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2020 International Conference on networking and network Applications, NaNA 2020

作者： Xia, Hong Zhang, YongKang Wang, Han Chen, YanPing Wang, ZhongMin Shannxi Key Laboratory of Network Data Analysis and Intelligent Progcessing Xi'an University of Posts and Telecommunications Xi'an710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781728189543

Crowdsourcing is an emerging distributed computing model that is widely used. Aiming at the uneven quality of crowdsourcing answers due to different workers' capabilities and attitudes, it is necessary to effectively study the hotspot issue of crowdsourcing answer integration. A crowdsourced answer integration algorithm based on 'filter-evaluate-vote' is proposed. This algorithm is implemented using MapReduce parallel programming model in the Hadoop platform, and experiments are performed on multiple data sets. The results show that the proposed algorithm can be effective. It improves the accuracy of crowdsourced answers, and has high computing performance and horizontal scalability, which is suitable for answer integration in a big data environment. © 2020 IEEE.

关键词： MapReduce

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An algorithm of crowdsourcing answer integration based on specialty categories of workers 5th

An algorithm of crowdsourcing answer integration based on sp...

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5th Euro-China Conference on intelligent data analysis and Applications, ECC 2018

作者： Chen, Yanping Wang, Han Xia, Hong Gao, Cong Wang, Zhongmin School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an China Shannxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (纸本)9783030037659

The effective integration of crowdsourcing answers has become research hot spots in crowdsourcing quality control. Taking into account the influence of the specialty categories of workers on the accuracy of crowdsourced answers, a crowdsourced answer integration algorithm based on the specialty categories of workers is proposed(SCAI). Firstly, SCAI use the crowdsourced answer set to determine the difficulty of the task. Secondly calculate the accuracy of each crowdsourced answer, then obtain the professional classification of the workers and update the professional accuracy. Experiments were conducted on real data sets and compared with classical majority voting method(MV) and expectation maximization evaluation algorithm(EM). The results show that the proposed algorithm can effectively improve the accuracy of crowd-sourced answer. © Springer Nature Switzerland AG 2019.

关键词： Quality control

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Crowdsourcing Answer Integration Algorithm For Big data Environment

Crowdsourcing Answer Integration Algorithm For Big Data Envi...

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International Conference on networking and network Applications (NaNA)

作者： Hong Xia YongKang Zhang Han Wang YanPing Chen ZhongMin Wang Shannxi Key Laboratory of Network Data Analysis and Intelligent Progcessing Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9781728189543

ISBN: (纸本)9781728189550

Crowdsourcing is an emerging distributed computing model that is widely used. Aiming at the uneven quality of crowdsourcing answers due to different workers' capabilities and attitudes, it is necessary to effectively study the hotspot issue of crowdsourcing answer integration. A crowdsourced answer integration algorithm based on “filter-evaluate-vote” is proposed. This algorithm is implemented using MapReduce parallel programming model in the Hadoop platform, and experiments are performed on multiple data sets. The results show that the proposed algorithm can be effective. It improves the accuracy of crowdsourced answers, and has high computing performance and horizontal scalability, which is suitable for answer integration in a big data environment.

关键词： Crowdsourcing Computational modeling Scalability Big data Quality assessment Time factors Task analysis

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A High Parallel Accelerator Design Based on the Sparsity of Convolutional Neural network

SSRN

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SSRN 2022年

作者： Pan, Xiao Ying Mu, Yuanzhen Jia, Ningxin Gao, Xuan Rong Feng, Tong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shannxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Shannxi Xi’an710121 China

Designing a domain-specific accelerator is an effective way to improve the performance of inference network on resource-constrained embedded devices. However, there are a large number of invalid memory accesses and calculations in the convolutional neural network. Based on this, this paper proposes an accelerator that adopts a flexible neural network structure that adjusts the processing flow of the network, reduces the size of the feature map sent into the activation function, and greatly reduces the inherent invalid calculation and memory access of the network. The accelerator uses loop expansion and loop reconstruction to solve the read-write problem in convolution operation, realize instruction-level parallelism and data-level parallelism. We determine the design parameters of parallel computing by modelling the constrained single-objective optimization problem to find the optimal cyclic expansion factor. Evaluate the performance, resource utilization, and energy consumption of accelerators in different neural networks. Compared with the general processor CPU and GPU, the running time of the accelerator is shortened by 6.89s and 1.16s, and the power consumption is only 7.47% of CPU and 3% of GPU. Compared with the state-of-the-art accelerator, the performance is increased by 2.8x and the power consumption is reduced by approximately 6W. © 2022, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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