Shigeng Zhang

RFID has become an attractive platform for battery-free sensing and human-centered systems. However, its practical deployment is often constrained by limited communication range or orientation dependency in real-world conditions, due to weak, omnidirectional backscatter communication. In this paper, we present VanTag, a novel UHF RFID tag architecture that integrates a Van Atta-style retrodirective array with a commodity RFID chip to enhance backscatter communication and improve measurement robustness for sensing. VanTag develops a novel coupler-based differential feed network co-designed with a symmetry-preserving matching circuit to overcome key challenges, including differential-port compatibility, differential impedance matching, and physical size constraints. We prototype VanTag designs and evaluate them on a commercial UHF RFID platform. Measurements show a 4.2 dB increase in radar cross-section, a 40.9% extension in read range, and good robustness to angle and orientation variations, all while preserving full compatibility with commodity readers and standard RFID protocols. These results demonstrate that modulated retrodirective backscatter can provide both communication enhancement and more stable backscatter measurements, offering a practical building block for robust battery-free sensing and IoT systems.

Missing modalities consistently lead to significant performance degradation in multimodal models. Existing approaches either synthesize missing modalities at high computational cost or apply prompt-based fine-tuning that relies only on adjacent-layer features and overlooks long-distance contextual information, which may offer additional tolerance to errors when one or more modalities are missing. To address this, we introduce REplay Prompting (REP): (1) construct modality-wise feature buffers via a residual bypass to cache early-layer representations and replay them in deeper layers, mitigating information loss as network depth increases; (2) employ a private-shared feature decoupling strategy, where private buffers preserve modality-specific signals and shared buffers encode cross-modal semantics; and (3) design a task-aware dynamic initialization mechanism to configure these buffers differently, improving stability and generalization under diverse missing-modality conditions. Experiments on vision-language, vision-language-audio, and temporal multimodal benchmarks demonstrate that REP consistently outperforms prior methods under both single- and multi-modality missing scenarios, while introducing only negligible parameter overhead. These results establish REP as a lightweight and effective paradigm for robust multimodal learning in challenging missing-modality environments.

Radio Frequency Identification (RFID) technology plays a pivotal role in modern applications ranging from retail and logistics to healthcare and security. However, a fundamental challenge persists in large-scale RFID systems: maximizing the coverage capacity of readers – the ability to reliably identify and communicate with the maximum number of tags within their operational range. While previous research has explored various aspects of RFID performance, the systematic optimization of coverage capacity remains underinvestigated. This paper addresses this gap by developing a comprehensive framework that integrates theoretical analysis with practical implementation strategies. We first establish a novel coverage capacity model that incorporates critical factors such as tag spatial distribution and link loss dynamics, providing a theoretical foundation for determining the upper bounds of reader performance. Building on this, we propose a cutoff-power-based optimization approach that dynamically adapts to real-world conditions without relying on predefined system parameters. Furthermore, to extend coverage across larger areas, we investigate advanced multi-reader configurations and present strategic deployment methodologies. Our framework is designed to characterize the baseline coverage capacity of existing RFID readers, rather than to enlarge the interrogation zone through additional hardware or protocol modifications. Moreover, it can be naturally extended to advanced configurations such as MIMO or phased-array systems once their antenna parameters are specified. The effectiveness of our approach is validated through extensive experiments using commercial RFID hardware, demonstrating measurable improvements in coverage capability. By bridging theoretical principles with practical constraints, this work offers actionable insights for designing and deploying high-performance RFID systems.

Tactics and techniques information in Cyber Threat Intelligence (CTI) represent the objectives of attackers and the means through which these objectives are achieved. The classification of tactics and techniques descriptions in CTI has been extensively studied to assist security experts in interpreting attack patterns. Although many recent studies have applied various deep learning methods to enhance classification performance, they mainly focus on improving performance from an average or top perspective. However, the imbalance between tactical and technical tag samples, as well as text sparsity, may lead to poor model performance, which has been under-explored. To address these issues, we propose a new tactics and techniques classification model based on adversarial contrastive learning and meta-path (TTC-ACLM). In TTC-ACLM, a novel text representation learning module is first designed. It includes pre-trained language model (PLM) and contrastive adversarial methods, which can better adapt to categories with smaller sample sizes while obtaining better text representations. Then, heterogeneous information networks are used to model the rich relationships between texts and labels (tactics and techniques), which can merge additional information, e.g., processes and tools, to address text sparsity. Next, we defined a meta-path based classifier learning module that maps text, tactics, and meta-path based context to a set of classifiers, which are applied to the text representation generated by the text representation module for better classification. Finally, the classification performance is further improved through the tactics and techniques correlation enhancement matrix. Through in-depth research, we demonstrate that the proposed model can effectively address the impact of sample imbalance and text sparsity. Extensive experimental results indicate that TTC-ACLM achieves state-of-the-art performance.

Machine learning-based Android malware detection has consistently demonstrated superior results. However, with the continual evolution of the Android framework, the efficacy of the deployed models declines markedly. Existing solutions necessitate frequent and expensive model retraining to resist the constant evolution of malware accompanying Android framework updates. To address this, we introduce a solution called ASDroid, which generalizes specific APIs into similar API clusters to counteract evolving Android malware threats. One primary challenge lies in identifying analogous API clusters that correspond to specific APIs. Our approach involves extracting semantic information from open-source API source code to construct a heterogeneous information graph, and utilizing embedding algorithms to obtain semantic vector representations of APIs. APIs that are close in embedding distance are presumed to have similar semantics. Our dataset encompasses Android applications spanning nine years from 2011 to 2019. In comparison to existing Android malware detection model aging mitigation solutions like APIGraph, SDAC and MaMaDroid, ASDroid demonstrates greater accuracy and more effective at resisting continuously evolving malware.