', 'D', 'D', 'A', 'B', 'A', 'C', 'D', 'A', 'B', 'A', 'B', 'B', 'A', 'A', 'A', 'C', 'B', 'C', 'A', 'A', 'B', 'D', 'A', 'C', 'A', 'B', 'A', 'C', 'C', 'C', 'D', 'B', 'C', 'C', 'A', 'A', 'D', 'D', 'A', 'C', 'C', 'B', 'A', 'C', 'A', 'D', 'C', 'B', 'C', 'A', 'C', 'C', 'D', 'C', 'B', 'B', 'B', 'A', 'C', 'A', 'D', 'C', 'A', 'D', 'A', 'D', 'C', 'D', 'B', 'A', 'D', 'B', 'A', 'C', 'C', 'B', 'D', 'A', 'D', 'A', 'D', 'D', 'A', 'C', 'A', 'A', 'B', 'C', 'B']","['2', '1', '1', '2', '2', '2', '2', '2', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '2', '1', '1', '2', '2', '1', '2', '2', '2', '1', '2', '2', '1', '1', '2']","{'openTime': '2021-08-18T14:52:11.898Z', 'loadTime': '2021-08-18T14:52:29.480Z', 'startTime': '2021-08-18T14:53:02.519Z', 'endTime': '2021-08-18T15:13:02.615Z', 'timeToCompleteFromOpenMsec': 1250717, 'timeToLoadMsec': 17582, 'timeToCompleteFromStartMsec': 1200096}","{'gender': 'female', 'ageGroup': 'ageGroup35', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'M32', 'isValidCode': True, 'coordinate': [666, 235]}, '1': {'rememberedCode': 'Q83', 'isValidCode': True, 'coordinate': [556, 254]}, '2': {'rememberedCode': 'Q83', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Q83', 'isValidCode': True, 'coordinate': [556, 254]}, '4': {'rememberedCode': 'L63', 'isValidCode': True, 'coordinate': [999, 232]}, '5': {'rememberedCode': 'L42', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'L42', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'L42', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'K54', 'isValidCode': True, 'coordinate': [986, 370]}, '9': {'rememberedCode': 'E81', 'isValidCode': True, 'coordinate': [955, 672]}, '10': {'rememberedCode': 'E81', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'E81', 'isValidCode': True, 'coordinate': [955, 672]}, '12': {'rememberedCode': 'B47', 'isValidCode': True, 'coordinate': [995, 412]}, '13': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'K17', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'C39', 'isValidCode': True, 'coordinate': [785, 420]}, '17': {'rememberedCode': 'X54', 'isValidCode': True, 'coordinate': [840, 558]}, '18': {'rememberedCode': 'X54', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'X54', 'isValidCode': True, 'coordinate': [840, 558]}, '20': {'rememberedCode': 'L32', 'isValidCode': True, 'coordinate': [1598, 17]}, '21': {'rememberedCode': 'S65', 'isValidCode': True, 'coordinate': [785, 334]}, '22': {'rememberedCode': 'S65', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'S65', 'isValidCode': True, 'coordinate': [785, 334]}, '24': {'rememberedCode': 'S94', 'isValidCode': True, 'coordinate': [786, 136]}, '25': {'rememberedCode': 'S85', 'isValidCode': True, 'coordinate': [555, 629]}, '26': {'rememberedCode': 'S85', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'S85', 'isValidCode': True, 'coordinate': [555, 629]}, '28': {'rememberedCode': 'M12', 'isValidCode': True, 'coordinate': [510, 912]}, '29': {'rememberedCode': 'W83', 'isValidCode': True, 'coordinate': [61, 923]}, '30': {'rememberedCode': 'W83', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'W83', 'isValidCode': True, 'coordinate': [61, 923]}, '32': {'rememberedCode': 'J45', 'isValidCode': True, 'coordinate': [343, 215]}, '33': {'rememberedCode': 'Y37', 'isValidCode': True, 'coordinate': [1137, 705]}, '34': {'rememberedCode': 'Y37', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Y37', 'isValidCode': True, 'coordinate': [1137, 705]}, '36': {'rememberedCode': 'J38', 'isValidCode': True, 'coordinate': [196, 428]}, '37': {'rememberedCode': 'R27', 'isValidCode': True, 'coordinate': [1308, 829]}, '38': {'rememberedCode': 'R27', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'R27', 'isValidCode': True, 'coordinate': [1308, 829]}}",35,737
\ No newline at end of file
diff --git a/VisRecall/answer_raw/group9.csv b/VisRecall/answer_raw/group9.csv
new file mode 100755
index 0000000..b9a9555
--- /dev/null
+++ b/VisRecall/answer_raw/group9.csv
@@ -0,0 +1,29 @@
+ID,QA_answers,reco_answers,timeing,surveyData,tasks,workerId,assId
+0,"['B', 'D', 'A', 'D', 'D', 'B', 'B', 'C', 'D', 'D', 'C', 'D', 'A', 'C', 'D', 'B', 'C', 'B', 'A', 'D', 'B', 'C', 'D', 'A', 'D', 'B', 'B', 'A', 'C', 'D', 'A', 'C', 'D', 'D', 'D', 'C', 'A', 'A', 'D', 'D', 'D', 'D', 'B', 'D', 'C', 'B', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'B', 'B', 'C', 'A', 'D', 'D', 'B', 'C', 'D', 'D', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'D', 'D', 'D', 'D', 'B', 'D', 'B', 'D', 'D', 'A', 'B', 'A', 'A', 'A', 'A', 'C', 'B', 'C', 'D', 'B', 'C', 'C', 'B', 'A', 'C', 'B', 'B', 'C', 'B']","['2', '1', '1', '2', '1', '2', '2', '1', '1', '1', '2', '2', '1', '2', '2', '1', '1', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '2', '2', '2', '1', '1', '1', '2', '2']","{'openTime': '2021-08-20T12:42:31.276Z', 'loadTime': '2021-08-20T12:42:49.392Z', 'startTime': '2021-08-20T12:42:51.830Z', 'endTime': '2021-08-20T13:01:08.338Z', 'timeToCompleteFromOpenMsec': 1117062, 'timeToLoadMsec': 18116, 'timeToCompleteFromStartMsec': 1096508}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'twoYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'S29', 'isValidCode': True, 'coordinate': [354, 732]}, '1': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'X68', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'A54', 'isValidCode': True, 'coordinate': [630, 515]}, '5': {'rememberedCode': 'C95', 'isValidCode': True, 'coordinate': [639, 1039]}, '6': {'rememberedCode': 'C95', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'C95', 'isValidCode': True, 'coordinate': [639, 1039]}, '8': {'rememberedCode': 'T25', 'isValidCode': True, 'coordinate': [801, 630]}, '9': {'rememberedCode': 'V94', 'isValidCode': True, 'coordinate': [785, 722]}, '10': {'rememberedCode': 'V94', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'V94', 'isValidCode': True, 'coordinate': [785, 722]}, '12': {'rememberedCode': 'B61', 'isValidCode': True, 'coordinate': [937, 1203]}, '13': {'rememberedCode': 'H56', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'H56', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'H56', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'R74', 'isValidCode': True, 'coordinate': [1118, 558]}, '17': {'rememberedCode': 'Z34', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'Z34', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Z34', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'P97', 'isValidCode': True, 'coordinate': [981, 740]}, '21': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [657, 152]}, '22': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [1554, 542]}, '23': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [657, 152]}, '24': {'rememberedCode': 'T76', 'isValidCode': True, 'coordinate': [348, 301]}, '25': {'rememberedCode': 'Y29', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'Y29', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'Y29', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'V71', 'isValidCode': True, 'coordinate': [227, 200]}, '29': {'rememberedCode': 'W52', 'isValidCode': True, 'coordinate': [664, 256]}, '30': {'rememberedCode': 'W52', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'W52', 'isValidCode': True, 'coordinate': [664, 256]}, '32': {'rememberedCode': 'F12', 'isValidCode': True, 'coordinate': [1394, 722]}, '33': {'rememberedCode': 'Q25', 'isValidCode': True, 'coordinate': [1255, 453]}, '34': {'rememberedCode': 'Q25', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'Q25', 'isValidCode': True, 'coordinate': [1255, 453]}, '36': {'rememberedCode': 'F19', 'isValidCode': True, 'coordinate': [1100, 417]}, '37': {'rememberedCode': 'G14', 'isValidCode': True, 'coordinate': [912, 812]}, '38': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G14', 'isValidCode': True, 'coordinate': [912, 812]}}",6,981
+1,"['A', 'B', 'A', 'C', 'B', 'B', 'C', 'D', 'D', 'D', 'C', 'A', 'A', 'C', 'D', 'B', 'A', 'B', 'D', 'D', 'B', 'C', 'B', 'D', 'B', 'B', 'B', 'B', 'C', 'D', 'A', 'D', 'B', 'B', 'D', 'C', 'A', 'A', 'A', 'D', 'A', 'D', 'A', 'D', 'D', 'B', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'A', 'C', 'C', 'A', 'B', 'B', 'B', 'A', 'D', 'D', 'A', 'A', 'A', 'C', 'A', 'C', 'A', 'B', 'A', 'A', 'A', 'B', 'C', 'D', 'A', 'D', 'B', 'A', 'A', 'D', 'C', 'A', 'A', 'D', 'B', 'A', 'D', 'C', 'C', 'C', 'D', 'A', 'C', 'B', 'D', 'C', 'B']","['2', '1', '2', '2', '1', '2', '2', '1', '1', '1', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '2', '1', '2', '2', '2', '1', '2', '1', '2', '2']","{'openTime': '2021-08-20T12:41:40.175Z', 'loadTime': '2021-08-20T12:42:02.882Z', 'startTime': '2021-08-20T12:42:06.875Z', 'endTime': '2021-08-20T13:09:05.721Z', 'timeToCompleteFromOpenMsec': 1645546, 'timeToLoadMsec': 22707, 'timeToCompleteFromStartMsec': 1618846}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': ''}","{'0': {'rememberedCode': 'Q67', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'U71', 'isValidCode': True, 'coordinate': [640, 632]}, '2': {'rememberedCode': 'U71', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'U71', 'isValidCode': True, 'coordinate': [640, 632]}, '4': {'rememberedCode': 'J86', 'isValidCode': True, 'coordinate': [915, 739]}, '5': {'rememberedCode': 'Q74', 'isValidCode': True, 'coordinate': [933, 455]}, '6': {'rememberedCode': 'Q74', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Q74', 'isValidCode': True, 'coordinate': [933, 455]}, '8': {'rememberedCode': 'U89', 'isValidCode': True, 'coordinate': [1216, 429]}, '9': {'rememberedCode': 'A59', 'isValidCode': True, 'coordinate': [491, 126]}, '10': {'rememberedCode': 'A59', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'A59', 'isValidCode': True, 'coordinate': [491, 126]}, '12': {'rememberedCode': 'D52', 'isValidCode': True, 'coordinate': [1281, 535]}, '13': {'rememberedCode': 'E95', 'isValidCode': True, 'coordinate': [882, 232]}, '14': {'rememberedCode': 'E95', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'E95', 'isValidCode': True, 'coordinate': [882, 232]}, '16': {'rememberedCode': 'A21', 'isValidCode': True, 'coordinate': [1548, 228]}, '17': {'rememberedCode': 'Z42', 'isValidCode': True, 'coordinate': [921, 267]}, '18': {'rememberedCode': 'Z42', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'Z42', 'isValidCode': True, 'coordinate': [921, 267]}, '20': {'rememberedCode': 'D16', 'isValidCode': True, 'coordinate': [479, 748]}, '21': {'rememberedCode': 'L51', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'L51', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'L51', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'C71', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'U31', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'U31', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'U31', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'V58', 'isValidCode': True, 'coordinate': [961, 232]}, '29': {'rememberedCode': 'E39', 'isValidCode': True, 'coordinate': [1285, 634]}, '30': {'rememberedCode': 'E39', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'E39', 'isValidCode': True, 'coordinate': [1285, 634]}, '32': {'rememberedCode': 'A89', 'isValidCode': True, 'coordinate': [1089, 524]}, '33': {'rememberedCode': 'H25', 'isValidCode': True, 'coordinate': [371, 467]}, '34': {'rememberedCode': 'H25', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'H25', 'isValidCode': True, 'coordinate': [371, 467]}, '36': {'rememberedCode': 'R69', 'isValidCode': True, 'coordinate': [625, 328]}, '37': {'rememberedCode': 'R79', 'isValidCode': True, 'coordinate': [666, 1128]}, '38': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'R79', 'isValidCode': True, 'coordinate': [666, 1128]}}",778,541
+2,"['C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'A', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'B', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'C', 'B', 'C', 'B', 'C', 'B', 'C', 'B', 'C']","['2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']","{'openTime': '2021-08-20T12:42:24.078Z', 'loadTime': '2021-08-20T12:42:53.509Z', 'startTime': '2021-08-20T12:43:01.392Z', 'endTime': '2021-08-20T13:10:36.941Z', 'timeToCompleteFromOpenMsec': 1692863, 'timeToLoadMsec': 29431, 'timeToCompleteFromStartMsec': 1655549}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'mastersDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'NICE'}","{'0': {'rememberedCode': 'E69', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'H74', 'isValidCode': True, 'coordinate': [1238, 840]}, '3': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'H72', 'isValidCode': False, 'coordinate': False}, '6': {'rememberedCode': 'H72', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'H72', 'isValidCode': False, 'coordinate': False}, '8': {'rememberedCode': 'A14', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'C21', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'C21', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'C21', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'L43', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'A14', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'A14', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'A14', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'X74', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'H16', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'H16', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'H16', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'X74', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'X74', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'X74', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'M72', 'isValidCode': True, 'coordinate': [34, 15]}, '25': {'rememberedCode': 'C21', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'C21', 'isValidCode': True, 'coordinate': [1425, 518]}, '27': {'rememberedCode': 'C21', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'M21', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'X57', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'E71', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'E71', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'E71', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'C59', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'X74', 'isValidCode': True, 'coordinate': [1079, 1096]}, '38': {'rememberedCode': 'X74', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'X74', 'isValidCode': True, 'coordinate': [1079, 1096]}}",49,150
+3,"['B', 'C', 'B', 'C', 'B', 'B', 'B', 'C', 'C', 'B', 'C', 'A', 'A', 'C', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'C', 'B', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'C', 'B', 'B', 'A', 'B', 'C', 'B', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'B', 'B', 'C', 'B', 'B', 'A', 'C', 'C', 'B', 'B', 'A', 'B', 'A', 'C', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'D', 'C', 'A', 'B', 'B', 'A', 'A', 'A', 'A', 'C', 'A', 'B', 'C', 'C', 'A', 'A', 'C', 'C', 'C', 'A', 'A', 'C', 'B', 'A', 'A', 'B']","['2', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '2', '1', '2', '2', '1', '1', '1', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '2', '2', '2', '2', '2', '1', '2', '2']","{'openTime': '2021-08-20T12:54:14.709Z', 'loadTime': '2021-08-20T12:54:36.670Z', 'startTime': '2021-08-20T12:54:37.391Z', 'endTime': '2021-08-20T13:10:44.206Z', 'timeToCompleteFromOpenMsec': 989497, 'timeToLoadMsec': 21961, 'timeToCompleteFromStartMsec': 966815}","{'gender': 'male', 'ageGroup': 'ageGroup18', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'L97', 'isValidCode': True, 'coordinate': [176, 38]}, '1': {'rememberedCode': 'Q95', 'isValidCode': True, 'coordinate': [971, 1164]}, '2': {'rememberedCode': 'Q95', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Q95', 'isValidCode': True, 'coordinate': [971, 1164]}, '4': {'rememberedCode': 'R46', 'isValidCode': True, 'coordinate': [323, 408]}, '5': {'rememberedCode': 'C35', 'isValidCode': True, 'coordinate': [794, 564]}, '6': {'rememberedCode': 'C35', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'C35', 'isValidCode': True, 'coordinate': [794, 564]}, '8': {'rememberedCode': 'D67', 'isValidCode': True, 'coordinate': [327, 135]}, '9': {'rememberedCode': 'D87', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'D87', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'D87', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'T65', 'isValidCode': True, 'coordinate': [371, 636]}, '13': {'rememberedCode': 'Y13', 'isValidCode': True, 'coordinate': [340, 219]}, '14': {'rememberedCode': 'Y13', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Y13', 'isValidCode': True, 'coordinate': [340, 219]}, '16': {'rememberedCode': 'T39', 'isValidCode': True, 'coordinate': [1385, 17]}, '17': {'rememberedCode': 'K34', 'isValidCode': True, 'coordinate': [166, 731]}, '18': {'rememberedCode': 'K34', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'K34', 'isValidCode': True, 'coordinate': [166, 731]}, '20': {'rememberedCode': 'F36', 'isValidCode': True, 'coordinate': [626, 637]}, '21': {'rememberedCode': 'G47', 'isValidCode': True, 'coordinate': [1111, 939]}, '22': {'rememberedCode': 'G47', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G47', 'isValidCode': True, 'coordinate': [1111, 939]}, '24': {'rememberedCode': 'S79', 'isValidCode': True, 'coordinate': [541, 1099]}, '25': {'rememberedCode': 'L74', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'L74', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'L74', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'K58', 'isValidCode': True, 'coordinate': [962, 729]}, '29': {'rememberedCode': 'G74', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'G74', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'G74', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'C83', 'isValidCode': True, 'coordinate': [352, 494]}, '33': {'rememberedCode': 'S81', 'isValidCode': True, 'coordinate': [1544, 1032]}, '34': {'rememberedCode': 'S81', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'S81', 'isValidCode': True, 'coordinate': [1544, 1032]}, '36': {'rememberedCode': 'K53', 'isValidCode': True, 'coordinate': [794, 1222]}, '37': {'rememberedCode': 'R79', 'isValidCode': True, 'coordinate': [666, 1128]}, '38': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'R79', 'isValidCode': True, 'coordinate': [666, 1128]}}",688,58
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+20,"['B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'C', 'B', 'A']","['2', '1', '1', '2', '1', '2', '2', '1', '1', '2', '2', '2', '1', '1', '1', '2', '1', '2', '1', '2', '1', '2', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '1', '1', '2', '2', '1', '1', '1', '2']","{'openTime': '2021-08-20T11:38:56.233Z', 'loadTime': '2021-08-20T11:38:59.223Z', 'startTime': '2021-08-20T11:39:00.596Z', 'endTime': '2021-08-20T11:55:42.447Z', 'timeToCompleteFromOpenMsec': 1006214, 'timeToLoadMsec': 2990, 'timeToCompleteFromStartMsec': 1001851}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'This survey was good.'}","{'0': {'rememberedCode': 'C29', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'A65', 'isValidCode': True, 'coordinate': [339, 266]}, '2': {'rememberedCode': 'A65', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'A65', 'isValidCode': True, 'coordinate': [339, 266]}, '4': {'rememberedCode': 'R62', 'isValidCode': True, 'coordinate': [471, 410]}, '5': {'rememberedCode': 'E63', 'isValidCode': True, 'coordinate': [787, 736]}, '6': {'rememberedCode': 'E63', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'E63', 'isValidCode': True, 'coordinate': [787, 736]}, '8': {'rememberedCode': 'Q30', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'V98', 'isValidCode': True, 'coordinate': [757, 1227]}, '10': {'rememberedCode': 'V98', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'V98', 'isValidCode': True, 'coordinate': [757, 1227]}, '12': {'rememberedCode': 'B75', 'isValidCode': True, 'coordinate': [1281, 428]}, '13': {'rememberedCode': 'Q18', 'isValidCode': True, 'coordinate': [589, 635]}, '14': {'rememberedCode': 'Q18', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'Q18', 'isValidCode': True, 'coordinate': [589, 635]}, '16': {'rememberedCode': 'G81', 'isValidCode': True, 'coordinate': [1255, 251]}, '17': {'rememberedCode': 'A89', 'isValidCode': True, 'coordinate': [362, 757]}, '18': {'rememberedCode': 'A89', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'A89', 'isValidCode': True, 'coordinate': [362, 757]}, '20': {'rememberedCode': 'T36', 'isValidCode': True, 'coordinate': [641, 837]}, '21': {'rememberedCode': 'E76', 'isValidCode': True, 'coordinate': [1429, 1057]}, '22': {'rememberedCode': 'E76', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'E76', 'isValidCode': True, 'coordinate': [1429, 1057]}, '24': {'rememberedCode': 'M45', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'S59', 'isValidCode': True, 'coordinate': [481, 742]}, '26': {'rememberedCode': 'S59', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'S59', 'isValidCode': True, 'coordinate': [481, 742]}, '28': {'rememberedCode': 'S64', 'isValidCode': True, 'coordinate': [504, 911]}, '29': {'rememberedCode': 'C11', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'C11', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'C11', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X36', 'isValidCode': True, 'coordinate': [1277, 89]}, '33': {'rememberedCode': 'R71', 'isValidCode': True, 'coordinate': [1089, 1149]}, '34': {'rememberedCode': 'R71', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'R71', 'isValidCode': True, 'coordinate': [1089, 1149]}, '36': {'rememberedCode': 'R65', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'E46', 'isValidCode': True, 'coordinate': [787, 334]}, '38': {'rememberedCode': 'E46', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E46', 'isValidCode': True, 'coordinate': [787, 334]}}",169,886
+21,"['B', 'D', 'A', 'C', 'A', 'B', 'C', 'D', 'D', 'D', 'C', 'A', 'A', 'D', 'D', 'B', 'A', 'B', 'D', 'D', 'B', 'C', 'D', 'A', 'D', 'B', 'B', 'D', 'D', 'D', 'A', 'C', 'D', 'B', 'D', 'C', 'D', 'A', 'C', 'D', 'B', 'C', 'B', 'D', 'C', 'B', 'D', 'D', 'D', 'D', 'A', 'D', 'D', 'A', 'D', 'C', 'A', 'A', 'D', 'B', 'C', 'D', 'D', 'A', 'B', 'A', 'A', 'A', 'C', 'A', 'B', 'A', 'D', 'A', 'B', 'C', 'B', 'A', 'D', 'B', 'A', 'B', 'A', 'D', 'A', 'A', 'D', 'B', 'D', 'D', 'B', 'C', 'C', 'B', 'A', 'C', 'B', 'B', 'D', 'D']","['2', '1', '1', '2', '1', '2', '2', '1', '1', '1', '2', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '2', '2', '1', '1', '2', '2', '2', '1', '2', '1', '2', '2']","{'openTime': '2021-08-20T11:45:17.223Z', 'loadTime': '2021-08-20T11:45:31.891Z', 'startTime': '2021-08-20T11:45:34.391Z', 'endTime': '2021-08-20T12:04:45.037Z', 'timeToCompleteFromOpenMsec': 1167814, 'timeToLoadMsec': 14668, 'timeToCompleteFromStartMsec': 1150646}","{'gender': 'female', 'ageGroup': 'ageGroup55', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'someExposure', 'feedback': ''}","{'0': {'rememberedCode': 'L93', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'B88', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'B88', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'B88', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'V59', 'isValidCode': True, 'coordinate': [348, 1229]}, '5': {'rememberedCode': 'N12', 'isValidCode': True, 'coordinate': [661, 559]}, '6': {'rememberedCode': 'N12', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'N12', 'isValidCode': True, 'coordinate': [661, 559]}, '8': {'rememberedCode': 'L28', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'R94', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'R94', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'R94', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'Z21', 'isValidCode': True, 'coordinate': [204, 337]}, '13': {'rememberedCode': 'L45', 'isValidCode': True, 'coordinate': [1331, 236]}, '14': {'rememberedCode': 'L45', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L45', 'isValidCode': True, 'coordinate': [1331, 236]}, '16': {'rememberedCode': 'Z28', 'isValidCode': True, 'coordinate': [1411, 129]}, '17': {'rememberedCode': 'A59', 'isValidCode': True, 'coordinate': [1536, 535]}, '18': {'rememberedCode': 'A59', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'A59', 'isValidCode': True, 'coordinate': [1536, 535]}, '20': {'rememberedCode': 'K13', 'isValidCode': True, 'coordinate': [1554, 542]}, '21': {'rememberedCode': 'G89', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'G89', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'G89', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'W81', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'A19', 'isValidCode': True, 'coordinate': [672, 764]}, '26': {'rememberedCode': 'A19', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'A19', 'isValidCode': True, 'coordinate': [672, 764]}, '28': {'rememberedCode': 'J74', 'isValidCode': True, 'coordinate': [676, 1123]}, '29': {'rememberedCode': 'G71', 'isValidCode': True, 'coordinate': [976, 769]}, '30': {'rememberedCode': 'G71', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'G71', 'isValidCode': True, 'coordinate': [976, 769]}, '32': {'rememberedCode': 'X38', 'isValidCode': True, 'coordinate': [1380, 393]}, '33': {'rememberedCode': 'N71', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'N71', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'N71', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'R95', 'isValidCode': True, 'coordinate': [1053, 492]}, '37': {'rememberedCode': 'F61', 'isValidCode': True, 'coordinate': [356, 907]}, '38': {'rememberedCode': 'F61', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'F61', 'isValidCode': True, 'coordinate': [356, 907]}}",782,277
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+24,"['B', 'C', 'A', 'C', 'B', 'A', 'B', 'B', 'C', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'D', 'D', 'B', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'C', 'B', 'C', 'A', 'B', 'C', 'A', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'C', 'B', 'A', 'B', 'C', 'A', 'C']","['2', '2', '2', '1', '2', '1', '2', '1', '2', '1', '2', '2', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']","{'openTime': '2021-08-20T12:42:24.969Z', 'loadTime': '2021-08-20T12:42:30.214Z', 'startTime': '2021-08-20T12:42:35.658Z', 'endTime': '2021-08-20T13:08:42.155Z', 'timeToCompleteFromOpenMsec': 1577186, 'timeToLoadMsec': 5245, 'timeToCompleteFromStartMsec': 1566497}","{'gender': 'female', 'ageGroup': 'ageGroup18', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Nice'}","{'0': {'rememberedCode': 'D49', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'E69', 'isValidCode': True, 'coordinate': [1242, 1041]}, '2': {'rememberedCode': 'E69', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'E69', 'isValidCode': True, 'coordinate': [1242, 1041]}, '4': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [1586, 440]}, '6': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [183, 300]}, '7': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [1586, 440]}, '8': {'rememberedCode': 'M72', 'isValidCode': True, 'coordinate': [648, 531]}, '9': {'rememberedCode': 'J75', 'isValidCode': True, 'coordinate': [1074, 1126]}, '10': {'rememberedCode': 'J75', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'J75', 'isValidCode': True, 'coordinate': [1074, 1126]}, '12': {'rememberedCode': 'Z49', 'isValidCode': True, 'coordinate': [790, 921]}, '13': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'L43', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'J46', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'H74', 'isValidCode': True, 'coordinate': [495, 433]}, '23': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'X52', 'isValidCode': True, 'coordinate': [943, 911]}, '25': {'rememberedCode': 'W47', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'W47', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'W47', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'U28', 'isValidCode': True, 'coordinate': [1423, 1226]}, '29': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'C21', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'K23', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'K23', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'K23', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'K23', 'isValidCode': False, 'coordinate': False}}",179,350
+25,"['A', 'A', 'B', 'C', 'B', 'B', 'A', 'B', 'B', 'C', 'B', 'B', 'A', 'B', 'B', 'B', 'A', 'B', 'C', 'B', 'B', 'B', 'A', 'B', 'A', 'B', 'B', 'B', 'A', 'C', 'A', 'A', 'B', 'A', 'B', 'C', 'A', 'A', 'B', 'B', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'C', 'B', 'A', 'A', 'B', 'B', 'B', 'A', 'C', 'B', 'B', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'B', 'C', 'A', 'B', 'A', 'B', 'B', 'A', 'B', 'B', 'B', 'A', 'B', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'B', 'B', 'A', 'C', 'A', 'A', 'C', 'A', 'A', 'A', 'B', 'A', 'A', 'B']","['2', '1', '1', '2', '1', '1', '2', '1', '1', '1', '2', '2', '1', '1', '1', '1', '1', '1', '2', '1', '1', '2', '1', '1', '2', '1', '2', '1', '1', '2', '2', '2', '1', '2', '2', '2', '1', '2', '1', '1', '1']","{'openTime': '2021-08-20T12:44:52.625Z', 'loadTime': '2021-08-20T12:44:56.091Z', 'startTime': '2021-08-20T12:44:58.941Z', 'endTime': '2021-08-20T13:10:47.633Z', 'timeToCompleteFromOpenMsec': 1555008, 'timeToLoadMsec': 3466, 'timeToCompleteFromStartMsec': 1548692}","{'gender': 'male', 'ageGroup': 'ageGroup35', 'ethnicity': 'Asian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Interesting task'}","{'0': {'rememberedCode': 'L43', 'isValidCode': True, 'coordinate': [975, 568]}, '1': {'rememberedCode': 'E71', 'isValidCode': False, 'coordinate': False}, '2': {'rememberedCode': 'E71', 'isValidCode': True, 'coordinate': [673, 239]}, '3': {'rememberedCode': 'E71', 'isValidCode': False, 'coordinate': False}, '4': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'Q42', 'isValidCode': True, 'coordinate': [643, 1174]}, '6': {'rememberedCode': 'Q42', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'Q42', 'isValidCode': True, 'coordinate': [643, 1174]}, '8': {'rememberedCode': 'N26', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'N58', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'N58', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'N58', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'E42', 'isValidCode': True, 'coordinate': [802, 609]}, '13': {'rememberedCode': 'V65', 'isValidCode': False, 'coordinate': False}, '14': {'rememberedCode': 'V65', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'V65', 'isValidCode': False, 'coordinate': False}, '16': {'rememberedCode': 'W48', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'C59', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'C59', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'C59', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'V93', 'isValidCode': True, 'coordinate': [180, 1051]}, '21': {'rememberedCode': 'K57', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'K57', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'K57', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'C89', 'isValidCode': False, 'coordinate': False}, '25': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'R79', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'B79', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'N21', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'N21', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'N21', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'H72', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'H74', 'isValidCode': True, 'coordinate': [1426, 145]}, '34': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'H74', 'isValidCode': True, 'coordinate': [1426, 145]}, '36': {'rememberedCode': 'X27', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'G49', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'G49', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'G49', 'isValidCode': False, 'coordinate': False}}",413,345
+26,"['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'A', 'C', 'B', 'A', 'C', 'B', 'C', 'A', 'C', 'B', 'A', 'B', 'C', 'B', 'C', 'B', 'C', 'C', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'A', 'C', 'A', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'C', 'B', 'A', 'A', 'C', 'D', 'A', 'B', 'C', 'A', 'B', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'C', 'A', 'C', 'A', 'C', 'B', 'A', 'D', 'B', 'A', 'C', 'B', 'A', 'C', 'C', 'A', 'C', 'B', 'A', 'C', 'B', 'A', 'B', 'A', 'C', 'A', 'B', 'A', 'C', 'A', 'C', 'B', 'C']","['2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '2', '1', '2', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2', '1', '2']","{'openTime': '2021-08-20T12:42:20.374Z', 'loadTime': '2021-08-20T12:42:26.640Z', 'startTime': '2021-08-20T12:42:36.446Z', 'endTime': '2021-08-20T13:14:56.011Z', 'timeToCompleteFromOpenMsec': 1955637, 'timeToLoadMsec': 6266, 'timeToCompleteFromStartMsec': 1939565}","{'gender': 'female', 'ageGroup': 'ageGroup25', 'ethnicity': 'Asian', 'education': 'someCollege', 'vizExperience': 'frequentInteraction', 'feedback': 'nice'}","{'0': {'rememberedCode': 'D49', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'E69', 'isValidCode': True, 'coordinate': [1242, 1041]}, '2': {'rememberedCode': 'E69', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'E69', 'isValidCode': True, 'coordinate': [1242, 1041]}, '4': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '5': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [1586, 440]}, '6': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [183, 300]}, '7': {'rememberedCode': 'F67', 'isValidCode': True, 'coordinate': [1586, 440]}, '8': {'rememberedCode': 'Z49', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '10': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '12': {'rememberedCode': 'F24', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'J46', 'isValidCode': True, 'coordinate': [896, 1236]}, '14': {'rememberedCode': 'J46', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'J46', 'isValidCode': True, 'coordinate': [896, 1236]}, '16': {'rememberedCode': 'H74', 'isValidCode': False, 'coordinate': False}, '17': {'rememberedCode': 'X52', 'isValidCode': False, 'coordinate': False}, '18': {'rememberedCode': 'X52', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'X52', 'isValidCode': False, 'coordinate': False}, '20': {'rememberedCode': 'U28', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '22': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'M74', 'isValidCode': False, 'coordinate': False}, '24': {'rememberedCode': 'C21', 'isValidCode': True, 'coordinate': [1425, 518]}, '25': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '26': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'F29', 'isValidCode': False, 'coordinate': False}, '28': {'rememberedCode': 'K23', 'isValidCode': False, 'coordinate': False}, '29': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '30': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'M67', 'isValidCode': False, 'coordinate': False}, '32': {'rememberedCode': 'X74', 'isValidCode': False, 'coordinate': False}, '33': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '34': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'G14', 'isValidCode': False, 'coordinate': False}, '36': {'rememberedCode': 'E17', 'isValidCode': False, 'coordinate': False}, '37': {'rememberedCode': 'E29', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'E29', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'E29', 'isValidCode': False, 'coordinate': False}}",900,666
+27,"['A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'C', 'A', 'C', 'A', 'B', 'C', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'C', 'A', 'B', 'C', 'B', 'A', 'B', 'A', 'B', 'C', 'A']","['2', '1', '1', '2', '1', '1', '2', '1', '1', '1', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '2', '1', '1', '2', '1', '2', '1', '2', '1', '2', '1', '1', '1', '1', '2', '1', '2', '1', '1', '2']","{'openTime': '2021-08-20T13:01:39.845Z', 'loadTime': '2021-08-20T13:01:49.060Z', 'startTime': '2021-08-20T13:01:50.348Z', 'endTime': '2021-08-20T13:15:51.286Z', 'timeToCompleteFromOpenMsec': 851441, 'timeToLoadMsec': 9215, 'timeToCompleteFromStartMsec': 840938}","{'gender': 'male', 'ageGroup': 'ageGroup25', 'ethnicity': 'White_Caucasian', 'education': 'fourYearDegree', 'vizExperience': 'frequentInteraction', 'feedback': 'Good'}","{'0': {'rememberedCode': 'D54', 'isValidCode': False, 'coordinate': False}, '1': {'rememberedCode': 'Z15', 'isValidCode': True, 'coordinate': [792, 340]}, '2': {'rememberedCode': 'Z15', 'isValidCode': False, 'coordinate': False}, '3': {'rememberedCode': 'Z15', 'isValidCode': True, 'coordinate': [792, 340]}, '4': {'rememberedCode': 'Z17', 'isValidCode': True, 'coordinate': [1359, 741]}, '5': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [944, 947]}, '6': {'rememberedCode': 'U79', 'isValidCode': False, 'coordinate': False}, '7': {'rememberedCode': 'U79', 'isValidCode': True, 'coordinate': [944, 947]}, '8': {'rememberedCode': 'N74', 'isValidCode': False, 'coordinate': False}, '9': {'rememberedCode': 'C16', 'isValidCode': True, 'coordinate': [790, 536]}, '10': {'rememberedCode': 'C16', 'isValidCode': False, 'coordinate': False}, '11': {'rememberedCode': 'C16', 'isValidCode': True, 'coordinate': [790, 536]}, '12': {'rememberedCode': 'K65', 'isValidCode': False, 'coordinate': False}, '13': {'rememberedCode': 'L24', 'isValidCode': True, 'coordinate': [936, 838]}, '14': {'rememberedCode': 'L24', 'isValidCode': False, 'coordinate': False}, '15': {'rememberedCode': 'L24', 'isValidCode': True, 'coordinate': [936, 838]}, '16': {'rememberedCode': 'V24', 'isValidCode': True, 'coordinate': [971, 849]}, '17': {'rememberedCode': 'H61', 'isValidCode': True, 'coordinate': [812, 556]}, '18': {'rememberedCode': 'H61', 'isValidCode': False, 'coordinate': False}, '19': {'rememberedCode': 'H61', 'isValidCode': True, 'coordinate': [812, 556]}, '20': {'rememberedCode': 'R34', 'isValidCode': False, 'coordinate': False}, '21': {'rememberedCode': 'A89', 'isValidCode': True, 'coordinate': [781, 556]}, '22': {'rememberedCode': 'A89', 'isValidCode': False, 'coordinate': False}, '23': {'rememberedCode': 'A89', 'isValidCode': True, 'coordinate': [781, 556]}, '24': {'rememberedCode': 'L89', 'isValidCode': True, 'coordinate': [815, 1008]}, '25': {'rememberedCode': 'P43', 'isValidCode': True, 'coordinate': [834, 662]}, '26': {'rememberedCode': 'P43', 'isValidCode': False, 'coordinate': False}, '27': {'rememberedCode': 'P43', 'isValidCode': True, 'coordinate': [834, 662]}, '28': {'rememberedCode': 'L74', 'isValidCode': True, 'coordinate': [500, 1124]}, '29': {'rememberedCode': 'Y59', 'isValidCode': True, 'coordinate': [818, 738]}, '30': {'rememberedCode': 'Y59', 'isValidCode': False, 'coordinate': False}, '31': {'rememberedCode': 'Y59', 'isValidCode': True, 'coordinate': [818, 738]}, '32': {'rememberedCode': 'W85', 'isValidCode': True, 'coordinate': [652, 596]}, '33': {'rememberedCode': 'N74', 'isValidCode': True, 'coordinate': [791, 329]}, '34': {'rememberedCode': 'N74', 'isValidCode': False, 'coordinate': False}, '35': {'rememberedCode': 'N74', 'isValidCode': True, 'coordinate': [791, 329]}, '36': {'rememberedCode': 'C58', 'isValidCode': True, 'coordinate': [756, 1012]}, '37': {'rememberedCode': 'U57', 'isValidCode': False, 'coordinate': False}, '38': {'rememberedCode': 'U57', 'isValidCode': False, 'coordinate': False}, '39': {'rememberedCode': 'U57', 'isValidCode': False, 'coordinate': False}}",662,318
