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A Multi-Objective Data Placement Method for IoT Applications Over Big Data Using NSGA-II

A Multi-Objective Data Placement Method for IoT Applications...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Xiaolong Xu Renhao Gu Yuan Yuan Kai Peng Yadang Chen Wanchun Dou Alex X. Liu Department of Computer Science and Engineering Michigan State University MI USA Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Engineering Huaqiao University Quanzhou Fujian China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The Internet of Things has been developing fast since it connects various devices for human beings to use, monitor or configure these devices, which brings great convenience. Generally, the big data generated by the sensors of IoT applications needs to be collected, stored and processed to realize the services of such applications. For the execution of task from the IoT applications, it may need a variety of datasets for tasks to access and process. However, it is still a challenge to realize the data placement method when taking into account the resource utilization and the energy consumption in data centers along with the data access time for the task sets to access the datasets. In view of this challenge, a multi-objectives data placement method for IoT applications over big data is proposed in this paper. Technically, multi-objectives are analyzed first. Then a heuristic data placement method is designed based on Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Consequentially, experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of our proposed method.

关键词： Manganese Handheld computers Internet of Things Switches Green computing Social computing Blockchain

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An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

An energy-aware virtual machine scheduling method for servic...

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作者： Dou, Wanchun Xu, Xiaolong Meng, Shunmei Zhang, Xuyun Hu, Chunhua Yu, Shui Yang, Jian State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical & Computer Engineering The University of Auckland Auckland New Zealand School of Computer and Information Engineering Hunan University of Commerce Hunan China School of Information Technology Deakin University Melbourne Australia Jiangsu Second Normal University Nanjing China

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Quality of service

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Research on Identification Method of Transformer Based on Improved Hu Invariant Moments

Research on Identification Method of Transformer Based on Im...

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International Conference on Mechatronics and Intelligent Robotics, ICMIR 2017

作者： Yu, Hua-dong Ou, Qing-hai Wu, Qing Zhang, Zhe Li, Wen-jing Wang, Yubo Liu, Guohua Lu, Yongling Hu, Chengbo Liu, Na Wang, Rong State Grid Information and Telecommunication Group Co. Ltd. Beijing China State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing100192 China Beijing Engineering Research Center of High-Reliability IC with Power Industrial Grade Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing100192 China State Grid Jiangsu Electric Power Company Research Institute Nanjing21110 China Control and Computer Engineering College North China Electric Power University NCEPU Beijing China

ISBN: (纸本)9783319709895

With the rapid development of electric power industry and the expansion of power grid scale, more and more attentions have been paid to the safe operation of electrical equipment. The target recognition method we used now couldn’t recognize power transformer well, which is based on gray information. In this paper, a transformer identification algorithm based on improved Hu moment invariants is proposed. The transformer identification algorithm proposed in this paper has the advantages of good recognition effect and high recognition accuracy. The correctness and feasibility of the proposed algorithm have been verified by experiments. © 2018, Springer International Publishing AG.

关键词： Template matching

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Context-dependent diffusion network for visual relationship detection

arXiv

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arXiv 2018年

作者： Cui, Zhen Xu, Chunyan Zheng, Wenming Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Key Laboratory of Child Development and Learning Science Ministry of Education Research Center for Learning Science Southeast University Nanjing210096 China

Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images. Different from pure object recognition tasks, the relation triplets of subject-predicate-object lie on an extreme diversity space, such as person-behind-person and car-behind-building, while suffering from the problem of combinatorial explosion. In this paper, we propose a context-dependent diffusion network (CDDN) framework to deal with visual relationship detection. To capture the interactions of different object instances, two types of graphs, word semantic graph and visual scene graph, are constructed to encode global context interdependency. The semantic graph is built through language priors to model semantic correlations across objects, whilst the visual scene graph defines the connections of scene objects so as to utilize the surrounding scene information. For the graph-structured data, we design a diffusion network to adaptively aggregate information from contexts, which can effectively learn latent representations of visual relationships and well cater to visual relationship detection in view of its isomorphic invariance to graphs. Experiments on two widely-used datasets demonstrate that our proposed method is more effective and achieves the state-of-the-art performance. Copyright © 2018, The Authors. All rights reserved.

