Demo Data from EDBT 2026 / reviewers in the wild / expert
JongHwan Oh
dblp:14/11457
· 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 |
Design research and methods · 33% Human-robot interaction · 33% Human-AI interaction · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › robot communication
conversational robot |
0.4 | 1 | 2019 | Apprentice of Oz: Human in the Loop System for Conversational Robot Wizard of Oz · HRI 2019 |
Human-AI interaction
human-in-the-loop |
0.4 | 1 | 2019 | Apprentice of Oz: Human in the Loop System for Conversational Robot Wizard of Oz · HRI 2019 |
Design research and methods › research methodology
wizard of oz prototyping |
0.4 | 1 | 2019 | Apprentice of Oz: Human in the Loop System for Conversational Robot Wizard of Oz · HRI 2019 |
Methods — techniques the papers use, named apart from their topics
wizard-of-oz · 0.4preliminary study · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Apprentice of Oz: Human in the Loop System for Conversational Robot Wizard of OzabstractConversational robots that exhibit human-level abilities in physical and verbal conversation are widely used in human-robot interaction studies, along with the Wizard of Oz protocol. However, even with the protocol, manipulating the robot to move and talk is cognitively demanding. A preliminary study with a humanoid was conducted to observe difficulties wizards experienced in each of four subtasks: attention, decision, execution, and reflection. Apprentice of Oz is a human-in-the-loop Wizard of Oz system designed to reduce the wizard's cognitive load in each subtask. Each task is co-performed by the wizard and the system. This paper describes the system design from the view of each subtask. Ahnjae Shin, JongHwan Oh, Joonhwan Lee |
HRI | 2 |