When samples from different classes over classification boundary are seriously overlapped, cluster assumption may not well reflect the real data distribution, so that semi-supervised classification methods based cluster assumption may yield even worse performance than their supervised counterparts. For the above unsafe semi-supervised classification problem, an Adjusted Cluster Assumption and Pairwise Constraints Jointly based Semi-Supervised Support Vector Machine classification method (ACA-JPC-S3VM) was proposed. On the one hand, the distances of individual unlabeled instances to the distribution boundary were considered in learning, which alleviated the degradation of the algorithm performance in such cases to some extent. On the other hand, the information of pairwise constraints was introduced to the algorithm to make up for its insufficient use of supervision information. The experimental results on the UCI dataset show that the performance of ACA-JPC-S3VM method would never be lower than that of SVM (Support Vector Machine), and the average accuracy is 5 percentage points higher than that of SVM when the number of labeled samples is 10. The experimental results on the image classification dataset show that the semi-supervised classification methods such as TSVM (Transductive SVM) have different degrees of unsafety learning (similar or worse performance than SVM) while ACA-JPC-S3VM can learn safely. Therefore, ACA-JPC-S3VM has better safety and correctness.

The accuracy of the speech endpoint detection has a direct impact on the precision of speech recognition, synthesis, enhancement, etc. To improve the effectiveness of speech endpoint detection, an algorithm based on critical band and energy entropy was proposed. It took full advantage of the frequency distribution of human auditory characteristics, and divided the speech signals according to critical bands. Combined with the different distribution of energy entropy of each critical band of the signals respectively in the speech segments and noise segments, speech endpoint detection under different background noises was completed. The experimental results indicate that the average accuracy of the newly proposed algorithm is 1.6% higher than the traditional short-time energy algorithm. The proposed method can achieve the detection of speech endpoint under various noise environment of low Signal to Noise Ratio (SNR).

Based on the Powell algorithm and Adaptive Artificial Fish Swarm Algorithm (AAFSA), a hybrid artificial fish swarm algorithm (AAFSA-Powell) for global optimization problems was presented by inserting Powell algorithm into AAFSA. As a local search operator, Powell algorithm has strong local search ability, whereas artificial fish swarm algorithm has global convergence. Therefore, the hybrid algorithm is capable of improving the global search ability of the algorithm, as well as reducing the computational burden. The numerical experimental results show that the algorithm can converge quickly with high adjustment.

A novel text zero-watermarking algorithm based on chaotic mapping was brought forward.The features of presented method are as follows: First,there are no modifications to the text document among the process of watermarking embedding,thus the watermarking is perceptually invisible.Second,the watermarking is robust against copying,cutting and formatted modifying.Third,the watermarking can be detected only by using a secret key and doesn’t need the original text.