Anomaly traffic detection method based on data augmentation and feature mining by AN Yishuai, FU Yu, YU Yihan, LIU Taotao

“…Secondly, a feature correlation matrix was calculated using the Pearson correlation coefficient, transforming traffic data into graph-structured representations to construct a graph dataset. …”
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An air target intention data extension and recognition model based on deep learning by Bo Cao, Qinghua Xing, Longyue Li, Weijie Lin

“…At the same time, the graph attention mechanism is introduced to mine and analyze the relationship between different features. …”
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Contrastive learning of similarity meta-path clustering for multi-behavior recommendation by Juan Liao, Aman Jantan, Zhe Liu, Himanshu Dhumras, Omed Hassan Ahmed

“…Second, we introduce a similarity meta-path framework, which constructs a meta-path-based similarity graph through node-level similarity computation, allowing the model to transfer prior knowledge to low-resource tasks. …”
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DSMF-Net: Dual Semantic Metric Learning Fusion Network for Few-Shot Aerial Image Semantic Segmentation by Xiyu Qi, Yidan Zhang, Lei Wang, Yifan Wu, Yi Xin, Zhan Chen, Yunping Ge

“…To exploit multiscale global semantic context, we construct scale-aware graph prototypes from different stages of the feature layers based on graph convolutional networks (GCNs), while also incorporating prior-guided metric learning to further enhance context at the high-level convolution features. …”
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Cross-Session Emotion Recognition by Joint Label-Common and Label-Specific EEG Features Exploration by Yong Peng, Honggang Liu, Junhua Li, Jun Huang, Bao-Liang Lu, Wanzeng Kong

“…To be specific, JCSFE imposes the <inline-formula> <tex-math notation="LaTeX">$\ell _{\text {2,1}}$ </tex-math></inline-formula>-norm on the projection matrix to explore the label-common EEG features and simultaneously the <inline-formula> <tex-math notation="LaTeX">$\ell _{{1}}$ </tex-math></inline-formula>-norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e., similar EEG samples are encouraged to have the same emotional state. …”
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Reliable Event Detection via Multiple Edge Computing on Streaming Traffic Social Data by Yipeng Ji, Jingyi Wang, Yan Niu, Hongyuan Ma

“…We also develop Binary Sample Graph Convolutional Neural Network (BS-GCN) and Binary Sample Graph Attention Network (BS-GAT) to improve the reliability of graph neural network models based on the characteristics of traffic event detection and design an incremental clustering algorithm based on event similarity to implement streaming social traffic event detection. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

“…As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. …”
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DLPLSR: Dual Label Propagation-Driven Least Squares Regression with Feature Selection for Semi-Supervised Learning by Shuanghao Zhang, Zhengtong Yang, Zhaoyin Shi

“…DLPLSR employs a fuzzy-graph-based clustering strategy to capture global relationships among all samples, and manifold regularization preserves local geometric consistency, so that it implements the dual label propagation mechanism for comprehensive utilization of unlabeled data. …”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…The system, evaluated on 3801 video samples of ADHD children, achieves 90.6% top-1 accuracy and 97.6% top-2 accuracy across six action categories. …”
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Vision-Based Fall Risk Assessment Through Attention Augmented Neural Encoding and Data Augmentation by Chunhua Pan, Rui Miao, Qing Zhang, Boting Qu, Xin Wang

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Unsupervised Feature Selection via a Dual-Graph Autoencoder with <inline-formula><math display="inline"><semantics><mrow><msub><mi mathvariant="bold-script">l</mi><mrow><mn mathvar... by Zhichao Song, Meiling Chen, Liang Xie, Xi Fang

“…Two separate adjacency graphs are constructed to capture the local geometric relationships among samples and among features, and their corresponding graph regularization terms are embedded in the training process to retain the intrinsic structure of the data. …”
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Research on Decision-Making for Automatic Operation of Heavy-Haul Trains in Automatic Block Sections by ZHANG Zhengfang, XIONG Jiayuan, SHU Quanlin, LUO Yuan, JIANG Jie

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Effect of Glass Fiber Reinforcement on the Mechanical Properties of Polyester Composites by I. R. Antipas

“…The technique of creating samples and methods of their testing were described. …”
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Host security threat analysis approach for network dynamic defense by Lixun LI, Bin ZHANG, Shuqin DONG

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Comparative Analysis of Deep Learning Methods for Classification of Ablated Regions in Hyperspectral Images of Atrial Tissue by Yeva Gabrielyan, Arpi Hunanyan, Sona Bezirganyan, Lusine Davtyan, Ani Avetisyan, Narine Sarvazyan, Aram Butavyan, Varduhi Yeghiazaryan

“…For both porcine and bovine data, models incorporating graph neural networks demonstrate superior performance. …”
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Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm by Fuwen Hu, Chun Wang, Xuefei Wu

“…The convolutional knowledge graph embedding (ConvE) method is employed for link prediction, converting entities and relationships into low-dimensional vectors to infer optimal spatial arrangements while addressing data sparsity through negative sampling. …”
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Study of Air-gap Magnetic Field &amp; Characteristic Simulation for PMSM by LI Huaxiang, YUAN Yuepin

“…It presents a calculation method of PMSM characteristics. As a sample of PMSM for the 2004 Toyota Prius motor drive system, FEA method is used to calculate static air-gap magnetic field of the PMSM. …”
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Full grading detection method of earth-rock materials by combining preliminary screening and video image recognition by Huawei Zhou, Liang Zhu, Yihong Zhou, Dongfeng Li, Tao Fang, Chunju Zhao, Zhipeng Liang, Fang Wang, Lei Lei

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BioGAN: Enhancing Transcriptomic Data Generation with Biological Knowledge by Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli

“…However, existing approaches based on generative artificial intelligence often fail to incorporate biological knowledge, limiting the realism and utility of generated samples. In this work, we present BioGAN, a novel generative framework that, for the first time, incorporates graph neural networks into a generative adversarial network architecture for transcriptomic data generation. …”
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Management of Digital Communications with Target Groups by Leading Russian Universities by E. V. Brodovskaya, A. Yu. Dombrovskaya, V. A. Lukushin

“…The article presents the results of an empirical study on the assessment of digital communications management with target groups of Russian universities in social media. A sample of universities is based on the «QS World University Ranking by Subjects 2021: Social Sciences and Management 2021». …”
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