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International Conference on Advanced Communication Technology (ICACT)

作者： Yong-Ju Lee Myungcheol Lee Mi-Young Lee Sung Jin Hur Okgee Min Big Data SW Platform Research Department Cloud Computing Research Department Electronics and Telecommunication Research Institute Daejeon South Korea

ISBN: (纸本)9781467381161

This paper outlines big data infrastructure for processing data streams. Our project is distributed stream computing platform that provides cost-effective and large-scale big data services by developing data stream management system. This research contributes to advancing feasibility of big data processing for distributed, real-time computation even when they are overloaded.

关键词： big data Ports (Computers) data models Twitter Sparks Synchronization

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An Entropy-based Probabilistic Forwarding Strategy in Named data Networking

An Entropy-based Probabilistic Forwarding Strategy in Named ...

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IEEE International Conference on Communications

作者： Kai Lei Jiawei Wang Jie Yuan Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology & Applications School of Electronics and Computer Engineering (SECE) Peking University

ISBN: (纸本)9781467364300

The forwarding strategy is the key to the resiliency and efficiency of Named data Networking (NDN), which is a new and fundamental research area. For forwarding strategy, dynamically selecting an optimal interface from multiple alternative interfaces to forward an Interest packet is indeed a multiple attribute decision making (MADM) problem. In this paper, an entropy-based probabilistic forwarding (EPF) strategy is proposed to make a stochastic interface selection based on the combination of interfaces' dynamic availabilities and static routing information, which achieves better load balance in comparison with deterministic interface selection. By objectively assigning weights to attributes and considering multiple real-time network condition metrics, EPF can obtain the availabilities of interfaces more accurately and comprehensively. Since additional network metrics can be easily added and integrated into interfaces' assessment model, EPF provides good extensibility. In addition, we innovatively define two parameters (γ, δ) which can be used to trade off the effect factors between static routing information and dynamic running status of interfaces to customize EPF strategy for different network and application scenarios. Experiments show that EPF can realize preferable load balance and achieve higher throughput compared to the representative BestRoute forwarding strategy.

关键词： Entropy Throughput Routing Probabilistic logic Load modeling Packet loss

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Kullback Leibler divergence for image quantitative evaluation

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AIP Conference Proceedings 2016年第1期1750卷

作者： Hang See Pheng Siti Mariyam Shamsuddin Wong Yee Leng Razana Alwee 1UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia 2Soft Computing Research Group Faculty of Computing Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia

Medical imaging has been expanding ever since to give diagnostic information through different types of modalities. Currently, there are many types of modalities such as Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRI), X-rays (plain radiography), Positron Emission Tomography (PET) scan and Ultrasonographic diagnostics (USG), available in the field of medical and surgical. These modalities are widely used in clinical diagnosis and development of research in education. In terms of image quality, the qualitative analysis was always used to evaluate the quality of output image from classification results. By qualitative analysis, the researchers were able to judge the precision of detected lesion and hence calculated the accuracy of detection through the testing cases. However, the qualitative analysis was sometimes subjective and the verification from more than one radiologist was needed to confirm the results of classification. Therefore, the quantitative analysis was also needed to ensure the results from the classification algorithm can be assessed objectively. In this study, we propose pixel-based approach of Kullback Leibler (KL) divergence in assessing the medical images. Unlike the standard statistical analysis, the evaluation using KL divergence does not require testing of hypothesis or confidence interval construction based on the mean and standard deviation. The proposed framework of KL is useful to provide a descriptive measure for the purpose of summarizing data. Firstly, both of the original and computed images are normalized where the sum of all intensities is equal to one. Then, the probability distribution is calculated by column using function of hist (HO) and hist (HA) and each of the column are expressed as data vector h0i = {h01, h02, h03, h0i} and hAi = {hA1, hA2, hA3, hAi} respectively. In the computation of probability distribution, the function of hist bins the elements in each data vector of and into 10 equally spaced container

