2931 papers
The paper proposes SMPD, a novel self-supervised meta-learning framework that leverages multi-modal temporal data and contrastive pre-training to predict IoT device compromises 24–48 hours in advance with high accuracy and few-shot adaptability, significantly outperforming existing intrusion detection baselines in resource-constrained environments.
This paper introduces GRADV, a reproducible signal-analysis workflow that discovers and evaluates target-class audio patterns in keyword-spotting models by optimizing waveforms and performing segment-level analysis, achieving high success rates without proposing new speech-recognition architectures.
This paper proposes a Pydantic schema and a few-shot small language model baseline to extract engineering change traceability from fragmented, bilingual, and truncated legacy CNC work orders, demonstrating high schema validity while revealing that contextually grounded pre-prompts degrade performance and deterministic post-filters improve precision without affecting recall.
OptiVerse AI is an explainable deep learning system that utilizes EfficientNetB7 and Grad-CAM to classify diabetic retinopathy and reconstruct 3D retinal structures from standard fundus images, achieving over 95% accuracy to enhance early disease detection and clinical decision-making.
This paper introduces RECON, a neuro-symbolic multi-agent architecture that combines deterministic rules, fuzzy detection, and LLM reasoning with a YAML-driven recipe system and tamper-evident auditing to deliver reproducible, defensible, and evidence-fused document compliance reviews across heterogeneous enterprise repositories.
This paper demonstrates that for correlative microscopy registration, cross-modal appearance similarity is a more critical determinant of success than field-of-view scale handling, as evidenced by the superior performance of cross-modal-trained backbones and the failure of naive scale-aware wrappers to overcome appearance mismatches.
This paper introduces a cost-efficient, training-free Fourier correction protocol that quantifies and adjusts specific low-frequency amplitude mismatches between synthetic and real construction images to significantly improve cold-start segmentation performance for rare safety-critical classes without requiring extensive unlabeled target data or generative models.
This paper introduces CB-SentiLex, an auditable weak-supervision framework and the first reproducible NLP benchmark for a South Asian central bank, which successfully generates stance labels for a Bangladesh Bank corpus and demonstrates strong model performance alongside significant correlations with monetary policy directions, despite challenges from temporal concept drift.
This study introduces the Ethical Field Theory framework to demonstrate through large-scale simulations that adaptive targeted stabilization is significantly more effective than uniform regulation or no intervention in preventing recursive cascade instabilities across diverse multi-agent AI network topologies.
This paper presents a joint discrimination algorithm that synergistically identifies vibration states, structural looseness, and surface defects in industrial equipment videos by integrating spatiotemporal features from a Video Swin Transformer, micro-displacement features from RAFT optical flow, and multi-task appearance analysis, thereby significantly reducing missed detections in composite anomaly scenarios compared to single-task models.