To improve the performance of k-Nearest Neighbor (kNN) model on class-imbalanced data, a new kNN classification algorithm was proposed. Different from the traditional kNN, for the learning process, the majority set was partitioned into several clusters by using partitioning method (such as K-Means), then each cluster was merged with the minority set as a new training set to train a kNN model, therefore a classifier library was constructed consisting of serval kNN models. For the prediction, using a partitioning method (such as K-Means), a model was selected from the classifier library to predict the class category of a sample. By this way, it is guaranteed that the kNN model can efficiently discover local characteristics of the data, and also fully consider the effect of imbalance of the data on the performance of the classifier. Besides, the efficiency of kNN was also effectively promoted. To further enhance the performance of the proposed algorithm, Synthetic Minority Over-sampling TEchnique (SMOTE) was applied to the proposed algorithm. Experimental results on KEEL data sets show that the proposed algorithm effectively enhances the generalization performance of kNN method on evaluation measures of recall, g-mean, f-measure and Area Under ROC Curve (AUC) on majority set partitioned by random partition strategy, and it also shows great superiority to other state-of-the-art methods.

Locally Weighted Naive Bayes (LWNB) is a good improvement of Naive Bayes (NB) and Discriminative Frequency Estimate (DFE) remarkably improves the generalization accuracy of Naive Bayes. Inspired by LWNB and DFE, this paper proposed Gradually Contracting Spaces (GCS) algorithm to learn parameters of Naive Bayes. Given a test instance, GCS found a series of subspaces in global space which contained all training instances. All of these subspaces contained the test instance and any of them must be contained by others that are bigger than it. Then GCS used training instances contained in those subspaces to gradually learn parameters of Naive Bayes (NB) by Modified version of DFE (MDFE) which was a modified version of DFE and used NB to classify test instances. GSC trained Naive Bayes with all training data and achieved an eager version, which was the essential difference between GSC and LWNB. Decision tree version of GCS named GCS-T was implemented in this paper. The experimental results show that GCS-T has higher generalization accuracy compared with C4.5 and some Bayesian classification algorithms such as Naive Bayes, BaysianNet, NBTree, Hidden Naive Bayes (HNB), LWNB, and the classification speed of GCS-T is remarkably faster than LWNB.

At present, most of the privacy protection schemes are based on sophisticate zero-knowledge protocol or bilinear mapping computation, so their efficiency is low. In order to address this problem, an anonymous negotiation credentials scheme was proposed based on ring signature. On the foundation of anonymous credentials framework, an efficient discrete logarithm based ring signature scheme was constructed to protect the negotiation credentials, compared with two schemes proposed by ZHANG, et al. (ZHANG MING-WU, YANG BO, ZHU SHENG-LIN, et al. Policy-Based Signature Scheme for Credential Privacy Protecting in Trust Negotiation. Journal of Electronics & Information Technology, 2009(1): 224-227) and LIU, et al. (LIU BAILING, LU HONGWEI, ZHAO YIZHU. An efficient automated trust negotiation framework supporting adaptive policies. Proceedings of the Second International Workshop on Education Technology and Computer Science. Washington, DC: IEEE Computer Society, 2010: 96-99) the proposed scheme is more efficient.

Given that the original maximum flow algorithm set a fixed edge capacity to each edge, which caused poor quality and improper size of communities, this paper proposed an improved algorithm for mining Web communities. The algorithm considered the differences between edges in terms of importance, and assigned different capacities to different edges by transforming the significant measurements of pages evaluated by weighted PageRank algorithm to edge-transferring probability scores to measure the importance of edges, and assigning them to corresponding edges as their capacities. The experimental results show that the improved maximum flow algorithm improves the quality of Web community effectively.

An efficient way calling masking to prevent DPA(Differential Power Analysis) attacks was introduced,and the modified simple fixed-value masking method was spreaded to fixed-value masking method to prevent SODPA(Second-order Differential Power Analysis).

MBSA(Map-based BitSet Associaition Rule) algorithm was presented which used TreeMap class and a compressed BitSet class in Java to store Boolean values. MBSA algorithm scanned the transaction database only once and further database scans were replaced by BitSet logical AND operation, which efficiently speeded up the computation. MBSA algorithm had been applied to mine the association rules of red, green and blue bands associated with crop yield from remote sensing image of crop. It is useful for improve crop production.