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Jiahui Li 0010

dblp:153/2952-10 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Interaction techniques and input
mobile interaction
0.612022
Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones · CHI 2022
Human-AI interaction › automation
mobile task automation
0.612022
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
YearPublicationVenuePosition
2022 Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones
abstract
Using 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
CHI3