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IMLNB: Multi-label Naive Bayes classification based on ICA

Journal of Computational Information Systems 2014年第12期10卷 5067-5074页

作者： Zheng, Xiyuan Zhang, Huaxiang Gao, Shuang School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 250014 China Jinan 250101 China

Multi-label learning is initially used to deal with the difficult text classification problems of ambiguity concept, in which each document can belong to multiple topics simultaneously. In this paper, a novel multi-label learning method named IMLNB is proposed based on the traditional Naive Bayes algorithm. IMLNB first deals with the multi-label data set by using Independent Component Analysis (ICA), and then calculates the prior probabilities of each label. After that, it estimates the Gaussian distribution parameters of each dimensionality vector and then calculates the weight of each dimensionality vector and the posteriori probabilities. IMLNB can give the label predictions using the computed posteriori probabilities. Experimental results show that IMLNB can predict the possible labels of a new instance accurately. 1553-9105/Copyright © 2014 Binary Information Press.

关键词： Gaussian distribution

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PEVTS: Privacy-Preserving Electric Vehicles Test-Bedding Scheme

PEVTS: Privacy-Preserving Electric Vehicles Test-Bedding Sch...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xu Yang Joseph K. Liu Wei Wu Man Ho Au Willy Susilo Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University China and State Key Laboratory of Cryptology Beijing China Faculty of Information Technology Monash University Australia Department of Computing Hong Kong Polytechnic University Hong Kong School of Computer Science and Software Engineering University of Wollongong Australia

Electric Vehicle (EV) infrastructure is relatively new in many countries. Due to the recency of an EV infrastructure, it is important to carry out a series of testing programs. Furthermore, authenticity for collection of data is necessary for testing programs in order to provide accurate results. At the same time, user privacy should not cease since tracing one's daily logistic movements or behaviour from the EV testing programs means breaching one's privacy. In this paper, we propose a novel solution PEVTS for enabling both data authenticity and user privacy concurrently. Our proposed system provides great flexibility to the authority to choose any arbitrary set of authenticated users for testing in every time period. At the same time, it provides anonymity for all participating users. Yet it can trace any vehicle within a time period for statistical purpose. We give a detailed description of our system. We also implement the prototype of our system to show its practicality.

关键词： Testing Privacy Cryptography Electric vehicles Data privacy Authentication

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Ranking based activity trajectory search

Ranking based activity trajectory search

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15th International Conference on Web Information Systems Engineering, WISE 2014

作者： Chen, Wei Zhao, Lei Jiajie, Xu Zheng, Kai Zhou, Xiaofang School of Computer Science and Technology Soochow University China Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University China School of ITEE The University of Queensland Brisbane Australia

ISBN: (纸本)9783319117485

With the proliferation of the GPS-enabled devices and mobile techniques, there has been a lot of work on trajectory search in the last decade. Previous trajectory search has focused on spatio-temporal features and text descriptions. Different from them, we study a novel problem of searching trajectories with activities and corresponding ranking information. Given a query q, which is attached with a set of activities and a threshold of distance, the results of ranking based activity trajectory search (RTS) are k trajectories such that the given activities are performed with the highest ranking within the threshold of distance. In addition, we also extend the query with an order, i.e., order-sensitive ranking based activity trajectory search (ORTS), which takes both the order of activities in a query q and the order of trajectories into account. It is challenging to answer RTS and ORTS efficiently due to the structural complexity of trajectory data with ranking information. In this paper, a hybrid index AC-tree and its optimized variant RAC-tree are proposed to achieve higher efficiency. Extensive experiments verify the high efficiency and scalability of the proposed algorithms. © Springer International Publishing Switzerland 2014.

关键词： Trajectories

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Group recommendation for Flickr images by 4-order tensor decomposition

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Journal of Computational Information Systems 2014年第3期10卷 1315-1322页

作者： Wang, Xiaofang Ma, Jun Xu, Ming School of Computer Science and Technology Shandong University Jinan 250101 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan 250022 China Shandong Dareway Computer Software Co. Ltd. Jinan 250101 China

