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共 1772 条记录，以下是421-430 订阅

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Accelerating Conjugate Gradient using OmpSs

Accelerating Conjugate Gradient using OmpSs

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Sandra Catalán Xavier Martorell Jesús Labarta Tetsuzo Usui Leonel Antonio Toledo Díaz Pedro Valero-Lara Barcelona Supercomputing Center Barcelona Spain Universitat Polit??cnica de Catalunya Barcelona Spain Universitat Polit`ecnica de Catalunya Barcelona Spain Fujitsu Limited Kawasaki Japan

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this paper, we present the benefits of using the clause concurrent of OmpSs when performing reductions, more specifically, when applied to the dot product (DOT) operations. We analyze its benefits through the implementation of different versions of the Conjugate Gradient (CG) method. We start from a parallel version of the code based on tasks and dependencies; later, we introduce the use of the concurrent clause, which allows to overlap the execution of tasks that have data dependencies among them. In this way, we want to show the benefits of the concurrent clause, which might be included in OpenMP standard as previously done with other OmpSs features. Our tests, performed on a single node of the (Intel-based) Marenostrum 4 Supercomputer and a single socket of the (ARM-based) Dibona cluster, show that the use of the concurrent clause may improve performance with respect to the version where only tasks and dependencies are used around 37% and 23% respectively.

关键词： Task analysis US Department of Transportation Kernel parallel processing Sockets Libraries Runtime

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parallel distributed Compensation for Voltage Controlled Active Magnetic Bearing System using Integral Fuzzy Model 9

Parallel Distributed Compensation for Voltage Controlled Act...

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9th international conference on Intelligent Systems (IS)

作者： Tatchum, Martial Gegov, Alexander Jafari, Raheleh Razvarz, Sina Univ Portsmouth Sch Comp Portsmouth Hants England Univ Agder CAIR Grimstad Norway Natl Polytech Inst Dept Control Automat Mexico City DF Mexico

ISBN: (纸本)9781538670972

parallel distributed Compensation (PDC) for current-controlled Active Magnetic Bearing System (AMBS) has been quite effective in recent years. However, this method does not take into account the dynamics associated with the electromagnet. this limits the method to smaller scale applications where the electromagnet dynamics can be neglected. Voltage-controlled AMBS is used to overcome this limitation but this comes with serious challenges such as complex mathematical modelling and higher order system control. In this work, a PDC with integral part is proposed for position and input tracking control of voltage controlled AMBS. PDC method is based on nonlinear TakagiSugeno (T-S) fuzzy model. It is shown that the proposed method outperforms the conventional fuzzy PDC. It stabilizes the bearing shaft at any chosen operating point and tracks any chosen smooth trajectory within the air gap with a high external disturbance rejection capability.

关键词： Takagi-Sugeno Active Magnetic Bearings parallel distributed compensation AMB voltage-controlled

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A Spark-based distributed Whale Optimization Algorithm for Feature Selection

A Spark-based Distributed Whale Optimization Algorithm for F...

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IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems

作者： Hongwei Chen Zhou Hu Lin Han Qiao Hou Zhiwei Ye Jiansen Yuan Jun Zeng School of Computer Science Hubei University of Technology Wuhan Fiberhome Technical Services Co. Ltd.

ISBN: (纸本)9781728140704

With the rapid development of the Internet and big data technologies, high-dimensional data generated in various fields has increased dramatically. Feature selection is an effective way to solve data processing problems caused by high dimensionality and high computational complexity. the traditional feature selection method shows the problem of insufficient classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. the traditional feature selection method shows low classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. this paper proposed a feature selection method based on Whale Optimization Algorithm to learn mining feature selection rules, then improve the accuracy of feature selection. However, when the data size is very large, the efficiency of single node execution is low. therefore, this paper combined the Whale Optimization Algorithm with the parallel computing model of the Spark platform, and proposed a feature selection method based on the Spark platform for distributed Whale Optimization Algorithm. the results showed that the excellent result search ability of the Whale Optimization Algorithm combined with the distributed and efficient calculation speed can realize the efficient solution of the feature selection optimization model.

关键词： Feature selection Whale optimization algorithm Spark platform parallel computing Feature extraction selection methods parallel PROCESSING (COMPUTERS) Optimization algorithms Whale data size Distribute

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3rd international Workshop on Big Data Management in Cloud Systems, BDMICS 2018, 9th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2018 and 15th international Workshop on technologies for Information Retrieval, TIR 2018 held at 29th international conference on Database and Expert Systems applications, DEXA 2018

3rd International Workshop on Big Data Management in Cloud S...

