Author

Qiang He

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2026

Cross-Patch Guided Reconstruction With Graph Contrastive Learning for Smartphone-Based Early Parkinson’s Disease Detection

Self-supervised learning for time-series data has broad application potential in smartphone-based early disease detection. However, time-series data often exhibit complex dynamic patterns and spatiotemporal correlations. These characteristics make it difficult to capture discriminative features and reconstruct local fe...

Tongyue He, Qiang He, Jun Mou et al. · IEEE Transactions on Instrum... · 0 citations

2026

Toward Trustworthy Coordination in Intelligent Edge Agent Networking: A Trust-Driven Transaction Propagation Mechanism

Intelligent edge networks increasingly use blockchain to support trustworthy collaboration among distributed autonomous edge agents. However, dynamic and heterogeneous edge environments expose intelligent edge agents to spam transaction attacks, where adversaries exploit repeated per-hop verification to exhaust limited...

Xi-Jia Lu, Xing-Wei Wang, Qiang He et al. · IEEE Transactions on Network... · 0 citations

#machine learning Preprint Sep 2026

Robust Graph Clustering Network for Multiple Missing Data

Clustering on graphs where both node attributes and structural links are partially missing remains a challenging task. Existing methods typically rely on imputation-then-clustering on single-view missingness incomplete graphs, which are vulnerable to cross-view error propagation and cluster-boundary blurring under simu...

Ke-Yuan Qiu, Ren-Da Han, Zhen Tang et al. · 0 citations

#machine learning Preprint Sep 2026

FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices

The Federated Parallel Scaling (FPS) algorithm is proposed, which jointly trains multiple sampled subnetworks in parallel with self-distillation so that larger sampled subnetworks can supervise smaller ones during local updates.

Jia-Xin Zhang, Xing-Wei Wang, Bo Yi et al. · 0 citations

Review Jul 2026

History, Development, and Principles of Representation Learning—An Introductory Survey

This survey deeply explains the basic principles of representation learning, and introduces its practical application cases in various fields, and points out the main limitations of current models and prospects the future research directions.

Zhiyong Wang, Qiang He, Jun Mou et al. · Expert systems · 0 citations

#edge computing Sep 2026

Energy-Efficient Task Allocation for Green Aerial Edge Computing Based on Metaverse Users: A Mean Field Game Approach

We consider the energy-constrained task allocation problem in large-scale Aerial Edge Computing (AEC) systems, which encompasses a series of tightly coupled decision-making processes, including which tasks need to be processed by uncrewed aerial vehicles (UAVs), how to allocate these tasks and balance energy across UAV...

Lianbo Ma, Ding-Xuan Chen, Yuee Zhou et al. · IEEE Transactions on Mobile... · 0 citations

#edge computing Sep 2026

Truthful Online Double Auction-Based Resource Allocation Mechanisms for Partial Computation Offloading in Collaborative Edge Computing

As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service provi...

Dongkuo Wu, Xing-Wei Wang, Xue-Yi Wang et al. · IEEE Transactions on Mobile... · 0 citations