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检索条件"任意字段=7th International Conference on the Art, Science, and Engineering of Programming, Programming 2023"

共 1136 条记录，以下是581-590 订阅

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TranskV: A Networking Support for Transaction Processing in Distributed Key-value Stores 7

TranskV: A Networking Support for Transaction Processing in ...

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7th IEEE international conference on Big Data Computing Service and Applications, BigDataService 2021

作者： Eldakiky, Hebatalla Du, David Hung-Chang Department of Computer Science and Engineering University of Minnesota Twin Cities United States

ISBN: (纸本)9781665434836

through the massive use of mobile devices, data clouds, and the rise of Internet of things, enormous amount of data has been generated and analyzed for the benefit of society. NoSQL Databases and specially key-value stores become the backbone in managing these large amounts of data. Most of key-value stores ignore transactions due to their effect on degrading key-value store's performance. Meanwhile, programmable switches with the software-defined networks and the programming Protocol-Independent Packet Processor (P4) lead to a programmable network where in-network computation can help accelerating the performance of applications. In this paper, we proposed a networking support for transaction processing in distributed key-value stores. Our system leverages the programmable switch to act as a transaction coordinator. Using a variation of the time stamp ordering concurrency control approach, the programmable switch can decide to proceed in transaction processing or abort the transaction directly from the network. Our experimental results on an initial prototype show that our proposed approach, while supporting transactions, improves the throughput by up to 4X and reduces the latency by 35% when compared to the existing architectures. © 2021 IEEE.

关键词： Concurrency control

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Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers 26th

Feature Selection on Epistatic Problems Using Genetic Algor...

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26th international conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Carvalho, Pedro Ribeiro, Bruno Rodrigues, Nuno M. Batista, João E. Vanneschi, Leonardo Silva, Sara University of Coimbra Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering Coimbra Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal LASIGE Department of Informatics Faculty of Sciences University of Lisbon Lisbon Portugal Universidade Nova de Lisboa Campus de Campolide Lisboa1070-312 Portugal

ISBN: (纸本)9783031302282

Feature selection is becoming an essential part of machine learning pipelines, including the ones generated by recent AutoML tools. In case of datasets with epistatic interactions between the features, like many datasets from the bioinformatics domain, feature selection may even become crucial. A recent method called SLUG has outperformed the state-of-the-art algorithms for feature selection on a large set of epistatic noisy datasets. SLUG uses genetic programming (GP) as a classifier (learner), nested inside a genetic algorithm (GA) that performs feature selection (wrapper). In this work, we pair GA with different learners, in an attempt to match the results of SLUG with less computational effort. We also propose a new feedback mechanism between the learner and the wrapper to improve the convergence towards the key features. Although we do not match the results of SLUG, we demonstrate the positive effect of the feedback mechanism, motivating additional research in this area to further improve SLUG and other existing feature selection methods. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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A Hybrid Data Acquisition Model for Precision Agriculture using IoT, engineering Nanomaterials and artificial Intelligence

A Hybrid Data Acquisition Model for Precision Agriculture us...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： K.Sankara Moorthy Ms. Peddinti Neeraia P. Pavitra R. Arivumalar K. Veeranjaneyulu S.Mohana Murali College of Management SRM Institute of Science and Technology Kattankulathur Department of Computer Applications School of Computing Mohan Babu University Tirupati Department of Basic science and humanities Dadi Institute of Engineering and Technology visakhapatnam Anakapalli Sri Sairam Engineering College West Tambaram Chennai-44 CVR College of Engineering Rangareddy Telangana Dept of commerce Govt Degree College Andhra Pradesh India

Two key ideas for wireless sensor network (WSN) optimization in precision farming serve as the guidelines for this study. According to its definition, a wireless sensor network is a collection of sensor nodes that link to one another and/or with a node outside of their coverage area via wireless communication. these nodes frequently have one or more sensors for evaluating internal or external elements as well as environmental characteristics. A CPU then controls the sensors, compiling, storing, processing, and, if requested, transmitting the data. In some systems, the sensor nodes' processing power is constrained to make them more energy-efficient. In this application, the sensor nodes transfer the collected data to a remote node with more powerful processing capabilities and greater storage in specified time intervals or condition-based events storage. Because of its ability to monitor processes at a small scale, nanotechnology is regarded as a leading technology for controlling agriculture. So, it paves the way for crucial advantages such as improving food quality and quantity, lowering the input needed for agricultural production, fully utilizing soil nutrients, etc. the availability of natural resources, detecting the right nutrients from the soil for crop-specific production, and crop cultivation are issues in these models. In order to boost crop yield by assessing the nutrients in the soil, various nano-sensors are used in this research study. the type of crop that can be employed for cultivation or irrigation depends on the accuracy of acquisition and detection. Real-time nanosensors are used to collect the soil constituents that are necessary for crop productivity. In comparison to state-of-the-art models, simulation results utilizing a deep learning detector based on input obtained from nanosensors, which demonstrate increased productivity

