2019 IEEE International Conference on Intelligence and Security Informatics (ISI)最新文献_第2页

Performance Modeling of Hyperledger Sawtooth Blockchain 超级账本锯齿状区块链的性能建模

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823238

Benjamin Ampel, Mark W. Patton, Hsinchun Chen

引用次数: 44

Exploring Cognitive Dissonance on Social Media 探索社交媒体上的认知失调

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823262

Jie Bai, Qingchao Kong, Linjing Li, Lei Wang, D. Zeng

引用次数: 3

Towards an Understanding of Cryptocurrency: A Comparative Analysis of Cryptocurrency, Foreign Exchange, and Stock 对加密货币的理解:加密货币、外汇和股票的比较分析

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823373

Jiaqi Liang, Linjing Li, Weiyun Chen, D. Zeng

{"title":"Towards an Understanding of Cryptocurrency: A Comparative Analysis of Cryptocurrency, Foreign Exchange, and Stock","authors":"Jiaqi Liang, Linjing Li, Weiyun Chen, D. Zeng","doi":"10.1109/ISI.2019.8823373","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823373","url":null,"abstract":"Cryptocurrency is a cutting-edge Fintech innovation and currently a worldwide hotspot. However, the high-speed evolution of it has already caused a series of public security related events all around the world. Cryptocurrency was built initially as a possible implementation of digital currency, then various derivatives were created in a variety of fields such as financial transactions, capital management, and even nonmonetary applications. This paper aims to offer analytical insights to help understand cryptocurrency by treating it as a financial asset. We position cryptocurrency by comparing its dynamic characteristics with two traditional and massively adopted financial assets: foreign exchange and stock. Based on the daily close prices about four years, we first construct the correlation matrices and asset trees of all three markets, then conduct comparisons on five properties: volatility, centrality, clustering structure, robustness, and risk. Our investigation suggests that the dynamics of cryptocurrency are more similar to stock. As to the robustness and clustering structure, our analysis shows cryptocurrency market is more fragile than stock market, thus it is currently a high-risk financial market. Our work is the first to study cryptocurrency with the help of well-understood financial assets and may shed some light on investment decisions, regulation, and legislation.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"16 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114118910","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: 14

Consensus Mechanism in Enterprise Blockchain 企业区块链共识机制

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823142

Ayokomi Lasisi, Sonya H. Y. Hsu

引用次数: 1

A Framework for Policy Information Popularity Prediction in New Media 新媒体政策信息人气预测框架

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823415

Yin Luo, Fangfang Wang, Feifei Zhao, Jianbin Guo, Lei Wang, Yanni Hao, D. Zeng

{"title":"A Framework for Policy Information Popularity Prediction in New Media","authors":"Yin Luo, Fangfang Wang, Feifei Zhao, Jianbin Guo, Lei Wang, Yanni Hao, D. Zeng","doi":"10.1109/ISI.2019.8823415","DOIUrl":"https://doi.org/10.1109/ISI.2019.8823415","url":null,"abstract":"With the rapid development and wide application of new media, predicting the popularity of policy information on new media is of great significance for understanding and managing public opinion. However, the complexity of the diffusion patterns of policy information has brought great challenges for predicting the popularity of such information. Inspired by the methods of popularity prediction for short text information from social networks, we propose a framework for the popularity prediction of policy information. In our framework, first, the features of policy information are extracted from three dimensions: contextual information, social information and textual information. Then, effective features, such as the topic distribution, popularity competition intensity and hot information relevance, are identified by empirical analysis. Finally, the effective features are input into the prediction model to predict the popularity of policy information. We evaluate the performance of our proposed framework using a real-world dataset and the experimental results show that the framework can efficiently predict the popularity of policy information and that the features that we used are effective in improving the accuracy of policy information popularity prediction. The accurate prediction result could benefit policy makers, allowing them to make better decisions, understand and manage public opinion.","PeriodicalId":156130,"journal":{"name":"2019 IEEE International Conference on Intelligence and Security Informatics (ISI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133110146","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

SimON-Feedback: An Iterative Algorithm for Performance Tuning in Online Social Simulation SimON-Feedback:一种用于在线社交模拟性能调整的迭代算法

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823438

M. Vora, Wingyan Chung, Cagri Toraman, Yifan Huang

引用次数: 1

Context-Aware Multi-View Attention Networks for Emotion Cause Extraction 基于上下文感知的多视角注意网络情感原因提取

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823225

Xinglin Xiao, Penghui Wei, W. Mao, Lei Wang

引用次数: 13

Identifying Risks of the Internet Finance Platforms Using Multi-Source Text Data 基于多源文本数据的互联网金融平台风险识别

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823525

Donglei Zhang, Jie Bai, Lei Wang, Min He, Yin Luo

引用次数: 0

Sentiment Analysis Based on Background Knowledge Attention 基于背景知识关注的情感分析

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/ISI.2019.8823324

Changliang Li, Yujun Zhou, Saike He, Hailiang Wang

引用次数: 1

ISI 2019 TOC

2019 IEEE International Conference on Intelligence and Security Informatics (ISI) Pub Date : 2019-07-01 DOI: 10.1109/isi.2019.8823361

引用次数: 0