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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Extracting backbones from weighted complex networks with incomplete information

Abstract and Applied Analysis

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Abstract and Applied Analysis 2015年第1期2015卷

作者： Qian, Liqiang Bu, Zhan Lu, Mei Cao, Jie Wu, Zhiang School of Computer Science and Technology Soochow University Suzhou 215006 China Jiangsu Provincial Key Lab. of E-Business Nanjing University of Finance and Economics Nanjing 210046 China

The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency - the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity. © 2015 Liqiang Qian et al.

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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 scalab.lity of the proposed algorithms. © Springer International Publishing Switzerland 2014.

关键词： Trajectories

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Efficient Parallel Community Detection in Large Edge-Intensive Networks 2

Efficient Parallel Community Detection in Large Edge-Intensi...

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2nd International Conference on Advanced Cloud and Big Data, CBD 2014

作者： Gao, Guangliang Bu, Zhan Wu, Zhiang Li, Yuan Cao, Jie College of Computer Sci. and Eng. Nanjing University of Science and Technology China Jiangsu Provincial Key Lab. of E-Business Nanjing University of Finance and Economics China

ISBN: (纸本)9781479980857

Community detection is a classic and very difficult task in social network analysis. A large number of methods have been developed for both efficient and effective community detection. However, much of the existing methods are heavily dependent on the number of links in the network, and thus they often suffer from the computational inefficiency when meeting large edge-intensive networks. In this paper, we present a novel SIMPLifying and Ensembling (SIMPLE) framework for parallel community detection. It employs the random link sampling to simplify the network and obtain basic partitionings on every sampled graphs. Then, the K-means-based Consensus Clustering is used to ensemble a number of basic partitionings to get high-quality community structures. Meanwhile, steps of random sampling and sampled graph partitioning are encapsulated into MapReduce to further improve the efficiency. Experiments on four real-world social networks analyze key parameters and factors inside SIMPLE, and demonstrate the effectiveness of the SIMPLE. © 2014 IEEE.

关键词： MapReduce

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Hidden space discriminant neighborhood embedding

Hidden space discriminant neighborhood embedding

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International Joint Conference on Neural Networks (IJCNN)

作者： Chuntao Ding Li Zhang Bangjun Wang School of Computer Science and Technology & Provincial Key laboratory for Computer Information Processing Soochow University Suzhou Jiangsu China

Discriminant neighborhood embedding (DNE) algorithm is one of supervised linear dimensionality reduction methods. Its nonlinear version kernel discriminant neighborhood embedding (KDNE) is expected to behave well on classification tasks. However, since KDNE constructs an adjacent graph in the original space, the adjacency graph could not represent the adjacent information in the kernel mapping space. By introducing hidden space, this paper proposes a novel nonlinear method for DNE, called hidden space discriminant neighborhood embedding (HDNE). This algorithm first maps the data in the original space into a high dimensional hidden space by a set of nonlinear hidden functions, and then builds an adjacent graph incorporating neighborhood information of the dataset in the hidden space. Finally, DNE is used to find a transformation matrix which would map the data in the hidden space to a low-dimensional subspace. The proposed method is applied to ORL face and MNIST handwritten digit databases. Experimental results show that the proposed method is efficiency for classification tasks.

关键词： Joints Conferences Neural networks

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3D Representation of Chinese Event Factuality

3D Representation of Chinese Event Factuality

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第十五届汉语词汇语义学国际研讨会

作者： CAO Yuan ZHU Qiaoming LI Peifeng School of Computer Science and Technology of Soochow University Key Lab of Computer Information Processing Technology of Jiangsu Province

In general,Chinese event factuality is determined by the specific vocabularies and syntactic structures of *** Chinese event factuality corpus is to annotate these specific vocabularies and syntactic *** these information is raw and complex,it will result in high computation complexity and low correct rate if we use these information to compute factuality *** paper proposes a 3D representation of Chinese event factuality based on the annotated factual information in a Chinese event factuality *** also presents the transformation rules between the factual information and 3D representation and those between the 3D representation and event *** experimental results demonstrate the effectiveness of our 3D representation.

