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Sure Bak

dblp:374/9404 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2026
0009-0001-0155-6460ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Applied, interdisciplinary, general and emerging 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 · 81% Collaborative and social computing · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
robot acceptance
1.012026
"Too Crowded for a Robot?": Modeling Human Acceptance Criteria for Elevator-Riding Robots · CHI 2026
Human-robot interaction › long-term interaction
human-robot coexistence
0.312026
"Too Crowded for a Robot?": Modeling Human Acceptance Criteria for Elevator-Riding Robots · CHI 2026
Collaborative and social computing
shared spaces
0.312026
"Too Crowded for a Robot?": Modeling Human Acceptance Criteria for Elevator-Riding Robots · CHI 2026

Methods — techniques the papers use, named apart from their topics

acceptance modeling · 1.0
YearPublicationVenuePosition
2026 "Too Crowded for a Robot?": Modeling Human Acceptance Criteria for Elevator-Riding Robots
Seoktae Kim, Sangyoung Cho, Kahyeon Kim, Sure Bak
CHI4
2024 Investigating Privacy in the Context of Office Delivery Robots
abstract
Robots powered by Artificial Intelligence require continuous sensing to function and interact autonomously with their environment. This may create concerns for their users. Here, we describe a study in the context of an office environment equipped with autonomous delivery robots. Our study aims to understand the employees’ current comprehension of the data captured by the robots, the nature of their concerns and ways to mitigate those. We found that the employees have little knowledge about data capture and that providing such knowledge can go either way, reassuring or generating new concerns which are often contextual. We qualitatively analysed reasons for having or not having concerns. Our findings include that trust in the employer is an important factor limiting them.
Antonietta Grasso, Jutta Willamowski, Jisun Park 0005, Sure Bak
RO-MAN4