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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

共 2794 条记录，以下是411-420 订阅

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Research on Real-time Learning Prediction Method Based on Spark 10

Research on Real-time Learning Prediction Method Based on Sp...

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10th IEEE international conference on Software Engineering and Service Science (ICSESS)

作者： Gong, Shaolin Qin, Xuan Nanjing Les Cybersecur & Informatizat Technol Res Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728109459

Based on the research of real-time prediction and big data processing platform, an effective solution is proposed to solve the shortcomings of current real-time learning prediction in engineering application. By analyzing learners' learning behaviors related to a certain course, learners' learning behaviors can be divided into three categories in terms of time and space: online learning behaviors, offline learning behaviors and performance of relevant basic courses. Based on the parallel computation and binary logistic regression algorithm in Spark framework, the off-line learning prediction model is created. In the real-time environment, large scale real-time learning prediction can be realized based on Spark Streaming and kafka. With the increase of learning behaviors data, the scalability problem of prediction scheme can be solved by expanding Spark cluster nodes. the advantages of the proposed scheme have been verified in the practical application of smart campus.

关键词： Real-tune prediction parallel Computing Logistic regression Spark

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parallel SAT Simplification on GPU architectures 25th

Parallel SAT Simplification on GPU Architectures

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25th international conference on Tools and algorithms for the Construction and Analysis of Systems (TACAS) held as part of the 22nd European Joint conferences on theory and Practice of Software (ETAPS)

作者： Osama, Muhammad Wijs, Anton Eindhoven Univ Technol NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9783030174620;9783030174613

the growing scale of applications encoded to Boolean Satisfiability (SAT) problems imposes the need for accelerating SAT simplifications or preprocessing. parallel SAT preprocessing has been an open challenge for many years. therefore, we propose novel parallel algorithms for variable and subsumption elimination targeting Graphics processing Units (GPUs). Benchmarks show that the algorithms achieve an acceleration of 66x over a state-of-the-art SAT simplifier (SatELite). Regarding SAT solving, we have conducted a thorough evaluation, combining both our GPU algorithms and SatELite with MiniSat to solve the simplified problems. In addition, we have studied the impact of the algorithms on the solvability of problems with Lingeling. We conclude that our algorithms have a considerable impact on the solvability of SAT problems.

关键词： Satisfiability Variable elimination Subsumption elimination parallel SAT preprocessing GPU

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Capturing push-processing using enriched semantic mesh equipped with functionals-and-hops model 10

Capturing push-processing using enriched semantic mesh equip...

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10th Annual international conference on Biologically Inspired Cognitive architectures, BICA 2019

作者： Ismailova, Larisa Wolfengagen, Viacheslav Kosikov, Sergey Dohrn, Melanie National Research Nuclear University Moscow Engineering Physics Institute Kashirskoye Shosse 31 Moscow115409 Russia Nao JurInfoR M. Pirogovskaya str. 5 Moscow119435 Russia

One of the aims of this paper is to study how to define meshes, a kind of wireframe semantic models hidden behind the variety of external forms of manifestation of information processes on the Web. their purpose is to fix non-logical patterns, some of which are constant, and some of then are varying with time/events. An enriched semantic model arises at the next stage of knowledge extraction and establishes conceptual dependencies in the form of a graph in which the nodes correspond to variable domains and/or variable concepts, and the edges are natural transformations. A computational model based on parametric functionals is generated. the areas of semantic stability are the imposed conditions of commutativity, which correspond to the emergence of information channels and the possibility of their equal use for promoting information over the network. In the computational model, this gives rise to a machinery for preventing confusion/entanglement of information processes based on the equality of representing functionals. Arguments in favor of its importance are given by the example of some tasks of social engineering (phishing). © 2020 the Authors. Published by Elsevier B.V.

关键词： Semantics

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Generic methodology for the design of parallel algorithms based on pattern languages 9th

Generic methodology for the design of parallel algorithms ba...

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9th international conference on Supercomputing, ISUM 2018

作者： Serrano-Rubio, A. Alejandra Meneses-Viveros, Amilcar Morales-Luna, Guillermo B. Paredes-López, Mireya Computer Science Department Cinvestav-IPN Mexico City Mexico Mathematics Department Cinvestav-IPN Mexico City Mexico

ISBN: (纸本)9783030104474

A parallel system to solve complex computational problems involve multiple instruction, simultaneous flows, communication structures, synchronisation and competition conditions between processes, as well as mapping and balance of workload in each processing unit. the algorithm design and the facilities of processing units will affect the cost-performance ratio of any algorithm. We propose a generic methodology to capture the main characteristics of parallel algorithm design methodologies, and to add the experience of expert programmers through pattern languages. Robust design considering the relations between architectures and programs is a crucial item to implement high-quality parallel algorithms. We aim for a methodology to exploit algorithmic concurrencies and to establish optimal process allocation into processing units, exploring the lowest implementation details. Some basic examples are described, such as the k-means algorithm, to illustrate and to show the effectiveness of our methodology. Our proposal identifies essential design patterns to find models of Data Mining algorithms with string self-adaptive mechanisms for homogeneous and heterogeneous parallel architectures. © Springer Nature Switzerland AG 2019.

