Traditional Mathematical Morphology （TMM） is not well in structure-preserving， and the existing adaptive modified methods usually miss mathematical properties. To address the problems， a Guided Adaptive Mathematical Morphology （GAMM） for multimodal images was proposed. Firstly， the structure elements were constructed by considering the joint information of the input and the guidance images， so that the corresponding operators were more robust to the noise. Secondly， according to 3σ rule， the selected members of structure elements were able to be adapted to image contents. Finally， by using the Hadamard product of sparse matrices， the structure elements were imposed with a symmetry constraint. Both of the theoretical verification and simulation show that the corresponding operators of the proposed mathematical morphology can have important mathematical properties， such as order preservation and adjunction， at the same time. Denoising experimental results on multimodal images show that the Peak Signal-to-Noise Ratio （PSNR） of GAMM is 2 to 3 dB higher than those of TMM and Robust Adaptive Mathematical Morphology （RAMM）. Meanwhile， comparison of subjective visual effect shows that GAMM significantly outperforms TMM and RAMM in noise removal and structure preservation.

At present, the research of face frontalization mainly solves the face yaw problem, and pays less attention to the face frontalization of the side face affected by yaw and pitch at the same time in real scenes such as surveillance video. Aiming at this problem and the problem of incomplete identity information retained in front face image generated by multi-angle side faces, a Generative Adversarial Network (GAN) based on feature map symmetry and periocular feature preserving loss was proposed. Firstly, according to the prior of face symmetry, a symmetry module of the feature map was proposed. The face key point detector was used to detect the position of nasal tip point, and mirror symmetry was performed to the feature map extracted by the encoder according to the nasal tip, so as to alleviate the lack of facial information at the feature level. Finally, benefiting from the idea of periocular recognition, the periocular feature preserving loss was added in the existing identity preserving method of generated image to train the generator to generate realistic and identity-preserving front face image. Experimental results show that the facial details of the images generated by the proposed algorithm were well preserved, and the average Rank-1 recognition rate of faces with all angles under the pitch of CAS-PEAL-R1 dataset is 99.03%, which can effectively solve the frontalization problem of multi-angle side faces.

With the popularity and development of social software, more and more people join the social network, which produces a lot of valuable information, including sensitive private information. Different users have different private requirements and therefore require different levels of privacy protection. The level of user privacy leak in social network is affected by many factors, such as the structure of social network graph and the threat level of the user himself. Aiming at the personalized differential privacy preserving problem and user privacy leak level problem, a Personalized Differential Privacy based on Skyline (PDPS) algorithm was proposed to publish social network relational data. Firstly, user's attribute vector was built. Secondly, the user privacy leak level was calculated by Skyline computation method and the user dataset was segmented according to this level. Thirdly, with the sampling mechanism, the users with different privacy requirements were protected at different levels to realize personalized differential privacy and noise was added to the integreted data. Finally, the processed data were analyzed for security and availability and published. The experimental results demonstrate that compared with the traditional Personalized Differential Privacy (PDP) method on the real data set, PDPS algorithm has better privacy protection quality and data availability.

Most of existing manifold learning algorithms are not capable of dealing with new arrival samples. Although some incremental algorithms are developed via extending a specified manifold learning algorithm, most of them have some disadvantages more or less. In this paper, a novel and more Generalized Incremental Manifold Learning algorithm based on local smoothness is proposed (GIML). GIML algorithm first extracts the local smoothness structure of data set via local PCA. Then the optimal linear transformation, which transforms the local smoothness structure of new arrival sample’s neighborhood to its corresponded low-dimensional embedding coordinates, is computed. Finally the low-dimensinal embedding coordinates of new arrival samples are obtained by the optimal transformation. Extensive and systematic experiments are conducted on both artificial and real image data sets. Experimental results demonstrate that our GIML algotithm is an effective incremental manifold learning algorithm and outperforms other existing algirthms.

IP supporting network traffic of a large-scale commercial iPAS System was analyzed with variance-time graph. The result was that the traffic fits to light level self-similarity process, meaings that the traffic had the characteristic of burst, but not very strong. Analysis indicated that IP supporting network traffic distribution was very complicated and could’t be expressed as typical distribution usually used. Peak value of the traffic showed the burst. Study in this paper showed that for the same traffic process, varying sampling time scale results from different Peak value. So, in order to gain the necessary detail of the traffic, sampling time scale must be selected correctly.