Using face images in the diagnosis of Obstructive Sleep Apnea （OSA） in children can reduce the burden of doctors and improve the accuracy of diagnosis. Firstly， the current methods and their limitations of OSA in children clinical diagnosis were briefly described. Then， on the basis of studying the existing methods， combining with the methods of computer-aided face diagnosis of other diseases， the computer-aided face diagnosis methods of OSA in children were divided into three types： traditional computer-aided face diagnosis methods， transfer learning based diagnosis methods， and 3D face data based diagnosis methods. The key steps of the three types of methods were summarized， and the methods used in these key steps were compared， which provides different entry points for the future research of computer-aided face diagnosis for OSA in children. Finally， the opportunities and challenges in the future research of OSA in children diagnosis were analyzed.

Support Vector Machine (SVM) is a widely used classification technique. But the scalability of SVM to handle large data sets still needs much of exploration. Core Vector Machine (CVM) is a technique for scaling up a two class SVM to handle large data sets. However, it is computationally infeasible to use CVM to deal with the data set with mass Support Vectors (SV), as its training time is related to the number of SV. In this paper, a two-stage training algorithm combining CVM with SVM (CCS) was proposed. It first employed Minimum Enclosing Ball (MEB) based CVM algorithm to determine the potential core vectors, and then used labeling method to rapidly reconstruct training set, which aim is to reduce the scale of training set. After obtaining new training samples, SVM was adopted to deal with them. The experimental results indicate that the proposed approach can reduce the training time by 30% without losing the classification accuracy, and it is an efficient method for handling large-scale classification.

A new data sharing scheme was proposed to solve the problem of error-tolerant search and fine-grained access control. This new scheme adopted the technology of locality-sensitive hashing and the predicate encryption, which allowed users to search for keywords in an error-tolerant manner, and modified the users access rights easily by updating the encrypted data. The computational complexity of updating is more optimized than the existing scheme. The theoretical analysis shows that the proposed solution is correct, safe and effective.

An unsymmetrical cross-search algorithm (UDCS) was proposed based on motion vector distribution characteristics. Search steps of using small cross-search pattern to find small motion vectors in initial phase and unsymmetrical cross-search pattern to determine search large motion vectors in subsequent steps were brought forward. Finally, the architecture of the algorithm for implementation was introduced and performance was analyzed.