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检索条件"任意字段=International Symposium Distributed Computing and Applications Business Engineering and Science"

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A Unified Framework for Multidisciplinary Design Optimization Application

A Unified Framework for Multidisciplinary Design Optimizatio...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Ning Zhang Baowei Song Peng Wang School of Marine Science and Technology Northwestern Polytechnical University Xi'an PR China

ISBN: (纸本)9781538621639

Research on the development of optimization modeling systems is relatively mature, but for multidisciplinary design optimization (MDO) application there are still some issues of accessibility and high efficiency. Based on the existing MDO algorithms, this paper presents a unified framework for formulating and solving the multidisciplinary optimization problem with various MDO methods in an efficient, simple and friendly manner. A multidisciplinary descriptive language is also proposed at the same time for the unified framework. The framework is designed to make researchers focus on the multidisciplinary optimization problem, rather than on algorithms implementation and software programming. Using the unified framework, researchers can solve the multidisciplinary optimization problem by creating a configuration file without any coding. The framework has integrated a variety of MDO architectures and also provides interface for other developers to add new optimization algorithm. Furthermore, examples demonstrate the usage of the framework as well as the advantages of the unified framework.

关键词： Computer architecture Software Couplings Design optimization Programming Algorithm design and analysis

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Review of Social Networking applications for Infectious Disease Management Systems in Saudi Arabia: Current Status & Future Prospects

Review of Social Networking Applications for Infectious Dise...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Fahad Alanezi Miznah H. Alshammary Dept. computer science Bedfordshire University London U.K. Dept. computer science King's College London U.K.

ISBN: (纸本)9781538621639

Internet Technologies are widely used in healthcare services and information management. Various applications have been proved to be highly beneficial in delivering healthcare services, information and disease management. Social Networking is one of the best internet technologies that is being used in various business and management areas, and it has been revolutionary in enhancing the various operations through effective interactions between the various users. This paper focuses on reviewing the social networking, its architecture, and the prospects of using it for Infectious Disease Management (IDM) in Saudi Arabia. Methods: Review of Various literature sources and Social Networking applications for Healthcare/IDM Management. Results: The Study has found that Social Networking can be an effective approach for integrating it with the healthcare/IDM Systems as it would enable effective user interactions, which could enhance behavioral aspects in delivering/receiving healthcare services. However, there has been no study identified that has used social networking for IDM in Saudi Arabia. This paper suggests the wide scope for future research in integrating the social networking concept across various healthcare systems in Saudi Arabia.

关键词： Infectious diseases Facebook Tools

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Symptom Distribution Regulation of Core Symptoms in Insomnia Based on Informap-SA Algorithm

Symptom Distribution Regulation of Core Symptoms in Insomnia...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Fang Hu Yulu Qiao Guangjing Xie Yanhui Zhu Yalin Jia PanPan Huang College of Information Engineering Hubei University of Chinese Medicine Wuhan P.R. China College of Basic Medicine Hubei University of Chinese Medicine Wuhan P.R. China

ISBN: (纸本)9781538621639

In the recent decade, clinical data mining in Traditional Chinese Medicine (TCM) based on complex networks has been becoming a hot topic. In this paper, we construct the "Symptom-Prescription" bipartite network in insomnia, which can intuitively reflect the relationship between prescriptions and symptoms in insomnia. And then, through projection of this bipartite network, the "symptom" network is generated. Based on the "symptom" network, the idea of node centrality is introduced to identify the core symptom nodes, which disclose the key factors in clinical diagnosis and treatment. Furthermore, the "symptom" network is divided into several communities detected by Infomap-SA algorithm, the nodes in the same community reveal the concurrent rule of symptoms in insomnia, which has practical guiding significance for clinical diagnosis and treatment in TCM.

关键词： Complex networks Channel coding Mathematical model Clustering algorithms Fatigue Clinical diagnosis

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Structure Maintaining Discriminant Maps (SMDM) for Grassmann Manifold Dimensionality Reduction with applications to the Image Set Classification

Structure Maintaining Discriminant Maps (SMDM) for Grassmann...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Rui Wang Xiao-Jun Wu School of Internet of Things Engineering Jiangnan University JNU WuXi China

ISBN: (纸本)9781538621639

For the image-set based classification, a considerable advance has been made by representing original image sets on Grassmann manifold. In order to extend the advantages of the Euclidean based dimensionality reduction methods to the Grassmann Manifold, several methods have been suggested recently to jointly perform dimensionality reduction and metric learning on Grassmann manifold and they have achieved good results in some computer vision tasks. Nevertheless, when handling the classification tasks on the complicated datasets, the learned features do not exhibit enough discriminatory ability and the data distribution of the resulted Grassmann manifold also be ignored which may lead to overfitting. To overcome the two problems, we propose a new method named Structure Maintaining Discriminant Maps (SMDM) for manifold dimensionality reduction problems. As to SMDM, we mainly design a new discriminant function for metric learning. We make experiments on two tasks: face recognition and object categorization to evaluate the proposed method, the achieved better results compared with the state-of-the-art methods, showing the feasibility and effectiveness of the proposed algorithm.

