Teng Zhou

Enhancing Railway Network Security and Privacy With Blockchain and Attribute-Based Encryption

Railway systems are gradually adopting Internet of Things (IoT) and artificial intelligence (AI)-driven technologies to improve operational efficiency, implement preventive maintenance, and enable real-time monitoring. However, this technological shift has raised significant challenges for security, privacy, and data i...

CausalSTKT: Disentangled Spatiotemporal Heterogeneous Graph Learning for Robust Knowledge Tracing Under Distribution Shifts

CausalSTKT is proposed, an SCM-guided knowledge tracing framework that integrates spatiotemporal modeling over a global item–knowledge-component bipartite graph with disentangled representation learning and derives an out-of-distribution risk bound showing that a smaller representation-entanglement residual leads to a...

Causal Benefit-Aware Recommendation for Personalized Learning-Path Features: A Targeting-Policy Framework with Provable Guarantees and Randomized Evaluation

This work formalizes feature recommendation as a causal targeting-policy problem: rank students by the estimated conditional average treatment effect (CATE) of a feature and recommend to the top of the ranking, proving causal top-CATE targeting maximizes policy value at any budget and weakly dominates predictive (outco...

Cost-effective and scalable traffic forecasting: A graph-free multi-view MLP architecture.

The Spatio-Temporal Multi-view MLP Network (STMMN), a highly scalable, graph-free computational framework that introduces a temporal multi-view aggregation module that seamlessly integrates distinct temporal branches, to dynamically fuse multi-scale temporal dependencies without relying on exhaustive attention maps.

Conditional Flow Matching-based Semi-supervised Segmentation for Quality Assessment of Fundus Images.

This framework provides an annotation-efficient solution for objective fundus image quality assessment, demonstrating a high concordance with expert measurements and outperforms other compared semi-supervised methods.

VulGNN: A high-fidelity graph neural network framework for robust smart contract vulnerability detection