Image data in actual business scenarios usually presents the characteristics of rich content and complex distortion performance， which is a great challenge to the generalization of objective Image Quality Assessment （IQA） algorithms. In order to solve this problem， a No-Reference IQA （NR-IQA） algorithm was proposed， which is mainly composed of three sub-networks： Feature Extraction Network （FEN）， Feature Fusion Network （FFN）， and Adaptive Prediction Network （APN）. Firstly， the global view， local patch， and saliency view of the sample were input into the FEN together， and the global distortion， local distortion， and saliency features were extracted by Swim Transformer. Then， the cascaded Transformer encoder was used to fuse the global distortion features and local distortion features， and the potential correlation patterns of the two were explored. Inspired by the human visual attention mechanism， the saliency features were used in the FFN to activate the attention module， so that the module was able to pay additional attention to the visual salient region， so as to improve the semantic parsing ability of the algorithm. Finally， the prediction score was calculated by the dynamically constructed MultiLayer Perceptron （MLP） regression network. Experimental results on main stream synthetic and real-world distortion datasets show that compared with the DSMix （Distortion-induced Sensitivity map-guided Mixed augmentation） algorithm， the proposed algorithm improves the Spearman Rank-order Correlation Coefficient （SRCC） by 4.3% on TID2013 dataset， and the Pearson Linear Correlation Coefficient （PLCC） by 1.4% on KonIQ dataset. The proposed algorithm also demonstrates excellent generalization ability and interpretability， which can deal with the complex distortion performance in business scenarios effectively， and can make adaptive prediction according to the individual characteristics of the sample.

With the intensive research and application of cloud computing, the problems about the security and privacy protection of the data stored on the remote server become hot issues of business and academia. Traditional cryptography methods can partly solve the above problems, but bring lots of obstacles to the remote user for data query and usage as well. Searchable encryption is an option to solve this contradiction, because of which enables users to search encrypted data stored on remote server, and has become a research hotspot in the field of information security at present. An overview of the public key searchable encryption was made in this paper, of which especially focused on the origin, background and research results in recent years of the Public key Encryption with Keyword Search (PEKS). The PEKS formal definition and security model definitions were given, followed by the detail discussion about the other aspects of this domain, such as the PEKS secure channel dependency problem, the query functional improvement, et al. Finally, the future trends, hotspot and open-ended problems of this novel research area were put forward.

The existing Provable Data Possession (PDP) schemes cannot be directly applied in mobile cloud computing because the terminal' storage space and computing power are limited for the computationally intensive operations in those schemes. Addressing this issue, an Agent-based PDP scheme was proposed for mobile cloud computing. A semi-trusted security computation Agent was introduced between the terminal and the cloud server in order to help end-users complete the computationally intensive operations, so that the PDP scheme can be applied in mobile computing. A concrete PDP scheme was presented and a formal proof shows it satisfies the Chosen Plaintext Attack (CPA) security under random oracle model. The quantitative analysis results show that the scheme is fit for mobile cloud computing.