Group headed by Andreas Bulling of the Human-Computer Interaction and Cognitive Systems department of the University of Stuttgart.
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781 to 790 of 1,117 Results
generate_sentinels.py
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Python Source Code - 7.1 KB - MD5: f67356936d2112db57719174c75d9eec
generate_spotlight.py
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Python Source Code - 4.5 KB - MD5: 51660bf3ceac4ddaf8141c2c2fe3bcb8
generate_tutorials.py
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Python Source Code - 3.7 KB - MD5: 8ccf47ffcce71cd21c9f0a6e9b950f9d
.gitignore
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Unknown - 93 B - MD5: b221a56d3dce4dc5c0108143ebb0b0a8
.gitignore
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Unknown - 735 B - MD5: 7bc924f06f144cb2ae1ef7bdf1b50313
gl.js
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Javascript Code - 924 B - MD5: d99b1fedaa86d011818cfbab11dd355c
grid.css
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Cascading Style Sheet - 69.4 KB - MD5: b6be8e65076fd6b7efad0e9e8df7b8fe
grid.min.css
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Cascading Style Sheet - 54.7 KB - MD5: c5688d59f89a82e39fbebe1cb0e7f81c
header.css
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Cascading Style Sheet - 12.8 KB - MD5: 2457601970caa79fc40f72d7c6789661
header.min.css
Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"
Cascading Style Sheet - 8.4 KB - MD5: 775dadbb64d52c0d22430bd5295498e4
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