Titlebook: Collaborative Computing: Networking, Applications and Worksharing; 19th EAI Internation Honghao Gao,Xinheng Wang,Nikolaos Voros Conference

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书目名称Collaborative Computing: Networking, Applications and Worksharing影响因子(影响力)




书目名称Collaborative Computing: Networking, Applications and Worksharing影响因子(影响力)学科排名




书目名称Collaborative Computing: Networking, Applications and Worksharing网络公开度




书目名称Collaborative Computing: Networking, Applications and Worksharing网络公开度学科排名




书目名称Collaborative Computing: Networking, Applications and Worksharing被引频次




书目名称Collaborative Computing: Networking, Applications and Worksharing被引频次学科排名




书目名称Collaborative Computing: Networking, Applications and Worksharing年度引用




书目名称Collaborative Computing: Networking, Applications and Worksharing年度引用学科排名




书目名称Collaborative Computing: Networking, Applications and Worksharing读者反馈




书目名称Collaborative Computing: Networking, Applications and Worksharing读者反馈学科排名

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CORE

armistice

Defeating the Non-stationary Opponent Using Deep Reinforcement Learning and Opponent Modelingscenario, it is not easy to capture its behavior strategy when confronted with a long-term latent, highly dynamic and unpredictable opponent. FlipIt game can model the stealth interaction of advanced persistent threat. However, it is insufficient for traditional reinforcement learning approach to so

deviate

有其法作用

D-AE: A Discriminant Encode-Decode Nets for Data Generationmain. The first is to use algorithms to learn the main features of minority class samples, and the second is to differentiate the generated data from the majority class samples. To tackle these challenges in binary classification, we propose the Discriminant-Autoencoder (D-AE) algorithm. It has two

有其法作用

使满足

留恋

MD-TransUNet: TransUNet with Multi-attention and Dilated Convolution for Brain Stroke Lesion Segment large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke le

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