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Evolving Big Data Stream Classification with MapReduce

Evolving Big Data Stream Classification with MapReduce

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IEEE international conference on Cloud Computing, CLOUD

作者： Ahsanul Haque Brandon Parker Latifur Khan Bhavani thuraisingham Department of computer Science The University of Texas at Dallas Richardson USA

Big Data Stream mining has some inherent challenges which are not present in traditional data mining. Not only Big Data Stream receives large volume of data continuously, but also it may have different types of features. Moreover, the concepts and features tend to evolve throughout the stream. Traditional data mining techniques are not sufficient to address these challenges. In our current work, we have designed a multi-tiered ensemble based method HSMiner to address aforementioned challenges to label instances in an evolving Big Data Stream. However, this method requires building large number of AdaBoost ensembles for each of the numeric features after receiving each new data chunk which is very costly. thus, HSMiner may face scalability issue in case of classifying Big Data Stream. To address this problem, we propose three approaches to build these large number of AdaBoost ensembles using MapReduce based parallelism. We compare each of these approaches from different aspects of design. We also empirically show that, these approaches are very useful for our base method to achieve significant scalability and speedup.

关键词： Big data Indexes Data mining Scalability Sorting Distributed databases parallel processing

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A parallel PCA-SIFT Algorithm Based on Raspberry Pi 4B 23

A Parallel PCA-SIFT Algorithm Based on Raspberry Pi 4B

引用

Proceedings of the 2023 7th international conference on Electronic Information Technology and Computer Engineering

作者： Wei Wang Wang Li Zongliang Zhang College of Computer Engineering Jimei University China

ISBN: (纸本)9798400708305

Aiming at the problems of limited computing power of embedded platform, low target detection probability and high false alarm rate in the process of video small target tracking, a parallel PCA-SIFT algorithm based on Raspberry Pi 4B microprocessor is proposed. the data-level parallel method is used to realize parallel feature extraction and feature point matching, and the computing tasks are assigned to each CPU for parallel processing to improve the efficiency of the algorithm. the experimental results show that the algorithm has good adaptability to factors such as target and background clutter, geometric deformation, noise interference and illumination influence, and is close to the serial PCA-SIFT algorithm in image matching effect.

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Server-uncertain spectrum trading in cognitive radio networks: A queueing-theoretic modeling approach

Server-uncertain spectrum trading in cognitive radio network...

引用

international conference on Cognitive Radio Oriented Wireless Networks and Communications

作者： Lixia Liu Gang Hu Ming Xu Yuxing Peng National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Network Engineering College of Computer National University of Defense Technology Changsha China

Spectrum trading is the promising method to improve spectrum efficiency from the perspective of economics. In this paper we propose a queueing-theory based spectrum trading model, where the primary user plays the server role providing spectrum to the secondary user who acts as the customer. the most significant challenge is how to optimize the spectrum trading model considering the server uncertainty which includes service state, service time, service area, service content and service price. We design a STACP queueing model according to the server attributes, so that the secondary user can choose the right queue quickly and reasonably according to its demand. Moreover, we further analyze the optimizing strategies for STACP model which can maximize the profit of the primary user and minimize the service cost of the secondary user. the simulation results demonstrate the analysis results.

关键词： Servers Uncertainty Loss measurement Queueing analysis Economics Analytical models Business

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GPU Passthrough Performance: A Comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL Applications

GPU Passthrough Performance: A Comparison of KVM, Xen, VMWar...

引用

IEEE international conference on Cloud Computing, CLOUD

作者： John Paul Walters Andrew J. Younge Dong In Kang Ke thia Yao Mikyung Kang Stephen P. Crago Geoffrey C. Fox Information Sciences Institute University of Southern California Arlington VA Pervasive Technology Institute Indiana University IN Bloomington University of Southern California Los Angeles CA US

As more scientific workloads are moved into the cloud, the need for high performance accelerators increases. Accelerators such as GPUs offer improvements in both performance and power efficiency over traditional multi-core processors, however, their use in the cloud has been limited. Today, several common hypervisors support GPU passthrough, but their performance has not been systematically characterized. In this paper we show that low overhead GPU passthrough is achievable across 4 major hypervisors and two processor microarchitectures. We compare the performance of two generations of NVIDIA GPUs within the Xen, VMWare ESXi, and KVM hypervisors, and we also compare the performance to that of Linux Containers (LXC). We show that GPU passthrough to KVM achieves 98 -- 100\% of the base system's performance across two architectures, while Xen and VMWare achieve 96 -- 99\% of the base systems performance, respectively. In addition, we describe several valuable lessons learned through our analysis and share the advantages and disadvantages of each hypervisor/GPU passthrough solution.

