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Image segmentation based on Iterative Self-Organizing data clustering threshold of PCNN 2

Image segmentation based on Iterative Self-Organizing Data c...

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2nd international conference on Cloud computing and Internet of Things (CCIOT)

作者： Li, Haiyan Guo, Lei Yu, Pengfei Chen, Jianhua Tang, Yiying Yunnan Univ Elect Engn Dept Sch Informat Sci & Engn Kunming Peoples R China Med Univ Kunming Hosp Affiliated 3 Breast Surg Dept Kunming Peoples R China

ISBN: (纸本)9781467398237

Tn order to solve the problem of PCNN with improper parameter selection and determination of circulation iterations which leads to the image owe-segmentation or over-segmentation, an Iterative Self-organizing data Clustering (ISODC) model is used in this paper to resolve the problems of the PCNN parameters selection and requiring multiple circulation. By using ISODC clustering search decision-making ability, the best threshold value is obtained. ISODC can solve the dilemma that PCNN needs to determine appropriate model parameters and circulation iterations. ISODC-PCNN makes use of the grey level of the image to cluster, and then uses the improved ISOdata to determine the initial number and the center of clustering which can be used as the optimal threshold value of PCNN. Therefore ISODC-PCNN can automatically segment an image with one time of iteration. Experimental results show that the proposed method improves the segmentation speed and achieves good segmentation results.

关键词： Pulse Coupled Neural Network (PCNN) Iterative Self-Organizing data Clustering (ISODC) Image segmentation

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Molecular computation based on tile assembly model: modular-multiplication and modular-square over finite field GF(2ⁿ)

Molecular computation based on tile assembly model: modular-...

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18th IEEE international conference on High Performance computing and Communications (HPCC) / 14th IEEE international conference on Smart City (Smart City) / 2nd IEEE international conference on data science and Systems (DSS)

作者： Li, Yongnan Xiao, Limin Peoples Publ Secur Univ China Sch Invest & Counter Terrorism Beijing 100038 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781509042975

In recent years, there are some questions worth study that how to implement the arithmetic operations based on DNA computing. This paper proposes that how to calculate modular-multiplication and modular-square simultaneously in one DNA computing model. This model approaches modular-multiplication and modular-square in the same way. It is given that the bitwise assembly rules manifested as 7746 types of computation tiles performing six different functions. The assembly time is 3n-3 and the space complexity is 2n(2) - 3n+ 1. Compared to previous works, this model achieves more functionalities and it is easier to encode the seed configuration. It's assembly speed is more faster. Without considering its functionality, this model could be improved continually by adding 32 types of computation tiles and adjusting seed configuration. Time complexity and space complexity could respectively reach to 3n - 4 and 2n(2) - 4n+ 2.

关键词： Modular-multiplication Modular-square Assembly rules Molecular computation

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Performance Optimization of the SSVD Collaborative Filtering Algorithm on MapReduce Architectures

Performance Optimization of the SSVD Collaborative Filtering...

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14th IEEE international conference on Dependable, Autonomic and Secure computing, DASC 2016, 14th IEEE international conference on Pervasive Intelligence and computing, PICom 2016, 2nd IEEE international conference on Big data Intelligence and computing, dataCom 2016 and 2016 IEEE Cyber science and Technology Congress, CyberSciTech 2016, DASC-PICom-dataCom-CyberSciTech 2016

作者： Yu, ShiouCheng Kao, Quey-Liang Lee, Che-Rung Department of Computer Science National Tsing Hua University HsinChu Taiwan

ISBN: (纸本)9781509040650

MapReduce that attends data locality and fault tolerance is one of the most successful programming paradigms for data intensive and high-throughput applications. While many researches focus on the system performance of MapReduce, the application level performance optimization techniques are seldom discussed. In this paper, we present strategies to optimize the performance of Stochastic Singular Value Decomposition Collaborative Filtering (SSVD-CF) algorithm on MapReduce architectures. We applied the performance optimization techniques from scientific computing, including BLAS 3 matrix operations, Tall and Skinny QR (TSQR) decomposition, tiled algorithm, and distributed cache, to the SSVD-CF computation. Experiments showed although the MapReduce architecture is different from traditional high performance computing environments, those techniques can be still very effective. Near eight times speedup can be achieved for the most time consuming job, and more than five times speedup can be obtained for the entire program for large datasets. © 2016 IEEE.

