Demo Data from VLDB 2026 / reviewers in the wild / expert
Jiahui Li 0010
dblp:153/2952-10
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 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
1 paper |
Interaction techniques and input · 61% Human-AI interaction · 30% User interface design and tools · 9% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
mobile interaction |
0.6 | 1 | 2022 | Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones · CHI 2022 |
Human-AI interaction › automation
mobile task automation |
0.6 | 1 | 2022 | Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones · CHI 2022 |
Methods — techniques the papers use, named apart from their topics
user study · 0.6q&a dialogue · 0.6operation sequence mining · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Automatically Generating and Improving Voice Command Interface from Operation Sequences on SmartphonesabstractUsing voice commands to automate smartphone tasks (e.g., making a video call) can effectively augment the interactivity of numerous mobile apps. However, creating voice command interfaces requires a tremendous amount of effort in labeling and compiling the graphical user interface (GUI) and the utterance data. In this paper, we propose AutoVCI, a novel approach to automatically generate voice command interface (VCI) from smartphone operation sequences. The generated voice command interface has two distinct features. First, it automatically maps a voice command to GUI operations and fills in parameters accordingly, leveraging the GUI data instead of corpus or hand-written rules. Second, it launches a complementary Q&A dialogue to confirm the intention in case of ambiguity. In addition, the generated voice command interface can learn and evolve from user interactions. It accumulates the history command understanding results to annotate the user’s input and improve its semantic understanding ability. We implemented this approach on Android devices and conducted a two-phase user study with 16 and 67 participants in each phase. Experimental results of the study demonstrated the practical feasibility of AutoVCI. Lihang Pan, Chun Yu, Jiahui Li 0010, Tian Huang, Xiaojun Bi 0001, Yuanchun Shi |
CHI | 3 |