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IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Qian Yu WeiTao Weng Kai Zhang Kai Lei Kuai Xu Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) Peking University Shenzhen P.R. China School of Mathematical and Natural Sciences Arizona State University

ISBN: (纸本)9781479975761

Sina Weibo has become an increasingly critical social media in China for sharing latest news, marketing new products, and discussing controversial issues. The rising importance of Sina Weibo on the society makes it very important to understand “what”, “when”, “who” on hot topics that are being continuously tweeted and searched by millions of active users. In this paper, we develop a systematic approach to characterize temporal distribution of hot topics searched by Sina Weibo users over a four-month time-span and to uncover correlated hot topics that are not only tweeted by the same users, but also appear in the similar set of tweet messages. We analyze real-time Sina Weibo tweet data streams and study volume correlations and temporal gaps between user searches and tweeting activities on hot topics. In addition, we examine the correlations between hot topic searches on social media and on search engines to understand hot topics and user behaviors across different platforms. Given the challenges of analyzing massive amount of tweet data, we explore Hadoop MapReduce framework to effectively process millions of tweets from the collected data-sets, and quantify the performance benefits of MapReduce on analyzing tweet streams. To the best of our knowledge, this paper is the first effort to characterize temporal search patterns of hot topics on Sina Weibo and to study their correlations with tweeting data streams as well as search engine statistics.

关键词： Media Search engines Correlation Web search Engines TV Real-time systems

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Internet Traffic Analysis Using Community Detection and Apache Spark

Internet Traffic Analysis Using Community Detection and Apac...

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第九届网络分布式计算与知识发现国际会议( 2017 International Conference on Cyber-enabled distributed computing and knowledge discovery)

作者： Jiake Ni Weitao Weng Jiayu Chen Kai Lei Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronic and Computer Engineering(SECE)Peking University SHENZHEN 518055P.R.CHINA

With the rapid development of Internet,Internet traffic and end hosts continue to grow in *** behavior analysis for a large-scale network is becoming more and more *** address these challenges,this paper proposes an Internet traffic analysis approach based on community detection to discover community consisted of end hosts with similar traffic behavior in a large campus ***,we use only the IP-to-IP information without packet payloads to model the similarity of end hosts in campus *** the similarity graph which represent the social behavior similarity of all end hosts is ***,we leverage lab.l Propagation algorithm to discover end hosts community on the similarity *** satisfy demands for the scalab.e analysis of evergrowing Internet traffic data,a Spark-based Internet traffic analysis system is developed,including implementing the above *** experimental results based on real campus network traffic show the benefits of the proposed approach in analyzing traffic behavior of a large-scale network on host community level and detecting potential anomalous traffic *** proposed approach reduces the complexity of analyzing the traffic behavior of a large network compare with analyzing individual *** addition,the experimental results also demonstrate the Spark-based Internet traffic analysis system can analyze Internet traffic efficiently.

关键词： Internet traffic analysis community detection Apache Spark data mining

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The real implementation of NDN forwarding strategy on Android smartphone

The real implementation of NDN forwarding strategy on Androi...

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Xuewei Piao Lei Huang Kaiqi Yuan Jie Yuan Kai Lei School of Electronic and Computer Engineering (SECE) Peking University Shenzhen P.R. China Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) Peking University Shenzhen P.R. China

ISBN: (纸本)9781509014972

A prototype system of Named Data Networking (NDN) wireless communication for Android is established based on NDN Forwarding Daemon (NFD). By porting NDN to Raspberry Pi and Android smartphone, the NDN-based Android application can connect to an NDN-supported access point built on Raspberry Pi, and communicate with remote NDN Testbed. As a contribution, this paper make a preliminary exploration on deploying NDN on Android terminals and interfacing Android smartphones with multiple NDN networks, on which various NDN forwarding strategies can be tested.

