EDBT 2026 Demo / reviewers in the wild / expert
Yingxi Chen
dblp:56/1066
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 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 · 50% Wearable and physiological sensing · 50% | |
| Artificial intelligence
1 paper |
Motion planning and robot control · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › eye tracking
electrooculography |
0.0 | 1 | 2004 | A Human-robot Interface Based on Electrooculography · ICRA 2004 |
Human-robot interaction
human-robot interface |
0.0 | 1 | 2004 | A Human-robot Interface Based on Electrooculography · ICRA 2004 |
Robotics › Motion planning and robot control › robot control
motion control |
0.0 | 1 | 2004 | A Human-robot Interface Based on Electrooculography · ICRA 2004 |
Robotics › Motion planning and robot control
robot control |
0.0 | 1 | 2004 | A Human-robot Interface Based on Electrooculography · ICRA 2004 |
Methods — techniques the papers use, named apart from their topics
pattern recognition · 0.1electrooculography · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | AOMD: An analogy-aware approach to offensive meme detection on social media
Lanyu Shang, Yang Zhang 0031, Yuheng Zha, Yingxi Chen, Christina Youn, Dong Wang 0002 |
Inf. Process. Manag. | 4 |
| 2004 | A Human-robot Interface Based on ElectrooculographyabstractDesign and implementation of an electrooculography based gaze-controlled robotic system is presented. The robot system consists of signal acquisition, pattern recognition, control strategy and robot motion modules. The user's eye gaze movements are reconstructed from electrooculogram (EOG) signals, which are recorded from the face in real time. The eye movement patterns, e.g., saccades, fixation and blinks are detected from the raw eye gaze movement data by a pattern recognition module. The control strategy module interprets the user's intention from the eye movement patterns based on predefined protocols. A horizontally mounted robot, emulating the skeletomuscular configuration of the human arm, is controlled by the robot motion control module to execute the interpreted user intention. The performance results of two control strategies are discussed. Yingxi Chen, Wyatt S. Newman |
ICRA | 1 |