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检索条件"任意字段=4th International Conference on Art, Science, and Engineering of Programming, Programming 2020"

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Cloud Secure Resource Sharing Algorithm from Object Based Sharable Environment

Cloud Secure Resource Sharing Algorithm from Object Based Sh...

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international conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Uday Moghe Brajesh Chaturvedi Prashant Lakkadwala Computer Science and Engineering Indore India

Cloud Computing is emerging as a major technology in the field of information and communication, now it has been proved, it is a key technology for develop a market, analyzing users of different field. In this paper our focus on, how to achieve data integrity, data compromise and availability of services to form a cloud computing environment for resource sharing with privacy and security. this work proposed a new approach “Cloud Secure - Resource Sharing Algorithm (CS - RSA) from Object Oriented Based Sharable Environment”. In this proposed work we will achieve reduce running time, reaction time, minimizing the risk in deploying physical infrastructure and security occur in cloud computing. In this paper we have three different clouds in which replete and aggregation operation had been performed that includes many object oriented aspects based on some inheritance were analyzed. CS - RSA approach mainly concentrates on the following four phases: interconnectivity, security, resource sharing and object oriented programming (OOP) mapping.

关键词： Security Computers Computational modeling Servers Reliability

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Fault-Tolerant Real-Time Collaborative Network Edge Analytics for Industrial IoT and Cyber Physical Systems with Communication Network Diversity

Fault-Tolerant Real-Time Collaborative Network Edge Analytic...

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international conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Emmanuel Oyekanlu Electrical and Computer Engineering Department Drexel University Philadelphia USA

In developing state of the art applications for Internet of things (IoT) and Cyber Physical Systems (CPS), most applications software and associated hardware are always developed from ground up. However, this approach always involves huge capital outlay, extend time-to-market for products, and contribute to integration delay, especially in the case of Industrial IoT (IIoT). In this paper, it is shown that already existing, widely-available, and low-cost hardware can participate successfully in collaborative computing for new analytics applications, at edges of IIoT and CPS networks. A well-known, low-cost, embedded digital signal processor (DSP) that is fully integrated in many industries is selected and used as a case study. the selected DSP is made more scalable by applying it to new, novel uses at the edge of an IIoT network. the new application includes using it to design useful waveforms needed for collaborative computing at IIoT network edges. Embedded-C, a programming language that is suitable for programming resource-constrained network edge devices is used to successfully design needed waveforms for the selected DSP. Collaborative computing is achieved by sending the designed waveforms from the network edge, across diverse communication channels, to the fog layer, and then, use it at the fog layer to remove noise in selected IIoT data. Correlation coefficient is positive and high between output of noise removal achieved by waveforms from the low-cost DSP when compared to noise removal achieved by waveforms from systems with more computing resources at the fog layer. this signifies a successful collaborative computing using such legacy, low-cost DSP. For low-cost hardware to participate successfully in distributed, real-time collaborative network edge-computing, impact of communication network disturbances must be examined. Hence, for the selected DSP, impact of network Bit Error Rate (BER) is examined for both wired and wireless networks. It is discovered t

关键词： Collaboration Computer languages Real-time systems Wireless communication Software Wavelet analysis Wavelet transforms

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Reactive-Optimized Sentence Detection In Kubernetes Using OpenNLP And Native GraalVM Image With Framework Metric Comparison

Reactive-Optimized Sentence Detection In Kubernetes Using Op...

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Emerging Technology (INCET), international conference for

作者： Aditya Sharma Komal Tahiliani Ghanshyam Prasad Dubey Department of CSE Sagar Institute of Science & Technology (SISTec) Bhopal MP India Dept. of CSE Sagar Institute of Science & Technology (SISTec) Bhopal MP India Dept. of CSE Amity School of Engineering & Technology Amity University Gwalior MP India

Java was chosen over Python for data science endeavours since it is a widely-used programming language. the default JVM was GraalVM, an industry-leading and high-performance JVM, rather than Oracle’s default. GraalVM has several advantages, including native image generation, which pre-compiles Java code into an executable, resulting in a smaller footprint and better performance. Apache OpenNLP, a Java-based natural language processing framework, was utilized for sentence detection. OpenNLP has pre-built models for language recognition, sentence detection, etc. the default OpenNLP code was made completely reactive and functional by modifying it. To achieve the goals, Spring WebFlux was leveraged and the advanced capabilities of Java 17, including lambda and streams. WebFlux, an event-driven asynchronous framework, can provide backpressure-capable, non-blocking clients and servers. Additionally, some of the most widely-used Java frameworks were employed, namely Micronaut, Quarkus, Helidon, and Spring Boot, to develop the code. Two codes were written for each framework: one utilizing traditional Java code and the default OpenNLP sentence detection implementation. At the same time, the other was entirely reactive and functional, with the OpenNLP implementation modified to be reactive and functional. Docker images were created for each and deployed in a Kubernetes environment using Minikube. Finally, concurrent load-testing of the endpoint for 1-1000 users was conducted using an Apache JMeter to assess the performance. Vital metrics such as CPU, memory, docker image size, executable size, executable start-up time, throughput, and error per cent were collected. It was found that reactive-functional programming with Spring Boot resulted in superior scalability, responsiveness, and fault tolerance.

