Mohammad A. Tayebi

Henry Ruckman-Utting, Vrushal Nedungadi, Taiga Okuma et al.

Open-source software (OSS) dependencies introduce systemic risks that are difficult to manage at scale. Existing Software Composition Analysis (SCA) and reachability tools generate severe alert fatigue by treating risk as an intrinsic component property, ignoring semantic context and forcing enterprises into rigid compliance frameworks. We present Deptex, an organization-first, graph-based platform treating supply chain risk as emergent. Deptex introduces Execution Path Dominance (EPD), fusing Code Property Graph (CPG) slicing with Large Language Model (LLM) semantic verification to calculate a vulnerability's true operational blast radius. To handle bespoke compliance, Deptex abstracts governance into a programmable ``As Code'' engine, enabling security teams to natively enforce dynamic pull request policies, custom asset tiers, and external API integrations. By shifting from reactive scanning to context-aware governance, Deptex enables proactive, efficient, and aligned supply chain risk management.

Amirhossein Mozafari, Kourosh Hashemi, Erfan Shafagh et al.

Healthcare fraud detection remains a critical challenge due to limited availability of labeled data, constantly evolving fraud tactics, and the high dimensionality of medical records. Traditional supervised methods are challenged by extreme label scarcity, while purely unsupervised approaches often fail to capture clinically meaningful anomalies. In this work, we introduce CleverCatch, a knowledge-guided weak supervision model designed to detect fraudulent prescription behaviors with improved accuracy and interpretability. Our approach integrates structured domain expertise into a neural architecture that aligns rules and data samples within a shared embedding space. By training encoders jointly on synthetic data representing both compliance and violation, CleverCatch learns soft rule embeddings that generalize to complex, real-world datasets. This hybrid design enables data-driven learning to be enhanced by domain-informed constraints, bridging the gap between expert heuristics and machine learning. Experiments on the large-scale real-world dataset demonstrate that CleverCatch outperforms four state-of-the-art anomaly detection baselines, yielding average improvements of 1.3\% in AUC and 3.4\% in recall. Our ablation study further highlights the complementary role of expert rules, confirming the adaptability of the framework. The results suggest that embedding expert rules into the learning process not only improves detection accuracy but also increases transparency, offering an interpretable approach for high-stakes domains such as healthcare fraud detection.