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Deep learning based point cloud registration: an overview Virtual Reality & Intelligent Hardware 2, 222 (2020); A deep learning "ticket" for single-molecule analysis of protein stoichiometry

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Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.

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We propose a novel hybrid method to analyze the security vulnerabilities in Android applications. Our method combines static analysis, which consists of metadata and data flow analyses with dynamic analysis, which includes dynamic executable scripts and application program interface hooks. Our hybrid method can effectively analyze nine major categories of important security vulnerabilities in Android applications. We design dynamic executable scripts that record and perform manual operations to customize the execution path of the target application. Our dynamic executable scripts can replace most manual operations, simplify the analysis process, and further verify the corresponding security vulnerabilities. We successfully statically analyze 5547 malwares in Drebin and 10 151 real-world applications. The average analysis time of each application in Drebin is 4.52 s, whereas it reaches 92.02 s for real-word applications. Our system can detect all the labeled vulnerabilities among 56 labeled applications. Further dynamic verification shows that our static analysis accuracy approximates 95% for real-world applications. Experiments show that our dynamic analysis can effectively detect the vulnerability named input unverified, which is difficult to be detected by other methods. In addition, our dynamic analysis can be extended to detect more types of vulnerabilities.