2787 papers
This paper demonstrates that explicit intention modeling improves cooperative performance over implicit methods specifically in environments requiring distinct best responses per partner type by establishing a division of labor that separates a partner's stable character from their dynamic mental state, thereby reducing partner incompatibility.
This paper introduces a large-scale, HamNoSys-grounded dataset of 144,000 RGB images from 15 participants for 160 handshape classes and establishes baseline performance using various deep learning and machine learning models under both subject-dependent and leave-one-subject-out evaluation protocols to advance fine-grained sign language recognition.
This paper introduces SEEDS, a role-aware framework for sustainable requirements engineering in digital health that operationalizes multidimensional sustainability assessments into actionable decision records and requirements, supported by a novel LLM-assisted drafting tool to enhance practical adoption.
This study proposes a hybrid Random Forest and Differential Equation (RF-DE) controller that effectively reduces fragmentation in de-duplicated storage systems, achieving a lower fragmentation ratio, significantly reduced I/O latency, and a higher storage health index compared to existing No Fragmentation Control and Storage Fragmentation-Informed Garbage Collection methods.
This paper introduces PH-RAG, a parallel hybrid retrieval framework that simultaneously executes sparse and dense retrieval with fusion and reranking to achieve state-of-the-art accuracy and improved latency on open-domain question answering, outperforming complex agentic baselines without requiring knowledge graphs or iterative critics.
This paper reviews causality-constrained Generative Adversarial Networks (CC-GANs), which integrate structural causal models and interventions to generate bias-resilient data by moving beyond associational patterns to address discriminatory pathways, while proposing a comparative taxonomy, summarizing fairness-utility trade-offs, and identifying key challenges for high-stakes adoption.
The paper proposes a lightweight, secure Federated Learning framework that integrates Differential Privacy with Homomorphic Vector Encryption to protect data-sensitive applications like IoMT from reverse-engineering attacks while quantifying the associated predictive and cryptographic overheads.
This paper presents a reproducible, three-phase engineering framework that systematically translates multi-stakeholder needs and regulatory constraints into formal, verifiable system requirements compliant with ISO/IEC/IEEE 29148 and the FUTURE-AI taxonomy, demonstrated through the creation of a publicly available requirements corpus for a high-risk clinical AI system in the AI4HF project.
This paper presents YOLO-WKPose v3.1, a comprehensive update that extends previous fixed-wing aircraft keypoint estimation by integrating five specific modules to address high-altitude turbulence, motion blur, and temporal prediction, providing a detailed implementation guide with defined losses and scope rather than reported experimental results.
This paper presents and formally validates a policy-gated firmware trust mechanism for autonomous UAVs that enforces anti-rollback and fail-closed security through manifest-based verification, while identifying critical dependencies on persistent state integrity and outlining a roadmap for hardware-backed attestation.