Causality extraction is a kind of relation extraction task in Natural Language Processing (NLP), which mines event pairs with causality from text by constructing event graph, and play important role in applications of finance, security, biology and other fields. Firstly, the concepts such as event extraction and causality were introduced, and the evolution of mainstream methods and the common datasets of causality extraction were described. Then, the current mainstream causality extraction models were listed. Based on the detailed analysis of pipeline based models and joint extraction models, the advantages and disadvantages of various methods and models were compared. Furthermore, the experimental performance and related experimental data of the models were summarized and analyzed. Finally, the research difficulties and future key research directions of causality extraction were given.

Natural Language Generation (NLG) technologies use artificial intelligence and linguistic methods to automatically generate understandable natural language texts. The difficulty of communication between human and computer is reduced by NLG, which is widely used in machine news writing, chatbot and other fields, and has become one of the research hotspots of artificial intelligence. Firstly, the current mainstream methods and models of NLG were listed, and the advantages and disadvantages of these methods and models were compared in detail. Then, aiming at three NLG technologies:text-to-text, data-to-text and image-to-text, the application fields, existing problems and current research progresses were summarized and analyzed respectively. Furthermore, the common evaluation methods and their application scopes of the above generation technologies were described. Finally, the development trends and research difficulties of NLG technologies were given.

Aiming at the problem of low detection rate of depth feature based object detection method Faster R-CNN （Faster Region-based Convolutional Neural Network） in flame detection tasks, a color-guided anchoring strategy was proposed. In this strategy, a flame color model was designed to limit the generation of anchors, which means the flame color was used to limit the generation locations of the anchors, thereby reducing the number of initial anchors and improving the computational efficiency. To further improve the computational efficiency of the network, the masked convolution was used to replace the original convolution layer in the region proposal network. Experiments were conducted on BoWFire and Corsician datasets to verify the detection performance of the proposed method. The experimental results show that the proposed method improves detection speed by 10.1% compared to the original Faster R-CNN, has the F-measure of flame detection of 0.87 on BoWFire, and has the accuracy reached 99.33% on Corsician.The proposed method can improve the efficiency of flame detection and can accurately detect flames in images.

Focusing on the issues such as high cost, long time consumption and background holes in the disparity map in naked-eye 3D applications, learning and prediction algorithm based on Convolutional Neural Network (CNN) was introduced. Firstly, the change rules of a dataset could be mastered through training and learning the dataset. Secondly, the depth map with continuous lasting depth value was attained by extracting and predicting the features of the left view in the input CNN. Finally, the right view was produced by the superposition of diverse stereo pairs after folding the predicted depth and original maps. The simulation results show that the pixel-wise reconstruction error of the proposed algorithm is 12.82% and 10.52% lower than that of 3D horizontal disparity algorithm and depth image-based rendering algorithm. In addition, the problems of background hole and background adhesion have been greatly improved. The experimental results show that CNN can improve the image quality of disparity maps.

The newly developed botnet defense technologies pose a severe challenge to botnet survivability. In order to improve the survivability of the botnet, from an attackers perspective, this article proposed a new anti-strike mechanism based on regroup, which was suitable for semi-distributed botnet. In the case that semi-distributed botnet suffered a severe blow, which caused topology broken, this mechanism could perceive the state of botnet, detect survival nodes, recover survival node and reassemble them into a new botnet. The experiments verify the effectiveness of the mechanism to effectively enhance the survivability of the semi-distributed botnet.

At present there are many kinds of technologies which can track, detect and counter botnet effectively, which are serious threats to botnet. In order to improve the survivability of botnets, with the analysis on the existing anti-botnet technology, the paper proposd a new P2P-botnet based on Kademlia protocol from an attacker's prospective. A communication encryption and node authentication mechanism was designed. The theoretical analysis shows that the mechanism can effectively address improper command attack and sybil attack. Eventually, the experimental results verify that this botnet has high survivability.

Based on certain detection characteristics of process, hidden process could be uncovered by memory searching. However, malware, with the help of developing Rootkit, could hardly be detected because its feature has been manipulated or virtual memory scan could be invalid, thus increasing the difficulty of detection. In order to address this issue, a new multi-characteristics matching approach was proposed. It was to obtain the whole physical memory image by Page Table Entry (PTE) patching, to extract the key fields from process data structure and construct a template to improve the reliability of characteristics, and to introduce similarity for preventing the detection leakage. The results show that the new detection is effective in the hidden process searching.

Trained by the Expectation Maximization (EM) algorithm, whose model parameters are randomly initialized, the performance of Probabilistic Latent Semantic Analysis (PLSA) model is quite dependent on the initialization of the model, and the result of iteration is not a global maximum, but a local one. The authors derived probabilities from Latent Semantic Analysis (LSA), and then used it to initialize the parameters of PLSA model in documents clustering. The improved PLSA could effectively solve the puzzle of random initializing of EM. It is shown that the improved algorithm has a distinct improvement in Normalized Mutual Information (NMI) and accuracy.