31 citations · 71 across the 26 of their papers we have counts for
27 papers
Counterfactual Benchmarking and Training for Factuality Consistency and Order-Robust Grounded Reasoning in LLMs over Heterogeneous Knowledge
Shibo Chu, Yuze Liu, Tiehua Zhang +4
Large language models (LLMs) have increasingly supported response generation grounded in user-provided knowledge spanning heterogeneous structures. However, existing benchmarks pro…
HyperAgent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation
Jinze Wang, Yuze Liu, Tiehua Zhang +2
Next Point-of-Interest (POI) recommendation benefits from textual and visual content that describes venue semantics, yet such content is often incomplete in real-world services. Mi…
Route What Remains: A Meta-Modal Agent for Missing-Modality Candidate Reranking in Recommender Systems
Jinze Wang, Yangchen Zeng, Tiehua Zhang +5
Missing-modality recommenders usually reconstruct absent representations, although the observed evidence may not determine the missing content. We formulate candidate reranking as…
ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies
Yuze Liu, Shibo Chu, Tiehua Zhang +5
Edge-cloud synergies provide a promising paradigm for privacy-preserving deployment of foundation models, where lightweight on-device models adapt to domain-specific data and cloud…
A Structure-Agnostic Co-Tuning Framework for LLMs and SLMs in Cloud-Edge Systems
Yuze Liu, Yunhan Wang, Tiehua Zhang +5
The surge in intelligent applications driven by large language models (LLMs) has made it increasingly difficult for bandwidth-limited cloud servers to process extensive LLM workloa…
Do We Really Need SFT? Prompt-as-Policy over Knowledge Graphs for Cold-start Next POI Recommendation
Jinze Wang, Lu Zhang, Yiyang Cui +5
Next point-of-interest (POI) recommendation is a key component of smart urban services, yet it remains challenging under cold-start conditions with sparse user-POI interactions. Re…
Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment
Yuze Liu, Tiehua Zhang, Zhishu Shen +3
Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI)…
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Zhuocheng Liu, Zhishu Shen, Qiushi Zheng +3
Low Earth Orbit (LEO) satellites are emerging as key components of 6G networks, with many already deployed to support large-scale Earth observation and sensing related tasks. Feder…
Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs
Dawen Jiang, Zhishu Shen, Qiushi Zheng +3
Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital tr…
MetaSTH-Sleep: Towards Effective Few-Shot Sleep Stage Classification for Health Management with Spatial-Temporal Hypergraph Enhanced Meta-Learning
Jingyu Li, Tiehua Zhang, Jinze Wang +6
Accurate classification of sleep stages based on bio-signals is fundamental not only for automatic sleep stage annotation, but also for clinical health management and continuous sl…
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
Jinze Wang, Tiehua Zhang, Lu Zhang +3
Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising result…
Grey-Box Fuzzing in Constrained Ultra-Large Systems: Lessons for SE Community
Jiazhao Yu, Yanlun Tu, Zhanlei Zhang +5
Testing ultra-large microservices-based FinTech systems presents significant challenges, including restricted access to production environments, complex dependencies, and stringent…
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning
Yuze Liu, Tingjie Liu, Tiehua Zhang +5
Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing (NLP) tasks due to their extensive general knowledge of the world.…
Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks
Ziqi Rong, Qiushi Zheng, Zhishu Shen +4
With the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent information services are being increasingly integrated across various secto…
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning
Jinze Wang, Jiong Jin, Tiehua Zhang +3
The accurate diagnosis of machine breakdowns is crucial for maintaining operational safety in smart manufacturing. Despite the promise shown by deep learning in automating fault id…
Multimodal Fusion via Hypergraph Autoencoder and Contrastive Learning for Emotion Recognition in Conversation
Zijian Yi, Ziming Zhao, Zhishu Shen +1
Multimodal emotion recognition in conversation (MERC) seeks to identify the speakers' emotions expressed in each utterance, offering significant potential across diverse fields. Th…
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks
Youhua Xia, Tiehua Zhang, Jiong Jin +2
Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly ce…
CHASE: A Causal Hypergraph based Framework for Root Cause Analysis in Multimodal Microservice Systems
Ziming Zhao, Zhenwei Wang, Tiehua Zhang +7
In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and r…
Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs
Kang Wang, Zhishu Shen, Zhen Lei +1
Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into st…
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Yuze Liu, Ziming Zhao, Tiehua Zhang +5
Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data…
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Tiehua Zhang, Yuze Liu, Zhishu Shen +4
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learni…
DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities
Tiehua Zhang, Rui Xu, Jianping Zhang +4
Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded b…
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Tiehua Zhang, Yuze Liu, Yao Yao +4
Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and…
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
Tiehua Zhang, Yuze Liu, Zhishu Shen +4
Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medi…