2585 papers
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.
This paper introduces GridWorld3DEnv, a reproducible 3D voxel-based simulation benchmark with standardized metrics and evaluation protocols, to address the lack of fair cross-study comparisons in multi-UAV cooperative coverage by systematically evaluating MARL algorithms like QMIX and VDN while releasing all code and datasets.
This paper proposes a bidirectional Path Integral Monte Carlo algorithm enhanced by Multiple Importance Sampling to efficiently estimate quantum circuit transition amplitudes in extremely sparse path spaces, demonstrating superior convergence and scalability for circuits with up to 4096 qubits compared to unidirectional approaches.
This paper evaluates local credit assignment methods for CPU-based Transformers, finding that while asynchronous readout-gradient consensus improves training throughput, both it and predictive coding approaches fail to match backpropagation's quality or establish a general, quality-preserving acceleration alternative.
This paper introduces AIRouteOpt, a trust-aware deep policy-routing framework that leverages Proximal Policy Optimization to dynamically select next hops based on link quality and recursively updated trust scores, significantly improving packet delivery rates in tactical vehicle MANETs under joint mobility and routing-plane attacks compared to traditional trust-blind and threshold-based approaches.
This paper introduces TurkCuisineBench, a 72-item, source-grounded short-answer benchmark that evaluates large language models' factual knowledge of Turkish cuisine and heritage, demonstrating that semantic evaluation significantly improves accuracy assessment over exact-string matching while highlighting substantial performance variations across different model endpoints.
This study demonstrates that nearly half of clinical trial eligibility criteria for non-small-cell lung cancer involve logical disjunctions rather than simple conjunctions, revealing that the common assumption of independent requirements leads to significant safety and efficiency errors in automated patient-trial matching.
This study introduces the UzHistQA dataset and demonstrates that while retrieval-augmented generation significantly reduces hallucinations in Uzbek history question answering, enforcing citation and abstention mechanisms is necessary to completely eliminate fabricated answers, highlighting the need to separately evaluate retrieval quality and generation faithfulness in under-resourced languages.