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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

DRAM is a significant source of server power consumption especially when the server runs memory intensive applications. Current power aware scheduling assumes that DRAM is as energy proportional as other components. However, the non-energy proportionality of DRAM significantly affects the power and energy consumption of the whole server system when running memory intensive applications. Thus good knowledge of server power characterization under memory intensive workloads can help better workload placement with power reduction. In this paper, we investigate the power characteristics of memory intensive applications on real rack servers of different generations. Through comprehensive analysis we find that (1) Server power consumption changes with workload intensity and concurrent execution threads. However, fully utilized memory systems are not the most energy efficient. (2) Powered memory modules of installed memory capacity, i.e. the memory capacity per processor core has significant impact on the application’s performance and server power consumption even if the memory system is not fully utilized. (3) Memory utilization is not always a good indicator for server power consumption when it is running memory intensive applications. Our experiments show that hardware configuration, workload types, as well as concurrently running threads have significant impact on a server’s energy efficiency when running memory intensive applications. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy efficiency aware job scheduling and power reductions. © 2019, Springer Nature Switzerland AG.

关键词： Energy efficiency

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Scalability Evaluation of Big Data Processing Services in Clouds 1st

Scalability Evaluation of Big Data Processing Services in Cl...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Zhou, Xin Jiang, Congfeng Qiu, Yeliang Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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HiKeyb: High-Efficiency Mixed Reality System for Text Entry

HiKeyb: High-Efficiency Mixed Reality System for Text Entry

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Haiyan Jiang Dongdong Weng Zhenliang Zhang Yihua Bao Yufei Jia Mengman Nie Beijing Institute of Technology AICFVE of Beijing Film Academy Science and Technology on Complex Land Systems Simulation Laboratory

ISBN: (纸本)9781538675939;9781538675922

Text entry is an imperative issue to be addressed in current entry systems for virtual environments (VEs). The entry method using a physical keyboard is still the most dominant choice for an efficient interaction regarding text entry. In this paper, we propose a typing system with a style of mixed reality, which is called HiKeyb, and it possesses a similar high-efficiency with the single physical keyboard in the real environment. The HiKeyb system consists of a depth camera, a pose tracking module, a head-mounted display (HMD), a QWERTY keyboard and a black table mat. This system can guarantee the entry efficiency and the amenity by not only introducing the force feedback from a movable physical keyboard, but also improving the immersion with the real hand image. In addition, the infrared absorption material helps improve the robustness of the system against different lighting environments. Experiments have proved that users wearing HMDs in Virtual Phrases session can achieve an entry rate of 23.1 words per minute and an error rate of 2.76%, and the rate ratio of virtual reality to real world is 78% when typing phrases. Besides, we find that the proposed system can provide a relatively close entry efficiency to that using a pure physical keyboard in the real environment.

关键词： Augmented reality

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Deep Modular Co-Attention Networks for Visual Question Answering

Deep Modular Co-Attention Networks for Visual Question Answe...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Zhou Yu Jun Yu Yuhao Cui Dacheng Tao Qi Tian Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney Noah's Ark Lab

ISBN: (纸本)9781728132945

Visual Question Answering (VQA) requires a finegrained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective 'co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the question-guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set.

关键词： Modularity images Visualization system buses compensatory amounts Maximum credible accidents Layer models

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A Trend Prediction Method for Failures Time Series Data by Exploring Singular Spectrum Analysis and Support Vector Machines Regression

A Trend Prediction Method for Failures Time Series Data by E...

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作者： Wang Xin Liu Xingke Wu Jiantao Sang Yun Li Ying Wang Tingshu Science and Technology on Complex Aviation Systems Simulation Laboratory East China Institute of Computing Technology China Electronics Technology Group Corporation Unit 93658 of People's Liberation Army International School Beijing University of Posts and Telecommunications

Recently, reliability prediction problems have been sufficiently studied by applying various failures time series prediction models. However, these models' performance could easily reach the bottleneck on account of the noise interference of the failures data. This paper focuses on the trend component of failures time series data as we believe its noiseless curve will achieve better prediction results and help to provide scientific guidance for reliability activities. We propose a trend prediction method which mainly involves two steps, trend extraction by singular spectrum analysis(SSA) and noise test, and trend prediction by support vector machines regression(SVR). Moreover, we further develop a multi-layer grid search algorithm to obtain optimized parameters of SSA and SVR models. The performance of the proposed method is measured against other time series processing techniques such as seasonal-trend decomposition procedure based on loess(STL), Holt-Winters, autoregressive moving average(ARMA), multiple linear regression(MLR) and k-nearest neighbors(KNN). The comparison results indicate that the proposed method outperforms other techniques and can be utilized as a promising tool for failures time series trend prediction.

关键词： failures time series trend prediction singular spectrum analysis support vector machines regression grid search method

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Mass- and energy-preserving exponential Runge-Kutta methods for the nonlinear Schrödinger equation

arXiv

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arXiv 2020年

作者： Cui, Jin Xu, Zhuangzhi Wang, Yushun Jiang, Chaolong Department of Basic Sciences Nanjing Vocational College of Information Technology Nanjing210023 China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical Sciences Nanjing Normal University Nanjing210023 China Department of Mathematics College of Liberal Arts and Science National University of Defense Technology Changsha410073 China School of Statistics and Mathematics Yunnan University of Finance and Economics Kunming650221 China

