Demo Data from SIGIR 2026 / reviewers in the wild / expert
Joe P. Mazer
dblp:265/2264
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1
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
1 paper |
Virtual and augmented reality · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › virtual humans
virtual human crowd |
0.4 | 1 | 2020 | Effects of Interacting with a Crowd of Emotional Virtual Humans on Users' Affective and Non-Verbal Behaviors · VR 2020 |
Human-robot interaction
affective interaction |
0.1 | 1 | 2020 | Effects of Interacting with a Crowd of Emotional Virtual Humans on Users' Affective and Non-Verbal Behaviors · VR 2020 |
Methods — techniques the papers use, named apart from their topics
electrodermal activity measurement · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Effects of Interacting with a Crowd of Emotional Virtual Humans on Users' Affective and Non-Verbal BehaviorsabstractIn this contribution we examined the effects on users during interaction with a virtual human crowd in an immersive virtual reality environment. We developed an agent-based crowd model with rich properties including eye gaze, facial expression, body motion, and verbal and non-verbal behaviors. The scenario was a virtual market in which the users needed to gather specific items. In a betweensubjects design, users interacted with a virtual human crowd that showed opposite valenced emotional expressions. There are four conditions in the between-subjects design. These includes different affective virtual crowds namely, Positive, Negative, Neutral, and a Mix condition. The Mix group is defined by a combination of Positive, Negative and Neutral emotional expressive characters. Depending on the specific condition, the virtual humans showed specific verbal and non-verbal behaviors. During the experiment we collected objective measures such as skin electrodermal activity, total time in the simulation, the number of interactions with the agents and performance measures. The subjective measures were the differential emotional survey (DES), and a user experience survey. We reported our findings with an in-depth analysis. Matias Volonte, Yu-Chun Hsu, Kuan-Yu Liu, Joe P. Mazer, Sai-Keung Wong, Sabarish V. Babu |
VR | 4 |