Titlebook: Interactive IR User Study Design, Evaluation, and Reporting; Jiqun Liu,Chirag Shah Book 2019 Springer Nature Switzerland AG 2019

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书目名称Interactive IR User Study Design, Evaluation, and Reporting影响因子(影响力)




书目名称Interactive IR User Study Design, Evaluation, and Reporting影响因子(影响力)学科排名




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书目名称Interactive IR User Study Design, Evaluation, and Reporting网络公开度学科排名




书目名称Interactive IR User Study Design, Evaluation, and Reporting被引频次




书目名称Interactive IR User Study Design, Evaluation, and Reporting被引频次学科排名




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书目名称Interactive IR User Study Design, Evaluation, and Reporting年度引用学科排名




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书目名称Interactive IR User Study Design, Evaluation, and Reporting读者反馈学科排名

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Interactive IR User Study Design, Evaluation, and Reporting

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1947-945X ng different facet values. Through bridging diverse combinations of facet values with the study design decisions made for addressing research problems, the face978-3-031-01191-7978-3-031-02319-4Series ISSN 1947-945X Series E-ISSN 1947-9468

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on. The integration of fundamental method optimization with GPU parallel computing consistently improves encryption efficiency. Our encryption algorithm consistently outperforms the CUDA-based SM4 algorithm for large volumes of data, especially beyond 30 MB. With a data volume of 1 KB, our improved

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Jiqun Liu,Chirag Shahtures as edge features instead of node features within one task, allowing each transaction to be streamed in 2DynEthNet, aiming to capture the evolutionary features of the Ethereum transaction network at a fine-grained level in continuous time. ., we adopt the strategy of incremental information tra

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Jiqun Liu,Chirag Shahelational blockchain transaction graphs. Theoretical analysis and experimental results on datasets from multiple blockchain platforms demonstrate that TRacer can perform transaction tracking tasks more efficiently at lower costs and achieve better tracking results compared to existing methods or eve

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Jiqun Liu,Chirag Shah aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance

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