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Machine Learning based Energy Efficient Wireless Sensor network

Machine Learning based Energy Efficient Wireless Sensor Netw...

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International conference on Power Electronics, Control and Automation (ICPECA)

作者： Lata, Sonam Mehfuz, Shabana Jamia Millia Islamia Dept Elect Engn Delhi India

ISBN: (纸本)9781728139586

In case of no fixed infrastructure (military applications and emergency rescue operations) and we need to build a network with low cost, Wireless sensor networks (WSNs) are useful. We have no fixed routing protocol, or intrusion detection technique available for them because WSNs arc dynamic in nature and individual nodes of the network are required for this to be done. Nodes are mobile in most of the applications of WSNs, so they depend on battery power and availability of limited resources which shows that power consumption is an effective research area for performing a set of tasks in WSNs. To deal with such an issue, machine learning (ML) techniques (sell-learning algorithms, working without programming or human intervention) can be applied effectively according to the application requirement. In this paper, we have done comparative about several ML-based techniques for WSNs. In addition, we also analyzed ML techniques for clustering and energy harvesting. At the end, we present a summary of ML techniques for both clustering and energy harvesting with some open issues.

关键词： Wireless Sensor network network-Lifetime Machine Learning Energy Efficiency Clustering

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Peculiarities of Human Machine Interaction for Synthesis of the Intelligent Dialogue Chatbot 10

Peculiarities of Human Machine Interaction for Synthesis of ...

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10th ieee International conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Sidenko, Ievgen Kondratenko, Galyna Kushneryk, Pavlo Kondratenko, Yuriy Petro Mohyla Black Sea Natl Univ Intelligent Informat Syst Dept 68th Desantnykiv Str 10 UA-54003 Mykolaiv Ukraine

ISBN: (纸本)9781728140681

Research in the field of human machine interaction and machine learning contributed to the revival of the chatbots. They are virtual interlocutors whose logical apparatus is based on artificial intelligence. However, recent reviews show that chatbots are perceived as unwise systems. These results contributed to the rapid introduction of chatbots in social networks. At the same time, the question of choosing the structure of a neural network for learning dialogue systems, the principles and features of human machine interaction remains important. In this paper various architectures of neural networks are being compared, and it's own chatbot using encoder-decoder architecture with attention mechanism is developed. For implementation, the Python programming language is used. TensorFlow framework is used for deep learning. The simulation results confirm the effectiveness of the proposed approach to speech recognition and human machine interaction.

关键词： neural network linguistic recognition human machine interaction deep learning dialogue system chatbot

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Delay-Optimal Temporal-Spatial Computation Offloading Schemes for Vehicular Edge Computing Systems

Delay-Optimal Temporal-Spatial Computation Offloading Scheme...

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ieee Wireless Communications and networking conference (WCNC)

作者： Tang, Dian Zhang, Xuefei Tao, Xiaofeng Beijing Univ Posts & Telecommun Natl Engn Lab Mobile Network Technol Beijing Peoples R China Sci & Technol Commun Networks Lab Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9781538676462

Vehicular edge computing (VEC) is a potential solution to meet delay-sensitive and computation-intensive vehicular applications demands. However, the nature of mobility brings a significant challenge, i.e., when and where to offload task can capture the optimal delay performance for VEC. To address this challenge, firstly, we characterizes the temporal-spatial correlation for VEC system. With the aid of this, we formulate the energy-constrainted delay minimization problem as a mixed integer nonlinear programming (MINLP). Due to the fact that it is hard to tackle the features about non-convex and coupling of designed MINLP problem, this paper transforms the original problem into task placement sub-problem and delayed offloading sub-problem. Specifically, the former is solved by two-stage decision tree algorithm and the latter is obtained by dynamic programming technique. On this basis, a temporal-spatial computation offloading scheme is proposed. Simulations demonstrate that our proposed scheme achieves superior delay performance and provides useful guides for choosing suitable schemes in different conditions.

关键词： Delays Task analysis Servers Wireless communication Edge computing Minimization Gold

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Neural network based patterns detection in object-oriented program 9

Neural network based patterns detection in object-oriented p...