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Strategy Gradient Algorithm for the Stem Balancing Problem

Strategy Gradient Algorithm for the Stem Balancing Problem

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Electronic Information engineering and Computer science (EIECS), 2021 international conference on

作者： Jiyuan Wang School of Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Policy Gradient is a policy-based reinforcement learning algorithm that approximates the optimal policy through a parametric function. the algorithm classifies the observations by softmax through neural network to derive the policy with reference. At the same time, the objective function is set by evaluating the reward after training. Finally, the iterative formula is derived by maximizing the value of the objective function, and the parameters are updated iteratively to approach the optimal policy. As a typical multivariate, nonlinear, strongly coupled and naturally unstable problem, the stem balancing problem is very suitable for testing the ability of the policy gradient algorithm in solving the optimal policy. In this paper, the objective function is constructed, the proof of the policy gradient theorem and the derivation of the gradient iteration formula are completed, the specific setting of the stem balancing problem in the reinforcement learning under the theoretical model is introduced in detail, and the flow of the round updating policy gradient algorithm is designed.

关键词： Training Neural networks Stochastic processes Reinforcement learning Linear programming Approximation algorithms Classification algorithms Iterative methods Testing Convergence

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Risk Prediction and Optimization of Electricity Retail Market Based on Risk Factors of Electricity Retail Companies

Risk Prediction and Optimization of Electricity Retail Marke...

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Power and Energy engineering (ICPEE), international conference on

作者： Yongbo Li Chuan He Yahai Zhang Hanhan Qian Power Exchange Center Anhui Electric Power Company Hefei Anhui School of Electrical Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781728191256

ISBN: (纸本)9781728191263

With deregulation of electrical market in the China, the electricity retail companies (ERCs) and the electricity retail market (ERM) experiences fast development in the recent years. Different from the generation and transmission market, the retail market is more open to the participators, and has more competition and higher risk. Considering the risk factors e.g. the business scale, the capital, the employee, and the complaints, an optimization model to reduce the risk of the ERCs and ERM with higher predicted risk is newly proposed in this paper, where the risk factors are normalized by the threshold value and the time before quantifying the risk. the risk optimization problem with constraints to adjustable risk factors is nonlinear and solved by the interior point method. the numerical results validate the optimization effect on the risk of the ERCs and the ERM.

关键词： Companies Business Optimization Indexes Qualifications Power systems Linear programming

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Virtual Machine packing algorithms for lower power consumption

Virtual Machine packing algorithms for lower power consumpti...

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IEEE international conference on Cloud Computing Technology and science (CloudCom)

作者： Satoshi Takahashi Hidemoto Nakada Atsuko Takefusa Tomohiro Kudoh Maiko Shigeno Akiko Yoshise JSPS Research Fellow (DC2) Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki Japan Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibaraki Japan

Virtual Machine(VM)-based flexible capacity management is an effective scheme to reduce total power consumption in the data centers. However, there remain the following issues, trade-off between power-saving and user experience, decision on VM packing plans within a feasible calculation time, and collision avoidance for multiple VM live migration processes. In order to resolve these issues, we propose two VM packing algorithms, a matching-based (MBA) and a greedy-type heuristic (GREEDY). MBA enables to decide an optimal plan in polynomial time, while GREEDY is an aggressive packing approach faster than MBA. We investigate the basic performance and the feasibility of proposed algorithms under both artificial and realistic simulation scenarios, respectively. the basic performance experiments show that the algorithms reduce total power consumption by between 18% and 50%, and MBA makes suitable VM packing plans within a feasible calculation time. the feasibility experiments show that the proposed algorithms are feasible to make packing plans for an actual supercomputer, and GREEDY has the advantage in power consumption, but MBA shows the better performance in user experience.

关键词： Power demand Servers Heuristic algorithms Degradation Cloud computing IP networks Linear programming

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Coordinated Optimization of Mobile Power Scheduling and Repair Crew Scheduling for Service Restoration in Distribution Networks

Coordinated Optimization of Mobile Power Scheduling and Repa...

