In view of the fact that users' preferences and social interaction data are very sparse, and the fact that users may prefer products recommended by friends than recommended by foes, a regularized matrix decomposition recommendation algorithm integrating with social network and interest preference similarity was proposed. First of all, for the problem of sparse data of social relations. Global and local topological characteristics of the network were used to extract trust and distrust matrices between users respectively. Secondly, a method for calculating interest preference similarity between users was defined. Finally, in the process of matrix decomposition, the trust matrix, the distrust matrix, and the interest correlation were synthetically taken into consideration to make recommendations for the users. Experiments show that this method is superior to other regularization recommendation methods. Compared with the basic matrix decomposition model (SocialMF), SoRec, TrustMF, CTRPMF and RecSSN algorithm, the proposed algorithm reduces 1.1% to 9.5% and 2% to 10.1% respectively in the root mean square error (RMSE) and the mean absolute error (MAE), improved recommendations effectively.

In hyperspectral remote sensing image classification, the traditional active learning algorithms only use labeled data for training sample, massive unlabeled data is ignored. In order to solve the problem, a new active learning algorithm combined with unlabeled information was proposed. Firstly, by realizing triple screening of K neighbor consistency principle,predict consistency principle, and information evaluation of active learning, the unlabeled sample with a certain amount of information and highly reliable prediction label was obtained. Then, the prediction label was added to the label sample set as real label. Finally, an optimized classification model was produced by training the sample. The experimental results show that, compared with the passive learning algorithms and the traditional active learning algorithms, the proposed algorithm can obtain higher classification accuracy under the precondition of the same manual labeling cost and get better parameter sensitivity.

Lossless video coding is increasingly used because of the need of high quality videos in digital video areas. For this reason, a lossless video compression algorithm based on fuzzy logic was designed in this paper. It utilized fuzzy-logic-based method to calculate the correlation between two subblocks from neighbor frames and the interior correlation in the subblock, which can be used to decide the selection between temporal prediction and spatial prediction. A new matching rule of motion estimation was defined in temporal prediction. At last, the correlation can be adopted to estimate the parameter of Golomb coding and realize fast and efficient Golomb coding without complex calculation of estimation. The experimental results show that the proposed method has significant improvement in coding efficiency compared with the JPEG-LS.

To solve the problem that the prefetching algorithm of the current Linux kernel might mistakenly prefetch blocks under the circumstances of multithreading, according to the characteristics of disk file reading in a multi-threaded environment, this paper proposed a file prefetching algorithm with multiple prefetching points. On the basis of the original prefetching algorithm and in combination with the data access patterns of applications, the algorithm was implemented on the page caching layer of Linux. Through the experiments and analysis with IOzone, this proposed algorithm showed comparable performance with the existing algorithm of Linux in the single threaded environment and in a multithreaded one, and it took less time by 1/3 at least. The proposed algorithm helps to improve the I/O parallelism, thereby enhancing the entire computer system parallelism.

Traditional network management can not meet the needs of the Next Generation Network (NGN), so service-oriented network management is the inevitable development trend. Based on the research of active network technology and using advanced ideas of active network technology and telecommunication management network, we proposed the organization model, the function model, the communication model and the information model. To propose a new network management model: service-oriented network management model based on active network technology. The network management system based on this model can preferably and effectively manage network services.