Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sangheng Xu
dblp:11/6910
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1995
—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 |
Human-robot interaction · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
human-robot coordination |
0.0 | 1 | 1995 | Towards Human-Robot Coordination: Skill Modeling and Transferring via Hidden Markov Model · ICRA 1995 |
Methods — techniques the papers use, named apart from their topics
hidden markov model · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Towards Human-Robot Coordination: Skill Modeling and Transferring via Hidden Markov ModelabstractAutomatic modeling and transferring human skill to a robot is an important step towards creating an intelligent robot in a cooperative environment where humans and robots can complement and enhance each other's performance in a reciprocal manner. We first classify two distinct categories of skills, i.e., action skill and reaction skill. Then we address how the hidden Markov model can be used for modeling these two types of human skill. We focus on the reaction learning scheme and discuss the issues and problems associated with the hidden Markov model approach. Sangheng Xu, Jie Yang 0001 |
ICRA | 1 |