关键词： Productivity Wireless sensor networks Protocols Nanosensors Crops Process control Soil

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Comparative Assessment of State-of-the-art Image Fusion Techniques for Fusion of Multi-modality Medical Images 7th

Comparative Assessment of State-of-the-art Image Fusion Tec...

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7th international conference on Microelectronics, Electromagnetics and Telecommunication, ICMEET 2022

作者： Singh, Vineeta Kaushik, Vandana Dixit Department of Computer Science and Engineering Harcourt Butler Technical University East Campus Nawabganj Uttar Pradesh Kanpur208002 India

ISBN: (纸本)9789811988646

Clinical imaging is a vital part in clinical field for analysis of illnesses, for example, growth recognition, malignant growth identification and a lot more where inside organ infections are required to have been analyzed. Clinical instruments are exorbitant and accompanied impediment. For instance, CT images give better insights concerning hard designs in a human body, while MRI portrays better insights concerning delicate tissues. Be that as it may, there might be sure cases where both of the subtleties are expected by a clinical specialist to productively analyze. Here, machine vision, human insight, PC vision, clinical imaging, minute imaging and a lot more are such encouraging fields where clinical images combination plays out a vital job. In this examination paper, creators have assessed various images amalgamation strategies on clinical picture dataset for exhibition of best performing techniques through specific execution measurements. Trial results outlined that pulse coupled neural network has performed better when contrasted with non-sub sampled contourlet transform, sparse representation as well as NSCT+SR methodologies including execution evaluators: average gradient, standard deviation and spatial frequency with a best performing values of 10.4, 82.2 and 8.1 separately. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical imaging

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Design and Implementation of Congestion-Control Strategy for LEO Satellite Based on NS3 7

Design and Implementation of Congestion-Control Strategy for...

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7th international conference on Computer and Communications, ICCC 2021

作者： Chen, Jinling Wang, Cheng Zhao, Xiaoyan Tong, Jianfei Wang, Weidong Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (纸本)9781665409506

As a branch of the simulation field, network simulation technology is increasingly becoming a powerful tool for analysis, research and design. In recent years, the Network Simulator 3 (NS3) simulator has made significant progress with its rich network component object library and relatively perfect bottom-level programming modelling. However, most of the current research on NS3 is based on the existing algorithms for differentiation and comparative analysis, lacking the open-source platform design, the training means of satellite communication system simulation and research on the overall platform building framework based on NS3. this article proposes a complete congestion-control open-source platform construction strategy for Low Earth Orbit (LEO) satellite. the top-level building process of the satellite simulation platform is introduced in detail, and the bottom layer is modularized into four parts to achieve. the NS3 top-level frame realization process and the bottom-level network element unit are modularized to build a complete satellite model simulation platform. It provides some ideas for promoting the development of satellite routing algorithm simulation experiments. © 2021 IEEE.

关键词： Satellite communication systems

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Demand Side Management of a Battery-Supported Manufacturing Process with On-Site Generation

Demand Side Management of a Battery-Supported Manufacturing ...

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IEEE international Energy conference and Exhibition (EnergyCon)

作者： Philipp Wohlgenannt Markus Preiß inger Mohan Lai Kolhe Peter Kepplinger Research Center Energy Vorarlberg University of Applied Sciences Dornbirn Austria Faculty of Engineering and Science University of Agder Grimstad Norway

Due to the global competition in manufacturing, flexibility to provide for individually customized products is considered an important selling point. Constantly changing manufacturing processes face higher production costs than well known reoccurring schedules. To lower these costs in general, we propose a model predictive control concept to reduce manufacturing energy costs in particular, using an existing digital twin to estimate the load of the different manufacturing steps. Based on a mixed integer linear programming formulation of the battery-supported manufacturing process, the system makes optimum use of the on-site photovoltaic generation by production scheduling and adaptive battery control. A simulation study considering a time of use and a real-time pricing scenario provides a proof of concept.

