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Congress on Evolutionary Computation

作者： Ye-shi Jiang Ying Lin Jing-Jing Li Zheng-jia Dai Jun Zhang Xinglin Zhang Ministry of Education Key Laboratory of Machine Intelligence - & Sensor Networks P.R. China School of Data & Computer Science Sun Yat-sen University School of Computer Science South China Normal University Department of Psychology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing School of Comouter Science & Engineering South China University of Technology

Cognitive diagnostic models (CDMs) are a new class of test models developed for educational assessment. They have gained growing attention in recent years for their distinctive ability to provide detailed feedback about examinees' ability. Automatic test assembly (ATA), as in other test models, has been one of the most critical issues in the development and applications of CDMs. However, developing ATA methods for CDMs is especially challenging because no close-form expressions can measure the quality of a test form based on the items used. Although some heuristic methods have been proposed for building a single test form of CDMs, few ATA methods can construct uniform test forms of CDMs, in which each test form contains a different set of items but meets equivalent demand of test quality. In order to fill the gap, this paper proposes a novel genetic algorithm (GA) for constructing uniform test forms of CDMs. The effectiveness and efficiency of the proposed method is validated on a synthetic item pool under different conditions.

关键词： Genetic algorithms Error analysis Biological cells Computational modeling Sociology Statistics Computer science

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Improved protein residue-residue contacts prediction using learning-to-rank

Improved protein residue-residue contacts prediction using l...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoyang Jing Qiwen Dong School of Computer Science Fudan University Shanghai People's Republic of China Shanghai Key Laboratory of Intelligent Information Processing Shanghai People's Republic of China Key Laboratory of Network Oriented Intelligent Computation Harbin Institute of Technology Shenzhen Graduate School Shenzhen Guangdong People's Republic of China Institute for Data Science and Engineering East China Normal University Shanghai People's Republic of China

ISBN: (纸本)9781509016129

Protein residue-residue contacts dictate the topology of protein structure and play an important role in structural biology, especially in de novo protein structure prediction. Accurate prediction of residue contacts could improve the performance of de novo protein structure prediction methods. In this study, a novel method based on learning-to-rank (RRCRank) has been presented to predict protein residue-residue contacts. The proposed method formulates the contacts prediction problem as a ranking problem. Firstly, the contact probabilities of residue pairs are predicted by ensemble machine-learning classifiers and correlated mutations approaches. And then, the proposed method integrates the complementary outputs of machine-learning and correlated mutations approaches and uses the learning-to-rank algorithm to rank residue pairs based on their probabilities to be contacts. Benchmarked on the CASP11 dataset, the proposed method achieves an improved performance for all three categories of contacts (short-range, medium-range and long-range contacts), which shows the proposed method based on learning-to-rank could take advantage of machine-learning and correlated mutations approaches and could provide the state-of-the-art performance.

关键词： Proteins Protein engineering Training Predictive models Feature extraction Classification algorithms Prediction algorithms

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

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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

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Enhancing distributed differential evolution with a space-driven topology

Enhancing distributed differential evolution with a space-dr...

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Congress on Evolutionary Computation

作者： Yong-Feng Ge Wei-Jie Yu Jing-Jing Li Zhi-Wen Yu Jun Zhang School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Information Management Sun Yat-sen University Guangzhou China School of Computer South China Normal University Guangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing China School of Computer Science and Engineering South China University of Technology Guangzhou China

Differential evolution (DE) is a simple and efficient evolutionary algorithm for global optimization. In distributed differential evolution (DDE), the population is divided into several sub-populations and each sub-population evolves independently for enhancing population diversity as well as algorithmic performance. Sub-populations in DDE share their elite individuals with neighborhood through a predefined migration topology. However, the construction of traditional migration topologies does not consider the position information of sub-populations in the search space. The position information is helpful in controlling the degree of diversity between the sub-populations and their migrated individuals. A proper degree of diversity could promote the balance between exploration and exploitation for DDE algorithms. To achieve this target, a dynamic space-driven migration topology is proposed in this paper. The proposed topology is constructed and updated according to the distances between sub-populations. Based on this proposed topology, some sub-populations receive diverse individuals from neighborhood far away while others communicate with neighborhood nearby. Numerical experiments have been performed on 13 diverse test functions. Results verify the advantage of DDE with the proposed migration topology compared to those with classic topologies.

