The feature representation extracted from the functional connection network based on single brain map template is not sufficient to reveal complex topological differences between patient group and Normal Control (NC) group. However, the traditional multi-template-based functional brain network definitions mostly use independent templates, ignoring the potential topological association information in functional brain networks built with each template. Aiming at the above problems, a multi-level brain map template and a method of Relationship Induced Sparse (RIS) feature selection model were proposed. Firstly, an associated multi-level brain map template was defined, and the potential relationship between templates and network structure differences between groups were mined. Then, the RIS feature selection model was used to optimize the parameters and extract the differences between groups. Finally, the Support Vector Machine (SVM) method was used to construct classification model and was applied to the diagnosis of patients with depression. The experimental results on the clinical diagnosis database of depression in the First Hospital of Shanxi University show that the functional brain network based on multi-level template achieves 91.7% classification accuracy by using the RIS feature selection method, which is 3 percentage points higher than that of traditional multi-template method.

Traditional Fuzzy C-Means (FCM) clustering algorithm only considers the characteristics of a single pixel when applied to liver CT image segmentation, and it can not overcome the influence of uneven gray scale and the problem of boundary leakage caused by blurred liver boundary. In order to solve the problems, a Spatial Fuzzy C-Means (SFCM) clustering segmentation algorithm combined with spatial constraints was proposed. Firstly, the convolution kernel was constructed by using two-dimensional Gauss distribution function, and the feature matrix could be obtained by using the convolution kernel to extract the spatial information of the source image. Then, the penalty term of spatial constraint was introduced to update and optimize the objective function to obtain a new iteration equation. Finally, the liver CT image was segmented by using the new algorithm. As shown in results, the shape of liver contour splited by SFCM is more regular when segmenting liver CT images with gray unevenness and boundary leakage. The accuracy of SFCM reaches 92.8%, which is 2.3 and 4.3 percentage points higher than that of FCM and Intuitionistic Fuzzy C-Means (IFCM). Also, over-segmentation rate of SFCM is 4.9 and 5.3 percentage points lower than that of FCM and IFCM.

Partial Least Squares (PLS) as the traditional data-driven method has the problem of poor performance of multi-modal data fault detection. In order to solve the problem, a new fault detection method was proposed, which called PLS based on Local Neighborhood Standardization (LNS) (LNS-PLS). Firstly, the original data was Gaussized by LNS method. On this basis, the monitoring model of PLS was established, and the control limits of T ² and Squared Prediction Error (SPE) were determined. Secondly, the test data was also standardized by the LNS, and then the PLS monitoring indicators of test data were calculated for process monitoring and fault detection, which solved the problem of unable to deal with multi-modal by PLS. The proposed method was applied to numerical examples and penicillin production process, and its test results were compared with those of Principal Component Analysis (PCA), K Nearest Neighbors ( KNN) and PLS. The experimental results show that, the proposed method is superior to PLS, KNN and PCA in fault detection. The proposed method has high accuracy in classification and multi-modal process fault detection.

Large amount of user rating data, involving plentiful users' opinion and preference, is produced in e-commerce applications. An construction and inference method for latent variable model (i.e., Bayesian Network with a latent variable) oriented to user preference discovery from rating data was proposed to accurately infer user preference. First, the unobserved values in the rating data were filled by Biased Matrix Factorization (BMF) model to address the sparseness problem of rating data. Second, latent variable was used to represent user preference, and the construction of latent variable model based on Mutual Information (MI), maximal semi-clique and Expectation Maximization (EM) was given. Finally, an Gibbs sampling based algorithm for probabilistic inference of the latent variable model and the user preference discovery was given. The experimental results demonstrate that, compared with collaborative filtering, the latent variable model is more efficient for describing the dependence relationships and the corresponding uncertainties of related attributes among rating data, which can more accurately infer the user preference.

In order to create novel artistic effects, a period-dynamic-image model was proposed, in which each element is a periodic function. Instead of using an array of color pixels to represent a digital image, a Fourier model was used to represent a periodic dynamic image as an array of functional pixels, and the output of each pixel was computed by a Fourier synthesis process. Then three applications with three rendering styles were put forward, including dynamic painting, dynamic distortion effects and dynamic speech balloons, to visually display the periodic dynamic images. A prototype system was constructed and a series of experiments were performed. The results demonstrate that the proposed method can effectively explore the novel artistic effects of periodic dynamic images, and it can be used as a new art media.

