Great convenience has been brought by the centralized control plane of Software-Defined Network (SDN), but a lot of security risks have been introduced into it as well. In the light of single point failure, unknown vulnerabilities and back doors, static configuration and other security problems of the controller, a secure architecture for SDN based on Byzantine protocol was proposed, in which the Byzantine protocol was executed between controllers and each switching device was controlled by a controller view and control messages were decided by several controllers. Furthermore, the dynamics and heterogeneity were introduced into the proposed structure, so that the attack chain was broken and the capabilities of network active defense were enhanced; moreover, based on the quantification of the controller heterogeneity, a two-stage algorithm was designed to seek for the controller view, so that the availability of the network and the security of the controller view were ensured. Simulation results show that compared with the traditional structure, the proposed structure is more resistant to attacks.

To solve the problem of relevance model based on unstable clustering estination and its effect on retrieval performance, a new Stable Semantic Relevance Model (SSRM) was proposed. The feedback data set was first formed by using the top N documents from user initial query, after the stable number of semantic clusters had been detected, SSRM was estimated by those stable semantic clusters selected according to higher user-query similarity. Finally, the SSRM retrieval performance was verified by experiments. Compared with Relevance Model (RM), Semantic Relevance Model (SRM) and the clustering-based retrieval methods including Cluster-Based Document Model (CBDM), LDA-Based Document Model (LBDM) and Resampling, SSRM has improvement of MAP by at least 32.11%, 0.41%, 23.64%,19.59%, 8.03% respectively. The experimental results show that retrieval performance can benefit from SSRM.

In order to effectively improve the accuracy of sentiment orientation and intensity analysis of unstructured Web financial text, a sentiment analysis algorithm for Web financial text based on semantic rule (SAFT-SR) was proposed. The algorithm extracted features of financial text based on Apriori, constructed financial sentiment lexicon and semantic rules to recognize sentiment unit and intensity, and figured out the sentiment orientation and intensity of text. Experiment results demonstrate that SAFT-SR is a promising algorithm for sentiment analysis on financial text. Compared with Ku’s algorithm, in sentiment orientation classification, SAFT-SR has better classification performance and increases F-measure, recall and precision; in sentiment intensity analysis, SAFT-SR reduces error and is closer to expert mark.

An important component of reconfigurable task scheduling is how to hide and reduce the configuration time. A reconfigurable hybrid task scheduling algorithm was proposed at solving the problem that the hybrid task was relevant for software and hardware simultaneously. The task and its chronological order should be figured out first by means of pre-configuration and priority algorithm and the successive task should be hidden into the run-time for predecessor task afterwards. In the meantime, the strategy of configuration reuse can be adopted in order to reduce the quantity of configuration for same tasks. Compared to the existing algorithms, the new algorithm is much more effective and its cost is less.

To solve the low classification accuracy and poor interpretability of selected features in traditional credit risk evaluation, a new model using Sparse Bayesian Learning (SBL) to evaluate personal credit risk (SBLCredit) was proposed in this paper. The SBLCredit utilized the advantages of SBL to get as sparse as possible solutions under the priori knowledge on the weight of features, which led to both good classification performance and effective feature selection. SBLCredit improved the classification accuracy of 4.52%, 6.40%, 6.26% and 2.27% averagely when compared with the state-of-the-art K-Nearest Neighbour (KNN), Nave Bayes, decision tree and support vector machine respectively on real-world German and Australian credit datasets. The experimental results demonstrate that the proposed SBLCredit is a promising method for credit risk evaluation with higher accuracy and fewer features.