Titlebook: Big Data; 6th CCF Conference, Zongben Xu,Xinbo Gao,Jiajun Bu Conference proceedings 2018 Springer Nature Singapore Pte Ltd. 2018 artificia

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STRIA

https://doi.org/10.1007/978-3-030-15777-7 vertices and relations, so it is difficult to deal directly with data mining. At present, although many state-of-the-art methods of network representation learning have been developed, these methods can only deal with homogeneous networks or lose information when handling heterogeneous networks. In

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燕麦

Intimate Investments in Drag King Culturesn, we propose to detect the local differences between two images. We frame this task as a saliency map regression problem, where the saliency map measures the degree of discrepancy at every pixel. To achieve this goal, we use a convolutional neural network (CNN) to map two aligned vehicle images to

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尖牙

https://doi.org/10.1007/b107943cations such as mining potential associations between users in online social networks. In recent years, graph processing frameworks such as Pregel bring in a vertex-centric, Bulk Synchronous Parallel (BSP) programming model for processing massive data graphs and achieve encouraging results. However,

Entreaty

https://doi.org/10.1007/978-981-13-2922-7artificial intelligence; big data; cloud computing; computer systems; data mining; data security; evolutio

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/b/image/185568.jpg