Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ryuki Matsuoka
dblp:404/5912
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1 · 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 · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
social robot |
0.9 | 1 | 2025 | Semantic Babbling: Interactive Baby Robot System Using Large Language Models · HRI 2025 |
Human-robot interaction › assistive robotics
therapeutic robot |
0.3 | 1 | 2025 | Semantic Babbling: Interactive Baby Robot System Using Large Language Models · HRI 2025 |
Methods — techniques the papers use, named apart from their topics
sentiment analysis · 0.9large language model · 0.9crowd-sourcing survey · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Voice Mitigates Expectation Deficiency: A Modality Comparison with Theory-Aligned Subjectivity Metrics in Co-Creative Image Generation
Ryuki Matsuoka, Michita Imai |
ICAART (5) | 1 |
| 2025 | Semantic Babbling: Interactive Baby Robot System Using Large Language ModelsabstractTherapeutic robots are used in facilities for older people, but many of them can only provide mechanical responses to users. To improve the quality of interactions, it is crucial to address emotional consistency. Thus, we propose Semantic Babbling, a system that enables interaction with users by utilizing the baby robot's “inner voice”. By generating inner voices and selecting babbling based on sentiment, Semantic Babbling aims to mimic infant-like responses. A crowd-sourcing survey demonstrated the significance of emotional consistency and the display of inner voice in Semantic Babbling, revealing that it improves the impression of baby robots. Serina Miyake, Ryuki Matsuoka, Kohei Okuoka, Takuto Akiyoshi, Hidenobu Sumioka, Masahiro Shiomi, Michita Imai |
HRI | 2 |