关键词： MapReduce

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Performance Evaluation of Different Similarity Functions and Classification Methods using Web Based Hindi Language Question Answering System 2

Performance Evaluation of Different Similarity Functions and...

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2nd international conference on Intelligent computing, Communication & Convergence (ICCC)

作者： Devi, Rajni Dua, Mohit Banasthali Vidyapith Dept Comp Engn Banasthali Rajasthan India Natl Inst Technol Dept Comp Engn Kurukshetra Haryana India

Question Answering (QA) system is an approach to extract the correct answer for the query asked by the user in its own language. The work discussed is implemented for Hindi Language objective type questions and answers. The paper implements the comparison of nine different similarity functions and two classification methods used to retrieve the desired information. The results revealconclude that Smith Waterman outperforms the other similarity functions in perforamnce evaluation. The K-Nearest Neighbor(K-NN) algorithm gives 97%, 95.6% and Neasret Neighbor (NN) algorithm gives 93.3%,95% for two differtent test data sets, respectively. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Hindi Question Answering System Machine Learning data Mining Similarity functions Text Similarity measure classification method Nearest Neighbor K-Nearest Neighbor

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Learning-based Characterizing and Modeling Performance Bottlenecks of Big data Workloads

Learning-based Characterizing and Modeling Performance Bottl...

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作者： Guo, Zhongxin Hu, Zheng Zhang, Chunhong Pu, Youer Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Minist Educ Beijing Peoples R China Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781509042975

As the increasing demands of large-scale data analytics, the understanding of performance bottlenecks on big data workloads becomes critical for the optimization of distribution platforms. Existing work focused on qualitatively characterizing the behaviors and performance of workloads. However little effort has been spent on quantification of performance bottlenecks and building bottleneck models. In this paper, we define a series of bottleneck ratios to quantify bottlenecks according to resource utilizations. Then based on features parsed from original logs, a stage-level modeling approach is proposed to characterize bottlenecks of workloads. By modeling, we can estimate bottleneck ratios using original logs, without collecting resource utilizations. To generalize the models for diverse workloads, we propose a workload generator: TrainBench, which is flexible to generate workloads with multifarious behaviors at stage-level. In addition, taking hardware performance into account, three key features are extracted to improve the estimation accuracy. Our bottleneck models perform well for diverse workloads in different clusters.

关键词： Big data workloads Distributed computing Performance bottlenecks

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Evaluating the effectiveness of soft K-Means in detecting overlapping clusters 16

Evaluating the effectiveness of soft K-Means in detecting ov...

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2nd international conference on Information and Communication Technology for Competitive Strategies, ICTCS 2016

作者： Dkhar, Ronald Ashley Nath, Keshab Roy, Swarup Bhattacharyya, Dhruba K. Nandi, Sukumar Dept of Information Technology North-Eastern Hill University Shillong India Dept of Computer Sc. and Engg. Tezpur University Tezpur India

ISBN: (纸本)9781450339629

Traditional clustering techniques are limited in detecting clusters of overlapping in nature. To identify overlapping clusters in a real data is a challenging task in data mining. A number of clustering methods proposed so far to address the issue. They have their own merits and limitations in producing quality outcomes. soft computing is relatively a new computing paradigm to handle uncertainty and vague-ness in the data. Recently, soft computing concepts are in-corporated in data clustering where data are overlapping in distribution. Various literatures reveal that soft clustering techniques are effective and give promising results. However, very few soft clustering approaches are evolved so far. Most of them are the modified and hybrid version of well-known k-means algorithm. In this paper, we evaluate the performance of all soft ver-sions of k-means such as fuzzy, rough and fuzzy-rough c-means in the light of synthetic data to measure its effective-ness in detecting overlapping clusters. © 2016 ACM.

关键词： soft computing

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A Visual Analytics Framework to Study Honey Bee Behaviour

A Visual Analytics Framework to Study Honey Bee Behaviour

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作者： Engelke, Ulrich Marendy, Peter Susanto, Ferry Williams, Raymond Mahbub, Selim Nguyen, Huyen de Souza, Paulo CSIRO Data61 Sandy Bay Tas 7005 Australia Univ Tasmania Sch Engn & ICT Sandy Bay Tas 7005 Australia Victoria Univ Coll Engn & Sci Footscray Vic 3011 Australia

ISBN: (纸本)9781509042975

We present an end-to-end visual analytics framework that aims to facilitate prediction and decision making about honey bee health based on micro sensing data. The framework is particularly tailored to cope with heterogeneous data from micro sensors and environmental sensors that are deployed to collect information about bees and their environment. The framework design allows for a wide range of end users, including scientists, bee keepers, and decision makers, to effectively explore the bee data through interactive visual interfaces. User centred design is deployed throughout the development to meet the various requirements of the users. A large scale study is being planned to evaluate and further refine the framework based on user experiences.

