Demo Data from VLDB 2026 / reviewers in the wild / expert
Chang-Chun Wang
dblp:202/2310
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2022
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 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.
| Computer graphics and multimedia
2 papers |
Virtual and augmented reality · 100% | |
| Human-computer interaction and pervasive computing
2 papers |
Collaborative and social computing · 65% Human-robot interaction · 35% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
virtual humans |
0.6 | 1 | 2022 | An Evaluation of Native versus Foreign Communicative Interactions on Users' Behavioral Reactions towards Affective Virtual Crowds · VR 2022 |
Virtual and augmented reality › virtual humans
virtual human crowd |
0.5 | 1 | 2021 | Effects of Language Familiarity in Simulated Natural Dialogue with a Virtual Crowd of Digital Humans on Emotion Contagion in Virtual Reality · VR 2021 |
Collaborative and social computing › computer-mediated communication
cultural differences |
0.2 | 1 | 2022 | An Evaluation of Native versus Foreign Communicative Interactions on Users' Behavioral Reactions towards Affective Virtual Crowds · VR 2022 |
Human-robot interaction
nonverbal communication |
0.2 | 1 | 2022 | An Evaluation of Native versus Foreign Communicative Interactions on Users' Behavioral Reactions towards Affective Virtual Crowds · VR 2022 |
Methods — techniques the papers use, named apart from their topics
between-subject experiment · 1.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | An Evaluation of Native versus Foreign Communicative Interactions on Users' Behavioral Reactions towards Affective Virtual CrowdsabstractThis investigation compared the impact on the users’ non-verbal behaviors elicited by interacting with a crowd of emotional virtual humans (VHs) in native and non-native language settings. In a between-subject experiment, we collected objective metrics regarding the users’ behaviors during interaction with a crowd of VHs who expressed verbal and non-verbal communicative behaviors in response to users’ speech-based interaction. The study presented four VH crowd conditions based on their emotional disposition, namely Positive, Negative, Neutral, and Mixed, in accordance with which VHs exhibited the appropriate behaviors. Participants were tasked with collecting items in a virtual flea market, and they interacted using natural speech-based dialogue with the VHs. The language conditions were collected in the USA and under two different conditions in Taiwan. The participants in the USA group interacted with the VHs in English (a native language for the USA setting); and two different groups in Taiwan interacted with the VHs in either a foreign (English) or native (Mandarin) language, respectively. Results reveal that in the (TMA, TEN) and (UEN, TEN) conditions, native versus non-native language communication can alter social behaviors (e.g., interaction time) of participants towards virtual interlocutors. Our results also revealed strong effects of cultural differences on participants’ non-verbal social behaviors with VHs in the UEN and TMA conditions. Chang-Chun Wang, Matias Volonte, Elham Ebrahimi, Kuan-Yu Liu, Sai-Keung Wong, Sabarish V. Babu |
VR | 1 |
| 2021 | Effects of Language Familiarity in Simulated Natural Dialogue with a Virtual Crowd of Digital Humans on Emotion Contagion in Virtual RealityabstractThis investigation compared the emotional impact caused by a crowd of affective virtual humans (VHs) that communicated in the users' native or foreign language. We evaluated the users' affective reactions to a crowd of VHs that exhibited distinct emotional expressions. A total of four emotions were presented, which were Positive, Negative, Neutral, and Mixed. The VHs performed verbal and non-behaviors accordingly. Under the Mixed condition, the VHs were divided into three groups equally and each group was uniquely assigned one of the three emotions (i.e., positive, negative, and neutral). Users collected ten items from a virtual reality market. To complete the tasks, they interacted using natural speech with the emotional VHs. Three language conditions were investigated: one condition in USA and another two conditions in Taiwan. The group of participants in USA interacted with the VHs in English; and the two groups of participants in Taiwan interacted with the VHs using a foreign (English) language and a native (Mandarin) language respectively. We discovered that the medium of communication or language familiarity had a strong influence on participants' emotional reactions. When participants interacted in a foreign language with VHs with a positive emotional disposition, we found their positive emotional reactions were subdued and negative reactions were elevated. However, this was not the case when participants interacted with VHs in their native language, as their emotional reactions were contingent on the emotional disposition of the VHs. Matias Volonte, Chang-Chun Wang, Elham Ebrahimi, Yu-Chun Hsu, Kuan-Yu Liu, Sai-Keung Wong, Sabarish V. Babu |
VR | 2 |