\ No newline at end of file
diff --git a/VisRecall/merged/image_annotation.csv b/VisRecall/merged/image_annotation.csv
new file mode 100644
index 0000000..e69978b
--- /dev/null
+++ b/VisRecall/merged/image_annotation.csv
@@ -0,0 +1,201 @@
+;image;group;vistype;title;attr: data-ink ratio;attr: # distinct colors;attr: black&white;attr: visual density;attr: human recognizable object
+0;02wEIH1uspxsk1E7VW-KRA==.0.png;1;bar;Confirmed COVID-19 Cases in the United States as of 23 Mar 2020;1;;n;3;
+1;3PuDl5gc8C7FWTFXB8kmqw==.0.png;1;line;Figure 2: There was a peak in birth following World War One;1;;n;1;
+2;_Di4yk2H64QEBkEncRGMhg==.0.png;1;scatter;Height and Weight by Sex;1;;n;1;
+3;economist_daily_chart_127.png;1;scatter;Military spending and GDP;1;;n;1;
+4;economist_daily_chart_184.png;1;line;Urbanisation;2;;n;2;
+5;economist_daily_chart_200.png;1;bar;CO2 emissions;2;;n;1;
+6;economist_daily_chart_202.png;1;scatter;Christmas gifts;1;1;n;1;n
+7;economist_daily_chart_267.png;1;other;The world’s biggest employers;2;;n;2;
+8;economist_daily_chart_319.png;1;bar;Emerging-market overheating index*;2;;n;3;
+9;economist_daily_chart_513.png;1;table;Best-selling political memoirs in Britain;2;;n;1;
+10;treasuryI01_1.png;1;table;Reno Housing Unit Growth Outpaced Population and Household Growth During the Past Decade;1;;n;3;
+11;treasuryJ02_2.png;1;bar;16.2 Million Homeowners Have Refinanced Since April 1, 2009;1;;n;1;
+12;treasuryK03_3.png;1;bar;Total Net Position (In Billions);2;1;n;2;n
+13;treasuryL03_2.png;1;pie;Manufacturing 2009-2011;1;3;n;2;n
+14;whoB04_1.png;1;line;Figure 3 Female life expectancy at birth by country income group and region, 1950-2005;2;;n;1;
+15;whoB08_1.png;1;pie;Figure 7 Distribution of those who usually collect water in 35 developing countries, 2005-2006;1;;n;3;
+16;whoJ01_1.png;1;other;Figure 1 Percentage of population using improved sanitation facilities (2020);2;;n;2;
+17;whoN06_1.png;1;line;Figure 4. Recorded adult per capita consumption of specific alcoholic beverages, 1990-2005a;1;;n;1;
+18;whoN27.png;1;bar;Figure 22. Legal definition of an alcoholic beverage (% pure alcohol by volume), as reported by countries, 2008;1;;y;1;
+19;wsj593.png;1;bar;Off Season;1;2;n;2;n
+20;3lG2Suf1vlUNqfO9pvq1kw.0.png;2;scatter;Prevalence of undernourishment vs. Daily supply of calories, 2013;2;;n;1;
+21;143.png;2;scatter;Trade statistics of Luxembourg with developing economies in South Asia;1;;n;1;
+22;79359.png;2;line;Percentage of male workers employed in Service sector;1;;n;1;
+23;__uID4lztqrYTniGMJdDWQ.0.png;2;scatter;Sound Mitigation at all Frequencies;1;;n;1;
+24;economist_daily_chart_76.png;2;line;Three-month $ LIBOR;2;;n;1;
+25;economist_daily_chart_100.png;2;line;Football wage costs;2;;n;1;
+26;economist_daily_chart_244.png;2;table;Historic stockmarket returns;3;;n;1;
+27;treasuryD10.png;2;bar;Servicer Outreach to 60+ Day Delinquent Homeowners: Cumulative Servicer Results, August 2011 - July 2012;2;;n;2;
+28;treasuryL16.png;2;bar;Figure II-3 : 2011 Critical Technology Transactions by Region Within Each Sector;1;;n;1;
+29;whoB13_1.png;2;table;Table 2 Leading risk factors for mortality in girls under five years of age, 2004;3;;n;3;
+30;whoB24_1.png;2;other;Figure 4 Distribution of countries by numbers of deaths attributable to unsafe abortion per 100 000 live births and legal grounds for abortion;2;;n;1;
+31;whoB26_1.png;2;other;Figure 8 Incidence rates of cervical cancer (age-standardized per 100 000 women, all ages), 2004;2;;n;2;
+32;whoB34_1.png;2;line;Figure 2 Persons aged 60 years and over as a percentage of total population by sex and country income group, 1950-2050;2;;n;1;
+33;whoJ23.png;2;pie;One third of countries indicate that revenues cover less than 80% of operating costs for urban utilities (Figure 3.11).;1;;n;1;
+34;whoJ33.png;2;line;Aid for drinking-water and sanitation has risen slowly as a percentage of total development aid since the low point of 2002, but is still significantly below aid for social sectors, such as health and education (Figure 6.2).;3;;n;1;
+35;whoK06_2.png;2;pie;Ten countries are home to two thirds of the global population without an improved drinking water source;2;;n;2;
+36;whoN32_1.png;2;bar;Figure 26. Blood alcohol concentration (BAC) limits for young/novice drivers, by number of countries, 2008;1;;y;1;
+37;whoO22_3.png;2;bar;Figure 13: Potential gain in life expectancy in the absence of selected risks to health, world, 2004.;2;;n;2;
+38;wsj135.png;2;bar;Home-Loan Hiatus;1;;n;2;
+39;wsj277.png;2;bar;In the Mix;2;;n;3;
+40;0yflU4o2ze89IvWqYRKGKA==.0.png;3;scatter;As good as it gets;1;;n;1;
+41;_1IiNGPpj1s86KzEjQ93YQ==.0.png;3;scatter;-;1;;n;1;
+42;economist_daily_chart_20.png;3;line;Urbanisation and income;3;;n;2;
+43;economist_daily_chart_162.png;3;line;Personal computing;1;;n;1;
+44;economist_daily_chart_207.png;3;line;Income inequality;2;;n;1;
+45;economist_daily_chart_209.png;3;line;Manufacturing activity;1;;n;1;
+46;economist_daily_chart_215.png;3;other;Cluster-munition producers;1;1;n;2;y
+47;economist_daily_chart_238.png;3;other;Selected leaning buildings;1;;n;3;
+48;economist_daily_chart_250.png;3;bar;Nobel prizes in chemistry;2;;n;2;
+49;treasuryA8.png;3;line;Auto industry employment has grown since the rescue.;2;;n;1;
+50;treasuryK04.png;3;table;Summary Financial Information (dollars in billions);3;;n;1;
+51;whoJ24_2.png;3;pie;Less than 20% of respondent countries consider the supply of skilled labour and technicians adequately developed to meet needs in rural sanitation (Figure 4.2);2;;n;1;
+52;whoJ44.png;3;line;Despite rising total ODA, general budget support has not risen appreciably (Figure 6.16).;1;1;n;1;n
+53;whoL06.png;3;bar;Figure 2.5: The under-5 child mortality as a function of socio-economic group in five countries;2;1;n;2;n
+54;whoN19_1.png;3;bar;Figure 15. Alcohol-attributable deaths per million inhabitants by income group and the world, 2004;2;;y;1;
+55;whoQ01.png;3;table;Table 1.1 Percentage of reporting forms received by month and by WHO Region, 2012;3;;n;2;
+56;whoQ42_1.png;3;bar;C-Annual blood examination rate, 2007-2011;1;1;n;1;n
+57;wsj11.png;3;bar;Upturn Amid Downturn;1;1;n;2;n
+58;wsj76.png;3;bar;Cutting The Lifeline;2;;n;2;
+59;wsj340.png;3;pie;Running Dry;2;2;n;3;n
+60;1tVCmxZoFO6hq5H_rC0j_w==.0.png;4;scatter;-;1;;n;1;
+61;8XT9tstaX3P7OjmkeT1xTg==.0.png;4;scatter;Hans Rosling Bubble Chart;1;;n;1;
+62;129462.png;4;bar;Balance of payments for transportation services (% of service imports);2;;n;2;
+63;_Fbq9iW1SCuAYaHmYUXAUA==.0.jpg;4;scatter;Relationship between ACSI and Temkin NPS scores by industry for 2015-2018;1;;n;1;
+64;economist_daily_chart_84.png;4;line;Saudi Arabia;2;;n;1;
+65;economist_daily_chart_95.png;4;other;Rubbish;1;;n;2;
+66;economist_daily_chart_225.png;4;bar;Change in emerging markets’ foreign-currency reserves;2;;n;1;
+67;economist_daily_chart_235.png;4;line;Ten-year government bond yields;2;;n;1;
+68;economist_daily_chart_384.png;4;pie;Global citations in scientific journals;2;;n;2;
+69;economist_daily_chart_447.png;4;table;The Economist poll of forecasters, December averages(previous month’s, if changed);3;;n;2;
+70;treasuryG01_2.png;4;table;Marketable Treasury Coupon Flows (including SOMA);1;1;n;1;n
+71;treasuryL02_2.png;4;line;Covered Transactions by Sector (2009-2011);2;;n;1;
+72;treasuryL15_2.png;4;bar;Figure II-2: 2011 Critical Technology Transactions by Region of Foreign Acquirer;3;;n;1;
+73;whoB13_2.png;4;pie;Figure 2 Distribution of major causes of death in girls under five years of age, including disease-specific contribution of undernutrition, 2004;3;3;n;3;n
+74;whoB14_1.png;4;other;Figure 3 Sex differences in key child health indicators, selected countries, 2002-2007;1;;n;1;
+75;whoK01.png;4;bar;The MDG drinking water target has been met;2;;n;2;
+76;whoL05.png;4;bar;Figure 2.4: Changes in the burden of disease associated with economic development;1;;n;1;
+77;whoN36.png;4;bar;Figure 33. Countries reporting awareness campaigns on alcohol, 2008;1;;y;1;
+78;whoQ06_2.png;4;line;Figure 3.6 Change over time in allocation of international funds for malaria control.;1;1;n;1;n
+79;wsj320.png;4;line;Summer Scorcher;2;;n;2;
+80;0BmWZbQdEukHi79Lit01oQ==.0.png;5;other;;1;;y;1;
+81;3LY3OX8bU7uKhgcRPgDRxw==.0.png;5;scatter;;1;;n;1;
+82;3iRYXLvZ8oVQDMLR-CebnQ==.0.png;5;scatter;;1;;n;1;
+83;economist_daily_chart_150.png;5;bar;;3;;n;1;
+84;economist_daily_chart_194.png;5;bar;;2;;n;1;
+85;economist_daily_chart_242.png;5;other;Global malaria deaths and R&D funding;3;2;n;2;n
+86;economist_daily_chart_243.png;5;other;;1;;n;2;
+87;economist_daily_chart_257.png;5;scatter;;1;;n;1;
+88;economist_daily_chart_260.png;5;bar;;2;;n;2;
+89;economist_daily_chart_262.png;5;table;;3;;n;2;
+90;treasuryD07_3.png;5;table;;3;;n;3;
+91;whoB10_1.png;5;bar;;3;;n;2;
+92;whoF03.png;5;bar;;1;;n;3;
+93;whoJ36_2.png;5;line;Figure 6.7  Trends in sanitation and water aid commitments for fragile or conflict-affected states, 2004-2010;1;2;n;1;n
+94;whoJ43_1.png;5;pie;;2;;n;1;
+95;whoK21.png;5;pie;;2;;n;2;
+96;whoQ12_2.png;5;line;;2;;n;1;
+97;wsj3.png;5;bar;;2;;n;2;
+98;wsj86.png;5;line;;3;;n;2;
+99;wsj265.png;5;line;;1;;n;2;
+100;1EMgXSmCnY1PeksJZMhlHA==.0.png;6;scatter;;2;;n;1;
+101;4bm2MTzbrTpK41Fhy3Oh2g==.0.png;6;scatter;;2;;n;1;
+102;79027.png;6;line;;1;;n;1;
+103;economist_daily_chart_189.png;6;bar;;2;;n;1;
+104;economist_daily_chart_211.png;6;line;;1;;n;1;
+105;economist_daily_chart_229.png;6;bar;;3;;n;2;
+106;economist_daily_chart_441.png;6;scatter;;2;;n;2;
+107;economist_daily_chart_451.png;6;table;;3;;n;2;
+108;economist_daily_chart_473.png;6;bar;;3;;n;2;
+109;economist_daily_chart_523.png;6;other;;2;;n;3;
+110;economist_daily_chart_531.png;6;line;;2;;n;1;
+111;treasuryI02_1.png;6;other;New and Existing Home Sales: Reno Compared to the Nation;2;2;n;2;n
+112;treasuryI02_3.png;6;line;;1;;n;1;
+113;whoB17_1.png;6;other;;2;;n;1;
+114;whoC02_2.png;6;table;;3;;n;3;
+115;whoF06.png;6;bar;;2;;n;2;
+116;whoJ38_2.png;6;pie;;1;;n;1;
+117;whoN35_1.png;6;pie;;2;;n;2;
+118;whoO15.png;6;bar;;2;;n;2;
+119;wsj88.png;6;bar;;2;;n;2;
+120;1_WnR3D8lP-2AK6XsHM74Q==.0.png;7;line;;1;;n;1;
+121;2yyamO0gVGUftCeTJ9WU2Q==.0.png;7;scatter;;2;;n;1;
+122;economist_daily_chart_191.png;7;other;;2;;n;2;
+123;economist_daily_chart_206.png;7;other;;3;;n;3;
+124;economist_daily_chart_236.png;7;line;;2;;n;1;
+125;economist_daily_chart_336.png;7;bar;;3;;n;2;
+126;economist_daily_chart_420.png;7;scatter;;1;;n;1;
+127;treasuryJ01_2.png;7;line;;1;;n;1;
+128;treasuryL07_2.png;7;pie;;2;;n;1;
+129;whoB19_1.png;7;scatter;Figure 4 Students aged 13-15 not meeting recommended levels of physical activity, selected  countries, 2003-2008;1;3;n;1;n
+130;whoB20_1.png;7;bar;Figure 1 Mortality and disease burden (DALYs) in women aged 15-44 years by region and broad causes, 2004;2;3;n;2;n
+131;whoC13.png;7;bar;;2;;n;2;
+132;whoI09_1.png;7;table;;1;;y;1;
+133;whoJ22_1.png;7;table;;2;;n;2;
+134;whoJ40_2.png;7;pie;ESAs indicate that 68% of aid for sanitation and water is directed to urban areas (Figure 6.13).;1;2;n;1;n
+135;whoQ51_1.png;7;bar;;1;;n;2;
+136;wsj72.png;7;line;;1;;n;2;
+137;wsj107.png;7;bar;;2;;n;3;
+138;wsj151.png;7;other;;2;;n;2;
+139;wsj553.png;7;other;Female Representation;3;3;n;2;y
+140;0Gc4n4lYDn4V19CtJzo_eg==.0.png;8;scatter;;1;;n;1;
+141;4dCSXyCVYOxhf_SLYhGkIw==.0.png;8;scatter;;2;;n;3;
+142;economist_daily_chart_93.png;8;bar;;1;;n;1;
+143;economist_daily_chart_197.png;8;bar;;2;;n;2;
+144;economist_daily_chart_198.png;8;line;;1;;n;1;
+145;economist_daily_chart_208.png;8;scatter;;2;;n;1;
+146;economist_daily_chart_218.png;8;line;;1;;n;2;
+147;economist_daily_chart_253.png;8;line;;1;;n;1;
+148;economist_daily_chart_355.png;8;bar;;3;;n;2;
+149;treasuryL02_1.png;8;table;;3;;n;3;
+150;whoB05_1.png;8;line;Figure 4 The health transition among Mexican women: death rates by overall cause of death (age-standardized per 1000 women), 1955-2005;2;1;n;2;n
+151;whoB11_1.png;8;bar;;2;;n;1;
+152;whoC22.png;8;pie;;1;;n;2;
+153;whoF15.png;8;pie;;1;;n;3;
+154;whoJ10.png;8;pie;;2;;n;1;
+155;whoJ13_1.png;8;table;;2;;n;2;
+156;whoJ46_1.png;8;bar;;2;;n;1;
+157;whoM01.png;8;table;;1;;y;1;
+158;wsj8.png;8;other;;3;;n;2;
+159;wsj342.png;8;bar;;3;;n;3;
+160;1JwHfqlSB3z3qu78N-3Law==.0.png;9;line;;2;;n;1;
+161;168.png;9;scatter;;1;;n;1;
+162;economist_daily_chart_77.png;9;bar;;1;;n;1;
+163;economist_daily_chart_137.png;9;other;;3;;n;1;
+164;economist_daily_chart_145.png;9;pie;;2;;n;2;
+165;economist_daily_chart_264.png;9;bar;;2;;n;2;
+166;economist_daily_chart_376.png;9;table;;3;;n;2;
+167;economist_daily_chart_377.png;9;other;;2;;n;2;
+168;economist_daily_chart_532.png;9;table;;2;;n;1;
+169;treasuryD05_2.png;9;table;;2;;n;3;
+170;treasuryL01.png;9;table;;2;;n;3;
+171;whoB25_1.png;9;line;;2;;n;1;
+172;whoB36_1.png;9;line;;2;;n;1;
+173;whoH01_1.png;9;pie;;1;;n;3;
+174;whoJ24_1.png;9;bar;;2;;n;1;
+175;whoK04_1.png;9;pie;;1;;y;2;
+176;whoM05.png;9;table;Proportion of all deaths attributable to tobacco (%);1;1;y;1;n
+177;wsj56.png;9;bar;;2;;n;2;
+178;wsj60.png;9;line;;1;;n;2;
+179;wsj180.png;9;line;;2;;n;1;
+180;2K-S52z1bcV0xQD4mnuCbw==.0.jpg;10;line;;2;;n;1;
+181;129470.png;10;bar;;1;;n;1;
+182;JVoKAJWwEgervxjbg2tqMA==.0.jpg;10;scatter;;2;;n;1;
+183;economist_daily_chart_65.png;10;other;;2;;n;1;
+184;economist_daily_chart_203.png;10;line;;1;;n;3;
+185;economist_daily_chart_223.png;10;scatter;;1;;n;1;
+186;economist_daily_chart_247.png;10;other;;3;;n;2;
+187;economist_daily_chart_308.png;10;table;;2;;n;1;
+188;economist_daily_chart_387.png;10;pie;;2;;n;2;
+189;treasuryB02.png;10;bar;;2;;n;3;
+190;treasuryB06.png;10;other;;2;;n;2;
+191;treasuryB13.png;10;bar;;1;;n;2;
+192;treasuryJ03_3.png;10;other;;1;;n;1;
+193;treasuryK05_2.png;10;pie;;2;;n;2;
+194;whoB22_1.png;10;table;Table 2 Deaths in women aged 15-44 years attributable to six leading risk factors, 2004 (percentage);2;1;n;3;n
+195;whoH12_1.png;10;line;;2;;n;1;
+196;whoH13_3.png;10;bar;;2;;n;1;
+197;whoK08.png;10;table;(paragraph) TABLE 3 Selected countries in sub-Saharan Africa that have performed above the regional average in terms of the proportion of their 2010 population that gained access to improved drinking water sources since 1995;1;1;y;1;n
+198;wsj128.png;10;line;;2;;n;3;
+199;wsj168.png;10;scatter;;2;;n;1;
\ No newline at end of file
diff --git a/VisRecall/merged/qa/02wEIH1uspxsk1E7VW-KRA==.0.json b/VisRecall/merged/qa/02wEIH1uspxsk1E7VW-KRA==.0.json
new file mode 100755
index 0000000..d59d976
--- /dev/null
+++ b/VisRecall/merged/qa/02wEIH1uspxsk1E7VW-KRA==.0.json
@@ -0,0 +1,50 @@
+{
+    "name": "02wEIH1uspxsk1E7VW-KRA==.0.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Confirmed COVID-19 Cases in the United States",
+        "B": "Recovered COVID-19 Cases in the United States",
+        "C": "COVID-19 Deaths in the United States",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "Jan 2020-Sep 2020",
+        "B": "Jan 2020-Dec 2020",
+        "C": "Jan 2020-Mar 2020",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Which of the following date has more than 10000 confirmed cases?",
+        "A": "19mar2020",
+        "B": "04mar2020",
+        "C": "18mar2020",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "As of 23 Mar 2020, how many confirmed cases does United States have?",
+        "A": "10000-20000",
+        "B": "20000-30000",
+        "C": "more than 30000",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "When did the number of confirmed cases start rapid increasing?",
+        "A": "04mar2020",
+        "B": "19feb2020",
+        "C": "05feb2020",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/0BmWZbQdEukHi79Lit01oQ==.0.json b/VisRecall/merged/qa/0BmWZbQdEukHi79Lit01oQ==.0.json
new file mode 100755
index 0000000..370eb67
--- /dev/null
+++ b/VisRecall/merged/qa/0BmWZbQdEukHi79Lit01oQ==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "0BmWZbQdEukHi79Lit01oQ==.0.png",
+    "vistype": "other",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Breeding bird numbers",
+        "B": "Breeding bird species abundance",
+        "C": "Breeding bird colors",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which areas are considered in this experience?",
+        "A": "grassland area",
+        "B": "desert area",
+        "C": "Urban fringe",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which area has the highest value?",
+        "A": "Rural area",
+        "B": "Urban area",
+        "C": "Urban fringe",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which area has the lowest value?",
+        "A": "Rural area",
+        "B": "Urban area",
+        "C": "Urban fringe",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which area has the highest average value?",
+        "A": "Rural area",
+        "B": "Urban area",
+        "C": "Urban fringe",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/0Gc4n4lYDn4V19CtJzo_eg==.0.json b/VisRecall/merged/qa/0Gc4n4lYDn4V19CtJzo_eg==.0.json
new file mode 100755
index 0000000..628e576
--- /dev/null
+++ b/VisRecall/merged/qa/0Gc4n4lYDn4V19CtJzo_eg==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "0Gc4n4lYDn4V19CtJzo_eg==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "What your Car says about your Salary",
+        "B": "What your Cat says about your Salary",
+        "C": "What your Car says about your Payment",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the correlation between car price and salary?",
+        "A": "Positive",
+        "B": "Negative",
+        "C": "Not obvious",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "What does the X-axis stand for?",
+        "A": "Salary",
+        "B": "Car Price",
+        "C": "Year",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What does the Y-axis stand for?",
+        "A": "Salary",
+        "B": "Car Price",
+        "C": "Year",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "What is the unit of values?",
+        "A": "Euro",
+        "B": "American dollar",
+        "C": "RMB",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/0yflU4o2ze89IvWqYRKGKA==.0.json b/VisRecall/merged/qa/0yflU4o2ze89IvWqYRKGKA==.0.json
new file mode 100755
index 0000000..bd0bd57
--- /dev/null
+++ b/VisRecall/merged/qa/0yflU4o2ze89IvWqYRKGKA==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "0yflU4o2ze89IvWqYRKGKA==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It plots the Human Development Index against the Gross Domestic Product for different countries",
+        "B": "It plots the Human Development Index against self-reported happiness for different countries",
+        "C": "It plots the Gross Domestic Product against self-reported happiness for different countries",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "question": "Which regions achieve the highest self-reported happiness?",
+		"type": "1",
+        "A": "Latin America & Caribbean",
+        "B": "European Union",
+        "C": "Sub-Saharan Africa",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "question": "What is the earliest year the data is taken from?",
+		"type": "2",
+        "A": "2013",
+        "B": "2014",
+        "C": "2015",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "question": "Which region achieves the lowest Human Development Index",
+		"type": "1",
+        "A": "South Asia",
+        "B": "Sub-Saharan Africa",
+        "C": "Middle East",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "question": "How many regions are reported?",
+		"type": "3",
+        "A": "6",
+        "B": "7",
+        "C": "8",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/129462.json b/VisRecall/merged/qa/129462.json
new file mode 100755
index 0000000..97398ce
--- /dev/null
+++ b/VisRecall/merged/qa/129462.json
@@ -0,0 +1,49 @@
+{
+    "name": "129462.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Balance of Payments for Robots",
+        "B": "Balance of payments for transport services",
+        "C": "Balance of payments for Cross-Border Trade",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2000-2010",
+        "B": "2004-2008",
+        "C": "2005-2007",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest value during this period?",
+        "A": "Argentina",
+        "B": "Armenia",
+        "C": "Australia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest value during this period?",
+        "A": "Australia",
+        "B": "Austria",
+        "C": "Azerbaijan",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "What is the maximum value approximately during this period?",
+        "A": "Around 15%",
+        "B": "Around 25%",
+        "C": "Around 35%",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/129470.json b/VisRecall/merged/qa/129470.json
new file mode 100755
index 0000000..be9952a
--- /dev/null
+++ b/VisRecall/merged/qa/129470.json
@@ -0,0 +1,49 @@
+{
+    "name": "129470.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualisation?",
+        "A": "Earnings from international visitors",
+        "B": "Earnings from international shops",
+        "C": "Earnings from international trade",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is not displayed in this visualisation?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest value in 2008?",
+        "A": "Pakistan",
+        "B": "Azerbaijan",
+        "C": "Tonga",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the lowest value in 2009?",
+        "A": "Pakistan",
+        "B": "Azerbaijan",
+        "C": "Tonga",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which currency has been chosen in this visualisation?",
+        "A": "Euro",
+        "B": "US dollar",
+        "C": "yen",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/143.json b/VisRecall/merged/qa/143.json
new file mode 100755
index 0000000..5d5caca
--- /dev/null
+++ b/VisRecall/merged/qa/143.json
@@ -0,0 +1,50 @@
+{
+    "name": "143.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of the visualization?",
+        "A": "Trade statistics of Luxembourg with developing economics in Africa",
+        "B": "Trade statistics of Luxembourg with developing economics in South America",
+        "C": "Trade statistics of Luxembourg with developing economics in South Asia",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What is the range of the years this study spanned?",
+        "A": "2003 - 2007",
+        "B": "2003 - 2008",
+        "C": "2003 - 2009",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "When did the highest exports rate show?",
+        "A": "2009",
+        "B": "2008",
+        "C": "2007",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "When did the lowest exports rate show?",
+        "A": "2003",
+        "B": "2004",
+        "C": "2005",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Does the export rates exceed the import rates?",
+        "A": "No",
+        "B": "They are equal",
+        "C": "Yes",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/168.json b/VisRecall/merged/qa/168.json
new file mode 100755
index 0000000..fcf5445
--- /dev/null
+++ b/VisRecall/merged/qa/168.json
@@ -0,0 +1,49 @@
+{
+    "name": "168.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Mobile Applications",
+        "B": "Patent Applications",
+        "C": "License Applications",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which year is not displayed in this visualization?",
+        "A": "2005",
+        "B": "2009",
+        "C": "2013",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which year has the highest value of pattern applications?",
+        "A": "2005",
+        "B": "2009",
+        "C": "2013",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What color stands for Residents?",
+        "A": "Red",
+        "B": "Blue",
+        "C": "Green",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "In which year Non-Residents has lower number of applications than Residents do?",
+        "A": "2006",
+        "B": "2010",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/1EMgXSmCnY1PeksJZMhlHA==.0.json b/VisRecall/merged/qa/1EMgXSmCnY1PeksJZMhlHA==.0.json
new file mode 100755
index 0000000..f66203b
--- /dev/null
+++ b/VisRecall/merged/qa/1EMgXSmCnY1PeksJZMhlHA==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "1EMgXSmCnY1PeksJZMhlHA==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Efficiency of popular model of Cars by class ",
+        "B": "Efficiency of popular model of Cars by Brands",
+        "C": "Efficiency of popular model of Cars by color",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the y-axis?",
+        "A": "Popular Model of Cars",
+        "B": "Highway Miles per Gallon",
+        "C": "Engine Displacement",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "What interval of data does the visualization show in terms of Engine displacement?",
+        "A": "2-7",
+        "B": "1-8",
+        "C": "2-6",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which car is most prevelant in the visualization?",
+        "A": "subcompact",
+        "B": "SUV",
+        "C": "midsize",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which car is least prevelant in the visualization?",
+        "A": "compact",
+        "B": "minivan",
+        "C": "2seater",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/1JwHfqlSB3z3qu78N-3Law==.0.json b/VisRecall/merged/qa/1JwHfqlSB3z3qu78N-3Law==.0.json
new file mode 100755
index 0000000..8fd4942
--- /dev/null
+++ b/VisRecall/merged/qa/1JwHfqlSB3z3qu78N-3Law==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "1JwHfqlSB3z3qu78N-3Law==.0.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Self-reported life expectation across the income distribution,country by country",
+        "B": "Self-reported life satisfaction across the income distribution,country by country",
+        "C": "Self-reported life style across the income distribution,country by country",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What does the Y-axis(vertical) stands for?",
+        "A": "Income per month",
+        "B": "Income per year",
+        "C": "Life satisfaction",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the lowest income per year in each income quintile?",
+        "A": "Panama",
+        "B": "Guinea",
+        "C": "Togo",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which country has not reached life satisfaction more than 7?",
+        "A": "Greece",
+        "B": "Chile",
+        "C": "USA",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which country has reached life satisfaction more than 7?",
+        "A": "Costa Rica",
+        "B": "Argentina",
+        "C": "Czech Republic",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/1_WnR3D8lP-2AK6XsHM74Q==.0.json b/VisRecall/merged/qa/1_WnR3D8lP-2AK6XsHM74Q==.0.json
new file mode 100755
index 0000000..721a321
--- /dev/null
+++ b/VisRecall/merged/qa/1_WnR3D8lP-2AK6XsHM74Q==.0.json
@@ -0,0 +1,49 @@
+{
+  "name": "1_WnR3D8lP-2AK6XsHM74Q==.0.png",
+  "vistype": "line",
+    "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "House Prices trend Forecast in US",
+    "B": "House proces trend Forecast in Europe",
+    "C": "House prices tredn Forecast in Germany",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2003-2013",
+    "B": "2003-2011",
+    "C": "2003-2012",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q3": {
+    "type": "2",
+    "question": "Which year has the highest value?",
+    "A": "2012-2013",
+    "B": "2009-2010",
+    "C": "2006-2007",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "3",
+    "question": "By how much percent are the house prices forecasted to rise by Early 2016?",
+    "A": "10",
+    "B": "30",
+    "C": "50",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "What is the name of the organization that prepared the forecasting prices?",
+    "A": "MarketOracle.co.de",
+    "B": "MarketOracle.co.uk",
+    "C": "MarketOracle.co.us",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/1tVCmxZoFO6hq5H_rC0j_w==.0.json b/VisRecall/merged/qa/1tVCmxZoFO6hq5H_rC0j_w==.0.json
new file mode 100755
index 0000000..8fed648
--- /dev/null
+++ b/VisRecall/merged/qa/1tVCmxZoFO6hq5H_rC0j_w==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "1tVCmxZoFO6hq5H_rC0j_w==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Changes in employment rate 65-69 with material deprivation in different regions",
+        "B": "Comparison of employment rate of different regions",
+        "C": "Comparison of the material deprivation of different regions",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "1",
+        "question": "Which country has the most material deprivation?",
+        "A": "Bulgaria",
+        "B": "Romania",
+        "C": "Latvia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the least material deprivation?",
+        "A": "Spain",
+        "B": "Belgium",
+        "C": "Luxembourg",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the highest employment rate 65-69?",
+        "A": "Romania",
+        "B": "Poland",
+        "C": "Portugal",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the lowest employment rate 65-69?",
+        "A": "Bulgaria",
+        "B": "France",
+        "C": "Slovakia",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/2K-S52z1bcV0xQD4mnuCbw==.0.json b/VisRecall/merged/qa/2K-S52z1bcV0xQD4mnuCbw==.0.json
new file mode 100755
index 0000000..7caba43
--- /dev/null
+++ b/VisRecall/merged/qa/2K-S52z1bcV0xQD4mnuCbw==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "2K-S52z1bcV0xQD4mnuCbw==.0.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "New York Times'Print revenue has declined",
+        "B": "New York Times' digital revenue has increased",
+        "C": "New York Times' print revenue has declined while digital revenue increased",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which print revenue has a decrease of 36%?",
+        "A": "Ad revenue",
+        "B": "Subscription revenue",
+        "C": "combined revenue ",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "Which digital revenue has been soared by 4 times?",
+        "A": "ad revenue",
+        "B": "Subscription revenue",
+        "C": "Malta",
+        "D": "combined revenue",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which revenue of New York Times Company has the highest value?",
+        "A": "Combined revenue from print",
+        "B": "ad revenue from digital",
+        "C": "subscription revenue from print",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which revenue has fast no change?",
+        "A": "ad revenue from digital",
+        "B": "subscription revenue from print",
+        "C": "Combined revenue from print",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/2yyamO0gVGUftCeTJ9WU2Q==.0.json b/VisRecall/merged/qa/2yyamO0gVGUftCeTJ9WU2Q==.0.json
new file mode 100755
index 0000000..b7227de
--- /dev/null
+++ b/VisRecall/merged/qa/2yyamO0gVGUftCeTJ9WU2Q==.0.json
@@ -0,0 +1,49 @@
+{
+  "name": "2yyamO0gVGUftCeTJ9WU2Q==.0.png",
+  "vistype": "scatter",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "B": "Correlation between how well a country's population speaks english and Human Development Index",
+    "A": "Correlation between how well a country's population speaks more than one language and Human development index",
+    "C": "Correlation between how well a country's population speaks german and Human development index",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q2": {
+    "type": "0",
+    "question": "What is the title of this visualization?",
+    "A": "Better Education Better Quality of Life",
+    "B": "Better English Better Quality of Life",
+    "C": "Commonwelath Nations have Better Quality of Life",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which country has the lowest EF EPI score?",
+    "A": "Saudi arabia",
+    "B": "India",
+    "C": "China",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which country has the Highest EF EPI score?",
+    "A": "Norway",
+    "B": "India",
+    "C": "China",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q5": {
+    "type": "1",
+    "question": "Which country has Lowest Human Development Index?",
+    "A": "India",
+    "B": "Russia",
+    "C": "Greece",
+    "D": "I can not remember",
+    "answer": "A"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/3LY3OX8bU7uKhgcRPgDRxw==.0.json b/VisRecall/merged/qa/3LY3OX8bU7uKhgcRPgDRxw==.0.json
new file mode 100755
index 0000000..66653d4
--- /dev/null
+++ b/VisRecall/merged/qa/3LY3OX8bU7uKhgcRPgDRxw==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "3LY3OX8bU7uKhgcRPgDRxw==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Geothermal power total installed costs by project, technology and capacity",
+        "B": "Wind power total installed costs by project, technology and capacity",
+        "C": "Thermal power total installed costs by project, technology and capacity",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2008-2013",
+        "B": "2007-2020",
+        "C": "2012-2033",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which type of data has the highest value?",
+        "A": "Binary",
+        "B": "Flash types",
+        "C": "Enhanced geothermal",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which type of data has the lowest value?",
+        "A": "Binary",
+        "B": "Flash types",
+        "C": "Enhanced geothermal",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Where does the data source come from?",
+        "A": "IRENA Renewable Cost Database and Global Data, 2016",
+        "B": "IRENA Renewable price Database and local Data, 2016",
+        "C": "Arena Renewable Cost Database and Global Data, 2020",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/3PuDl5gc8C7FWTFXB8kmqw==.0.json b/VisRecall/merged/qa/3PuDl5gc8C7FWTFXB8kmqw==.0.json
new file mode 100755
index 0000000..628067d
--- /dev/null
+++ b/VisRecall/merged/qa/3PuDl5gc8C7FWTFXB8kmqw==.0.json
@@ -0,0 +1,50 @@
+{
+    "name": "3PuDl5gc8C7FWTFXB8kmqw==.0.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Live births in UK",
+        "B": "Live births in US",
+        "C": "Live births in Germany",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1913-1940",
+        "B": "1913-1930",
+        "C": "1913-1950",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "4",
+        "question": "What does the gray area represent?",
+        "A": "World War One",
+        "B": "Great Depression",
+        "C": "World War Two",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "What was the number of live births in Britain after the end of the first World War?",
+        "A": "Around 1,000,000",
+        "B": "Around 800,000",
+        "C": "Around 600,000",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "When was the peak in births?",
+        "A": "1920",
+        "B": "1922",
+        "C": "1919",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/3iRYXLvZ8oVQDMLR-CebnQ==.0.json b/VisRecall/merged/qa/3iRYXLvZ8oVQDMLR-CebnQ==.0.json
new file mode 100755
index 0000000..f95965e
--- /dev/null
+++ b/VisRecall/merged/qa/3iRYXLvZ8oVQDMLR-CebnQ==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "3iRYXLvZ8oVQDMLR-CebnQ==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Financial development and foreign bank presence",
+        "B": "Environment development and creature species abundance",
+        "C": "The number and distribution of banks in a given region",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2007",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "4",
+        "question": "What is the y-axis?",
+        "A": "Private credit/Total bank assets",
+        "B": "Foreign bank assets/Total bank assets",
+        "C": "Private credit/GDP",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "4",
+        "question": "What is the x-axis?",
+        "A": "Private credit/Total bank assets",
+        "B": "Foreign bank assets/Total bank assets",
+        "C": "Private credit/GDP",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "How many countries' data are showed in this figure?",
+        "A": "98",
+        "B": "99",
+        "C": "100",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/3lG2Suf1vlUNqfO9pvq1kw.0.json b/VisRecall/merged/qa/3lG2Suf1vlUNqfO9pvq1kw.0.json
new file mode 100755
index 0000000..f626d87
--- /dev/null
+++ b/VisRecall/merged/qa/3lG2Suf1vlUNqfO9pvq1kw.0.json
@@ -0,0 +1,50 @@
+{
+    "name": "3lG2Suf1vlUNqfO9pvq1kw.0.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the main topic of the visualization?",
+        "A": "comparision between the prevalence of undernourishment and daily supply of calories",
+        "B": "comparision between GDP per capita and daily supply of calories",
+        "C": "comparision between the prevalence of undernourishment and GDP per capita",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year was the data collected?",
+        "A": "2003",
+        "B": "2013",
+        "C": "2018",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest prevalence of undernourishment?",
+        "A": "Haiti",
