Demo Data from EDBT 2026 / reviewers in the wild / expert
Minyeop Choi
dblp:292/5588
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 first-author · 1 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.
| Network and information security
1 paper |
Network security · 50% Security and privacy of machine learning · 50% | |
| Human-computer interaction and pervasive computing
1 paper |
Games and playful interaction · 77% Wearable and physiological sensing · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Security and privacy of machine learning
adversarial robustness |
0.7 | 1 | 2023 | BotScreen: Trust Everybody, but Cut the Aimbots Yourself · USENIX Security Symposium 2023 |
Network security › intrusion detection and prevention
bot detection |
0.7 | 1 | 2023 | BotScreen: Trust Everybody, but Cut the Aimbots Yourself · USENIX Security Symposium 2023 |
Games and playful interaction › multiplayer games
esports |
0.5 | 1 | 2021 | Secrets of Gosu: Understanding Physical Combat Skills of Professional Players in First-Person Shooters · CHI 2021 |
Wearable and physiological sensing
multimodal sensing |
0.1 | 1 | 2021 | Secrets of Gosu: Understanding Physical Combat Skills of Professional Players in First-Person Shooters · CHI 2021 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.7statistical hypothesis testing · 0.5data collection study · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | BotScreen: Trust Everybody, but Cut the Aimbots Yourself
Minyeop Choi, Gihyuk Ko, Sang Kil Cha |
USENIX Security Symposium | 1 |
| 2021 | Secrets of Gosu: Understanding Physical Combat Skills of Professional Players in First-Person ShootersabstractIn first-person shooters (FPS), professional players (a.k.a., Gosu) outperform amateur players. The secrets behind the performance of professional FPS players have been debated in online communities with many conjectures; however, attempts of scientific verification have been limited. We addressed this conundrum through a data-collection study of the gameplay of eight professional and eight amateur players in the commercial FPS Counter-Strike: Global Offensive. The collected data cover behavioral data from six sensors (motion capture, eye tracker, mouse, keyboard, electromyography armband, and pulse sensor) and in-game data (player data and event logs). We examined conjectures in four categories: aiming, character movement, physicality, and device and settings. Only 6 out of 13 conjectures were supported with statistically sufficient evidence. Eunji Park, Sangyoon Lee 0002, Auejin Ham, Minyeop Choi, Sunjun Kim, Byungjoo Lee |
CHI | 4 |