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Titlebook: Knowledge Science, Engineering and Management; 16th International C Zhi Jin,Yuncheng Jiang,Wenjun Ma Conference proceedings 2023 The Editor

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书目名称Knowledge Science, Engineering and Management
副标题16th International C
编辑Zhi Jin,Yuncheng Jiang,Wenjun Ma
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Knowledge Science, Engineering and Management; 16th International C Zhi Jin,Yuncheng Jiang,Wenjun Ma Conference proceedings 2023 The Editor
描述This volume set constitutes the refereed proceedings of the 16th International Conference on Knowledge Science, Engineering and Management, KSEM 2023, which was held in Guangzhou, China, during August 16–18, 2023. .The 114 full papers and 30 short papers included in this book were carefully reviewed and selected from 395 submissions. They were organized in topical sections as follows: knowledge science with learning and AI; knowledge engineering research and applications; knowledge management systems; and emerging technologies for knowledge science, engineering and management. .
出版日期Conference proceedings 2023
关键词artificial intelligence; computational linguistics; computer networks; data mining; databases; directed g
版次1
doihttps://doi.org/10.1007/978-3-031-40283-8
isbn_softcover978-3-031-40282-1
isbn_ebook978-3-031-40283-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Boosting LightWeight Depth Estimation via Knowledge Distillationlexity and inference performance. In this paper, we propose a lightweight network that can accurately estimate depth maps using minimal computing resources. We achieve this by designing a compact model that maximally reduces model complexity. To improve the performance of our lightweight network, we
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Graph Neural Network with Neighborhood Reconnectionveness of GNNs is compromised by two limitations. First, they implicitly assume that networks are homophilous, leading to decreased performance on heterophilous or random networks commonly found in the real world. Second, they tend to ignore the known node labels, inferring node labels merely from t
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Critical Node Privacy Protection Based on Random Pruning of Critical Treesy obvious. Especially for critical node users, if the critical node users suffer from background knowledge attacks during data publishing, it can not only lead to the privacy information leakage of the critical user but also lead to the privacy leakage of their friends. To address this issue, we pro
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DSEAformer: Forecasting by De-stationary Autocorrelation with Edgeboundl due to the increasing data volume and dimensionality. Current MLTF methods face challenges such as over-stationarization and distribution shift, affecting prediction accuracy. This paper proposes DSEAformer, a unique MLTF method that addresses distribution shift by normalizing and de-normalizing t
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RTAD-TP: Real-Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two-Parameteications. However, due to the continuous influx of data streams and the dynamic changes in data patterns, real-time anomaly detection still poses challenges. Algorithms such as SPOT, DSPOT, and FluxEV are efficient unsupervised anomaly detection algorithms for data streams, but their detection perfo
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