引用

3rd international Workshop on Big Data Management in Cloud Systems, BDMICS 2018, 9th international Workshop on Biological Knowledge Discovery from Data, BIOKDD 2018 and 15th international Workshop on technologies for Information Retrieval, TIR 2018 held at 29th international conference on Database and Expert Systems applications, DEXA 2018

ISBN: (纸本)9783319991320

the proceedings contain 25 papers. the special focus in this conference is on Database and Expert Systems applications. the topics include: Classifying big DNA methylation data: A gene-oriented approach;classifying leukemia and gout patients with neural networks;incremental wrapper based random forest gene subset selection for tumor discernment;protein identification as a suitable application for fast data architecture;Mining geometrical motifs co-occurrences in the CMS dataset;suitable overlapping set visualization techniques and their application to visualize biclustering results on gene expression data;can We quantify domainhood? Exploring measures to assess domain-specificity in web corpora;a case study of closed-domain response suggestion with limited training data;what to Read Next? Challenges and preliminary results in selecting representative documents;thespisDIIP: distributed integrity invariant preservation;text-based annotation of scientific images using wikimedia categories;detecting link and landing page misalignment in marketing emails;toward validation of textual information retrieval techniques for software weaknesses;investigating the effect of attributes on user trust in social media;analysing author self-citations in computer science publications;a semantic-based personalized information retrieval approach using a geo-social user profile;privacy issues for cloud systems;script based migration toolkit for cloud computing architecture in building scalable investment platforms;space-adaptive and workload-aware replication and partitioning for distributed rdf triple stores;performance comparison of three spark-based implementations of parallel entity resolution;Big data analytics: Exploring graphs with optimized SQL queries;new modeling ideas for the exact solution of the closest string problem;ensemble clustering based dimensional reduction.

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Low Computational Data Fusion Approach Using INS and UWB for UAV Navigation Tasks in GPS-Denied Environments

Low Computational Data Fusion Approach Using INS and UWB for...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Xiaoying Kong Gengfa Fang Li Liu Tich Phuoc Tran Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Faculty of Engineering and Information Technologies The University of Sydney Sydney Australia School of Science UNSW Canberra Canberra Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

this paper presents a low computational approach for unmanned aerial vehicles (UAV) navigation in GPS-denied environments. this approach is aiming to reduce computation load for UAV flying mission constraints. Small size, light weight on board hardware are constraints for UAV deployment and flying missions. the on board processor should not be built with high complexity and should consume as little computing as possible. Most existing approaches use Kalman filter, extended Kalman filter, Unscented filter, or particle filter to fuse different types of onboard sensor data to estimate UAV position. We developed a data fusion architecture that does not use these filters. We use an ultra-light-coupling fusion architecture. In this architecture, primary sensor and secondary sensor data are fused. When the secondary sensor is unavailable in most of the time, the UAV navigation uses the output of the primary sensor. When the secondary sensor signal is available, the primary sensor is re-aligned using the secondary sensor signal to bond the errors. In our approach, the primary sensor is Inertial Measurement Unit (IMU), and the secondary sensor inputs are from Ultra-wideband system (UWB). this approach is validated using demonstration of comparison of computing load, and simulation results for accuracy and reliability testing using UAV flying mission scenario.

关键词： Handheld computers conferences distributed computing Systems architecture Couplings Computational modeling Load modeling

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A distributed Constrained Non-negative Matrix Factorization Algorithm for Time-Series Gene Expression Data 18

A Distributed Constrained Non-negative Matrix Factorization ...

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9th ACM international conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Dyer, Matthew Dymacek, Julian Longwood Univ Dept Math & Comp Sci Farmville VA 23909 USA

ISBN: (纸本)9781450357944

We present a new distributed computing algorithm, parallel Pattern Discovery (PPD), for constrained Non-negative Matrix Factorization (NMF). Our implementation offers the ability to constrain a specific pattern for optimization of the data while minimizing reconstruction error. parallel Pattern Discovery operates within a distributed environment using a message passing interface. Distribution of the PPD algorithm provides better scalability and allows operation in single- or multiple-system environments. the algorithm was tested on a set of time-series, dose-dependent mRNA gene expression data. parallel Pattern Discovery was found to accurately identify patterns within the data and reconstruct the original matrices. Our NMF algorithm found a smaller reconstruction error when compared against standard NMF algorithms. Development focused on running PPD as part of a system which identifies significantly contributing genes. parallel Pattern Discovery is first run to find patterns from biological data. It is followed by Gene Set Enrichment (GSE) which takes the pattern data and relates it back to genetic pathways.

