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20242026
most citedCo-clustering for Federated Recommender System

27 citations · 32 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.SE2024

Practitioners' Expectations on Log Anomaly Detection

Xiaoxue Ma, Yishu Li, Jacky Keung +5

Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over th…

cs.IR2024★ 27 cited

Co-clustering for Federated Recommender System

Xinrui He, Shuo Liu, Jackey Keung +1

As data privacy and security attract increasing attention, Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendation…

cs.SE2024★ 3 cited

Exploring and Unleashing the Power of Large Language Models in Automated Code Translation

Zhen Yang, Fang Liu, Zhongxing Yu +7

Code translation tools (transpilers) are developed for automatic source-to-source translation. Although learning-based transpilers have shown impressive enhancement against rule-ba…

cs.SE2024

On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations

Xiaoxue Ma, Huiqi Zou, Pinjia He +4

Numerous Deep Learning (DL)-based approaches have gained attention in software Log Anomaly Detection (LAD), yet class imbalance in training data remains a challenge, with anomalies…

cs.SE2024★ 2 cited

Delving into Parameter-Efficient Fine-Tuning in Code Change Learning: An Empirical Study

Shuo Liu, Jacky Keung, Zhen Yang +3

Compared to Full-Model Fine-Tuning (FMFT), Parameter Efficient Fine-Tuning (PEFT) has demonstrated superior performance and lower computational overhead in several code understandi…