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DASH: A C++ PGAS library for distributed data structures and parallel algorithms

DASH: A C++ PGAS library for distributed data structures and...

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18th IEEE international conference on High Performance computing and Communications, 14th IEEE international conference on Smart City and 2nd IEEE international conference on data science and Systems, HPCC/SmartCity/DSS 2016

作者： Fuerlinger, Karl Fuchs, Tobias Kowalewski, Roger Munich Computer Science Department MNM Team Oettingenstr. 67 Munich80538 Germany

ISBN: (纸本)9781509042968

We present DASH, a C++ template library that offers distributed data structures and parallel algorithms and implements a compiler-free PGAS (partitioned global address space) approach. DASH offers many productivity and performance features such as global-view data structures, efficient support for the owner-computes model, flexible multidimensional data distribution schemes and interoperability with STL (standard template library) algorithms. DASH also features a flexible representation of the parallel target machine and allows the exploitation of several hierarchically organized levels of locality through a concept of Teams. We evaluate DASH on a number of benchmark applications and we port a scientific proxy application using the MPI two-sided model to DASH. We find that DASH offers excellent productivity and performance and demonstrate scalability up to 9800 cores. © 2016 IEEE.

关键词： Productivity

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Skin carcinoma data analysis using morphological filters 2

Skin carcinoma data analysis using morphological filters

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2nd IEEE international conference on Next Generation computing Technologies, NGCT 2016

作者： Sree Hari Raju, S. Rajan, E.G. Department of Computer Science University of Mysore Manasa Gangotri Mysore Karnataka India Avatar MedVision US. LLC United States Pentagram Labs Inc California United States Helios and Matheson Analytics United States

ISBN: (纸本)9781509032570

Skin is a complex 3D tessellation and it is difficult to study its texture from acquired images. Images of a skin acquired in different lighting conditions would not be the same simply because of the reason that spatial distribution of light reflections from a particular skin due to an incident light would not remain the same for a different lighting condition. Moreover, skin pores absorb ultraviolet and it is not possible to detect pores and skin flaws even when skin is exposed to a near perfect lighting. This paper addresses the problem of detecting pores and dead cells due to skin carcinoma which are not visible to naked eyes. © 2016 IEEE.

关键词： Pattern recognition

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Optimal Power Flow, Sizing and Location of Thermal Generating Units Using Meta - Heuristic soft computing Algorithms 2

Optimal Power Flow, Sizing and Location of Thermal Generatin...

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

作者： Shilaja, C. Ravi, K. VIT Univ Sch Elect Engn Vellore 632014 Tamil Nadu India

This paper presents an approach on stochastically approximated optimal power flow (OPF) in thermal generation system. It also considers the optimal sizing and location of thermal generation units for finding the optimal generator with minimal losses in distributed environment. The proposed hybrid approach optimizes the power flow using stochastic approximation technique with Flower Pollination Algorithm (FPA). With OPF as a key objective, the system chooses the optimal location and sizing of distributed thermal generators using Krill Herd Algorithm (KH). Here, the objective function for FPA is concerned with minimal cost, emission and voltage stability index. Likewise, KH algorithm is concerned with minimal loss and installation of thermal generators. Proposed hybrid system that combines optimal power flow, sizing and location is compared with conventional techniques to prove its effectiveness. The comparative analysis of proposed hybrid approach is implemented over standard IEEE 30-bus and IEEE 57-bus distribution system. Simulation results proved that the meta-heuristic hybrid approach performs well when compared with available conventional techniques. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Optimal Power Flow Sizing and Location Flower Pollination Algorithm Krill Herd Algorithm Fuel Cost Emission Voltage Stability Index

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Understanding data Analytics Workloads on Intel®Xeon Phi™

Understanding Data Analytics Workloads on Intel®Xeon Phi™

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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： Xie, Biwei Liu, Xu Mckee, Sally A. Zhan, Jianfeng Jia, Zhen Wang, Lei Zhang, Lixin Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Coll William & Mary Williamsburg VA 23187 USA Chalmers Univ Technol Gothenburg Sweden Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781509042975

