@inproceedings{be20b3eda8674095a8c30a140c9aaad6,
title = "Killing-Time Detection from Smartphone Screenshots",
abstract = "Finding good moments to deliver interruptions has drawn research attention. Since users have attention surplus at these moments, killing-Time is considered one such a kind of moment. However, detection on killing-Time has been under researched. In this paper, we propose a screenshot-based killing-Time detection with deep learning. Our model achieves an accuracy 79.71%, recall 90.24%, precision 84.51%, and AUROC 65.50%. This suggests that using screenshots to detect users' kill time behavior on smartphones is a promising approach. It may be worthwhile to investigate how the fusion of screenshots and sensor data can further improve detection.",
keywords = "Deep Learning, Kill time, Opportune Moment, Screenshot",
author = "Chen, {Yu Chun} and Kao, {Keui Chun} and Lee, {Yu Jen} and Faye Shih and Chiu, {Wei Chen} and Chang, {Yung Ju}",
note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2021 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2021 ; Conference date: 21-09-2021 Through 25-09-2021",
year = "2021",
month = sep,
day = "21",
doi = "10.1145/3460418.3479295",
language = "English",
series = "UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers",
publisher = "Association for Computing Machinery, Inc",
pages = "15--16",
booktitle = "UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers",
}