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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Xu, Suping Yang, Xibei Tsang, Eric C. C. Mantey, Eric A. School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang212003 China School of Economics and Management Nanjing University of Science and Technology Nanjing210094 China Faculty of Information Technology Macau University of Science and Technology C-Macau519020 China Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan030006 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China

ISBN: (纸本)9781509003891

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC;2) NCC reduces the size of dictionary when compared with CRC. © 2016 IEEE.

关键词： Classification (of information)

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Comparative analysis of identification methods for wheat leaf diseases

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International Agricultural Engineering Journal 2016年第3期25卷 197-203页

作者： Zhang, D.Y. Xie, X.H. Liang, D. Huang, L.S. Zhang, X.Q. Teng, L. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University School of Electronic and Information Engineering Hefei Anhui230601 China

How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image processing technique was applied to segment the infected regions of disease leaves. Twenty disease features were extracted, and eighteen larger weight features were selected by Relief-F algorithm to generate the models of Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Back Propagation Neural Network (BPNN). Subsequently, these models were used to identify two kinds of wheat diseases, namely, wheat stripe rust and powdery mildew. Total 136 samples, including 68 training samples and 68 test samples with different infection severities were used to study the recognition capabilities of the three models. Results showed that high predictive accuracies in identification of two wheat diseases with varying severity for all three models. Overall accuracy of RVM was 89.71%, which was superior to 83.82% of SVM and inferior to 92.64% of BPNN. Meanwhile, the recognition accuracies of SVM, RVM and BPNN models for mild-to-moderate disease were 83.33%, 88.33% and 91.67%, respectively. The prediction time of RVM was less than those of SVM and BPNN, with differences as large as 7.96 and 31.68 times, respectively. Therefore, RVM appeared to be the most suitable for real-time identifying wheat leaf diseases among the three models, which can provide important technical support for wheat diseases management.

关键词： Support vector machines

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Flickr group recommendation via heterogeneous information networks 15

Flickr group recommendation via heterogeneous information ne...

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

作者： Wang, Yueyang Xia, Yuanfang Tang, Siliang Wu, Fei Zhuang, Yueting College of Computer Science and Technology Zhejiang University Key Laboratory of Big Data Intelligent Computing of Zhejiang Province Innovation Joint Research Center for ICPS China

ISBN: (纸本)9781450335287

As an important characteristic of social media (i.e. Flickr or Facebook), user communities or groups are beginning to attract increasing attention. Most of the previous studies on group recommendation only consider a homogeneous relationship, such as the rating of users to groups in social network. In real world, each group has its textual descriptions (e.g. the "Sky & Cloud" group in Flickr), and the describing words about a given group may mention particular entities as well as their belonging categories (e.g. "Nature"). In fact, the group recommendation can be conducted in a heterogeneous information network, where informative cues are in general multi-typed. Motivated by the assumption that different types of relationships in heterogeneous information network can be potentially used to boost the performance of the group recommendation. We propose to combine heterogeneous relationship information for users with implicit user feedback. In general, the group recommendation in this paper is formulated as a non-negative matrix factorization (NMF) method regularized with user-user similarity via heterogeneous information networks. Experiments show that our proposed approach outperforms other counterpart recommendation techniques. © 2015 ACM.

关键词： Non-negative matrix factorization

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Qian Yu-Hui Chang 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)9781509003914

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets.

关键词： Cybernetics Acceleration Time complexity Rough sets Heuristic algorithms data mining Algorithm design and analysis

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SI2P: A restaurant recommendation system using preference queries over incomplete information 42

SI2P: A restaurant recommendation system using preference qu...