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9th Annual Information Technology, Electromechanical Engineering and Microelectronics conference, IEMECON 2019

作者： Dwivedi, Ashish Kumar Rath, Santanu Kumar Satapathy, Shashank Mouli Department of CSE Gayatri Vidya Parishad College of Engineering Visakhapatnam India Department of CSE National Institute of Technology Rourkela India School of Computer Science and Engineering VIT Vellore India

ISBN: (纸本)9781538693254

These days, different design methodologies are proposed for actualizing various types of answers for the product wanted for complex frameworks. Among them design patterns are as often as possible used to develop the object oriented program. The Determination of design patterns in open source program empowers the viability of conventional question situated projects. The proposed system demonstrates the assurance of software patterns in open source program. The procedure of pattern retrieval is carried out by utilizing quality estimation parameters e.g., programming measurements. The introduced strategy considers different sort of neural network models to decide the existence of patterns in open source framework. The proposed method is assessed by utilizing an open source program e.g., MapperXML. © 2019 ieee.

关键词： Neural network models

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An Energy-driven network Function Virtualization for Multi-domain Software Defined networks

An Energy-driven Network Function Virtualization for Multi-d...

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ieee conference on Computer Communications (ieee INFOCOM)

作者： Kaur, Kuljeet Garg, Sahil Kaddoum, Georges Gagnon, Francois Kumar, Neeraj Ahmed, Syed Hassan Ecole Technol Super Elect Engn Dept Montreal PQ H3C 1K3 Canada Thapar Inst Engn & Technol CSED Patiala Punjab India Georgia Southern Univ Dept Comp Sci Statesboro GA 30460 USA

ISBN: (纸本)9781728118789

network Functions Virtualization (NFV) in Software Defined networks (SDN) emerged as a new technology for creating virtual instances for smooth execution of multiple applications. Their amalgamation provides flexible and programmable platforms to utilize the network resources for providing Quality of Service (QoS) to various applications. In SDN-enabled NFV setups, the underlying network services can be viewed as a series of virtual network functions (VNFs) and their optimal deployment on physical/virtual nodes is considered a challenging task to perform. However, SDNs have evolved from single-domain to multi-domain setups in the recent era. Thus, the complexity of the underlying VNF deployment problem in multi-domain setups has increased manifold. Moreover, the energy utilization aspect is relatively unexplored with respect to an optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the VNF deployment problem in multi-domain SDN setup has been addressed with a primary emphasis on reducing the overall energy consumption for deploying the maximum number of VNFs with guaranteed QoS. The problem in hand is initially formulated as a "Multi-objective Optimization Problem" based on Integer Linear programming (ILP) to obtain an optimal solution. However, the formulated ILP becomes complex to solve with an increasing number of decision variables and constraints with an increase in the size of the network. Thus, we leverage the benefits of the popular evolutionary optimization algorithms to solve the problem under consideration. In order to deduce the most appropriate evolutionary optimization algorithm to solve the considered problem, it is subjected to different variants of evolutionary algorithms on the widely used MOEA framework (an open source java framework based on multi-objective evolutionary algorithms). The experimental results demonstrate that the proposed scheme achieves better results in comparison to the epsilon-Non-dominated Sorting Genetic

关键词： network Function Virtualization Software Defined network Evolutionary Optimization Energy Consumption Multi-objective Optimization

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A Runtime Resource Management Policy for openCL Workloads on Heterogeneous Multicores 22

A Runtime Resource Management Policy for OpenCL Workloads on...

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22nd Design, Automation and Test in Europe conference and Exhibition (DATE)

作者： Angioletti, Daniele Bertani, Francesco Bolchini, Cristiana Cerizzi, Francesco Miele, Antonio Politecn Milan Dip Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9783981926323

Nowadays, runtime workload distribution and resource tuning for heterogeneous multicores running multiple openCL applications is still an open quest. This paper proposes an adaptive policy capable at identifying an optimal working point for an unknown multiprogrammed openCL workload without using any design-time application profiling or analysis. The approach compared against a design-time optimization strategy demonstrates to be effective in converging to an solution guaranteeing required performance while minimizing power consumption and maximum temperature;it achieves on average values 0.085 W (5.15%) and 0.83 degrees C (1.47%) worse than the static optimal solution.

关键词： openCL Hetenrogeneous System architectures Runtime Resource Management

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Bare-Metal Compute, Storage and networking in Metro Optical Access 24

Bare-Metal Compute, Storage and Networking in Metro Optical ...

