2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)最新文献_第2页

A survey on social image understanding 社会形象理解调查

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256394

Rucai Zhou, Kuojian Lu, Yi Long, Jiaying Lu, Xinghua Cheng, Di Hu, Yanhui Gu

引用次数: 2

Modeling of political web pages spreading in WeChat networks 微信网络政治网页传播建模

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256402

Liang Liu, Bin Chen, W. Jiang, X. Qiu, Lingnan He, Kaisheng Lai

{"title":"Modeling of political web pages spreading in WeChat networks","authors":"Liang Liu, Bin Chen, W. Jiang, X. Qiu, Lingnan He, Kaisheng Lai","doi":"10.1109/BESC.2017.8256402","DOIUrl":"https://doi.org/10.1109/BESC.2017.8256402","url":null,"abstract":"Modern social media has greatly facilitated the ability and efficiency of people to access and consume information, as well as intentionally or unintentionally spread political rumors and nationalist sentiments. This paper addresses the problem of modeling of political web pages spreading in WeChat networks. At first, a large number of web pages diffused in WeChat are collected, in which more than two hundred million users are involved. The widely disseminated pages are extracted and divided into two categories: political and non-political pages. Then the topological and temporal features of these web pages are analyzed and compared with respect to cascade size, life span, width, height, average depth, and average path length. The properties of involved user's behaviors are examined in terms of viewing delay, sharing delay, and sharing probability. At last, the Unknown-View-Share-Removed (UVSR) model is employed to characterize the dynamic diffusion process of political web pages. The model is driven and validated by the empirical observations of political web pages diffused in WeChat networks. Our findings contribute to predicting and even regulating political rumors and nationalist sentiments.","PeriodicalId":142098,"journal":{"name":"2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128926342","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}

引用次数: 2

Coarse-to-fine facial landmarks localization based on convolutional feature 基于卷积特征的人脸标志粗到精定位

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256378

Huifang Li, Yidong Li, Wenhua Liu, Hai-rong Dong

{"title":"Coarse-to-fine facial landmarks localization based on convolutional feature","authors":"Huifang Li, Yidong Li, Wenhua Liu, Hai-rong Dong","doi":"10.1109/BESC.2017.8256378","DOIUrl":"https://doi.org/10.1109/BESC.2017.8256378","url":null,"abstract":"Accurate facial landmarks localization (FLL) plays an important role in face recognition, face tracking and 3D face reconstruction. It can be formulated as a regression problem, which outputs facial landmarks positions from the detected face image. Deep constitutional neural network (CNN) has achieved great success in vision tasks, but it is insignificant to use it directly. In this paper, instead of adopting CNN model straightforwardly, we combine different convolutional features with extreme machine learning (ELM) in a cascade framework to achieve accurate FLL. Specifically, we extract globally and spatially convolutional feature in the first stage for containing better localization property by training deep CNN, which takes the whole face region as input and concatenates lower layers with higher layers. Then, we extract locally and correlatedly convolutional feature in the following stages for preserving shape constraint by building multi-objective CNN, which inputs local patches centered at the current landmarks and concatenates independent subnetwork of each landmark together. Moreover, the regressor embedded in CNN is replaced by the robust ELM for accurate shape regression. Extensive experiments demonstrate that our method performs better in challenging datasets.","PeriodicalId":142098,"journal":{"name":"2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127034603","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}

引用次数: 1

Study on the social networks based on Japanese social events from name card data 基于名片数据的日本社交事件社交网络研究

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256390

Ling Tan, Shihan Wang, T. Terano

引用次数: 1

Cognitive systems in human resources 人力资源认知系统

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256384

R. Chwastek

引用次数: 1

The volatility of Bitcoin returns and its correlation to financial markets 比特币回报的波动性及其与金融市场的相关性

