2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence最新文献_第5页

Review and comparison of face detection algorithms 人脸检测算法的回顾和比较

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943228

K. Dang, Shanu Sharma

引用次数: 80

Online influence maximization using rapid continuous time independent cascade model 利用快速连续时间无关级联模型实现在线影响最大化

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943175

Annu Kumari, S. Singh

{"title":"Online influence maximization using rapid continuous time independent cascade model","authors":"Annu Kumari, S. Singh","doi":"10.1109/CONFLUENCE.2017.7943175","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943175","url":null,"abstract":"Do one really know the meaning of Online Influence (OI) Maximization? Do you know why do we need to calculate the influence of social networking sites? How to measure the influence of online maximization? How does it really works? If you have been pondering for the answers of the questions then this paper will assist you to identify and connect with influences of online maximization. Influence Maximization is the problem in which subset of seed nodes are found within the social networks which maximizes influence on other nodes in their ties and relationships. Influence maximization has been developed to find out how influence gets propagated through its network. The concept of Influence Maximization lies in the selection of minimal set of seed nodes which propagates maximum of its influenciality within a network. This paper firstly comprises of previous models used in appropriate selection of seed nodes i.e. Linearly Threshold Model (LT), Classic Cascade Independent s Model(IC), Extended Classic Independent Model(EIC). Then, I proposed a new Model Rapid Continuous Time (RCT) Independent Cascade Model that can be used in the Classic Independent Model(IC).","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"35 6 1","pages":"356-361"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77063718","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: 6

Efficient management of distributive cloud computing system for scientific applications 高效管理科学应用的分布式云计算系统

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943160

Anil Bamwal

引用次数: 0

Dual band compact antenna with series of hexagonal cut and coupling structure for isolation enhancement 双频紧凑型天线，采用一系列六角形切割和耦合结构，以增强隔离

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943250

Sanjay Chouhan, Manish Gupta, D. K. Panda

引用次数: 6

Role of predictive modeling in cloud services pricing: A survey 预测建模在云服务定价中的作用:调查

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943158

Meetu Kandpal, Monica Gahlawat, Kalyani Patel

引用次数: 7

Distributed and highly-scalable WAN network attack sensing and sophisticated analysing framework based on Honeypot technology 基于蜜罐技术的分布式、高扩展性广域网攻击感知与复杂分析框架

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943186

Daniel Fraunholz, Marc Zimmermann, S. D. Antón, Jörg Schneider, H. Dieter Schotten

{"title":"Distributed and highly-scalable WAN network attack sensing and sophisticated analysing framework based on Honeypot technology","authors":"Daniel Fraunholz, Marc Zimmermann, S. D. Antón, Jörg Schneider, H. Dieter Schotten","doi":"10.1109/CONFLUENCE.2017.7943186","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943186","url":null,"abstract":"Recently, the increase of interconnectivity has led to a rising amount of IoT enabled devices in botnets. Such botnets are currently used for large scale DDoS attacks. To keep track with these malicious activities, Honeypots have proven to be a vital tool. We developed and set up a distributed and highly-scalable WAN Honeypot with an attached backend infrastructure for sophisticated processing of the gathered data. For the processed data to be understandable we designed a graphical frontend that displays all relevant information that has been obtained from the data. We group attacks originating in a short period of time in one source as sessions. This enriches the data and enables a more in-depth analysis. We produced common statistics like usernames, passwords, username/password combinations, password lengths, originating country and more. From the information gathered, we were able to identify common dictionaries used for brute-force login attacks and other more sophisticated statistics like login attempts per session and attack efficiency.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"27 1","pages":"416-421"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83303294","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: 17

Performance improvement of communication based high speed train control system with packet drops during handover under worst case scenario 基于通信的高速列车控制系统在最坏情况下切换时丢包的性能改进

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943185

Rajesh Mishra, Bhupendra Singh, Shobhna Tiwari

引用次数: 4

Comparative analysis of joint encryption and watermarking algorithms for security of biomedical images 生物医学图像安全联合加密与水印算法的比较分析

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943224

Siddhant Bansal, Garima Mehta

引用次数: 3

A study on prediction of breast cancer recurrence using data mining techniques 基于数据挖掘技术的乳腺癌复发预测研究

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943207

Uma Ojha, Savita Goel

引用次数: 76

Lexicalizing linked data for a human friendly web 为人类友好的网络词汇化链接数据

2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence Pub Date : 2017-01-01 DOI: 10.1109/CONFLUENCE.2017.7943119

Rivindu Perera, P. Nand, Wen-Hsin Yang, Kohichi Toshioka

{"title":"Lexicalizing linked data for a human friendly web","authors":"Rivindu Perera, P. Nand, Wen-Hsin Yang, Kohichi Toshioka","doi":"10.1109/CONFLUENCE.2017.7943119","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2017.7943119","url":null,"abstract":"The consumption of Linked Data has dramatically increased with the increasing momentum towards semantic web. Linked data is essentially a very simplistic format for representation of knowledge in that all the knowledge is represented as triples which can be linked using one or more components from the triple. To date, most of the efforts has been towards either creating linked data by mining the web or making it available for users as a source of knowledgebase for knowledge engineering applications. In recent times there has been a growing need for these applications to interact with users in a natural language which required the transformation of the linked data knowledge into a natural language. The aim of the RealText project described in this paper, is to build a scalable framework to transform Linked Data into natural language by generating lexicalization patterns for triples. A lexicalization pattern is a syntactical pattern that will transform a given triple into a syntactically correct natural language sentence. Using DBpedia as the Linked Data resource, we have generated 283 accurate lexicalization patterns for a sample set of 25 ontology classes. We performed human evaluation on a test sub-sample with an inter-rater agreement of 0.86 and 0.80 for readability and accuracy respectively. This results showed that the lexicalization patterns generated language that are accurate, readable and emanates qualities of a human produced language.","PeriodicalId":6651,"journal":{"name":"2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence","volume":"29 1","pages":"30-35"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91540813","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