From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance
本文通过回顾AI招聘系统的发展，分析了从匹配模型到招聘代理的转变，探讨了评估和治理问题，并提出了一种基于证据的阶段性评价方法。
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Tangut Word Segmentation under Extreme Resource Scarcity: Integrating Traditional Lexicons and Unlabeled Text
研究通过结合传统词典、未标注文本及预训练字符编码器，解决了西夏文在资源极度稀缺下的分词问题，达到了约0.91的F1分数。
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VAD-Net: Multidimensional Facial Expression Recognition in Intelligent Education System
Existing facial expression recognition (FER) datasets predominantly provide only discrete emotion category labels, failing to capture fine-grained, continuous affective variations. Although some works incorporate Valence-Arousal (VA) annotations, the Dominance (D) dimension remains largely absent. This paper addresses this gap by introducing the first complete, human-verified three-dimensional Valence-Arousal-Dominance (VAD) continuous annotation for the FER2013 dataset. We further propose an orthogonal convolution-based ResNet regression architecture that enforces feature orthogonality to improve VAD value prediction accuracy. Experiments demonstrate that, despite its high annotation difficulty, the D dimension is effectively learnable; orthogonal convolutions significantly enhance predictive performance across all three dimensions—particularly for Dominance. The released VAD-annotated FER2013 dataset and open-source code establish a new benchmark for multidimensional affective computing, enabling more precise, granular emotion analysis in applications such as intelligent education.
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From Matching Models to Recruiting Agents: A Systematized Narrative Review of AI Recruitment Systems, Evaluation, and Governance
本文通过回顾AI招聘系统的发展，分析了从匹配模型到招聘代理的转变，探讨了评估和治理问题，并提出了一种基于证据的阶段性评价方法。
0 citationsRead paper
Tangut Word Segmentation under Extreme Resource Scarcity: Integrating Traditional Lexicons and Unlabeled Text
研究通过结合传统词典、未标注文本及预训练字符编码器，解决了西夏文在资源极度稀缺下的分词问题，达到了约0.91的F1分数。
0 citationsRead paper
VAD-Net: Multidimensional Facial Expression Recognition in Intelligent Education System
Existing facial expression recognition (FER) datasets predominantly provide only discrete emotion category labels, failing to capture fine-grained, continuous affective variations. Although some works incorporate Valence-Arousal (VA) annotations, the Dominance (D) dimension remains largely absent. This paper addresses this gap by introducing the first complete, human-verified three-dimensional Valence-Arousal-Dominance (VAD) continuous annotation for the FER2013 dataset. We further propose an orthogonal convolution-based ResNet regression architecture that enforces feature orthogonality to improve VAD value prediction accuracy. Experiments demonstrate that, despite its high annotation difficulty, the D dimension is effectively learnable; orthogonal convolutions significantly enhance predictive performance across all three dimensions—particularly for Dominance. The released VAD-annotated FER2013 dataset and open-source code establish a new benchmark for multidimensional affective computing, enabling more precise, granular emotion analysis in applications such as intelligent education.
0 citationsRead paper