+        "B": "North Korea",
+        "C": "Zambia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which following country has the relative higher daily caloric supply?",
+        "A": "China",
+        "B": "Russia",
+        "C": "Egypt",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "What is the highest value of daily caloric supply in this visualization?",
+        "A": "Among 3600 kcal",
+        "B": "3000-3500 kcal",
+        "C": "2500-3000 kcal",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/4bm2MTzbrTpK41Fhy3Oh2g==.0.json b/VisRecall/merged/qa/4bm2MTzbrTpK41Fhy3Oh2g==.0.json
new file mode 100755
index 0000000..1704943
--- /dev/null
+++ b/VisRecall/merged/qa/4bm2MTzbrTpK41Fhy3Oh2g==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "4bm2MTzbrTpK41Fhy3Oh2g==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Using Icon markers in Highcharts",
+        "B": "Using Line markers in Highcharts",
+        "C": "Using Image markers in Highcharts",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What does the red icon represent?",
+        "A": "Male",
+        "B": "Female",
+        "C": "Girls",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "What does the blue icon represent?",
+        "A": "Female",
+        "B": "Girls",
+        "C": "Male",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What is the minimum value of x-axis?",
+        "A": "90-100",
+        "B": "80-90",
+        "C": "70-80",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "What is the maximum value of x-axis?",
+        "A": "170-180",
+        "B": "180-190",
+        "C": ">190",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/4dCSXyCVYOxhf_SLYhGkIw==.0.json b/VisRecall/merged/qa/4dCSXyCVYOxhf_SLYhGkIw==.0.json
new file mode 100755
index 0000000..128dfa6
--- /dev/null
+++ b/VisRecall/merged/qa/4dCSXyCVYOxhf_SLYhGkIw==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "4dCSXyCVYOxhf_SLYhGkIw==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "GDP versus tourism",
+        "B": "GDP versus global temperature",
+        "C": "GDP versus life expectancy",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2001",
+        "B": "2007",
+        "C": "2009",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which continent has the largest population?",
+        "A": "Americas",
+        "B": "Asia",
+        "C": "Europe",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which continent has the lowest life expectancy?",
+        "A": "Americas",
+        "B": "Asia",
+        "C": "Africa",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "What is the rough relationship between life expectancy and GDP per capita?",
+        "A": "Positively related",
+        "B": "Negatively related",
+        "C": "No obvious relationship",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/79027.json b/VisRecall/merged/qa/79027.json
new file mode 100755
index 0000000..76926e5
--- /dev/null
+++ b/VisRecall/merged/qa/79027.json
@@ -0,0 +1,49 @@
+{
+    "name": "79027.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Population growth",
+        "B": "Technological growth",
+        "C": "Educational growth",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What does the purple curve represent?",
+        "A": "Urban",
+        "B": "Rural",
+        "C": "Total",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "Which scale is used for depicting the growth?",
+        "A": "In terms of values",
+        "B": "In terms of quantiles",
+        "C": "In terms of percentage",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "What is the period of year shown in the visualization?",
+        "A": "1969 - 1975",
+        "B": "1970 - 1972",
+        "C": "1980 - 1990",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the name of the country used in visualization?",
+        "A": "Kenya",
+        "B": "Ghana",
+        "C": "Cabo Verde",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/79359.json b/VisRecall/merged/qa/79359.json
new file mode 100755
index 0000000..bd03b8e
--- /dev/null
+++ b/VisRecall/merged/qa/79359.json
@@ -0,0 +1,50 @@
+{
+    "name": "79359.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the topic of the visualization?",
+        "A": "Percentage of female workers employed in the Service sector",
+        "B": "Percentage of male workers employed in the Service sector",
+        "C": "Percentage of workers employed in the Service sector",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What was the range of years of this study?",
+        "A": "2005 - 2013",
+        "B": "2005 - 2014",
+        "C": "2004 - 2013",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "What is the maximum Employment value in this visulization?",
+        "A": "Among 60",
+        "B": "50-60",
+        "C": "40-50",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "What year has the maximum employment value?",
+        "A": "2006",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What the countries did the study cover?",
+        "A": "Germany",
+        "B": "Bhutan, Ethiopia",
+        "C": "Malawi, Myanmar, Cuba",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/8XT9tstaX3P7OjmkeT1xTg==.0.json b/VisRecall/merged/qa/8XT9tstaX3P7OjmkeT1xTg==.0.json
new file mode 100755
index 0000000..be82c8c
--- /dev/null
+++ b/VisRecall/merged/qa/8XT9tstaX3P7OjmkeT1xTg==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "8XT9tstaX3P7OjmkeT1xTg==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Change in life expectancy with GDP per capita in different regions",
+        "B": "Comparison of GDP per capita of different regions",
+        "C": "Comparison of the average life expectancy of different regions",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2005",
+        "B": "2006",
+        "C": "2007",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "4",
+        "question": "How does life expectancy change with GDP growth?",
+        "A": "always higher",
+        "B": "Becoming higher then staying at a certain level",
+        "C": "always lower",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest GDP per capita during this period?",
+        "A": "Africa",
+        "B": "Americas",
+        "C": "Asia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the highest GDP per capita during this period?",
+        "A": "Asia",
+        "B": "Americas",
+        "C": "Europe",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/JVoKAJWwEgervxjbg2tqMA==.0.json b/VisRecall/merged/qa/JVoKAJWwEgervxjbg2tqMA==.0.json
new file mode 100755
index 0000000..38aff56
--- /dev/null
+++ b/VisRecall/merged/qa/JVoKAJWwEgervxjbg2tqMA==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "JVoKAJWwEgervxjbg2tqMA==.0.jpg",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The relationship between ACT scores, GPA and college admissions",
+        "B": "The relationship between SAT scores, GPA and job admissions",
+        "C": "The relationship between SAT scores, GPA and college admissions",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which organization does the data of this figure come from?",
+        "A": "Suffolk University",
+        "B": "Sheffield University",
+        "C": "Sussex University",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What is the lowest GPA for admissions?",
+        "A": "Over 1.50",
+        "B": "Over 2.50",
+        "C": "Over 2.00",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "What is the lowest SAT score for addmission?",
+        "A": "over 400",
+        "B": "over 500",
+        "C": "over 600",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the composition of the SAT score?",
+        "A": "Math + Reading",
+        "B": "Math + Writing",
+        "C": "Reading + Writing",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
diff --git a/VisRecall/merged/qa/_1IiNGPpj1s86KzEjQ93YQ==.0.json b/VisRecall/merged/qa/_1IiNGPpj1s86KzEjQ93YQ==.0.json
new file mode 100755
index 0000000..50dea57
--- /dev/null
+++ b/VisRecall/merged/qa/_1IiNGPpj1s86KzEjQ93YQ==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "_1IiNGPpj1s86KzEjQ93YQ==.0.jpg",
+    "vistype": "scatter",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It plots the validation loss over epochs",
+        "B": "It plots the training loss over epochs",
+        "C": "It plots the test loss over epochs",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "Which models are compared?",
+		"type": "2",
+        "A": "Two different model types",
+        "B": "An original model with a smaller version",
+        "C": "An original model with a bigger version",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "For how many epochs were the models trained?",
+		"type": "3",
+        "A": "10",
+        "B": "20",
+        "C": "15",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "question": "Which markers are used to visualize the data points?",
+		"type": "4",
+        "A": "Plus and Circle",
+        "B": "Cross and Circle",
+        "C": "Cross and Rectangle",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "What is the highest validation loss achieved by any model?",
+		"type": "1",
+        "A": "about 0.8",
+        "B": "about 1.1",
+        "C": "about 0.9",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/_Di4yk2H64QEBkEncRGMhg==.0.json b/VisRecall/merged/qa/_Di4yk2H64QEBkEncRGMhg==.0.json
new file mode 100755
index 0000000..a8d9216
--- /dev/null
+++ b/VisRecall/merged/qa/_Di4yk2H64QEBkEncRGMhg==.0.json
@@ -0,0 +1,50 @@
+{
+    "name": "_Di4yk2H64QEBkEncRGMhg==.0.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Height and Weight by Continents",
+        "B": "Height and Weight by Eating Habits",
+        "C": "Height and Weight by Sex",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "1",
+        "question": "Who is the tallest male in this visualization?",
+        "A": "Philip",
+        "B": "Ronald",
+        "C": "William",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Who is the tallest female in this visualization?",
+        "A": "Barbara",
+        "B": "Jane",
+        "C": "Mary",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Who is the weightest male in this visualization?",
+        "A": "Philip",
+        "B": "Ronald",
+        "C": "William",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Who is the shortest person in this visualization?",
+        "A": "Joyce",
+        "B": "Alice",
+        "C": "Thomas",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/_Fbq9iW1SCuAYaHmYUXAUA==.0.json b/VisRecall/merged/qa/_Fbq9iW1SCuAYaHmYUXAUA==.0.json
new file mode 100755
index 0000000..33b289c
--- /dev/null
+++ b/VisRecall/merged/qa/_Fbq9iW1SCuAYaHmYUXAUA==.0.json
@@ -0,0 +1,49 @@
+{
+    "name": "_Fbq9iW1SCuAYaHmYUXAUA==.0.jpg",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Relationship between ACSI and GDP scores by industry",
+        "B": "Relationship between GDP and Temkin NPS scores by industry",
+        "C": "Relationship between ACSI and Temkin NPS scores by industry",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2015-2018",
+        "B": "2010-2015",
+        "C": "2013-2017",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "4",
+        "question": "what is the approximate relationship between the two scores?",
+        "A": "Linear function",
+        "B": "Quadratic function",
+        "C": "Exponential function",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "4",
+        "question": "What is the interval of the horizontal coordinate?",
+        "A": "50-80",
+        "B": "60-85",
+        "C": "70-100",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "4",
+        "question": "What is the interval of the vertical coordinate?",
+        "A": "-10-60",
+        "B": "0-70",
+        "C": "10-80",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/__uID4lztqrYTniGMJdDWQ.0.json b/VisRecall/merged/qa/__uID4lztqrYTniGMJdDWQ.0.json
new file mode 100755
index 0000000..1dfff9b
--- /dev/null
+++ b/VisRecall/merged/qa/__uID4lztqrYTniGMJdDWQ.0.json
@@ -0,0 +1,50 @@
+{
+    "name": "__uID4lztqrYTniGMJdDWQ.0.jpg",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the main topic of the visualization?",
+        "A": "Sound mitigation at low frequencies",
+        "B": "Sound mitigation at high frequencies",
+        "C": "Sound mitigation at all frequencies",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What was the range of the frequencies?",
+        "A": "10 Hz - 1000 Hz",
+        "B": "100 Hz - 10000 Hz",
+        "C": "1000 Hz - 10000 Hz",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "In 10000Hz, which one has the highest Insertion Loss(db)?",
+        "A": "Classic Racetrack Baffle",
+        "B": "Betafoam AFI",
+        "C": "Medium Expansion Baffle",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "In 100Hz, which one has the highest Insertion Loss(db)?",
+        "A": "Classic Racetrack Baffle",
+        "B": "Betafoam AFI",
+        "C": "Medium Expansion Baffle",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "How does the insertion loss behave with increasing frequency?",
+        "A": "Decreases",
+        "B": "Stays constant",
+        "C": "Increases",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_100.json b/VisRecall/merged/qa/economist_daily_chart_100.json
new file mode 100755
index 0000000..d3fed97
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_100.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_100.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the main topic of the visualization?",
+        "A": "Football broadcasting revenues",
+        "B": "Football revenues",
+        "C": "Football wage costs",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2000-2010",
+        "B": "2001-2010",
+        "C": "1995-2010",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Where was the study conducted?",
+        "A": "South America",
+        "B": "Europe",
+        "C": "Middle East",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country's league spends the most on wages?",
+        "A": "Spain",
+        "B": "England",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which league spends the least on wages?",
+        "A": "La Liga",
+        "B": "Serie A",
+        "C": "Ligue 1",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_127.json b/VisRecall/merged/qa/economist_daily_chart_127.json
new file mode 100755
index 0000000..444923d
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_127.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_127.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Tourism and GDP",
+        "B": "Foreign exchange reserves and GDP",
+        "C": "Military spending and GDP",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2001-2010",
+        "B": "2002-2010",
+        "C": "2002-2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the biggest GDP growth in this period?",
+        "A": "China",
+        "B": "Angola",
+        "C": "Ethiopia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the biggest Military spending growth in this period?",
+        "A": "Ecuador",
+        "B": "Kazakhstan",
+        "C": "Armenia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Which country has a negative growth in military spending?",
+        "A": "Italy",
+        "B": "Canada",
+        "C": "Singapore",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_137.json b/VisRecall/merged/qa/economist_daily_chart_137.json
new file mode 100755
index 0000000..cbaf30d
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_137.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_137.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Wildfire",
+        "B": "Tree planting",
+        "C": "Paper consumption",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What is the height of trees in the visualization?",
+        "A": "40-foot trees",
+        "B": "42-foot trees",
+        "C": "44-foot trees",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country consumes the most 40-foot trees per person?",
+        "A": "Belgium",
+        "B": "Austria",
+        "C": "Finland",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country consumes the fewest 40-foot trees per person?",
+        "A": "China",
+        "B": "India",
+        "C": "Azerbaijian",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which country is not in the visualization?",
+        "A": "Switzerland",
+        "B": "Norway",
+        "C": "Portugal",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_145.json b/VisRecall/merged/qa/economist_daily_chart_145.json
new file mode 100755
index 0000000..d8ebd52
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_145.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_145.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Global arms inports",
+        "B": "Global arms exports",
+        "C": "Global arms profits",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2007-11",
+        "B": "2008-12",
+        "C": "2009-13",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country takes the highest percentage?",
+        "A": "Russia",
+        "B": "USA",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country takes the highest percentage of arms exports from Russia?",
+        "A": "India",
+        "B": "Algeria",
+        "C": "China",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which country is not in the five biggest arms exports countries?",
+        "A": "Britain",
+        "B": "France",
+        "C": "Australia",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_150.json b/VisRecall/merged/qa/economist_daily_chart_150.json
new file mode 100755
index 0000000..5cbca93
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_150.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_150.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Employment Outlook",
+        "B": "Company Finances",
+        "C": "Number of companies in different countries",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest Percentage?",
+        "A": "China",
+        "B": "Poland",
+        "C": "India",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest Percentage?",
+        "A": "United States",
+        "B": "Greece",
+        "C": "Mexico",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Compared with last year, which country has the largest increase in percentage?",
+        "A": "Spain",
+        "B": "Italy",
+        "C": "Greece",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_162.json b/VisRecall/merged/qa/economist_daily_chart_162.json
new file mode 100755
index 0000000..6a9080f
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_162.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_162.png",
+    "vistype": "line",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It shows the number of smartphones shipped per year",
+        "B": "It shows the number of server devices shipped per year",
+        "C": "It shows the number of personal computer devices shipped per year",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "question": "What type of phone was sold the most within a year?",
+		"type": "2",
+        "A": "Nokia",
+        "B": "Android",
+        "C": "iPhone",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "question": "What period of data does this visualization show?",
+		"type": "3",
+        "A": "1975-2011",
+        "B": "1985-2010",
+        "C": "1980-2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "question": "What is the most selling group of devices every year since 1985?",
+		"type": "1",
+        "A": "Android phones",
+        "B": "Macintosh",
+        "C": "PC total",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "question": "What is the source of the data?",
+		"type": "3",
+        "A": "Brantico",
+        "B": "Asymco",
+        "C": "Asyntra",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_184.json b/VisRecall/merged/qa/economist_daily_chart_184.json
new file mode 100755
index 0000000..7d9c21d
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_184.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_184.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Ageing of population",
+        "B": "Urbanisation",
+        "C": "Population growth",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1950-2010",
+        "B": "1950-2045",
+        "C": "1950-2030",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "By 2045, which area is expected to have the highest level of urbanization?",
+        "A": "US",
+        "B": "Western Europe",
+        "C": "Brazil",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which area had the lowest level of urbanization in 1950?",
+        "A": "China",
+        "B": "India",
+        "C": "South-East Asia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "What percentage of Indians are expected to live in urban areas by 2045?",
+        "A": "About 50%",
+        "B": "About 60%",
+        "C": "About 70%",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_189.json b/VisRecall/merged/qa/economist_daily_chart_189.json
new file mode 100755
index 0000000..30fbfee
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_189.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_189.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Executive growth in FTSE 350 companies",
+        "B": "Executive skillset in FTSE 350 companies",
+        "C": "Executive pay and performance in FTSE 350 Companies",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What does the light blue represent?",
+        "A": "performance",
+        "B": "pay",
+        "C": "earnings",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which category has the maximum value?",
+        "A": "Bonus",
+        "B": "Long-term incentive plan",
+        "C": "Total earnings",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which category has the minimum value?",
+        "A": "Earnings per share",
+        "B": "Year-end share price",
+        "C": "Pre-tax profit",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the name of the country used in visualization?",
+        "A": "Kenya",
+        "B": "Ghana",
+        "C": "Cabo Verde",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_191.json b/VisRecall/merged/qa/economist_daily_chart_191.json
new file mode 100755
index 0000000..97557ed
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_191.json
@@ -0,0 +1,49 @@
+{
+  "name": "economist_daily_chart_191.png",
+  "vistype": "other",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "election result prediction in 2012",
+    "B": "Student elections and selections in 2012",
+    "C": "Leadership elections and selections in 2012 ",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What is the source of data that is displayed in this visualization?",
+    "A": "The washington Post",
+    "B": "New york Times",
+    "C": "The Economist",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q3": {
+    "type": "2",
+    "question": "Which country's Leadership election doesn't have the month of election mentioned?",
+    "A": "Egypt",
+    "B": "China",
+    "C": "Malta",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "Which country has the presedential election on Two different days?",
+    "A": "Kenya",
+    "B": "France",
+    "C": "Mexico",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "Which is the only european nation having its election in 2012?",
+    "A": "France",
+    "B": "Hungary",
+    "C": "Greece",
+    "D": "I can not remember",
+    "answer": "A"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_194.json b/VisRecall/merged/qa/economist_daily_chart_194.json
new file mode 100755
index 0000000..697bc07
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_194.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_194.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "Which two countries are compared in this visualization?",
+        "A": "China,United States",
+        "B": "Canada,India",
+        "C": "Vietnam,Brazil",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1999-2019",
+        "B": "1999-2020",
+        "C": "1999-2025",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which type of data has the highest China/US ratio in 2011?",
+        "A": "Steel consumption",
+        "B": "Mobile phones",
+        "C": "Car sales",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which type of the following data has the lowest China/US ratio in 2011?",
+        "A": "Imports",
+        "B": "Defence spending",
+        "C": "Oil consumption",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "In 2011-2025, China will overtakes the United States in ____?",
+        "A": "Mobile phones",
+        "B": "Beer consumption",
+        "C": "Retail Sales",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_197.json b/VisRecall/merged/qa/economist_daily_chart_197.json
new file mode 100755
index 0000000..bd0ca51
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_197.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_197.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Latin America of total population are middle-class",
+        "B": "Latin America of total population having high income",
+        "C": "Latin America of total population living in poverty",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What is the range of values?",
+        "A": "0-70",
+        "B": "0-80",
+        "C": "0-90",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest value in 2010?",
+        "A": "Paraguay",
+        "B": "Uruguay",
+        "C": "Colombia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the lowest value in 2010?",
+        "A": "Paraguay",
+        "B": "Uruguay",
+        "C": "Colombia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which item is marked bold?",
+        "A": "Colombia",
+        "B": "Latin America",
+        "C": "Brazil",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_198.json b/VisRecall/merged/qa/economist_daily_chart_198.json
new file mode 100755
index 0000000..4313f18
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_198.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_198.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "World economies",
+        "B": "World imports",
+        "C": "World exports",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1990-2010",
+        "B": "1990-2011",
+        "C": "1990-2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What range of percentage does this visualization show?",
+        "A": "0-60",
+        "B": "0-70",
+        "C": "0-80",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What decreases as time goes by?",
+        "A": "Developed economies",
+        "B": "Emerging economies",
+        "C": "General economies",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "What increases as time goes by?",
+        "A": "Developed economies",
+        "B": "Emerging economies",
+        "C": "General economies",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_20.json b/VisRecall/merged/qa/economist_daily_chart_20.json
new file mode 100755
index 0000000..3cde608
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_20.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_20.png",
+    "vistype": "line",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It compares the population growth rate with the GPD per person",
+        "B": "It compares the urbanisation rate with the population growth rate",
+        "C": "It compares the urbanisation rate with the GPD per person",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "question": "Which regions are compared?",
+		"type": "2",
+        "A": "West Asia and Sub-Saharan Africa",
+        "B": "European Union and Sub-Saharan Africa",
+        "C": "Sub-Saharan Africa and East Asia/Pacific",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "What period of data does this visualization show?",
+		"type": "3",
+        "A": "1985-2010",
+        "B": "1990-2015",
+        "C": "1985-2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "question": "What is the highest urbanisation rate achieved by a country?",
+		"type": "1",
+        "A": "about 85%",
+        "B": "about 55%",
+        "C": "about 75%",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "question": "What is the source of the data?",
+		"type": "3",
+        "A": "World Health Organization",
+        "B": "World Bank",
+        "C": "World Trade Organization",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_200.json b/VisRecall/merged/qa/economist_daily_chart_200.json
new file mode 100755
index 0000000..c53a844
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_200.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_200.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "CO2 emissions",
+        "B": "Greenhouse gas emissions",
+        "C": "Carbon emissions",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1990-2010",
+        "B": "1990-2009",
+        "C": "1999-2009",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the most CO2 emissions in this visualization?",
+        "A": "United States",
+        "B": "China",
+        "C": "Russia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the least CO2 emissions in this visualization?",
+        "A": "Austria",
+        "B": "Australia",
+        "C": "Romania",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the biggest reduction in CO2 emissions during this period?",
+        "A": "Poland",
+        "B": "France",
+        "C": "Ukraine",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_202.json b/VisRecall/merged/qa/economist_daily_chart_202.json
new file mode 100755
index 0000000..5fc560a
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_202.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_202.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Anticipated personal spending on healthcare service",
+        "B": "Anticipated personal spending on public transportation",
+        "C": "Anticipated personal spending on Christmas gifts",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest anticipated personal spending on Christmas gifts in this visualization?",
+        "A": "United States",
+        "B": "Finland",
+        "C": "Luxembourg",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest anticipated personal spending on Christmas gifts in this visualization?",
+        "A": "Greece",
+        "B": "Germany",
+        "C": "Netherlands",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the lowest GDP per person in this visualization?",
+        "A": "South Africa",
+        "B": "Poland",
+        "C": "Ukraine",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_203.json b/VisRecall/merged/qa/economist_daily_chart_203.json
new file mode 100755
index 0000000..a61e55c
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_203.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_203.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualisation?",
+        "A": "the global revenue and the market size of puzzel games in different countries  ",
+        "B": "the global revenue and the market size of mobil games in different countries ",
+        "C": "the global revenue and the market size of video games in different countries ",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "From which year in this picture is the forecasting?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which of these global revenues has the highest value all the time?",
+        "A": "Mobile",
+        "B": "PC",
+        "C": "In-game advertising",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which global revenue has the lowest value all the time ?",
+        "A": "Online ",
+        "B": "Console",
+        "C": "donate",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which of these countries has the biggest Market size?",
+        "A": "Germany",
+        "B": "China",
+        "C": "France",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_206.json b/VisRecall/merged/qa/economist_daily_chart_206.json
new file mode 100755
index 0000000..f5b5440
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_206.json
@@ -0,0 +1,49 @@
+{
+  "name": "economist_daily_chart_206.png",
+  "vistype": "other",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Energy in Czech Republic",
+    "B": "Energy in Germany",
+    "C": "Energy in United states of america",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "1990-2009",
+    "B": "1999-2009",
+    "C": "1990-2010",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q3": {
+    "type": "3",
+    "question": "By what percentage did coal production drop by during 1990-2010 in Czech?",
+    "A": "51",
+    "B": "21",
+    "C": "38",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which country has the highest drop in coal production during 1990-2010 in percentage?",
+    "A": "Czech republic",
+    "B": "Australia",
+    "C": "Turkey",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q5": {
+    "type": "1",
+    "question": "which is the largest source of electricity production in Greece?",
+    "A": "Nuclear",
+    "B": "Coal",
+    "C": "Oil and gas",
+    "D": "I can not remember",
+    "answer": "C"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_207.json b/VisRecall/merged/qa/economist_daily_chart_207.json
new file mode 100755
index 0000000..92930e9
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_207.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_207.png",
+    "vistype": "line",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It shows the income inequality of multiple countries over time",
+        "B": "It shows the income increase of multiple countries over time",
+        "C": "It shows the income decrease of multiple countries over time",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "Which metric is used for the visualization?",
+		"type": "2",
+        "A": "OECD coefficient",
+        "B": "Spearman's coefficient",
+        "C": "Gini coefficient",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "What period of data does this visualization show?",
+		"type": "3",
+        "A": "1975-2006",
+        "B": "1975-2007",
+        "C": "1975-2008",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "Which country has the highest income inequality at the end (2008)?",
+		"type": "1",
+        "A": "Germany",
+        "B": "United States",
+        "C": "Britain",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "question": "Which country has the lowest income inequality?",
+		"type": "1",
+        "A": "United States",
+        "B": "Sweden",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_208.json b/VisRecall/merged/qa/economist_daily_chart_208.json
new file mode 100755
index 0000000..727bfec
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_208.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_208.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Corrumption and human development",
+        "B": "Corrumption and country development",
+        "C": "Consumption and human development",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "What does the red curve stand for?",
+        "A": "Confidence",
+        "B": "Average",
+        "C": "R2",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "What is the source of the visualization?",
+        "A": "Transparency Internet; UN Country Development Report",
+        "B": "Transparency Internet; UN Human Development Report",
+        "C": "Transparency Internet; UN Consumption Report",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the range of Y-axis?",
+        "A": "0.2-1.0",
+        "B": "0.1-1.0",
+        "C ": "0-1.0",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_209.json b/VisRecall/merged/qa/economist_daily_chart_209.json
new file mode 100755
index 0000000..150c03c
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_209.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_209.png",
+    "vistype": "line",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It shows the worker acitivty of different countries",
+        "B": "It shows the sales activity of different countries",
+        "C": "It shows the manufacturing activity of different countries",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "question": "Which metric is used for the visualization?",
+		"type": "2",
+        "A": "Employee hiring rate",
+        "B": "Number of employees",
+        "C": "Purchasing manager index",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "What period of data does this visualization show?",
+		"type": "2",
+        "A": "2008-2011",
+        "B": "2007-2012",
+        "C": "2009-2013",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "question": "Which value does the horizonal red line indicate?",
+		"type": "3",
+        "A": "50",
+        "B": "100",
+        "C": "75",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "Whats the lowest purchasing manager index achieved?",
+		"type": "1",
+        "A": "around 20",
+        "B": "around 30",
+        "C": "around 25",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_211.json b/VisRecall/merged/qa/economist_daily_chart_211.json
new file mode 100755
index 0000000..c0dc23f
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_211.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_211.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Computing power and stockmarkets",
+        "B": "Computing only stock markets",
+        "C": "Computing only a power on specific commodity",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What are the sources of the visualization?",
+        "A": "Intel and The Economist",
+        "B": "Intel,NYSE Euronext and The Economist",
+        "C": "Intel",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "What is the name of the city present in visualization?",
+        "A": "Washington",
+        "B": "New Jersey",
+        "C": "New York",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What does the brown line represent?",
+        "A": "Transistor count on integrated circuits",
