Yinyu Chen | Searcharxiv

arXiv2026

A Bi-Layer TSN Formulation for Separable Scheduling of Mobile Emergency Resources

Separable scheduling unleashes the deployment flexibility of mobile emergency resources by dispatching carriers and functional modules separately yet in a coordinated manner, offering a promising avenue to enhance power system resilience. However, this flexibility induces a distinct carrier-supported module routing structure, where non-self-mobile modules must be routed through compatible carrier movements. The resulting carrier-module spatio-temporal coupling makes exact and tractable optimization challenging. This letter identifies this structure and develops a novel exact bi-layer time-space network formulation as a mixed-integer linear program. The proposed formulation represents carrier and module trajectories as interacting network flows and enforces their support relations through explicit arc-level coupling. Compared with the prior logic-based model, the proposed formulation preserves exactness while improving modeling flexibility by eliminating mandatory post-arrival dwelling. Numerical studies validate its correctness and demonstrate substantial computational advantages.

eess.SY↗

arXiv2025

Uncertainty-Driven Hierarchical Sampling for Unbalanced Continual Malware Detection with Time-Series Update-Based Retrieval

Android malware detection continues to face persistent challenges stemming from long-term concept drift and class imbalance, as evolving malicious behaviors and shifting usage patterns dynamically reshape feature distributions. Although continual learning (CL) mitigates drift, existing replay-based methods suffer from inherent bias. Specifically, their reliance on classifier uncertainty for sample selection disproportionately prioritizes the dominant benign class, causing overfitting and reduced generalization to evolving malware. To address these limitations, we propose a novel uncertainty-guided CL framework. First, we introduce a hierarchical balanced sampler that employs a dual-phase uncertainty strategy to dynamically balance benign and malicious samples while simultaneously selecting high-information, high-uncertainty instances within each class. This mechanism ensures class equilibrium across both replay and incremental data, thereby enhancing adaptability to emerging threats. Second, we augment the framework with a vector retrieval mechanism that exploits historical malware embeddings to identify evolved variants via similarity-based retrieval, thereby complementing classifier updates. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art methods under strict low-label conditions (50 labels per phase). It achieves a true positive rate (TPR) of 92.95\% and a mean accuracy (mACC) of 94.26\%, which validates its efficacy for sustainable Android malware detection.

cs.CE↗