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SIAM International Conference on Data Mining 2015, SDM 2015

作者： Liu, Hongfu Wu, Junjie Tao, Dacheng Zhang, Yuchao Fu, Yun Department of Electrical and Computer Engineering College of Engineering College of Computer and Information Science Northeastern University Boston United States School of Economics and Management Beihang University Beijing China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia Beijing Institute of System Engineering Beijing China

ISBN: (纸本)9781510811522

Upon extensive studies, text clustering remains a critical challenge in data mining community. Even by various techniques proposed to overcome some of these challenges, there still exist problems when dealing with weakly related or even noisy features. In response to this, we propose a Dlssemble-ASsemble (DIAS) framework for text clustering. DIAS employs simple random feature sampling to disassemble high-dimensional text data and gains diverse structural knowledge. This also does good to avoiding the bulk of noisy features. Then the multi-view knowledge is assembled by weighted information-theoretic Consensus Clustering (IC-C) in order to gain a high-quality consensus partitioning. Extensive experiments on eight real-world text data sets demonstrate the advantages of DIAS over other widely used methods. In particular, DIAS shows strengths in learning from very weak basic partitionings. In addition, it is the natural suitability to distributed computing that makes DIAS become a promising candidate for big text clustering. Copyright © SIAM.

关键词： Distributed computer systems

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Hybrid color image steganography method used for copyright protection and content authentication

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Journal of information Hiding and Multimedia Signal Processing 2015年第4期6卷 686-696页

作者： Lyu, Wan-Li Chang, Chin-Chen Chou, Yeh-Chieh Lin, Chia-Chen Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd Seatwen Taichung40724 Taiwan Department of Computer Science and Information Management Providence University Taichung43301 Taiwan

In this paper, we proposed a hybrid image data hiding method used for watermarking applications in order to make sure the hidden dual watermarks survive after the image manipulation and enhancement processes. To achieve this goal, the proposed method is divided into two phases. In the first phase, the YCbCr color space is exploited, and, then, LSB techniques are used to embed the first level watermark bits in the YCbCr channel. In the second phase, the modification direction method is used to embed the second level watermark bits in the RGB channel. The experimental results confirmed that the proposed method resists most geometric attacks more effectively than Lusson et al.'s method. Therefore, the method is suitable for protecting the ownership of images and integrity of the potential tampered images. © 2015 ISSN.

关键词： Steganography

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Erratum to: Theories in Business and information Systems engineering

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Business & information Systems engineering 2016年第5期58卷 327-328页

作者： Martin Bichler Ulrich Frank David Avison Julien Malaurent Peter Fettke Dirk Hovorka Jan Krämer Daniel Schnurr Benjamin Müller Leena Suhl Bernhard Thalheim Department of Informatics Technical University of Munich Garching/Munich Germany Chair of Information Systems and Enterprise Modelling Institute for Computer Science and Business Information Systems University of Duisburg-Essen Essen Germany Department of Information Systems Decision Sciences and Statistics ESSEC Business School Cergy Pontoise Cedex France German Research Center for Artificial Intelligence (DFKI GmbH) and Saarland University Saarbrücken Germany Business Information Systems The University of Sydney Business School The University of Sydney Sydney Australia Chair of Internet and Telecommunications Business Faculty of Business Administration and Economics University of Passau Passau Germany Innovation Management and Strategy Faculty of Economics and Business University of Groningen Groningen The Netherlands Decision Support and Operations Research Lab Department Business Information Systems Universität Paderborn Paderborn Germany Information Systems Engineering (ISE) Institut für Informatik Christian-Albrechts-Universität zu Kiel Kiel Germany

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Risk management Using Big Real Time Data

Risk Management Using Big Real Time Data

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IEEE International Conference on Cloud Computing Technology and science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

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Large scale online kernel learning

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Jing Lu Steven C. H. Hoi Jialei Wang Peilin Zhao Zhi-Yong Liu Google MPI for Intelligent Systems School of Information Systems Singapore Management University Singapore Department of Computer Science University of Chicago Chicago IL Institute for Infocomm Research A*STAR Connexis Singapore State Key Lab of Management and Control for Complex System Chinese Academy of Sciences Beijing China

In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nyström Online Gradient Descent (NOGD) algorithm that applies the Nyström method to approximate large kernel matrices. We explore these two approaches to tackle three online learning tasks: binary classification, multi-class classification, and regression. The encouraging results of our experiments on large-scale datasets validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget online kernel learning approaches.

关键词： kernel approximation large scale machine learning online learning

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An intelligent market making strategy in algorithmic trading

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Frontiers of computer science 2014年第4期8卷 596-608页

作者： Xiaodong LI Xiaotie DENG Shanfeng ZHU Feng WANG Haoran XIE Department of Computer Science City University of Hong Kong Hong Kong China AIMS Lab Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai 200240 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China School of Computer Science Fudan University Shanghai 200433 China State Key Lab of Software Engineering School of Computer Science Wuhan University Wuhan 430072 China Department of Computer Science Hong Kong Baptist University Hong Kong China

Market making （MM） strategies have played an important role in the electronic stock market. However, the MM strategies without any forecasting power are not safe while trading. In this paper, we design and implement a twotier framework, which includes a trading signal generator based on a supervised learning approach and an event-driven MM strategy. The proposed generator incorporates the information within order book microstructure and market news to provide directional predictions. The MM strategy in the second tier trades on the signals and prevents itself from profit loss led by market trending. Using half a year price tick data from Tokyo Stock Exchange （TSE） and Shanghai Stock Exchange （SSE）, and corresponding Thomson Reuters news of the same time period, we conduct the back-testing and simulation on an industrial near-to-reality simulator. From the empirical results, we find that 1） strategies with signals perform better than strategies without any signal in terms of average daily profit and loss （PnL） and sharpe ratio （SR）, and 2） correct predictions do help MM strategies readjust their quoting along with market trending, which avoids the strategies triggering stop loss procedure that further realizes the paper loss.