+        "B": "Shares traded on the New York Stock",
+        "C": "Shares traded on the New Jersey Stock",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "What does the blue line represent?",
+        "A": "Transistor count on integrated circuits",
+        "B": "Shares traded on the New York Stock",
+        "C": "Shares traded on the New Jersey Stock",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_215.json b/VisRecall/merged/qa/economist_daily_chart_215.json
new file mode 100755
index 0000000..9409298
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_215.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_215.png",
+    "vistype": "other",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It shows which countries produce cluster munition",
+        "B": "It shows which countries produce nuclear bombs",
+        "C": "It shows which countries produce weapons",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "What is the color of current producers?",
+		"type": "4",
+        "A": "Light Gray",
+        "B": "Dark blue",
+        "C": "Light blue",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "question": "What is the color of past producers?",
+		"type": "4",
+        "A": "Light Gray",
+        "B": "Dark blue",
+        "C": "Light blue",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "From which year is the data?",
+		"type": "3",
+        "A": "2010",
+        "B": "2012",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "question": "Only one country in africa is currently producing, which one is it?",
+		"type": "2",
+        "A": "Egypt",
+        "B": "South Africa",
+        "C": "Central Africa",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_218.json b/VisRecall/merged/qa/economist_daily_chart_218.json
new file mode 100755
index 0000000..1db1ce3
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_218.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_218.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "US troops abroad",
+        "B": "North America troops abroad",
+        "C": "America troops abroad",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What years' data is displayed in this visualization?",
+        "A": "1900-2011",
+        "B": "1920-2011",
+        "C": "1950-2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "Which region is displayed in deep green?",
+        "A": "Americas",
+        "B": "East Asia",
+        "C": "Middle East",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "Which region is displayed in blue?",
+        "A": "Americas",
+        "B": "East Asia",
+        "C": "Middle East",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "What is the range of Y-axis?",
+        "A": "0-1.0",
+        "B": "0-1.1",
+        "C": "0-1.2",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_223.json b/VisRecall/merged/qa/economist_daily_chart_223.json
new file mode 100755
index 0000000..17b64b4
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_223.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_223.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Alcohol production and breakeven costs",
+        "B": "Electronic devices production and breakeven costs",
+        "C": "Oil and gas production and breakeven costs",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which two years the data is compared in this visualization?",
+        "A": "2011 and 2015",
+        "B": "2011 and 2025",
+        "C": "2011 and 2035",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which district has the largest production in 2011?",
+        "A": "OECD Americas",
+        "B": "Middle East and North Africa",
+        "C": "Easter Europe / Eurasia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which district has the smallest production in 2011?",
+        "A": "Latin America",
+        "B": "Asia",
+        "C": "Rest of Africa",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "In which district the production is predicted to decrease and the breakeven cost to increase?",
+        "A": "OECD Americas",
+        "B": "Rest of OECD",
+        "C": "Rest of Africa",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_225.json b/VisRecall/merged/qa/economist_daily_chart_225.json
new file mode 100755
index 0000000..34f40f1
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_225.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_225.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Change in emerging markets' foreign-currency reserves",
+        "B": "Change in emerging markets' material  reserves",
+        "C": "Change in emerging markets' technology  reserves",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has made the biggest negative change during this period?",
+        "A": "China",
+        "B": "Russia",
+        "C": "Singapore",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has made the smallest change during this period?",
+        "A": "Peru",
+        "B": "Colombia",
+        "C": "Philippines",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has made the biggest positive change during this period?",
+        "A": "China",
+        "B": "Mexico",
+        "C": "Saudi Arabia",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_229.json b/VisRecall/merged/qa/economist_daily_chart_229.json
new file mode 100755
index 0000000..863f18a
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_229.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_229.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Human development index according to UN Human Development Report",
+        "B": "Human development index according to EU Human Development Report",
+        "C": "Human development index according to WHO Human Development Report",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What does the grey icon represent?",
+        "A": "Life expectancy at birth",
+        "B": "Income per person",
+        "C": "Expected years of schooling",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest rank?",
+        "A": "Norway",
+        "B": "United States",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which years has the lowest rank?",
+        "A": "Brazil",
+        "B": "Russia",
+        "C": "Congo",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "How many countries are taken into consideration for the ranking?",
+        "A": "Around 100 and below",
+        "B": "Around 150 and below",
+        "C": "Around 180 and above",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_235.json b/VisRecall/merged/qa/economist_daily_chart_235.json
new file mode 100755
index 0000000..4b32491
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_235.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_235.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Government bond yields",
+        "B": "Government Property yields",
+        "C": "Government fund yields",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "2009-2010",
+        "B": "2010-2011",
+        "C": "2011-2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest yields during this period?",
+        "A": "Greece",
+        "B": "Italy",
+        "C": "France",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest yields during this period?",
+        "A": "Greece",
+        "B": "Italy",
+        "C": "France",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What are the highest yields in Greece during this period?",
+        "A": "Around 5%",
+        "B": "Around 15%",
+        "C": "Around 25%",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_236.json b/VisRecall/merged/qa/economist_daily_chart_236.json
new file mode 100755
index 0000000..f017594
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_236.json
@@ -0,0 +1,49 @@
+{
+  "name": "economist_daily_chart_236.png",
+  "vistype": "line",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Real Average after-tax income in US",
+    "B": "Real Average after-tax income in Germany",
+    "C": "Real Average after-tax income in Europe",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "1979-2007",
+    "B": "1989-2005",
+    "C": "1995-2005",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q3": {
+    "type": "4",
+    "question": "How many times did the recession occur?",
+    "A": "1",
+    "B": "2",
+    "C": "4",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "3",
+    "question": "In what range does Top 1 percent be around 2005-2007?",
+    "A": "Around 250-300",
+    "B": "Around 350-400",
+    "C": "Around 450-500",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "In what range does 21st to 80th percentiles be around 1990-2000?",
+    "A": "Around 100-150",
+    "B": "Around 150-200",
+    "C": "Around 200-250",
+    "D": "I can not remember",
+    "answer": "A"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_238.json b/VisRecall/merged/qa/economist_daily_chart_238.json
new file mode 100755
index 0000000..c904c4f
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_238.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_238.png",
+    "vistype": "other",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It shows tourist attractions",
+        "B": "It shows leaning buildings",
+        "C": "It shows the tallest buildings",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "question": "What is the tallest building shown?",
+		"type": "1",
+        "A": "Tower of Bologna",
+        "B": "Big Ben",
+        "C": "Tower of Pisa",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "question": "What is the most leaning building?",
+		"type": "1",
+        "A": "Suurhusen church tower",
+        "B": "Tower of Pisa",
+        "C": "Bad Frankenhausen church tower",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "question": "What is the least leaning building?",
+		"type": "1",
+        "A": "Nevyansk tower",
+        "B": "Tiger Hill Pagoda",
+        "C": "Big Ben",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "question": "How high is the highest building?",
+		"type": "2",
+        "A": "about 90m",
+        "B": "about 100m",
+        "C": "about 75m",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_242.json b/VisRecall/merged/qa/economist_daily_chart_242.json
new file mode 100755
index 0000000..82f567c
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_242.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_242.png",
+    "vistype": "other",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Global malaria deaths and R&D funding",
+        "B": "Global smallpox deaths and R&D funding",
+        "C": "Global malaria deaths and P&G funding",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2000-2009",
+        "B": "2001-2021",
+        "C": "2007-2009",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which area has the highest value in 2000?",
+        "A": "Africa",
+        "B": "Americas",
+        "C": "South-East Asia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which area has the lowest value in 2003?",
+        "A": "Africa",
+        "B": "Americas",
+        "C": "South-East Asia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "What year did R&D funding have the highest value?",
+        "A": "2000",
+        "B": "2005",
+        "C": "2009",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_243.json b/VisRecall/merged/qa/economist_daily_chart_243.json
new file mode 100755
index 0000000..53e901a
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_243.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_243.png",
+    "vistype": "other",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Economic situation of all continents",
+        "B": "'Occupy' protests around the world",
+        "C": "Youth unemployment rate",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest unemployment rate in 2011?",
+        "A": "Spain",
+        "B": "Italy",
+        "C": "US",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest unemployment rate in 2011?",
+        "A": "Greece",
+        "B": "Japan",
+        "C": "Chile",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the highest percentage of government gross debt in 2011?",
+        "A": "Portugal",
+        "B": "Britain",
+        "C": "Japan",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_244.json b/VisRecall/merged/qa/economist_daily_chart_244.json
new file mode 100755
index 0000000..2bebe85
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_244.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_244.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of the visualization?",
+        "A": "Historic Stockmarket Returns",
+        "B": "Home-Loan Hiatus",
+        "C": "Three-month LIBOR",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What is the first period listed in the visualization?",
+        "A": "1910-1914",
+        "B": "1914-1918",
+        "C": "1919-1928",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "What is the latest period listed in the visualization",
+        "A": "2008",
+        "B": "2005",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the largest stockmarket return in the visualization?",
+        "A": "Japan",
+        "B": "United States",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "During Which period the stockmarket return is the lowest for World??",
+        "A": "1914-1918, First world war",
+        "B": "1929-1931, Wall Street crash",
+        "C": "1939-1948, Second world war and aftermath",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_247.json b/VisRecall/merged/qa/economist_daily_chart_247.json
new file mode 100755
index 0000000..bd3407d
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_247.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_247.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Exchange rate and merchandise trade-deficit between the US and China",
+        "B": "Exchange rate and merchandise trade-deficit between the US and Russia",
+        "C": "Exchange rate and merchandise trade-deficit between Europe and China",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period's data is displayed in this visualization?",
+        "A": "2006-2010",
+        "B": "2007-2011",
+        "C": "2008-2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "Numerically speaking, what is the trend of the exchange rate in the period?",
+        "A": "The exchange rate decreases",
+        "B": "The exchange rate increases",
+        "C": "The exchange rate remains stable",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "In which quarter the merchandise trade-deficit is the largest?",
+        "A": "2009",
+        "B": "2010",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "In which range the merchandise trade-deficit in the period?",
+        "A": "100-200 billion",
+        "B": "150-250 billion",
+        "C": "200-300 billion",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_250.json b/VisRecall/merged/qa/economist_daily_chart_250.json
new file mode 100755
index 0000000..d790d6d
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_250.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_250.png",
+    "vistype": "bar",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It shows nobel prices in chemistry",
+        "B": "It shows nobel prices in physics",
+        "C": "It shows nobel prices in mathematics",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "What is the first decade the visualization reports?",
+		"type": "2",
+        "A": "1910",
+        "B": "1890",
+        "C": "1900",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "question": "Which sub-discipline is always shown on the bottom of each bar?",
+		"type": "3",
+        "A": "Organic",
+        "B": "Biological",
+        "C": "Physical",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "How many nobel prizes are awarded at most each decade?",
+		"type": "2",
+        "A": "10",
+        "B": "20",
+        "C": "5",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "Which sub-discipline is awarded the least amount of nobel prized on average?",
+		"type": "1",
+        "A": "Biological",
+        "B": "Organic",
+        "C": "Physical",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_253.json b/VisRecall/merged/qa/economist_daily_chart_253.json
new file mode 100755
index 0000000..57a17b9
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_253.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_253.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Currencies against the dollar",
+        "B": "Currencies against the euro",
+        "C": "Currencies against the rupee",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which months' data are displayed in this visualization?",
+        "A": "Jan to Sep",
+        "B": "Jan to Oct",
+        "C": "Jan to Dec",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "Which currency continues to rise?",
+        "A": "Yuan",
+        "B": "Zloty",
+        "C": "Real",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Which currency is displayed in brown?",
+        "A": "Dollar",
+        "B": "Euro",
+        "C": "Rupee",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_257.json b/VisRecall/merged/qa/economist_daily_chart_257.json
new file mode 100755
index 0000000..9fedbd5
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_257.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_257.png",
+    "vistype": "scatter",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Democracy and inequality between the sexes",
+        "B": "Democracy and equality between the sexes",
+        "C": "Democracy and inequality between the youth and senior",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "What area does the green circle represent?",
+        "A": "Western Europe",
+        "B": "Americas",
+        "C": "Asia & Oceania",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the highest UN gender-inequality index?",
+        "A": "UAE",
+        "B": "Yemen",
+        "C": "Russia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the highest EIU democracy index?",
+        "A": "Cuba",
+        "B": "Japan",
+        "C": "Norway",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_260.json b/VisRecall/merged/qa/economist_daily_chart_260.json
new file mode 100755
index 0000000..17ebdef
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_260.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_260.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Gross government debt",
+        "B": "Gross government costs for education",
+        "C": "GDP forecast of government",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest value?",
+        "A": "Japan",
+        "B": "Italy",
+        "C": "Belgium",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest value?",
+        "A": "Britain",
+        "B": "Spain",
+        "C": "Canada",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the highest debt that held abroad?",
+        "A": "Spain",
+        "B": "United States",
+        "C": "Greece",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_262.json b/VisRecall/merged/qa/economist_daily_chart_262.json
new file mode 100755
index 0000000..30bd41e
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_262.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_262.png",
+    "vistype": "table",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Excess female deaths",
+        "B": "Excess male deaths",
+        "C": "Excess human deaths",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2008",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest number of female deaths between the ages of 5-14?",
+        "A": "Sub-Saharan Africa",
+        "B": "India",
+        "C": "South Asia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest number of female deaths between the ages of 15-49?",
+        "A": "China",
+        "B": "Europe and Central Asia",
+        "C": "Latin America and the Caribbean",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the lowest number of female deaths under 60?",
+        "A": "Europe and Central Asia",
+        "B": "India",
+        "C": "Latin America and the Caribbean",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_264.json b/VisRecall/merged/qa/economist_daily_chart_264.json
new file mode 100755
index 0000000..5f89aa1
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_264.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_264.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Public opinion on reallocation",
+        "B": "Public opinion on austerity",
+        "C": "Public opinion on corruption",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "What country has the highest opinion of increasing spending according to the poll?",
+        "A": "Italy",
+        "B": "Netherlands",
+        "C": "Britain",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest Forecast net debt as percentage of GDP, 2011?",
+        "A": "Britain",
+        "B": "Italy",
+        "C": "Netherlands",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the highest opinion of cut spending according to the poll?",
+        "A": "Portugal",
+        "B": "Germany",
+        "C": "Slovakia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which country has the lowest Forecast net debt as percentage of GDP, 2011?",
+        "A": "Poland",
+        "B": "Slovakia",
+        "C": "Sweden",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_267.json b/VisRecall/merged/qa/economist_daily_chart_267.json
new file mode 100755
index 0000000..c9e4244
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_267.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_267.png",
+    "vistype": "other",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The Chinese biggest employers",
+        "B": "The world's biggest employers",
+        "C": "The American biggest employers",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2000",
+        "C": "2020",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which one is the biggest employer in this visualization?",
+        "A": "US Department of Defence",
+        "B": "Walmart",
+        "C": "McDonald's",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which of the following companies has the least employees in this visualization?",
+        "A": "Chinese People's Liberation Army",
+        "B": "State Grid Corporation of China",
+        "C": "Hon Hai Precision Industry",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "How many employees does Walmart have?",
+        "A": "Around 1 million",
+        "B": "Around 1.5 million",
+        "C": "Around 2 million",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_308.json b/VisRecall/merged/qa/economist_daily_chart_308.json
new file mode 100755
index 0000000..1025142
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_308.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_308.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualisation?",
+        "A": "recorded homeless people during protests between civilians and Arab government security forces",
+        "B": "recorded deaths during protests between civilians and Arab government security forces",
+        "C": "recorded jobless during protests between civilians and Arab government security forces",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualisation",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest value of the number of population?",
+        "A": "Syria",
+        "B": "Egypt",
+        "C": "Tunisia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the longest time of unrest ?",
+        "A": "Syria",
+        "B": "Tunisia",
+        "C": "Yemen",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which country has the different status than other countries?",
+        "A": "Egypt",
+        "B": "Yemen",
+        "C": "Bahrain",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_319.json b/VisRecall/merged/qa/economist_daily_chart_319.json
new file mode 100755
index 0000000..f4dcc9a
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_319.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_319.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Developed-market overheating index",
+        "B": "Emerging-market overheating index",
+        "C": "Developed-market economic recession index",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "1",
+        "question": "Which country has the highest overheating index?",
+        "A": "Argentina",
+        "B": "Brazil",
+        "C": "Singapore",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the lowest overheating index?",
+        "A": "Saudi Arabia",
+        "B": "Malaysia",
+        "C": "Hungary",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "What is the minimum value of economic index in this visualization?",
+        "A": "Less than 10",
+        "B": "10-20",
+        "C": "20-30",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Which country doesn't appear in this visualization?",
+        "A": "China",
+        "B": "Russia",
+        "C": "US",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_336.json b/VisRecall/merged/qa/economist_daily_chart_336.json
new file mode 100755
index 0000000..7a2a12e
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_336.json
@@ -0,0 +1,49 @@
+{
+  "name": "economist_daily_chart_336.png",
+  "vistype": "bar",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Biggest Healthcare spenders",
+    "B": "Biggest Military spenders",
+    "C": "Biggest Video game makers",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2010",
+    "B": "2011",
+    "C": "2012",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which country has the highest value?",
+    "A": "Turkey",
+    "B": "United states",
+    "C": "Canada",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which country has the lowest value?",
+    "A": "China",
+    "B": "Japan",
+    "C": "Britain",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "whats the Unit of spending used to represent the spending",
+    "A": "$bn (billion dollars)",
+    "B": "$mn (million dollars)",
+    "C": "$tr (trillion dollars)",
+    "D": "I can not remember",
+    "answer": "A"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_355.json b/VisRecall/merged/qa/economist_daily_chart_355.json
new file mode 100755
index 0000000..9800906
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_355.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_355.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "World temperature",
+        "B": "World population",
+        "C": "World birth rate",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which is the earliest year displayed in this visualization?",
+        "A": "1900",
+        "B": "1950",
+        "C": "2000",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which is the last year displayed in this visualization?",
+        "A": "2050",
+        "B": "2100",
+        "C": "2150",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What are the two classes in the X-axis?",
+        "A": "Young and Old",
+        "B": "Poor and Rich",
+        "C": "Male and Female",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the source?",
+        "A": "UK",
+        "B": "US",
+        "C": "UN",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_376.json b/VisRecall/merged/qa/economist_daily_chart_376.json
new file mode 100755
index 0000000..b32e039
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_376.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_376.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Earthquake danger",
+        "B": "Nuclear danger",
+        "C": "Fire danger",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What is the maximum level of nuclear danger?",
+        "A": "6",
+        "B": "7",
+        "C": "8",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which of the following places has the highest level of nuclear danger?",
+        "A": "Fukushima, Japan",
+        "B": "Kyshtym, Russia",
+        "C": "Three Mile Island, United States",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which of the following places has the lowest level of nuclear danger?",
+        "A": "Tokaimura, Japan",
+        "B": "Windscale, Britain",
+        "C": "Gravelines, France",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which of the following places occured nuclear incident after 2000?",
+        "A": "Chernobyl, Ukraine",
+        "B": "Sellafiend, Britain",
+        "C": "Tokaimura, Japan",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_377.json b/VisRecall/merged/qa/economist_daily_chart_377.json
new file mode 100755
index 0000000..000b9f5
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_377.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_377.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Manned spaceflight",
+        "B": "Spaceflight Nationality",
+        "C": "Spaceflight missions",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "When is the earlist year in this visualization?",
+        "A": "1959",
+        "B": "1960",
+        "C": "1961",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which country is not ploted in the figure?",
+        "A": "Russia",
+        "B": "Britain",
+        "C": "China",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "During which decade has the average length of mission reached the peak (highest value)?",
+        "A": "1970-80",
+        "B": "1980-90",
+        "C": "1990-2000",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the maximun missions in a year?",
+        "A": "10-12",
+        "B": "12-14",
+        "C": "14-16",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_384.json b/VisRecall/merged/qa/economist_daily_chart_384.json
new file mode 100755
index 0000000..6af032c
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_384.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_384.png",
+    "vistype": "pie",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Global citations in scientific journals",
+        "B": "Global citations in art journals",
+        "C": "Global citations in scientific conferences",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "1999-2008",
+        "B": "1999-2010",
+        "C": "1999-2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "3",
+        "question": "what is the total number of citations from 1999 to 2003?",
+        "A": "Around 10m",
+        "B": "Around 20m",
+        "C": "Around 40m",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the highest citations over this period?",
+        "A": "United States",
+        "B": "Britain",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the lowest citations over this period?",
+        "A": "Canada",
+        "B": "Italy",
+        "C": "China",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_387.json b/VisRecall/merged/qa/economist_daily_chart_387.json
new file mode 100755
index 0000000..81cd80a
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_387.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_387.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Global lagre conventional weapons exports",
+        "B": "Global agricultural products exports",
+        "C": "Global army dispatch",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which period of data is displayed in this visualization?",
+        "A": "2010-2016",
+        "B": "2006-2010",
+        "C": "2006-2016",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest percentage?",
+        "A": "United States",
+        "B": "Russia",
+        "C": "China",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the lowest percentage?",
+        "A": "France",
+        "B": "Britain",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which country is Russia's largest exporter of this product?",
+        "A": "Saudi Arabia",
+        "B": "China",
+        "C": "India",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
diff --git a/VisRecall/merged/qa/economist_daily_chart_420.json b/VisRecall/merged/qa/economist_daily_chart_420.json
new file mode 100755
index 0000000..0ebf243
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_420.json
@@ -0,0 +1,49 @@
+{
+  "name": "economist_daily_chart_420.png",
+  "vistype": "scatter", 
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Deaths due to increased Bool pressure",
+    "B": "Deaths due to type 2 diabetes",
+    "C": "Traffic cameras and road deaths",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2001-2005",
+    "B": "2005-2009",
+    "C": "2009-2013",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which country has the highest value of deaths?",
+    "A": "Spain",
+    "B": "Slovenia",
+    "C": "Ukraine",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which country has the highest number of cameras?",
+    "A": "Cyprus",
+    "B": "Britain",
+    "C": "Estonia",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "How many deaths are there in Ukraine?",
+    "A": "Around 50-60",
+    "B": "Around 40-50",
+    "C": "Around 30-40",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_441.json b/VisRecall/merged/qa/economist_daily_chart_441.json
new file mode 100755
index 0000000..a20b3d0
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_441.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_441.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Adult and youth unemployment as % of labour force",
+        "B": "Adult and youth unemployment as % of labour wage",
+        "C": "Adult and youth unemployment as % of labour market ",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which year is seen in the visualization?",
+        "A": "2011",
+        "B": "2010",
+        "C": "2015",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest unemployment rank?",
+        "A": "Spain",
+        "B": "Estonia",
+        "C": "Slovakia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which years has the lowest unemployment rank?",
+        "A": "Germany",
+        "B": "Austria",
+        "C": "Switzerland",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What age interval is considered as Adult?",
+        "A": "Aged around 25 - 54",
+        "B": "Aged around 20 - 50",
+        "C": "Aged around 25 - 40",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_447.json b/VisRecall/merged/qa/economist_daily_chart_447.json
new file mode 100755
index 0000000..3ec3ecc
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_447.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_447.png",
+    "vistype": "table",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The Economist poll of forecasters",
+        "B": "The Sociologist poll of forecasters",
+        "C": "The Historian poll of forecasters",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which month's data is displayed in this visualization?",
+        "A": "May",
+        "B": "August",
+        "C": "Decemeber",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "2009-2010",
+        "B": "2010-2011",
+        "C": "2011-2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the highest average GDP in 2010?",
+        "A": "Australia",
+        "B": "Germany",
+        "C": "Sweden",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the highest average GDP in 2011?",
+        "A": "Australia",
+        "B": "Germany",
+        "C": "Sweden",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_451.json b/VisRecall/merged/qa/economist_daily_chart_451.json
new file mode 100755
index 0000000..ca392b7
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_451.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_451.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The hottest years on record in degree celsius",
+        "B": "The coldest years on record in degree celsius",
+        "C": "The hottest years on record in Fahrenheit",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What is the ranking order seen in the visualization? ",
+        "A": "1 - 50",
+        "B": "1 - 20",
+        "C": "1 - 100",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which year has recorded the highest deviation according to all research centers?",
+        "A": "1990",
+        "B": "1998",
+        "C": "2010",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "According to NOAA national climatic data center what is the lowest deviation value reported?",
+        "A": "Around 0.20",
+        "B": "Around 0.30 ",
+        "C": "Around 0.50",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the source of the data visualized?",
+        "A": "Met office",
+        "B": "UN climate organization",
+        "C": "EPA",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_473.json b/VisRecall/merged/qa/economist_daily_chart_473.json
new file mode 100755
index 0000000..1256336
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_473.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_473.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "US mid term elections",
+        "B": "US senate elections",
+        "C": "UN elections",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What is the year of latest projection?",
+        "A": "2015",
+        "B": "2010",
+        "C": "2018",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "What is the name of president in the latest projection?",
+        "A": "Bush Junior",
+        "B": "Clinton",
+        "C": "Obama",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "What is the name of president in the first projection?",
+        "A": "Truman",
+        "B": "Kennedy",
+        "C": "Eisenhower",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the interval of seats lost according to the visualization?",
+        "A": "0 to 80",
+        "B": "+10 to - 80",
+        "C": "0 to 100",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_513.json b/VisRecall/merged/qa/economist_daily_chart_513.json
new file mode 100755
index 0000000..f56525c
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_513.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_513.png",
+    "vistype": "table",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Best-selling children's books in Britain",
