Structure fusion based on graph convolutional networks for semi-supervised classification

Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multi-graph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks

A comparison of heuristic algorithms for custom instruction selection

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A CD8 T Cell/Indoleamine 2,3-Dioxygenase Axis Is Required for Mesenchymal Stem Cell Suppression of Human Systemic Lupus Erythematosus

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Location-Based Services and Privacy Protection under Mobile Cloud Computing

Location-based services can provide personalized services based on location information of moving objects and have already been widely used in public safety services, transportation, entertainment and many other areas. With the rapid development of mobile communication technology and popularization of intelligent terminals, there will be great commercial prospects to provide location-based services under mobile cloud computing environment. However, the high adhesion degree of mobile terminals to users not only brings facility but also results in the risk of privacy leak. The paper introduced the necessities and advantages to provide location-based services under mobile cloud computing environment, stressed the importance to protect location privacy in LBS services, pointed out new security risks brought by mobile cloud computing, and proposed a new framework and implementation method of LBS service. The cloud-based LBS system proposed in this paper is able to achieve privacy protection from the confidentiality of outsourced data and integrity of service results, and can be used as a reference while developing LBS system under mobile cloud computing environment