Titlebook: Data Science; 10th International C Chengzhong Xu,Haiwei Pan,Zeguang Lu Conference proceedings 2024 The Editor(s) (if applicable) and The Au

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书目名称Data Science影响因子(影响力)




书目名称Data Science影响因子(影响力)学科排名




书目名称Data Science网络公开度




书目名称Data Science网络公开度学科排名




书目名称Data Science被引频次




书目名称Data Science被引频次学科排名




书目名称Data Science年度引用




书目名称Data Science年度引用学科排名




书目名称Data Science读者反馈




书目名称Data Science读者反馈学科排名

表示向前

A Lightweight Edge Network Intrusion Detection System Based on MobileVitthms, and the large resource overhead required to deploy deep learning algorithms to edge networks, we proposes a lightweight intrusion detection system based on MobileViT, which can detect cyber-attack traffic in edge networks with little resource consumption. The system uses smote to balance the d

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Prologue

Enhancing Relevance and Efficiency in Visual Question Generation Through Redundant Object Filtering provides answers. The quality of question generation is vital for the task, but existing methods do not consider the redundant objects brought by Faster RCNN for object detection, leading to meaningless, repetitive questions. To address this, we propose Question Improvement and Redundancy Eliminati

Prologue

Chinese Named Entity Recognition Algorithm Integrating Vocabulary Informationicient learning of Chinese character vocabulary information during the training process. This article proposes an entity recognition model LEBERT-IDGRU-CRF based on BERT and introducing external dictionaries for training. The model performs lexical matching on the data text through an external dicti

aggrieve

WSDSum: Unsupervised Extractive Summarization Based on Word Weight Fusion and Document Dynamic Compaverall content. Numerous prevalent research methods predominantly prioritize the significance of sentences within a document, potentially overlooking the importance of varying keywords within a sentence. Moreover, many methods confine the summarization to information present only in the current docu

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Expertise

Multi-modal Variable-Channel Spatial-Temporal Semantic Action Recognition Networktion. However, existing models often lack the spatial-temporal discriminative ability required for fine-grained recognition tasks. To address this issue, we introduce a flexible attention block called Variable-Channel Spatial-Temporal attention (VCSTA) to enhance the discriminative capacity of spati