+        "B": "Best-selling science fictions in Britain",
+        "C": "Best-selling political memoirs in Britain",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1998-2008",
+        "B": "1998-2005",
+        "C": "1998-2010",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which book sells best in this visualization?",
+        "A": "My Life",
+        "B": "Dreams of My Father",
+        "C": "Living History",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which book sells the worst in this visualization?",
+        "A": "Chance Witness",
+        "B": "Diaries: In Power",
+        "C": "The Third Man",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "How many copies did My Life sell?",
+        "A": "Around 700,000",
+        "B": "Around 180,000",
+        "C": "Around 60,000",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_523.json b/VisRecall/merged/qa/economist_daily_chart_523.json
new file mode 100755
index 0000000..734aafd
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_523.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_523.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Beer consumption around the world",
+        "B": "Beer consumption around Europe only",
+        "C": "Beer consumption around US only",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What are the years seen in the visualization?",
+        "A": "2008 and 2010",
+        "B": "2000 and 2001",
+        "C": "2008 and 2009",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which country has recorded the highest consumption in terms of literes per person?",
+        "A": "Czech Republic",
+        "B": "US",
+        "C": "Germany",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which country has recorded the lowest consumption in terms of literes per person?",
+        "A": "Vietnam",
+        "B": "France",
+        "C": "China",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "How much Hectoliters/m of beer is produced by Asia and Europe in the year 2009?",
+        "A": "Around 500",
+        "B": "Above 550",
+        "C": "Below 400",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_531.json b/VisRecall/merged/qa/economist_daily_chart_531.json
new file mode 100755
index 0000000..5fe1af4
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_531.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_531.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Commodity prices for the year 2009 and 2010",
+        "B": "Commodity prices for the year 2020 and 2021",
+        "C": "Commodity prices for 2009",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What are the commodities seen in the visualization?",
+        "A": "Coffee and Lemon juice",
+        "B": "Tea and Coffee",
+        "C": "Coffee, Orange juice and Wheat",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which commodity has recorded the highest price?",
+        "A": "Wheat",
+        "B": "Tea",
+        "C": "Orange juice",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has recorded the lowest price?",
+        "A": "Wheat",
+        "B": "Orange juice",
+        "C": "Tea",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What interval values does the visualization show?",
+        "A": "60 to 100",
+        "B": "60 to 150",
+        "C": "60 to 600",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_532.json b/VisRecall/merged/qa/economist_daily_chart_532.json
new file mode 100755
index 0000000..3ebbaf4
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_532.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_532.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Car accidents in America",
+        "B": "Car manufactures in America",
+        "C": "Car thefts in America",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Where does the source come from?",
+        "A": "Insurance-claim",
+        "B": "Police-claim",
+        "C": "Consultant-claim",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which model has the highest overall theft loss?",
+        "A": "Cadillac Escalade",
+        "B": "Ford f-250 Crew 4WD(2008-2009)",
+        "C": "Chevrolet Corvette Z06",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which model has the lowest claim frequency?",
+        "A": "Volvo S80",
+        "B": "Saturn VUE(2008-2009)",
+        "C": "Toyota Sienna 4WD",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which brand occured most times in the list of HIGHEST LOSSES?",
+        "A": "Cadillac",
+        "B": "Ford",
+        "C": "Chevrolet",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_65.json b/VisRecall/merged/qa/economist_daily_chart_65.json
new file mode 100755
index 0000000..fa56234
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_65.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist daily chart65.jpg",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualisation?",
+        "A": "The average age of cabinets and the population in different countries",
+        "B": "The average age of cabinets and the policeman in different counties ",
+        "C": "The average age of cabinets and the fireman in different countries ",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data did not display in this visualisation?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest value of the age of cabinet ministers?",
+        "A": "China",
+        "B": "United States",
+        "C": "India",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "What is the average age of cabinet ministers in China ?",
+        "A": "65",
+        "B": "63",
+        "C": "60",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Which color did not used to display the data in this visualisation?",
+        "A": "yellow",
+        "B": "red",
+        "C": "blue",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_76.json b/VisRecall/merged/qa/economist_daily_chart_76.json
new file mode 100755
index 0000000..7f8bb06
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_76.json
@@ -0,0 +1,50 @@
+{
+    "name": "economist_daily_chart_76.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the main topic of the visualization?",
+        "A": "Historic stockmarket returns",
+        "B": "Football wage costs",
+        "C": "Three-month LIBOR",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "When does the peak of JPMorgan Chase LIBOR rate appears?",
+        "A": "September 1st - October 1st",
+        "B": "October 1st - November 1st",
+        "C": "November 1st - December 1st",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which one has the highest LIBOR rate?",
+        "A": "Barclays",
+        "B": "JPMorgan Chase",
+        "C": "Official rate",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which event is not appeared in the visualization?",
+        "A": "LEHMAN BROTHERS COLLAPES",
+        "B": "BARCLAYS CONTACT WITH BANK OF ENDLAND",
+        "C": "JPMORGAN CHASE CONTACT WITH BANK OF ENGLAND",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the range of highest LIBOR rate in the visualization?",
+        "A": "5.0-5.5",
+        "B": "4.5-5.0",
+        "C": "4.0-4.5",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/economist_daily_chart_77.json b/VisRecall/merged/qa/economist_daily_chart_77.json
new file mode 100755
index 0000000..c962079
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_77.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_77.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "The Standard Model of quantum physics",
+        "B": "The Standard Model of particle physics",
+        "C": "The Standard Model of electronic physics",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which particle is the earliest discovered?",
+        "A": "Photon",
+        "B": "Muon",
+        "C": "Electron",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which particle is the latest discovered?",
+        "A": "Tau neutrino",
+        "B": "HIGGS BOSON",
+        "C": "Top",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which particle is Bosons?",
+        "A": "Photon",
+        "B": "Electron",
+        "C": "Muon",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which particle takes the longest time from theorised to explained?",
+        "A": "Electron neutrino",
+        "B": "Tao neutrino",
+        "C": "HIGGS BOSON",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_84.json b/VisRecall/merged/qa/economist_daily_chart_84.json
new file mode 100755
index 0000000..bc038bb
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_84.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_84.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Median age of population",
+        "B": "Median height of population",
+        "C": "Median weight of population",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1900-2010",
+        "B": "1930-2010",
+        "C": "1950-2010",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "3",
+        "question": "What is the median value in 1980?",
+        "A": "Around 15",
+        "B": "Around 30",
+        "C": "Around 60",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "What is the median value in 2010?",
+        "A": "Around 15",
+        "B": "Around 30",
+        "C": "Around 60",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which region has the highest age?",
+        "A": "SAUD",
+        "B": "FAISAL",
+        "C": "ABDULIAH",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_93.json b/VisRecall/merged/qa/economist_daily_chart_93.json
new file mode 100755
index 0000000..b6e746a
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_93.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_93.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Retirement ranked by life expectancy at effective retirement age in 2010, men, years",
+        "B": "Retirement ranked by GDP at effective retirement age in 2010, men, years",
+        "C": "Retirement ranked by alphabetical order at effective retirement age in 2010, men, years",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is compared with 2010's data in this visualization?",
+        "A": "1970",
+        "B": "1980",
+        "C": "1990",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the largest effective retirement age in 2010?",
+        "A": "Ireland",
+        "B": "Poland",
+        "C": "Switzerland",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the smallest official retirement age in 2010?",
+        "A": "Italy",
+        "B": "Australia",
+        "C": "Greece",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the range of year in this visualization?",
+        "A": "60-90",
+        "B": "55-90",
+        "C": "50-90",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/economist_daily_chart_95.json b/VisRecall/merged/qa/economist_daily_chart_95.json
new file mode 100755
index 0000000..eb87b02
--- /dev/null
+++ b/VisRecall/merged/qa/economist_daily_chart_95.json
@@ -0,0 +1,49 @@
+{
+    "name": "economist_daily_chart_95.png",
+    "vistype": "other",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Municipal solid waste",
+        "B": "Municipal liquid waste",
+        "C": "Municipal gas waste",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "4",
+        "question": "Which areas on the chart are more serious?",
+        "A": "East",
+        "B": "West",
+        "C": "Middle",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "3",
+        "question": "What is the approximate level of waste in China?",
+        "A": ">2.50",
+        "B": "1.0-1.49",
+        "C": "0.0-0.49",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "What is the approximate level of waste in Australia?",
+        "A": ">2.50",
+        "B": "2.0-2.49",
+        "C": "0.5-0.99",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "What is the approximate level of waste in SEE INSET?",
+        "A": ">2.50",
+        "B": "1.5-1.99",
+        "C": "0.0-0.49",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryA8.json b/VisRecall/merged/qa/treasuryA8.json
new file mode 100755
index 0000000..1eae446
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryA8.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryA8.png",
+    "vistype": "line",
+    "Q1": {
+        "question": "What is the theme of this visualization?",
+		"type": "0",
+        "A": "It shows the employment growth in the logistics industry",
+        "B": "It shows the employment growth in the finance industry",
+        "C": "It shows the employment growth in the auto industry",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "question": "What is the employment growth since June 2009?",
+		"type": "2",
+        "A": "about 20,000",
+        "B": "about 200,000",
+        "C": "about 2,000,000",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "question": "What period of data does this visualization show?",
+		"type": "3",
+        "A": "2009-2016",
+        "B": "2009-2014",
+        "C": "2009-2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "When did the restructuring of GM & Chrysler happen?",
+		"type": "3",
+        "A": "June 2009",
+        "B": "July 2009",
+        "C": "August 2009",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "What is the lowest amount of payrolls at any given time (in thousands)?",
+		"type": "1",
+        "A": "225",
+        "B": "2250",
+        "C": "5220",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryB02.json b/VisRecall/merged/qa/treasuryB02.json
new file mode 100755
index 0000000..7518d4c
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryB02.json
@@ -0,0 +1,50 @@
+{
+    "name": "treasuryB02.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The crisis response along with quarter national income growth",
+        "B": "The crisis response along with quarter population growth",
+        "C": "The crisis response along with quarter real GDP growth",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period's data is displayed in this visualization?",
+        "A": "2006-2010",
+        "B": "2007-2011",
+        "C": "2008-2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "In which quarter the growth rate is the lowest?",
+        "A": "The fourth quarter of 2007",
+        "B": "The third quarter of 2008",
+        "C": "The fourth quarter of 2008",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "In which quarter the growth rate turns from negative to positive?",
+        "A": "The first quarter of 2009",
+        "B": "The second quarter of 2009",
+        "C": "The third quarter of 2009",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "In which year the average growth rate for four quarters is the highest?",
+        "A": "2009",
+        "B": "2010",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/treasuryB06.json b/VisRecall/merged/qa/treasuryB06.json
new file mode 100755
index 0000000..060e3ee
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryB06.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryB06.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The crisis response helped stabilize the housing market",
+        "B": "The crisis response helped stabilize the car market",
+        "C": "The crisis response helped stabilize the stock market",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "How does the conventional mortgage rates change in the figure?",
+        "A": "Increase",
+        "B": "Decrease",
+        "C": "Remain stable",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "What program helped the borrowers to stay in their home?",
+        "A": "Loan modification programs",
+        "B": "Mortgage modification programs",
+        "C": "Finace modification programs",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "How many permanent loan modifications have been there since April 2009?",
+        "A": "2.6 million",
+        "B": "5 million",
+        "C": "6.5 million",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Where is the data from?",
+        "A": "U.S Department of the Treasury",
+        "B": "U.S Department of Housing and Urban Development",
+        "C": "U.S Depratment of Commerce",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
diff --git a/VisRecall/merged/qa/treasuryB13.json b/VisRecall/merged/qa/treasuryB13.json
new file mode 100755
index 0000000..99e5088
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryB13.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryB13.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The crisis response helped prevent the collapse of the financial system and stabilized AIA",
+        "B": "The crisis response helped prevent the collapse of the insurance system and stabilized AIA",
+        "C": "The crisis response helped prevent the collapse of the financial system and stabilized AIG",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "For the government, what is expected to realize on its investment?",
+        "A": "Gain",
+        "B": "Loss",
+        "C": "Balance out",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "When does the maximum commitment happen?",
+        "A": "March 2009",
+        "B": "March 2012",
+        "C": "Mach 2008",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "How many percentage of the maximum committment returned or cancelled until March 2012?",
+        "A": "Over 25%",
+        "B": "Over 50%",
+        "C": "Over 75%",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Where does the total committment come from?",
+        "A": "Treasury",
+        "B": "Federal Reserve",
+        "C": "Treasury and Federal Reserve",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
diff --git a/VisRecall/merged/qa/treasuryD05_2.json b/VisRecall/merged/qa/treasuryD05_2.json
new file mode 100755
index 0000000..1dae54e
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryD05_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryD05_2.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Covered Transactions by Sector and Year, 2009-2011",
+        "B": "Select Median Characteristics of Active Permanent Modifications",
+        "C": "Homeowner's HOPE Hotline Volume",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the trend of Front-End Debt-to-Income Ratio from Before Modification to After Modification?",
+        "A": "Decreased",
+        "B": "Didn't change",
+        "C": "Increased",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "What is the trend of Back-End Debt-to-Income Ratio from Before Modification to After Modification?",
+        "A": "Decreased",
+        "B": "Didn't change",
+        "C": "Increased",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "Compare Front-End Debt-to-Income Ratio with Back-End, which one is larger before modification??",
+        "A": "Front-End",
+        "B": "Back-End",
+        "C": "Same",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "How large is the Median Decrease of Median Monthly Housing Payment countries?",
+        "A": "0 to -200",
+        "B": "-200 to -400",
+        "C": "-400 to -600",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryD07_3.json b/VisRecall/merged/qa/treasuryD07_3.json
new file mode 100755
index 0000000..8e45f6c
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryD07_3.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryD07_3.png",
+    "vistype": "table",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Homeowner's HOPE Hotline Volume",
+        "B": "HOPE Access Voulume",
+        "C": "Homeowner's Program Volume",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "3",
+        "question": "How many calls have been taken to 1-888-995-HOPE program to date?",
+        "A": "3330696",
+        "B": "1601398",
+        "C": "71177",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "3",
+        "question": "How many calls have been taken to 1-888-995-HOPE in August?",
+        "A": "3330696",
+        "B": "1601398",
+        "C": "71177",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "How many borrowers referred for free housing counseling assistance through the homeowner's HOPE Hotline program to date?",
+        "A": "1601398",
+        "B": "3330696",
+        "C": "71177",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "How many borrowers referred for free housing counseling assistance through the homeowner's HOPE Hotline in August?",
+        "A": "1601398",
+        "B": "37589",
+        "C": "71177",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryD10.json b/VisRecall/merged/qa/treasuryD10.json
new file mode 100755
index 0000000..0897457
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryD10.json
@@ -0,0 +1,50 @@
+{
+    "name": "treasuryD10.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the main topic of the visualization?",
+        "A": "Servicer Outreach to 30+ Day Delinquent Homeowners",
+        "B": "Servicer Outreach to 60+ Day Delinquent Homeowners",
+        "C": "Servicer Outreach to 90+ Day Delinquent Homeowners",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "When were the data collected?",
+        "A": "August 2011 - July 2012",
+        "B": "August 2010 - July 2012",
+        "C": "August 2011 - July 2013",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which bank does the most outreach?",
+        "A": "GMAC",
+        "B": "Bank of America",
+        "C": "OneWest",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which bank does the least outreach?",
+        "A": "CitiMortgage",
+        "B": "Wells Fargo",
+        "C": "SPS",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which bank does not show in this viusalization?",
+        "A": "Homeward Residential",
+        "B": "JPMorgan Chase",
+        "C": "Royal Bank of Canada",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/treasuryG01_2.json b/VisRecall/merged/qa/treasuryG01_2.json
new file mode 100755
index 0000000..bdee8a8
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryG01_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryG01_2.png",
+    "vistype": "table",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Marketable Treasury Coupon Flows",
+        "B": "Maturing Coupon Securities",
+        "C": "Coupon Payments",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2009",
+        "B": "2010",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which date has the highest total outflows?",
+        "A": "February 15,2010",
+        "B": "March 15,2010",
+        "C": "April 15, 2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which date has the highest maturing coupon securities?",
+        "A": "February 15,2010",
+        "B": "March 15,2010",
+        "C": "April 15, 2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which date has the highest total outflows?",
+        "A": "February 15,2010",
+        "B": "March 15,2010",
+        "C": "April 15, 2010",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryI01_1.json b/VisRecall/merged/qa/treasuryI01_1.json
new file mode 100755
index 0000000..5d66c35
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryI01_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "treasuryI01_1.png",
+    "vistype": "table",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Reno housing unit growth outpaced population and house hold growth during the past decade",
+        "B": "Reno population growth outpaced housing unit and house hold growth during the past decade",
+        "C": "Reno house hold growth outpaced housing unit and population growth during the past decade",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2010-2020",
+        "B": "2000-2010",
+        "C": "2000-1990",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which one has the highest annual growth rate?",
+        "A": "Reno-Sparks Population",
+        "B": "Reno-Sparks Households",
+        "C": "Reno-Sparks Housing Units",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "4",
+        "question": "Which one is higher, annual growth rate of Reno-Sparks Households or annual growth rate of Reno-Sparks Housing Units?",
+        "A": "Reno-Sparks Housing Units",
+        "B": "Reno-Sparks Households",
+        "C": "The same",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "What is the population in 2000",
+        "A": "Around 350,000",
+        "B": "Around 400,000",
+        "C": "Around 450,000",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/treasuryI02_1.json b/VisRecall/merged/qa/treasuryI02_1.json
new file mode 100755
index 0000000..f92dc48
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryI02_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryI02_1.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "New and Exisiting Home sales comparing Nation in 2010 and Nation in 2011",
+        "B": "New and Exisiting Home sales comparing Reno in 2010 and Reno in 2011",
+        "C": "New and Exisiting Home sales comparing Reno and Nation in 2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the scale used for determining the sale?",
+        "A": "Thousands",
+        "B": "Hundreds",
+        "C": "Lakhs",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What is the highest value of sale reported?",
+        "A": "Above 6500",
+        "B": "Below 5000",
+        "C": "Around 5000",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which year has recorded the highest number of new sale by Reno MSA?",
+        "A": "2006",
+        "B": "2005",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which year has recorded least number of sales for both Nation and Reno in terms of exisiting sales?",
+        "A": "2000",
+        "B": "2008",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryI02_3.json b/VisRecall/merged/qa/treasuryI02_3.json
new file mode 100755
index 0000000..0490152
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryI02_3.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryI02_3.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Rental vacancy Rates Declination",
+        "B": "Rental vacancy Rates Inclination",
+        "C": "Rental vacancy Rates Saturation",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "How often does the vacancy rates are calculated?",
+        "A": "Yearly",
+        "B": "Monthly",
+        "C": "Quarterly",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which year's data is seen in the visualization?",
+        "A": "Since 2009",
+        "B": "Since 2010",
+        "C": "Since 2011",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which year has recorded the highest value in Reno Metro Area?",
+        "A": "2009",
+        "B": "2009 and 2010",
+        "C": "2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "What is the highest recorded value seen in the visualization?",
+        "A": "Above 10",
+        "B": "Above 20",
+        "C": "Above 30",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryJ01_2.json b/VisRecall/merged/qa/treasuryJ01_2.json
new file mode 100755
index 0000000..cc122ba
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryJ01_2.json
@@ -0,0 +1,49 @@
+{
+  "name": "treasuryJ01_2.png",
+  "vistype": "line",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Total Professional services, Scientific and technical services",
+    "B": "Expectations On House Prices Above 2009 Projections",
+    "C": "Deaths due to increased Bool pressure",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q2": {
+    "type": "4",
+    "question": "What does the line stands for?",
+    "A": "House Price per capita",
+    "B": "Dollars",
+    "C": "S&P/Case-Shiller Index",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q3": {
+    "type": "2",
+    "question": "Which year has the highest S&P/Case-Shiller Index?",
+    "A": "2006-2007",
+    "B": "2009-2010",
+    "C": "2011-2012",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "Which year has the lowest House Price Futures Index?",
+    "A": "2006",
+    "B": "2009",
+    "C": "2012",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "What is the source of this visualization?",
+    "A": "Standard and Poor's and Radar Logic",
+    "B": "Global School-based Student Health",
+    "C": "SIPRI",
+    "D": "I can not remember",
+    "answer": "A"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryJ02_2.json b/VisRecall/merged/qa/treasuryJ02_2.json
new file mode 100755
index 0000000..984f259
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryJ02_2.json
@@ -0,0 +1,50 @@
+{
+    "name": "treasuryJ02_2.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Quarterly refinance mortgage originations",
+        "B": "Quarterly commercial loan originations",
+        "C": "Quarterly secured loans",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2003-2008",
+        "B": "2008-2012",
+        "C": "2003-2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "When have the 16.2 million homeowners refinanced?",
+        "A": "Since April 1, 2008",
+        "B": "Since April 1, 2009",
+        "C": "Since April 1, 2010",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "In which year does the maximum quarterly refinance mortgage originations appear?",
+        "A": "2003",
+        "B": "2005",
+        "C": "2007",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "In which year does the minimum quarterly refinance mortgage originations appear?",
+        "A": "2004",
+        "B": "2006",
+        "C": "2008",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/treasuryJ03_3.json b/VisRecall/merged/qa/treasuryJ03_3.json
new file mode 100755
index 0000000..99e14b5
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryJ03_3.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryJ03_3.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "FHA supports loan lending during crisis",
+        "B": "FHA supports mortgage lending during crisis",
+        "C": "FHA suppresses mortgage lending during crisis",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "When did the mortgage start decreasing?",
+        "A": "2008 Q1",
+        "B": "2009 Q1",
+        "C": "2008 Q3",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "What is the most used origination for mortgage in the figure?",
+        "A": "Purchase",
+        "B": "Refinance",
+        "C": "Both are the same",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "What is the highest percentage for purchase as a mortgage origination?",
+        "A": "around 50%",
+        "B": "around 40%",
+        "C": "around 30%",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Around when did the combination of purchase and refinance reach the lowest percentage in the figure?",
+        "A": "2003-2004",
+        "B": "2008-2010",
+        "C": "2005-2007",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
diff --git a/VisRecall/merged/qa/treasuryK03_3.json b/VisRecall/merged/qa/treasuryK03_3.json
new file mode 100755
index 0000000..cd1f3d7
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryK03_3.json
@@ -0,0 +1,50 @@
+{
+    "name": "treasuryK03_3.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Total long position",
+        "B": "Total short position",
+        "C": "Total net position",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2005-2010",
+        "B": "2008-2012",
+        "C": "2009-2014",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which year has the highest total net position?",
+        "A": "2009",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which year has the lowest total net position?",
+        "A": "2009",
+        "B": "2011",
+        "C": "2010",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "What is the total net position(in Billions) in 2010?",
+        "A": "Around 300",
+        "B": "Around 100",
+        "C": "Around 400",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/treasuryK04.json b/VisRecall/merged/qa/treasuryK04.json
new file mode 100755
index 0000000..28b2b54
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryK04.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryK04.png",
+    "vistype": "table",
+    "Q1": {
+        "question": "What type of information is given in this chart?",
+		"type": "0",
+        "A": "Number of cars sold in America",
+        "B": "Most successful box office film of 2011",
+        "C": "Summary Financial Information",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "question": "Which currency is used in the chart?",
+		"type": "2",
+        "A": "Euros",
+        "B": "Dollars",
+        "C": "Yen",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "question": "How are negative numbers represented?",
+		"type": "3",
+        "A": "Negative sign",
+        "B": "Red font",
+        "C": "Parentheses",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "Does the net custodial revenue compensate for the net federal debt interest costs in 2012?",
+		"type": "4",
+        "A": "Yes",
+        "B": "No",
+        "C": "They're the same",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "Which time range is represented in this chart?",
+		"type": "3",
+        "A": "2011-2012",
+        "B": "2010-2011",
+        "C": "2012-2013",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
diff --git a/VisRecall/merged/qa/treasuryK05_2.json b/VisRecall/merged/qa/treasuryK05_2.json
new file mode 100755
index 0000000..79c2468
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryK05_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryK05_2.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "FY Partial Liabilities",
+        "B": "FY Total Liabilities",
+        "C": "FY Total capitals",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "0",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2012",
+        "B": "2013",
+        "C": "2014",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which liabilities has the highest value?",
+        "A": "Due to the General Fund",
+        "B": "Federal Debt and Interest Payable(Intra-Gov'tal)",
+        "C": "Federal Debt and Interest Payable(With the Public)",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which liabilities has the lowest value?",
+        "A": "Due to the General Fund",
+        "B": "Other Liabilities",
+        "C": "Federal Debt and Interest Payable(With the Public)",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which liabilities accounts for about a quarter of the total liabilities?",
+        "A": "Due to the General Fund",
+        "B": "Federal Debt and Interest Payable(Intra-Gov'tal)",
+        "C": "Federal Debt and Interest Payable(With the Public)",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryL01.json b/VisRecall/merged/qa/treasuryL01.json
new file mode 100755
index 0000000..f10167e
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryL01.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryL01.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Covered Transactions, Withdrawals, and Presidential Decisions, 2009-2011",
+        "B": "Covered Transactions by Sector and Year, 2009-2011",
+        "C": "Homeowner's HOPE Hotline Volume",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which year has the highest Number of Notices?",
+        "A": "2009",
+        "B": "2010",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which year has the highest Notices Withdrawn During Review?",
+        "A": "2009",
+        "B": "2010",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which kind of transactions, withdrawals, or presidential decisions has the largest number in total?",
+        "A": "Number of Notices",
+        "B": "Notices Withdrawn During Review",
+        "C": "Number of Investigations",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which kind of transactions, withdrawals, or presidential decisions has the lowest number in total?",
+        "A": "Number of Investigations",
+        "B": "Presidential Decisions",
+        "C": "Notices Withdrawn During Investigations",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryL02_1.json b/VisRecall/merged/qa/treasuryL02_1.json
new file mode 100755
index 0000000..75e636a
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryL02_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryL02_1.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Covered Transactions by Sector and Year",
+        "B": "Covered Transactions by Field and Year",
+        "C": "Covered Transactions by Industry and Year",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which years' data are displayed in this visualization?",
+        "A": "2009-2011",
+        "B": "2008-2011",
+        "C": "2008-2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which year has the highest total value?",
+        "A": "2008",
+        "B": "2009",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which year has the lowest total value?",
+        "A": "2008",
+        "B": "2009",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which one of the followings has the highest total value?",
+        "A": "Manufacturing",
+        "B": "Finance, Information, and Services",
+        "C": "Mining, Utilies, and Construction",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryL02_2.json b/VisRecall/merged/qa/treasuryL02_2.json
new file mode 100755
index 0000000..dd4de34
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryL02_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj320.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Covered Transactions by Region",
+        "B": "Covered Transactions by Country",
+        "C": "Covered Transactions by Sector",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "2009-2011",
+        "B": "2010-2012",
+        "C": "2011-2013",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which sector has the highest number of transactions in 2011?",
+        "A": "Manufacturing",
+        "B": "Finance, Information and Services",
+        "C": "Mining, Utilities and Construction",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "How many transactions did Manufacturing covered?",
+        "A": "Around 10",
+        "B": "Around 30",
+        "C": "Around 50",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which sector has the lowest number of transactions in 2011?",
