Focused on the lack of basic theories for structural testing of disjunctive answer set programs， concepts of structured test coverage for disjunctive answer set programs was proposed systematically. Firstly， the test cases of disjunctive answer set programs were defined， and the logic rules in the program were determined to be the main test entities of disjunctive answer set programs. Then， the basic concepts such as rule coverage， definition coverage and loop coverage were constructed for different test targets such as rule header， rule body， and rule set to simulate the concepts such as statement coverage and branch coverage in structural testing. Finally， the calculation formula of test coverage rate for disjunctive answer set programs was proposed， and different coverage calculation methods under different types of coverage were illustrated in samples. At the same time， some special characteristics and key indicators of the disjunctive answer set programs were discussed.

Most of the current image classification methods use supervised learning or semi-supervised learning to reduce image dimension. However, supervised learning and semi-supervised learning require image carrying label information. Aiming at the dimensionality reduction and classification of unlabeled images, a mixed-order feature stacked sparse autoencoder was proposed to realize the unsupervised dimensionality reduction and classification learning of the images. Firstly, a serial stacked sparse autoencoder network with three hidden layers was constructed. Each hidden layer of the stacked sparse autoencoder was trained separately, and the output of the former hidden layer was used as the input of the latter hidden layer to realize the feature extraction of image data and the dimensionality reduction of the data. Secondly, the features of the first hidden layer and the second hidden layer of the trained stacked autoencoder were spliced and fused to form a matrix containing mixed-order features. Finally, the support vector machine was used to classify the image features after dimensionality reduction, and the accuracy was evaluated. The proposed method was compared with seven comparison algorithms on four open image datasets. The experimental results show that the proposed method can extract features from unlabeled images, realize image classification learning, reduce classification time and improve image classification accuracy.

In view of the friend recommendation problem in social networks, a friend recommendation algorithm based on the theory of three-degree influence was proposed. The relationships between social network users include not only the mutual friends, but also the other connecting relations with different lengths. By introducing the theory of three-degree influence, the algorithm took all the relationships within three-degree between users into account, while not only considering the number of mutual friends between users as the main basis of the friend recommendation. By assigning corresponding weights to connections with different distances, the strength of friend relationship between users could be calculated, which would be used as the standard for recommendation. The experimental results on Sina microblog and Facebook show that the precision and recall rate of the proposed algorithm are improved by about 5% and 0.8% respectively than that merely based on mutual friends, which indicates the better recommendation performance of the improved recommendation algorithm. It can be helpful for the social platform to improve the recommendation system and enhance the user experience.

To meet the requirement of non-uniform coverage of nodes, a Three-Dimensional Coverage Algorithm based on Virtual Force (3D-CAVF) in sensor network was introduced. In this algorithm the virtual force was applied in wireless sensor network to implement node arrangement. By the means of virtual force and the congestion degree control, the nodes could automatically cover the events, and then the nodes and density of events could present a balanced effect. According to the simulation experiment in Matlab, when the events are in T-shaped non-uniform arrangement and linear non-uniform arrangement, the efficiency of event set covering by the proposed algorithm is 3.6% and 3.1% higher than the APFA3D (Artificial Potential Field Algorithm in Three-Dimensional Space) and ECA3D (Exact Covering Algorithm in Three-Dimensional Space) respectively. The simulation results indicate that the proposed algorithm can arrange the nodes efficiently in three-dimensional wireless sensor networks.

Concerning the problems of large amount of calculation and poor accuracy of traditional matching method of plane image target point, a fast and highly precise matching method of plane image target point was proposed based on RANdom SAmple Consensus (RANSAC) random sampling algorithm. Firstly, based on the dual conic model, the ellipse's gradient vector was estimated by exploiting directly the raw gradient information in the neighborhood of an ellipse's boundary. Secondly, the ellipse's parameters and centers with the gradient vector field were matched. At last, the points in image with the target point in calibration board were matched with the help of RANSAC random sampling algorithm. The experimental results verify the method is simple and has a high degree of accuracy.

The de-aliasing Super-Resolution (SR) algorithm in image frequency domain demands some limits on frame numbers and sub-pixel shifts between frames of input Lower Resolution (LR) images, which limits the application range of this algorithm. Using single frame super-resolution method and resample function, the authors produced 16 frames images which have the same resolution as input images from each input LR image and then selected images that meet the requirements from these produced images. Therefore, a novel de-aliasing SR algorithm in image frequency domain from two to many frames of input LR images was proposed. Three simulation experimental results indicate that the proposed algorithm removes the limits on frame numbers and sub-pixel shifts between frames and makes the PSNR of SR images to be increased by about 5dB.

In this work, DDoS based on UDP was simulated in NS-2, Results show that traffic of network still has self-similarity when DDoS based on UDP is happening in the network regardless of different kind of attack model and different network load. This is significant to research of characteristic of DDoS traffic and defense against DDoS.

With the development of Web technology, accessing OLAP services through Web is a strong trend. The characteristics of accessing OLAP services based on Web were analyzed. In order to solve the problems of poor performance and license limitation, the concept of object pool was introduced into OLAP service systems. Due to the support of OLAP Object pool, web users can access the OLAP services easily and efficiently.