Demo Data from EDBT 2026 / reviewers in the wild / expert
Faye Shih
dblp:302/4084
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0003-1058-9683ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
2 papers |
Ubiquitous computing and smart environments · 67% Interaction techniques and input · 20% Wearable and physiological sensing · 7% |
Topics — the 4 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Ubiquitous computing and smart environments
mobile sensing |
0.7 | 1 | 2023 | Are You Killing Time? Predicting Smartphone Users' Time-killing Moments via Fusion of Smartphone Sensor Data and Screenshots · CHI 2023 |
Ubiquitous computing and smart environments
attention management |
0.6 | 1 | 2022 | Because I'm Restricted, 2 - 4 PM Unable to See Messages: Exploring Users' Perceptions and Likely Practices around Exposing Attention Management Use on IM Online Status · CHI 2022 |
Interaction techniques and input
mobile interaction |
0.6 | 1 | 2022 | Because I'm Restricted, 2 - 4 PM Unable to See Messages: Exploring Users' Perceptions and Likely Practices around Exposing Attention Management Use on IM Online Status · CHI 2022 |
Collaborative and social computing › computer-mediated communication
instant messaging |
0.2 | 1 | 2022 | Because I'm Restricted, 2 - 4 PM Unable to See Messages: Exploring Users' Perceptions and Likely Practices around Exposing Attention Management Use on IM Online Status · CHI 2022 |
Methods — techniques the papers use, named apart from their topics
deep learning fusion model · 0.7clustering · 0.7speed-dating design method · 0.6qualitative analysis · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Are You Killing Time? Predicting Smartphone Users' Time-killing Moments via Fusion of Smartphone Sensor Data and ScreenshotsabstractTime-killing on smartphones has become a pervasive activity, and could be opportune for delivering content to their users. This research is believed to be the first attempt at time-killing detection, which leverages the fusion of phone-sensor and screenshot data. We collected nearly one million user-annotated screenshots from 36 Android users. Using this dataset, we built a deep-learning fusion model, which achieved a precision of 0.83 and an AUROC of 0.72. We further employed a two-stage clustering approach to separate users into four groups according to the patterns of their phone-usage behaviors, and then built a fusion model for each group. The performance of the four models, though diverse, yielded better average precision of 0.87 and AUROC of 0.76, and was superior to that of the general/unified model shared among all users. We investigated and discussed the features of the four time-killing behavior clusters that explain why the models’ performance differ. Yu-Chun Chen, Yu-Jen Lee, Kuei-Chun Kao, Jie Tsai, En-Chi Liang, Walon Wei-Chen Chiu, Faye Shih, Yung-Ju Chang |
CHI | 7 |
| 2022 | Because I'm Restricted, 2 - 4 PM Unable to See Messages: Exploring Users' Perceptions and Likely Practices around Exposing Attention Management Use on IM Online StatusabstractAttention-management tools can restrict online communication, but may cause collateral damage to their users’ fulfillment of communication expectations. This paper explores the idea of integrating attention management into instant messaging (IM), by 1) disclosing restriction status via an online status indicator (OSI) to manage contacts’ expectations, and 2) imposing communication limits to reduce communication distraction. We used a speed-dating design method to allow 43 participants to rapidly compare 48 types of OSI restriction in various conversational contexts. We identified two “tug-of-wars” that take place when attention management is integrated into IM apps: one between fulfilling one’s contacts’ expectations and protecting one’s own attention, and the other, between protecting one’s privacy and asserting the justifiability of using communication restrictions. We also highlighted the participants’ desire to be diplomatic for sustaining their positive images and maintaining relational connectedness. Finally, we provide design recommendations for integrating attention management into IM apps. Yu-Ling Chou, Yu-Ling Chien, Yu-Hsin Lin 0004, Kung-Pai Lin, Faye Shih, Yung-Ju Chang |
CHI | 5 |