The Intel (R) Xeon Phi (TM) is gaining popularity for high-performance computing (HPC) applications, but the performance of this many-core coprocessor with wide floating point SIMD units has yet to be explored on data analytics workloads. We construct a benchmark suite to explore the Xeon Phi (TM)'s potential for use in data center servers. Our resulting PhiBench consists of eight representative data analytics workloads covering six application domains. These workloads are optimized for Xeon Phi (TM) and implemented with openMP and Cilk Plus. We run them on real-world datasets and compare their performances for different programming models, input data sizes, and thread counts. Most benefit from the Xeon Phi (TM)'s high computational capacity, delivering speedups by factors of four to almost 29. We further analyze their microarchitecture-level performance characteristics, including vectorization intensities and cache behaviors, and we investigate the impact of affinities and scheduling policies on performance and scalability. Our observations should help other researchers and practitioners to understand and optimize the behaviors of data analytics workloads on the Xeon Phi (TM).

关键词： Xeon Phi (TM) data analytics performance characterization

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Security-Aware Efficient Mass Distributed Storage Approach for Cloud Systems in Big data 2

Security-Aware Efficient Mass Distributed Storage Approach f...

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2nd IEEE international conference on Big data Security on Cloud (BigdataSecurity)

作者： Gai, Keke Qiu, Meikang Zhao, Hui Pace Univ Dept Comp Sci New York NY 10038 USA Henan Univ Software Sch Kaifeng 475000 Henan Peoples R China

ISBN: (纸本)9781509024032

Implementing cloud computing empowers numerous paths to Web-based computing service offerings for meeting diverse needs. However, cloud data security and privacy information protection have also become a critical issue restraining the cloud applications. One of the major concerns in security is that cloud operators will have a chance to reach sensitive data, which dramatically increases users' anxiety and reduces the adoptability of cloud computing in many fields, such as the financial industry and governmental agencies. This paper focuses on this issues and proposes a novel approach that can efficiently split the file and separately store the data in the distributed cloud servers, in which the data cannot be directly reached by cloud service operators. The proposed scheme is entitled as Security-Aware Efficient Distributed Storage (SAEDS) model, which is mainly supported by the proposed algorithms, named Secure Efficient data Distributions (SED2) Algorithm and Efficient data Conflation (EDCon) Algorithm. Our experimental evaluations have assessed both security and efficiency performances.

关键词： Security-aware cybersecurity mass distributed storage cloud computing big data

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Br2Br: A Vector-based Trust Framework for WebRTC Calling Services

Br2Br: A Vector-based Trust Framework for WebRTC Calling Ser...

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作者： Javed, Ibrahim Tariq Toumi, Khalifa Crespi, Noel Mohammadinejad, Amir Telecom SudParis Inst Mines Telecom Evry France

ISBN: (纸本)9781509042975

WebRTC provides web calling services by enabling communication between browsers in a P2P fashion. To achieve security and enhance user privacy it is essential to define the level of trust between the various entities involved in WebRTC security architecture. The existing P2P trust models are not directly applicable as the characteristics of browser to browser communication are not taken into account. We, therefore present ' Br2Br' a vector based trust framework for defining trust in WebRTC. The concepts of trust requirement, trust context, trust policy and trust evaluation are proposed to define trust relationships in WebRTC architecture. The framework considers identification, reputation and experience parameters to evaluate the amount of trust, distrust and mistrust. Finally we present set of characteristics and behaviors essential for the evaluation of trust in WebRTC communications.

关键词： WebRTC Trust Identity IdP Authentication Reputation

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A Change-Point Detection Scheme Based on Subspace Tracking for Mobile Access Traffic

A Change-Point Detection Scheme Based on Subspace Tracking f...

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作者： Hirabaru, Senri Matsuda, Takahiro Hirota, Yuki Izumikawa, Haruki Hanano, Hiroshi Ono, Chihiro Takine, Tetsuya Osaka Univ Grad Sch Engn Dept Informat & Commun Technol 2-1 Yamada Oka Suita Osaka 5650871 Japan KDDI Corp Chiyoda Ku Garden Air Tower3-10-10 Iidabashi Tokyo 1028460 Japan KDDI Res 2-1-15 Ohara Fujimino Saitama 3568502 Japan

ISBN: (纸本)9781509042975

Because a huge number of base stations are deployed due to the recent development of mobile access services, network monitoring in mobile networks becomes more challenging. In this paper, we propose a novel change-point detection scheme to efficiently detect a time instant (change-point) when the spatial distribution of mobile terminals connecting to base stations is changed dramatically. In the proposed scheme, we regard the numbers of connections at multiple base stations as multi-dimensional data, and from the correlation matrix of the multi-dimensional data, the eigenvector associated with the largest eigenvalue is computed sequentially by means of subspace tracking. The eigenvector is transformed to a one-dimensional sequence and change-points are detected by an existing one-dimensional change-point detection scheme. We evaluate the performance of the proposed scheme with simulation experiments.