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42nd International Conference on Very Large data Bases, VLDB 2016

作者： Miao, Xiaoye Gao, Yunjun Chen, Gang Cui, Huiyong Guo, Chong Pan, Weida College of Computer Science Zhejiang University Hangzhou China The Key Lab of Big Data Intelligent Computing of Zhejiang Province Zhejiang University Hangzhou China

The incomplete data is universal in many real-life applications due to data integration, the limitation of devices, etc. In this demonstration, we present SI2P, a restaurant recommendation System with Preference queries on Incomplete Information. SI2P is capable of friendly recommending desirable restaurants based on preference queries that take the incomplete ratings information into consideration. It adopts the browser-server model, and incorporates three functionality modules including friendly and convenient query submission, flexible and useful result explanation, timely and incremental dataset interaction. SI2P provides the server side based on an extended PostgreSQL database that integrates two types of preference queries, namely, skyline and top-k dominating queries over incomplete data. It also offers the browser-based interface for the users to interact with the system. Using a real restaurant dataset from TripAdvisor, we demonstrate SI2P can recommend and explore the restaurants in a friendly way. © 2016 VLDB.

关键词： Query processing

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Research on bearing micro deformation based on the frequency domain technique of white light speckle image

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2016年 37卷 46-52页

作者： Feng, Bo Xu, Chao Shao, Deyang Luo, Shixian Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

In this paper, a new method of non contact and on-line measurement is presented for the change of the axis of the rotation axis. Under the condition of a constant light source, the image of the original position and the offset image in the original position are collected by the micro camera, These two pictures are calculated by the improved White light digital image point by point analysis method in this paper, and get the black and white stripes. The number and the angle of the fringe in the white light digital frequency domain interference image can be known as the offset and direction of the rotating shaft in the rotating process. Because this method only use a camera and a light source, and this method is very simple, and it can always detect, and it does not affect the normal work, so there is a wide range of industrial application prospects. This method can provide alarm before the plastic deformation occurs in the shaft, Reduce the risk of shaft damage. © 2016, Science Press. All right reserved.

关键词： Frequency domain analysis

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Neighborhood collaborative classifiers

Neighborhood collaborative classifiers

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Suping Xu Xibei Yang Eric C. C. Tsang Eric A. Mantey Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Economics and Management Nanjing University of Science and Technology Nanjing China Faculty of Information Technology Macau University of Science and Technology Macau China

ISBN: (纸本)9781509003914

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC; 2) NCC reduces the size of dictionary when compared with CRC.

关键词： Collaboration Rough sets Training Cybernetics Numerical models Error analysis Measurement

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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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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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Comparison of Polarized Radiative Transfer Codes Used by the EHT Collaboration

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The Astrophysical Journal 2023年第1期950卷 35-35页

