Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jooyun Han
dblp:126/9660
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 1 first-authorArtificial intelligence and machine learning · 2Applied, interdisciplinary, general and emerging computing · 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
2 papers |
Human-robot interaction · 87% Usability and user experience research · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › affective interaction
empathy in human-robot interaction |
0.2 | 1 | 2013 | Empathy between human and robot? · HRI 2013 |
Human-robot interaction › nonverbal communication
robot gesture |
0.2 | 1 | 2013 | The effects of familiarity and robot gesture on user acceptance of information · HRI 2013 |
Usability and user experience research
technology acceptance |
0.0 | 1 | 2013 | The effects of familiarity and robot gesture on user acceptance of information · HRI 2013 |
Methods — techniques the papers use, named apart from their topics
experiment · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Empathy between human and robot?
Doori Jo, Jooyun Han, Kyungmi Chung, Sukhan Lee 0001 |
HRI | 2 |
| 2013 | The effects of familiarity and robot gesture on user acceptance of information
Aelee Kim, Younbo Jung, Kwan Min Lee, Jooyun Han |
HRI | 4 |
| 2013 | Analysis of Human Attention toward Context-Related Images for Understanding Visual Information ProcessingabstractThe purpose of this study is to understand human's visual information processing mechanisms. We emphasize the importance of "context" for attracting visual attention as an effect of top-down process. We created indices for saliency index by degree of saliency using Itti's saliency model and for context index by object importance from pre-experimental survey for analyzing the present data. In terms of bottom-up process, the effect of bottom-up saliency was insignificant and correlation between the saliency map and human fixation map was rather low. We compared the context index within both salient and non-salient area that top-down processing is a strong attentional guidance. If the contextually meaningful objects and visually salient area was overlapped, it robustly attracts attentions. Furthermore, the counts of fixations on objects and the order in sequence of eye-movement showed that the subjects tended to fixate more often and more frequently toward more importantly considered objects. Jooyun Han, Yu-Bu Lee, Sukhan Lee 0001 |
SMC | 1 |