2608 papers
TetraFence is a production pipeline for commodity smartphones that ensures re-auditable mobile field evidence by binding image acquisition to device context and anchoring raw data on a permissioned Ethereum ledger, achieving high classification agreement between off-chain and on-chain implementations while highlighting the necessity of version-binding to prevent retroactive misclassifications.
This paper proposes TRRS, a target-state-driven reactive replanning strategy that utilizes a receding-horizon mixed-integer linear programming formulation to dynamically handle position shifts and priority updates in heterogeneous multi-robot systems under counting LTL constraints, demonstrating superior performance over static and greedy baselines through simulations and physical experiments.
This paper demonstrates that the SCFCE-R risk-gated policy, while effective in synthetic network simulations for containing harmful information, catastrophically fails to transfer to real-world Twitter rumor cascades without retraining, thereby proving that strong controlled-network performance does not guarantee deployment readiness.
This paper introduces TOMAgent, a budget-aware multi-agent framework that optimizes unit test generation by modeling target selection as a marginal-utility problem, achieving a 40% fault-detection success rate on Defects4J benchmarks—significantly outperforming uniform and coverage-guided baselines—while maintaining competitive mutation scores and token efficiency.
To address the challenges of scale variation, weak textures, and background similarity in UAV-based insulator defect detection, this paper proposes the Scale-Aware Context Aggregation Network (SACANet), which integrates scale-aware receptive-field aggregation, four-scale bidirectional feature refinement, and spatially aligned multi-scale prediction to achieve state-of-the-art performance on the FLOWID dataset.
This paper introduces an evidence-aware, provenance-controlled benchmark using 295 lacquerware objects from the National Palace Museum Taipei to audit the consistency of museum image metadata against visual evidence, revealing significant gaps in current automated and human evaluation capabilities and highlighting the necessity of explicitly modeling evidential sufficiency and field-specific uncertainty in future metadata-auditing systems.
The paper proposes MDKT, a robust knowledge tracing model that enhances prediction accuracy and resilience in noisy, sparse data scenarios by jointly modeling knowledge at question, concept, and overall levels through an adaptive dual-phase encoder and adversarial smoothing.
This paper proposes a novel, scalable, and robust probabilistic approach for multi-dimensional classification with mixed and incomplete data, providing theoretical foundations for its algorithms and empirical evidence of its effectiveness across various missingness scenarios.
This paper proposes a smart data analytics framework utilizing machine learning models (Linear Regression, Random Forest, and XGBoost) to evaluate and predict sustainability indicators aligned with UN SDGs, demonstrating that XGBoost offers the most stable performance while providing governments with an interpretable, evidence-based tool for optimizing resource allocation and long-term strategic planning.
This paper introduces the concept of Effective Segregation of Duties (SoD) to distinguish between nominal identity separation and actual independent assurance in AI-driven authorization, demonstrating through a large-scale P2P benchmark that effective risk mitigation relies on evidence mediation and heterogeneous checker models rather than mere principal distinctness.