关键词： Graphics processing units Benchmark testing Virtual machine monitors Kernel Virtual machining Linux Cloud computing

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Composite field GF(((22)2)2) AES S-Box with direct computation in GF(24) inversion

Composite field GF(((22)2)2) AES S-Box with direct computati...

引用

international conference on Information Technology in Asia (CITA)

作者： M. M. Wong M. L. D. Wong I. Hijazin A. K. Nandi School of Engineering Computing and Science Swinburne University of Technology Kuching Malaysia Engineering and Electronics Signal Processing and Communications Group University of Liverpool Liverpool UK

Composite field arithmetic (CFA) has been widely used in designing combinatorial logic circuits for the S-Box function in the Advanced Encryption Standard (AES) in order to mitigate the performance bottleneck in VLSI implementation. In this work, we first categorize all of the possible composite field AES S-box constructions into four main architectures based on their field representations and the chosen algebraic properties. Each category is then investigated thoroughly. Next, we show that by computing the F(2 4 ) inversion directly in the composite field F(((2 2 ) 2 ) 2 ), we can further reduce the total area gate count as well as the critical path gate count. the architecture that leads to the maximum reduction in both total area coverage and critical path gate count through the exploitation of direct computation in F(2 4 ) inversion is found and reported. Our best architecture has a total area gate count of 35 AND gates and 117 XOR gates and critical path gate count of 3 AND gates and 20 XOR gates.

关键词： Polynomials Logic gates Computer architecture Complexity theory Galois fields Encryption Hardware

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A Privacy and Price-Aware Inter-Cloud System

A Privacy and Price-Aware Inter-Cloud System

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IEEE international conference on Cloud Computing Technology and Science (CloudCom)

作者： Yuanfang Chi Wei Cai Zhen Hong Henry C. B. Chan Victor C. M. Leung Department of Electrical and Computer Engineering The University of British Columbia Canada Department of Computing The Hong Kong Polytechnic University Hong Kong

Cloud service selection and financial expense are two main concerns of users considering adoption of cloud computing services. In this paper, we propose a novel cloud federation that is cognitive to the dynamic prices. the cloud federation system first determines on which cloud services should the user applications be deployed. then, when a cloud provider is charging too high for a VM, the proposed system automatically migrates user tasks to a cloud system that is charging at a lower rate. We discuss the architectural framework and platform design, provide a mathematic formulation and investigate a total service fee minimization approach with privacy constraints. Preliminary simulation results demonstrate the proposed system can lower the cost of cloud services by exploiting the advantages of different price policies provided by multiple cloud providers.

关键词： Cloud computing Pricing parallel processing Privacy Distributed databases Data security

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Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals

Compact Deep Neural Networks for Computationally Efficient G...

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IEEE international conference on Biomedical Robotics and Biomechatronics

作者： Adam Hartwell Visakan Kadirkamanathan Sean R. Anderson Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Sl 3JD Department of Automatic Control and Systems Eng

ISBN: (纸本)9781538681848

Machine learning classifiers using surface electromyography are important for human-machine interfacing and device control. Conventional classifiers such as support vector machines (SVMs) use manually extracted features based on e.g. wavelets. these features tend to be fixed and non-person specific, which is a key limitation due to high person-to-person variability of myography signals. Deep neural networks, by contrast, can automatically extract person specific features - an important advantage. However, deep neural networks typically have the drawback of large numbers of parameters, requiring large training data sets and powerful hardware not suited to embedded systems. this paper solves these problems by introducing a compact deep neural network architecture that is much smaller than existing counterparts. the performance of the compact deep net is benchmarked against an SVM and compared to other contemporary architectures across 10 human subjects, comparing Myo and Delsys Trigno electrode sets. the accuracy of the compact deep net was found to be 84.2 ±0.06% versus 70.5 ±0.07% for the SVM on the Myo, and 80.3 ±0.07% versus 67.8 ± 0.09% for the Delsys system, demonstrating the superior effectiveness of the proposed compact network, which had just 5,889 parameters - orders of magnitude less than some contemporary alternatives in this domain while maintaining better performance.