关键词： Androids Humanoid robots Wireless communication Java Libraries Routing Protocols

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A Knowledge Graph based Solution for Entity Discovery and Linking in Open-domain Questions

arXiv

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arXiv 2018年

作者： Lei, Kai Zhang, Bing Deng, Yang Zhang, Dongyu Shen, Ying Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications Peking University SHENZHENP.R518055 China C.S. Depart. Harbin Institute of Technology

Named entity discovery and linking is the fundamental and core component of question answering. In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement. To overcome these difficulties, we proposed a knowledge graph based solution for QEDL and developed a system consists of Question Entity Discovery (QED) module and Entity Linking (EL) module. The method of QED module is a tradeoff and ensemble of two methods. One is the method based on knowledge graph retrieval, which could extract more entities in questions and guarantee the recall rate, the other is the method based on Conditional Random Field (CRF), which improves the precision rate. The EL module is treated as a ranking problem and Learning to Rank (LTR) method with features such as semantic similarity, text similarity and entity popularity is utilized to extract and make full use of the information in short texts. On the official dataset of a shared QEDL evaluation task, our approach could obtain 64.44% F1 score of QED and 64.86% accuracy of EL, which ranks the 2nd place and indicates its practical use for QEDL problem. Copyright © 2018, The Authors. All rights reserved.

关键词： Knowledge graph

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Performance Comparison and Evaluation of Web Development Technologies in PHP, Python, and ***

Performance Comparison and Evaluation of Web Development Tec...

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Kai Lei Yining Ma Zhi Tan Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) Peking University SHENZHEN P.R. CHINA GuangDong Poya Information & Technology Co. LTD GUANGZHOU P.R. CHINA

ISBN: (纸本)9781479979820

Large scale, high concurrency, and vast amount of data are important trends for the new generation of website. *** becomes popular and successful to build data-intensive web applications. To study and compare the performance of ***, Python-Web and PHP, we used benchmark tests and scenario tests. The experimental results yield some valuable performance data, showing that PHP and Python-Web handle much less requests than that of *** in a certain time. In conclusion, our results clearly demonstrate that *** is quite lightweight and efficient, which is an idea fit for I/O intensive websites among the three, while PHP is only suitable for small and middle scale applications, and Python-Web is developer friendly and good for large web architectures. To the best of our knowledge, this is the first paper to evaluate these Web programming technologies with both objective systematic tests (benchmark) and realistic user behavior tests (scenario), especially taking *** as the main topic to discuss.

关键词： Benchmark testing Web servers Concurrent computing Java Educational institutions

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Extracting Unknown Words from Sina Weibo via Data Clustering

Extracting Unknown Words from Sina Weibo via Data Clustering

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IEEE International Conference on Communications

作者： Kai Lei WeiYang Zhang Kai Zhang Kuai Xu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronics and Computer Engineering (SECE) Peking University School of Mathematical and Natural Sciences Arizona State University

ISBN: (纸本)9781467364300

Sina Weibo, a Twitter-like microblogging site attracting over 240 million monthly active users to tweet, retweet, and comment, has rapidly become one of the most popular social media sites in China. As many users create new and innovative words on their tweets and comments, it is necessary to extract these emerging words, which do not exist in today's Chinese vocabulary or dictionary. Towards this end, this paper proposes a novel method based on data clustering of Weibo users and tweets for extracting unknown words from Weibo tweets and comments. Specifically, relying on the similarity of the users who post the tweets, we apply a hierarchical clustering to divide Weibo data into distinct groups, e.g., sports, news stories, movies, before extraction. Comparing with the method of unclustered Weibo data, our experimental results have successfully demonstrated the benefits of the proposed data clustering scheme for improving the recall and accuracy of extracting unknown Chinese words from tweets and comments.

关键词： Entropy Context Data mining Clustering algorithms Social network services Clustering methods Couplings

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Profiling the Followers of the Most Influential and Verified Users on Sina Weibo

Profiling the Followers of the Most Influential and Verified...

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IEEE International Conference on Communications

作者： Huiyu Wang Kai Lei Kuai Xu Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronics and Computer Engineering (SECE) Peking University School of Mathematical and Natural Sciences Arizona State University

ISBN: (纸本)9781467364300

The new social media such as Twitter and Sina Weibo has become an increasingly popular channel for spreading influence, challenging traditional media such as TVs and news-papers. The most influential and verified users, also called big-V accounts on Sina Weibo often attract million of followers and fans, creating massive "celebrity-centric" social networks on the social media, which play a key role in disseminating breaking news, latest events, and controversial opinions on social issues. Given the importance of these accounts, it is very crucial to understand social networks and user influence of these accounts and profile their followers' behaviors. Towards this end, this paper monitors a selected group of influential users on Sina Weibo and collects their tweet streams as well as retweeting and commenting activities on these tweets from their followers. Our analysis on tweet data streams from Sina Weibo reveals when and what the followers comment on the tweets of these influential users, and discovers different temporal patterns and word diversity in the comments. Based on the insight gained from follower characteristics, we further develop simple and intuitive algorithms for classifying the followers into spammers and normal fans. Our experimental results demonstrate that the proposed algorithms are able to achieve an average accuracy of 95.20% in detecting spammers from the followers who have commented on the tweets of these influential accounts.