关键词： parallel algorithms

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Mixup Based Privacy Preserving Mixed Collaboration Learning 13

Mixup Based Privacy Preserving Mixed Collaboration Learning

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13th IEEE international conference on Service-Oriented System Engineering (SOSE) / 10th international Workshop on Joint Cloud Computing (JCC) / IEEE international Workshop on Cloud Computing in Robotic Systems (CCRS)

作者： Fu, Yingwei Wang, Huaimin Xu, Kele Mi, Haibo Wang, Yijie Natl Univ Def Technol Natl Key Lab Parallel & Distributed Proc Coll Comp Changsha Hunan Peoples R China

ISBN: (数字)9781728114422

ISBN: (纸本)9781728114422

the amount of high-quality data determines the performance of the deep learning model. In reality, the data is often physically distributed in different organizations, and model averaging can train a deep model on the distributed data, while providing competitive performance compared with training a model on the centralized data. However, it cannot prevent inversion attack, as the intermediate parameters are transmitted during training. Some data enhancement methods, such as mixup, can effectively enhance the data privacy. In this paper, we propose a novel model averaging method combined with mixup, which provides protection against inversion attack. Besides we conduct experiments using state-of-the-art deep network architectures on multiple types of dataset to show that our method improves the classification accuracy of models.

关键词： distributed machine learning data privacy protection model aggregation

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Efficient parallel GCD algorithms for Multicore Shared Memory architectures

Efficient Parallel GCD Algorithms for Multicore Shared Memor...

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international conference on Advances in ICT for Emerging Regions

作者： Gihan tharaka Pathirana Sittampalam Sotheeswaran Nagulan Ratnarajah Faculty of Science Eastern University Sri Lanka Faculty of Applied Science Vavuniya Campus of the University of Jaffna Sri Lanka

ISBN: (数字)9781728186559

ISBN: (纸本)9781728186566

the greatest common divisor (GCD) is used for numerous applications in number theory, modular arithmetic, encryption algorithms such as RSA, random number generation, and solving linear Diophantine equations. High-performance algorithms, which are efficiently and accurately find the GCD of two integers and n (>2) integers, are needed in the modern world for many applications in science and engineering. parallel hardware and parallel programming solve such emerging challenges that are not possible in a serial world. Modern desktop and laptop computers are equipped with multicore processors with shared memory architecture. In this paper, we develop novel efficient parallel n integers GCD algorithms for multicore shared memory architecture. the brute force, divide-and-conquer, linear recursive and finding minimum first techniques are adopted in our novel algorithms to reduce the size and the complexity of the n integers GCD problem. Various working models of OpenMP, such as the thread-centric, loop-centric and task-centric models are utilized, which promised a more natural way of exploiting and expressing regular and irregular algorithms. A comprehensive performance analysis applies to prove the efficiency of the proposed algorithms.

关键词： parallel algorithms Task analysis Multicore processing Time complexity Software algorithms Memory architecture Heuristic algorithms

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GPU-based parallel Computing for Activity-based Travel Demand Models 10

GPU-based Parallel Computing for Activity-based Travel Deman...

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10th international conference on Ambient Systems, Networks and Technologies (ANT) / 2nd international conference on Emerging Data and Industry 4.0 (EDI40)

作者： Zhou, H. Dorsman, J. L. Snelder, M. de Romph, E. Mandjes, M. TNO Dutch Appl Sci Org Sustainable Urban Mobil & Safety The Hague Netherlands Univ Amsterdam Korteweg de Vries Inst Math Amsterdam Netherlands

Activity-based travel demand models (ABMs) are gaining popularity in the field of traffic modeling because of their high level of detail compared to traditional travel demand models. Due to this, however, ABMs have high computational requirements, making ABMs hard to use for analysis and optimization purposes. We address this problem by relying on the concept of parallel computing using a computer's graphics processing unit (GPU). To illustrate the potential of GPU computing for ABM, we present a pilot study in which we compare the observed computation time of an ABM GPU implementation that we built using NVIDIA's CUDA framework with similar, non-parallel implementations. We conclude that speed-ups up to a factor 50 can be realized, enabling the use of ABMs both for fast analysis of scenarios and for optimization purposes. (C) 2019 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the conference Program Chairs.

