Due to the complexity of network penetration and the diversity of penetration methods, traditional analysis approaches analyse only a single penetration method or part of the network penetration process. Moreover, the lack of customized exploration makes it difficult to discover and analyse network penetration behaviors. Characterizing and summarizing the penetration testing process based on an interpretive visual analysis approach can enhance researchers' comprehension of penetration testing and further promote the development of network security technologies. To assist with this process, we design PTVis, a visual approach for the penetration testing process summarization based on visual narrative and auxiliary decision. PTVis consists of two primary components: (1) a visual interface that displays customized penetration testing paths, and (2) a component that effectively displays the results of penetration testing. To design PTVis, penetration testing paths that combine penetration testing methods and tools are built via cooperative multi-view and customized exploration, which facilitates the exploration of penetration testing. For evaluation, a qualitative user study is performed on two groups. The feedback from the study demonstrates that PTVis can enhance the user's knowledge of the penetration testing process.

Serious feature heterogeneity and semantic gaps exist between multi-source heterogeneous data, and existing cross-modal retrieval methods cannot effectively extract common semantic and complementary information between multi-source heterogeneous data. In this regard, an adversarial cross-modal retrieval method that fuses collaborative attention networks is proposed. The method addresses two major challenges in the cross-modal retrieval process, firstly, an information extraction algorithm based on the cooperative attention mechanism is designed, and secondly, the cooperative attention network is combined with the adversarial subspace learning algorithm to enhance the complementary capability of information in the feature subspace. The experimental results show that the proposed method has better retrieval results than similar cross-modal retrieval methods in terms of MAP metrics.

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According to the structure and environment of the spacecrafts, its necessary to have mechanical experiments on them. Besides, analysis models are also required. The combination of experiments and analysis is useful. Analysis can guide experiments ,which will lead to improve the accuracy of experiments. At the same time, the experiment data can be used to update the models. Nowadays, analysis softwares are lack of capacity in post-processing, and they cant complete the task of contrasting analysis and experiment data. This paper designs a system which is based on MSC.Patran , Oracle and Java to solve that problem.