Aiming at the problem that the influence of time and space information of the location was not considered when making the location recommendation by Gated Recurrent Unit (GRU) of recurrent neural network, the spatiotemporal-aware GRU model was proposed. In addition, aiming at the noise problem generated by the unrelated check-in data in check-in sequence, the next location recommendation method of SpatioTemporal-aware GRU and Attention (ST-GRU+Attention) was proposed. Firstly, time gate and distance gate were added in the GRU model by counting the time slot and distance gap between two locations. The influence of time and space information on recommending next location was controlled by setting the weight matrices. Secondly, the attention mechanism was introduced. The attention weight coefficients of the user were obtained by calculating the attention weight scores of the user preferences, and the personalized preference of the user was obtained. Finally, the objective function was constructed and the model parameters were learned by Bayesian Personalized Ranking (BPR) algorithm. The experimental results show that the accuracy of ST-GRU+Attention is improved significantly compared to the recommendation methods of Factorizing Personalized Markov Chain and Localized Region (FPMC-LR), Personalized Ranking Metric Embedding (PRME) and Spatial Temporal Recurrent Neural Network (ST-RNN), and the precision and recall of ST-GRU+Attention are increased by 15.4% and 17.1% respectively compared to those of ST-RNN which is the best of the three methods. The recommendation method of ST-GRU+Attention can effectively improve the effect of next location recommendation.

In recent years, test question resources in online education has grown at an explosive rate. It is difficult for students to find appropriate questions from the mass of question resources. Many test question recommendation methods for students have been proposed to solve this problem. However, many problems exist in traditional test question recommendation methods based on unified probalilistic matrix factorization; especially information of student knowledge points is not considered, resulting in low accuracy of recommendation results. Therefore, a kind of personalized test question recommendation method based on unified probalilistic matrix factorization was proposed. Firstly, through a cognitive diagnosis model, the student knowledge point mastery information was obtained. Secondly, the process of unified probalilistic matrix factorization was executed by combining the information of students, test questions and knowledge points. Finally, according to the difficulty range, the test questions were recommended. The experimental results show that the proposed method gets the best recommedation results in the aspect of accuracy of question recommendation for different range of difficulty, compared to other traditional recommendation methods, and has a good application prospect.

A data destruction model based on lifecycle control under cloud storage environment was proposed to solve the lack of effective data destruction mechanism for user data, and that data security was threatened and destruction time was controlled in the life cycle, which greatly limited the development of cloud services. The plain text was processed by functional transformation to generate the cipher text and metadata and avoid the complex key management. Secondly, in order to improve the controllability of data destruction, a self-destruction data objects based on controllable time was designed, which made any illegal access of expired objects to trigger the assured deletion by rewriting program, and realized the data destruction based on lifecycle control. The analysis and experimental results show that the scheme can enhance the flexibility and controllability of data destruction and reduce the performance cost, while protecting the data safely and effectively.

In order to overcome the non-person entity's interference, the difficulties in selection of feature words and muti-person influence on target personal relation extraction, this paper proposed person judgment based on decision tree, relation feature word generation based on minimum set cover and statistical approach based on three-layer sentence pattern rules. In the first step, 18 features were extracted from attribute files of China Conference on Machine Learning (CCML) competition 2015, C4.5 decision was used as the classifier, then 98.2% of recall rate and 92.6% of precision rate were acquired. The results of this step were used as the next step's input. Next, the algorithm based on minimum set cover was used. The feature word set covers all the personal relations as the scale of feature word set is maintained at a proper level, which is used to identify the relation type in text headline. In the last step, a method based on statistics of three-layer sentence pattern rules was used to filter small proportion rules and specify the sentence pattern rules based on positive and negative proportions to judge whether the personal relation is correct or not. The experimental result shows the approach acquires 82.9% in recall rate and 74.4% in precision rate and 78.4% in F1-measure, so the proposed method can be applied to personal relation extraction from text headlines, which helps to construct personal relation knowledge graph.

No method could be used to get the exact numbers of these variables in the original Koschmieder model, whose solution is an ill-posed problem. Thus, we propose a novel Koschmieder model whose solution is much easier, while the proposed model has something in common with Atmosphere Degradation Model. Then a novel method for fast image dehazing is proposed based on the proposed model, compared with the dehazing method proposed by He, the experimental results yields that out method could realize fast dehazing, while could keep the scene’s color constancy and get the same or even better contrast promotion

Directed towards the test sequencing problem in the sequential fault diagnosis for large scale systems, an algorithm based on information entropy for design the fault diagnosis strategy with least test cost was presented. The algorithm requires less computation than the traditional methods, and uses the test results, test cost, and fault probabilities efficiently. This algorithm is suitable for on-line or off-line diagnosis and maintenance process. The design process of the algorithm was presented, and an example was used to illustrate the validity of the method.