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256365

Nhi N. Y. Vo, Guandong Xu

{"title":"The volatility of Bitcoin returns and its correlation to financial markets","authors":"Nhi N. Y. Vo, Guandong Xu","doi":"10.1109/BESC.2017.8256365","DOIUrl":"https://doi.org/10.1109/BESC.2017.8256365","url":null,"abstract":"The 2008 financial crisis had scattered incredulity around the globe regarding traditional financial systems, which made investors and non-financial customers turn to other alternative such as digital banking systems. The existence and development of blockchain technology make cryptocurrency in recent years believably become a complete alternative to traditional ones. Bitcoin is the world's first peer-to-peer and decentralized digital cash system initiated by Nakamoto [1]. Though being the most prominent cryptocurrency, Bitcoin has not been a legal trading currency in various countries. Its exchange rate has appeared to be an exceptionally high-risk portfolio with extreme volatility, which requires a more detailed evaluation before making any decision. This paper utilizes knowledge of statistics for financial time series and machine learning to (i) fit the parametric distribution and (ii) model and forecast the volatility of Bitcoin returns, and (iii) analyze its correlation to other financial market indicators. The fitted parametric time series model significantly outperforms other standard models in explaining the stylized facts and statistical variances in the behavior of Bitcoin returns. The model forecast also outperforms some machine learning methodologies, which would benefit policy makers, banks and financial investors in trading activities for both long-term and short-term strategies.","PeriodicalId":142098,"journal":{"name":"2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130506999","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}

引用次数: 10

Urban emergency evacuation strategy 城市紧急疏散战略

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256371

Zhongwei Xie, Lin Li, Yueqing Sun, Wangping Li, Guiming Xu

引用次数: 0

Digital humanities using content-based image retrieval: The Visual Studies Toolkit 使用基于内容的图像检索的数字人文:视觉研究工具包

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256409

A. Olszewska, J. Gancarczyk

引用次数: 0

Identifying herding effect in Chinese stock market by high-frequency data 利用高频数据识别中国股市羊群效应

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256359

Yunfei Hou, Jianbo Gao, Fangli Fan, Feiyan Liu, Changqing Song

{"title":"Identifying herding effect in Chinese stock market by high-frequency data","authors":"Yunfei Hou, Jianbo Gao, Fangli Fan, Feiyan Liu, Changqing Song","doi":"10.1109/BESC.2017.8256359","DOIUrl":"https://doi.org/10.1109/BESC.2017.8256359","url":null,"abstract":"Herding behavior is thought to often occur during market frenzy, stock crashes, financial crises, as well as strong bull markets. The issue has been gaining increasing attention in recent years, in the hope that timely detection of herding behavior can be used to implement effective means to mitigate them, thus to make the market more rational. So far, herding behavior has been mainly studied using low-frequency data with methods such as LSV, PCM, CH, CKK, and HS. Such studies can only report whether herding behavior exists in a long time span, such as a few months to even a few years, and thus essentially renders all those studies irrelevant to the design of any policies for curbing herding behavior. To achieve the latter goal, it is important to realize that herding behavior is a dynamic process that may only last for a short time span, such as a few minutes. This dictates that to timely detect the herding behavior in a stock market, high frequency data must be used. Guided by this rationale, we show that computation of mutual information and cross correlation coefficient from high frequency data can indeed effectively identify herding behavior from Chinese stock markets.","PeriodicalId":142098,"journal":{"name":"2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120974827","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}

引用次数: 2

Prediction of social risk perception on petition in China 中国信访社会风险感知的预测

2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) Pub Date : 2017-10-01 DOI: 10.1109/BESC.2017.8256404

T. Xue, Huiqi Liu

{"title":"Prediction of social risk perception on petition in China","authors":"T. Xue, Huiqi Liu","doi":"10.1109/BESC.2017.8256404","DOIUrl":"https://doi.org/10.1109/BESC.2017.8256404","url":null,"abstract":"Petition attracts more attention because of its unique impact on social life and its increasing trends in China. In this study, we analyzed the causes and classification of petition in terms of social risk perception, and constructed a system of predicting indices by using online big data. First, we reclassified offline petitions in terms of social risk perception, and built online searching indices of certain kinds of petition by using data from “Google trend” and “Baidu index”. Second, we analyzed the predicting effect of social risk perception on online searching indices of petition by Granger causality analysis. Finally, we built an integral predicting model of petition by considering social risk perceptions and online searching indices at the same time. We found that the correlation between offline petitions and Baidu index of petition is more significant than that of Google index. We also found a more significant predicting effect between social risk perception and Baidu index of petition. Moreover, certain kinds of social risk perception such as economy & finance risk perception, have significant predicting effect not only on their corresponding kind of online searching indices of petitions, but also on other relevant kinds of online searching indices of petitions. Therefore, we have demonstrated the possibility of using the correlation among social risk perception indices, online searching indices of petitions and offline petitions to construct online predicting indices of petitions, from which the dominant social contradictions and their relationship in modern China are reflected.","PeriodicalId":142098,"journal":{"name":"2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC)","volume":"309 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122859834","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}

引用次数: 1