Wenchao Cui, Wei Dong, Xinya Sun, K. Feng, Jun Zhang
{"title":"A Global Dynamic Capacity Risk Assessment and Prediction Method of Regional Rail Transit Network Based on Passenger Flow Monitoring","authors":"Wenchao Cui, Wei Dong, Xinya Sun, K. Feng, Jun Zhang","doi":"10.1145/3474963.3474977","DOIUrl":"https://doi.org/10.1145/3474963.3474977","url":null,"abstract":"Regional rail transit is a comprehensive rail transit system with multiple standards that is formed to meet the needs of urban agglomeration economic integration. With the continuous development of regional rail transit, the connection between different standards and between stations continues to increase, and the ripple effect of transportation risks is more prominent. Therefore, in order to reduce the impact of transportation risks on the safety of the road network, it is urgent to evaluate and predict the dynamic transportation risks of the regional rail transit network from a global perspective. In response to this problem, this paper proposes a method for evaluating and predicting regional rail transit dynamic capacity risk based on dynamic passenger flow monitoring, and establishes an SVM-based capacity risk assessment and prediction model, and finally takes the rail transit network in Chengdu as an example to verify the effectiveness of this method.","PeriodicalId":277800,"journal":{"name":"Proceedings of the 13th International Conference on Computer Modeling and Simulation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125102253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaoxu Li, Leixiao Li, Dan Deng, Hao Lin, Jing Gao, Yongsheng Wang
{"title":"A Virtual Machine Placement Strategy with Low Resource Consumption","authors":"Shaoxu Li, Leixiao Li, Dan Deng, Hao Lin, Jing Gao, Yongsheng Wang","doi":"10.1145/3474963.3474976","DOIUrl":"https://doi.org/10.1145/3474963.3474976","url":null,"abstract":"A virtual machine placement strategy based on sine and cosine perturbation and reverse learning particle swarm optimization is proposed to solve the problem of insufficient optimization of internal resource consumption in data center. First of all, the integer encoding method is used to solve the shortcoming of the tedious operation of binary encoding on the virtual machine placement problem. Secondly, the quality of the initial solution is improved by the inverse learning strategy to initialize the population, the method of sine and cosine perturbation is used to avoid the particle swarm optimization algorithm falling into the locally optimal solution, and the ability of exploration and development is explored by the open downward parabola adaptive adjustment. Then, with minimizing resource consumption as the optimization goal, a constrained optimization model for virtual machine placement in the data center is established. Finally, the relevant experiments prove that this strategy can effectively reduce resource consumption and ensure service quality, and it has a good application prospect.","PeriodicalId":277800,"journal":{"name":"Proceedings of the 13th International Conference on Computer Modeling and Simulation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115569062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Multi-layered Friend Recommendation System on Twitter","authors":"Fufeng Zheng, Long Ma","doi":"10.1145/3474963.3475848","DOIUrl":"https://doi.org/10.1145/3474963.3475848","url":null,"abstract":"Nowadays, the number of active users on social media is growing. Therefore, the friend recommendation plays a critical role in building a substantial social network. Compared with previous recommendation systems in social networks, our research is not focused on a particular direction (e.g., geographic location, tag) but introduces another field of social media, common interests among users. In the proposed recommendation system on Twitter, the common interests between two users are determined by four features retrieved from a Twitter user account: user introduction, geographic distance between the target user and candidate users, keywords in tweets, and hashtags of tweets. These features are utilized to calculate the similarities between the candidate users and the target user. In the end, a candidate user with a high similarity score is recommended to the target user.","PeriodicalId":277800,"journal":{"name":"Proceedings of the 13th International Conference on Computer Modeling and Simulation","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124122886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis and Design of Adversarial Virtual Simulation Training System","authors":"Xuwen Sun, Shixiong Li, Yangang Xing","doi":"10.1145/3474963.3474970","DOIUrl":"https://doi.org/10.1145/3474963.3474970","url":null,"abstract":"In order to improve the high degree of close to training reality for college teaching and training, an antagonistic virtual simulation training system is constructed by analyzing and designing. First, the basic process of antagonistic training is analyzed for preparation, training implantation, and training evaluation, then the process is modeled, and modeled. And then, functional requirements of adversarial virtual simulation training is analyzed and studied, after which, the technical architecture is designed, and core technique is discussed. The study lays a foundation for the design and implementation of the antagonistic virtual simulation training system in future.","PeriodicalId":277800,"journal":{"name":"Proceedings of the 13th International Conference on Computer Modeling and Simulation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131001629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"To Distinguish Full and Short Papers using Commonness of Words","authors":"Toshiro Minami, Y. Ohura","doi":"10.1145/3474963.3475846","DOIUrl":"https://doi.org/10.1145/3474963.3475846","url":null,"abstract":"Our eventual goal regarding this study is to support students with developing paper-writing skill. In order to achieve this goal, we have been trying to find characteristic features of good papers through analyzing educational and other kinds of data. We take conference papers as target data and suppose full/regular papers are good because they are chosen as reviewers evaluate them more valuable to be presented in the conference than other papers. In our series of study, we have been surveying the differences of full and short papers. In this paper, we aim to investigate further differences of them by taking different analysis method. We have been using the numbers of occurrences of each word in full/short papers as the data for analysis. In this paper we use the numbers of full/short papers that contain the word instead of the total numbers of occurrences of words. We define a new index of a word which shows how likely it is used in full or short papers. We discuss its effectiveness by applying it to an experiment of distinguishing full and short papers.","PeriodicalId":277800,"journal":{"name":"Proceedings of the 13th International Conference on Computer Modeling and Simulation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115523376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fundus images classification for Diabetic Retinopathy using Deep Learning","authors":"Chu-Hui Lee, Yi Ke","doi":"10.1145/3474963.3475849","DOIUrl":"https://doi.org/10.1145/3474963.3475849","url":null,"abstract":"Diabetes is a worldwide chronic disease, which can even affect life and has several complications. Diabetic Retinopathy is the most serious complication of diabetes. Early detection still has a chance of cure, but there are many cases of serious blindness. Today's machine learning and deep learning are significant technology, where perform excellently in many classification fields. In this paper, we modify the architecture of the VGG-16 and ResNet-50 models to classify the severity grading of Diabetic Retinopathy with the dropout concept. In addition, contrast-limited adaptive histogram equalization (CLAHE) is used in data pre-processing to improve the quality of the fundus image of diabetic retinopathy, and data expansion is used to solve the problem of data imbalance and improve training over-fitting. After the pre-processing of the fundus image and the models are modified with dropout, the confusion matrix is used to evaluate the model. The classification accuracy of the two models is 94.03% and 97.21%. The average sensitivity is over 70%, and the specificity is over 90%.","PeriodicalId":277800,"journal":{"name":"Proceedings of the 13th International Conference on Computer Modeling and Simulation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}