检索结果-内蒙古大学图书馆

big data Research in Italy： A Perspective

Engineering 2016年第2期2卷 163-170页

作者： Sonia Bergamaschi Emanuele Carlini Michelangelo Ceci Barbara Furletti Fosca Giannotti Donato Malerba Mario Mezzanzanica Anna Monreale Gabriella Pasi Dino Pedreschi Raffele Perego Salvatore Ruggieri Department of Engneering "Enzo Ferrari " University of Modena and Reggio Emilia Modena 41125 Italy High Performance Computing Laboratory Institute of Information Science and Technologies of the Italian National Research Council (ISTI-CNR) Pisa 56124 Italy Department of Computer Science University ofBari Aldo Moro Bari 70125 Italy Knowledge Discovery and Data Mining Laboratory ISTI-CNR Pisa 56127 Italy Big Data Laboratory National Interuniversity Consortium for Informatics Rome 00185 Italy Department of Statistics and Quantitative Methods University of Milano-Bicocca Milan 20126 Italy Department of Computer Science Systems and Communications University of Milano-Bicocca Milan 20126 Italy Department of Computer 5denee University of Pisa Pisa 56127 Italy

The aim of this article is to synthetically describe the research projects that a selection of Italian univer- sities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.

关键词： big data Smart cities EnergyJob offersPrivacy

来源：评论

学校读者我要写书评

暂无评论

No reference video quality assessment based on 3D convolutional neural network

引用

Ruan Jian Xue Bao/Journal of Software 2016年 27卷 103-112页

作者： Wang, Chun-Feng Su, Li Zhang, Wei-Gang Huang, Qing-Ming Key Laboratory on Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology The Chinese Academy of Sciences Beijing100190 China Weihai264209 China

No reference video quality assessment (NR-VQA) measures distorted videos quantitatively without the reference of original high quality videos. Conventional NR-VQA methods are generally designed for specific types of distortions, or not consistent with human's perception. This paper innovatively introduces 3D deep convolutional neural network (3D-CNN) into VQA and proposes a 3D-CNN based NR-VQA method, which is universal for non-specific types of distortions. First, the proposed method utilizes 3D patches to learn spatio-temporal features that represent video content effectively. Second, the original 3D-CNN model is modified which is used to classify videos to make it adapt to VQA task. Experiments demonstrate that the proposed method is highly consistent with human's perception across numerous distortions and metrics. Compared with other state-of-the-art no-reference VQA methods, the proposed method runs much faster while keeping the similar performance. As a no-reference VQA method, it is even comparable with many of the state-of-the-art full-reference VQA methods, which provides the proposed method with better application prospects. © Copyright 2016, institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Automatic salient object segmentation via shape prior based active contour model

引用

ICIC Express Letters, Part B: Applications 2016年第11期7卷 2491-2496页

作者： Gao, Shangbing Zhang, Youdong Zhou, Jun Zheng, Hao The Key Laboratory for Traffic and Transportation Security of Jiangsu Province Faculty of Computer and Software Engineering No. 1 Meicheng Rd. Huai’an223003 China Jiangsu Provincial Key Laboratory for Advanced Manufacturing Technology Huaiyin Institute of Technology No. 1 Meicheng Rd. Huai’an223003 China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University No. 3601 Hongjing Ave. Jiangning Dist. Nanjing211171 China

In this paper, we propose a novel model for unsupervised segmentation of viewer's attention object from natural images based on localizing region-based active con-tour (LRAC). Firstly, we proposed the saliency detection model via the multi-scale super-pixel. Then, object-level shape prior is extracted combining saliency with object boundary information. Finally, this contour is improved by the edge-preserving to generate the initial contour for our automatic object segmentation system. In contrast with localiz-ing region-based active contours that require considerable user interaction, the proposed method does not require it, i.e., the segmentation task is ful_lled in a fully automatic manner. Extensive experiments results on a large variety of natural images demonstrate that our algorithm consistently outperforms the popular existing salient object segmenta-tion methods, yielding higher precision and better recall rates. © 2016 ICIC International.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Tools in data science for better processing

引用

AIP Conference Proceedings 2016年第1期1750卷

作者： Nur Syahela Hussien Sarina Sulaiman Siti Mariyam Shamsuddin 1UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Faculty of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Johor Malaysia

Analysing the data is an important part of a research in data science. There are many tools that can be used in analysing a data set to get the experiment results for classification, clustering and others. However, the researchers are concerned about how to increase the efficiency in analysing a data set. In this paper, three open source tools which are the Waikato Environment for Knowledge Analysis (WEKA), Konstanz Information Miner (KNIME) and Salford Predictive Modular (SPM) were compared to identify the better processing tools in evaluating the presented data. All of these tools have their own different characteristics. WEKA can handle pre-processing of data and then analyses it based on different algorithms. It is suitable to be used for classification, regression, clustering, association rules, and visualisation. The algorithms can be applied directly to a data set or called from its own Java code. KNIME is more inclined towards producing graphical view, while SPM is a highly accurate and ultra-fast analytics which also data mines platforms for any sizes, complexity or organisation. The results illustrate the tools capability in analysing data sets and evaluators in an efficient and effective manner.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Design of a scalable data stream channel for big data processing

Design of a scalable data stream channel for big data proces...

引用

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

来源：评论

学校读者我要写书评

暂无评论

An Entropy-based Probabilistic Forwarding Strategy in Named data Networking

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Kullback Leibler divergence for image quantitative evaluation

引用

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

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

关键词：

来源：评论

学校读者我要写书评

暂无评论

Extracting Unknown Words from Sina Weibo via data Clustering

Extracting Unknown Words from Sina Weibo via Data Clustering

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：