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Erratum: “Influence of heavy metal materials on magnetic properties of Pt/Co/heavy metal tri-layered structures” [Appl. Phys. Lett. 110, 012405 (2017)]

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Applied Physics Letters 2017年第10期110卷

作者： Boyu Zhang Anni Cao Junfeng Qiao Minghong Tang Kaihua Cao Xiaoxuan Zhao Sylvain Eimer Zhizhong Si Na Lei Zhaohao Wang Xiaoyang Lin Zongzhi Zhang Mingzhong Wu Weisheng Zhao 1Fert Beijing Research Institute Beihang University Beijing 100191 China 2Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Beijing 100191 China 3School of Electronic and Information Engineering Beihang University Beijing 100191 China 4Key Laboratory of Micro and Nano Photonic Structures (Ministry of Education) Department of Optical Science and Engineering Fudan University Shanghai 200433 China 5Department of Physics Colorado State University Fort Collins Colorado 80523 USA

The following errors appeared in the above article.1

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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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Video saliency prediction through machine learning with semantic information

Video saliency prediction through machine learning with sema...

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IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)

作者： Xiaohui Fu Li Su Lei Qin University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Lab on Big Data Mining and Knowledge Management Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9781479919499

Saliency prediction is valuable in many video applications, such as intelligent retrieval, advertisement design and delivering, video coding and video summarization generating. Although image saliency is well explored, less works have been done on videos. Compared to images, the semantic orientation is more obvious for video saliency. In this paper, we propose a method to predict video saliency by introducing semantic information. Different from existing approaches, we simultaneously consider the bottom-up and top-down factors in a machine learning framework and utilize a semantic object learning model to compute the semantic related saliency map. The proposed method is tested on two datasets. The experiment results show that the proposed method keeps higher consistent with human's gaze tracks data on various video contents. Furthermore, the computation efficiency is also improved as we don't need to process every pixel of each frame during prediction features extraction.

关键词： Decision support systems Feature extraction Semantics Training Videos Indexes Training data

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Translating maps and coordinates from the Great War

Translating maps and coordinates from the Great War

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Terra Cognita - Semantic Sensor Networks, TC-SSN 2014 - 6th International Workshop on the Foundations, Technologies and Applications of the Geospatial Web and 7th International Workshop on Semantic Sensor Networks, co-located with 13th International Semantic Web Conference, ISWC 2014

作者： Warren, Rob Evans, David Big Data Institute Dalhousie University Halifax Canada Department of Computing and Mathematics University of Derby Derby United Kingdom

Many obsolete coordinate systems used in the past have fallen into disuse. However, the contents of historical documents still refer to these obsolete coordinates and thus translation systems are important in locating historical events. We present a specialized Linked Open data API constructed to translate obsolete British Trench Map coordinates from the Great War into modern WGS84 coordinates. We report on the design of the API, the construction of the triple structures used to answer queries and the methods used to enrich query results while ensuring network performance. Several use cases for the API are presented and we close with a discussion of our experiences in linking to external data providers.

关键词： Query processing

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Personalized Financial News Recommendation Algorithm Based on Ontology

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Procedia Computer Science 2015年 55卷 843-851页

作者： Rui Ren Lingling Zhang Limeng Cui Bo Deng Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

To deal with the challenge of information overload, in this paper, we propose a financial news recommendation algorithm which help users find the articles that are interesting to read. To settle the ambiguity problem, a new presented OF-IDF method is employed to represent the unstructured text data in the form of key concepts, synonyms and synsets which are all stored in the domain ontology. For users, the recommendation algorithm build the profiles based on their behaviors to detect the genuine interests and predict current interests automatically and in real time by applying the thinking of relevance feedback. Finally, the experiment conducted on a financial news dataset demonstrates that the proposed algorithm significantly outperforms the performance of a traditional recommender.

关键词： news recommendation algorithm ontology relevence feedback OF-IDF

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big data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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