Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sean Dallas
dblp:401/9184
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 1 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 |
Human-robot 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 |
|---|---|---|---|---|
Human-robot interaction
human-robot collaboration |
0.9 | 1 | 2025 | Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface · ICRA 2025 |
Collaborative and social computing
team training |
0.3 | 1 | 2025 | Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator Interface · ICRA 2025 |
Methods — techniques the papers use, named apart from their topics
between-subjects experiment · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Training Human-Robot Teams by Improving Transparency Through a Virtual Spectator InterfaceabstractAfter-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies comparing different formats of AARs have focused mainly on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, and designed a 1$\times 3$between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects' SA over the other conditions. Sean Dallas, Hongjiao Qiang, Motaz AbuHijleh, Wonse Jo, Kayla Riegner, Jonathon M. Smereka, Lionel P. Robert Jr., Wing-Yue Geoffrey Louie, Dawn M. Tilbury |
ICRA | 1 |