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The Second IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(ICSDM 2015)

作者： Xueying Zhang Xianghan Zheng College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9781479977505

Online social networks have played an important role in people's common life. Most existing social network platforms,however, face the challenges of dealing with undesirable users and their malicious spam activities that disseminate content,malware, viruses, etc. to the legitimate users of the service. In this paper, an Extreme Learning Machine based supervised machine is proposed for effective spammer detection. The experiment and evaluation show that the proposed solution provides excellent performance with a true positive rate of spammers and non-spammers reaching 99% and 99.95%,respectively. As the results suggest, the proposed solution could achieve better reliability and feasibility compared with existing SVM based approaches.

关键词： Social network Spammer Machine learning Extreme learning machine

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Machine Learning Based Social Media Recommendation

Machine Learning Based Social Media Recommendation

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The Second IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(ICSDM 2015)

作者： Taiping Lai Xianghan Zheng College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9781479977505

In view of the problems existing in traditional recommendation algorithm of low accuracy and low efficiency, this paper presents a machine learning based social media recommendation algorithm. The algorithm is based on the traditional personalized collaborative filtering algorithm, and combines with the correlation characteristics among users in a social network. Besides, the algorithm also considers the network rating factors and upgrade its efficiency by using clustering algorithm. At last, the algorithm is realized on the Hadoop cloud platform.

关键词： Social network Recommendation Clustering Collaborative Filtering MapReduce

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Gradient compared ℓp-LMS algorithms for sparse system identification

Gradient compared ℓp-LMS algorithms for sparse system ident...

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Chinese Control and Decision Conference, CCDC

作者： Yong Feng Jiasong Wu Rui Zeng Limin Luo Huazhong Shu School of Biological Science and Medical Engineering Southeast University Nanjing China Laboratory of Image Science and Technology the Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China Laboratory of Image Science and Technology Southeast University Nanjing China

In this paper, we propose two novel p-norm penalty least mean square (ℓ p -LMS) algorithms as supplements of the conventional ℓ p -LMS algorithm established for sparse adaptive filtering recently. A gradient comparator is employed to selectively apply the zero attractor of p-norm constraint for only those taps that have the same polarity as that of the gradient of the squared instantaneous error, which leads to the new proposed gradient compared p-norm constraint LMS algorithm (ℓ p GC-LMS). We explain that the ℓ p GC-LMS can achieve lower mean square error than the standard ℓ p -LMS algorithm theoretically and experimentally. To further improve the performance of the filter, the ℓ p NGC-LMS algorithm is derived using a new gradient comparator which takes the sign-smoothed version of the previous one. The performance of the ℓpNGC-LMS is superior to that of the ℓpGC-LMS in theory and in simulations. Moreover, these two comparators can be easily applied to other norm constraint LMS algorithms to derive some new approaches for sparse adaptive filtering. The numerical simulation results show that the two proposed algorithms achieve better performance than the standard LMS algorithm and ℓ p -LMS algorithm in terms of convergence rate and steady-state behavior in sparse system identification settings.

关键词： Least squares approximations Standards Convergence System identification Signal processing algorithms Steady-state Signal to noise ratio

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Improved recommendation system using friend relationship in SNS 19th

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19th International Conference on Transactions on Computational Collective Intelligence, TCCI 2015

作者： Liao, Qing Wang, Bin Ling, Yanxiang Zhao, Jingling Qiu, Xinyue School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China Beijing Key Laboratory of Network System Architecture and Convergence Beijing100876 China

ISBN: (纸本)9783662490167

With the rapid development of the Internet, SNS services and 3G commercial mobile applications there have been tremendous opportunities although the development of SNS is very short in China, and the social web game is in the early stage of development. Because of massive users, the potential commercial value of Chinese SNS is still a great mining space. However, a relatively large defects is the precipitation and accumulation on content. The dynamic of friends will affect our own decisions largely, it is favorable for the activity of SNS to increase the number of friends. We have improved the existing models, and conduct experiments to validate it and compare it with previous methods. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Social networking (online)

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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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MPSICA: An intelligent routing recovery scheme for heterogeneous wireless sensor networks

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information Sciences 2015年 308卷 49-60页

作者： Ding, Yongsheng Hu, Yifan Hao, Kuangrong Cheng, Lijun Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Donghua University Shanghai 201620 China College of Information Sciences and Technology Donghua University Shanghai 201620 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan 250014 China Shandong Provincial Key Laboratory of Computer Network Jinan 250014 China