关键词： Classification algorithms Fans Social network services Media Feature extraction Standards Entropy

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Exploring the Benefits of Introducing Network Coding into Named Data Networking

Exploring the Benefits of Introducing Network Coding into Na...

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IEEE International Conference on Computational Science and Engineering, CSE

作者： Kai Lei Tao Chen Chen Peng Zhi Tan Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) School of Electronics and Computer Engineering (SECE) Peking University Shenzhen P.R. China GuangDong Poya Information & Technology Co. LTD Guangzhou P.R. China

ISBN: (纸本)9781479979820

In recent years, the focus to optimize network transmission efficiency has evolved to adopt methods that let those intermediate data transferring nodes get involved with routing, forwarding and caching. In other words, the new network architecture designs become in favor of hop-to-hop model, instead of traditional TCP-like end-to-end model. Named data networking is a promising future internet data oriented architecture which uses names instead of addresses and exchanges or forwards interest/data pair packets at each node along the path to route data for delivery. And meanwhile Network coding (NC) is a content oriented and effective method to reduce redundancy, increase network throughput and improve robustness. Nonetheless, due to NDN's current preliminary research, less research has combined these two technologies together. This paper presents some new thoughts to study on the benefits brought by integrating network coding to NDN, which can effectively improve network utilization, strengthen caching privacy, and also promote development of the NDN architecture itself.

关键词： Network coding Security Peer-to-peer computing Communication networks Encoding Bandwidth Internet

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Combining parametric and nonparametric topic model to discover microblog event

Combining parametric and nonparametric topic model to discov...

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International Conference on Information Science, Electronics and Electrical Engineering (ISEEE)

作者： Shengbing Liu Li Liu Ruzhong Cheng Peking University Beijing Beijing CN Dept. of Electronic and Computer Engineering South University of Science and Technology of China Shenzhen P.R. China Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) Peking University Shenzhen P.R. China

Compared with traditional news media, microblog holds overwhelming superiority in fast-diffusion and comprehensive coverage of topics. Microblog becomes an effective, particular and important carrier of affair information and many other text analysis tasks, e.g., event discovering based on microblog have special significance. Common tools of content analysis, such as topic model, however, experience severe data sparsity problems due to short length of microblog. Following previous researchers' idea, such as separating personal interest post from global event post, we further differentiate general topics from event topics and adopt nonparametric method to model the birth and death of event. We conduct experiments on Twitter data set, and the experimental results demonstrate that our method can not only discover event effectively, but also mine higher quality general topics.

关键词： Computational modeling Educational institutions Coherence Media Data models Twitter Computational linguistics

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A novel framework of online network coding for multicast switches with constrained buffers

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Journal of Electronics 2011年第4期28卷 460-467页

作者： Wang, Wei Yu, Li Zhu, Guangxi Li, Hui Wuhan National Lab. for Optoelectronics Division of Communiction and Intelligent Networks Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China Shenzhen Key Lab of Cloud Computing Technology and Application Shenzhen Graduate School Peking University Shenzhen 518055 China Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China

Network coding is able to address output conflicts when fanout splitting is allowed for multicast switching. Hence, it successfully achieves a larger rate region than non-coding approaches in crossbar switches. However, network coding requires large coding buffers and a high computational cost on encoding and decoding. In this paper, we propose a novel Online Network Coding framework called Online NC for multicast switches, which is adaptive to constrained buffers. Moreover, it enjoys a much lower decoding complexity O(n ²) by a Vandermonde matrix based approach, as compared to conventional randomized network coding O(n ³). Our approach realizes online coding with one coding algorithm that synchronizes buffering and coding. Therefore, we significantly reduce requirements on buffer space, while also sustaining high throughputs. We confirm the superior advantages of our contributions using empirical studies. © 2011 Science Press, Institute of Electronics, CAS and Springer-Verlag Berlin Heidelberg.

关键词： Multicast Network coding Packet switching Vandermonde matrix

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