+        "A": "Manufacturing",
+        "B": "Finance, Information and Services",
+        "C": "Wholesale, Retail adn Transportatio",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryL03_2.json b/VisRecall/merged/qa/treasuryL03_2.json
new file mode 100755
index 0000000..fe71591
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryL03_2.json
@@ -0,0 +1,50 @@
+{
+    "name": "treasuryL03_2.png",
+    "vistype": "pie",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Manufacturing",
+        "B": "Agriculture",
+        "C": "Tourism",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2005-2010",
+        "B": "2008-2011",
+        "C": "2009-2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which of the following categories accounts for the highest proportion?",
+        "A": "Machinery",
+        "B": "Transportation Equipment",
+        "C": "Computer and Electronic Products",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "2",
+        "question": "Which of the following categories accounts for relative lower proportion?",
+        "A": "Transportation Equipment",
+        "B": "Machinery",
+        "C": "Computer and Electronic Products",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Which of the following accounts for more than 1%?",
+        "A": "Textile Product Mills",
+        "B": "Fabricated Metal Products",
+        "C": "Plastics and Rubber Products",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/treasuryL07_2.json b/VisRecall/merged/qa/treasuryL07_2.json
new file mode 100755
index 0000000..aa312d5
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryL07_2.json
@@ -0,0 +1,49 @@
+{
+  "name": "treasuryL07_2.png",
+  "vistype": "pie",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Total Professional services",
+    "B": "Total Professional services and Scientific services",
+    "C": "Total Professional services, Scientific and technical services",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2009-2010",
+    "B": "2009-2011",
+    "C": "2007-2009",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which Field has the highest share of contribution?",
+    "A": "Computer systems and related services",
+    "B": "Scientific research and development",
+    "C": "Architectural Engineering",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which Field has the lowest share of contribution?",
+    "A": "Computer systems and related services",
+    "B": "Scientific research and development",
+    "C": "Other professional, scientific and technical services",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "Which keyword stands for the visualization the best?",
+    "A": "Professional",
+    "B": "Transactions",
+    "C": "Technologies",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryL15_2.json b/VisRecall/merged/qa/treasuryL15_2.json
new file mode 100755
index 0000000..094f809
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryL15_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "treasuryL15_2.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Critical Technology Transactions by Region of Foreign Acquirer",
+        "B": "Critical Product Transactions by Region of Foreign Acquirer",
+        "C": "Critical Project Transactions by Region of Foreign Acquirer",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2011",
+        "B": "2012",
+        "C": "2013",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which region has the lowest value?",
+        "A": "Middle East & North Africa",
+        "B": "Other",
+        "C": "East Asia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which region has the highest value?",
+        "A": "East Aisa",
+        "B": "Canada, Australia & New Zealand",
+        "C": "Europe (excluding Russia)",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "How many transactions does Europe(excluding Russia) have?",
+        "A": "Around 20",
+        "B": "Around 50",
+        "C": "Around 70",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/treasuryL16.json b/VisRecall/merged/qa/treasuryL16.json
new file mode 100755
index 0000000..3a64274
--- /dev/null
+++ b/VisRecall/merged/qa/treasuryL16.json
@@ -0,0 +1,50 @@
+{
+    "name": "treasuryL16.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the main topic of the visualization?",
+        "A": "Critical technology transactions by region within different industry sectors",
+        "B": "Critical technology spendings by region within different industry sectors",
+        "C": "Critical technology earnings by Region within different industry sectors",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year were the data collected?",
+        "A": "2012",
+        "B": "2013",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which region has the highest number of critical technology transactions in most sectors?",
+        "A": "Europe(excluding Russia)",
+        "B": "East Asia",
+        "C": "Canada, Australia & New Zealand",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which sector has the highest number of critical technology transactions?",
+        "A": "Electronics",
+        "B": "Information Technology",
+        "C": "Machinery & Equipment",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which sector has the lowest number of critical technology transactions?",
+        "A": "Metals & Mining",
+        "B": "Aerospace & Defence",
+        "C": "Pharma & Biotech",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoB04_1.json b/VisRecall/merged/qa/whoB04_1.json
new file mode 100755
index 0000000..cc611da
--- /dev/null
+++ b/VisRecall/merged/qa/whoB04_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoB04_1.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Male life expectancy at birth by country income group and region",
+        "B": "Female life expectancy at birth by country income group and region",
+        "C": "Life expectancy at birth by country income group and region",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1950-2005",
+        "B": "1960-2015",
+        "C": "1950-2015",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "3",
+        "question": "What is the female life expectancy at birth in high-income countries in 2000-2005?",
+        "A": "Higher than 80",
+        "B": "75-80",
+        "C": "70-75",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "What is the female life expectancy at birth in low-income countries in 1950-1955?",
+        "A": "45-50",
+        "B": "40-45",
+        "C": "35-40",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "When did female life expectancy in low-income countries start to be higher than that in East and Southern African countries?",
+        "A": "1960-65",
+        "B": "1970-75",
+        "C": "1965-70",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoB05_1.json b/VisRecall/merged/qa/whoB05_1.json
new file mode 100755
index 0000000..0b890ab
--- /dev/null
+++ b/VisRecall/merged/qa/whoB05_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoB05_1.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "The health transition among Mexican: death rates by overall cause of death",
+        "B": "The health transition among Mexican men: death rates by overall cause of death",
+        "C": "The health transition among Mexican women: death rates by overall cause of death",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What years' data is displayed in this visualization?",
+        "A": "1950-2000",
+        "B": "1955-2005",
+        "C": "1960-2010",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "How many causes were displayed in this visualization?",
+        "A": "2",
+        "B": "3",
+        "C": "4",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which cause the most death?",
+        "A": "Communicable, maternal, perinatal and nutritional diseases",
+        "B": "Noncommunicable diseases",
+        "C": "Injuries",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which cause the least death?",
+        "A": "Communicable, maternal, perinatal and nutritional diseases",
+        "B": "Noncommunicable diseases",
+        "C": "Injuries",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB08_1.json b/VisRecall/merged/qa/whoB08_1.json
new file mode 100755
index 0000000..4c9580f
--- /dev/null
+++ b/VisRecall/merged/qa/whoB08_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoB08_1.png",
+    "vistype": "pie",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Distribution of those who usually collect water in 15 developing countries",
+        "B": "Distribution of those who usually collect water in 15 developed countries",
+        "C": "Distribution of those who usually collect water in 15 underdeveloped countries",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2005-2006",
+        "B": "2006-2007",
+        "C": "2007-2008",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Who usually goes to collect water than others?",
+        "A": "Women",
+        "B": "Men",
+        "C": "Boys",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "What is the percentage of Men?",
+        "A": "25%",
+        "B": "64%",
+        "C": "4%",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "What is the percentage of Girls?",
+        "A": "25%",
+        "B": "4%",
+        "C": "7%",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoB10_1.json b/VisRecall/merged/qa/whoB10_1.json
new file mode 100755
index 0000000..bfbf1e0
--- /dev/null
+++ b/VisRecall/merged/qa/whoB10_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoB10_1.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Share of women in the health workforce, selected countries",
+        "B": "Share of men in the health workforce, selected countries",
+        "C": "Share of women in the digital workforce, selected countries",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1989-2007",
+        "B": "1989-1997",
+        "C": "1994-1997",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest value?",
+        "A": "Russian Federation",
+        "B": "Uganda",
+        "C": "Hungary",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest value?",
+        "A": "Germany",
+        "B": "Cambodia",
+        "C": "Thailand",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "In Thailand, what percentage of women are in the health workforce?",
+        "A": "50%",
+        "B": "75%",
+        "C": "100%",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB11_1.json b/VisRecall/merged/qa/whoB11_1.json
new file mode 100755
index 0000000..0cc44d1
--- /dev/null
+++ b/VisRecall/merged/qa/whoB11_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoB11_1.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Motality and disease burden (DALYs) in girls under 10 years of age by region and broad causes",
+        "B": "Motality and disease burden (DALYs) in girls under 20 years of age by region and broad causes",
+        "C": "Motality and disease burden (DALYs) in women under 30 years of age by region and broad causes",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2004",
+        "B": "2005",
+        "C": "2006",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which region has the highest DALYs and deaths?",
+        "A": "Eastern Mediterranean",
+        "B": "South-East Asia",
+        "C": "Africa",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which region has the lowest DALYs and deaths?",
+        "A": "Americas",
+        "B": "High-income countries",
+        "C": "Europe",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What caused the largest DALYs and deaths in South-East Asia?",
+        "A": "Diarrhoeal diseases",
+        "B": "Maternal conditions",
+        "C": "Perinatal conditions",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB13_1.json b/VisRecall/merged/qa/whoB13_1.json
new file mode 100755
index 0000000..a408cd3
--- /dev/null
+++ b/VisRecall/merged/qa/whoB13_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoB13_1.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the main topic of the visualization?",
+        "A": "Leading risk factors for mortality in girls under five years of age",
+        "B": "Leading risk factors for mortality in boys under five years of age",
+        "C": "Leading risk factors for mortality in children under five years of age",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "When was the study conducted?",
+        "A": "2004",
+        "B": "2005",
+        "C": "2006",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "What is the the risk factor that leads to the most death cases in low-income countries?",
+        "A": "Unsafe water, sanitation, hygiene",
+        "B": "Zinc deficincy",
+        "C": "Childhood underweight",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "What is the the risk factor that leads to the most death cases in middle-income countries?",
+        "A": "Vitamin A deficinvy",
+        "B": "Suboptimal breastfeeding",
+        "C": "Indoor smoke from solid fuels",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "What is the the risk factor that leads to the most death cases in high-income countries?",
+        "A": "Unsafe water, sanitation, hygiene",
+        "B": "Suboptimal breastfeeding",
+        "C": "Childhood underweight",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoB13_2.json b/VisRecall/merged/qa/whoB13_2.json
new file mode 100755
index 0000000..89d1487
--- /dev/null
+++ b/VisRecall/merged/qa/whoB13_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj320.png",
+    "vistype": "pie",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Distribution of major causes of death in girls under five years of age",
+        "B": "Distribution of major causes of death in girls under ten years of age",
+        "C": "Distribution of major causes of death in boys  under five years of age",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2002",
+        "B": "2004",
+        "C": "2006",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which cause of death accounts for the largest percentage?",
+        "A": "Pneumonia",
+        "B": "Diarrhoea",
+        "C": "Measles",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "In which cause does the contribution of undernutrition account for the largest percentage?",
+        "A": "Pneumonia",
+        "B": "Diarrhoea",
+        "C": "Severe neoatal infections",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which cause of death accounts for the least percentage?",
+        "A": "Injuries",
+        "B": "Measles",
+        "C": "Nutritional deficiencies",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB14_1.json b/VisRecall/merged/qa/whoB14_1.json
new file mode 100755
index 0000000..3726105
--- /dev/null
+++ b/VisRecall/merged/qa/whoB14_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoB14_1.png",
+    "vistype": "other",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Sex differences in key child health indicators",
+        "B": "Ethnic differences in key child health indicators",
+        "C": "Age differences in key child health indicators",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which country are not displayed in this visualization?",
+        "A": "Africa",
+        "B": "Americas",
+        "C": "China",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "2002-2005",
+        "B": "2002-2007",
+        "C": "2002-2010",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which indicator has the least difference in all seletecd countries?",
+        "A": "DPT3 coverage",
+        "B": "Mosquito net use",
+        "C": "Diarrhoea treatment",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which indicator has the largest difference in Africa?",
+        "A": "Mosquito net use",
+        "B": "Stunting",
+        "C": "Under-five morality rate",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB17_1.json b/VisRecall/merged/qa/whoB17_1.json
new file mode 100755
index 0000000..42f787e
--- /dev/null
+++ b/VisRecall/merged/qa/whoB17_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoB17_1.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Adolescence pregnancy rates by educational level in selected countries",
+        "B": "Adolescence pregnancy rates by educational level in all countries",
+        "C": "Adolescence pregnancy rates by educational level in selected cities",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which category has the highest rate?",
+        "A": "Primary education only",
+        "B": "No education",
+        "C": "Secondary education or higher",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which category has the lowest rate?",
+        "A": "Secondary education or higher",
+        "B": "No education",
+        "C": "Primary education only",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "What is the median pregnancy rate in No education category?",
+        "A": "Above 50%",
+        "B": "Below 40%",
+        "C": "Above 60%",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the year value seen in the visualization?",
+        "A": "1990 - 2005",
+        "B": "1995 - 2005",
+        "C": "2000 - 2005",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB19_1.json b/VisRecall/merged/qa/whoB19_1.json
new file mode 100755
index 0000000..4ea1b6e
--- /dev/null
+++ b/VisRecall/merged/qa/whoB19_1.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoB19_1.png",
+  "vistype": "scatter",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Students of specific age not meeting recommended levels of physical activity",
+    "B": "Students of specific age not meeting recommended levels of Nutrition",
+    "C": "Students of specific age not interested in mathematics",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2003-2010",
+    "B": "2003-2008",
+    "C": "2003-2011",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "3",
+    "question": "In How many countries was the survey conducted?",
+    "A": "13",
+    "B": "63",
+    "C": "36",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "Only one country has girls doing better than boys, Which region does this country belong to?",
+    "A": "Africa",
+    "B": "Americas",
+    "C": "Western-pacific",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "What does each data point represent?",
+    "A": "One Country",
+    "B": "One state",
+    "C": "One Continent",
+    "D": "I can not remember",
+    "answer": "A"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB20_1.json b/VisRecall/merged/qa/whoB20_1.json
new file mode 100755
index 0000000..c83d4d1
--- /dev/null
+++ b/VisRecall/merged/qa/whoB20_1.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoB20_1.png",
+  "vistype": "bar",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Mortality and Disease burden in women aged 15-44",
+    "B": "Mortality and Disease burden in men aged 15-44",
+    "C": "Mortality and Disease burden in women aged 55-64",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2008",
+    "B": "2011",
+    "C": "2004",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q3": {
+    "type": "2",
+    "question": "Which continent has the highest deaths due to HIV?",
+    "A": "Europe",
+    "B": "America",
+    "C": "Africa",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "Which condition caused most deaths in Europe?",
+    "A": "Sensory disorders",
+    "B": "Mental disorders",
+    "C": "Cancers",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "How many DALYs pre 1000 women in Africa?",
+    "A": "Around 250-300",
+    "B": "Around 300-350",
+    "C": "Around 350-400",
+    "D": "I can not remember",
+    "answer": "C"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB22_1.json b/VisRecall/merged/qa/whoB22_1.json
new file mode 100755
index 0000000..f136802
--- /dev/null
+++ b/VisRecall/merged/qa/whoB22_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoB22_1.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Deaths in animals attributable to six leading risk factors",
+        "B": "Deaths in men attributable to six leading risk factors",
+        "C": "Deaths in women attributable to six leading risk factors",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2004",
+        "B": "2005",
+        "C": "2006",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which factor accounts for the largest percentage of deaths among the world?",
+        "A": "Unmet contraceptive need",
+        "B": "Unsafe sex",
+        "C": "Alcohol use",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which factor accounts for the largest percentage of deaths among the high-income countries?",
+        "A": "Unmet contraceptive need",
+        "B": "Unsafe sex",
+        "C": "Alcohol use",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "In the countries at which income level Tobacco use brings about higher percentage of deaths?",
+        "A": "Low-income countries",
+        "B": "Middle-income countries",
+        "C": "High-income countries",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoB24_1.json b/VisRecall/merged/qa/whoB24_1.json
new file mode 100755
index 0000000..354688a
--- /dev/null
+++ b/VisRecall/merged/qa/whoB24_1.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoB24_1.png",
+  "vistype": "other",
+  "Q1": {
+    "type": "0",
+    "question": "What is the main topic of the visualization?",
+    "A": "Distribution of countries by numbers of cervical cancer cases attributable to unsafe abortion and legal grounds for abortion",
+    "B": "Distribution of countries by numbers of hospitalizations attributable to unsafe abortion and legal grounds for abortion",
+    "C": "Distribution of countries by numbers of deaths attributable to unsafe abortion and legal grounds for abortion",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q2": {
+    "type": "3",
+    "question": "What were the scaling factor of this visualization?",
+    "A": "10 000",
+    "B": "100 000",
+    "C": "1 000 000",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "What is the highest number of deaths attribute to unsafe abortion in this visualization?",
+    "A": ">200",
+    "B": "150-200",
+    "C": "100-150",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "Which legal ground has the relative lower death cases?",
+    "A": "To save the women's life only, or no grounds",
+    "B": "Also to preserve health",
+    "C": "Also for economic or social reasons",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "Which legal ground has the relative higher death cases?",
+    "A": "Also in cases of rape or incest",
+    "B": "Also to preserve health",
+    "C": "Also for economic or social reasons",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB25_1.json b/VisRecall/merged/qa/whoB25_1.json
new file mode 100755
index 0000000..0e9277a
--- /dev/null
+++ b/VisRecall/merged/qa/whoB25_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoB25_1.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Covered Transactions by Sector and Year, 2009-2011",
+        "B": "The Standard Model of particle physics",
+        "C": "HIV prevalence in women aged 15-49 years by region, 1990-2007",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What color in the visualization stands for Africa?",
+        "A": "Purple",
+        "B": "Orange",
+        "C": "Blue",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "In what range does the HIV prevalence reach the peak?",
+        "A": "1990-1996",
+        "B": "1996-2002",
+        "C": "2002-2007",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which of these regions has not shown in the legend of this visualization?",
+        "A": "Asia",
+        "B": "Western Pacific",
+        "C": "Europe",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Where does the source come from?",
+        "A": "NMCP reports",
+        "B": "UNAIDS and WHO Database 2009",
+        "C": "2011 GLAAS country survey",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoB26_1.json b/VisRecall/merged/qa/whoB26_1.json
new file mode 100755
index 0000000..27c81aa
--- /dev/null
+++ b/VisRecall/merged/qa/whoB26_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoB26_1.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What does the visualization illustrate",
+        "A": "Incidence rates of COVID-19 infections",
+        "B": "Incidence rates of HIV infections",
+        "C": "Incidence rate of cervical cancer",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year was the data collected?",
+        "A": "2003",
+        "B": "2004",
+        "C": "2014",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What is the highest incidence rate in this visualization?",
+        "A": "30 to <50",
+        "B": "50 to <100",
+        "C": "20 to <30",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which continent is the least affected?",
+        "A": "Europe",
+        "B": "North America",
+        "C": "Oceania",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "How many colors were used in the legend?",
+        "A": "5",
+        "B": "6",
+        "C": "7",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoB34_1.json b/VisRecall/merged/qa/whoB34_1.json
new file mode 100755
index 0000000..b492643
--- /dev/null
+++ b/VisRecall/merged/qa/whoB34_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoB34_1.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Persons aged 60 years and over as a percentage of total population",
+        "B": "Persons aged 50 years and over as a percentage of total population",
+        "C": "Persons aged 40 years and over as a percentage of total population",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period of historical estimate does the visualization show?",
+        "A": "1940-2005",
+        "B": "1950-2005",
+        "C": "1950-2050",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which category has the highest percentage?",
+        "A": "Females, low and middle-income countries",
+        "B": "Males, high-income countries",
+        "C": "Females, high-income countries",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which category has the lowest percentage?",
+        "A": "Females, low and middle-income countries",
+        "B": "Males, low and middle-income countrie",
+        "C": "Males, high-income countries",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which year of projection does the data reflect to?",
+        "A": "2005",
+        "B": "2020",
+        "C": "2050",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoB36_1.json b/VisRecall/merged/qa/whoB36_1.json
new file mode 100755
index 0000000..ca70a9f
--- /dev/null
+++ b/VisRecall/merged/qa/whoB36_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoB36_1.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "HIV prevalence in women aged 15-49 years",
+        "B": "Public opinion on austerity",
+        "C": "Mortality rates due to cardiovascular diseases",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the scale of Y-axis?",
+        "A": "Rates per 1 000 women",
+        "B": "Rates per 10 000 women",
+        "C": "Rates per 100 000 women",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What is the highest rates ranged in?",
+        "A": "12-14 per 100 000 women",
+        "B": "14-16 per 100 000 women",
+        "C": "16-18 per 100 000 women",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the highest rates in the visualization?",
+        "A": "USA",
+        "B": "Sweden",
+        "C": "France",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which country has the lowest rates in the visualization?",
+        "A": "Sweden",
+        "B": "France",
+        "C": "Canada",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoC02_2.json b/VisRecall/merged/qa/whoC02_2.json
new file mode 100755
index 0000000..243c6e8
--- /dev/null
+++ b/VisRecall/merged/qa/whoC02_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoC02_2.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Number of people reached by preventive chemotherapy for at least five neglected tropical disease",
+        "B": "Number of people reached by preventive chemotherapy for at least one neglected tropical disease",
+        "C": "Number of people reached by preventive chemotherapy for at least two neglected tropical disease",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which WHO region has the highest number of countries?",
+        "A": "European",
+        "B": "South-East Asia",
+        "C": "African",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which category has the lowest rate?",
+        "A": "Secondary education or higher",
+        "B": "No education",
+        "C": "Primary education only",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "What is the total number of people globally reached according to the visualization?",
+        "A": "Around 660000000",
+        "B": "Around 550000000",
+        "C": "Around 400000000",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the year value seen in the visualization?",
+        "A": "2008",
+        "B": "2018",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoC13.json b/VisRecall/merged/qa/whoC13.json
new file mode 100755
index 0000000..832fb18
--- /dev/null
+++ b/VisRecall/merged/qa/whoC13.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoC13.png",
+  "vistype": "bar",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Number of new cases of yaws reported in indonesia",
+    "B": "Number of cases of yaws reported in indonesia",
+    "C": "Number of cases of crimes reported in indonesia",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2010",
+    "B": "2001-2009",
+    "C": "2001-2010",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which year has the highest number of new cases reported?",
+    "A": "2006",
+    "B": "2007",
+    "C": "2001",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which year has the lowest number of new cases reported?",
+    "A": "2002",
+    "B": "2001",
+    "C": "2009",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "How many new cases reported in 2001?",
+    "A": "around 1000",
+    "B": "around 2000",
+    "C": "around 3000",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoC22.json b/VisRecall/merged/qa/whoC22.json
new file mode 100755
index 0000000..7dae09e
--- /dev/null
+++ b/VisRecall/merged/qa/whoC22.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoC22.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Burden of leishmaniasis, by WHO region",
+        "B": "Burden of rabies, by WHO region",
+        "C": "Burden of tuberculosis, by WHO region",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2007",
+        "B": "2008",
+        "C": "2009",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which region has the highest value?",
+        "A": "Africa",
+        "B": "Eastern Mediterranean",
+        "C": "South-East Asia",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which region has the second highest value?",
+        "A": "Africa",
+        "B": "Eastern Mediterranean",
+        "C": "South-East Asia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Which color is used to represent the region with the highest value?",
+        "A": "Deep Blue",
+        "B": "Light Blue",
+        "C": "Grey",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoF03.json b/VisRecall/merged/qa/whoF03.json
new file mode 100755
index 0000000..466b40b
--- /dev/null
+++ b/VisRecall/merged/qa/whoF03.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoF03.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Per cent distribution of age at death by region, 2004",
+        "B": "Per cent distribution of age at diabetes by region, 2004",
+        "C": "Value distribution of age at diabetes by region, 2004",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "1",
+        "question": "Which region has the highest death percent distribution between 0-14 years old?",
+        "A": "South-East Asia",
+        "B": "Africa",
+        "C": "Europe",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which region has the lowest death percent distribution between 0-14 years old?",
+        "A": "South-East Asia",
+        "B": "Western Pacific",
+        "C": "High income",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which region has the highest percent distribution of death for 60 years and older?",
+        "A": "High income",
+        "B": "Africa",
+        "C": "Americas",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which region has the lowest percent distribution of death for 60 years and older?",
+        "A": "High income",
+        "B": "Africa",
+        "C": "Americas",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoF06.json b/VisRecall/merged/qa/whoF06.json
new file mode 100755
index 0000000..b9835e0
--- /dev/null
+++ b/VisRecall/merged/qa/whoF06.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoF06.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Child mortality rates by cause and region",
+        "B": "Animal mortality rates by cause and region",
+        "C": "Bird mortality rates by cause and region",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which region has the highest mortality?",
+        "A": "Africa",
+        "B": "America",
+        "C": "Europe",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which two regions has same mortality?",
+        "A": "Americas and Europe",
+        "B": "Americas and Western Pacific",
+        "C": "Americas and Africa",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "How many categories are present in the visualization?",
+        "A": "Less than 3",
+        "B": "More than 3",
+        "C": "More than 5",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which year's data is seen in the visualization?",
+        "A": "2008",
+        "B": "2005",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoF15.json b/VisRecall/merged/qa/whoF15.json
new file mode 100755
index 0000000..5123d2b
--- /dev/null
+++ b/VisRecall/merged/qa/whoF15.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoF15.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Age distribution of burden of water shortage by income group",
+        "B": "Age distribution of burden of disease by income group",
+        "C": "Age distribution of burden of air pollution by income group",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2014",
+        "B": "2009",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which age group takes the largest proportion in high-income countries?",
+        "A": "60 years and over",
+        "B": "15-59 years",
+        "C": "0-4 years",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which age group takes the second largest proportion in high-income countries?",
+        "A": "60 years and over",
+        "B": "15-59 years",
+        "C": "0-4 years",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Which age group takes the second largest proportion in low- and middle-income countries?",
+        "A": "60 years and over",
+        "B": "15-59 years",
+        "C": "0-4 years",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoH01_1.json b/VisRecall/merged/qa/whoH01_1.json
new file mode 100755
index 0000000..cf83218
--- /dev/null
+++ b/VisRecall/merged/qa/whoH01_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoH01_1.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Figure 1: Urban VS Rural Births in Perceng During 2006-2007",
+        "B": "Select Median Characteristics of Active Permanent Modifications",
+        "C": "Covered Transactions by Sector and Year, 2009-2011",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "Which color stands for Urban birth?",
+        "A": "Blue",
+        "B": "Pink",
+        "C": "Orange",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "Which color stands for Rural birth?",
+        "A": "Blue",
+        "B": "Pink",
+        "C": "Orange",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which region has higher births?",
+        "A": "Urban",
+        "B": "Same",
+        "C": "Rural",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the Urban births in percentange?",
+        "A": "40%-50%",
+        "B": "30%-40%",
+        "C": "20%-30%",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoH12_1.json b/VisRecall/merged/qa/whoH12_1.json
new file mode 100755
index 0000000..88be1da
--- /dev/null
+++ b/VisRecall/merged/qa/whoH12_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoH12_1.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Rate of Progress towards MSG4",
+        "B": "Rate of Progress towards MPEG4",
+        "C": "Rate of Progress towards MDG4",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1980-2015",
+        "B": "1990-2021",
+        "C": "1970-2000",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "How did the mortality rates of IMR (per 1,000 live births) varies from 1980 to 2006?",
+        "A": "Increase",
+        "B": "Constant",
+        "C": "Decrease",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which one of the predicted mortality trends is relatively low?",
+        "A": "Based on 2000-2005 trend",
+        "B": "Based on 1990-2005 trend",
+        "C": "Based on 1980-2005 trend",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the target mortality rate (per 1,000 live births) for MDG4-29?",
+        "A": "Around 30",
+        "B": "Around 0",
+        "C": "Around 60",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoH13_3.json b/VisRecall/merged/qa/whoH13_3.json
new file mode 100755
index 0000000..aa12544
--- /dev/null
+++ b/VisRecall/merged/qa/whoH13_3.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoH13_3.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Absolute difference in under four mortality",
+        "B": "Absolute difference in under five mortality",
+        "C": "Absolute difference in under six mortality",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which one of the criterias appears on this visualization?",
+        "A": "Place of education",
