Demo Data from EDBT 2026 / reviewers in the wild / expert
Ruida Tang
dblp:384/2964
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0005-9452-9355ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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
1 paper |
Immersive interaction · 77% Collaborative and social computing · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Immersive interaction › virtual reality
virtual reality user behavior |
1.0 | 1 | 2026 | Monkey See, Monkey Break? Study of Rule-Breaking Imitation in Virtual Crowds · IEEE Trans. Vis. Comput. Graph. 2026 |
Collaborative and social computing › online communities
social norms |
0.3 | 1 | 2026 | Monkey See, Monkey Break? Study of Rule-Breaking Imitation in Virtual Crowds · IEEE Trans. Vis. Comput. Graph. 2026 |
Methods — techniques the papers use, named apart from their topics
gaze tracking · 1.0VR study · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Monkey See, Monkey Break? Study of Rule-Breaking Imitation in Virtual CrowdsabstractRule-breaking behaviors, such as jaywalking or skipping queues, are common in crowds but difficult to study in real-world settings due to limited control and observability. Virtual reality (VR) provides a controlled alternative, but its validity depends on whether VR elicits realistic rule-breaking behavior. We conducted a VR study with 65 participants navigating a virtual city with four scenarios differing in social norms: walking on grass, crossing outside a crosswalk, jaywalking at a red light, and skipping a line. In each scenario, the proportion of rule-breaking virtual characters was manipulated (0%, 25%, 50%). Participants' movements and gaze were recorded to assess behavior and attention. Results showed higher rule-breaking as the number of violators increased, except in the low-stakes crossing scenario. Rule-breakers attended more to violating characters, whereas rule-followers focused on compliant ones. Participants cited efficiency, safety, and social norms as key factors guiding their decisions. Overall, VR reproduced natural patterns of social compliance and noncompliance, supporting its use for studying rule-breaking and applications in crowd simulation, urban design, safety training, and immersive media. Kexiang Huang, Tairan Yin, Jose Luis Ponton, Ruida Tang, Reiya Itatani, Oscar Argudo, Nuria Pelechano |
IEEE Trans. Vis. Comput. Graph. | 4 |
| 2024 | Choreographing multi-degree of freedom behaviors in large-scale crowd simulations
Kexiang Huang, Ruida Tang, Nuria Pelechano |
Comput. Graph. | 4 |