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This paper presents an empirical study on sentiment classifier behavior with sarcastic and AI-paraphrased social text, revealing lower confidence on sarcasm, higher accuracy on AI paraphrases, and an abstention method that improves performance by handling low-confidence inputs.
This paper proposes a Multi-Modal Generative Fuzzy System (MMGFS) to enhance multimodal question answering by addressing modality bias and uncertainty through fuzzy inference and multi-hop reasoning, demonstrating improved performance on multiple benchmarks.
This paper introduces a probabilistic polygonal representation for plane curves using Gaussian Mixture Models, preserving local tangent, normal, and arc length while encoding uncertainty in the normal direction. The framework applies to various plane curves and supports uncertainty-aware geometric modeling for CAD, robotics, and trajectory planning.
This paper proposes a novel evidential information fusion framework based on a reversible transformation between belief functions and possibilistic structure, using triangular norms for flexible combination beyond Dempster's rule.