5333 papers
This paper introduces the Required-edge Cycle Cover Problem (RCCP) and a corresponding flow model to establish an ASP-completeness framework that resolves open complexity questions for Constraint Graph Satisfiability and Kakuro while proving the ASP-completeness of several other pencil-and-paper puzzles.
This paper presents Mica, a confidential computing architecture built on Arm CCA that decouples confidentiality from trust by enabling tenants to explicitly define, restrict, and attest communication paths between distrustful TEE components, thereby preventing sensitive data leakage without significantly expanding the trusted computing base.
This paper introduces LDP-Slicing, a lightweight, training-free framework that overcomes the utility limitations of applying Local Differential Privacy to high-dimensional images by decomposing pixel values into binary bit-planes and integrating perceptual obfuscation and optimized budget allocation to achieve rigorous privacy with high downstream task performance.
This paper introduces RISCBench, a benchmark suite and methodology that quantifies orchestration efficiency in heterogeneous RISC-V systems using a new Sustained Instantaneous Throughput (SIT) metric to address the limitations of conventional performance indicators in FPGA and accelerator-class platforms.
This paper presents an empirical review comparing binary floating-point to decimal string conversion algorithms, highlighting that modern techniques like Schubfach and Dragonbox offer significant speedups over legacy methods like Dragon4, though many implementations still fail to consistently generate the shortest possible decimal strings.
This design-science study introduces and validates the GSI D-Hub, an AI-powered platform that leverages explainable algorithms and synthetic data to enhance transparency, trust, and decision-making within multi-stakeholder global development ecosystems.
This study evaluates the vulnerability of Sub-Saharan African migrants to AI-enabled deception, finding that prior exposure to targeting is the strongest predictor of risk, while confidence in identifying AI content and high verification effort serve as significant protective factors.
This paper proposes an ethics-by-design control architecture that operationalizes AI governance across the entire lifecycle by embedding philosophical reasoning into a triple-gate enforcement structure (Metric, Governance, and Eco) with measurable triggers and audit trails, thereby translating normative commitments into testable controls compatible with existing MLOps pipelines and major regulatory frameworks like the EU AI Act and NIST RMF.
Using causal inference on a dataset of 530 papers, this study quantifies statistically significant disadvantages in academic peer review rankings for authors from minority racial groups, female authors, and those affiliated with institutions in the Global South, highlighting the urgent need for fairness interventions in both traditional and AI-driven assessment systems.
This paper proposes a stateful, hierarchical multi-agent framework for 5G/6G Intent-Based Networking orchestration that leverages both Large and Small Language Models, demonstrating that while both achieve similar translation accuracy, Small Language Models improve the overall lifecycle completion speed by 20%.
This paper introduces ObjChangeVR, a novel framework and corresponding dataset designed to enhance object state change reasoning in virtual reality by addressing the challenges of detecting background changes without direct interaction through viewpoint-aware retrieval and cross-view reasoning.
This paper proposes a margin-consistent deep subtyping framework for invasive lung adenocarcinoma that integrates attention-weighted aggregation, contrastive regularization, and a novel Perturbation Fidelity scoring mechanism to achieve robust, high-accuracy classification across multiple architectures and demonstrate cross-institutional generalizability on whole-slide images.
PaLMR is a novel framework that enhances the faithfulness of multimodal large language models by aligning both the reasoning process and outcomes through a perception-aligned data layer and a hierarchical reward fusion scheme, thereby significantly reducing visual hallucinations while achieving state-of-the-art performance on key benchmarks.
This paper proposes a Digital Twin-enabled framework (DAPR) that integrates asynchronous federated learning, a GRU-VAE prediction model, and deep reinforcement learning to optimize client selection and content request prediction, thereby significantly improving cache hit ratios and reducing transmission latency in vehicular edge computing systems.
This paper introduces FCBNet, a parameter-efficient convolutional model featuring a frozen ConvNeXt backbone and a Feature Correction Block that achieves superior weed segmentation accuracy (over 85% mIoU) and computational efficiency across RGB and multispectral aerial imagery compared to existing state-of-the-art models.
The paper introduces GameVerse, a comprehensive benchmark featuring a novel reflect-and-retry paradigm and a hierarchical taxonomy across 15 games, demonstrating that Vision-Language Models can effectively improve their gameplay policies through video-based reflection by combining failure trajectories with expert tutorials.
The paper introduces ASMIL, a unified framework that addresses unstable attention dynamics, overfitting, and over-concentrated attention in attention-based multiple instance learning for whole slide imaging by employing an anchor model with a normalized sigmoid function and token random dropping, resulting in significant performance improvements over state-of-the-art methods.
This chapter explores the potential of generative AI to enhance K-16+ science literacy by proposing a coherent architectural framework that aligns the teaching, learning, and assessment of scientific knowledge and reasoning, while addressing associated challenges and outlining future research needs.
The paper proposes Graph-of-Mark (GoM), a novel pixel-level visual prompting technique that overlays scene graphs onto images to capture object relationships, thereby significantly enhancing the spatial reasoning and zero-shot performance of multimodal language models.
This paper introduces a system-level inference optimization for Diffusion Transformer-based video generation that employs a sequence-parallel Causal-RoPE mechanism and operator fusion to overcome memory and latency bottlenecks, achieving near real-time speeds and sub-second first-frame latency on an eight-GPU cluster.