Search

With the ever-increasing demand for Cyberspace in Defense Technology, researches surrounding Cyberspace have become a hot topic. This article firstly elaborates on the concept of Cyberspace and its attributes from the geographic, informational and social perspectives. Then based on the angle of modeling, the framework of “ Three Dimensions and Five Levels ” is proposed, and the modeling of Cyberspace map is discussed. Finally, this article discusses the possibility of intelligent defense from the perspective of knowledge space.

To solve the problem of Collaborative Pattern Mining in heterogeneous networks, we propose a collaborative pattern mining model based on mixed meta-path for heterogeneous networks. According to the heterogeneous network structure and information, multiple meta-paths are extracted, and the multiple meta-paths extracted are fused and analyzed. Then, random walk strategy is used to generate a large number of sequences. Frequent item-sets are extracted by the sequences in FP algorithm, and the collaborative patterns of nodes in the network are analyzed. The experimental analysis was carried out in two public data sets, tmdb5000 and DBLP, to verify the validity and scalability of the model.

Considering the inter-scale dependency between the coefficients, a novel progressive rotation-invariant texture retrieval means based on inter-scale dependency is proposed in this paper. Firstly, Logpolar transform and Non-Subsampled Contourlet Transform (NSCT) are combined to get rotationinvariant multi-scale and multi-direction coefficients, then Generalized Gaussian Distribution (GGD) model is used to extract the profile information from low band which could be employed further as coarse retrieval features. Afterwards, the inter-scale dependency is modeled by Non Gaussian Bivariate Model and is used as fine retrieval foundations. Experiments on Brodatz standard texture database show that, our method provides better efficiency and accuracy with lower feature dimension compared to wavelet transform and intra-scale model GGD and is proved to be an efficient rotation-invariant texture retrieval means.