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37th Chinese Control Conference, CCC 2018

作者： Yuan, Cao Yue-Ping, Wang Xue-Mei, Tang Xiao-Fang, Zhao Science and Technology on Complex Land Systems Simulation Laboratory Beijing100012 China Beijing Institute of Spacecraft System Engineering 100094 China

ISBN: (纸本)9789881563941

The number of experimental data obtained under different conditions is large, and there are some differences in the distribution characteristics of samples. If a large number of different types of data are mixed and the network communication Effectiveness evaluation is carried out, the characteristics of some samples may be drowned out, on the other hand, the information of many experimental samples is not fully utilized. In order to solve this problem, this paper proposes a method of network communication effectiveness evaluation based on multi-source information fusion. This method firstly analyzes the communication success rate of the cell communication node by longitudinal fusion of experimental information. Based on this, the confidence degree is used to construct the weighting coefficient, and the whole communication success rate of the network information system is evaluated by the experiment information of multiple nodes. Thus, the comprehensive evaluation of the communication efficiency of the unit node and the network Information system is realized. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Efficiency

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On Exploring Undetermined Relationships for Visual Relationship Detection

On Exploring Undetermined Relationships for Visual Relations...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yibing Zhan Jun Yu Ting Yu Dacheng Tao 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

ISBN: (纸本)9781728132945

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.

关键词： Visualization Fuses Dataset

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Priority-Based Optimization of I/O Isolation for Hybrid Deployed Services 15th

Priority-Based Optimization of I/O Isolation for Hybrid Depl...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Jiancheng Li, Youhuizi Zhou, Li Ren, Zujie Wan, Jian Wang, Yuan Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Zhejiang Lab Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Key Enterprise Research Institute of NetEase Big Data of Zhejiang Province Netease Hangzhou Network Co.Ltd Hangzhou China

ISBN: (纸本)9783030301453

With the increasing of software complexity and user demands, collaborative service is becoming more and more popular. Each service focuses on its own specialty, their cooperation can support complicated task with high efficiency. To improve the resources utilization, virtualization technology like container is used and it enables multiple services running in the same physical machine. However, since the host physical machine is shared by several services, the resource competition is inevitable. Isolation is an effective solution, but the weak isolation mechanisms of container cannot handle such complicated scenarios. In the worst situation, the performance of services cannot meet the requirements and the system may crash. In order to solve this problem, we propose a priority-based optimization mechanism for I/O isolation after analyzing the characteristics of typical service workloads. Based on the real-time performance data, priority is automatically assigned to each service and corresponding optimization methods are applied. We evaluate the optimization effects of the priority-based mechanism in both static and dynamic workload cases, besides, the influence of different priority order is also analyzed. The experimental results show that our approach can indeed improve the system performance and guarantee the requirements of all the running services are satisfied. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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Power Characterization of Memory Intensive Applications: Analysis and Implications 1st

Power Characterization of Memory Intensive Applications: Ana...

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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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The Study of simulation Architecture in Meta-Synthetic Hall

The Study of Simulation Architecture in Meta-Synthetic Hall

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2018 2nd International Conference on Data Mining,Communications and Information technology (DMCIT 2018)

作者： X J Zhao L J Kong J Y Zhao Science and Technology on Complex Land Systems Simulation Laboratory

The meta-synthetic methodology is the scientific and effective way to solve the problems of the complicated large *** the meta-synthetic Hall,we combined the expert,repository,and simulation test together,building an environment to realize the Meta-Synthesis *** this article,we put forward simulation architecture for the Meta-Synthesis *** the architecture,we can build the simulation system and do experiment for the Hall,supporting expert discussion and repository *** can solve the system experiment and data collection problems for complicated large system analysis,enforces system improve and perfect.

关键词： simulation The Study of simulation Architecture in Meta-Synthetic Hall

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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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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

引用

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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