The mixed traffic flow has an increasingly impact on the operation of urban traffic. To study the evolution law of multi-group behaviors in pedestrian crossing, we used the evolutionary game theory to establish a multi-group behavior evolution model for pedestrian crossing. The process of concern started from the risk perception and multi-group behavior choice. The evolutionary stability strategies, evolution trends, and factors affecting the evolutionary path of multi-group behaviors are discussed in this paper. This study found that evolutionary strategy equilibrium of pedestrians, drivers, and traffic managers not only relied on their own earning, but also on those of the other two groups. The factors affecting its behavior included the revenue factor and the limiting factor. Evolutionary game theory was used to analyze the multi-group interaction behavior of pedestrians, vehicle drivers, and traffic managers in the process of pedestrian crossing, as well as to analyze the behavior of traffic subjects in the process of pedestrian crossing. This paper provides a basis for decision-making for the traffic management department to manage road traffic, offering a new idea from the perspective of evolution for solving the conflict of interest at the crosswalk of the road section.
Security risks brought by web page information has been a matter that can no longer be ignored. Malicious script is a major challenge the web sites security is facing currently. According to the data from the Google Research Centre, more than 10% of web pages is malicious. Especially in China, the proportion of malicious web pages has reached 43.21%. This paper presents a detection system which is used to locate the malicious scripts in web pages. It acquires and builds up malicious code features base, URL of hidden links base etc. based on safety data published on security research web sites. The web crawler is applied to collecting web pages source code in this system and learning algorithm for classification is used to train the classifier. The classification results would be evaluated and improved in the end.
The current visualization has been difficult to adapt to the growing of big data. The paper presents method to use binned aggregation to reduce big data for visualize a variety of data types. Givens a data storage scheme of the dimensionality reduction block. Then for interactive visualization in binned plots through multivariate data tiles. For a large range of data, we cut into a lot of small pieces. When carrying out a range query, just use the divided pieces of data for this range be spliced. The big data through reduction into data block and be interactive checking among binned plots through many multivariate data tiles.
The paper presents a new way to apply text similarity computing to the Clinical Decision Support System. It can be applied to all kinds of diseases. Our method includes some traditional algorithms and their improvements, such as TF-IDF algorithm and Cosine Similarity algorithm. Besides, a new approach using TF-IDF algorithm combined eigenvector associated model to determine the case feature weights is proposed. Based on this method, the one-to-one relationship between terms and words would be changed to many-to-many. The focus of this Clinical Decision Support Track will be the retrieval of relevant biomedical articles and locating the most relevant and timely information for answering clinical questions.
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