4157 papers
This study evaluates nine machine learning algorithms on a simulated dataset of residential buildings in Cairo, Egypt, demonstrating that ensemble-based models outperform traditional methods in accurately predicting heating and cooling loads while identifying window-to-wall ratio and glazing distribution as critical design factors for energy efficiency.
This paper proposes GKMSLO-MDC, a novel framework that integrates Grid-based K-Means clustering with a Mobile Data Collector-assisted Sea Lion Optimization algorithm to achieve energy-efficient, QoS-aware cluster head localization and significantly extend network lifetime in large-scale Wireless Sensor Networks.
This paper introduces TRACE-IT, a self-verifying temporal knowledge evolution architecture that integrates contradiction detection, evidence assessment, and calibrated confidence to enable safe information repair, validated by the new T-CONFLICT benchmark and significant performance gains over existing methods.
This paper introduces the Opt-In Radius Auction (ORA), an exact two-sided hypergraph VCG mechanism that enhances ride-sharing welfare and match rates while strictly adhering to passengers' individualized pickup and detour constraints, outperforming standard radius-based approaches in both synthetic and real-world New York City datasets.
This study demonstrates that while deep learning classifiers for pneumonia show strong and transferable discrimination across adult and pediatric chest radiographs, their fixed decision thresholds and probability calibration fail to generalize to external cohorts, highlighting the critical need to report and re-evaluate these metrics beyond internal validation.
This study introduces the bounded-delegation protocol and Eva-Bench to demonstrate that while delegating tasks to agent teams can marginally improve success rates in specific scenarios, it generally incurs significant latency and token costs without providing a consistent advantage over monolithic approaches, suggesting delegation should be selectively earned based on request structure rather than applied universally.
This paper argues that improving long-lived personal AI agents requires shifting from prompt-based persuasion to a formal runtime incentive layer, demonstrating through theoretical modeling and empirical experiments that contractible resource allocation and verification significantly outperform prompt engineering in driving agent effort, success rates, and resource efficiency.
This paper proposes and validates six metamorphic relations for three quantum query algorithms, demonstrating through systematic experiments that these relations effectively detect faults with high mutation scores and maintain this effectiveness even when using significantly reduced input sets.
This paper addresses the lack of interoperable enforcement in Open Digital Rights Language (ODRL) by formalizing its operational semantics for policy evaluation and compliance checking, thereby enabling consistent, fine-grained usage control guarantees for AI agents accessing enterprise systems.
ProtoRefine is a lightweight, prototype-conditioned refinement method that corrects strawberry instance mask errors caused by occlusion by reusing existing instance representations to reallocate pixel locations while preserving the original detection's count and confidence, thereby significantly improving mask accuracy on the StrawDI dataset with minimal additional parameters.