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共 4332 条记录，以下是1011-1020 订阅

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Comparative study on skyline query processing techniques on big data 4

Comparative study on skyline query processing techniques on ...

引用

4th International conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020

作者： Kumar Sadineni, Praveen Mahantech Corporation CharlestonWV United States

ISBN: (纸本)9781728154640

In this digital era, humans are intended to access huge amount of data to fulfill their needs. The so called big data has been generated across the globe from various sources such as e-commerce, social networking sites, banking applications etc. and makes it difficult to capture, store, access and manage the huge amount of data resources. To overcome this disadvantage, a new technique is proposed to access the useful insights from a pool of data to deliver better *** query is one such efficient and popular decision support approaches, which remains as an enhanced version of database queries that help to extract the desired data from large databases and recognizes the objects that present along with the optimum combination of the features of the dataset in an efficient manner. This paper deals with various skyline query processing techniques such as basic distributed skyline (BDS), improved distributed skyline (IDS), progressive distributed skyline (PDS), mobile ad-hoc network (MANET), SKYPEERISKYPEER+, parallel distributed skyline (PaDSkyline), feedback based distributed Skyline (FDS), distributed skyline (DSL), skyline space partitioning (SSP), SKYFRAME, iSky which can be used on Big Data to extract the required information and summarizes its comparison. © 2020 IEEE.

关键词： Large dataset

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An Online Learning Approach for Client Selection in Federated Edge Learning under Budget Constraint 22

An Online Learning Approach for Client Selection in Federate...

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Proceedings of the 51st International conference on parallel processing

作者： Lina Su Ruiting Zhou Ne Wang Guang Fang Zongpeng Li School of Computer Science Wuhan University China School of Computer Science Wuhan University China and School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences And Cyberspace Tsinghua University China

ISBN: (纸本)9781450397339

Federated learning (FL) has emerged as a new paradigm that enables distributed mobile devices to learn a global model collaboratively. Since mobile devices (a.k.a, clients) exhibit diversity in model training quality, client selection (CS) becomes critical for efficient FL. CS faces the following challenges: First, the client’s availability, the training data volumes, and the network connection status are time-varying and cannot be easily predicted. Second, clients for training and the number of local iterations would seriously affect the model accuracy. Thus, selecting a subset of available clients and controlling local iterations should guarantee model quality. Third, renting clients for model training needs cost. It is necessary to dynamically administrate the use of the long-term budget without knowledge of future inputs. To this end, we propose a federated edge learning (FedL) framework, which can select appropriate clients and control the number of training iterations in real-time. FedL aims to reduce the completion time while reaching the desired model convergence and satisfying the long-term budget for renting clients. FedL consists of two algorithms: i) the online learning algorithm makes CS and iteration decisions according to historic learning results; ii) the online rounding algorithm translates fractional decisions derived by the online learning algorithm into integers to satisfy feasibility constraints. Rigorous mathematical proof reveals that dynamic regret and dynamic fit have sub-linear upper-bounds with time for a given budget. Extensive experiments based on realistic datasets suggest that FedL outperforms multiple state-of-the-art algorithms. In particular, FedL reduces at least 38% completion time compared with others.

关键词： online learning federated learning client selection budget

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Moving Enterprise Integration Middleware toward the distributed Stream processing Architecture 8

Moving Enterprise Integration Middleware toward the Distribu...

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8th Mediterranean conference on Embedded Computing (MECO)

作者： Bakulev, Alexander Bakuleva, Marina RSREU Ryazan Russia

This article discusses the results of practical research on the transfer of the architecture of the integration middleware layer to the distributed stream processing platform. The selection of the framework for stream... 详细信息

ISBN: (纸本)9781728117393

关键词： distributed stream processing middleware enterprise service bus message queue Apache Kafka Apache Flink parallel processing distributed cluster

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GR-ADMM: A Communication Efficient Algorithm based on ADMM

GR-ADMM: A Communication Efficient Algorithm Based on ADMM

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Xin Huang Guozheng Wang Yongmei Lei School of Computer Engineering and Science Shanghai University Shanghai China

In recent work, the decentralized algorithm has received more attention. In the centralized network, the worker nodes need to communicate with the central nodes, which results in the growth of communication traffic with the network expansion. based on the purpose of reducing the communication costs in the distributed system, we proposed a decentralized algorithm based on ADMM - Grouping Ring All-Reduce ADMM (GR-ADMM) in this paper. First, GR-ADMM adopts decentralized architecture to avoid the problem of communication bottleneck in the central network. Second, to ensure the scalability of the distributed system, GR-ADMM introduces the Ring All-Reduce to the ADMM. Ring All-Reduce architecture has the advantage of its constant communication overhead. However, its performance is bounded by the stragglers (i.e., slow nodes). Third, GR-ADMM adopts the grouping strategy to alleviate the problem of stragglers. Experiments show that our algorithm has better convergence performance than QSGD and GADMM, especially in massive clusters. Compared with GADMM's, the overall communication cost of GR-ADMM is reduced by 72%.