关键词： Base stations

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Efficient Incremental Itemset Tree for approximate Frequent Itemset mining on data Stream 2

Efficient Incremental Itemset Tree for approximate Frequent ...

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2nd international conference on Applied and Theoretical computing and Communication Technology, iCATccT 2016

作者： Pavitra Bai, S. Ravi Kumar, G.K. Dept. of Information Science S.J.B Institute of Technology Bangalore India Belgaum India

ISBN: (纸本)9781509023981

Mining frequent itemsets and association rules on data stream is an important and challenging task. Tree based approaches have been extensively studied and widely used for their parallel processing capability. Itemset Tree is an efficient data structure to represent the transactions for performing selective mining of frequent itemsets and association rules. The transactions are inserted incrementally and provide on-demand ad-hoc querying on the tree for fining frequent itemsets and association rules for different support and confidence values. However the size of tree grows larger for unbounded data streams limiting the scalability. In this paper, we propose an Approximation based Incremental Memory Efficient Itemset Tree (AIMEIT) algorithm which is an extension to Memory Efficient Itemset Tree (MEIT) algorithm to construct the itemset tree from data stream. The user defined minimum support of interest has been used along with Lossy counting algorithm to prune transactions before inserting them into the tree. Experimental results show that the proposed algorithm is more memory efficient and takes lesser processing time for constructing the tree. © 2016 IEEE.

关键词： data mining

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PyramidViz: Visual Analytics and Big data Visualization for Frequent Patterns

PyramidViz: Visual Analytics and Big Data Visualization for ...

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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： Leung, Carson K. Kononov, Vadim V. Pazdor, Adam G.M. Jiang, Fan Department of Computer Science University of Manitoba WinnipegMB Canada

ISBN: (纸本)9781509040650

Frequent pattern mining aims discover implicit, previously unknown and potentially useful knowledge in the form of frequently co-occurring items, events, or objects. These discovered frequent patterns helps reveal interesting relationships such as consumer shopper behaviour. Existing mining algorithms mostly return a long textual list of frequent patterns to users. Such a long list may not be comprehensible by many users. As a picture is worth a thousand words, visual representation of frequent patterns is more comprehensible. Consequently, several visualizers have been proposed. While they are popular and benefit from a few advantages, they also suffer from some disadvantages. In this paper, we present a visual analytic solution, called PyramidViz, for visualizing and analyzing frequent patterns. PyramidViz shows frequent patterns in an informative and intuitive fashion so that users can easily get an insight about frequency of frequent patterns and relationships (e.g., prefix-extension relationships) among related frequent patterns. Evaluation results show the effectiveness and practicality of PyramidViz in visual analytics and big data visualization of frequent patterns for various reallife applications. © 2016 IEEE.

关键词： Visualization

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Distributed Mean-Field-Type Filters for Big data Assimilation

Distributed Mean-Field-Type Filters for Big Data Assimilatio...

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作者： Gao, Jian Tembine, Hamidou New York Univ Abu Dhabi Learning & Game Theory Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9781509042975

The combination of observed data and dynamical models of mean-field type over networked systems is a challenging problem because of non-linearity, high dimensionality and partial observations. In many networked systems, the effective extraction and utilization of the information contained in observed data should be accomplished by exploiting the availability of accurate predictive, proactive tools of mean-field type dynamical systems. Incorporating observed big data into dynamical models of mean-field type has two problems. One is the curse of dimensionality, and the other is the control of error accumulation. This paper presents a distributed mean-field filter (DMF) framework for large scale networked systems. The proposed filter exploits the topology of the network and decomposes it into highly independent components with respect to the marginal mean-field correlations. The upper bound of global filtering error can be estimated using mean-field-type game theory. Numerical experiments in two object tracking scenarios are carried out to illustrate the performance of our algorithm. Evaluation results show that DMF significantly outperforms the existing filtering algorithms.

关键词： Filtering Mean-field Vehicle tracking

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