2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)最新文献_第8页

Low Power Wireless Sensor Node Platform for Agriculture Monitoring in Argentina 阿根廷农业监测低功耗无线传感器节点平台

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00029

A. Valenzuela, Mauro Schwab, Adolfo A. Silnik, Alfredo F. Debattista, Roberto A. Kiessling

引用次数: 2

The Optimization of Big Data Platform Under the Internet of Things 物联网下的大数据平台优化

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00034

Suzhen Wang, Yanpiao Zhang, Lu Zhang, Ning Cao

引用次数: 0

Research on Probability Statistics Method for Multi-sensor Data Fusion 多传感器数据融合的概率统计方法研究

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00079

Maoli Ran, Xiangyu Bai, Fangshuo Xin, Yaping Xiang

{"title":"Research on Probability Statistics Method for Multi-sensor Data Fusion","authors":"Maoli Ran, Xiangyu Bai, Fangshuo Xin, Yaping Xiang","doi":"10.1109/CYBERC.2018.00079","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00079","url":null,"abstract":"In multi-sensor systems, data fusion is one of the key technologies for solving information diversification in wireless sensor networks. Data fusion is a process of information processing to automatically analyze and synthesize data collected by multiple sensors under certain rules to complete the required decisions or tasks, including information fusion, feature fusion, relationship fusion and decision fusion. It extends the lifespan of wireless sensor networks and improves data accuracy. It is generally considered that data fusion is an integrated process of information processing. It is generally considered that data fusion is a process of information synthesis and processing, making various information and data detected, correlated, estimated, and synthesized at multiple levels and from many aspects to obtain accurate and complete information. There are many methods for sensor data fusion, such as Bayesian method, D-S method, neural network, fuzzy reasoning, genetic algorithm, deep learning, etc. This article focuses on the application, analysis and comparison of probabilistic statistical methods in multi-sensor data fusion. The data fusion methods of probability statistics are divided into three categories: data fusion method based on estimation theory, data fusion method based on regression theory, and data fusion method based on information theory. This article just has a simple analysis on the three types from the perspective of theory and has a detailed analysis on the core Bayesian fusion in probability statistics.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121287226","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

QAM Division Based Space-Time Modulation for Two-User Uplink Massive MIMO Systems 基于QAM分割的双用户上行海量MIMO系统空时调制

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00065

G. Han, Linxin Zhang, X. Mu, Dalong Zhang, Yi Sun

引用次数: 0

Neural Network Multi-label Learning Based on Enhancing Pairwise Labels Discrimination for Obstetric Auxiliary Diagnosis 基于神经网络多标签学习的产科辅助诊断双标签识别增强方法

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00060

Weibing Long, Kunli Zhang, Hongchao Ma, Donghui Yue, Zhuang Lei

引用次数: 0

Loading Analysis of Channelized SATCOM System with Link-Margin Degree Optimization 链路裕度优化的信道化卫星通信系统负载分析

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00088

Yuewei Jia, D. Qi, Yan Shi, Jianghua Li, Zhuyun Chen, Xiaokai Zhang

引用次数: 1

Detecting Spammer on Micro-blogs Base on Fuzzy Multi-class SVM 基于模糊多类支持向量机的微博垃圾信息检测

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00016

Guangxia Xu, G. Gao, Mengxiao Hu

{"title":"Detecting Spammer on Micro-blogs Base on Fuzzy Multi-class SVM","authors":"Guangxia Xu, G. Gao, Mengxiao Hu","doi":"10.1109/CYBERC.2018.00016","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00016","url":null,"abstract":"Micro-blog has become an important information dissemination and exchange platform in people's social lives. Massive micro-blog data contains a large number of valuable information, but the micro-blog platform appears to have a lot of spam behavior problems in recent years; behavior consistent with spammers and spam micro-blogs. The spam not only affects the impact of micro-blog's data mining and decision analysis, but also seriously affects the healthy development of micro-blog platform and user experience. In this paper, a new spammer detection method based on fuzzy multi-class support vector machines (FMCSVM) is proposed in micro-blog, it combines the SVM multi-class classifier with the fuzzy mathematics theory in spammer detection. Current researches on micro-blog spammers is to analyze the characteristics of the global spammers, so that the strength of these analyses is not enough, and these researches lack the feature analysis for a certain type spammer. As a result, this will enable the spammer to escape the spam detection system. In this paper, we divide spammers into three categories by analyzing the features of micro-blog spammers, and then construct one-versus-rest SVM multi-class classifier. The fuzzy clustering method is used to deal with the mixed samples generated by the multi class classifier, and the combination classifier is obtained, which improves the detection accuracy.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128854441","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: 0