关键词： Diffusion

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Glucose Screening Measurements and Noninvasive Glucose Monitor Methods

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Procedia computer Science 2018年 139卷 613-621页

作者： Hui Zheng Jing He Peng Li Mengjiao Guo Hui Jin Jie Shen Zhijun Xie Chihung Chi School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China North China Institute of Aerospace Engineering Hebei China College of Information Engineering Nanjing University of Finance and Economics China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Jiangsu Province Nanjing China Endocrinology Department Zhongda Hospital Southeast University Nanjing Jiangsu China Endocrinology Department The Third Affiliated Hospital of Southern Medical University Guangzhou Guangdong China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang Province China Commonwealth Scientific and Industrial Research Organisation (CSIRO) Tasmania Australia

Diabetes alone does not affect the health, but its complications always bring danger to people’s health. To avoid the development of diabetes and posterior to discover diabetes, checking blood glucose level is an effective way. The development of glucose screening measurements is introduced. Non-invasive glucose monitor methods, as accurate, safe, convenient and comfortable methods, are also illustrated and compared in this paper. Also, an Intelligent dynamic health inspection is designed with a proposed non-invasion glucose monitor measurement.

关键词： diabetes blood glucose screening noninvasive methods

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A flexible I/O arbitration framework for netCDF-based big data processing workflows on high-end supercomputers

A flexible I/O arbitration framework for netCDF-based big da...

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作者： Liao, Jianwei Gerofi, Balazs Lien, Guo-Yuan Miyoshi, Takemasa Nishizawa, Seiya Tomita, Hirofumi Liao, Wei-Keng Choudhary, Alok Ishikawa, Yutaka College of Computer and Information Science Southwest University of China Chongqing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China RIKEN Advanced Institute for Computational Science Kobe Japan Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

On the verge of the convergence between high-performance computing and Big Data processing, it has become increasingly prevalent to deploy large-scale data analytics workloads on high-end supercomputers. Such applications often come in the form of complex workflows with various different components, assimilating data from scientific simulations as well as from measurements streamed from sensor networks, such as radars and satellites. For example, as part of the Flagship 2020 (post-K) supercomputer project of Japan, RIKEN is investigating the feasibility of a highly accurate weather forecasting system that would provide a real-time outlook for severe guerrilla rainstorms. One of the main performance bottlenecks of this application is the lack of efficient communication among workflow components, which currently takes place over the parallel file *** this paper, we present an initial study of a direct communication framework designed for complex workflows that eliminates unnecessary file I/O among components. Specifically, we propose an I/O arbitration layer that provides direct parallel data transfer (both synchronous and asynchronous) among job components that rely on the netCDF interface for performing I/O operations. Our solution requires only minimal modifications to application code. Moreover, we propose a configuration file–based approach that allows users to specify the desired data transfer pattern among workflow components, offering a general solution for different application contexts. We present a preliminary evaluation of the proposed framework on the K computer (running on up to 4800 compute nodes) using RIKEN's experimental weather forecasting workflow as a case study. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Supercomputers

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SQLS:A Storm-Based Query Language System for Real-Time Stream Data Analysis

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Chinese Journal of Electronics 2016年第6期25卷 1025-1033页

作者： JI Yimu ZHANG Dianchao SUN Yanfei ZHAO Chuanxin HE Jing WANG Ruchuan CHEN Guoliang Computer of Science Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Key Laboratory of Computer Network and Information Integration Southeast University Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Computer of Telecommunications Nanjing University of Posts and Telecommunications Department of Computer Anhui Normal University Engineering and Science Victoria University

In this work, a Storm-based query language System(SQLS) is proposed for real-time data stream analysis. The system is compatible with Continuous query language(CQL) specification. It supports both continuous queries and one-time queries over streaming data, and meets the requirements of user experience(traditional SQL queries) and Qo S(such as real-time and throughput). In order to better meet the requirement of throughput and enhance the processing efficiency, the load shedding algorithm and cache optimization are employed during the implementation of SQL-like operators. Finally, performance testing of the proposed SQLS has been conducted on standalone Storm platform and Storm clusters. Experimental results show that our system can not only meet the needs of users, but also extend the function of real-time streaming queries processing.