+        "B": "Place of birth",
+        "C": "Place of residence",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which criteria has the most absolute difference in under five mortality?",
+        "A": "Education of Mother",
+        "B": "Wealth quintile",
+        "C": "Place of residence",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which criteria has the least absolute difference in under five mortality?",
+        "A": "Education of Mother",
+        "B": "Wealth quintile",
+        "C": "Place of residence",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the absolute difference in under five mortality of Education of Mother?",
+        "A": "Around 35",
+        "B": "Around 5",
+        "C": "Around 65",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoI09_1.json b/VisRecall/merged/qa/whoI09_1.json
new file mode 100755
index 0000000..63c89cd
--- /dev/null
+++ b/VisRecall/merged/qa/whoI09_1.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoI09_1.png",
+  "vistype": "table",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Survey of animals in studies",
+    "B": "Break down of development aid",
+    "C": "Number of cases of yaws reported in indonesia",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "4",
+    "question": "What does the n column stands for?",
+    "A": "age",
+    "B": "number of animals",
+    "C": "number of studies",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which animal has the highest number of studies?",
+    "A": "juvenile pig",
+    "B": "adult pig",
+    "C": "adult cattle",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which animal has the lowest ranking?",
+    "A": "adult deer",
+    "B": "adult cats",
+    "C": "adult horses",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q5": {
+    "type": "1",
+    "question": "Which animal has the highest Mean prevalence?",
+    "A": "adult pig",
+    "B": "juvenile pig",
+    "C": "adult cattle",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ01_1.json b/VisRecall/merged/qa/whoJ01_1.json
new file mode 100755
index 0000000..0d469fd
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ01_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoJ01_1.png",
+    "vistype": "other",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Percentage of population using the medical mask",
+        "B": "Percentage of population using improved sanitation facilities",
+        "C": "Percentage of population using Automated External Defibrillator (AED)",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2000",
+        "B": "2010",
+        "C": "2015",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "How much percentage of the population in Germany using improved sanitation?",
+        "A": "91-100%",
+        "B": "76-90%",
+        "C": "50-75%",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "2",
+        "question": "How much percentage of the population in China using improved sanitation?",
+        "A": "91-100%",
+        "B": "76-90%",
+        "C": "50-75%",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "4",
+        "question": "What color means that 50-75% of population using improved sanitation facilities?",
+        "A": "Light green",
+        "B": "Yellow",
+        "C": "Pink",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoJ10.json b/VisRecall/merged/qa/whoJ10.json
new file mode 100755
index 0000000..cf77cb7
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ10.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ10.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Is there a national information system used to inform decision-making?",
+        "B": "Is there a global information system used to inform decision-making?",
+        "C": "Is there a regional information system used to inform decision-making?",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which answer has the lowest value?",
+        "A": "Yes and used",
+        "B": "Under development",
+        "C": "No",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which answer has the same value with Under development?",
+        "A": "Yes and used",
+        "B": "Under evaluation",
+        "C": "No",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "How many country repondents are in this visualization?",
+        "A": "64",
+        "B": "74",
+        "C": "84",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ13_1.json b/VisRecall/merged/qa/whoJ13_1.json
new file mode 100755
index 0000000..699fb6f
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ13_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ13_1.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0", 
+        "question": "What is the theme of this visualization?",
+        "A": "Contribution of household tariffs (and costs associated with self-supply)",
+        "B": "Contribution of industry tariffs (and costs associated with self-supply)",
+        "C": "Contribution of tourism tariffs (and costs associated with self-supply)",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest values in both columns?",
+        "A": "Lesotho",
+        "B": "Thailand",
+        "C": "Iran (Islamic Rebulic of)",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the lowest values in both columns?",
+        "A": "Lesotho",
+        "B": "Thailand",
+        "C": "Iran (Islamic Rebulic of)",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which country has not available data?",
+        "A": "Lesotho",
+        "B": "Thailand",
+        "C": "Iran (Islamic Rebulic of)",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ22_1.json b/VisRecall/merged/qa/whoJ22_1.json
new file mode 100755
index 0000000..ccaf4ee
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ22_1.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoJ22_1.png",
+  "vistype": "table", 
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "New cases of yaws",
+    "B": "Sanitation and drinking water",
+    "C": "Population at risk",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2008-2010",
+    "B": "2010-2012",
+    "C": "2012-2014",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which country in 2010 has the highest value of expenditure?",
+    "A": "Burkina Faso",
+    "B": "Kenya",
+    "C": "Lesotho",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which country in 2008 has the lowest value of expenditure?",
+    "A": "Lesotho",
+    "B": "Madagascar",
+    "C": "Nepal",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "What is the expenditure of Kenya in 2008?",
+    "A": "Around 3 million dollars",
+    "B": "Around 30 million dollars",
+    "C": "Around 300 million dollars",
+    "D": "I can not remember",
+    "answer": "C"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ23.json b/VisRecall/merged/qa/whoJ23.json
new file mode 100755
index 0000000..12a0d04
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ23.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ23.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Revenues coverage of operating costs for urban utilities",
+        "B": "Revenues coverage of operating costs for rural utilities",
+        "C": "Revenues coverage of operating costs for all utilities",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2001",
+        "B": "2011",
+        "C": "2021",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What percentage of the countries have operating ratio greater than 1.2?",
+        "A": "24",
+        "B": "34",
+        "C": "42",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which of the following accounts for the most?",
+        "A": "operating ratio greater than 1.2",
+        "B": "operating ratio between 0.8 and 1.2",
+        "C": "operating ratio less than 0.8",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "How many countries respond to the survey?",
+        "A": "50",
+        "B": "80",
+        "C": "66",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ24_1.json b/VisRecall/merged/qa/whoJ24_1.json
new file mode 100755
index 0000000..4eed2be
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ24_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ24_1.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Covered Transactions by Sector and Year, 2009-2011",
+        "B": "Countries Report Insufficient Staff to Operate and Maintain Urban and Rural Drinking-water Systems (Figure 4.1)",
+        "C": "Figure 1: Urban VS Rural Births in Perceng During 2006-2007",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the label of Y-axis (vertical)?",
+        "A": "Number of staffs",
+        "B": "Number of countries",
+        "C": "Number of cities",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "Which color stands for Urban Drinking-water?",
+        "A": "Darker blue",
+        "B": "Lighter blue",
+        "C": "Orange",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "Which one is higher in the bar plot of Yes, sufficient to meet demand?",
+        "A": "Urban Drinking-water",
+        "B": "Rural Drinking-water",
+        "C": "Same",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which one is higher in the bar plot of Yes, but insufficient to meet demand?",
+        "A": "Urban Drinking-water",
+        "B": "Rural Drinking-water",
+        "C": "Same",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ24_2.json b/VisRecall/merged/qa/whoJ24_2.json
new file mode 100755
index 0000000..ef3545f
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ24_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ24_2.png",
+    "vistype": "pie",
+    "Q1": {
+        "question": "Which infrastructure is discussed in this figure?",
+		"type": "3",
+        "A": "Sanitation",
+        "B": "Healthcare buildings",
+        "C": "Roads",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "Which living area is mentioned in this chart?",
+		"type": "3",
+        "A": "Cities",
+        "B": "Suburbs",
+        "C": "Rural areas",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "What percentage of respondent countries find the supply of skilled labour and technicians adequately developed to meet needs in rural sanitation?",
+		"type": "3",
+        "A": "68%",
+        "B": "20%",
+        "C": "18%",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "question": "How many countries responded to the survey?",
+		"type": "2",
+        "A": "73",
+        "B": "68",
+        "C": "18",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "In what year was the survey publicized?",
+		"type": "2",
+        "A": "2011",
+        "B": "1973",
+        "C": "2021",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
diff --git a/VisRecall/merged/qa/whoJ33.json b/VisRecall/merged/qa/whoJ33.json
new file mode 100755
index 0000000..21604c1
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ33.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ33.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Trends in aid for water and sanitation, education and (health population HIV AIDS) as a percentage of total ODA commitments",
+        "B": "rends in aid for water and education and (health population HIV AIDS) as a percentage of total ODA commitments",
+        "C": "Trends in aid for water and sanitation and (health population HIV AIDS) as a percentage of total ODA commitments",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1900-2010",
+        "B": "1995-2010",
+        "C": "1995-2015",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which of the following has the highest percentage of total ODA commitments in 2010?",
+        "A": "Sanitation and wate",
+        "B": "Education",
+        "C": "Health, population, (HIV AIDS)",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "How much ODA commitments does educatoin use in 2010?",
+        "A": "US$ 19.5 billion",
+        "B": "US$ 13.3 billion",
+        "C": "US$ 7.8 billion",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "What year has lowest aid for sanitaion and water?",
+        "A": "1999",
+        "B": "2000",
+        "C": "2002",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ36_2.json b/VisRecall/merged/qa/whoJ36_2.json
new file mode 100755
index 0000000..27a9421
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ36_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ36_2.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this figure?",
+        "A": "Trends in sanitation and water aid commitments for fragile or conflict-affected states, 2004-2010",
+        "B": "Trends in sanitation and food aid commitments for fragile or conflict-affected states, 2004-2010",
+        "C": "Trends in sanitation and tent aid commitments for fragile or conflict-affected states, 2004-2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "3",
+        "question": "What percentage of water and sanitation first aid in 2004?",
+        "A": "5%",
+        "B": "7%",
+        "C": "9%",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "3",
+        "question": "What percentage of water and sanitation first aid in 2010?",
+        "A": "5%",
+        "B": "7%",
+        "C": "11%",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "In which year was the highest first aid commitment value?",
+        "A": "2008",
+        "B": "2009",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "When was the data source published",
+        "A": "2004",
+        "B": "2010",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ38_2.json b/VisRecall/merged/qa/whoJ38_2.json
new file mode 100755
index 0000000..d5d38b1
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ38_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ38_2.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Comparing donor commitments on Sanitation and drinking-water",
+        "B": "Comparing private commitments on Sanitation and drinking-water",
+        "C": "Comparing public commitments on Sanitation and drinking-water",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What is the percentage of aid commitments for sanitation in the visualization?",
+        "A": "30%",
+        "B": "34%",
+        "C": "40%",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What is maximum percentage of aid is allocated?",
+        "A": "More than 75%",
+        "B": "More than 65%",
+        "C": "More than 80%",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What does the blue represent for?",
+        "A": "Drinking-water",
+        "B": "Sanitation",
+        "C": "AEDs",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which year's data is seen in the visualization?",
+        "A": "2008",
+        "B": "2005 and 2006",
+        "C": "2011 and 2012",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ40_2.json b/VisRecall/merged/qa/whoJ40_2.json
new file mode 100755
index 0000000..10d6752
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ40_2.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoJ40_2.png",
+  "vistype": "pie",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Break down of military aid",
+    "B": "Break down of unemployement aid",
+    "C": "Break down of development aid",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2010",
+    "B": "2011",
+    "C": "2012",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which category has the highest value?",
+    "A": "Rural",
+    "B": "Urban",
+    "C": "Urban and Rural",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q4": {
+    "type": "3",
+    "question": "What is the amount of aid that was disbursed in  2010?",
+    "A": "1 Billion dollars",
+    "B": "1.1 Billion dollars",
+    "C": "2.1 Billion dollars",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "What is the percentage of aid directed to urban areas?",
+    "A": "28%",
+    "B": "68%",
+    "C": "78%",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
diff --git a/VisRecall/merged/qa/whoJ43_1.json b/VisRecall/merged/qa/whoJ43_1.json
new file mode 100755
index 0000000..1f1d379
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ43_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ43_1.png",
+    "vistype": "pie",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Sector budget support from donors for WASH is 3% of total WASH aid",
+        "B": "Direct implementation for WASH is 3% of total WASH aid",
+        "C": "Programmes and projects via other institutions for WASH is 3% of total WASH aid",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which channel has the most funds?",
+        "A": "Sector budget support",
+        "B": "Direct implementation",
+        "C": "Programmes and projects via other institutions",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which channel has the least funds?",
+        "A": "Sector budget support",
+        "B": "Direct implementation",
+        "C": "Programmes and projects via other institutions",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Where does the data come from?",
+        "A": "2011 GLAAS ESA surveys",
+        "B": "2010 WASH surveys",
+        "C": "20 ESAs surveys",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoJ44.json b/VisRecall/merged/qa/whoJ44.json
new file mode 100755
index 0000000..30f0174
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ44.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ44.png",
+    "vistype": "line",
+    "Q1": {
+        "question": "What trend does the ODA have?",
+		"type": "4",
+        "A": "It's increasing",
+        "B": "It's decreasing",
+        "C": "It stays the same",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "In what currency is the budget support shown?",
+		"type": "2",
+        "A": "US dollars",
+        "B": "Canadian dollars",
+        "C": "Euros",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "question": "How much total ODA was spent in 2007?",
+		"type": "3",
+        "A": "US$ 164 billion",
+        "B": "US$ 4.4 billion",
+        "C": "US$ 125 billion",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "What is the earliest year shown in this graph?",
+		"type": "2",
+        "A": "2010",
+        "B": "2005",
+        "C": "2000",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "question": "What is the general trend to general budget support?",
+		"type": "4",
+        "A": "It is increasing",
+        "B": "It stays the same",
+        "C": "It is decreasing",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
diff --git a/VisRecall/merged/qa/whoJ46_1.json b/VisRecall/merged/qa/whoJ46_1.json
new file mode 100755
index 0000000..e3c0b03
--- /dev/null
+++ b/VisRecall/merged/qa/whoJ46_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoJ46_1.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Are hygiene promotion programmes implemented in primary schools?",
+        "B": "Are hygiene promotion programmes implemented in high schools?",
+        "C": "Are hygiene promotion programmes implemented in colleges?",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "In which percentage of schools do the number of countries are the most similar between urban and rural sanitation?",
+        "A": ">75%",
+        "B": "25-75%",
+        "C": "<25%",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "In which percentage of schools do most countries implement urban sanitations?",
+        "A": ">75%",
+        "B": "25-75%",
+        "C": "<25%",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "In which percentage of schools do most countries implement rural sanitations?",
+        "A": ">75%",
+        "B": "25-75%",
+        "C": "<25%",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoK01.json b/VisRecall/merged/qa/whoK01.json
new file mode 100755
index 0000000..6e78f2c
--- /dev/null
+++ b/VisRecall/merged/qa/whoK01.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoK01.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "1",
+        "question": "What is the theme of this visualization?",
+        "A": "Trends in global forest coverage",
+        "B": "Trends in global sea water coverage",
+        "C": "Trends in global drinking water coverage",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "1990-2000",
+        "B": "1990-2005",
+        "C": "1990-2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "4",
+        "question": "What are the trends of coverage over this period?",
+        "A": "lower",
+        "B": "higher",
+        "C": "fast constant",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "2",
+        "question": "Which year is projected in this visualization?",
+        "A": "2013",
+        "B": "2014",
+        "C": "2015",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "In which year has the MDG target been met?",
+        "A": "2000",
+        "B": "2005",
+        "C": "2010",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoK04_1.json b/VisRecall/merged/qa/whoK04_1.json
new file mode 100755
index 0000000..c629dac
--- /dev/null
+++ b/VisRecall/merged/qa/whoK04_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoK04_1.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "HIV prevalence in women aged 15-49 years by region, 1990-2007",
+        "B": "Countries Report Insufficient Staff to Operate and Maintain Urban and Rural Drinking-water Systems (Figure 4.1)",
+        "C": "Almost half of the two billion people who have gained access to drinking water since 1990 live in China or India",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the scale of the visualization?",
+        "A": "Billions",
+        "B": "Millions",
+        "C": "Thousands",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the largest population to gain access to drinking water since 1990?",
+        "A": "China",
+        "B": "India",
+        "C": "Other",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "Which region is not represent in blue colormaps?",
+        "A": "WESTERN ASIA",
+        "B": "SOUTHERN ASIA",
+        "C": "SUB-SAHARAN AFRICA",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Which region is represented in blue colormaps",
+        "A": "WESTERN ASIA",
+        "B": "SOUTHERN ASIA",
+        "C": "DEVELOPED REGIONS",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoK06_2.json b/VisRecall/merged/qa/whoK06_2.json
new file mode 100755
index 0000000..1e7fba4
--- /dev/null
+++ b/VisRecall/merged/qa/whoK06_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoK06_2.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Ten countries with the largest population without access to a stable internet access",
+        "B": "Ten countries with the largest population without access to an improved electrical system",
+        "C": "Ten countries with the largest population without access to an improved drinking water source",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2000",
+        "B": "2010",
+        "C": "2020",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which of the following countries accounts for the highest proportion?",
+        "A": "China",
+        "B": "India",
+        "C": "Nigeria",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which of the following countries accounts for the lowest proportion?",
+        "A": "Bangladesh",
+        "B": "Sudan",
+        "C": "Kenya",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What's the population in the rest of the world without improved drinking water source?",
+        "A": "97",
+        "B": "119",
+        "C": "292",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
diff --git a/VisRecall/merged/qa/whoK08.json b/VisRecall/merged/qa/whoK08.json
new file mode 100755
index 0000000..9c1df0e
--- /dev/null
+++ b/VisRecall/merged/qa/whoK08.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoK08.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "Which region are the selected contries from?",
+        "A": "South America",
+        "B": "South Africa",
+        "C": "Sub-Saharan Africa",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's Population is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which country has the highest value in Population in 2010?",
+        "A": "Togo",
+        "B": "Ghana",
+        "C": "Malawi",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the highest water supply coverage in 2010?",
+        "A": "Namibia",
+        "B": "Gambia",
+        "C": "Ghana",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which country has the most population that gained access to improved sources of drinking water since 1995?",
+        "A": "Ghana",
+        "B": "Malawi",
+        "C": "Namibia",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoK21.json b/VisRecall/merged/qa/whoK21.json
new file mode 100755
index 0000000..a69e607
--- /dev/null
+++ b/VisRecall/merged/qa/whoK21.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoK21.png",
+    "vistype": "pie",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Most of those using an improved drinking water source also use improved sanitation",
+        "B": "Few of those using an improved drinking water source also use improved sanitation",
+        "C": "Most of those using an improved drinking water source don't use improved sanitation",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "3",
+        "question": "How many developing countries are included in the data?",
+        "A": "16",
+        "B": "59",
+        "C": "25",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Which type of data in the figure is marked in blue?",
+        "A": "Water only",
+        "B": "Neither water nor sanitation",
+        "C": "Sanitation only",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which type of data accounted for the most?",
+        "A": "Water only",
+        "B": "Neither Neither water nor sanitation",
+        "C": "Both water and sanitation",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which type of data accounted for the least?",
+        "A": "Sanitation only",
+        "B": "Neither Neither water nor sanitation",
+        "C": "Both water and sanitation",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoL05.json b/VisRecall/merged/qa/whoL05.json
new file mode 100755
index 0000000..8717071
--- /dev/null
+++ b/VisRecall/merged/qa/whoL05.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoL05.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Changes in the burden of disease associated with social development",
+        "B": "Changes in the burden of disease associated with economic development",
+        "C": "Changes in the burden of disease associated with population development",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "1",
+        "question": "Which country has the lowest value in terms of Non-communicable disease?",
+        "A": "Sub-Saharan Africa",
+        "B": "India",
+        "C": "Middle East",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest value in terms of Non-communicable disease?",
+        "A": "China",
+        "B": "Middle East",
+        "C": "Established Market Economies",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest value in terms of Communicable disease?",
+        "A": "Sub-Saharan Africa",
+        "B": "Middle East",
+        "C": "Established Market Economies",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which country has the highest value in terms of Communicable disease?",
+        "A": "China",
+        "B": "Sub-Saharan Africa",
+        "C": "India",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoL06.json b/VisRecall/merged/qa/whoL06.json
new file mode 100644
index 0000000..b9496d1
--- /dev/null
+++ b/VisRecall/merged/qa/whoL06.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoL06.png",
+    "vistype": "bar",
+    "Q1": {
+        "question": "What is the theme of this table?",
+		"type": "0",
+        "A": "Child mortality",
+        "B": "Income tax",
+        "C": "Budget",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "In what year was this data gathered?",
+		"type": "2",
+        "A": "2014",
+        "B": "2008",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "Which region had the lowest percentage of reporting forms received in September?",
+		"type": "1",
+        "A": "Western pacific",
+        "B": "European",
+        "C": "Eastern Mediterranean",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "Which region has the fewer number of countries per area?",
+		"type": "2",
+        "A": "European",
+        "B": "Western pacific",
+        "C": "African",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "What is the source of the data?",
+		"type": "2",
+        "A": "Commission on Social Determinants of Health",
+        "B": "WHO",
+        "C": "World Health Organization",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
diff --git a/VisRecall/merged/qa/whoM01.json b/VisRecall/merged/qa/whoM01.json
new file mode 100755
index 0000000..5d259ce
--- /dev/null
+++ b/VisRecall/merged/qa/whoM01.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoM01.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Depression rate due to Lung cancer by smoking status in the CPSII reference population",
+        "B": "Disability rate due to Lung cancer by smoking status in the CPSII reference population",
+        "C": "Mortality rate due to Lung cancer by smoking status in the CPSII reference population",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which age group has the highest values on both smokers and never smokers?",
+        "A": "60-69",
+        "B": "70-79",
+        "C": "80+",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "On which population were the rates calculated?",
+        "A": "100,000",
+        "B": "10,000",
+        "C": "1,000",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which group has the highest value over 80 years old?",
+        "A": "Female smokers",
+        "B": "Male smokers",
+        "C": "Male never smokers",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which group has the lowest value in 60-69 age group?",
+        "A": "Female never smokers",
+        "B": "Male never smokers",
+        "C": "Male smokers",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoM05.json b/VisRecall/merged/qa/whoM05.json
new file mode 100755
index 0000000..f84d2d0
--- /dev/null
+++ b/VisRecall/merged/qa/whoM05.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoM05.png",
+    "vistype": "table",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Tobacco",
+        "B": "Alcohol",
+        "C": "Cardiovascular",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "What Region has the highest propotion of deaths among Men?",
+        "A": "African",
+        "B": "European",
+        "C": "South East Asian",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "What Region has the highest propotion of deaths among Women?",
+        "A": "Americas",
+        "B": "Eastern Mediterannean",
+        "C": "Western Pacific",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "What Region has the lowest propotion of deaths among All adults?",
+        "A": "Eastern Mediterannean",
+        "B": "African",
+        "C": "Western Pacific",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "Which gender has higher propotion of deaths of Global?",
+        "A": "Men",
+        "B": "Women",
+        "C": "Same",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoN06_1.json b/VisRecall/merged/qa/whoN06_1.json
new file mode 100755
index 0000000..ade8e86
--- /dev/null
+++ b/VisRecall/merged/qa/whoN06_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoN06_1.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Recorded adult per capita consumption of specific alcoholic beverages",
+        "B": "Recorded adult per capita consumption of specific carbonated beverages",
+        "C": "Recorded adult per capita consumption of specific juices",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "1995-2005",
+        "B": "2000-2015",
+        "C": "1990-2005",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which of the following beverages consumed the most per capita during this period?",
+        "A": "Beer",
+        "B": "Spirits",
+        "C": "Wine",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which of the following beverages consumed the least per capita during this period?",
+        "A": "Beer",
+        "B": "Spirits",
+        "C": "Wine",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "How many alcoholic beverages does each person consume per year?",
+        "A": "Around 5 litres",
+        "B": "Around 4.5 litres",
+        "C": "Around 4 litres",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoN19_1.json b/VisRecall/merged/qa/whoN19_1.json
new file mode 100755
index 0000000..452ffd9
--- /dev/null
+++ b/VisRecall/merged/qa/whoN19_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoN19_1.png",
+    "vistype": "bar",
+    "Q1": {
+        "question": "What is the theme of this table?",
+		"type": "0",
+        "A": "C-Annual blood examination rate",
+        "B": "Alcolhol-attrubutable death",
+        "C": "Child mortality",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "question": "In what year was this data gathered?",
+		"type": "2",
+        "A": "2000",
+        "B": "2002",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "Which income group has the highest death rate?",
+		"type": "1",
+        "A": "High income",
+        "B": "Upper middle income",
+        "C": "Lower middle income",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "question": "Which income group has the lowest death rate?",
+		"type": "1",
+        "A": "High income",
+        "B": "Upper middle income",
+        "C": "Lower middle income",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "How many deaths per million does the Upper middle income group have?",
+		"type": "3",
+        "A": "Around 700",
+        "B": "Around 800",
+        "C": "Around 900",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
diff --git a/VisRecall/merged/qa/whoN27.json b/VisRecall/merged/qa/whoN27.json
new file mode 100755
index 0000000..b340b53
--- /dev/null
+++ b/VisRecall/merged/qa/whoN27.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoN27.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Legal definition of a pure juice",
+        "B": "Legal definition of an alcoholic beverage",
+        "C": "Legal definition of an cordial",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2008",
+        "B": "2009",
+        "C": "2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "3",
+        "question": "What is the minimum alcohol content used by the most countries?",
+        "A": "<1",
+        "B": "3-3.99",
+        "C": "None",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "3",
+        "question": "What is the minimum alcohol content used in almost no country?",
+        "A": "1-1.99",
+        "B": "2-2.99",
+        "C": "4-4.99",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "How many countries use <1 as the minimum alcohol content?",
+        "A": "Around 40",
+        "B": "Around 10",
+        "C": "Around 15",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoN32_1.json b/VisRecall/merged/qa/whoN32_1.json
new file mode 100755
index 0000000..cbd1575
--- /dev/null
+++ b/VisRecall/merged/qa/whoN32_1.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoN32_1.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Blood lipid concentration limits for young novice drivers",
+        "B": "Blood glucose concentration limits for young novice drivers",
+        "C": "Blood alcohol concentration limits for young novice drivers",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2004",
+        "B": "2008",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "At which concentration has the largest number of countries?",
+        "A": "0.05 and 0.08",
+        "B": "0.06 and 0.10",
+        "C": "0.00 and 0.02",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "At which concentration has the smallest number of countries?",
+        "A": "0.05 and 0.08",
+        "B": "0.01 and 0.06",
+        "C": "0.00 and 0.02",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Approximately how many countries have blood alcohol concentration limits for young drivers of 0?",
+        "A": "22",
+        "B": "42",
+        "C": "52",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoN35_1.json b/VisRecall/merged/qa/whoN35_1.json
new file mode 100755
index 0000000..632bb57
--- /dev/null
+++ b/VisRecall/merged/qa/whoN35_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoN35_1.png",
+    "vistype": "pie",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Regulation of product placements on television",
+        "B": "Regulation of product placements on internet",
+        "C": "Regulation of product placements on newspaper",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which regulation has the highest percentage?",
+        "A": "No regulation",
+        "B": "Regulation",
+        "C": "Self-regulation",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which regulation has the lowest percentage?",
+        "A": "Self-regulation",
+        "B": "Regulation",
+        "C": "No regulation",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "What does the Green represent?",
+        "A": "Regulation",
+        "B": "No data available",
+        "C": "Self-regulation",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which year's data is seen in the visualization?",
+        "A": "2008",
+        "B": "2005",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoN36.json b/VisRecall/merged/qa/whoN36.json
new file mode 100755
index 0000000..5621416
--- /dev/null
+++ b/VisRecall/merged/qa/whoN36.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoN36.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Countries reporting awareness campaigns on cancer",
+        "B": "Countries reporting awareness campaigns on alcohol",
+        "C": "Countries reporting awareness campaigns on tobacco",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2006",
+        "B": "2007",
+        "C": "2008",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which awareness campaign get most reported?",