A Binary Feature Extraction Based Data Provenance System Implemented on Flink Platform 基于二进制特征提取的数据溯源系统在Flink平台上实现

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00045

Yang Wang, Lan Li, Lei Fan

{"title":"A Binary Feature Extraction Based Data Provenance System Implemented on Flink Platform","authors":"Yang Wang, Lan Li, Lei Fan","doi":"10.1109/CYBERC.2018.00045","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00045","url":null,"abstract":"Data protection and the control of information flow are basic requirements for the security operation of enterprises or organizations. The data provenance of documents is a function that records the transmission of a specific document and provenance afterwards. As an important function of enterprise information security control, it has been confronted with the trouble of high management costs. Therefore, this paper attempts to recover the document content by proactively monitoring the internal traffic data of the enterprise and restore the document and find the parent document accurately through the proposed algorithm, thereby getting rid of the shackle of traditional document tracing. In order to ensure the flexibility and scalability of the streaming data restoration, this paper tries to build algorithm modules based on Flink, a streaming process platform, by migrating key computing services to its platform. In the process, the capture agent is set at the key node to collect traffic data, which is put into the stream processing system through the message queue. The stream processing system restores the file using document restoration algorithm, and finally the file is handed over to the feature extraction module. After the feature extraction module completes the file analysis, it is stored on file systems or structed data storage systems and waits for document tracking requests. The entire system solution achieved above and the daily business of the enterprise are completely seperated, while the load on the internal network flow is also very small. On the other hand, relying on the advantages of Flink's excellent distributed features, the experiments show that the data provenance results are satisfactory.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127979672","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: 0

Design and Implementation of Video Analytics System Based on Edge Computing 基于边缘计算的视频分析系统的设计与实现

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00035

Yuejun Chen, Yinghao Xie, Yihong Hu, Yaqiong Liu, Guochu Shou

引用次数: 10

A Dynamic Proxy Based Crawler Strategy for Data Collection on CyberGIS 基于动态代理的网络地理信息系统数据采集爬虫策略

2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2018-10-01 DOI: 10.1109/CYBERC.2018.00094

Shumiao Yu, Weifeng Sun, Minghan Jia

{"title":"A Dynamic Proxy Based Crawler Strategy for Data Collection on CyberGIS","authors":"Shumiao Yu, Weifeng Sun, Minghan Jia","doi":"10.1109/CYBERC.2018.00094","DOIUrl":"https://doi.org/10.1109/CYBERC.2018.00094","url":null,"abstract":"With the development of geographic information system, digital earth and digital city play more and more important roles in life. The data generated by sensors or other edge nodes need to be collected by crawlers in the distributed systems in IoT, such as the GIS data in CyberGIS. In some edge networks, network operators have adopted methods to limit crawlers, such as blocking the request IP addresses, requiring logging in verification codes and other measures to avoid disturbance to servers. To collect data from web servers in these types of edge networks, a dynamic IP address based strategy DP-crawler is proposed to solve the anti-crawler strategies in the edge networks. DP-crawler can dynamic get proper IP addresses from a security-aware list and select the best available proxies. The security-aware list is designed to use the block-chain. Security and dynamic storage can be achieved by this method. DP-crawler is used to crawler webs, and the detailed information of Douban movies are obtained in the experiments. The experiment results show that the DP-Crawler can get more information by using the DP-Crawler.","PeriodicalId":282903,"journal":{"name":"2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128230690","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