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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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RDF partitioning for scalable SPARQL query processing

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Frontiers of Computer Science 2015年第6期9卷 919-933页

作者： Xiaoyan WANG Tao YANG Jinchuan CHEN Long HE Xiaoyong DU School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University Beijing 100872 China Information Center Supreme People's Court Beijing 100745 China State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China

The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalability. Previous work on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect system performance. Specifically, a good partitioning solution would greatly reduce or even to- tally avoid cross-node joins, and significantly cut down the cost in query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture, where Map/Reduce takes charge of the computing tasks, and RDF query engines like RDF-3X store the data and execute join operations. According to the analysis of query workloads, this work proposes a novel algorithm for automatically parti- tioning RDF data and an approximate solution to physically place the partitions in order to reduce data redundancy. It also discusses how to make a good trade-off between query evaluation efficiency and data redundancy. All of these pro- posed approaches have been evaluated by extensive experiments over large RDF data sets.

关键词： RDF data data partitioning SPARQL query

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Provable multiple replication data possession with full dynamics for secure cloud storage

Provable multiple replication data possession with full dyna...

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作者： Zhang, Yafang Ni, Jianbing Tao, Xiaoling Wang, Yong Yu, Yong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada College of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Cloud Security and Cloud Service Engineering Research Center Guilin University of Electronic Technology Guilin541004 China

Cloud storage has been gaining tremendous popularity among individuals and corporations because of its low maintenance cost and on-demand services for the clients. To improve the availability and the reliability of critical data, storing multiple replicas on multiple servers is a commonly used strategy. Currently, several provable data possession (PDP) protocols for multiple replicas of dynamic data have been proposed to ensure the integrity of outsourced multi-copy data, but the efficiency of these protocols on verifying multiple replicas one by one is not satisfactory. In this paper, we propose a provable multiple replication data possession protocol with full dynamics, named MR-DPDP. In MR-DPDP, we utilize a novel authenticated data structure called Merkle hash tree with rank to support both full dynamic data updates and efficient integrity verification. In addition, our construction with RSA signature can support both variable-sized file blocks and public verification. Through security proof and performance evaluation, we demonstrate that MR-DPDP not only is sound but also incurs less communication overhead when updating data blocks as well as verifying a proof of the integrity of multiple replicas. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： Cloud storage

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Construction of a Class of Quantum Boolean Functions Based on the Hadamard Matrix

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Acta Mathematicae Applicatae Sinica 2015年第4期31卷 1013-1020页

作者： Jiao DU Shan-qi PANG Qiao-yan WEN Jie ZHANG College of Mathematics and Information Science Henan Normal University Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control Henan Normal University State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Science Beijing University of Posts and Telecommunications

In this study, a new methodology based on the Hadamard matrix is proposed to construct quantum Boolean functions f such that f ： I2n --2P2n, where I2n is an identity matrix of order 2n and P2n is a projective matrix ... 详细信息

关键词： quantum information matrix image quantum boolean function projective matrix

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Exploring the heterogeneity of social media: dataset and a pilot study 15

Exploring the heterogeneity of social media: Dataset and a p...

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7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Lu, Dongyuan Zheng, Nan School of Information Technology and Management University of International Business and Economics Beijing100029 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781450335287

Social media has exploded beyond anyone's wildest imagination, and User Generated Content (UGC) is propagated online at an unparalleled level. Organizing and associating the heterogeneous UGC data in different social media networks is playing a more and more important role in comprehensive user modeling and social media applications. Since social media data are essentially user-centric, which are generated from user and customized for user, we introduce a user-centric dataset to analyze and exploit the social media heterogeneity, which involves with six popular social media networks and 180, 000 overlapped users. A pilot study on the collected dataset is conducted to demonstrate the limitations of traditional content-centric metrics in analyzing the cross-OSN data. Finally, we conclude this work with discussions on the potentials of usercentric framework for cross-OSN organization and association. © 2015 ACM.

关键词： Social networking (online)

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Market Value of Innovation: An Empirical Analysis on China's Stock Market

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Procedia Computer Science 2015年 55卷 1275-1284页

作者： Meng Xiangying Zhang Yueyan Wei Xianhua School of Management University of Chinese Academy Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Science The State Key Laboratory of Automotive Safety and Energy Tsinghua University Department of Automotive Engineering Tsinghua University Beijing 100084 China

Studies on market value of innovation investment have been based on US and European database. However, few re-searchers investigate Chinese stock market about the market value of firm's expected R&D performance. In this paper, we use constructed panel dataset from three representative stock markets in China: Main Board Market, Small and Media-sized Enterprise Board Market and Growth Enterprise Market and then use intangible assets increment as innovation indicator to examine the innovation investment-firm performance association in both manufacturing and service industries by a dataset over an 11-year period covering 1455 firms to evaluate the differences in their relative contribution to market performance. By comparing the results of these three stock markets, we find out the effect of R&D investment and increasing R&D input to market value is insignificant in all three stock markets, resulting from the weak protection for minority investors and loose regulation of information disclosure. Different constructions of intangible assets of listed firms on MBM and GEM account for market's relatively low efficiency to reflect real value of R&D investment. On industry-level, we conclude that R&D investment in both service and manufacturing sector contributes positively to market performance of firms and R&D investment in service industry shows stronger and more significant linkage to market value than manufacturing industry.