关键词： Electrodes Neural networks Feature extraction Support vector machines Graphics processing units Convolution Embedded systems Support Vector Network Neural network Embedded systems Convolution Feature extraction Electrodes Graphics processing Unit Compact Compact Pattern Electromyogram human subject Myography

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Quadrature Coupler with Enhanced Coupling Bandwidth and DC Isolation for C-Band Applications

Quadrature Coupler with Enhanced Coupling Bandwidth and DC I...

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international conference on Signal processing and Integrated Networks (SPIN)

作者： V. Nagisha V. Nivesha Madhur Deo Upadhayay Electrical Engineering Shiv Nadar University Gr Noida

ISBN: (数字)9781728154756

ISBN: (纸本)9781728154763

this paper presents an alternate design for a conventional 3dB Branch line coupler (Quadrature Coupler) for C Band applications. the proposed coupler is designed for 5.2GHz. the proposed coupler has parallel-coupled lines at the port of conventional 3dB coupler with slotted ground plane. the novel design of the coupler allows 82.6% of bandwidth improvement with DC(Direct Current) isolation. the designs have been simulated and fabricated on FR4 dielectric substrate and measured using Vector Network Analyzer. Simulated and fabricated results of the S-parameters are presented.

关键词： Couplers Bandwidth Power transmission lines Couplings Scattering parameters Impedance Signal processing

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Average Jane, Where Art thou? – Recent Avenues in Efficient Machine Learning Under Subjectivity Uncertainty 18th

Average Jane, Where Art Thou? – Recent Avenues in Efficient...

引用

18th international conference on Information processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020

作者： Rizos, Georgios Schuller, Björn W. GLAM Imperial College London LondonSW7 2AZ United Kingdom Chair of EIHW University of Augsburg Augsburg86159 Germany audEERING Gilching82205 Germany

ISBN: (纸本)9783030501457

In machine learning tasks an actual ‘ground truth’ may not be available. then, machines often have to rely on human labelling of data. this becomes challenging the more subjective the learning task is, as human agreement can be low. To cope with the resulting high uncertainty, one could train individual models reflecting a single human’s opinion. However, this is not viable, if one aims at mirroring the general opinion of a hypothetical ‘completely average person’ – the ‘average Jane’. Here, I summarise approaches to optimally learn efficiently in such a case. First, different strategies of reaching a single learning target from several labellers will be discussed. this includes varying labeller trustability and the case of time-continuous labels with potential dynamics. As human labelling is a labour-intensive endeavour, active and cooperative learning strategies can help reduce the number of labels needed. Next, sample informativeness can be exploited in teacher-based algorithms to additionally weigh data by certainty. In addition, multi-target learning of different labeller tracks in parallel and/or of the uncertainty can help improve the model robustness and provide an additional uncertainty measure. Cross-modal strategies to reduce uncertainty offer another view. From these and further recent strategies, I distil a number of future avenues to handle subjective uncertainty in machine learning. these comprise bigger, yet weakly labelled data processing basing amongst other on reinforcement learning, lifelong learning, and self-learning. Illustrative examples stem from the fields of Affective Computing and Digital Health – both notoriously marked by subjectivity uncertainty. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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A design of analog C-matrix circuits used for signal/data processing

A design of analog C-matrix circuits used for signal/data pr...

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Asia and South Pacific Design Automation conference

作者： T. Sugawara Y. Miyanaga N. Yoshida Graduate Schoolof Engineering Hokkaido University Sapporo Japan

ISBN: (纸本)0769514413

Various calculations of matrices and vectors are used in many digital signal processing systems. Although the calculation simply repeats multiplication and addition, the reiteration processing is usually heavy. therefore, in order to perform the calculations with high speed, it is necessary to apply parallel precessing. Although there is an issue with increased circuit area in the case of digital LSI, the proposed analog circuit can realize multiplication and addition simultaneously with a simple structure which arranges capacitors in a matrix form. Furthermore, a vowel speech recognition system is designed using this circuit.

关键词： Signal design Signal processing Data processing Capacitors Analog circuits Large scale integration Voltage Speech recognition Switches Digital signal processing

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