Most existing visual sentiment analysis methods mainly construct visual sentiment feature representation based on the whole image. However, the local regions with objects in the image are able to highlight the sentiment better. Concerning the problem of ignorance of local regions sentiment representation in visual sentiment analysis, a visual sentiment analysis method by combining global and local regions of image was proposed. Image sentiment representation was mined by combining a whole image with local regions of the image. Firstly, an object detection model was used to locate the local regions with objects in the image. Secondly, the sentiment features of the local regions with objects were extracted by deep neural network. Finally, the deep features extracted from the whole image and the local region features were utilized to jointly train the image sentiment classifier and predict the sentiment polarity of the image. Experimental results show that the classification accuracy of the proposed method reaches 75.81% and 78.90% respectively on the real datasets TwitterⅠand TwitterⅡ, which is higher than the accuracy of sentiment analysis methods based on features extracted from the whole image or features extracted from the local regions of image.

Current recognition algorithm of pneumonia image faces two problems. First, the extracted features can not fit the pneumonia image well because the transfer learning model used by the pneumonia feature extractor has large image difference between the source dataset and the pneumonia dataset. Second, the softmax classifier used by the algorithm can not well process high-dimensional features, and there is still room for improvement in recognition accuracy. Aiming at these two problems, a pneumonia image recognition algorithm based on Deep Convolution Neural Network (DCNN) was proposed. Firstly, the GoogLeNet Inception V3 network model trained by ImageNet dataset was used to extract the features. Then, a feature fusion layer was added and random forest classifier was used to classify and forecast. Experiments were implemented on Chest X-Ray Images pneumonia standard dataset. The experimental results show that the recognition accuracy, sensitivity and specificity of the proposed model reach 96.77%, 97.56% and 94.26% respectively. The proposed model is 1.26 percentage points and 1.46 percentage points higher than the classic GoogLeNet Inception V3+Data Augmentation (GIV+DA) algorithm in the index of recognition accuracy and sensitivity, and is close to the optimal result of GIV+DA in the index of specificity.

Cloud computing data are vulnerable to be theft illegally and tampered maliciously. To solve these problems, a Dynamic Updating Ciphertext-Policy Attribute-Based Encryption (DU-CPABE) scheme which enables both data dynamic updating and security protection was proposed. Firstly, by using linear partitioning algorithm, data information was divided into fixed size blocks. Secondly, the data blocks were encrypted by using Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithm. Finally, based on conventional Merkle Hash Tree (MHT), an Address-MHT (A-MHT) was proposed for the operation of dynamically updating data in cloud computing. The theoretical analysis proved the security of the scheme, and the simulation in ideal channel showed that, for five updates, compared with CP-ABE method, the average time overhead of data update was decreased by 14.6%. The experimental results show that the dynamic updating of DU-CPABE scheme in cloud computng services can effectively reduce data update time and system overhead.

An evaluation algorithm based on HRank was proposed to evaluate the users' influence in microblog social networking platform. By introducing H parameter which used for judging the scientific research achievements of scientists and considering the user's followers and their microblog forwarding numbers, two new H-index models of followers H-index and microblog-forwarded H-index were given. Both of them could represent the users' static characters and their dynamic activities in microblog, respectively. And then the HRank model was established to make comprehensive assessment on users' influence. Finally, the experiments were conducted on Sina microblog data using the HRank model and the PageRank model, and the results were analyzed by correlation on users' influence rank and compared to the results given by Sina microblog. The results show that user influence does not have strong correlation with the number of fans, and the HRank model outperforms the PageRank model. It indicates that the HRank model can be used to identify users influence effectively.

A parallel bi-level image sharpening methodology in Compute Unified Device Architecture (CUDA) circumstance was proposed especially for the improvements on fuzzy boundaries and poor quality when enlarging low resolution photos or images. A GPU-based parallel sharpening algorithm with two stages was designed and implemented. Firstly, the parallel linear interpolation algorism was repeatedly adopted by the calculation of non-edge region and the sharpening treatments of edge area. Secondly, an improved gradient method was utilized for the further optimized images. The jagged edges of the enlarged images were basically eliminated by the proposed method, making the images much more smooth, natural, and legible. The experimental results prove that the GPU-based parallel image sharpening algorithm is superior to the currently popular algorithms in calculation efficiency and image quality, and it can be applied in sharpening images and amplifying photos.