4157 papers
The paper introduces SIMR-NO, a spectrally-informed multi-resolution neural operator that significantly outperforms existing interpolation and deep learning baselines in reconstructing high-resolution turbulent flow fields from severely under-resolved observations by effectively recovering fine-scale vortex structures and high-wavenumber content across various Reynolds numbers.
FL-Compliance-Guard is a machine-checkable framework that ensures regulatory adherence in Federated Learning by encoding controls as traceable predicates, performing static and runtime verification against GDPR, EU AI Act, and HIPAA requirements, and generating hash-chained evidence with negligible performance overhead.
The paper introduces StemGround, a framework that uses source-interventional auditing and a Selective Response Alignment (SRA) head to train music foundation models to accurately link instrument labels to their physical sources, significantly reducing false positives and improving response consistency compared to standard training methods.
This paper presents a ROS 2-based Decision Support System that integrates YOLOv8-powered real-time potato late blight detection with weather data to optimize fungicide spraying schedules for Algerian farmers, achieving high detection accuracy while reducing costs and chemical usage.
This paper presents a practical, end-to-end framework for real-time anomaly detection in Software-Defined Networks that integrates an ELK stack with machine learning capabilities to successfully monitor, visualize, and classify security threats like DDoS attacks within a centralized architecture.
This methodological paper introduces the University AI Sovereignty Index (UASI), a multidimensional framework comprising eight dimensions and 40 indicators designed to diagnose and assess higher education institutions' practical capacity to govern, scrutinize, and exit AI dependencies while preserving digital autonomy without equating it to technological isolation.
The paper proposes CP-XNet, a lightweight, explainable dual-branch deep learning framework that combines 1D-CNN and BiGRU with a compact attention mechanism to achieve high-accuracy, low-latency industrial IoT intrusion detection and transparent decision-making across multiple benchmark datasets.
This paper presents a multidimensional semantic retrieval framework for classical Chinese poetry, featuring a large-scale annotated dataset and a Gain-aware Listwise Ordinal Loss (GLO) optimization method that significantly enhances the discovery of poems based on nuanced aspects like emotion, intent, and theme.
This paper proposes the SASI framework, an interpretable Hidden Markov Model-based approach that effectively captures sequential customer behavior dynamics for banking segmentation, achieving competitive clustering performance and direct visualizability on both synthetic and real-world datasets.
This paper demonstrates that the standard Neighbourhood Rough-Set (NRS) attribute reduction fails for high-dimensional small-sample data due to saturation of its search criterion, showing that while the alternative Maximal Homogeneous Metric Granules (MHMG) method outperforms NRS in nearest-neighbour classification, tuned L1-regularised logistic regression ultimately offers superior stability and speed.