关键词： Information visualisation Modelling User interface Visual analytics

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Smart Energy-Aware data Allocation for Heterogeneous Memory

Smart Energy-Aware Data Allocation for Heterogeneous Memory

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作者： Gai, Keke Qiu, Meikang Zhao, Hui Qiu, Longfei Pace Univ Dept Comp Sci New York NY 10038 USA Columbia Univ Dept Elect Engn New York NY 10027 USA Henan Univ Software Sch Kaifeng 475000 Henan Peoples R China Nanjing Foreign Language Sch Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509042975

Heterogeneous memory is an emerging concept that uses multiple kinds of memories to balance the contradictions between the performance and cost. Many prior researches have addressed the heterogeneous memory designs, which attracted both researchers and practitioners. However, the main challenge in memory design is that the moving costs between memories cannot be ignored, such that forming an effective data allocation plan is a critical part for reducing total costs. This paper focuses on this issue and proposes an adaptive energy-aware approach that is designed for efficiently generating adaptive data allocation plans in heterogeneous memories. Two crucial variables are considered the condition constraints, including time and energy consumptions. The proposed method is called Smart data Allocation Model (SDAM), which is supported by our algorithm, Smart Switch Allocation Algorithm (SA2). The experimental results have depicted that our approach is superior to prior developed methods. The likelihood of generating optimal solutions is 80.4% -90.1%.

关键词： Smart computing energy-aware data allocation heterogeneous memory

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Space and Time Constrained data Offloading in Vehicular Networks

Space and Time Constrained Data Offloading in Vehicular Netw...

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作者： Yuan, Quan Li, Jinglin Liu, Zhihan Yang, Fangchun Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781509042975

Mobile data offloading is a feasible and costeffective solution to ease the burden of cellular networks. In Internet of Vehicles, however, existing offloading techniques are hardly applicable to the ubiquitous location-dependent services, which impose strict spatiotemporal constraints on content delivery. Particularly, the spatiotemporal constraints cause a phenomenon where the delivery deadlines are different even for the vehicles who subscribe to the same content. To this end, we propose a space and time constrained data offloading scheme (STCDO). The scheme maintains a probability-based contact graph to represent the near-term transmission opportunities between vehicles. Furthermore, a dynamic structure called offloading tree is introduced to evaluate the influence of each vehicle on opportunistic dissemination. Finally, the scheme uses a greedy algorithm to effectively select appropriate vehicles as offloading seeds. We perform extensive experiments based on the real-world map-driven movement model in the ONE simulator. The experimental results show that the proposed scheme largely offloads the overloaded cellular networks while satisfying the spatiotemporal constraints.

关键词： mobile data offloading location-dependent service vehicular networks

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Big data: The V's of the Game Changer Paradigm

Big Data: The V's of the Game Changer Paradigm

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作者： Patgiri, Ripon Ahmed, Arif Natl Inst Technol Silchar Dept Comp Sci & Engn Grant 788010 Assam India

ISBN: (纸本)9781509042975

The Big data is the most prominent paradigm now-a-days. The Big data starts rule slowly from 2003, and expected to rule and dominate the IT industries at least up to 2030. Furthermore, the Big data conquer the technological war and easily capture the entire market since 2009. The Big data is blasting everywhere around the World in every domain. The Big data, a massive amount of data, able to generate billions of revenue. The secret behind of these billions of revenue is ever growing volume. This paper presents the redefinition of volume of Big data. The volume is redefined by engaging three other V's, namely, voluminosity, vacuum, and vitality. Furthermore, this paper augments two new V's to the Big data paradigm, namely, vendee and vase. This paper explores all V's of Big data. There are lots of controversy and confusion regarding V's of Big data. This paper uncovers the confusions of the V family of the Big data.

关键词： Big data V family of Big data All V's of Big data Trends of Big data Redefinition of Big data Redefinition of Volume

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