关键词： Topology Sociology Statistics Heuristic algorithms Evolutionary computation Optimization Aerospace electronics

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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018)

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NATURE GENETICS 2018年第12期50卷 1755-1755页

作者： Evangelou, Evangelos Warren, Helen R. Mosen-Ansorena, David Mifsud, Borbala Pazoki, Raha Gao, He Ntritsos, Georgios Dimou, Niki Cabrera, Claudia P. Karaman, Ibrahim Fu Liang Ng Evangelou, Marina Witkowska, Katarzyna Tzanis, Evan Hellwege, Jacklyn N. Giri, Ayush Edwards, Digna R. Velez Sun, Yan V. Cho, Kelly Gaziano, J. Michael Wilson, Peter W. F. Tsao, Philip S. Kovesdy, Csaba P. Esko, Tonu Magi, Reedik Milani, Lili Almgren, Peter Boutin, Thibaud Debette, Stephanie Ding, Jun Giulianini, Franco Holliday, Elizabeth G. Jackson, Anne U. Li-Gao, Ruifang Lin, Wei-Yu Luan, Jian'an Mangino, Massimo Oldmeadow, Christopher Prins, Bram Peter Qian, Yong Sargurupremraj, Muralidharan Shah, Nabi Surendran, Praveen Theriault, Sebastien Verweij, Niek Willems, Sara M. Zhao, Jing-Hua Amouyel, Philippe Connell, John de Mutsert, Renee Doney, Alex S. F. Farrall, Martin Menni, Cristina Morris, Andrew D. Noordam, Raymond Pare, Guillaume Poulter, Neil R. Shields, Denis C. Stanton, Alice Thom, Simon Abecasis, Goncalo Amin, Najaf Arking, Dan E. Ayers, Kristin L. Barbieri, Caterina M. Batini, Chiara Bis, Joshua C. Blake, Tineka Bochud, Murielle Boehnke, Michael Boerwinkle, Eric Boomsma, Dorret I. Bottinger, Erwin P. Braund, Peter S. Brumat, Marco Campbell, Archie Campbell, Harry Chakravarti, Aravinda Chambers, John C. Chauhan, Ganesh Ciullo, Marina Cocca, Massimiliano Collins, Francis Cordell, Heather J. Davies, Gail de Borst, Martin H. de Geus, Eco J. Deary, Ian J. Deelen, Joris Del Greco, Fabiola M. Demirkale, Cumhur Yusuf Dorr, Marcus Ehret, Georg B. Elosua, Roberto Enroth, Stefan Erzurumluoglu, A. Mesut Ferreira, Teresa Franberg, Mattias Franco, Oscar H. Gandin, Ilaria Gasparini, Paolo Giedraitis, Vilmantas Gieger, Christian Girotto, Giorgia Goel, Anuj Gow, Alan J. Gudnason, Vilmundur Guo, Xiuqing Gyllensten, Ulf Hamsten, Anders Harris, Tamara B. Harris, Sarah E. Hartman, Catharina A. Havulinna, Aki S. Hicks, Andrew A. Hofer, Edith Hofman, Albert Hottenga, Jouke-Jan Huffman, Jennifer E. Hwang, Shih-Jen Ingelsson, Erik James, Alan Jansen Department of Epidemiology and Biostatistics Imperial College London London UK Department of Hygiene and Epidemiology University of Ioannina Medical School Ioannina Greece William Harvey Research Institute Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK National Institute for Health Research Barts Cardiovascular Biomedical Research Center Queen Mary University of London London UK MRC-PHE Centre for Environment and Health Imperial College London London UK Department of Mathematics Imperial College London London UK Division of Epidemiology Department of Medicine Institute for Medicine and Public Health Vanderbilt Genetics Institute Vanderbilt University Medical Center Tennessee Valley Healthcare System (626)/Vanderbilt University Nashville TN USA Vanderbilt Genetics Institute Vanderbilt Epidemiology Center Department of Obstetrics and Gynecology Vanderbilt University Medical Center Tennessee Valley Health Systems VA Nashville TN USA Department of Epidemiology Emory University Rollins School of Public Health Atlanta GA USA Department of Biomedical Informatics Emory University School of Medicine Atlanta GA USA Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) VA Boston Healthcare System Boston MA USA Division of Aging Department of Medicine Brigham and Women’s Hospital Department of Medicine Harvard Medical School Boston MA USA Atlanta VAMC and Emory Clinical Cardiovascular Research Institute Atlanta GA USA VA Palo Alto Health Care System Division of Cardiovascular Medicine Stanford University School of Medicine Stanford CA USA Nephrology Section Memphis VA Medical Center and University of Tennessee Health Science Center Memphis TN USA Estonian Genome Center University of Tartu Tartu Estonia Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA Department Clinical Sciences Malmö Lund University Malmö Sweden MRC Human Genetics Un