关键词： parallel computing GPU Activity-based travel demand modeling Speed-up ActivitySim

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Monitoring gait events by image processing 24

Monitoring gait events by image processing

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24th KES international conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Young, Jeffery Simic, Milena Simic, Milan School of Engineering RMIT University Melbourne3083 Australia Faculty of Health Sciences University of Sydney Sydney2141 Australia

this paper presents a novel methodology in measuring Foot Progression Angle (FPA) and other gait parameters, using digital image processing, based on body and foot speeds. Measurements of body parts' movement speeds allow identification of gait events such as initial contact, mid stance and Flat Foot Phase (FFP). algorithms were developed to segment FFP based on the motion patterns and their relationships. Digital data and graphic presentation results are generated synchronously from the same optic device. this could allow clinicians to confirm results of the both data sets. New methodology requires participant to walk along a 10-meter flat walkway, at self-selected speed, wearing comfortable shoes. Measurement set-up involves a mobile-phone camera, attached to a torso-mounted strap, pointing downwards, a walkway with parallel straight lines outlining progression direction, and printed strips placed along the long axis of the foot. Several methods were evaluated to find the best way to detect initial contact, flat foot and mid stance phases. Our new methodology may present a solution for mobile gait analysis, which could be used at home, or community / outdoor environment, basically outside of the sophisticated well equipped and expensive labs. © 2020 the Authors. Published by Elsevier B.V.

关键词： Image processing

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PhD Forum: Towards Embedded Heterogeneous FPGA-GPU Smart Camera architectures for CNN Inference 13

PhD Forum: Towards Embedded Heterogeneous FPGA-GPU Smart Cam...

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13th international conference on Distributed Smart Cameras (ICDSC)

作者： Carballo-Hernandez, Walther Berry, Francois Pelcat, Maxime Arias-Estrada, Miguel Inst Pascal Dept Images Percept Syst & Robot Aubiere France UMR CNRS Inst Natl Sci Appliquees INSA Rennes IETR Dept Images Rennes France INAOE Dept Comp Sci Puebla Mexico

ISBN: (纸本)9781450371896

the success of Deep Learning (DL) algorithms in computer vision tasks have created an on-going demand of dedicated hardware architectures that could keep up with the their required computation and memory complexities. this task is particularly challenging when embedded smart camera platforms have constrained resources such as power consumption, processing Element (PE) and communication. this article describes a heterogeneous system embedding an FPGA and a GPU for executing CNN inference for computer vision applications. the built system addresses some challenges of embedded CNN such as task and data partitioning, and workload balancing. the selected heterogeneous platform embeds an Nvidia (R) Jetson TX2 for the CPU-GPU side and an Intel Altera (R) Cyclone10GX for the FPGA side interconnected by PCIe Gen2 with a MIPI-CSI camera for prototyping. this test environment will be used as a support for future work on a methodology for optimized model partitioning.

关键词： Heterogeneous Computing Edge Computing Internet of things parallel Programming Single Instruction Multiple Data Pipelining Models of Computation and Architecture

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parallel Spectrum Reconstruction in Fourier Transform Imaging Spectroscopy Based on the Embedded System 1

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10th international conference on Image and Graphics (ICIG)

作者： Zhang, Weikang Wen, Desheng Song, Zongxi Wei, Xin Liu, Gang Li, Zhixin Jiang, Tuochi Chinese Acad Sci Xian Inst Opt & Precis Mech Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9783030341107

ISBN: (纸本)9783030341107;9783030341091

In this paper, we design a parallel-processing pipeline for spectrum reconstruction in Fourier transform imaging spectroscopy (FTIS), which works well with the embedded system, NVIDIA Jetson TX2. this embedded system has great performance in parallel computing and can be developed easily by programmers using CUDA C in a single development board. this is very important for data processing on satellite and mobile devices. On the other hand, because of the huge amount of interference data acquired by the Fourier transform spectrometer, traditional interference data processing mechanism is not efficient and time-saving. these data should be processed in a fast way for real time, especially on satellite, to save memory and bandwidth. We take advantage of parallel computing to enable higher efficiency and reduced operation time. Furthermore, traditional serial algorithms for processing interferograms on the ARMs are introduced for comparison. the experimental results show that our parallel spectrum reconstruction pipeline has much higher performance than the serial one, and for huge data, our parallel mechanism also achieves great result in high performance.

关键词： Spectrum reconstruction GPU Embedded system High performance

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