To solve the problem of the game of detection and stealth in the presence of clutter between Multiple Input Multiple Output (MIMO) radar and target, a new two-step water-filling was proposed. Firstly, space-time coding model was built. Then based on mutual information, water-filling was applied to distribute target interference power, and generalized water-filling was applied to distribute radar signal power. Lastly, optimization schemes in Stackelberg game of target dominant and radar dominant were achieved under strong and weak clutter. The simulation results indicate that both radar signal power allocation and trend of generalized water-filling level are affected by clutter, therefore two optimization schemes' mutual information in strong clutter environment is about half and interference factor decreases 0.2 and 0.25 separately, mutual information is less sensitive to interference. The availability of the proposed algorithm is proved.

Since the common continuous data protection system just backups the modified data directly and consumes a large amount of storage space, this paper presented a continuous data protection system based on Timely Recovery to Any Point-in-time 4 (TRAP-4). This system captured the modified data from the user by volume filter driver, and the data would be backed up to the backup center after calculation and compression. The recovery process can recover the data volume to any time-point through reverse decompressing and reorganizing the compressed data. Experiment shows this system can effectively save the storage space compared to common method. And, as the block size increases and decreases to modify the file, the system further reduces the storage space usage.

As microblog application grows rapidly, how to extract users' interested popular topic from massive microblog information automatically becomes a challenging research area. This paper studied and proposed a topic extraction algorithm of Chinese microblog based on extended topic model. In order to deal with data sparse problem of microblog, the content related microblog text would be firstly clustered to generate synthetic document. Based on the assumption that posting relationship among microblogs implied topical correlation, the traditional LDA (Latent Dirichlet Allocation) topic model was extended to model the posting relationship among microblogs. At last, Mutual Information (MI) measurement was utilized to calculate topic vocabulary after extracting topics by proposing extended LDA topic model for topic recommendation. Furthermore, a prototype system for domain-specific topical mining system, named BTopicMiner, was implemented so as to verify the effectiveness of the proposed algorithm. The experimental result shows that the proposed algorithm can extract topics from microblogs more accurately. Meanwhile, the semantic similarity between automatically calculated topic vocabulary and manually selected topic vocabulary exceeds 75% while automatically calculating topic vocabulary based on MI.

Social annotation is a form of folksonomy, which allows Web users to categorize Web resource with text tags freely. It usually implicates fundamental and valuable semantic information of Web resources. Consequently, social annotation is helpful to improve the quality of information retrieval when applied to information retrieval system. This paper investigated and proposed an improved text classification algorithm based on social annotation. Because social annotation is a kind of folksonomy and social tags are usually generated arbitrarily without any control or expertise knowledge, there has been significant variance in the quality of social tags. Under this consideration, the paper firstly proposed a quantitative approach to measure the quality of social tags by utilizing the semantic similarity between Web pages and social tags. After that, the social tags with relatively low quality were filtered out based on the quality measurement and the remained social tags with high quality were applied to extend traditional vector space model. In the extended vector space model, a Web page was represented by a vector in which the components were the words in the Web page and tags tagged to the Web page. At last, the support vector machine algorithm was employed to perform the classification task. The experimental results show that the classification result can be improved after filtering out the social tags with low quality and embedding those high quality social tags into the traditional vector space model. Compared with other classification approaches, the classification result of F1 measurement has increased by 6.2% on average when using the proposed algorithm.

Web service choreography and orchestration defines the global interaction of composite Web service and the local behavior of each participant from global and local perspectives, respectively. The conformance of each participant's local behavior to global interaction is the guarantee of the correctness of Web service composition. The paper firstly presented a set of definitions to formally describe the global interaction of composite Web service, the local behavior of each participant and the mapping rules between them based on process algebra. Accordingly, two formal judgmental rules for the conformance of each participant's local behavior to global interaction were proposed. The two formal rules were based on the relationship between the transition of global interaction and local process and bisimulation theorem. At last, the conformance formal checking approach was described through a case study. The result of the case study shows that the proposed conformance definition of Web service composition and conformance checking approach can formally check the conformance of Web service composition effectively. As a result, the correctness of Web service composition can be guaranteed.

With the rapid development of semantic Web,ontology played a prominent role on it.In the process of information integration,ontology solved the semantic heterogeneity problem of distributed, heterogeneous and autonomous data source.This paper introduces a ontology-based hybrid approach of inforamtion integration , which provides users the same interface for accessing to data corresponding to the semantic.

The biological Immune System(IS) is highly complicated and aimed at detecting and removing the viruses. There’re many similarities between the computer security system and living organism’s IS. So the researches on the similarities could provide important clues about how to construct robust computer security system. Based on the theory of lymphocyte activation, a behavioral characteristic detecting model based on the self-replicating behavior of the viruses is brought forward. A validity analysis and experiment was made. The results of the experiment show that this model could become a new try to detect the viruses according to the replication behavior of the viruses. And at the same time it effectively reduces the problem of false negative and false positive in the process of distinguish between self and non-self.