关键词： Representation of Chinese Event Factuality

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Research on improved coding and decoding algorithm for fountain codes

Research on improved coding and decoding algorithm for fount...

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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Zhifeng Zhu Liulei Zhou Jia Hou School of Electronics and Information Engineering Soochow University Jiangsu China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Jiangsu China

ISBN: (纸本)9781479973408

Fountain code is a class of graph-based linear erasure codes, which can effectively solve the problems such as network congestion and feedback cracking for its characteristics of rateless and can resume when interrupted, and has a lower complexity of encoding and decoding. However, there are still some problems in the process of encoding and decoding, including the degree distribution structure may be destroyed, parameters of the generating matrix are not fixed, and it cannot recover source datas from the remaining encoded packets with no degree one. So, the basic theory of fountain codes from three aspects are introduced in this paper, i.e., degree distribution, encoding and decoding principles. Therefore the improved algorithms according to the above three aspects are presented. Simulation results show that the proposed algorithm is more efficient than the previous one.

关键词： Decoding Encoding Algorithm design and analysis Solitons Robustness Vectors Signal processing algorithms

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An evolution-based robust social influence evaluation method in Online Social Networks

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8787卷 141-157页

作者： Zhu, Feng Liu, Guanfeng Liu, An Zhao, Lei Zhou, Xiaofang School of Computer Science and Technology Jiangsu Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Information Technology and Electrical Engineering University of Queensland Brisbane4072 Australia

Online Social Networks (OSNs) are becoming popular and attracting lots of participants. In OSN based e-commerce platforms, a buyer’s review of a product is one of the most important factors for other buyers’ decision makings. A buyer who provides high quality reviews thus has strong social influence, and can impact a large number of participants’ purchase behaviours in OSNs. However, the dishonest participants can cheat the existing social influence evaluation models by using some typical attacks, like Constant and Camouflage, to obtain fake strong social influence. Therefore, it is significant to accurately evaluate such social influence to recommend the participants who have strong social influences and provide high quality product reviews. In this paper, we propose an Evolutionary-Based Robust Social Influence (EB-RSI) method based on the trust evolutionary models. In our EB-RSI, we propose four influence impact factors in social influence evaluation, i.e., Total Trustworthiness (TT), Fluctuant Trend of Being Advisor (FTBA), Fluctuant Trend of Trustworthiness (FTT) and Trustworthiness Area (TA). They are all significant in the influence evaluation. We conduct experiments onto a real social network dataset Epinions, and validate the effectiveness and robustness of our EB-RSI by comparing with state-of-the-art method, SoCap. The experimental results demonstrate that our EB-RSI can more accurately evaluate participants’ social influence than SoCap. © Springer International Publishing Switzerland 2014.

关键词： Social networking (online)

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Ranking based activity trajectory search

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8786卷 170-185页

关键词： Trajectories

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A parallel ant colonies approach to de novo prediction of protein backbone in CASP8/9

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Science China(information Sciences) 2013年第10期56卷 226-238页

作者： LV Qiang WU HongJie WU JinZhen HUANG Xu LUO XiaoHu QIAN PeiDe School of Computer Science and Technology Soochow University Jiangsu Provincial Key Lab for Information Processing Technologies School of Electronic and Information Engineering Suzhou University of Science and Technology

Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational *** modeling of the protein backbone represents the initial phase of a protein structure prediction *** a parallel ant colony optimization based on sharing one pheromone matrix,this report proposes a parallel approach to predict the structure of a protein *** parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy *** free modeling targets in CASP8/9 are used to evaluate the performance of the *** 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top *** 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.

关键词： protein backbone prediction parallel ant colony optimization

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