Knowledge tracing models mainly use three types of learning behaviors data， including learning process， learning end and learning interval， but the existing studies do not fuse the above types of learning behaviors and cannot accurately describe the interactions of multiple types of learning behaviors. To address these issues， a Multi-Learning Behavior collaborated Knowledge Tracing （MLB-KT） model was proposed. First， the multi-head attention mechanism was used to describe the homo-type constraint for each type of learning behavior， then the channel attention mechanism was used to model the multi-type collaboration in three types of learning behaviors. Comparison experiments of MLB-KT， Deep Knowledge Tracing （DKT） and Temporal Convolutional Knowledge Tracing with Attention mechanism （ATCKT） models were conducted on three datasets. Experimental results show that the MLB-KT model has a significant increase in Area Under the Curve （AUC） and performs best on ASSISTments2017 dataset， the AUC is improved by 12.26% and 2.77% compared to DKT and ATCKT respectively； the results of the representation quality comparison experiments also verify that the MLB-KT model has better performance. In summary， modeling the homo-type constraint and multi-type collaboration can better determine students' knowledge status and predict their future answers.

As a multi-task meta learning algorithm， Model Agnostic Meta Learning （MAML） can use different models and adapt quickly to different tasks， but it still needs to be improved in terms of training speed and accuracy. The principle of MAML was analyzed from the perspective of Gaussian stochastic process， and a new Model Agnostic Meta Learning algorithm based on Bayesian Weight function （BW-MAML） was proposed， in which the weight was assigned by Bayesian analysis. In the training process of BW-MAML， each sampling task was regarded as following a Gaussian distribution， and the importance of the task was determined according to the probability of the task in the distribution， and then the weight was assigned according to the importance， thus improving the utilization of information in each gradient descent. The small sample image learning experimental results on Omniglot and Mini-ImageNet datasets show that by adding Bayesian weight function， for training effect of BW-MAML after 2500 step with 6 tasks， the accuracy of BW-MAML is at most 1.9 percentage points higher than that of MAML， and the final accuracy is 0.907 percentage points higher than that of MAML on Mini-ImageNet averagely； the accuracy of BW-MAML on Omniglot is also improved by up to 0.199 percentage points averagely.

Traffic flow forecasting is an important research topic for the intelligent transportation system， however， this research is very challenging because of the complex local spatio-temporal relationships among traffic objects such as stations and sensors. Although some previous studies have made great progress by transforming the traffic flow forecasting problem into a spatio-temporal graph forecasting problem， in which the direct correlations across spatio-temporal dimensions among traffic objects are ignored. At present， there is still lack of a comprehensive modeling approach for the local spatio-temporal relationships. A novel spatio-temporal hypergraph modeling scheme was first proposed to address this problem by constructing a kind of spatio-temporal hyper-relationships to comprehensively model the complex local spatio-temporal relationships. Then， a Spatio-Temporal Hyper-Relationship Graph Convolutional Network （STHGCN） forecasting model was proposed to capture these relationships for traffic flow forecasting. Extensive comparative experiments were conducted on four public traffic datasets. Experimental results show that compared with the spatio-temporal forecasting models such as Attention based Spatial-Temporal Graph Convolutional Network （ASTGCN） and Spatial-Temporal Synchronous Graph Convolutional Network （STSGCN）， STHGCN achieves better results in Root Mean Square Error （RMSE）， Mean Absolute Error （MAE） and Mean Absolute Percentage Error （MAPE）； and the comparison of the running time of different models also shows that STHGCN has higher inference speed.

The effect data of actions show a significant randomness because of lots of uncertain elements in the course of action. In order to explore the rules of warfare hidden behind the data, the effectiveness evaluation was studied based on statistical analysis method. The basic concept of action and its effectiveness were analyzed. With the simulation data produced by enhanced irreducible semi-autonomous adaptive combat neural simulation toolkit (EINSTein), a single, a group and multi group experimental methods were used to study the statistical characteristics of offensive actions and find out that to a party who has a combat advantage, compared with increased number of personnel, the increased radius of firepower can achieve better operational results. On this basis, an evaluation method of action effectiveness was proposed and validated with simulation data. Therefore, a feasible resolution is provided to evaluate the action effectiveness based on actual combat data.

Overcomplete dictionary was used to represent an image sparsely in order to improve image denoising performance. The sparse representation may represent efficiently the singular geometry of the images with the redundancy of over-complete dictionary. Global image prior model based on the sparse representation of image patches was presented in Bayesian framework. Then maximum a posteriori probability estimator for denoising image was constructed. The dictionary was composed of two orthonormal bases. A method based on singular value decomposition was used for dictionary learning. The orthonormal property was used to update the one chosen basis effectively. The method can improve the performance of image denoising. The experimental results verify the validity of the method.

For Automated Trust Negotiation (ATN) consultative process may encounter the infinite cycling problem, the causes of the cycle were analyzed and the corresponding detection algorithm was designed to find and terminate the negotiation cycle. Interdependency relationships among policies in ATN were modeled as simple graph and the model's correctness was proved. The process of calculating simple grahp's reachability matrix was analyzed and cycle detection theorem was given. The algorithm of detecting cyclic policy interdependency was designed according to the theorem. Finally, a case study verifies the feasibility of the algorithm.

The technology of Network Adress Translator (NAT) is widely used in the Internet. With this technology, computers set behind the NAT are separated to the external net. Attacker can hardly find and invade those computer behind the NAT by the conventional technique. Some principles of DDoS attack were briefly introduced and a concrete analysis about the effect of NAT on DDoS attack was given. To overcome the weakness of traditional mode in describing the propagation of DDoS attack, a new scanning strategy based on the Teredo technology and search engines was presented. Attacker could more rapidly invade computers set behind the NAT and use those computers more efficiently to actualize the DDoS attack. Compared with the conventional invasive methods, the simulation results show that the new method is more effective and feasible.

We presented a novel hybrid layout algorithm based on skeleton subgraph, which could handle the power-law graph. The key idea was to decompose the original graph into a skeleton subgraph and several stub trees, and to layout them with different graph drawing algorithms. The experiments and analysis indicate that our algorithm outperforms the traditional K-K algorithm when the size of the graph is smaller than a certain constant, and the result seems to be easier to lead the user to identify the skeleton subgraph and the stub trees, and to understand the original graph.