2579 papers
This paper proposes a secure and efficient authentication scheme for resource-constrained IoT devices that leverages Elliptic Curve Cryptography and unique Electronic Product Codes to minimize energy consumption and execution time, outperforming existing methods in simulated wireless network environments.
This scoping review of 49 clinical XAI studies reveals that evaluations overwhelmingly prioritize user trust and perceptions over critical oversight capacities like failure and bias detection, relying heavily on non-validated measures and rarely assessing performance on deployed systems.
This paper proposes a Hyper-ISDR model on hypergraph networks that integrates group-level interactions, hyperedge selection strategies, and information overload to analyze the competitive dynamics of rumor and debunking propagation, deriving stability thresholds and stochastic persistence criteria validated by simulations and empirical data.
This paper analyzes 122,458 malicious domains to demonstrate that measured lifetimes are significantly distorted by the observing source, revealing that feed-based estimates overcount deaths by 55.06% compared to registration records primarily due to unobservable operator abandonment, thereby necessitating a two-instrument decomposition to accurately attribute domain exit causes.
The paper proposes YOLO-MAG, an enhanced YOLO11-based network featuring edge-preserving modules, hybrid attention mechanisms, and a bilateral dual-gated fusion pyramid, which significantly improves small object detection accuracy and real-time performance in UAV aerial imagery by mitigating challenges like low resolution and complex backgrounds.
This paper proposes Adaptive PINN, a physics-informed neural network framework with a certified repair layer, to solve a tri-objective robust military UAV routing problem that accounts for nonlinear battery depletion via Peukert's law and multiple sources of uncertainty.
This paper proposes LGM-Net, a morphology-guided YOLOv12n-based detector featuring novel LESE, GSSPAN, and MGDHead modules to effectively address challenges like scale variation and weak boundaries, achieving superior accuracy and real-time performance in detecting the toxic weed *Stellera chamaejasme* within complex grassland environments.
This survey traces the evolution of speech interaction from traditional cascaded systems to unified omni-modal agents, systematically analyzing the architectures, training strategies, and data governance of SpeechLLMs and Omni-MLLMs while identifying key challenges and future directions for next-generation human-computer interaction.
This paper proposes MAGA, a novel multi-level attack framework that exploits cross-modal attention vulnerabilities in BLIP models by constructing and disrupting cross-modal attack graphs, resulting in significant performance degradation and linguistically plausible but visually inconsistent adversarial captions.
A controlled cross-platform evaluation of 600 mobile AR runs reveals that enabling depth APIs (ARCore/ARKit) does not improve anchor stability and may even increase error, whereas scene texture contrast emerges as the dominant factor influencing spatial drift.