Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sames Al Moubayed
dblp:97/7736
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 |
Haptics and multimodal interaction · 50% Human-AI interaction · 50% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-AI interaction
conversational agents |
0.1 | 1 | 2009 | Generating Robot/Agent backchannels during a storytelling experiment · ICRA 2009 |
Haptics and multimodal interaction
multimodal interaction |
0.1 | 1 | 2009 | Generating Robot/Agent backchannels during a storytelling experiment · ICRA 2009 |
Multimedia analysis and retrieval › multimedia dataset construction › multimodal dataset
multimodal corpus |
0.0 | 1 | 2009 | Generating Robot/Agent backchannels during a storytelling experiment · ICRA 2009 |
Methods — techniques the papers use, named apart from their topics
behavior modeling · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Generating Robot/Agent backchannels during a storytelling experimentabstractThis work presents the development of a real-time framework for the research of multimodal feedback of robots/talking agents in the context of Human Robot Interaction (HRI) and Human Computer Interaction (HCI). For evaluating the framework, a Multimodal corpus is built (ENTERFACE_STEAD), and a study on the important multimodal features was done for building an active Robot/Agent listener of a storytelling experience with Humans. The experiments show that even when building the same reactive behavior models for Robot and Talking Agents, the interpretation and the realization of the behavior communicated is different due to the different communicative channels Robots/Agents offer be it physical but less human-like in Robots, and virtual but more expressive and human-like in Talking agents. Sames Al Moubayed, Malek Baklouti, Mohamed Chetouani, Thierry Dutoit, Ammar Mahdhaoui, Jean-Claude Martin, Stanislav Ondás, Catherine Pelachaud, Jérôme Urbain |
ICRA | 1 |