Surface electromyography (sEMG), the electrical signals generated by muscle activity, plays an important role in prosthetics, orthotics, and human-computer interaction because of its non-invasive signal acquisition, real-time responsiveness, and ability to provide rich information for decoding complex movements. However, the complexity and nonlinear characteristics of sEMG signals present challenges in implementing sEMG-based tasks, such as hand gesture classification. Motivated by effectively addressing the challenges and extending the applicability of sEMG signals, we propose a nearest-centroid-based hand gesture classification framework using sEMG signals. We utilize the Dynamic Time Warping Barycenter Averaging (DBA) algorithm to generate centroids of sEMG signals and leverage Dynamic Time Warping (DTW) to measure signal discrepancies. Our pipeline features an effective data preprocessing approach and a heuristic, repeatable, optimal parameter search process. Additionally, our framework supports interpretability analysis using Shapley values. Through our extensive experiments, we demonstrate that our framework has achieved an average accuracy of 90.0% and a peak accuracy of 91.25% on a public dataset Mendeley data-sEMG, which outperforms the surveyed existing methods and establishes our framework as state-of-the-art on such dataset.

Smart contract auditing remains challenging because vulnerabilities often emerge only under complex execution conditions, cross-transaction interactions, and environment-dependent assumptions. Existing analysis techniques, including static analysis, symbolic execution, fuzzing, and recent LLM-assisted approaches, each provide useful but incomplete coverage, and monolithic auditing pipelines often struggle to balance search breadth, reproducibility, and reporting reliability. This paper presents SEMA, a self-evolving multi-agent auditing framework for smart contracts that formulates auditing as a resource-bounded discovery of concrete counterexamples under replay-certified reporting semantics. SEMA combines heterogeneous specialized agents, an orchestrator, a shared artifact-centric knowledge base, and a replay-based referee. During auditing, agents generate and consume reusable artifacts, such as candidate invariants, refuted hypotheses, transaction templates, and coverage cues, allowing the shared search state to evolve across rounds without modifying the analyzers themselves. To ensure reporting reliability, findings are accepted only when the referee can replay the candidate scenario under a pinned execution configuration and confirm violation of an executable security property. We further evaluate SEMA on an annotated smart contract benchmark under a fixed 300 s budget per contract. The full system achieves 0.9469 instance recall, 0.9441 success rate, and 0.9445 macro-average category recall on the retained executable subset, outperforming both symbolic-only and fuzzing-only baselines, as well as multi-agent ablations that disable dynamic knowledge evolution or cross-agent artifact reuse.

This paper performs a comprehensive evaluation of Smartwatch in-air signature classification based on multiple deep learning models. We leverage the Shapley value in dimension-wise feature selection to provide the in-air signature community with the most and least dominant dimension regarding the accuracy of in-air signature classification. Our experiment results highlight InceptionTime as the top-performing model, achieving an accuracy of 97.73%. Through our Shapley Value analysis, among all the sensors embedded in the Smartwatch, we find that the y dimension of the gyroscope and the z dimension of the gyroscope contribute the most to classification accuracy with 12.57% and 12.51% respectively, while the x dimension of the accelerometer produces the least contribution with 8.71%.

The growing adoption of context-aware reasoning in Large Language Model (LLM)-based applications, particularly through retrieval-augmented and prompt-enriched architectures, has substantially broadened the utility of LLMs in specialized, real-world scenarios. Yet this progress comes with an underexplored vulnerability: users must supply sensitive contextual information as part of the reasoning process, exposing private data to external LLM service providers and raising serious concerns about informational self-determination. Existing privacy-preserving approaches in NLP are largely ill-suited to this setting, as they either degrade reasoning quality unacceptably or fail to provide formal privacy guarantees. This paper addresses this gap by introducing Textual Differential Privacy (TDP), a principled framework that embeds differential privacy guarantees directly into the textual context supplied to LLMs, enabling privacy-preserving reasoning without sacrificing the semantic coherence that models depend upon. Central to the framework is a Differential Embedding Substitution mechanism, which perturbs sensitive entities in the input space while preserving the structural and semantic properties necessary for downstream reasoning tasks. The framework further introduces a formal privacy loss quantification scheme, providing practitioners with interpretable metrics to navigate the privacy–utility trade-off in deployment. We prove that the mechanism, instantiated as an exponential mechanism over a semantically constrained candidate set, satisfies metric differential privacy with a privacy budget that scales with the cosine distance between entities, and empirical evaluation across context-aware reasoning benchmarks validates its practical effectiveness. By treating privacy as a first-class design principle in LLM-based architectures, this work contributes toward a more trustworthy and broadly deployable foundation for intelligent, context-sensitive AI systems.