Users in Flickr can upload images and share them with appropriate groups based on their interests. Sharing images with a group would promote the communication between users and expand their social networks, so Flickr groups have gained great popularity and attracted a large number of users. However, repeatedly adding images to certain groups is quite tiring and it requires users to know the content of each group and which group they should add to. To solve this problem, suggesting groups for Flickr images has attracted a lot of attention recently. However, as far as we know, many previous works on Flickr group recommendation have difficulties in generating satisfactory suggestions because they only used visual features and tags associated with the images and rarely exploited the information of users. The interests of users are critical to high-quality and efficient group recommendation. Hence, in this paper, we present a model based on 4-order tensor to describe the relation among images, users, tags and groups. Particularly, we propose a Tetradic Decomposition Algorithm which employs the higher order singular value decomposition to reduce the dimensionality of the 4-order tensor and reveal the latent semantic association among images, users, tags and groups. Experiments on the dataset crawled from Flickr demonstrate that our approach outperforms other existing methods. © 2014 Binary Information Press.

关键词： Collaborative filtering

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Mobile anchor-assisted localization in 3D wireless sensor networks with hilbert curve 1

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7th China Conference of Wireless Sensor networks, CWSN 2013

作者： Zhou, Chuanai Cui, Huanqing Meng, Xiaojing Hua, Rong School of Information Engineering Qingdao Binhai University Qingdao 266555 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Jinan 250014 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao 266590 China

ISBN: (数字)9783642541742

ISBN: (纸本)9783642541735

Localization is one of the key technologies of wireless sensor networks (WSNs). This paper proposes a mobile anchor-assisted localization method for three-dimensional (3D) networks. In this method, an anchor is equipped with global positioning system (GPS) device, and traverses the network along the Hilbert space-filling curve. The ordinary nodes estimate their positions using trigonometric method based on the characteristics of Hilbert curve. We compare the localization error, communication cost, and computational complexity of our proposed algorithm and other existing mobile anchor-assisted algorithms. The simulation results show that our method is better than other methods. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Wireless sensor networks

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An unsupervised model for classification and recognition of household appliances

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Journal of Computational Information Systems 2014年第1期10卷 403-410页

作者： Liu, Li Ding, Jiaman Zhong, Jiayu Fu, Xiaodong Lv, Yang Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 China State-Province Joint Laboratory of Digital Home Interactive Applications School of Information Science and Technology Sun Yat-Sen University Guangzhou 510006 China

Detecting and monitoring the numerous household appliances in smart home is significant for home energy management, because the large various household appliances are complicated to identify and control. This paper proposes an intelligent unsupervised model for the classification and recognition of household appliances. The key idea in this work is to integrate appliance and activity recognition mechanism for effective management and operation within a domestic location. We first present the corresponding classification list of household appliances w. r. t their respective usage patterns and basic power factors. Then, the communication services among various appliances are established to recognize complex appliances and monitor the variation of their states. Finally, the proposed method is applicable for an inexpensive, unsupervised controlling of electrical appliances in a real-time way. Experimental results show that our method effectively provides a financially viable and easily applicable solution for gathering information and monitoring status of diverse household appliances. Copyright © 2014 Binary Information Press.

关键词： Domestic appliances

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Detecting Optic Disk Based on Structured Learning

Detecting Optic Disk Based on Structured Learning

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IEEE International Conference on Robotics and Biomimetics

作者： Zhun Fan Yibiao Rong Xinye Cai Wenji Li Huibiao Lin Zefeng Yu Jiewei Lu Key Laboratory of Digital Signal and Image Processing of Guangdong Provincial College of Engineering Shantou University. 515063 Shantou China College of Engineering Shantou University. 515063 Shantou China school of computer science and technology Nanjing University of Aeronautics and Astronautics Jiangsu China

ISBN: (纸本)9781467396769

Optic Disk (OD) detection plays an important role for fundus image analysis. In this paper, we propose an algorithm for detecting OD mainly based on a classifier model trained by structured learning. Then we use the model to achieve the edge map of OD. Thresholding is performed on the edge map to obtain a binary image. Finally, circle Hough transform is carried out to approximate the boundary of OD by a circle. The proposed algorithm has been evaluated on the public database and obtained promising results. The results (an area overlap and Dices coefficients of 0.8636 and 0.9196, respectively, an accuracy of 0.9770, and a true positive and false positive fraction of 0.9212 and 0.0106) show that the proposed method is a robust tool for the segmentation of OD and is very competitive with the stage-of-the-art methods.

