A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification
本文提出一种集成生成AI的多模态框架，用于解决多摄像头行人重识别中的低延迟问题，通过成本感知的提前退出级联方法优化准确性和延迟。
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Automating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction
This study addresses the challenge of objectivity in educational assessment and critical data leakage issues in EEG-based familiarity prediction. We benchmarked 15 machine learning and deep learning models, introducing a rigorous Group K-Fold independent validation paradigm to correct performance inflation caused by temporal leakage. Integrating SHAP interpretability analysis, we established a corrected CNN F1-score of 0.6038 as a reliable evaluation baseline and identified frontotemporal Gamma/Beta oscillations as key cognitive biomarkers. By effectively mitigating model overestimation, this work provides a robust methodological framework and physiological evidence for neurocognitive monitoring in educational technology, ensuring more valid assessments of learning states through electrophysiological signals.
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A Combined Feature-Based Framework for Disguise and Spoofing Detection in Face Recognition Systems
This study addresses the dual challenge in face verification systems: defending against presentation attacks (e.g., photos, videos) while maintaining robustness to legitimate appearance variations such as accessories, lighting, and pose changes. The authors propose a unified framework that, for the first time, integrates classical handcrafted features—including PCA, LBP, HOG, SURF, and Harris corner detectors—within five fusion strategies: Product Matching (PM), Linear Product Matching (LPM), Hierarchical Product Matching (HPM), Sum Matching (SM), and Hybrid Matching (HM). These strategies jointly optimize spoof detection and recognition robustness across preprocessing and classification stages. Experimental results demonstrate that HPM achieves 94.59% accuracy under mixed spoofing attacks, 81.5%–93.2% under lighting and pose variations, and 91.67% anti-spoofing performance; LPM yields the best anti-spoofing rate (93.2%) but exhibits weaker pose robustness. The work further reveals a quantifiable sensitivity–robustness trade-off between spoof detection and appearance invariance.
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When the Next Step Is Not One Step: Distribution-Aware Execution Modeling for Concurrent Go Programs
This work addresses the challenge of modeling concurrency bugs in Go programs, where scheduling nondeterminism leads to multiple valid next events from the same execution prefix—a scenario poorly captured by conventional single-label prediction models. The authors propose a distribution-aware modeling approach that collects empirical distributions of next events through repeated program executions and fine-tunes a 7B-parameter large language model using Kullback–Leibler divergence as the optimization objective to align its predictions with the observed distributions. By treating scheduling uncertainty as a training signal, the method formally characterizes goroutine leaks induced by select statement blocking. Evaluated on 798 real-world Go production defect prediction tasks, the model achieves an accuracy of 36.2%, outperforming Gemini 3.5 Flash in zero-shot settings, and attains a significantly reduced expected calibration error of 0.169.
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Towards Calibrating Prompt Tuning of Vision-Language Models
This work addresses the poor confidence calibration and unreliable prediction uncertainty commonly induced by prompt tuning in vision-language models. To this end, the authors propose a novel calibration framework that jointly optimizes calibration performance and semantic generalization while preserving the geometric structure of CLIP’s pretrained embedding space. The method introduces a dual-regularization mechanism built upon the cross-entropy loss, incorporating a mean-variance margin penalty and a textual moment-matching loss to effectively integrate prompt tuning with uncertainty calibration. Extensive experiments across seven prompt-tuning methods and eleven datasets demonstrate that the proposed approach significantly reduces Expected Calibration Error (ECE) and consistently outperforms existing calibration techniques.
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A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification
本文提出一种集成生成AI的多模态框架，用于解决多摄像头行人重识别中的低延迟问题，通过成本感知的提前退出级联方法优化准确性和延迟。
0 citationsRead paper
Automating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction
This study addresses the challenge of objectivity in educational assessment and critical data leakage issues in EEG-based familiarity prediction. We benchmarked 15 machine learning and deep learning models, introducing a rigorous Group K-Fold independent validation paradigm to correct performance inflation caused by temporal leakage. Integrating SHAP interpretability analysis, we established a corrected CNN F1-score of 0.6038 as a reliable evaluation baseline and identified frontotemporal Gamma/Beta oscillations as key cognitive biomarkers. By effectively mitigating model overestimation, this work provides a robust methodological framework and physiological evidence for neurocognitive monitoring in educational technology, ensuring more valid assessments of learning states through electrophysiological signals.
0 citationsRead paper
A Combined Feature-Based Framework for Disguise and Spoofing Detection in Face Recognition Systems
This study addresses the dual challenge in face verification systems: defending against presentation attacks (e.g., photos, videos) while maintaining robustness to legitimate appearance variations such as accessories, lighting, and pose changes. The authors propose a unified framework that, for the first time, integrates classical handcrafted features—including PCA, LBP, HOG, SURF, and Harris corner detectors—within five fusion strategies: Product Matching (PM), Linear Product Matching (LPM), Hierarchical Product Matching (HPM), Sum Matching (SM), and Hybrid Matching (HM). These strategies jointly optimize spoof detection and recognition robustness across preprocessing and classification stages. Experimental results demonstrate that HPM achieves 94.59% accuracy under mixed spoofing attacks, 81.5%–93.2% under lighting and pose variations, and 91.67% anti-spoofing performance; LPM yields the best anti-spoofing rate (93.2%) but exhibits weaker pose robustness. The work further reveals a quantifiable sensitivity–robustness trade-off between spoof detection and appearance invariance.
0 citationsRead paper
When the Next Step Is Not One Step: Distribution-Aware Execution Modeling for Concurrent Go Programs
This work addresses the challenge of modeling concurrency bugs in Go programs, where scheduling nondeterminism leads to multiple valid next events from the same execution prefix—a scenario poorly captured by conventional single-label prediction models. The authors propose a distribution-aware modeling approach that collects empirical distributions of next events through repeated program executions and fine-tunes a 7B-parameter large language model using Kullback–Leibler divergence as the optimization objective to align its predictions with the observed distributions. By treating scheduling uncertainty as a training signal, the method formally characterizes goroutine leaks induced by select statement blocking. Evaluated on 798 real-world Go production defect prediction tasks, the model achieves an accuracy of 36.2%, outperforming Gemini 3.5 Flash in zero-shot settings, and attains a significantly reduced expected calibration error of 0.169.
0 citationsRead paper
Towards Calibrating Prompt Tuning of Vision-Language Models
This work addresses the poor confidence calibration and unreliable prediction uncertainty commonly induced by prompt tuning in vision-language models. To this end, the authors propose a novel calibration framework that jointly optimizes calibration performance and semantic generalization while preserving the geometric structure of CLIP’s pretrained embedding space. The method introduces a dual-regularization mechanism built upon the cross-entropy loss, incorporating a mean-variance margin penalty and a textual moment-matching loss to effectively integrate prompt tuning with uncertainty calibration. Extensive experiments across seven prompt-tuning methods and eleven datasets demonstrate that the proposed approach significantly reduces Expected Calibration Error (ECE) and consistently outperforms existing calibration techniques.
0 citationsRead paper