This letter presents a novel tensor voting mechanism — analytic tensor voting (ATV), to get rid of the difficulties in original tensor voting, especially the efficiency. One of the main advantages is its explicit voting formulations, which benefit the completion of tensor voting theory and computational efficiency. Firstly, new decaying function was designed following the basic spirit of decaying function in original tensor voting (OTV). Secondly, analytic stick tensor voting (ASTV) was formulated using the new decaying function. Thirdly, analytic plate and ball tensor voting (APTV, ABTV) were formulated through controllable stick tensor construction and tensorial integration. These make the each voting of tensor can be computed by several non-iterative matrix operations, improving the efficiency of tensor voting remarkably. Experimental results validate the effectiveness of proposed method.

Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.

In order to improve the anomaly detection efficiency of network traffic, firstly, the model is established for network flows based on complex networks. Aiming at the uncertainty and fuzziness between network traffic characteristics and network states, the deviation extent is measured from the normal network state using deviation interval uniformly, and the intuitionistic fuzzy sets (IFSs) are established for the various characteristics on the network model that the membership degree, non-membership degree and hesitation margin of the IFSs are used to quantify the ownership of values to be tested and the corresponding network state. Then, the knowledge measure (KM) is introduced into the intuitionistic fuzzy weighted geometry (IFWGω) to weight the results of IFSs corresponding to the same network state with different characteristics together to detect network anomaly comprehensively. Finally, experiments are carried out on different network traffic datasets to analyze the evaluation indicators of network characteristics by our method, and compare with other existing anomaly detection methods. The experimental results demonstrate that the changes of various network characteristics are inconsistent under abnormal attack, and the accuracy of anomaly detection results obtained by our method is higher, verifying our method has a better detection performance.