3020 papers
This study introduces a curriculum analytics framework that maps Computer Science course signals to Data Scientist occupational requirements using a shared Detailed Work Activity ontology, revealing that while the curriculum covers all necessary tasks, it exhibits a significant emphasis gap in their relative weighting.
This paper presents a comprehensive case study of developing a production-grade medical platform using Claude Code's multi-agent orchestration, establishing a human-AI competency framework, quantifying performance gains across different AI configurations, and proposing token optimization strategies to enhance productivity while reducing costs.
This paper demonstrates that hospital-level prevalence mismatch is a dominant, quantifiable cause of calibration failure in cross-institutional ICU mortality models, showing that a simple Bayesian correction using only local mortality rates can effectively restore calibration without requiring labeled target data.
This paper demonstrates that reasoning capabilities in foundation models exhibit layer-specific fragility patterns that vary across architectures, revealing that while uniform quantization suffices at higher bit-widths, a mixed-precision strategy guided by layer-localized fragility maps enables significant compression below four bits without sacrificing performance.
This paper introduces a lightweight, backbone-agnostic safety envelope that wraps frozen foundation models to provide provably bounded prediction deviations, calibrated confidence, and robust out-of-distribution refusal, thereby ensuring the reliability required for clinical dental diagnosis across diverse centers and architectures.
This paper introduces VozConsultAI, a provider-agnostic microservice architecture for automated SOAP note generation in Brazilian Portuguese telehealth, and presents the first comparative benchmark of open-weight large language models using a novel, clinically grounded evaluation metric that prioritizes safety and penalizes hallucinations.
This paper proposes a human-supervised meta-model framework that combines multiple XAI methods to generate more plausible and human-aligned explanations for Arabic sentiment analysis, achieving a significant F1 score improvement of 0.84 with CatBoost while trading off some faithfulness compared to previous ensemble approaches.
This paper proposes AM-RAF, a conceptual six-layer architecture that integrates agentic reasoning, retrieval-augmented generation, and uncertainty-gated deferral to create a trustworthy, privacy-preserving clinical decision support system specifically designed for the connectivity and equity challenges of resource-constrained health systems.
This paper introduces a unified three-axis observability model for quantum risk that evaluates assets based on algorithmic resistance, channel protection, and crypto-agility, supported by an open-source implementation (Sezar) and empirical validation across TLS, QKD, and agility scenarios to address the limitations of current single-axis discovery tools.
This paper proposes a mobility-driven task offloading framework for Vehicular Edge Computing that utilizes the Asynchronous Advantage Actor-Critic (A3C) deep reinforcement learning algorithm to optimize scheduling decisions, effectively minimizing latency and energy overhead while ensuring reliability in dynamic vehicular environments.