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16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

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Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms

arXiv

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arxiv 2020年

作者： Mo, Haimiao Ding, Shuai Yang, Shanlin Vasilakos, Athanasios V. Zheng, xi The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory of Big Data Distribution and Exchange Technologies China Grimstad Norway The College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China The School of Computing Macquarie University Sydney Australia

Monitoring the respiratory rate is crucial for helping us identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from the face and monitor breathing. However, the current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract high-quality respiratory signals. By gathering the data and choosing several regions of interest on the face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than rival approaches while transferring fewer data. © 2020, CC BY.

关键词： Edge computing

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Accelerated Latent Factor Analysis for Recommender Systems via PID Controller

Accelerated Latent Factor Analysis for Recommender Systems v...

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IEEE International Conference on Networking, Sensing and Control

作者： Jinli Li Xuke Wu Ye Yuan Yajuan Wu Kangkang Ma Yue Zhou Computer School China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of information and software engineering University of Electronic Science and Technology of China Chengdu Sichuan China University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

High-dimensional and sparse (HiDS) matrices generated by recommender systems (RSs) contain rich knowledge. A latent factor (LF) model can address such data effectively. Stochastic gradient descent (SGD) is an efficient algorithm for building a LF model on an HiDS matrix. However, it suffers slow convergence. To address this issue, this study proposes to implement a LF model with a proportional integral derivative (PID) controller. The main idea is to continuously apply a correction for SGD to accelerate the training process. Based on such design, a PID-based LF (PLF) model is proposed. Empirical studies on two HiDS matrices from RSs indicate that a PLF model outperforms an LF model in terms of both convergence rate and prediction accuracy for missing data.

关键词： Training Predictive models data models Sparse matrices Acceleration Recommender systems Convergence

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A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network

arXiv

引用

arxiv 2020年

作者： Hua, Xuan Han, Jungang Zhao, Chen Tang, Haipeng He, Zhuo Tang, Jinshan Chen, Qing-Hui Tang, Shaojie Zhou, Weihua College of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing in Shaanxi Xi’an710121 China College of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China College of Computing Michigan Technological University HoughtonMI49931 United States School of Computing University of Southern Mississippi Long Beach39560 United States Deprtment of Kinesiology and Integrative Physiology Michigan Technological University HoughtonMI49931 United States School of Automation Xi’an University of Posts and Telecommunications Xi’an710121 China

This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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An IGS algorithm applied to DNA sequence design 17

An IGS algorithm applied to DNA sequence design

引用

17th IEEE International Conference on Dependable, Autonomic and Secure computing, IEEE 17th International Conference on Pervasive Intelligence and computing, IEEE 5th International Conference on Cloud and big data computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Liu, Kaiqiang Wang, Bin Liu, Yuan Wu, Ranfeng Zhang, Xun Liu, xin Zhang, Qiang Yin, Zhixiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

American scientist Adelman first used DNA molecules to solve the NP-complete problem, thus creating a new research field. As an advanced interdisciplinary subject, it has attracted many scholars because of its high parallelism, great storage density and low energy consumption. DNA sequence design is critical in the field of DNA computing. The success of DNA computing depends on the quality of DNA sequence. In this paper, we proposed an improved gravitational search algorithm for DNA sequence design. We improved the gravitational coefficient and introduced chaotic system and Gaussian mutation to the position update of the particles. We compared the result from our improved algorithm with the result obtained by other algorithms. The result shows that our proposed algorithm can generate high quality DNA sequence for DNA computing. © 2019 IEEE.

关键词： DNA computing DNA sequence design Gravitational search algorithm NP-complete problem

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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Convergence Analysis of a Fast Non-negative Latent Factor Model

Convergence Analysis of a Fast Non-negative Latent Factor Mo...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Yue Zhou Zhigang Liu xiaojiang Yu Yajuan Wu Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Computer School of China West Normal University Nanchong Sichuan China

A fast non-negative latent factor (FNLF) model adopts a single latent factor-dependent, non-negative, multiplicative and momentum-incorporated update (SLF-NM 2 U) algorithm, which can ensure fast convergence on a high-dimensional and sparse (HiDS) matrix according to empirical studies in prior researches. However, it is crucial to investigate the theoretical proof regarding the reason why incorporation of a generalized momentum method into an SLF-NM 2 U algorithm can ensure the fast convergence of an FNLF model, which has not been addressed in previous work. Therefore, this paper aims to unveil how a generalized momentum method improves the convergence rate of an FNLF model in the discrete time case by combining physical analysis. The FNLF model is superior to the NLF model in terms of the convergence rate and the prediction accuracy of missing data. This conclusion is obtained by empirically research on the HiDS matrix in industrial applications, which also provides empirical basis for theoretical proof.