关键词： Manifolds Measurement Face recognition YouTube Feature extraction

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A Dynamic Scheduling Scheme for Deadline Constrained Content Distribution in D2D Cellular Network

A Dynamic Scheduling Scheme for Deadline Constrained Content...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Yu Shi Fuyang Wang Zhenya Zhang Computer Science and Technology Anyang Institute of Technology Anyang China College of Electronic and Information Engineering Southwest University Chongqing China Department of Products and Solutions Huawei Corporation Xi’ an China

ISBN: (纸本)9781538621639

This paper studies the distribution of deadlineconstrained contents over a wireless cellular network. Affected by the channel condition between base station and users, some users could not directly receive the content from base station. Fortunately, D2D (Device to Device) multicast communication allows the users who obtained the content to further multicast it to other users. In this paper, we focus on the propagation of contents in overlay D2D cellular network. For the deadlineconstrained contents, considering the queuing time waiting in the buffer before transmission, how to properly schedule them to maximize the system capacity is crucial. We formulate the scheduling problem with the objective of optimizing the throughput of the system and solve it using the Markov Decision Process (MDP) theory. Extensive simulation results are provided to demonstrate that our scheduling algorithm can efficiently improve the throughput of system.

关键词： Device-to-device communication Base stations Optimal scheduling Cellular networks Artificial neural networks Schedules Throughput

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Study on Fast Human Activity Recognition Based on Optimized Feature Selection

Study on Fast Human Activity Recognition Based on Optimized ...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Hanyuan Xu Zhibin Huang Jue Wang Zilu Kang School of Computer Science Beijing University of Posts and Telecommunications Beijing China Institute of Internet of Things Technology China Information Engineering Research Center Beijing China

ISBN: (纸本)9781538621639

Sensor-based human activity recognition has attracted much scholarly attention due to its merit of wide applicability. However, because of such hardware limitations as battery capacity and computing power, most current generation of wearable devices cannot yet benefit from those activity recognition methods based on deep learning theory and high dimension features since implementing these methods are time-consuming and need a relatively large amount of calculation. A solution, therefore, is proposed for this situation, which aims to reduce computational complexity by reducing the feature dimension through analyzing the linear correlation between the features. Based on the support vector machine model of single-layer fully connected network, the training and recognition time are significantly reduced while the recognition accuracy is still ensured. The experiment is based on the public dataset in the UCI Machine Learning Repository, and it uses Caffe, a deep learning framework, to structure the support vector machine model. In the experiment, when the feature dimension is reduced from 561 to 130, the training time can be reduced by 70% while the recognition accuracy is kept at a promising 91%.

关键词： Support vector machines Correlation coefficient Training Activity recognition Computational modeling Hardware Machine learning

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Real-time communication for manufacturing cyber-physical systems 16

Real-time communication for manufacturing cyber-physical sys...

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16th IEEE international symposium on Network computing and applications, NCA 2017

作者： Nguyen, Ngoc-Tu Leu, Ming C. Liu, Xiaoqing Frank Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology United States Department of Computer Science and Computer Engineering University of Arkansas United States

ISBN: (纸本)9781538614655

The cloud manufacturing yields insights of manufacturing services over cyber space based on integration of advanced manufacturing with cloud computing. However, the different communication standards between the different system levels are main challenges for the integration of them without a conflicting communication. Ethernet appears as the best solution to support all levels for industrial manufacturing systems. Since the synchronous integration is fulfilled, the prerequisite for deciding the overall performance of systems is adaptable real-time communication. This motivates us to study the problem of designing a new real-time communication based on Ethernet standard to enable real-time processing for factory networks, termed Real-Time Ethernet (RTEthernet). The objective is to schedule maximum number of machines transmitting data through a controller in a data transmission window (Coordinated Cycle) as well as guarantee the real-time communication. The problem is called the Maximum Number of Real-time Data Transmission (MNRDT) problem. Two relevant solutions are proposed for addressing the MNRDT problem. Simulation results are provided to show the performance of proposed solutions. © 2017 IEEE.