In this paper, a family of arbitrarily high-order structure-preserving exponential Runge-Kutta methods is developed for the nonlinear Schrödinger equation by combing the scalar auxiliary variable approach and the exponential Runge-Kutta method. By introducing an auxiliary variable, we first transform the original model into an equivalent system which admits both mass and modified energy. Then applying the Lawson method and the symplectic Runge-Kutta method in time, we derive a class of mass- and energy-conserving time-discrete schemes. Numerical experiments are addressed to demonstrate the accuracy and effectiveness of the newly proposed schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Runge Kutta methods

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On exploring undetermined relationships for visual relationship detection

arXiv

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arXiv 2019年

作者： Zhan, Yibing Yu, Jun Yu, Ting Tao, Dacheng Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney DarlingtonNSW2008 Australia

In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undetermined relationships. Although a vast body of literature exists, few methods exploit these undetermined relationships for visual relationship detection. In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection. We propose a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieve great improvements in relationship detection. In detail, our MF-URLN automatically generates undetermined relationships by comparing object pairs with human-notated data according to a designed criterion. Then, the MF-URLN extracts and fuses features of object pairs from three complementary modals: Visual, spatial, and linguistic modals. Further, the MF-URLN proposes two correlated subnetworks: One subnetwork decides the determinate confidence, and the other predicts the relationships. We evaluate the MF-URLN on two datasets: The Visual Relationship Detection (VRD) and the Visual Genome (VG) datasets. The experimental results compared with state-of-the-art methods verify the significant improvements made by the undetermined relationships, e.g., the top-50 relation detection recall improves from 19.5% to 23.9% on the VRD dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Deep Modular Co-Attention networks for visual question answering

arXiv

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arXiv 2019年

作者： Yu, Zhou Yu, Jun Cui, Yuhao Tao, Dacheng Tian, Qi Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney Noah's Ark Lab Huawei

Visual Question Answering (VQA) requires a finegrained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective 'co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-ofthe- A rt. Our best single model delivers 70.63% overall accuracy on the test-dev set. Code is available at http://***/MILVLG/mcan-vqa. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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MobileEdge: Enhancing On-Board Vehicle Computing Units Using Mobile Edges for CAVs

MobileEdge: Enhancing On-Board Vehicle Computing Units Using...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Lin Wang Qingyang Zhang Youhuizi Li Hong Zhong Weisong Shi School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou China Department of Computer Science Wayne State University Detroit MI USA

ISBN: (纸本)9781728125848

As the rapid growth of connected and autonomous vehicles (CAVs) and 5G intensifies, more third-party applications are increasingly being deployed on CAVs. They not only improve user experience but also provide more helpful services, for example, enhancing public safety by recognizing criminals in real-time videos. Current CAVs prefer to process collected data on the vehicle to avoid long transmission latency and extra network cost. However, due to the limitations of the on-board vehicle computing unit (VCU) and increasing use of computing-intensive in-vehicle applications, the burden of on-board VCU has sharply increased, which may affect driving safety. In particular, for existing vehicles on the road, adding more computing devices is a challenge if not impossible due to cost concerns. Inspired by edge computing, we propose a novel platform, MobileEdge, to enhance the computing capability of the unchangeable on-board VCU, which leverages mobile devices as edge nodes, e.g., the passengers' smartphones, by offloading computing tasks to them for collaboratively computing. Moreover, MobileEdge provides the dynamic management of mobile devices, monitoring device status and interfaces for customizable task offloading strategies and eventually achieves optimal task scheduling. We build a prototype to demonstrate the designed platform and evaluate three task offloading strategies which were implemented based on the developed interfaces. The results show that MobileEdge significantly reduces the application response latency. Compared with the baseline which does not employ task offloading, the response latency is almost near real-time when more computing resources are available. In addition, the proposed shortest response latency strategy outperforms the best overall task scheduling among the three strategies.

关键词： Edge computing Vehicular data analysis Distributed computing Connected and autonomous vehicles Distributed computing TASK SCHEDULING Response Latency UNLOADING vehicle vinyl chloride-urethane copolymer Mobile devices unchangeable quasi-realtime language Public safety

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Theoretical analysis and simulation of phase separation in a driven bidirectional two-lane system

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Physical Review E 2019年第3期100卷 032133-032133页

作者： Qing-Yi Hao Rui Jiang Mao-Bin Hu Yunxin Zhang Chao-Yun Wu Ning Guo Key Laboratory of Modeling Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes School of Mathematics and Computational Science Anqing Normal University Anqing 246133 China School of Mathematical Sciences Fudan University Shanghai 200433 China MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology Beijing Jiaotong University Beijing 100044 China School of Engineering Science University of Science and Technology of China Hefei 230026 China School of Automotive and Transportation Engineering Hefei University of Technology Hefei 230009 China

The two-lane driven system is a type of important model to research some transport systems, and also a powerful tool to investigate properties of nonequilibrium state systems. This paper presents a driven bidirectional two-lane model. The dynamic characteristics of the model with periodic boundary are investigated by Monte Carlo simulation, simple mean field, and cluster mean field methods, respectively. By simulations, phase separations are observed in the system with some values of model parameters. When the phase separation does not occur, cluster mean field results are in good agreement with simulation results. According to the cluster mean field analysis and simulations, a conjecture about the condition that the phase separation happens is proposed. Based on the conjecture, the phase boundary distinguishing phase separation state and homogeneous state is determined, and a corresponding phase diagram is drawn. The conjecture is validated through observing directly the spatiotemporal diagram and investigating the coarsening process of the system by simulation, and a possible mechanism causing the phase separation is also discussed. These outcomes maybe contribute to understand deeply transport systems including the congestion and efficiency of the transport, and enrich explorations of nonequilibrium state systems.

关键词： Dynamical phase transitions Phase diagrams Phase separation Phase transitions Transport phenomena

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