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Electrical engineering and Control science (IC2ECS), international conference on

作者： Xunting Wang Bin Xu Jinjin Ding Ping Yu Ming Wang Han Wang Electric Power Research Institute of State Grid Anhui Electric Power Co. Ltd Hefei China Chizhou Power Supply Branch State Grid Anhui Electric Power Co. Ltd Chizhou China School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (数字)9798331510107

ISBN: (纸本)9798331510114

this paper proposes a coordinated optimization strategy for service restoration in distribution networks, which integrates mobile power sources (MPSs) scheduling and repair crews (RCs) scheduling. the primary objective is to accelerate the recovery of distribution networks after large-scale power outages caused by extreme natural disasters. the strategy incorporates three key emergency measures: MPSs scheduling, RCs scheduling, and island partitioning. this approach allows for dynamic adjustments to the island topology and the number of islands, based on the progress of repairs and MPS scheduling. the model is formulated as a mixed-integer second-order cone programming (MISOCP) problem and solved using commercial solvers. Case studies based on the IEEE 123-bus distribution network validate the effectiveness of the proposed strategy. the results show that the strategy enhances fault recovery efficiency by coordinating RCs and flexibly deploying MPS units.

关键词： Network topology Distribution networks Maintenance engineering programming Dynamic scheduling Real-time systems Topology MISO Optimization Resilience

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Application of artificial neural network in urban landscape water quality evaluation

Application of artificial neural network in urban landscape ...

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the 4th international conference on Bioinformatics and Biomedical engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Yi-mei Tian Li-hua Jia Bin-wei Wang School of Environmental Science and Engineering Tianjin University Tianjin China

Water quality model is a useful tool to evaluate the future state of river water through evaluation of actual pollution loading or different management options. Based on the human brain physiology research, artificial neural network(ANN), which simulates the structure and mechanism of the human brain, is a kind of dynamic information processing system that eventually achieves certain functions of the human brain. At present, there are hundreds of neural network approaches，and also BP neural network is one of the most frequently used techniques in recent years，whose application in environmental quality assessment of environmental science field was presented in this paper. In this paper, BP neural network was used to establish the landscape water quality evaluation system. this paper also gave an optimization method of BP artificial neural network, which mainly used MATLAB programming whose toolbox provided a function of BP network to avoid complicated mathematical calculations and cumbersome code editor. On the basis of sampling and monitoring of water quality continuously, artificial neural network was used here to establish landscape water evaluation model in order to evaluate the landscape water quality objectively and quickly.

关键词： artificial neural networks Water pollution Humans Biological neural networks Mathematical model Rivers Quality management Physiology Brain modeling Discrete event simulation

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Research on the Innovation of Media Marketing under the Upsurge of Webcast

Research on the Innovation of Media Marketing under the Upsu...

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2018 4th international conference on Education & Training,Management and Humanities science(ETMHS 2018)

作者： Feng Jujun Xi'an Peihua University

At present,as a novel form of programming,webcasting has created an upsurge in the entire population because of its diversity of content and *** paper analyzes the current status of webcast media marketing models,as well as the characteristics,content and marketing models of marketing,analyzes the advantages of webcast marketing models under the Internet to other media marketing methods,and analyzes the media industry's business with webcast economy as an *** view of issues such as it is difficult to maintain the enthusiasm of the user and malicious marketing that are faced by current webcast marketing,development suggestions have been put forward in terms of analysis of webcast marketing strategies and innovations in webcast marketing development.

关键词： webcast media marketing marketing model innovation and development

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Design and development of an Android application to process and display summarised corporate data

Design and development of an Android application to process ...

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international conference on Adaptive science & Technology (ICAST)

作者： Seth Y. Fiawoo Robert A. Sowah Department of Computer Engineering University of Ghana Ghana

In the corporate world today, managers are always faced with a lot of decisions to make based on data generated at their various offices. the decision-making process is very vital as decisions taken by management could make a company either overtake or stay within reach of its competitors. this paper presents the design and development of an Android application that would retrieve summarised data from a central database process and display that information on an android device so as to aid managers in their decision-making process. the components of designed and developed system include (1) a web application through which workers would input data at their various workplaces (2) a database hosted on a central server that would store information entered by workers (3) an application programming interface (API) that would take requests from the Android application, query the database and serve the results back to the Android application and (4) an android application that processes and displays results to users. the android application is developed using Eclipse in conjunction with android SDK tools. the application retrieves data from a database per user request and displays the retrieved information on an android device. Users of this application would be able to analyse data quicker hence make quick decisions as they would not be drowned in a flood of detailed information. there is also an added benefit of having access to company data on the go.

关键词： Decision support systems conferences

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