2833 papers
This study proposes an AI-based framework that integrates RoBERTa-derived semantic embeddings with handcrafted linguistic features to significantly outperform conventional methods in English text readability assessment, achieving high accuracy (R² = 0.9908) on the CLEAR dataset.
This study employs a design science approach to develop and validate semi-automatic artifacts combining text mining, a specialized dictionary, and generative AI prompts, which significantly enhance the efficiency and scalability of extracting and coding digital sustainability data from textual sources while maintaining high agreement with expert manual coding.
This paper audits legal large language models for gender bias in China's wrongful-dismissal compensation calculations, revealing that counterfactual instability rather than directional bias is the primary failure mode, and proposes two effective remedies—Counterfactual Symmetric Calibration and Fairness-Constrained Fine-Tuning—to mitigate these errors by addressing missing formula knowledge.
This paper introduces C3, a disparity-gated vision-language prior that combines VLM-reported distances with unit-baseline triangulation of a tracked semantic anchor to achieve partial metric-scale recovery in monocular visual odometry, demonstrating improved performance over other non-ground-truth priors on KITTI sequences while highlighting specific conditions for reliability.
This systematic literature review of 25 studies reveals that most AI mentoring tools for entrepreneurship rely on ungrounded conversational heuristics and lack persistent memory, creating a "cognitive offloading trap" where high user satisfaction correlates with diminished critical thinking and unmeasured cognitive development.
This paper presents a weakly supervised preprocessing pipeline that utilizes template matching and landmark-based pixel size calibration to generate aligned image pairs from disparate electron and light microscopy modalities, thereby facilitating accurate multimodal image registration and downstream analysis.
This paper proposes a novel hybrid deep reinforcement learning framework that integrates Temporal Convolutional Networks, Transformer encoders, and Soft Actor-Critic to achieve state-of-the-art, privacy-preserving wearable fall detection with 99.0% accuracy and high deployability on resource-constrained edge devices.
This paper argues that the persistent limitations of current AI in causal reasoning, social cognition, and symbolic composition stem from a lack of diverse data environments rather than insufficient scale, demonstrating through a survey of eighty systems that integrating physical, social, and thinking-space signals is essential for advancing from narrow pattern recognition to general intelligence.
This paper introduces CAMNet, a robust deep learning framework that leverages a cross-attention multi-stream architecture to integrate advanced audio and visual analysis techniques, thereby achieving superior deepfake detection accuracy by identifying modality-specific features and cross-modal inconsistencies across benchmark datasets.
This paper introduces Cross-granularity Ranking Uncertainty (CRU), a lightweight framework that directly estimates retrieval uncertainty from ranking-space disagreements between global and local matching distributions to improve error detection, calibration, and selective prediction in image-text retrieval without requiring multiple forward passes.