关键词： Convergence Analytical models Sparse matrices data models Computational modeling Acceleration Friction

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Momentum-incorporated Latent Factorization of Tensors for Extracting Temporal Patterns from QoS data

Momentum-incorporated Latent Factorization of Tensors for Ex...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Minzhi Chen Hao Wu Chunlin He Sili Chen Computer School of China West Normal University Nanchong Sichuan China Chongqing Institute of Green and Intelligent Technology Chongqing Key Laboratory of Big Data and Intelligent Computing Chinese Academy of Sciences Chongqing China

Quality-of-service (QoS) of Web services vary over time, making it a significant issue to discover temporal patterns from them for addressing various subsequent analyzing tasks like missing QoS prediction. A Latent factorization of tensors (LFT)-based approach proves to be highly efficient in addressing this issue, which can be built through a stochastic gradient descent (SGD) solver efficiently. However, an SGD-based LFT model frequently suffers low-tail convergence. For addressing this issue, we present a momentum-incorporated latent factorization of tensors (MLFT) model, which integrates a momentum method into an SGD-based LFT model, thereby improving its convergence rate as well as maintaining the prediction accuracy for missing QoS data. Empirical studies on two dynamic industrial QoS datasets show that compared with an SGD-based LFT model, an MLFT model achieves faster convergence rate and higher prediction accuracy.

关键词： Quality of service Tensile stress Convergence Predictive models Training Computational modeling Stochastic processes

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A Parallelized, Momentum-incorporated Stochastic Gradient Descent Scheme for Latent Factor Analysis on High-dimensional and Sparse Matrices from Recommender Systems

A Parallelized, Momentum-incorporated Stochastic Gradient De...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Wen Qin Hao Wu Qingkuan Lai Chaobin Wang Computer School of China West Normal University Nanchong Sichuan China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-related industrial applications like recommender systems. Latent factor (LF) analysis via stochastic gradient descent (SGD) is greatly efficient in discovering latent patterns from them. However, as a sequential algorithm, SGD suffers considerable time cost and low scalability when handling large-scale problems. To address these issues, this study proposes parallelized, momentum-incorporated stochastic gradient descent (PMSGD) scheme, which incorporates momentum effects into an SGD scheme as well as implementing its parallelization via careful data splitting. Based on a PMSGD method, we achieve a PMSGD-based LF (PLF) model to execute fast LF analysis on HiDS matrices from a recommender system. Experimental results on two HiDS matrices arising from industrial applications indicate that owing to the careful design of PMSGD, a PLF model outperforms state-of-the-art parallel LF models significantly in terms of computational efficiency.

关键词： Training Recommender systems Computational modeling Analytical models Stochastic processes Convergence Sparse matrices

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Pattern Synthesis of MIMO Radar Based on Immune Differential Evolution Algorithm

引用

Journal of Physics: Conference Series 2021年第1期1813卷

作者： Ling Jiang Yi Jiang Guangfu Chen Guimin Shi Qi Cheng School of Mathematics and Computer Wuyi University Wuyishan 354300 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyishan 354300 China Big Data Development Administration of Anji County Huzhou 313000 China

In order to optimize the pattern synthesis of multiple input and multiple output (MIMO) radar, immune mechanism is adopted to overcome the premature risk of differential evolution (DE) algorithm, namely immune differential evolution (IDE). Firstly, the modeling of MIMO radar is introduced by encoding the position of array in the binary. Secondly, the immune mechanism is employed to improve the DE. In IDE, two parameters are self-adapted for the mutation operator by immune mechanism to enhance the convergence ability of DE, including scaling factor and crossover rate. Several experiments are conducted to analysis the performance of IDE. The simulation results show that the IDE with variable parameters can get optimal results in MIMO radar with lower the peak side-lobe level (PSLL) and maintain the diversity with stronger convergence ability and shorter calculation speed.

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