+        "A": "Drink-driving",
+        "B": "Youth",
+        "C": "Social harm",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which awareness campaign get least reported?",
+        "A": "Social harm",
+        "B": "Youth",
+        "C": "Indigenous people",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "3",
+        "question": "What is the number of countries reporting Drink-driving?",
+        "A": "Around 100",
+        "B": "Around 120",
+        "C": "Around 140",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoO15.json b/VisRecall/merged/qa/whoO15.json
new file mode 100755
index 0000000..2f7d24f
--- /dev/null
+++ b/VisRecall/merged/qa/whoO15.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoO15.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Burden of disease attributable to lack of contraception",
+        "B": "Burden of disease attributable to lack of awareness",
+        "C": "Burden of disease attributable to lack of hygiene",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What is the age range of the data provider?",
+        "A": "15-44",
+        "B": "15-55",
+        "C": "15-50",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which region is most affected?",
+        "A": "Europe",
+        "B": "Africa",
+        "C": "Americas",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "What is approximately the maximum among all values?",
+        "A": "Above 15",
+        "B": "Above 25",
+        "C": "Above 35",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "Which year's data is seen in the visualization?",
+        "A": "2008",
+        "B": "2005",
+        "C": "2004",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoO22_3.json b/VisRecall/merged/qa/whoO22_3.json
new file mode 100755
index 0000000..cca7259
--- /dev/null
+++ b/VisRecall/merged/qa/whoO22_3.json
@@ -0,0 +1,50 @@
+{
+    "name": "whoO22_3.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Potential reduction in life expectancy in the absence of selected risks to health",
+        "B": "Potential gain in life expectancy with consideration of selected risks to health",
+        "C": "Potential gain in life expectancy in the absence of selected risks to health",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2004",
+        "B": "2011",
+        "C": "2014",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which region has the lowest current life expectancy?",
+        "A": "Africa",
+        "B": "Europe",
+        "C": "South-East Asia",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which region has the highest current life expectancy?",
+        "A": "Europe",
+        "B": "High income",
+        "C": "Africa",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What does the purple bar graph indicate?",
+        "A": "Increase in life expectancy",
+        "B": "Reduction in life expectancy",
+        "C": "Current life expectancy",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/whoQ01.json b/VisRecall/merged/qa/whoQ01.json
new file mode 100755
index 0000000..9c27cc3
--- /dev/null
+++ b/VisRecall/merged/qa/whoQ01.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoQ01.png",
+    "vistype": "table",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of the visualization?",
+        "A": "C-Annual blood examination rate, 2007-2011",
+        "B": "Alcohol-attribute deaths per million inhabitants by income group and the world, 2004",
+        "C": "Percentage of reporting forms received by month and by WHO region, 2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which region is not included in this visualization?",
+        "A": "Region of the Americas",
+        "B": "Middle-East Asia",
+        "C": "South-East Asia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Which region only has two data entries(September and October)?",
+        "A": "South-East Asia",
+        "B": "African",
+        "C": "European",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which region has the lowest number of Total coundtries/areas in this visualization?",
+        "A": "European",
+        "B": "African",
+        "C": "Western pacific",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which month has the highest number of percentage of reporting forms received by WHO Region?",
+        "A": "June",
+        "B": "August",
+        "C": "October",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
diff --git a/VisRecall/merged/qa/whoQ06_2.json b/VisRecall/merged/qa/whoQ06_2.json
new file mode 100755
index 0000000..9bca12d
--- /dev/null
+++ b/VisRecall/merged/qa/whoQ06_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoQ06_2.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Change over time in allocation of international funds for leukemia control",
+        "B": "Change over time in allocation of international funds for HIV control",
+        "C": "Change over time in allocation of international funds for malaria control",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "1999-2010",
+        "B": "2001-2010",
+        "C": "2003-2010",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "4",
+        "question": "How funding for African countries has changed over this time period?",
+        "A": "unchanged",
+        "B": "lower",
+        "C": "higher",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which region has the highest value in 2010?",
+        "A": "African Region",
+        "B": "20% of countries with largest population at risk",
+        "C": "20% of countries with lowest GNI per capita",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which region has the lowest value in 2010?",
+        "A": "African Region",
+        "B": "20% of countries with largest population at risk",
+        "C": "20% of countries with lowest GNI per capita",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoQ12_2.json b/VisRecall/merged/qa/whoQ12_2.json
new file mode 100755
index 0000000..f2a6e97
--- /dev/null
+++ b/VisRecall/merged/qa/whoQ12_2.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoQ12_2.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Proportion of population at malaria risk protected by IRS by WHO Region",
+        "B": "Proportion of population at HIV risk protected by IRS by WHO Region",
+        "C": "Proportion of population at malaria risk protected by WHO by IRS Region",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2002-2011",
+        "B": "2000-2010",
+        "C": "2009-2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which Region has the highest value in 2010?",
+        "A": "Western Pacific",
+        "B": "Americas",
+        "C": "Africa",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which Region has the lowest value in 2003?",
+        "A": "Western Pacific",
+        "B": "Americas",
+        "C": "Africa",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Where does the data come from?",
+        "A": "NMCP reports",
+        "B": "WHO reports",
+        "C": "IRS reports",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/whoQ42_1.json b/VisRecall/merged/qa/whoQ42_1.json
new file mode 100755
index 0000000..fed9725
--- /dev/null
+++ b/VisRecall/merged/qa/whoQ42_1.json
@@ -0,0 +1,49 @@
+{
+    "name": "whoQ42_1.png",
+    "vistype": "bar",
+    "Q1": {
+        "question": "Which time range is this graph visualizing?",
+		"type": "3",
+        "A": "2007-2011",
+        "B": "2007-2010",
+        "C": "2001-2007",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "What is the object being examined in the chart's content?",
+		"type": "3",
+        "A": "Blood",
+        "B": "Urine",
+        "C": "Saliva",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "question": "Which country has the highest blood examination rate?",
+		"type": "1",
+        "A": "Algeria",
+        "B": "Togo",
+        "C": "Liberia",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "Which country has an examination rate of around 15%?",
+		"type": "2",
+        "A": "Niger",
+        "B": "Gambia",
+        "C": "Mali",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "question": "Which country pairs have approximately similar annual blood examination rates?",
+		"type": "4",
+        "A": "Liberia and Niger",
+        "B": "Ghana and Nigeria",
+        "C": "Cape Verde and Togo",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
diff --git a/VisRecall/merged/qa/whoQ51_1.json b/VisRecall/merged/qa/whoQ51_1.json
new file mode 100755
index 0000000..4ffeb4f
--- /dev/null
+++ b/VisRecall/merged/qa/whoQ51_1.json
@@ -0,0 +1,49 @@
+{
+  "name": "whoQ51_1.png",
+  "vistype": "bar",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Population at risk",
+    "B": "Economy at risk",
+    "C": "Environment at risk",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2010",
+    "B": "2011",
+    "C": "2012",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which country has the highest 'High risk'?",
+    "A": "Sudan",
+    "B": "South Sudan",
+    "C": "Malta",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which country has the Lowest 'High risk'?",
+    "A": "Sudan",
+    "B": "South Sudan",
+    "C": "Iran",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "In Somalia, how many population is at risk?",
+    "A": "More than 80%",
+    "B": "60% to 80%",
+    "C": "40% to 60%",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj107.json b/VisRecall/merged/qa/wsj107.json
new file mode 100755
index 0000000..8550f6c
--- /dev/null
+++ b/VisRecall/merged/qa/wsj107.json
@@ -0,0 +1,49 @@
+{
+  "name": "wsj107.png",
+  "vistype": "bar",
+  "Q1": {
+    "type": "0",
+    "question": "What is the title of this visualization?",
+    "A": "Slow and Steady",
+    "B": "Slow but Steady",
+    "C": "Slow or Steady",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q2": {
+    "type": "3",
+    "question": "What is growth rate of the U.S. gross domestic product in the third quarter of 2012?",
+    "A": "2%",
+    "B": "4%",
+    "C": "6%",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q3": {
+    "type": "2",
+    "question": "Is the data adjusted for inflation?",
+    "A": "No",
+    "B": "Yes",
+    "C": "May be",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "What was the period of recision on an average?",
+    "A": "2008-2009",
+    "B": "2012-2015",
+    "C": "2010-2012",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q5": {
+    "type": "1",
+    "question": "When the biggest recession occur?",
+    "A": "First quarter of 2010",
+    "B": "Third quarter of 2009",
+    "C": "Fourth quarter of 2008",
+    "D": "I can not remember",
+    "answer": "C"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj11.json b/VisRecall/merged/qa/wsj11.json
new file mode 100755
index 0000000..cc50e61
--- /dev/null
+++ b/VisRecall/merged/qa/wsj11.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj11.png",
+    "vistype": "bar",
+    "Q1": {
+        "question": "Which ethnic group is targeted in this graph?",
+		"type": "2",
+        "A": "Chinese",
+        "B": "Japanese",
+        "C": "Americans",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "Which two years are being compared in this figure?",
+		"type": "2",
+        "A": "2009 and 2012",
+        "B": "2008 and 2013",
+        "C": "2000 and 2010",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "question": "Which ethnic group is least represented in Spain, according to this graph?",
+		"type": "1",
+        "A": "Germany",
+        "B": "France",
+        "C": "Romania",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "question": "Approximately how many Chinese are self-employed in Spain in 2012?",
+		"type": "2",
+        "A": "30,000",
+        "B": "40,000",
+        "C": "10,000",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "question": "Which ethnic group increased the least its number of self-employed members in Spain between 2009 and 2012?",
+		"type": "2",
+        "A": "U.K.",
+        "B": "Germany",
+        "C": "Italy",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
diff --git a/VisRecall/merged/qa/wsj128.json b/VisRecall/merged/qa/wsj128.json
new file mode 100755
index 0000000..635d1a3
--- /dev/null
+++ b/VisRecall/merged/qa/wsj128.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj128.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "How much German pension funds have invested in assets in each country",
+        "B": "How much Finnish pension funds have invested in assets in each country",
+        "C": "How much French pension funds have invested in assets in each country",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2011-2013",
+        "B": "2009-2011",
+        "C": "2010-2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "Which country is not mentioned in this visualization?",
+        "A": "Spain",
+        "B": "Hungary",
+        "C": "Greece",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which country has the highest value in 2010?",
+        "A": "Italy",
+        "B": "Spain",
+        "C": "Greece",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Compared to year 2010, which country's value decreased the most in year 2012?",
+        "A": "Portugal",
+        "B": "Italy",
+        "C": "Spain",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj135.json b/VisRecall/merged/qa/wsj135.json
new file mode 100755
index 0000000..2fbc6bf
--- /dev/null
+++ b/VisRecall/merged/qa/wsj135.json
@@ -0,0 +1,50 @@
+{
+    "name": "wsj135.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Home-Loan Hiatus",
+        "B": "In the Mix",
+        "C": "Historic stockmarket returns",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "3",
+        "question": "What does the green bar graph indicate?",
+        "A": "Home purchase",
+        "B": "Refinance",
+        "C": "Housing Market",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What does the orange bar graph indicate?",
+        "A": "Refinance",
+        "B": "Housing Market",
+        "C": "Home purchase",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "In which year does the highest Refinance value appear?",
+        "A": "2003",
+        "B": "2006",
+        "C": "2009",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "What is the value of Home purchase of the most recent year(2011)?",
+        "A": "2.4 million",
+        "B": "3.2 million",
+        "C": "4.3 million",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
+
diff --git a/VisRecall/merged/qa/wsj151.json b/VisRecall/merged/qa/wsj151.json
new file mode 100755
index 0000000..8cde5a7
--- /dev/null
+++ b/VisRecall/merged/qa/wsj151.json
@@ -0,0 +1,49 @@
+{
+  "name": "wsj151.png",
+  "vistype": "other",
+  "Q1": {
+    "type": "0",
+    "question": "What is the title of this visualization?",
+    "A": "Global Slowdown",
+    "B": "Slow but Steady",
+    "C": "Balancing Act",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q2": {
+    "type": "3",
+    "question": "How many share of global gross domestic product does Euro area has?",
+    "A": "11%",
+    "B": "18%",
+    "C": "22%",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which country has the biggest growth in gross domestic product in 2012?",
+    "A": "U.S.",
+    "B": "Euro area",
+    "C": "China",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "What is the correct ordering of growth in gross domestic product in 2012?(From highest to lowest)",
+    "A": "China, World, Euro area, U.S.",
+    "B": "China, Euro area, World, U.S.",
+    "C": "China, World, U.S., Euro area",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "What this the source of the visualization?",
+    "A": "SIPRI",
+    "B": "Wall Street Journal",
+    "C": "European Commission",
+    "D": "I can not remember",
+    "answer": "B"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj168.json b/VisRecall/merged/qa/wsj168.json
new file mode 100755
index 0000000..4956a7b
--- /dev/null
+++ b/VisRecall/merged/qa/wsj168.json
@@ -0,0 +1,50 @@
+{
+    "name": "wsj168.png",
+    "vistype": "scatter",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Several banks regarding the changes of their benchmark rates",
+        "B": "Several banks regarding the rate changes for their Libor submissions",
+        "C": "European banks regarding the rate changes for their Libor submissions",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2011",
+        "B": "2012",
+        "C": "2021",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which bank has the largest number of rate changes?",
+        "A": "RBC",
+        "B": "Bank of America",
+        "C": "Credit suisse",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Which bank has the largest average days at rate?",
+        "A": "Bank of America",
+        "B": "Societe Generale",
+        "C": "RBC",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "The banks from which district that Red dots refer to?",
+        "A": "European banks",
+        "B": "North American banks",
+        "C": "Japanese banks",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
+
diff --git a/VisRecall/merged/qa/wsj180.json b/VisRecall/merged/qa/wsj180.json
new file mode 100755
index 0000000..bd22d89
--- /dev/null
+++ b/VisRecall/merged/qa/wsj180.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj180.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Time by Time",
+        "B": "Brick by Brick",
+        "C": "Home by Home",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Home Price",
+        "B": "Brick Price",
+        "C": "Share Price",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "2",
+        "question": "What is the scale of x-axis(horizontal)?",
+        "A": "Daily",
+        "B": "Weekly",
+        "C": "Monthly",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "How big has Home prices changed in 2008?",
+        "A": "around 5%",
+        "B": "around -5%",
+        "C": "around -10%",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "How big has Home prices changed in 2012?",
+        "A": "around 5%",
+        "B": "around -5%",
+        "C": "around -10%",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj265.json b/VisRecall/merged/qa/wsj265.json
new file mode 100755
index 0000000..56c3077
--- /dev/null
+++ b/VisRecall/merged/qa/wsj265.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj265.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Growing Divide",
+        "B": "Seasonally adjusted unemployment rate",
+        "C": "The euro zone's unemployment rate",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What period of data does this visualization show?",
+        "A": "2006-2012",
+        "B": "2007-2010",
+        "C": "2008-2012",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest proportion in 2012?",
+        "A": "Euro zone",
+        "B": "Germany",
+        "C": "Spain",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest proportion in 2012?",
+        "A": "Euro zone",
+        "B": "Germany",
+        "C": "Spain",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Where the data comes from?",
+        "A": "The Wall Street Journal",
+        "B": "July",
+        "C": "Eurostat",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj277.json b/VisRecall/merged/qa/wsj277.json
new file mode 100755
index 0000000..d71ca3a
--- /dev/null
+++ b/VisRecall/merged/qa/wsj277.json
@@ -0,0 +1,50 @@
+{
+    "name": "wsj277.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the title of this visualization?",
+        "A": "Cook Political Report",
+        "B": "In the Mix",
+        "C": "Democrats",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which party hold the higher number of seats?",
+        "A": "Democrats",
+        "B": "Republicans",
+        "C": "the same",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Democrats holds the most seats in (  )?",
+        "A": "Solid D",
+        "B": "Likely D",
+        "C": "Lean D",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+        "type": "1",
+        "question": "Republicans holds the most seats in (  )?",
+        "A": "Toss up",
+        "B": "Solid R",
+        "C": "Likely R",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "3",
+        "question": "How many seats held by republicans are there in total?",
+        "A": "2",
+        "B": "22",
+        "C": "10",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/wsj3.json b/VisRecall/merged/qa/wsj3.json
new file mode 100755
index 0000000..5fc7bb2
--- /dev/null
+++ b/VisRecall/merged/qa/wsj3.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj3.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Direct Deposit",
+        "B": "Indirect Deposit",
+        "C": "Average Deposit",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "4",
+        "question": "What does the blue bar data represent in this visualization?",
+        "A": "Position",
+        "B": "Total compensation, percentage change 2008 to 2012",
+        "C": "Average 2012 total compensation",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which position has the largest proportion of total compensations?",
+        "A": "CEO/President",
+        "B": "Experienced teller",
+        "C": "Commercial loan officer",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which position has the smallest proportion of total compensations?",
+        "A": "Chief compliance officer",
+        "B": "Loan workout officer",
+        "C": "Call center representative",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "Which position has the smallest proportion of average 2012 total compensation?",
+        "A": "CEO/President",
+        "B": "Experienced teller",
+        "C": "Commercial loan officer",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj320.json b/VisRecall/merged/qa/wsj320.json
new file mode 100755
index 0000000..70afd2b
--- /dev/null
+++ b/VisRecall/merged/qa/wsj320.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj320.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Average temperature in the contiguous 48 states",
+        "B": "Average probability of precipitation in the contiguous 48 states",
+        "C": "Average Humidity in the contiguous 48 states",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What month's data is displayed in this visualization?",
+        "A": "May",
+        "B": "June",
+        "C": "July",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Which period of data does this visualization show?",
+        "A": "since 1895",
+        "B": "since 1900",
+        "C": "since 1950",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which year has the highest value?",
+        "A": "1900",
+        "B": "1936",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+		"type": "1",
+        "question": "what is the highest temperature in this visualization",
+        "A": "Around 70",
+        "B": "Around 74",
+        "C": "Around 78",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj340.json b/VisRecall/merged/qa/wsj340.json
new file mode 100755
index 0000000..14c1d0e
--- /dev/null
+++ b/VisRecall/merged/qa/wsj340.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj340.png",
+    "vistype": "pie",
+    "Q1": {
+        "question": "Which utility is this graph concerned with?",
+		"type": "2",
+        "A": "Electricity",
+        "B": "Telephone",
+        "C": "Sanitation",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "question": "This graph represents data from which country?",
+		"type": "2",
+        "A": "France",
+        "B": "Germany",
+        "C": "India",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "question": "Which energy source is the main one in India?",
+		"type": "2",
+        "A": "Nuclear",
+        "B": "Hydroelectric",
+        "C": "Coal",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "Which share of electricity source does hydroelectric represent?",
+		"type": "2",
+        "A": "1/5",
+        "B": "1/4",
+        "C": "1/4",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "question": "What is the source of data contained in this graph?",
+		"type": "2",
+        "A": "World Health Organization",
+        "B": "India's Ministry of Power",
+        "C": "India Electric",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
diff --git a/VisRecall/merged/qa/wsj342.json b/VisRecall/merged/qa/wsj342.json
new file mode 100755
index 0000000..1def5ce
--- /dev/null
+++ b/VisRecall/merged/qa/wsj342.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj342.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Cost and and chance of getting a seat during rush hour of public transport",
+        "B": "Cost and and chance of getting a seat of public transport",
+        "C": "Cost and and chance of getting a seat at night of public transport",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which line costs the most?",
+        "A": "5",
+        "B": "R",
+        "C": "Q",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "4",
+        "question": "Which lines have the smallest chance to get a seat?",
+        "A": "5 and 4",
+        "B": "R and 7",
+        "C": "Q and B",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q5": {
+        "type": "4",
+        "question": "Which lines have the biggest chance to get a seat?",
+        "A": "5 and 4",
+        "B": "R and 7",
+        "C": "Q and B",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj553.json b/VisRecall/merged/qa/wsj553.json
new file mode 100755
index 0000000..80f8f8e
--- /dev/null
+++ b/VisRecall/merged/qa/wsj553.json
@@ -0,0 +1,49 @@
+{
+  "name": "wsj553.png",
+  "vistype": "other",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Male representation on the boards of the largest listed companies in January 2012",
+    "B": "Female representation on the boards of the largest listed companies in January 201",
+    "C": "Male representation as CEO's of the largest listed companies in January 201",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q2": {
+    "type": "2",
+    "question": "What year's data is displayed in this visualization?",
+    "A": "2010",
+    "B": "2011",
+    "C": "2012",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q3": {
+    "type": "1",
+    "question": "Which country has the highest percentage of Female representation ?",
+    "A": "U.S.",
+    "B": "Italy",
+    "C": "Norway",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q4": {
+    "type": "1",
+    "question": "Which country has the highest percentage of Female representation ?",
+    "A": "U.S.",
+    "B": "Italy",
+    "C": "Norway",
+    "D": "I can not remember",
+    "answer": "A"
+  },
+  "Q5": {
+    "type": "2",
+    "question": "Whats the source of information visualization?",
+    "A": "Sud Deutsche",
+    "B": "Euro News",
+    "C": "European Commision",
+    "D": "I can not remember",
+    "answer": "C"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj56.json b/VisRecall/merged/qa/wsj56.json
new file mode 100755
index 0000000..f6ec602
--- /dev/null
+++ b/VisRecall/merged/qa/wsj56.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj56.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Clout of cloth",
+        "B": "Number of women",
+        "C": "election of congress",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "type": "4",
+        "question": "What is the trend of number of women in House Congress?",
+        "A": "Decreasing",
+        "B": "Didn't change",
+        "C": "Increasing",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q3": {
+        "type": "4",
+        "question": "Which color stands for Senate?",
+        "A": "Brown",
+        "B": "Green",
+        "C": "Orange",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "How many women are in House and Senate Congress after 2010?",
+        "A": "60-80",
+        "B": "80-100",
+        "C": "100-120",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the source of this visualization?",
+        "A": "The Wall Street Journal",
+        "B": "The Economist",
+        "C": "Democatic National Committee",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj593.json b/VisRecall/merged/qa/wsj593.json
new file mode 100755
index 0000000..cf81662
--- /dev/null
+++ b/VisRecall/merged/qa/wsj593.json
@@ -0,0 +1,50 @@
+{
+    "name": "wsj593.png",
+    "vistype": "bar",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Top10 EU countries in terms of travel and tourism contribution to GDP",
+        "B": "Top10 EU countries in terms of manufacturing contribution to GDP",
+        "C": "Top10 EU countries in terms of agriculture contribution to GDP",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "2",
+        "question": "What year's data is displayed in this visualization?",
+        "A": "2010",
+        "B": "2011",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+		"type": "1",
+        "question": "Which country has the highest value?",
+        "A": "Spain",
+        "B": "Slovenia",
+        "C": "Malta",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which country has the lowest value?",
+        "A": "Cyprus",
+        "B": "Hungary",
+        "C": "Estonia",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Which country's data has a different color from other countries?",
+        "A": "Spain",
+        "B": "Hungary",
+        "C": "Greece",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
+
diff --git a/VisRecall/merged/qa/wsj60.json b/VisRecall/merged/qa/wsj60.json
new file mode 100755
index 0000000..9945887
--- /dev/null
+++ b/VisRecall/merged/qa/wsj60.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj60.png",
+    "vistype": "line",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Blur Picture",
+        "B": "Film Cost",
+        "C": "Share Price",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "2",
+        "question": "Which company is described in this visualization?",
+        "A": "Nikon",
+        "B": "Kodak",
+        "C": "Fujifilm",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "type": "3",
+        "question": "What is the lowest daily share price?",
+        "A": "0.1",
+        "B": "0.2",
+        "C": "0.3",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "How many events are introduced in the visualization?",
+        "A": "3",
+        "B": "4",
+        "C": "5",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "1",
+        "question": "what is the latest event introduced in the visualization?",
+        "A": "Draws down 160 million on credit line",
+        "B": "Files for Chapter 11 bankruptcy protection",
+        "C": "Says it is seeking financing",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj72.json b/VisRecall/merged/qa/wsj72.json
new file mode 100755
index 0000000..9d72ee1
--- /dev/null
+++ b/VisRecall/merged/qa/wsj72.json
@@ -0,0 +1,49 @@
+{
+  "name": "wsj72.png",
+  "vistype": "line",
+  "Q1": {
+    "type": "0",
+    "question": "What is the theme of this visualization?",
+    "A": "Federal income as a percentage as GDP",
+    "B": "State income as a percentage as GDP",
+    "C": "Federal income and spending as a percentage as GDP",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q2": {
+    "type": "1",
+    "question": "When does the highest spending occur?",
+    "A": "1980-1990",
+    "B": "1990-2000",
+    "C": "2000-2010",
+    "D": "I can not remember",
+    "answer": "C"
+  },
+  "Q3": {
+    "type": "2",
+    "question": "What is the priority for next president?",
+    "A": "To widen the gap",
+    "B": "To narrow the gap",
+    "C": "To maintain it as it is",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q4": {
+    "type": "2",
+    "question": "What is the source of the data?",
+    "A": "Washington post",
+    "B": "White House",
+    "C": "Department of Social welfare",
+    "D": "I can not remember",
+    "answer": "B"
+  },
+  "Q5": {
+    "type": "3",
+    "question": "What percentage is the most recent year's income?",
+    "A": "15.7%",
+    "B": "18.7%",
+    "C": "22.7%",
+    "D": "I can not remember",
+    "answer": "A"
+  }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj76.json b/VisRecall/merged/qa/wsj76.json
new file mode 100755
index 0000000..d2799fb
--- /dev/null
+++ b/VisRecall/merged/qa/wsj76.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj76.png",
+    "vistype": "bar",
+    "Q1": {
+        "question": "Which household appliance is discussed in this chart?",
+		"type": "2",
+        "A": "Toaster",
+        "B": "Cellphone",
+        "C": "Car",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q2": {
+        "question": "Which time range is of interest here?",
+		"type": "2",
+        "A": "2012-2021",
+        "B": "2008-2011",
+        "C": "2018",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q3": {
+        "question": "What percentage of households had no corded phone in 2011?",
+		"type": "3",
+        "A": "20%",
+        "B": "30%",
+        "C": "34%",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+        "question": "What is the source of the data presented in this graph?",
+		"type": "2",
+        "A": "Wall Street Journal",
+        "B": "World Health Organization",
+        "C": "CDC/NCHS",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "question": "How many households were surveyed for this graph?",
+		"type": "3",
+        "A": "201,134",
+        "B": "136,228",
+        "C": "95,000",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
diff --git a/VisRecall/merged/qa/wsj8.json b/VisRecall/merged/qa/wsj8.json
new file mode 100755
index 0000000..3ec2c54
--- /dev/null
+++ b/VisRecall/merged/qa/wsj8.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj8.png",
+    "vistype": "other",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Gender gap in the large US banks and securities firms",
+        "B": "Gender gap in the large US banks and consulting firms",
+        "C": "Gender gap in the large US banks and accounting firms",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which bank or firm has the most women executives?",
+        "A": "Bank of America",
+        "B": "Citigroup",
+        "C": "Wells Fargo",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which bank or firm has the least women executives?",
+        "A": "Bank of America",
+        "B": "Citigroup",
+        "C": "Wells Fargo",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "3",
+        "question": "How many banks and firms are displayed in the visualization?",
+        "A": "3",
+        "B": "4",
+        "C": "5",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What is the source of the figure?",
+        "A": "CNN",
+        "B": "The Wall Street Journal",
+        "C": "The Times",
+        "D": "I can not remember",
+        "answer": "B"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj86.json b/VisRecall/merged/qa/wsj86.json
new file mode 100755
index 0000000..5fb22ab
--- /dev/null
+++ b/VisRecall/merged/qa/wsj86.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj86.png",
+    "vistype": "line",
+    "Q1": {
+		"type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Rising Tide",
+        "B": "Falling Tide",
+        "C": "Rising GDP",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+		"type": "4",
+        "question": "Which data is marked in red?",
+        "A": "Greek budget report",
+        "B": "IMF's World Economic Outlook",
+        "C": "Original bailout program",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+		"type": "2",
+        "question": "Which type of data overlapped with IMF's world Economic Outlook in 2009-2011?",
+        "A": "Greek budget report",
+        "B": "Original bailout program",
+        "C": "Review of bailout program",
+        "D": "I can not remember",
+        "answer": "C"
+    },
+    "Q4": {
+		"type": "1",
+        "question": "Which type of data has the lowest value in 2009?",
+        "A": "Greek budget report",
+        "B": "Original bailout program",
+        "C": "Review of bailout program",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+		"type": "2",
+        "question": "Which year's data are all estimates?",
+        "A": "2009",
+        "B": "2010",
+        "C": "2011",
+        "D": "I can not remember",
+        "answer": "A"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/qa/wsj88.json b/VisRecall/merged/qa/wsj88.json
new file mode 100755
index 0000000..a162d1e
--- /dev/null
+++ b/VisRecall/merged/qa/wsj88.json
@@ -0,0 +1,49 @@
+{
+    "name": "wsj88.png",
+    "vistype": "bar",
+    "Q1": {
+        "type": "0",
+        "question": "What is the theme of this visualization?",
+        "A": "Top 10 political entities that spent most of their money on behalf of candidates in the election",
+        "B": "Top 20 political entities that spent most of their money on behalf of candidates in the election",
+        "C": "Top 50 political entities that spent most of their money on behalf of candidates in the election",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q2": {
+        "type": "1",
+        "question": "Which entity has spent the maximum?",
+        "A": "Restore our future",
+        "B": "American Crossroads/Crossroads GPS",
+        "C": "SEIU",
+        "D": "I can not remember",
+        "answer": "A"
+    },
+    "Q3": {
+        "type": "1",
+        "question": "Which entity has spent the minimum?",
+        "A": "AFSCME",
+        "B": "Moveon.org",
+        "C": "AFT",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q4": {
+        "type": "2",
+        "question": "What currency is used to denote the spending?",
+        "A": "Euros",
+        "B": "Dollars",
+        "C": "Yens",
+        "D": "I can not remember",
+        "answer": "B"
+    },
+    "Q5": {
+        "type": "2",
+        "question": "What year's data is seen in the visualization?",
+        "A": "2008",
+        "B": "2005",
+        "C": "2012",
+        "D": "I can not remember",
+        "answer": "C"
+    }
+}
\ No newline at end of file
diff --git a/VisRecall/merged/src/02wEIH1uspxsk1E7VW-KRA==.0.png b/VisRecall/merged/src/02wEIH1uspxsk1E7VW-KRA==.0.png
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