4159 papers
This paper presents RansomShield, an adaptive backup architecture that dynamically adjusts snapshot frequency and immutability based on a continuous, multi-modal threat score derived from memory, API, I/O, and entropy analyses, achieving near-perfect detection accuracy and rapid escalation to air-gapped backups during simulated ransomware attacks.
This paper proposes a method for disambiguating arbitrary context-free grammars by treating parse tree selection as a minimum-cost optimization problem, where dynamic programming is used to find the single best tree based on penalized disambiguation patterns while preserving the original language.
This paper proposes a Wireless Sensor Network-based federated edge learning framework that integrates differential privacy, homomorphic encryption, and blockchain smart contracts to enable secure, auditable, and high-performance collaborative risk control for supply chain finance warehouse receipt pledges while ensuring data remains within local domains.
This study presents an explainable, multi-dataset computational framework that integrates phenotypic clustering with Long Short-Term Memory (LSTM) models and dynamic feature importance analysis to achieve robust early sepsis prediction and risk stratification while providing clinically interpretable insights into disease progression.
This paper demonstrates that representational changes caused by post-hoc edits primarily reflect the relocation of information to new coordinates rather than its permanent destruction, as fresh probes can often recover factors that appear lost to original readouts, with total information loss occurring only under extreme conditions like representational collapse.
This empirical study analyzes the React Native ecosystem's migration to its New Architecture, revealing that while major milestones accelerated adoption among popular libraries, a significant portion of dormant, unmigrated packages remains in use, leading to a fragmented landscape where compatibility layers have become long-term infrastructure.
The paper introduces ATLAS-EHR, a self-supervised foundation model that learns reusable, temporally-aware representations from structured electronic health records to outperform task-specific models across diverse clinical prediction tasks and enable robust imputation and physiological signal analysis.
This study reveals that dormancy is widespread and largely silent among the most downloaded npm packages, with explicit deprecation by maintainers being the primary signal that prompts dependent projects to remove these inactive dependencies, despite the fact that few dormant packages carry known vulnerabilities.
This study proposes a feedback-driven risk accumulation framework that leverages rolling historical outcomes and shipment signals to significantly improve late-delivery prediction accuracy and enable effective risk prioritization, as validated by substantial performance gains on a large-scale DataCo dataset and a semiconductor simulation.
This paper proposes the Uncertainty-Guided Belief World Model (UG-BWM), a framework that leverages posterior covariance and ensemble-based predictive uncertainty to guide belief updates and trigger selective replanning, thereby significantly improving long-horizon UAV navigation success and reducing collision rates under partial observability and visual degradation compared to state-of-the-art baselines.