Software security testing technology has become an essential method for software developers to improve software performance and resist network attacks in the Internet age. DevSecOps （Development， Security and Operations）， as a new generation software development pattern which integrates Security and Operations into Development and maintenance， can identify the possible threats to the software and effectively evaluate the security of software， and can make software security risks within control. Therefore， starting from the process of DevOps （Development and Operations）， the various stages of DevOps involving software security testing techniques were sorted out， including source code audit， fuzzing， vulnerability scanning， penetration testing， and security crowdsourced testing techniques. And by collecting and analyzing the relevant technical literature in the last three years in well-known index databases， such as SCI， EI， SCOPUS， CNKI， CSCD and WanFang， the research status of the above techniques was summarized and the recommendations for the use of relevant testing tools were given. At the same time， aiming at the advantages and disadvantages of each technical support means， the future development directions of software development mode DevSecOps were prospected.
When mining news features and user features， the existing news recommendation models often lack comprehensiveness since they often fail to consider the relationship between the browsed news， the change of time series， and the importance of different news to users. At the same time， the existing models also have shortcomings in more fine-grained content feature mining. Therefore， a news recommendation model with deep feature fusion injecting attention mechanism was constructed， which can comprehensively and non-redundantly conduct user characterization and extract the features of more fine-grained news fragments. Firstly， a deep learning-based method was used to deeply extract the feature matrix of news text through the Convolutional Neural Network （CNN） injecting attention mechanism. By adding time series prediction to the news that users had browsed and injecting multi-head self-attention mechanism， the interest characteristics of users were extracted. Finally， a real Chinese dataset and English dataset were used to carry out experiments with convergence time， Mean Reciprocal Rank （MRR） and normalized Discounted Cumulative Gain （nDCG） as indicators. Compared with Neural news Recommendation with Multi-head Self-attention （NRMS） and other models， on the Chinese dataset， the proposed model has the average improvement rate of nDCG from -0.22% to 4.91% and MRR from -0.82% to 3.48%. Compared with the only model with negative improvement rate， the proposed model has the convergence time reduced by 7.63%. on the English dataset， the proposed model has the improvement rates reached 0.07% to 1.75% and 0.03% to 1.30% respectively on nDCG and MRR； At the same time this model always has fast convergence speed. Results of ablation experiments show that adding attention mechanism and time series prediction module is effective.
Alzheimer’s Disease （AD） is a progressive neurodegenerative disease with hidden causes， and can result in structural changes of patients’ brain regions. For assisting the doctors to make correct judgment on the condition of AD patients， an improved Three-Dimensional Principal Component Analysis Network （3DPCANet） model was proposed to classify AD by combining the mean Amplitude of Low-Frequency Fluctuation （mALFF） image of the whole brain of the subject. Firstly， functional Magnetic Resonance Imaging （fMRI） data were preprocessed， and the mALFF image of the whole brain was calculated. Then， the improved 3DPCANet deep learning model was used for feature extraction. Finally， Support Vector Machine （SVM） was used to classify features of AD patients with different stages. Experimental results show that the proposed model is simple and robust， and has the classification accuracies on Subjective Memory Decline （SMD） vs. AD， SMD vs. Late Mild Cognitive Impairment （LMCI）， and LMCI vs. AD reached 92.42%， 91.80% and 89.33% respectively， which verifies the effectiveness and feasibility of the proposed method.
Concerning that the general hybrid genetic algorithms cannot give attention to both effectiveness and efficiency, a new hybrid genetic algorithm using two-dimensional variable neighborhood coding named VNHGA was proposed. Firstly, the traditional binary coding method was replaced by a new coding method, which was designed to separate coding and synchronous inheritance for individuals. Secondly, the traditional mutation operator was replaced by a new stable mutation operator to improve efficiency. VNHGA was tested by optimization problem of multi-dimensional functions. It was verified that, after adopting the new coding method, features with more effectiveness and less efficiency were maintained when using "Baldwin effect" relative to using "Lamarckian evolution" as embedding strategy. After introducing the stable mutation operator, effectiveness was maintained and efficiency was improved at the same time, and the running time was shortened about half of before. VNHGA was also compared with other two modified hybrid genetic algorithms to exhibit its advantages. The results indicate that VNHGA is both effective and efficient, and it can be used to solve optimization problems.
To the shortage of theoretical support in the policy-making process of traffic guidance management, the research method of choice behavior with confinement mechanism of traffic information was proposed. From the perspective of human perception, the deep analysis of Multi-Source Traffic Information (MSTI) constraint rule was presented based on fuzzy clustering algorithm, then the road network environment was simulated by VISSIM and the traffic state pattern recognition model was established to simulate the mental activity of traveler under restriction of information. Then by means of Biogeme software, the choice model was constructed based on the behavior survey data, which was obtained in the road network example by using Stated Preference (SP) investigate method. Results show that the sanction of traffic information on travel behavior is very limited and the travelers prefer the preference path when traffic of this preference path is not very heavy, while this sanction enhances gradually and the path change behavior, which is influenced by the information, becomes more frequent when the preference path is more congested. The conclusions provided a new idea and reference for incomplete rational behavior research under the information environment, and also provided decision support for traffic management department.
