54 citations · 61 across the 5 of their papers we have counts for
7 papers
The Operable Pareto Front: Distilling Offline Search into Run-Time Control for Multi-Objective UAV Edge-Computing Scheduling
Qiao Liao, Zhiyong Feng, Bin Wu +1
A UAV mobile edge computing (MEC) fleet trades energy against delay, and its schedules form a Pareto front; we call a scheduler operable when the fleet can be asked for any point o…
When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation
Guodong Fan, Cuiyun Gao, Chun Yong Chong +4
The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation appro…
RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment
Hongyue Wu, Hangyu Li, Guodong Fan +3
Federated learning (FL) enables collaborative model training without sharing raw data in edge environments, but is constrained by limited communication bandwidth and heterogeneous…
A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
Cuiyun Gao, Guodong Fan, Chun Yong Chong +5
Model hallucination is one of the most critical challenges faced by Large Language Models (LLMs), especially in high-stakes code intelligence tasks. As LLMs become increasingly int…
Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
Hangyu Li, Hongyue Wu, Guodong Fan +3
As privacy protection gains increasing importance, more models are being trained on edge devices and subsequently merged into the central server through Federated Learning (FL). Ho…
Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering
Ruiqi Wang, Jiyu Guo, Cuiyun Gao +3
Recently, large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation, significantly advancing the automation of SE tasks…
FairSort: Learning to Fair Rank for Personalized Recommendations in Two-Sided Platforms
Guoli Wu, Zhiyong Feng, Shizhan Chen +6
Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favourite items. This user-centric approach may lead to unfair exposure distribution am…