关键词： algorithm detecting method

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Erratum to: The sponge microbiome project

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Gigascience 2018年第12期7卷 giy145页

作者： Lucas Moitinho-Silva Shaun Nielsen Amnon Amir Antonio Gonzalez Gail L Ackermann Carlo Cerrano Carmen Astudillo-Garcia Cole Easson Detmer Sipkema Fang Liu Georg Steinert Giorgos Kotoulas Grace P McCormack Guofang Feng James J Bell Jan Vicente Johannes R Björk Jose M Montoya Julie B Olson Julie Reveillaud Laura Steindler Mari-Carmen Pineda Maria V Marra Micha Ilan Michael W Taylor Paraskevi Polymenakou Patrick M Erwin Peter J Schupp Rachel L Simister Rob Knight Robert W Thacker Rodrigo Costa Russell T Hill Susanna Lopez-Legentil Thanos Dailianis Timothy Ravasi Ute Hentschel Zhiyong Li Nicole S Webster Torsten Thomas Centre for Marine Bio-Innovation and School of Biological Earth and Environmental Sciences The University of New South Wales Sydney 2052 Australia. Department of Pediatrics University of California - San Diego La Jolla CA 92093 USA. Department of Life and Environmental Sciences Polytechnic University of Marche Ancona 60131 Italy. School of Biological Sciences University of Auckland Auckland New Zealand. Halmos College of Natural Sciences and Oceanography Nova Southeastern University Dania Beach FL 33004 USA. Wageningen University Laboratory of Microbiology Stippeneng 4 6708 WE Wageningen The Netherlands. State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai 200240 P.R. China. Hellenic Centre for Marine Research Institute of Marine Biology Biotechnology and Aquaculture Thalassocosmos 71500 Heraklion Greece. Zoology School of Natural Sciences Ryan Institute National University of Ireland Galway University Rd. Galway Ireland. School of Biological Sciences Victoria University of Wellington Wellington New Zealand. Hawaii Institute of Marine Biology 46-007 Lilipuna Road Kaneohe HI 96744-1346. Galvin Life Science Center University of Notre Dame Notre Dame IN 46556 USA. Ecological Networks and Global Change Group Theoretical and Experimental Ecology Station CNRS and Paul Sabatier University Moulis France. Department of Biological Sciences University of Alabama Tuscaloosa AL 35487 USA. INRA UMR1309 CMAEE Cirad UMR15 CMAEE 34398 Montpellier France. Department of Marine Biology Leon H. Charney School of Marine Sciences University of Haifa Haifa Israel. Australian Institute of Marine Science (AIMS) Townsville 4810 Queensland Australia. Department of Zoology George S. Wise Faculty of Life Sciences Tel Aviv University Tel Aviv 69978 Israel. Department of Biology and Marine Biology University of North Carolina Wilmington Wilmington NC 28409 USA. Institute for Chemistry and Biolog

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A new way for path planning based on BBO in crowd animation

A new way for path planning based on BBO in crowd animation

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2013 International Conference on Future computer and Information technology, ICFCIT 2013

作者： Gao, Kaige Zheng, Xiangwei Ma, Hongwei School of Information Science and Engineering Shandong Normal University China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology China

ISBN: (纸本)9781845648510

In crowd animation production, it is difficult to design realistic and natural crowd motion paths using traditional methods. In order to solve the problems in generating paths of a crowd, a new approach based on an interactive evolutionary algorithm (EA) named biogeography-based optimization (BBO) is proposed in this paper. The optimization process of individuals is considered as the motion process of a crowd. Specifically, the new approach is applied into a system to generate crowd motion paths and the simulation results demonstrate the efficiency and effectiveness of it. © 2014 WIT Press.

关键词： Motion planning

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A visualization method in virtual educational system

A visualization method in virtual educational system

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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Zhang, Guijuan Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

ISBN: (纸本)9789400776173

We present a visualization-based knowledge expression approach for virtual educational system in this paper. Our method allows teachers and students to understand complex algorithms and procedures more intuitively and conveniently during the process of teaching and learning. We take the decision tree and the random forest algorithm in the field of Data mining as examples in this paper. In our method, the decision tree is represented by a virtual 3D tree model that both the structure and the classification results can be showed clearly. In addition, random forest is represented by a group of virtual 3D trees and their positions denote the similarity between the decision trees. We also provide several user-interaction tools in our system. The tools help users to browse the forest, select a tree, delete a tree and even see the detail information of the decision tree. The effective and understandable results show the feasibility of applying visualization method in virtual educational system. © Springer science+Business Media Dordrecht 2014.

关键词： Visualization

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