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

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書目名稱Knowledge Science, Engineering and Management影響因子(影響力)




書目名稱Knowledge Science, Engineering and Management影響因子(影響力)學(xué)科排名




書目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開度




書目名稱Knowledge Science, Engineering and Management網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Knowledge Science, Engineering and Management被引頻次




書目名稱Knowledge Science, Engineering and Management被引頻次學(xué)科排名




書目名稱Knowledge Science, Engineering and Management年度引用




書目名稱Knowledge Science, Engineering and Management年度引用學(xué)科排名




書目名稱Knowledge Science, Engineering and Management讀者反饋




書目名稱Knowledge Science, Engineering and Management讀者反饋學(xué)科排名

他去就結(jié)束

misanthrope

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

公豬

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

idiopathic

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

Cosmopolitan

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

前奏曲

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Picks-Disease

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

chuckle