Existing learning-based single-image deraining networks mostly focus on the effect of rain streaks in rainy images on visual imaging， while ignoring the effect of fog on visual imaging due to the increase of humidity in the air in rainy environments， thus causing problems such as low generation quality and blurred texture detail information in the derained images. To address these problems， an asymmetric unsupervised end-to-end image deraining network model was proposed. It mainly consists of rain and fog removal network， rain and fog feature extraction network and rain and fog generation network， which form two different data domain mapping conversion modules： Rain-Clean-Rain and Clean-Rain-Clean. The above three sub-networks constituted two parallel transformation paths： the rain removal path and the rain-fog feature extraction path. In the rain-fog feature extraction path， a rain-fog-aware extraction network based on global and local attention mechanisms was proposed to learn rain-fog related features by using the global self-similarity and local discrepancy existing in rain-fog features. In the rain removal path， a rainy image degradation model and the above extracted rain-fog related features were introduced as priori knowledge to enhance the ability of rain-fog image generation， so as to constrain the rain-fog removal network and improve its mapping conversion capability from rain data domain to rain-free data domain. Extensive experiments on different rain image datasets show that compared to state-of-the-art deraining method CycleDerain， the Peak Signal-to-Noise Ratio （PSNR） is improved by 31.55% on the synthetic rain-fog dataset HeavyRain. The proposed model can adapt to different rainy scenarios， has better generalization， and can better recover the details and texture information of images.

With the widespread applications of Massive Open Online Courses （MOOCs） platforms， an effective method is needed for personalized course recommendation. In view of the existing course recommendation methods， which usually use the course learning records to establish the overall static representation for users’ learning interests， while ignoring the dynamic changes of learning interests and users’ short-term learning interests， a Hierarchical and Phased Attention Network （HPAN） was proposed to carry out personalized course recommendation. In the first layer of the network， the attention network was used to obtain the user’s long- and short-term learning interests. In the second layer of the network， the user’s long- and short-term learning interests and short-term interaction sequence were combined to obtain the user’s interest vector through the attention network， then the preference value of the user’s interest vector with each course vector was calculated， and courses were recommended for the user according to the result. Experimental results on public dataset XuetangX show that， compared with the second best SHAN （Sequential Hierarchical Attention Network） model， HPAN model has the Recall@5 increased by 12.7%； compared with FPMC （Factorizing Personalized Markov Chains） model， HPAN model has the MRR@20 increased by 15.6%. HPAN model has better recommendation effect than the comparison models， and can be used for practical personalized course recommendation.

Due to the threats of Cross-Site Scripting (XSS) attack in Online Social Network (OSN), a approach combined classifiers and improved n-gram model was proposed to detect the malicious OSN webpages infected with XSS code. Firstly, similarity-based features and difference-based features were extracted to build classifiers and the improved n-gram model. After that, the classifiers and model were combined to detect malicious webpages in OSN. The experimental results show that compared with the traditional classifier detection methods, the proposed approach is more effective and the false positive rate is about 5%.

Hierarchical Distributed Latent Dirichlet Allocation (HD-LDA), a popular topic modeling technique for exploring collections, is an improved Latent Dirichlet Allocation (LDA) algorithm running in distributed environment. Mahout has realized HD-LDA algorithm in the framework of Hadoop. However the algorithm processed the whole documents of a single node in sequence, and the execution time of the HD-LDA program was very long when processing a large amount of documents. A new method was proposed to combine Hadoop with Graphic Processing Unit (GPU) to solve the above problem when transferring the computation from CPU to GPU. The application results show that combining the Hadoop with GPU which processes many documents in parallel can decrease the execution time of HD-LDA program greatly and achieve seven times speedup.