863 papers
This paper proposes a cost-sensitive and explainable evaluation framework for Chronic Kidney Disease prediction that demonstrates how optimizing decision thresholds and prioritizing the avoidance of false negatives, alongside interpretability analysis confirming clinical plausibility, enhances the suitability of machine learning models for clinical decision support.
This paper presents a Neo4j-based knowledge graph that transforms the static MITRE ATLAS documentation into a queryable, LLM-grounded system cross-linked with the OWASP Top 10 for LLM Applications, enabling practitioners to perform threat reasoning, identify 40 unmitigated generative-AI techniques, and demonstrate the orthogonal nature of ATLAS tactics and OWASP risk categories.
This paper proposes the Security-Aware Semantic Validation Framework (SSVF), a novel execution-aware approach that transforms database execution plans into structured semantic knowledge to detect sophisticated SQL injection attacks and enhance relational database security through intelligent, explainable validation.
This paper demonstrates that prompt safety classifiers trained on synthetic benchmarks suffer from significant distribution mismatch when applied to human-authored inputs, leading to a substantial drop in recall and a sharp increase in false positives that cannot be resolved by simply increasing model complexity.
This paper presents MedGemma StructCore, a local-first, two-stage pipeline using fine-tuned MedGemma 4B models to convert unstructured clinical notes into auditable KVT4 facts for readmission risk assessment, demonstrating that while the structured data adds predictive signal and ensures format stability on consumer hardware, the system requires further validation for extraction accuracy and probability calibration before clinical deployment.
This paper proposes a constrained Markov decision process (CMDP) framework using factorized Lagrangian Q-learning to optimize shared micromobility rebalancing by explicitly enforcing equity thresholds on service failure rates, validated through both synthetic networks and a real-world case study in The Hague.
This paper presents a client-side security architecture for web-based credential vaults that achieves strong zero-knowledge properties by combining WebAuthn's PRF extension with symmetric cryptography and optimized browser storage patterns.
This paper introduces AeroGround, an open, citation-grounded aviation dataset derived from U.S. public-domain sources that enables the training and evaluation of trustworthy language models capable of providing authoritative, cited answers or calibrated abstentions when evidence is insufficient.
This paper presents a comparative analysis of machine learning algorithms applied to anonymized clinical records from the 'Aadarshvelu' dataset, demonstrating that a proposed labeled attention model achieves superior diagnostic accuracy (94.05%) and F1 score (95.05%) for predicting liver cirrhosis stages, thereby offering a cost-effective tool to enhance early detection and clinical decision-making.
The paper introduces SPIDER-WEB, an all-in-one coding framework that enables real-time, concurrent data retrieval during DNA sequencing, achieving efficiency gains of up to 9,083-fold over conventional methods and demonstrating compact-disc-level performance with successful video rendering in under 100 seconds.