Unsupervised feature selection attracts much attention in the field of machine learning, and is very important for dimensionality reduction and classification of high-dimensional data. The similarity between data points can be measured by several different criteria, which results in the inconsistency of the similarity measure criteria between different data points. At the same time, in existing methods, the similarity matrices are most obtained by allocation of neighbors, so that the number of the connected components is usually not ideal. To address the two problems, a Self-Adaptive Multi-measure unsupervised feature selection with Structured Graph Optimization (SAM-SGO) method was proposed with regarding the similarity matrix as a variable instead of a preset thing. By fusing different measure functions into a unified measure adaptively, various measure methods could be synthesized, the similarity matrix of data was obtained adaptively, and the relationships between data points were captured more accurately. In order to obtain an ideal graph structure, a constraint was imposed on the rank of similarity matrix to optimize the local structure of the graph and simplify the calculation. In addition, the graph based dimensionality reduction problem was incorporated into the proposed adaptive multi-measure problem, and the sparsity-inducing l _2,0 regularization constraint was introduced to obtain the sparse projection used for feature selection. Experiments on several standard datasets demonstrate the effectiveness of SAM-SGO. Compared with Local Learning-based Clustering Feature Selection (LLCFS), Dependence Guided Unsupervised Feature Selection (DGUFS) and Structured Optimal Graph Feature Selection (SOGFS) methods proposed in recent years, the clustering accuracy of this method is improved by about 3.6 percentage points averagely.

Aiming at the problems that it is difficult to improve performance and reduce power consumption in the process of High Level Synthesis (HLS), an automatic custom instructions identification method for high level synthesis was proposed. The enumeration and selection of custom instructions were implemented before high level synthesis, so as to provide a universal automatic custom instructions identification method for high level synthesis. Firstly, the high level source code was transformed into a Control Data Flow Graph (CDFG), and the source code was preprocessed. Secondly, a subgraph enumeration algorithm was used to enumerate all the connected convex subgraphs in a bottom-up manner from the Data Flow Graph (DFG) based on control data flow graph, which effectively improved the user's ability to flexibly modify the constraints. Then, considering the area, performance and code size, the subgraph selection algorithms were used to select partial optimal subgraphs as the final custom instructions. Finally, a new code was regenerated by incorporating the selected custom instructions as the input of high level synthesis. Compared with the traditional high level synthesis, the pattern selection based on frequency of occurrence reduced the area by an average of 19.1%. Meanwhile, the subgraph selection based on critical paths reduced the latency by an average of 22.3%. In addition, compared with Transitive Digraph (TD) algorithm, the enumeration efficiency of the proposed algorithm was increased by an average of 70.8%. The experimental results show that the automatic custom instructions identification method can significantly improve performance and reduce area and code size for high level synthesis in circuit design.

In order to better characterize the propagation of benign worm, this paper adopted a discrete-time model. Under discrete time, the paper analyzed the propagation of hybrid-benign worms and presented a mathematical modeling, which took account of the network influence due to the spread of malicious worms and benign worms. Finally, the simulation validated the propagation model, and the key parameters were compared and analyzed with Taylor Equation. The theoretical analysis and experimental results indicate that there exists a critical value of switch time under a defined release time of hybrid-benign worms and the certain performance of network. And when the sensitivity of the network is small enough, different switch time would have no influence on the movement of the number of the infected hosts.