关键词： Wireless communication Communication effectiveness Costs Scalability Clustering algorithms Computer architecture Mobile communication

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A knowledge graph embedding method based on neural network 5

A knowledge graph embedding method based on neural network

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5th IEEE International conference on Data Science in Cyberspace, DSC 2020

作者： Li, Chenchen Li, Aiping Tu, Hongkui Wang, Ye Wang, Changhai National University of Defense Technology College of Computer ChangSha China National University of Defense Technology College of Computer Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (数字)9781728195582

ISBN: (纸本)9781728195582

As the basis of many knowledge graph completion tasks, the embedding representation of entities and relations in knowledge graph (KG) is an important task in the fields of Natural Language processing (NLP) and Artificial Intelligence (AI). While most of the existing knowledge graph embedding (KGE) models based on convolutional neural network (CNN) can obtain abundant feature embedding, they may ignore an important fact that the triples in the KG come from the text, as they simply learn about the feature embedding of entities and relations without considering contextual information. Therefore, in this paper, we propose an effective KGE model based on neural network. First of all, we convert the triple (h, r, t) of the KG into a sentence [h r t]. Then, the LSTM neural network is used to learn the long-term dependence of sentences from the input feature vectors. Then, on this basis, the two-layer convolutional neural network with several different filters is used to extract different local features. Finally, the obtained feature vectors are connected together, and the inner product is carried out with the weight vectors to obtain the score of the triple, so as to judge the validity of the given triple. We evaluate our model on two benchmark datasets FB15k-237 and WN18RR, the experimental results show that the model can effectively improve the accuracy of link prediction, achieving better results compared with other baseline models. © 2020 IEEE.

关键词： Knowledge graph

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A Power Sharing Method for Microgrid Consisting of HAPF and VSC

A Power Sharing Method for Microgrid Consisting of HAPF and ...

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Image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Mingming Zhang Ping Qin Yongjin Chen Hengjie Jia Wenyang Deng Shaoguan Power Supply Bureau of Guangdong Power Grid Co. Ltd Shaoguan Guangdong Guangzhou Power Electrical Technology Co. Ltd Guangzhou Guangdong

Converters play an imperative part in joining conveyed generators and directing control stream of microgrid and active distributed network (ADN). The hybrid active power filter is proposed to improve responsive control with a moo amplitude of operation's voltage. Its control extends and characteristics are distinctive based on a routine voltage source converter (VSC). Regulation of parallel-connected HAPF and VSC in active distributed networks is examined in this work. To completely utilize the control efficiency of every sort of converter, the power output allocation calculation among these two devices is considered for reduce add up to converter rating. A control reference assurance strategy is hence proposed and inserted in various leveled control system of the active distributed network. Comes about demonstrate that converter capacity is decreased by combining HAPF and VSC. Decoupling regulation method is utilized as the essential regulation strategy to every converter. Reenactment comes about are given to appear the legitimacy of the proposed strategy.

关键词： Simulation Image processing Power control Microgrids Regulation Hybrid power systems Generators

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A survey on approaches and applications of knowledge representation learning 5

A survey on approaches and applications of knowledge represe...

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5th IEEE International conference on Data Science in Cyberspace, DSC 2020

作者： Li, Chenchen Li, Aiping Wang, Ye Tu, Hongkui Song, Yichen National University of Defense Technology College of Computer ChangSha China National University of Defense Technology College of Computer Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (纸本)9781728195582

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized. © 2020 IEEE.

关键词： Knowledge representation

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PC-BNA: parallel Convolution Binary Neural network Accelerator

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Haihui Liu Yuang Zhang Xiangwei Zheng Mengxin Yu Baoxu An Xiaojie Liu Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan China

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.

关键词： Performance evaluation Convolution Neural networks Digital signal processors Speech recognition Logic gates Mobile handsets

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A Simulation Study on Block Generation Algorithm based on TPS Model 20th

A Simulation Study on Block Generation Algorithm Based on TP...

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20th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Cai, Shubin Zhou, Huaifeng Yang, NingSheng Ming, Zhong Shenzhen Univ Shenzhen Peoples R China

ISBN: (纸本)9783030602482;9783030602475

Blockchain is a emerging decentralized infrastructure and distributed computing paradigm. However, the low TPS performance of blockchain technology can not meet the performance requirements of large-scale and high concurrency in application reality. A polling discrete event simulation platform is designed to investigate the performance of PoW based block generation algorithm. The operation of block generation algorithm is simulated from three aspects: network topology level, message queue of communication and protocol of PoW algorithm. The result shows that when the block size is 1 MB, the average relative error between the experimental results and the fixed TPS is 13.00%, and when the block size is 4 MB, the average relative error between the experimental results and the fixed TPS is 15.25%. Experiment result shows that the simulation platform can be use to investigate the transaction performance effectively.

关键词： Blockchain Bitcoin Simulation model TPS

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Proceedings - 2020 IEEE International conference on Cluster Computing, CLUSTER 2020

Proceedings - 2020 IEEE International Conference on Cluster ...

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22nd IEEE International conference on Cluster Computing, CLUSTER 2020

ISBN: (纸本)9781728166773

The proceedings contain 71 papers. The topics discussed include: implementing a comprehensive networks-on-chip generator with optimal configurations;ECS2: A fast erasure coding library for GPU-accelerated storage systems with parallel & direct IO;autoscaling high-throughput workloads on container orchestrators;Grade10: a framework for performance characterization of distributed graph processing;data life aware model updating strategy for stream-based online deep learning;energy optimization and analysis with EAR;parallel particle advection bake-off for scientific visualization workloads;HAN: a hierarchical AutotuNed collective communication framework;and evaluating worksharing tasks on distributed environments.

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