Due to the conflict of interest between the component services of the cloud composite service from different service providers, it is necessary to control the information flow of the cloud services to avoid the flow of sensitive information between the conflicting component services. For the conflict of interest in cloud composite service, it was formally described that the information flow of complex composite structure with the weighted directed graph model of cloud composite service, and defined that the concept of the dependency relation of data and the alliance relation of cloud services. Moreover, the conflict of interest relation was extended to the composite service-conflict of interest relation in Chinese Wall policy. Based on this, the Chinese Wall-based Cloud Composite Service Information Flow Control (CW-CCSIFC) model was proposed and the formal description as well as the proof of the relevant theorems was given. The analysis shows that the CW-CCSIFC model can block the illegal information flow between cloud services with conflicting interests and protect the information flow security of cloud composite service.

Aiming at the problem that the traditional Graph Transduction (GT) algorithm is computationally intensive and inaccurate, a semi-supervised classification algorithm based on C-means clustering and graph transduction was proposed. Firstly, the Fuzzy C-Means (FCM) clustering algorithm was used to pre-select unlabeled samples and reduce the range of the GT algorithm. Then, the k-nearest neighbor sparse graph was constructed to reduce the false connection of the similarity matrix, thereby reducing the time of composition, and the label information of the primary unlabeled samples was obtained by means of label propagation. Finally, combined with the semi-supervised manifold hypothesis model, the extended marker data set and the remaining unlabeled data set were used to train the classifier, and then the final classification result was obtained. In the Weizmann Horse data set, the accuracy of the proposed algorithm was more than 96%, compared with the traditional method of only using GT to solve the dependence problem on the initial set of labels, the accuracy was increased by at least 10%. The proposed algorithm was applied directly to the terracotta warriors and horses, and the classification accuracy was more than 95%, which was obviously higher than that of the traditional graph transduction algorithm. The experimental results show that the semi-supervised classification algorithm based on C-means clustering and graph transduction has better classification effect in image classification, and it is of great significance for accurate classification of images.

The neighboring relationship of sketch patches and photo patches on the manifold cannot always reflect their intrinsic data structure. To resolve this problem, a Locality-Constrained Neighbor Embedding (LCNE) based face sketch-photo synthesis algorithm was proposed. The Neighbor Embedding (NE) based synthesis method was first applied to estimate initial sketches or photos. Then, the weight coefficients were constrained according to the similarity between the estimated sketch patches or photo patches and the training sketch patches or training photo patches. Subsequently, alternative optimization was deployed to determine the weight coefficients, select K candidate image patches and update the target synthesis patch. Finally, the synthesized image was generated by merging all the estimated sketch patches or photo patches. In the contrast experiments, the proposed method outperformed the NE based synthesis method by 0.0503 in terms of Structural SIMilarity (SSIM) index and by 14% in terms of face recognition accuracy. The experimental results illustrate that the proposed method resolves the problem of weak compatibility among neighbor patches in the NE based method and greatly alleviates the noises and deformations in the synthetic image.

0/1 Knapsack Problems is a typical optimization problem in Operations Research, Genetic Algorithms is one of the most commonly evolutionary algorithms used to solve the problems. This paper proposed an improved adaptive genetic algorithm. In this algorithm, we introduce the evolution number into the crossover probability and mutation probability formula、improve the crossover operator and mutation operator, and add a pattern replacement operation. The simulation results show that, the solution speed and the optimal solution have greatly improved with our algorithms.