关键词： algorithmic trading market making strategy or- der book microstructure news impact analysis market simulation

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Energy management of cooperative microgrids with P2P energy sharing in distribution networks

Energy management of cooperative microgrids with P2P energy ...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Tian Liu Xiaoqi Tan Bo Sun Yuan Wu Xiaohong Guan Danny H. K. Tsang Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an China College of Information Engineering Zhejiang University of Technology Hangzhou China

To handle the mismatch problem between local demand and local generation in microgrids (MGs), the paradigm of peer-to-peer (P2P) energy sharing among neighboring MGs has been considered as a promising solution for improving the utilization of local distributed energy resources and saving the energy bills for all MGs. Existing works on cooperative MGs usually consider the high-level energy sharing and trading strategies but little about the physical constraints (e.g. voltage tolerance and power flow constraints) in the underlying distribution network. Hence, their solutions may not be applicable to practical power systems. This paper proposes an optimization problem that aims at minimizing the overall energy cost and the P2P energy sharing losses in a distribution network consisting of multiple MGs and explicitly incorporates the practical constraints (e.g., power balance and battery's operational constraints). The proposed optimization problem is difficult to solve directly due to the non-convex constraints. Nevertheless, motivated by the very recent result in radial distribution networks, the proposed non-convex optimization problem can be relaxed to a second-order cone programming (SOCP) problem without incurring any loss of optimality. We apply the proposed problem to a radial distribution network testbed and obtain the corresponding optimal energy management strategy, which exploits the diversified energy consumption profiles to dynamically coordinate multiple MGs and reduces the total energy bill of all MGs. Moreover, an interesting observation from the simulation results is that the cooperation scheme in the P2P sharing network is significantly affected by the MGs' relative locations in the distribution network.

关键词： Propagation losses Smart grids Microgrids Optimization Numerical models Renewable energy sources

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Preface: Human-centric computing and intelligent environments

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Journal of Ambient Intelligence and Smart Environments 2016年第4期8卷 377页

作者： Carles Gomez Andrea Prati Hamid Aghajan Juan Carlos Augusto Wireless Networks Group Department of Network Engineering Universitat Politecnica de Catalunya Fundacio i2cat Spain Department of Information Engineering University of Parma Italy iMinds Gent University BelgiumAmbient Intelligence Research (AIR) Lab Stanford University USA Department of Computer Science and Research Group on Development of Intelligent Environments Middlesex University UK

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TURNING MOBILE LASER SCANNING POINTS INTO 2D/3D ON-ROAD OBJECT MODELS: CURRENT STATUS

TURNING MOBILE LASER SCANNING POINTS INTO 2D/3D ON-ROAD OBJE...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Zongliang Zhang Ming Cheng Xinqu Chen Menglan Zhou Yifei Chen Jonathan Li Hongshan Nie Fujian Key Laboratory of Sensing and Computing for Smart City School of Information Science and Engineering Xiamen University GeoSTARS Lab Department of Geography and Environmental Management University of Waterloo Hunan Intelligent Things Technology Co. Ltd.

ISBN: (纸本)9781479979301

Traditional road surveying methods rely largely on in-situ measurements, which are time consuming and labor intensive. Recent Mobile Laser Scanning (MLS) techniques enable collection of road data at a normal driving speed. However, extracting required information from collected MLS data remains a challenging task. This paper focuses on examining the current status of automated on-road object extraction techniques from 3D MLS points over the last five years. Several kinds of on-road objects are included in this paper: curbs and road surfaces, road markings, pavement cracks, as well as manhole and sewer well covers. We evaluate the extraction techniques according to their method design, degree of automation, precision, and computational efficiency. Given the large volume of MLS data, to date most MLS object extraction techniques are aiming to improve their precision and efficiency.

关键词： Mobile laser scanning (MLS) 3D point clouds Object extraction Road

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Improved gene selection and classification method using quantum particle swarm optimization and projection extreme learning machine

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Journal of Computational and Theoretical Nanoscience 2015年第12期12卷 5781-5788页

作者： Zhang, Xinman Liang, Mujia Xu, Xuebin Jia, Pukun Chen, Sushing MOE Key Lab for Intelligent Networks and Network Security School of Electronics and Information Engineering Xi'An Jiaotong University Xi'an710049 China Guangdong xi'An Jiaotong University Academy Guangdong 528000 China School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer and Information Science and Engineering University of Florida 32611 United States

In order to improve and accelerate the speed of gene selection and classification, an optimal and intelligent method for microarray cancer data is investigated in this paper. Based on new machine learning method theory, projection extreme learning machine (PELM) is studied in searching key gene of disease-producing, combined of quantum particle swarm optimization (QPSO) intelligent search strategy. The method has stronger search ability and quicker convergence speed than other current methods. Experiments are performed on the four microarray datasets: colon, leukemia, lymphoma, and prostate cancers. We apply quantum particle swarm optimization (QPSO) as an effective selection method and projection extreme learning machine (PELM) for classification to reduce the dimensionality of microarray datasets. The resulting feature sets contain significantly smaller subsets of important genes in very rapid computing time, with higher test accuracy and a better evaluation index by calculating the mutual information. © 2015 American Scientific Publishers All rights reserved.

关键词： Genes

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