Aiming at the problems that traditional aquatic animal image recognition methods have complex steps, low accuracy and poor generalization, and it is difficult to develop Deep Convolutional Neural Network (DCNN) model, a method based on parameter transfer strategy using fine-tune to retrain pre-trained model was proposed. Firstly, the image was preprocessed by data enhancement and so on. Secondly, on the basis of modifying the source model's fully connected classification layer, the weights of high-level convolution modules were set to be trained for adaptive adjustment. Finally, using training time and recognition accuracy on validation set as the evaluation indexes, the performance experiments were conducted on various network structures and different proportion of trainable parameters. The experimental results show that the highest retrained model classification accuracy can reach 97.4%, 20 percentage points higher than the source model, the ideal performance can be obtained when the proportion of trainable parameters is around 75%. It is proved that the fine-tune method can obtain a deep neural network image classification model with good performance under low-cost development condition.

With the rapid growth of medical literature, it is difficult for physicians to maintain up-to-date knowledge by reading biomedical literatures. An algorithm named MedRank can be used to recommend influential medications from literature by analyzing information network, based on the assumption that "a good treatment is likely to be found in a good medical article published in a good journal, written by good author(s)", recomending the most effective drugs for all types of disease patients. But the algorithm still has several problems:1) the diseases, as the inputs, are not independent; 2) the outputs are not specific drugs; 3) some other factors such as the publication time of the article are not considered; 4) there is no definition of "good" for the articles, journals and authors. An improved algorithm named HIC-MedRank was proposed by introducing H-index of authors, impact factor of journals and citation count of articles as criterion for defining good authors, journals and articles, and recommended antihypertensive agents for the patients suffered from Hypertension with Chronic Kidney Disease (CKD) by considering published time, support institutions, publishing type and some other factors of articles. The experimental results on Medline datasets show that the recommendation drugs of HIC-MedRank algorithm are more precise than those of MedRank, and are more recognized by attending physicians. The consistency rate is up to 80% by comparing with the JNC guidelines.

Concerning the broadcast encryption security problem in reality, a new identity-based anonymous broadcast encryption scheme in the standard model was proposed. In a anonymous broadcast encryption scheme, broadcaster sent encrypted data to the user via a broadcast channel, which only authorized users could decrypt and access the data; meanwhile, no one knew whom the encrypted data was sent to. Thereby the recipient user's privacy was protected. The scheme was proposed by combining with dual system encryption and composite-order bilinear groups. Based on static assumptions, the proposed scheme is chosen plaintext secure in the standard model, the ciphertext and private key in the scheme has fix length. Compared with the contrast scheme, the length of key is only two group elements, and the proposed scheme can satisfy the anonymity.

In recent years, research of almost optimal resilient Boolean functions develops rapidly, and it is important to improve the nonlinearity degree of almost optimal functions. Analysis and improvement of an almost optimal function with good performance was given, and an almost optimal function with even variables was constructed using concatenating construction method. A nonlinear optimal function with higher nonlinearity was got while maintaining its resilience and algebraic degree, which improved the performance of the function. And the construction method was also given to construct an elastic Boolean function with high nonlinearity. Analysis shows that the proposed construction method is simple and easy to implement, the nonlinearity is improved with m resilience and unchanged algebraic degree.

Traditional workflow management systems lack the abilities of handling transactions, thus cannot recover quickly enough. This paper introduced a prototype of transactional workflow by adding failure mode in modeling. When a task failed, the system could invoke the transactional algorithm with loosed atomicity to assure the data consistence, as well as decrease the work of participants. The experimental result shows that the workflow management system with transaction charactors can recover efficiently after failure.