9 citations · 9 across the 6 of their papers we have counts for
7 papers
RP-CATE: Recurrent Perceptron-based Channel Attention Transformer Encoder for Industrial Hybrid Modeling
Haoran Yang, Yinan Zhang, Wenjie Zhang +5
Nowadays, industrial hybrid modeling which integrates both mechanistic modeling and machine learning-based modeling techniques has attracted increasing interest from scholars due t…
When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTI
Yanhui Li, Qi Zhou, Zhihong Xu +3
Large vision-language models (LVLMs) are increasingly used for tasks where detecting multimodal harmful content is crucial, such as online content moderation. However, real-world h…
CrossPL: Evaluating Large Language Models on Cross Programming Language Code Generation
Zhanhang Xiong, Dongxia Wang, Yuekang Li +2
As large language models (LLMs) become increasingly embedded in software engineering workflows, a critical capability remains underexplored: generating correct code that enables cr…
Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models
Kexin Chen, Dongxia Wang, Yi Liu +2
Despite the widespread use of Transformer-based text embedding models in NLP tasks, surprising 'sticky tokens' can undermine the reliability of embeddings. These tokens, when repea…
LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Yanhui Li, Dongxia Wang, Zhu Sun +2
Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-…
Enhancing New-item Fairness in Dynamic Recommender Systems
Huizhong Guo, Zhu Sun, Dongxia Wang +3
New-items play a crucial role in recommender systems (RSs) for delivering fresh and engaging user experiences. However, traditional methods struggle to effectively recommend new-it…