关键词： Non-negative matrix factorization distributed computing time-series data

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A parallel Uncertain Frequent Itemset Mining Algorithm with Spark

A Parallel Uncertain Frequent Itemset Mining Algorithm with ...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Jiaman Ding Haibin Li Yuanyuan Wang Lianyin Jia Jinguo You Yang Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability.

关键词： Itemsets Data mining Probability Database systems

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An adaptive cloud-based IoT back-end architecture and its applications 26

An adaptive cloud-based IoT back-end architecture and its ap...

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26th Euromicro international conference on parallel, distributed, and Network-Based Processing (PDP)

作者： Marosi, Attila Csaba Farkas, Attila Lovas, Robert Hungarian Acad Sci MTA SZTAKI Inst Comp Sci & Control Lab Parallel & Distributed Syst POB 63 H-1518 Budapest Hungary

ISBN: (纸本)9781538649756

Internet of things (IoT) is playing increasingly more fundamental role in wide range of sectors, including industry, agriculture, health care, and other services. In many cases, cloud computing serves as an elastic and efficient paradigm for implementing IoT back-ends. With the emerging lightweight software container technologies, the feasible approaches and design options for such IoT back-ends have been significantly enriched. In our paper we present the evolution of an IoT back-end, which is responsible for collecting (among others) meteorological, image and soil data from cultivated fields in order to enable precision farming. the different versions, namely the cloud VM-based and the Docker containerized variants, provide highly scalable and vendor independent (cloud provider agnostic) solutions, therefore they can form a robust and adaptive framework for further pilot applications areas, e.g. Connected Cars and Industry 4.0, as the presented benchmarks illustrate the throughput and other parameters of the current implementation in the paper.

关键词： IoT precision agriculture big data data ingestion containers

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Intrusion Detection in High-Speed Big Data Networks: A Comprehensive Approach

Intrusion Detection in High-Speed Big Data Networks: A Compr...

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12th KIPS international conference on Ubiquitous Information technologies and applications (CUTE) / 9th international conference on Computer Science and its applications (CSA)

作者： Siddique, Kamran Akhtar, Zahid Kim, Yangwoo Dongguk Univ Seoul South Korea Univ Quebec INRS EMT Quebec City PQ Canada

ISBN: (纸本)9789811076053;9789811076046

In network intrusion detection research, two characteristics are generally considered vital to build efficient intrusion detection systems (IDSs) namely, optimal feature selection technique and robust classification schemes. However, an emergence of sophisticated network attacks and the advent of big data concepts in anomaly detection domain require the need to address two more significant aspects. they are concerned with employing appropriate big data computing framework and utilizing contemporary dataset to deal with ongoing advancements. Based on this need, we present a comprehensive approach to build an efficient IDS with the aim to strengthen academic anomaly detection research in real-world operational environments. the proposed system is a representative of the following four characteristics: It (i) performs optimal feature selection using branch-and-bound algorithm;(ii) employs logistic regression for classification;(iii) introduces bulk synchronous parallel processing to handle computational requirements of large-scale networks;and (iv) utilizes real-time contemporary dataset named ISCX-UNB to validate its efficacy.

关键词： Anomaly detection Network intrusion detection systems Bulk synchronous parallel BSP Big data ISCX-UNB dataset Darpa KDD Cup '99

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Proceedings - 5th IEEE/ACM international conference on Big Data computing, applications and technologies, BDCAT 2018

Proceedings - 5th IEEE/ACM International Conference on Big D...

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5th IEEE/ACM international conference on Big Data computing, applications and technologies, BDCAT 2018

the proceedings contain 27 papers. the topics discussed include: CloneHadoop: process cloning to reduce hadoop9;s long tail;fault-tolerant query execution over distributed bitmap indices;skipping unused events to s...

ISBN: (纸本)9781538655023

the proceedings contain 27 papers. the topics discussed include: CloneHadoop: process cloning to reduce hadoop's long tail;fault-tolerant query execution over distributed bitmap indices;skipping unused events to speed up rollback-recovery in distributed data-parallel CEP;prediction of air pollution through machine learning approaches on the cloud;non-linear machine learning with active sampling for MOX Drift compensation;detecting system anomalies in multivariate time series with information transfer and random walk;a decentralized sns system based on XMPP with connection control in large-scale disasters;a mobile application for dog breed detection and recognition based on deep learning;prediction of bus delay over intervals on various kinds of routes using bus probe data;and development of a radiology decision support system for the classification of MRI brain scans.

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