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

邪惡的你

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

清晰

燕麥

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

教義

追逐

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

突變

Supplement

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