Demo Data from EDBT 2026 / reviewers in the wild / expert
Huachao Cui
dblp:343/1956
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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.
| Theoretical computer science
1 paper |
Mathematical optimization · 100% | |
| Artificial intelligence
1 paper |
Reinforcement learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning
reinforcement learning for combinatorial optimization |
0.8 | 1 | 2024 | ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem · IJCAI 2024 |
Mathematical optimization
combinatorial optimization |
0.8 | 1 | 2024 | ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem · IJCAI 2024 |
Mathematical optimization › metaheuristic optimization
neighborhood search |
0.2 | 1 | 2024 | ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem · IJCAI 2024 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 1.5neighborhood search · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | ReinforceNS: Reinforcement Learning-based Multi-start Neighborhood Search for Solving the Traveling Thief Problem
Huachao Cui, Yuesong Wang 0001 |
IJCAI | 2 |
| 2023 | Parallel Emergency Management of Incidents by Integrating OODA and PREA Loops: The C2 Mechanism and ModesabstractGiven the differences of command and control (C2) activities between the field command center and the emergency operations center (EOC), this article combined the edge C2 theory with the parallel C2 theory, and proposed a parallel incident C2 mode based on the observe–orient–decide–act (OODA) loop and planning–readiness–execution–assessment (PREA) loop. The aim is to build up a PREA loop-based parallel incident C2 mode and its related operating mechanism of edge empowerment and energy release in parallel incident C2 mode. The parallel incident C2 mode based on the PREA loop and OODA loop supports the co-existence and connection of the two roles of the incident C2 agent at the emergency scene. The two roles are the executive role of emergency response and operation and the command and organization role of the edge emergency system. This article initiates a deep integration of two different C2 process mechanisms in the emergency response and operation process, taking into account the local emergency scene and the global emergency system. Taken together, a key issue has been well addressed regarding the contradiction that the traditional emergency response cannot be reconciled in terms of rapidity and thoroughness. Fenghua Zhu, Huachao Cui, Jirong Qin |
IEEE Trans. Syst. Man Cybern. Syst. | 4 |