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

关键词： Cardiovascular diseases Genome informatics Genome-wide association studies

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Research on adaptive detection network attacks based on the improved data mining algorithm in cloud computing

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ICIC Express Letters 2016年第8期10卷 1957-1964页

作者： Song, Guangjun Li, Bo Lu, Xuyang School of Mathematics Physics and Information Science Zhejiang Ocean University No. 1 Haida South Road Lincheng Changzhi Island Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University No. 1 Haida South Road Lincheng Changzhi Island Zhoushan316022 China College of Computer and Control Engineering Qiqihar University No. 42 Wenhua Street Qiqihar161006 China

Network attacks are one of the biggest threats in cloud security. According to the characteristics of network attacks in cloud computing, an improved k-means algorithm to optimize the clustering number k by function is proposed. The improved k-means algorithm is applied to the intrusion detection model. In the data mining detection module, the improved clustering analysis algorithm is used to adapt and detect those network attacks of different types, which has effectively solved the problems existing in intrusion detection of cloud computing, such as large amount of alarms and high rate of false alarm. Experiment results show that the improved clustering analysis algorithm applied to network attack detection in cloud computing can detect the network attacks efficiently. © 2016, ICIC Express Letters Office. All rights reserved.

关键词： Cloud computing

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Road network modeling with layered abstraction for path discovery in vehicle navigation systems

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Journal of Information Hiding and Multimedia Signal Processing 2016年第4期7卷 791-801页

作者： Pan, Jeng-Shyang Wang, Chen Sung, Tien-Wen Fuzhou College of Foreign Studies and Trade No. 28 Yuhuan Road Changle Fuzhou CityFujian Province350202 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School University Town Shenzhen City518055 China College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road Fuzhou CityFujian Province350118 China

Algorithms for path planning have recently drawn renewed attention from researchers due to the advances of intelligent transportation applications. The difference on path planning between past and nowadays is the complexity of dynamic transportation network. This paper focuses on the road network modeling to speed up real time path discovery in dynamic and complex road networks. On the basis of community detection (partitioning), an agglomerative hierarchical clustering algorithm with greedy strategy is proposed in this paper. The algorithm performs a hierarchical network abstraction to make a layered road network with largest community modularity. Static and dynamic information of the real time road network conditions is concerned in the process of network abstraction. Based on the road network layering, the performance of path discovery can be improved due to the reduction of unnecessary searching. The performance of the proposed algorithm was evaluated and compared with the well-known Dijkstra and A-star algorithms. The result shows that the proposed algorithm performs better than the other two algorithms. © 2016.

关键词： Motion planning

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A MapReduce Optimization Method on Hadoop Cluster

A MapReduce Optimization Method on Hadoop Cluster

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International Conference on Industrial Informatics - computing Technology, intelligent Technology, Industrial Information Integration

作者： Xiaodong Wu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9781467383134

The MapReduce parallel and distributed computing framework has been widely applied in both academia and industry. MapReduce applications are divided into two steps: Map and Reduce. Then, the input data is divided into splits, which can be concurrently processed, and the amount of the splits determines the number of map tasks. In this paper, we present a regression-based method to compute the number of Map tasks as well as Reduce tasks such that the performance of the MapReduce application can be improved. The regression analysis is used to predict the executing time of MapReduce applications. Experimental results show that the proposed optimization method can effectively reduce the execution time of the applications.

关键词： MapReduce Hadoop Optimization Polynomial Regression

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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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Parallel knowledge acquisition algorithm for big data using MapReduce

Parallel knowledge acquisition algorithm for big data using ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Min Xia Ping Lv Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou China

ISBN: (纸本)9781467372220

With the rapid growth of data volume, knowledge acquisition for big data has become a new challenge. To address this issue, the hierarchical decision table is defined and implemented in this work. The properties of different hierarchical decision tables are discussed under the different granularity of conditional attributes. A novel knowledge acquisition algorithm for big data using MapReduce is proposed. Experimental results demonstrate that the proposed algorithm is able to deal with big data and mine hierarchical decision rules under the different granularity.

关键词： big data Hierarchical Rough Set Knowledge Acquisition MapReduce big data Information Extraction Decision tables

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