{"title":"Simulation Model Of Stampede Accident Based On Multi-Agent And Game Theory","authors":"Sui Yang, Baoyun Wang, Linna Li","doi":"10.1145/3456126.3456136","DOIUrl":"https://doi.org/10.1145/3456126.3456136","url":null,"abstract":"Crowd stampede accident, as one of the main man-made accidents in large public gathering places, has become a problem that science must face today. In order to explore the formation mechanism of crowd stampede in public places, this paper establishes a theoretical model of crowd evacuation based on multi-agent technology. This paper adopts a combination of theory-modeling-simulation method, with the Bandatia Bridge in India as the background, analyzes the changes in various stages of large-scale crowded stampede accidents from the walking path of the crowd, rationality and irrationality. The experimental results show that the model can simulate the behavior change of the crowd during evacuation, and provide theoretical and technical support for the study of the formation mechanism of crowd stampede.","PeriodicalId":174808,"journal":{"name":"Asia Service Sciences and Software Engineering Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115032370","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":"Call Attention to Stances: Detect Rumor with a Stance Attention Network","authors":"Lingyu Zeng, Bin Wu, Bai Wang","doi":"10.1145/3399871.3399890","DOIUrl":"https://doi.org/10.1145/3399871.3399890","url":null,"abstract":"With the advent of web2.0 era, social media such as microblogging platforms have become important information distribution channels. Considering the amount of users on microblogging platforms, the rumor spreading on microblogging platform could have a negative effect on individuals, groups and the whole society. Hence, automatic rumor detection method is required. Stance features are a crucial for rumor detection, because users discussing rumors tend to express more querying and denying stances. However, different user stances have different importance. Motivated by this inspiration, in this paper, we propose a Rumor Detection Model with a Stance Attention Network (RDM-SAN). The RDM-SAN consists of three modules: stance module, rumor module and integrate module. The first module is a stance module, in which recurrent neural network and convolutional neural network are used to capture grammatical and event-related information released by individual users respectively. Then attention mechanism is leveraged to make sure that the most valuable information is contained in the event stance latent representation. The second module is a rumor module in which the content features and temporal features of a microblogging event are captured. The third module is an integrate module in which stance representations and rumor representations are concatenated together to detect rumors. Experiments on a real-world dataset from Weibo platform demonstrate that our proposed model RDM-SAN improves the performance of rumor detection in terms of both efficiency and accuracy compared to other methods, and the accuracy of our model achieves 94.6%.","PeriodicalId":174808,"journal":{"name":"Asia Service Sciences and Software Engineering Conference","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123043458","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}