863 papers
This paper proposes a multi-agent reinforcement learning framework with game-theoretic incentives to achieve consensus among autonomous, learning blockchain participants, demonstrating that policies converge to equitable and adversarial-tolerant equilibria while identifying specific architectural limitations and refining theoretical convergence guarantees.
This study utilizes Structural Equation Modeling to demonstrate that while AI-driven cybersecurity awareness and digital literacy significantly promote the adoption of AI-based cybersecurity systems, trust in these systems does not directly influence behavioral intention, highlighting the critical need for organizations to prioritize cybersecurity awareness and digital literacy development over trust-building alone.
This paper introduces DustFormer, a deep learning-based encoder–decoder framework that simultaneously performs dust segmentation and density estimation in RGB images, achieving high accuracy and robustness for environmental monitoring, precision agriculture, and smart sensing applications.
This paper proposes a directed symbolic execution method enhanced with automatic symbolization and loop-bounding strategies to efficiently verify interprocedural control flow path feasibility, demonstrating superior recall and precision compared to existing tools like KLEEF when applied to real-world projects and static analysis integration.
This study evaluates five large language models on OKAP-style ophthalmology questions, revealing that general-purpose models, particularly Gemini-Pro-3, outperformed the specialized clinical AI tool OpenEvidence in accuracy and calibration, while highlighting significant performance heterogeneity and shared reasoning failures across all systems.
This paper proposes a novel intrusion detection framework that combines a two-stage stacked autoencoder for deep feature extraction with a Proximal Policy Optimization-based deep reinforcement learning agent, demonstrating competitive performance and efficiency across multiple benchmark datasets.
This paper proposes a confidence-convergence-based sequential recognition method for underwater moving targets that dynamically updates behavior-type confidence and accepts decisions only when dominance, temporal stability, and inter-class separation are jointly satisfied, thereby significantly improving accuracy and reducing errors under limited and uncertain observations compared to existing baselines.
The paper proposes HiRoC, a multimodal emotion recognition framework that enhances prediction accuracy by selectively routing relevant conversational history through speaker-typed graphs and calibrating decisions using cross-modal disagreement to address limitations in existing graph-based methods.
This paper presents an automated, scalable method that combines interprocedural static analysis with control-flow and data-dependency tracking to accurately detect natural loops and determine their iteration counts in optimized binary code, achieving high precision and scalability on real-world software and benchmark suites.
This paper establishes a reproducible benchmark demonstrating that while full HMD pose data can effectively model certain subjective VR outcomes like spatial presence, it serves best as a complementary behavioral signal to traditional questionnaires rather than a complete replacement, with recoverability varying significantly across different constructs such as usability, workload, and cybersickness.