关键词： Continuous query Ad hoc query Storm CQL Real-time query processing

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The M-computations induced by accessibility relations in nonstandard models M of Hoare logic

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Frontiers of computer Science 2016年第4期10卷 717-725页

作者： Cungen CAO Yuefei SUI Zaiyue ZHANG Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Department of Computer Science Jiangsu University of Sciences and Technology Zhenjiang 212003 China

Hoare logic is a logic used as a way of specifying semantics of programming languages, which has been extended to be a separation logic to reason about mutable heap structure. In a model M of Hoare logic, each program α induces an M-computable function fα M on the universe of M; and the M-recursive functions are defined on M. It will be proved that the class of all the M-computable functions fα M induced by programs is equal to the class of all the M- recursive functions. Moreover, each M-recursive function is ∑ 1 NM -definable in M, where the universal quantifier is a num- ber quantifier ranging over the standard part of a nonstandard model M.

关键词： Hoare logic recursive ：Function computable function nonstandard model of Peano arithmetic

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Learning convolutional neural network to maximize Pos@Top performance measure 25

Learning convolutional neural network to maximize Pos@Top pe...

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25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

作者： Geng, Yanyan Liang, Ru-Ze Li, Weizhi Wang, Jingbin Liang, Gaoyuan Xu, Chenhao Wang, Jing-Yan Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Suning Commerce RandD Center USA Inc. Palo AltoCA94304 United States Information Technology Service Center Intermediate People's Court of Linyi City Linyi China Jiangsu University of Technology Jiangsu213001 China New York University Abu Dhabi Abu Dhabi United Arab Emirates

ISBN: (纸本)9782875870391

In the machine learning problems, the performance measure is used to evaluate the machine learning models. Recently, the number positive data points ranked at the top positions (Pos@Top) has been a popular performance measure in the machine learning community. In this paper, we propose to learn a convolutional neural network (CNN) model to maximize the Pos@Top performance measure. The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation. We propose to minimize the loss function of Pos@Top over a training set to learn the filters of CNN and the classifier parameter. The classifier parameter vector is solved by the Lagrange multiplier method, and the filters are updated by the gradient descent method alternately in an iterative algorithm. Experiments over benchmark data sets show that the proposed method outperforms the state-of-the-art Pos@Top maximization methods. © ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.

关键词： Convolution

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Randomized mixture models for probability density approximation and estimation

arXiv

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arXiv 2018年

作者： Nguyen, Hien D. Wang, Dianhui McLachlan, Geoffrey J. Department of Mathematics and Statistics La Trobe University Bundoora MelbourneVIC3086 Australia Department of Computer Science and Information Technology La Trobe University Bundoora Melbourne VIC3086 Australia State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China School of Mathematics and Physics University of Queensland St. Lucia BrisbaneQLD4072 Australia

Randomized neural networks (NNs) are an interesting alternative to conventional NNs that are more used for data modeling. The random vector functional-link (RVFL) network is an established and theoretically well-grounded randomized learning model. A key theoretical result for RVFL networks is that they provide universal approximation for continuous maps, on average, almost surely. We specialize and modify this result, and show that RFVL networks can provide functional approximations that converge in Kullback-Leibler divergence, when the target function is a probability density function. Expanding on the approximation results, we demonstrate the the RFVL networks lead to a simple randomized mixture model (MM) construction for density estimation from random data. An expectation-maximization (EM) algorithm is derived for the maximum likelihood estimation of our randomized MM. The EM algorithm is proved to be globally convergent and the maximum likelihood estimator is proved to be consistent. A set of simulation studies is given to provide empirical evidence towards our approximation and density estimation results. Copyright © 2018, The Authors. All rights reserved.

关键词： Mixtures

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