关键词： Market value Innovation R&D China's stock market

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Which year is the hottest or coldest from 2001 to 2012 based on remote sensing data

Which year is the hottest or coldest from 2001 to 2012 based...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： K. B. Mao Y. Ma Z. Y. Zuo F. Wang Y. Q. Jiao X. Y. Shen Q. Liu National Hulunber Grassland Ecosystem Observation and Research Station Chinese Academy of Agricultural Sciences Beijing China Chinese Academy of Agricultural Big Data and Nutrition Hong Kong China State Key Laboratory of Remote Sensing Science Beijing Normal University Beijing China National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and Technology Xiangtan China College of Science of China University of Petroleum Beijing China Hydrometeorology and Remote Sensing Laboratory University of Oklahoma Norman Oklahoma

ISBN: (纸本)9781509033331

Increasing extreme weather events has made people confused with which year is the hottest or the coldest in recent years. To answer this question, the region scale and time must be considered. In this study, we calculate global surface temperature by using four surface temperature products at day and night retrieved from MODIS data. The global mean surface temperature is about 14.56° from 2001 to 2012, and the hottest and coldest surface temperatures of the global earth are in 2005 and 2008 in the recent twelve years. The hottest and coldest surface temperatures for the global land are in 2005 and 2001, and in 2010 and 2008 for Ocean. The hottest and coldest surface temperatures for Northern and Southern Hemisphere, Eastern and Western hemisphere, seven continents, and four Ocean are also obtained. The analysis indicates that the change is different in different region. We propose to build a spatial weather-climate model based on gravitational-magmatic change with the solar and the galactic system as its centre to analyze the long-term temperature changes.

关键词： Ocean temperature Sea surface Land surface temperature Temperature distribution Temperature measurement Meteorology

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Global water vapor content and vegetation change analysis based on remote sensing data

Global water vapor content and vegetation change analysis ba...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： K.B. Mao Y. Ma Z. Y. Zuo Y. Q. Jiao F. Wang Q. Liu Z. W. Sun National Hulunber Grassland Ecosystem Observation and Research Station Chinese Academy of Agricultural Sciences Beijing China Chinese Academy of Agricultural Big Data and Nutrition Hong Kong China State Key Laboratory of Remote Sensing Science Beijing Normal University Beijing China College of Science China University of Petroleum Beijing China National-Local Joint Engineering Laboratory of Geo-spatial Information Technology Hunan University of Science and Technology Xiangtan China Space Star Technology Co. Ltd. Beijing China

Water vapor in the Earth's upper atmosphere plays a crucial in the radiative balance, hydrological process, and climate change. Based on the latest MODIS data, this study probes the spatio-temporal variations of global water vapor content in the past decade. It is found that overall the global water vapor concentrations declined over 2003-2012 (b=- 0.0149, R 2 =0.797, p=0.0005). The decreasing trend over the ocean surface (b= -0.017, R 2 =0.824, p=0.0003) is more explicit than over terrestrial surface (b= -0.010, R 2 =0.612, p=0.007), more significant over the Northern Hemisphere (b= -0.0175, R 2 =0.852, p=0.0001) than over the Southern Hemisphere (b= -0.0123, R 2 =0.682, p=0.003). Vegetation cover change is critical for an understanding of the impacts and responses of vegetation to climate change. The global vegetation growth has been analysised from 2001 to 2012. After thorough analysis based on satellite data, we find evidence that the global vegetation change little, and it is increasing slightly in Northern hemisphere while it is deceasing slightly in Southern Hemisphere. For different latitudes, the vegetation is increasing 0.17% every year from 60°N to 70 °N (R 2 =0.47, P>0.013), while the vegetation is decreasing 0.11% every year from 10°N to 10°S (R 2 =0.54, P>0.004). For different continents, the vegetation in South America is decreasing 0.16% every year (R 2 =0.78, P>0.0001) and it is increasing 0.05% every year in Asia (R 2 =0.28, P>0.072) and 0.25% every year in Oceania (R 2 =0.24, P>0.1).

关键词： Vegetation mapping Vegetation MODIS Remote sensing Meteorology Market research Ocean temperature

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