To address the fault-tolerant recovery problem in a heterogeneous wireless sensor network consisting of several resource-rich supernodes, and a large number of energy-constrained ordinary sensor nodes, we propose a multi-particle-swarm immune cooperative algorithm (MPSICA) to provide intelligent routing recovery scheme. The MPSICA could maintain K disjoint paths from each source node to the nearest supernode and the available path from supernodes to the sink. Moreover, it could investigate the optimal alternative routing strategies, and solve the problem with cloning, high-frequency mutation, clone selection operations, which can improve the fault tolerant ability and reliability of inter-cluster and intra-cluster data transmission. Using this scheme, we can efficiently repair the broken path of intercluster supernodes or intracluster ordinary nodes network. Finally, we discuss the implementation of the MPSICA based fault-tolerant routing protocol and present the performance evaluation through experiments. The simulation results have verified that the MPSICA based protocol can provide reliable communication with intelligent bio-heuristic routing recovery scheme, thus extend the lifetime of WSNs. © 2015 Elsevier Inc. All rights reserved.

关键词： Heterogeneous sensor networks Immune cooperation Master-slave swarm Routing recovery

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An ant colony algorithm and improvement for the state transition diagram search

An ant colony algorithm and improvement for the state transi...

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2015 International Industrial Informatics and computer Engineering Conference(IIICEC 2015)

作者： Xiao Da Shengli Liu Yuefei Zhu Xiadong Wang Li Rui Computer network department Zheng Zhou Information Science University State Key Laboratory of Mathematical Engineering and Advanced Computing

Aiming at the state transition quick extraction problem for sequential logic chip,has desined a state transition graph extraction algorithm based on ant colony algorithm model,besides that,the ant colony algorithm model has been introduced in details,and according to the characteristics of the graph state transfer,the ant colony algorithm has been improved,has designed the state transition graph realization method in details oriented to the ant colony *** tests show that,the ant colony algorithm is designed in this paper can realize fast state transition graph extraction.

关键词： The state transition diagram Ant colony algorithm Sequential logic chip Shortest path Fast state test

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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On Efficient Feature Ranking Methods for High-Throughput Data Analysis

On Efficient Feature Ranking Methods for High-Throughput Dat...

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作者： Liao, Bo Jiang, Yan Liang, Wei Peng, Lihong Peng, Li Hanyurwimfura, Damien Li, Zejun Chen, Min Key Laboratory for Embedded and Network Computing of Hunan Province College of Information Science and Engineering Hunan University Changsha Hunan410082 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Engineering Changsha Medical University Changsha Hunan410219 China

Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology-related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are incorporated in an optimization framework, and feature ranking is achieved by introducing structured sparsity norm. Unlike existing methods, the presented methods have two advantages: (1) the feature subset simultaneously account for global margin information as well as locality manifold information. Consequently, both global and locality information are considered. (2) Features are selected by batch rather than individually in the algorithm framework. Thus, the interactions between features are considered and the optimal feature subset can be guaranteed. In addition, this study presents a theoretical justification. Empirical experiments demonstrate the effectiveness and efficiency of the two algorithms in comparison with some state-of-the-art feature ranking methods through a set of real-world gene expression data sets. © 2015 IEEE.

关键词： Convex optimization

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Sparse representation based CSI indoor localization method

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Ruan Jian Xue Bao/Journal of Software 2016年 27卷 21-27页

作者： Ma, Li Wang, Shan-Ting Ma, Dong-Chao Zhang, Yong-Mei College of Computer Science and Technology North China University of Technology Beijing100144 China College of Electronic Information Engineering North China University of Technology Beijing100144 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data North China University of Technology Beijing100144 China

Precise indoor positioning has important application value. GPS and other systems in the room often can be affected by many factors, and therefore may fail to provide accurate positioning. How to accurately locate the indoor environment location has become the focus of research and application. By analyzing the radio channel state information (CSI) of ordinary wireless device, indoor environment sensing technology can be realized without having to carry devices. The technology can provide personalized service based on location and apply to many kinds of location tracking and sensing. In order to solve the multipath effect and temporary noise impact on precise indoor positioning, this paper proposes a CSI indoor positioning method based on sparse representation. The method reduces the influence of multipath effect by utilizing the frequency diversity provided by CSI and the spatial diversity provided by multiple antennas. Furthermore, sparse representation method is employed to eliminate the fingerprint noise and to improve the algorithm's robustness and anti noise ability. CSI's sensitive phase feature is also exploited to improve the location accuracy. Using router as signal transmitter, 802.11n CSI-TOOL for collecting CSI, an experimental environment is created with desktop computers and Intel 5300. The test results show that, compared with the typical algorithms, the proposed algorithm can improve the accuracy and precision of positioning. The average precision is about 0.5m and accuracy reaches 91%. © Copyright 2016, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Channel state information

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