关键词： Ethernet

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Towards measuring the adaptability of an AO4BPEL process 17

Towards measuring the adaptability of an AO4BPEL process

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2017 Australasian Computer science Week Multiconference, ACSW 2017

作者： Botangen, Khavee Agustus Yu, Jian Sheng, Michael School of Engineering Computer and Mathematical Sciences Auckland University of Technology New Zealand Macquarie University Australia

ISBN: (纸本)9781450347686

Adaptability is a significant property which enables software systems to continuously provide the required functionality and achieve optimal performance. The recognised importance of adaptability makes its evaluation an essential task. However, the various adaptability dimensions and implementation mechanisms make adaptive strategies difficult to evaluate. In service oriented computing, several frameworks that extend the WS-BPEL, the de facto standard in composing distributed business applications, focus on enabling the adaptability of processes. We aim to evaluate the adaptability of processes specified from the extended-BPEL frameworks. In this paper, we propose metrics to measure the adaptability of an AO4BPEL process. The metrics is grounded in the perspective that a process is capable of dynamically adapting to changes in business requirements. This opens potential future work on evaluating the adaptability of processes specified from various aspect-oriented WS-BPEL frameworks. © 2017 ACM.

关键词： distributed computer systems

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A Review of Recent Developments in NOMA & SCMA Schemes for 5G Technology

A Review of Recent Developments in NOMA & SCMA Schemes for 5...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Miznah H. Alshammary Fahad A. Alanezi Dept. computer science King's College London U.K. Dept. computer science Bedfordshire University London U.K.

ISBN: (纸本)9781538621639

The race for developing 5G technology has taken a multiorganizational competition in the recent past, where the professional bodies, private organizations, and research units are carrying out research in developing various technologies relating to 5G. Multiple Access Scheme is one of the most important aspects that have been an area of interest for the majority as 5G is expected to accommodate large number of users with high speed of data transmission. This paper review the latest developments in the field of research concerning Sparse code multiple access (SCMA) and Non-orthogonal multiple access (NOMA). Ann extensive research has been carried out to identify the recent developments and their results are presented in this paper. This study has found that most of the recent developments in SCMA were focusing on achieving lower complexity levels, low BER, and effective and efficient energy utilization, and the recent developments identified regarding NOMA focused especially on the performance attributes like spectral efficiency, spatial efficiency, low BER and latency, and resource allocation and multiple access.

关键词： NOMA 5G mobile communication Complexity theory Resource management Receivers Organizations Uplink

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Scale-Out Acceleration for Machine Learning 17

Scale-Out Acceleration for Machine Learning

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50th Annual IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Park, Jongse Sharma, Hardik Mahajan, Divya Kim, Joon Kyung Olds, Preston Esmaeilzadeh, Hadi Georgia Inst Technol Alternat Comp Technol ACT Lab Atlanta GA 30332 USA Univ Calif San Diego Alternat Comp Technol ACT Lab La Jolla CA 92093 USA

ISBN: (纸本)9781450349529

The growing scale and complexity of Machine Learning (ML) algorithms has resulted in prevalent use of distributed general-purpose systems. In a rather disjoint effort, the community is focusing mostly on high performance single-node accelerators for learning. This work bridges these two paradigms and offers CoSMIC, a full computing stack constituting language, compiler, system software, template architecture, and circuit generators, that enable programmable acceleration of learning at scale. CoSMIC enables programmers to exploit scale-out acceleration using FPGAs and Programmable ASICs (P-ASICs) from a high-level and mathematical Domain-Specific Language (DSL). Nonetheless, CoSMIC does not require programmers to delve into the onerous task of system software development or hardware design. CoSMIC achieves three conflicting objectives of efficiency, automation, and programmability, by integrating a novel multi-threaded template accelerator architecture and a cohesive stack that generates the hardware and software code from its high-level DSL. CoSMIC can accelerate a wide range of learning algorithms that are most commonly trained using parallel variants of gradient descent. The key is to distribute partial gradient calculations of the learning algorithms across the accelerator-augmented nodes of the scale-out system. Additionally, CoSMIC leverages the parallelizability of the algorithms to offer multi-threaded acceleration within each node. Multi-threading allows CoSMIC to efficiently exploit the numerous resources that are becoming available on modern FPGAs/P-ASICs by striking a balance between multi-threaded parallelism and single-threaded performance. CoSMIC takes advantage of algorithmic properties of ML to offer a specialized system software that optimizes task allocation, role-assignment, thread management, and internode communication. We evaluate the versatility and efficiency of CoSMIC for 10 different machine learning applications from various domains. On av

关键词： Accelerator scale-out distributed cloud machine learning

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