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24th OptoElectronics and Communications conference (OECC) / International conference on Photonics in Switching and Computing (PSC)

作者： Gunning, Paul BT Appl Res Software Based Networking Grp Adastral Pk Ipswich IP5 3RE Suffolk England

ISBN: (纸本)9784885523212

Forwarding-, control-, and management-plane disaggregation with openAPIs offer automation of compute, storage and networking resources across optical carrier-switched and electronic packet-switched domains. open, bare-metal hardware outfitted with merchant silicon hosting open software reduce barriers to entry and have deep historical roots in computer evolution in the last century. History may be about to repeat itself.

关键词： O1. Core/Access /Data Center networks and Subsystems P2. Photonics in Switching Technologies Systems and architectures for Computing and BigData

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Real-Time Visualization of Neural network Training to Supplement Machine Learning Education 9

Real-Time Visualization of Neural Network Training to Supple...

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9th ieee Integrated STEM Education conference (ISEC)

作者： You, Michael Yin, Jessica Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Mech Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781728115023

In machine learning, neural networks have excelled at performing tasks at a high level with a simple and flexible implementation. Neural networks are particularly well-suited for novice programmers due to the availability of open-source libraries like TensorFlow and Caffe. However, novice programmers often neglect to learn beyond the black-box behaviors that these libraries provide. Introductory college students often lack the understanding of neural network internals, such as hidden layers and activation functions, and their interactions during training, which are crucial to efficiently solving more complex problems. Here, we present Omega(3), a device that opens up the black-box of neural networks by visually representing how hidden layers behave during training in real-time. In addition, Omega(3) provides an engaging tactile and visual educational experience to students, and waives the requirement for a strong programming background in order to learn about neural networks. In this paper, we will discuss the fabrication and set-up of Omega(3) as well as evaluate and compare Omega(3) to traditional lecture based learning.

关键词： Hidden layer modular neural networks visualization

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Optimizing Latency Sensitive Applications for Amazon's Public Cloud Platform

Optimizing Latency Sensitive Applications for Amazon's Publi...

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ieee Global Communications conference (ieee GLOBECOM)

作者： Czentye, Janos Pelle, Istvan Kern, Andras Gero, Balazs Peter Toka, Laszlo Sonkoly, Balazs MTA BME Network Softwarizat Res Grp Budapest Hungary Budapest Univ Technol & Econ Budapest Hungary Ericsson Res Stockholm Sweden

ISBN: (纸本)9781728109626

Recent cloud technologies enable a diverse set of novel applications with capabilities never seen before. Cloud native programming, microservices, serverless architectures are novel paradigms reducing the burden on both software developers and operators while enabling cloud-grade service deployments. Several types of applications fit in well with the new concepts, however, latency sensitive applications with strict delay constraints pose additional challenges on the platforms. Can we run these applications on today's public cloud platforms making use of the brand new tools and techniques? In this paper, we try to answer this question by addressing one of the most widely used and versatile public cloud platforms, namely Amazon's AWS, and we propose a novel mechanism to optimize the software "layout" based on dynamic performance measurements. Our contribution is threefold. First, we define a combined performance and cost model on CaaS/FaaS (Container/Function as a Service) platforms, specifically for AWS, based on a comprehensive performance analysis, and we also provide an application model capturing the performance requirements. Second, we formulate an optimization problem which minimizes the deployment costs on AWS while meeting the latency constraints. A polynomial algorithm finding the optimal solution is also given. Third, we evaluate the model and the algorithm for different scenarios and investigate the performance on today's system.

关键词： Cost benefit analysis

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Application of advanced learning methods for detecting network configuration in a smart water distribution system

Application of advanced learning methods for detecting netwo...

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ieee International conference on Automation, Quality and Testing, Robotics, AQTR

作者： Alexandru Predescu Mariana Mocanu Ciprian Lupu Automatic Control and Computers University POLITEHNICA of Bucharest

ISBN: (数字)9781728171661

ISBN: (纸本)9781728171678

Water distribution is one of the main pillars of modern society and accounts for a constant need of innovative solutions to age-old problems. There are many challenges associated to the aging infrastructure in large metropolitan areas as well as efficient operation of control structures, requiring improved decision support systems and autonomous operation based on available data. An extension of traditional methods with modern concepts is required such as using learning algorithms, smart meters and reactive programming for improving the quality of service in water distribution systems. This paper extends an IoT-based model with Deep Learning and automated test scenarios, while showing the effective application and comparison of learning techniques on experimental data in this domain. The experimental model is described from the hardware level to the IoT platform in a modern approach using the current state of software development and architectures for real-time data management.

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