In recent years， more and more schools tend to use online education widely. However， learners are hard to search for their needs from the massive learning resources in the Internet. Therefore， it is very important to research the online learning resource recommendation and perform personalized recommendations for learners， so as to help learners obtain the high-quality learning resources they need quickly. The research status of online learning resource recommendation was analyzed and summarized from the following five aspects. Firstly， the current work of domestic and international online education platforms in learning resource recommendation was summed up. Secondly， four types of algorithms were analyzed and discussed： using knowledge point exercises， learning paths， learning videos and learning courses as learning resource recommendation targets respectively. Thirdly， from the perspectives of learners and learning resources， using the specific algorithms as examples， three learning resource recommendation algorithms based on learners’ portraits， learners’ behaviors and learning resource ontologies were introduced in detail respectively. Moreover， the public online learning resource datasets were listed. Finally， the current challenges and future research directions were analyzed.

Clustering is a technique to find the internal structure between data， which is a basic problem in many data-driven applications. Clustering performance depends largely on the quality of data representation. In recent years， deep learning is widely used in clustering tasks due to its powerful feature extraction ability， in order to learn better feature representation and improve clustering performance significantly. Firstly， the traditional clustering tasks were introduced. Then， the representative clustering methods based on deep learning were introduced according to the network structure， the existing problems were pointed out， and the applications of deep learning based clustering in different fields were presented. At last， the development of deep learning based clustering was summarized and prospected.

Aiming at the low efficiency of ship stowage in automated terminals， a new Fixed Set Search （FSS） algorithm based on ship stowage characteristics was proposed in order to improve the utilization of equipment resources. Firstly， on the basis of considering the general principles of ship stowage， by introducing the block operation balance factor and taking the minimization of the number of rehandles and total loading on board time with as much block operation balance as possible as the objectives， a mixed integer programming model of ship stowage in automated terminals was established based on the quay crane working plan. Then， the optimal solution was searched by fixing the elements that appeared repeatedly in the better solutions. Experimental results show that， under the instances with different scales， compared with Cplex， the proposed FSS algorithm has the rehandle number and unbalanced container number reduced by 22.3% and 11.7% on average respectively， and the objective function value optimized by 6.5% on average.Compared with the Particle Swarm Optimization （PSO） algorithm， Genetic Algorithm （GA） and Ant Colony Optimization （ACO） algorithm， the proposed FSS algorithm has the objective function value optimized by 2.1% on average， highlighting the higher stowage efficiency of the FSS algorithm. In order to increase the diversity of instances， the distribution and proportion of block stacks were adjusted. Under this circumstance， compared with the above three algorithms， the FSS algorithm has the number of unbalanced containers reduced by 19.3% on average， and has higher utilization of equipment resources.

In order to recommend trustworthy Web services, the differences between Web service recommendation and electronic commerce recommendation were analyzed, and then based on the collaborative filtering recommendation algorithm, a trustworthy Web service recommendation approach was proposed. At first, non-functional requirements of trustworthy software were evaluated. According to the evaluation results, similar users were filtered for the first time. Then, by using the rating information and basic information, the similar users were filtered for the second time. After finishing these two filtering procedures, the final recommendation users were determined. When using users' ratings information to calculate the similarity between the users, the similarity of the different services to the users was taken into consideration. When using users' basic information to calculate the similarity between the users, the Euclidean distance formula was introduced because of the nonlinear characteristics of the users. The problems of the dishonesty and insufficient number of users were also considered in the approach. At last, the experimental results show that the recommendation approach for trustworthy Web services is effective.

IP traceback is an important way to defend against distributed denial of service attack. Gong et al's IP traceback method was analyzed and the disadvantage of low speed in reconstructing path was pointed out. An improved scheme was proposed to overcome the disadvantage. The presented scheme employed the information of router interface to mark a route so as to shorten the mark length in original method. In the proposed scheme, the speed of reconstructing path was enhanced and the false positive was lowered since it was decided whether the log was detected according to the deployment of router. Moreover, the scheme can well support incremental deployment.