Demo Data from SIGIR 2026 / reviewers in the wild / expert
Daryn Kalym
dblp:237/8500
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1
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 · 44% Learning and educational technologies · 44% Human-AI interaction · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies
language learning |
0.4 | 1 | 2019 | Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots · HRI 2019 |
Human-robot interaction
social robot |
0.4 | 1 | 2019 | Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots · HRI 2019 |
Human-AI interaction
speech synthesis |
0.1 | 1 | 2019 | Similarity Attraction for Robot's Dialect in Language Learning Using Social Robots · HRI 2019 |
Methods — techniques the papers use, named apart from their topics
user study · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Similarity Attraction for Robot's Dialect in Language Learning Using Social RobotsabstractThe gradual transition towards the Kazakh language in the Republic of Kazakhstan raises the emergence of applying new technologies for learning the language. Considering the fact that the Kazakh language has dialectal forms, it is important to investigate how these language features would affect the interaction with the synthesized speech of a robot or a computer program. This paper presents a preliminary study exploring the effect of dialectal language on the human-robot interaction in an education-oriented environment. Participants were involved in the interaction with two different robots with pre-programmed language dialectal patterns - South and non-South, to learn new vocabulary. Findings show that there is a low significance in correlation, however, it is suggested that a small sample size led to the obtained results. Askarbek Pazylbekov, Daryn Kalym, Anuar Otynshin, Anara Sandygulova |
HRI | 2 |