作者： Ben S. Prather Jason Dexter Monika Moscibrodzka Hung-Yi Pu Thomas Bronzwaer Jordy Davelaar Ziri Younsi Charles F. Gammie Roman Gold George N. Wong Kazunori Akiyama Antxon Alberdi Walter Alef Juan Carlos Algaba Richard Anantua Keiichi Asada Rebecca Azulay Uwe Bach Anne-Kathrin Baczko David Ball Mislav Baloković John Barrett Michi Bauböck Bradford A. Benson Dan Bintley Lindy Blackburn Raymond Blundell Katherine L. Bouman Geoffrey C. Bower Hope Boyce Michael Bremer Christiaan D. Brinkerink Roger Brissenden Silke Britzen Avery E. Broderick Dominique Broguiere Sandra Bustamante Do-Young Byun John E. Carlstrom Chiara Ceccobello Andrew Chael Chi-kwan Chan Dominic O. Chang Koushik Chatterjee Shami Chatterjee Ming-Tang Chen Yongjun Chen Xiaopeng Cheng Ilje Cho Pierre Christian Nicholas S. Conroy John E. Conway James M. Cordes Thomas M. Crawford Geoffrey B. Crew Alejandro Cruz-Osorio Yuzhu Cui Mariafelicia De Laurentis Roger Deane Jessica Dempsey Gregory Desvignes Vedant Dhruv Sheperd S. Doeleman Sean Dougal Sergio A. Dzib Ralph P. Eatough Razieh Emami Heino Falcke Joseph Farah Vincent L. Fish Ed Fomalont H. Alyson Ford Raquel Fraga-Encinas William T. Freeman Per Friberg Christian M. Fromm Antonio Fuentes Peter Galison Roberto García Olivier Gentaz Boris Georgiev Ciriaco Goddi Arturo I. Gómez-Ruiz José L. Gómez Minfeng Gu Mark Gurwell Kazuhiro Hada Daryl Haggard Kari Haworth Michael H. Hecht Ronald Hesper Dirk Heumann Luis C. Ho Paul Ho Mareki Honma Chih-Wei L. Huang Lei Huang David H. Hughes Shiro Ikeda C. M. Violette Impellizzeri Makoto Inoue Sara Issaoun David J. James Buell T. Jannuzi Michael Janssen Britton Jeter Wu Jiang Alejandra Jiménez-Rosales Michael D. Johnson Svetlana Jorstad Abhishek V. Joshi Taehyun Jung Mansour Karami Ramesh Karuppusamy Tomohisa Kawashima Garrett K. Keating Mark Kettenis Dong-Jin Kim Jae-Young Kim Jongsoo Kim Junhan Kim Motoki Kino Jun Yi Koay Prashant Kocherlakota Yutaro Kofuji Shoko Koyama Carsten Kramer Michael Kramer Thomas P. Krichbaum Cheng-Yu Kuo Noemi La Bella Tod R. Lauer Daeyoung L CCS-2 Los Alamos National Laboratory P.O. Box 1663 Los Alamos NM 87545 USA bprather@lanl.gov JILA and Department of Astrophysical and Planetary Sciences University of Colorado Boulder CO 80309 USA Department of Astrophysics/IMAPP Radboud University P.O. Box 9010 6500 GL Nijmegen The Netherlands Department of Physics National Taiwan Normal University No. 88 Sec.4 Tingzhou Road Taipei 116 Taiwan R.O.C. Center of Astronomy and Gravitation National Taiwan Normal University No. 88 Sec. 4 Tingzhou Road Taipei 116 Taiwan R.O.C. Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan R.O.C. Department of Astronomy and Columbia Astrophysics Laboratory Columbia University 550 W. 120th Street New York NY 10027 USA Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New York NY 10010 USA Department of Astrophysics Institute for Mathematics Astrophysics and Particle Physics (IMAPP) Radboud University P.O. Box 9010 6500 GL Nijmegen The Netherlands Mullard Space Science Laboratory University College London Holmbury St. Mary Dorking Surrey RH5 6NT UK Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 D-60438 Frankfurt am Main Germany Department of Physics University of Illinois 1110 West Green Street Urbana IL 61801 USA Department of Astronomy University of Illinois at Urbana-Champaign 1002 West Green Street Urbana IL 61801 USA NCSA University of Illinois 1205 W. Clark Street Urbana IL 61801 USA CP3-Origins University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark School of Natural Sciences Institute for Advanced Study 1 Einstein Drive Princeton NJ 08540 USA Princeton Gravity Initiative Princeton University Princeton NJ 08544 USA Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road Westford MA 01886 USA National Astronomical Observatory of Japan 2-21-1 Osaw

Interpretation of resolved polarized images of black holes by the Event Horizon Telescope (EHT) requires predictions of the polarized emission observable by an Earth-based instrument for a particular model of the black hole accretion system. Such predictions are generated by general relativistic radiative transfer (GRRT) codes, which integrate the equations of polarized radiative transfer in curved spacetime. A selection of ray-tracing GRRT codes used within the EHT Collaboration is evaluated for accuracy and consistency in producing a selection of test images, demonstrating that the various methods and implementations of radiative transfer calculations are highly consistent. When imaging an analytic accretion model, we find that all codes produce images similar within a pixel-wise normalized mean squared error (NMSE) of 0.012 in the worst case. When imaging a snapshot from a cell-based magnetohydrodynamic simulation, we find all test images to be similar within NMSEs of 0.02, 0.04, 0.04, and 0.12 in Stokes I, Q, U, and V, respectively. We additionally find the values of several image metrics relevant to published EHT results to be in agreement to much better precision than measurement uncertainties.

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