关键词： Adaptation models Schedules Manufacturing processes Costs Job shop scheduling Production Pricing

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A Novel Adaptive Graph Transformer For Point Cloud Object Detection

A Novel Adaptive Graph Transformer For Point Cloud Object De...

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IEEE international conference on Communication and Information Systems (ICCIS)

作者： Shaoyu Sun Chunhao Shi Chunyang Wang Xuelian Liu School of Electronic and Information Engineering Changchun University of Science and Technology Changchun China Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology Xi'an Technological University Xi'an China

With the advancement of autonomous driving technology, Lidar-driven 3D object detection has become a focal point of research. However, many existing point-driven approaches face challenges due to rigid local dependencies and entangled foreground-background points. the direct utilization of point clouds for 3D object detection is also complicated by the inherent sparsity and irregularities of Lidar data. To counter these challenges, we propose AGT-SSD a single-stage graph-based 3D detector to improve object performance especially for small objects. Which comprises two critical modules adaptive graph feature convolution module (AGF) and a point cloud transformer layer with self-attention mechanism (TSA). AGF module utilizes an adaptive graph kernel to process graph features, facilitating feature interactions within the feature domain to help detect small objects. Non-local transformer layer incorporates self-attention module, designed to capture long-range dependencies to encode global sematic and spatial feature to improve detection performance. this enhancement is attributed to the geometric structure refinement through graph convolution methods and the use of a self-attention mechanism to grasp long-range dependencies. Both modules employ non-local operations, adapt at discerning long-range correlations. Extensive experiments on the KITTI dataset demonstrated that our detector is not flexible and plug and play for other detectors but also highlight the superior accuracy of our method compared to other state-of-the-art detectors.

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Change Categories Based on Mutable artifacts in PLC Control Software Projects

Change Categories Based on Mutable Artifacts in PLC Control ...

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Industrial Cyber-Physical Systems (ICPS)

作者： Yizhi Wang Birgit Vogel-Heuser Eva-Maria Neumann Michael Gnadlinger Ziming Wen Jan Wilch Bernhard Rupprecht Institute of Automation and Information Systems TUM School of Engineering and Design Technical University of Munich Garching Germany

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

Automated Production Systems are highly complex mechatronic systems. the increasing complexity of automation software is a main challenge for companies to remain competitive. Added functionality entails software changes, which may cause side effects. Static code analysis and change impact analysis are key methods in computer science for assessing software regarding side effects. However, the requirements of real-time capability, reliability, and mixing different Programmable Logic Controller programming languages in the automation software lead to new requirements for code analysis and change management. this paper introduces a set of change categories designed to provide understandable and comprehensive classifications of changes in automation software. Mutable (i.e., changed or modified between versions) software artifacts serve to differentiate change categories. Aiming for unambiguousness and completeness, this set of categories supports the further work of the change analysis, such as automatic change detection and visualization. the change categories are evaluated through an industrial software project programmed in Siemens TIA Portal comprising 262 program organization units in the health sector.

关键词： Visualization Production systems Automation Codes Mechatronics Programmable logic devices Software

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Various Modulation Types Classification on the Air by Convolutional Neural Network 23

Various Modulation Types Classification on the Air by Convol...

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Proceedings of the 7th international conference on Future Networks and Distributed Systems

作者： Yousif I Hammadi Mokhalad Abdulameer Kadhim Alsaeedi Omar Abdulkareem Mahmood Aws Zuhair Sameen Mohammed Saleh Ali Muthanna Ahmed Aziz Ammar Muthanna Dep. of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instrumentation Engineering Techniques Al-Farahidi University Iraq Institute of Computer Technologies and Information Security Southern Federal University Russia Department of computer science Benha university Egypt and International Business Management Department Tashkent State University of Economics Uzbekistan Department of Telecommunication Networks and Data Transmission The Bonch Bruevich Saint Petersburg State University of Telecommunications Russia and Department of Applied Probability and Informatics Peoples Friendship University of Russia Russia

ISBN: (纸本)9798400709036

Deep learning (DL), a relatively recent AI technique, has been successfully applied to the problem of automated modulation categorization (AMC), with promising results. An essential part of developing the spectrum-sensing capabilities needed by software-defined radio is carrying out radio signal classification jobs, which we did in this research by introducing a better deep neural architecture. Different detection techniques were utilized in the literature; however, DL neural networks are recently utilized in this field. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) which is one variation of the RNN network, and other techniques are some of the employed DL architectures in the field of AMC. In this paper, Convolutional Neural Network (CNN) model has been built to automatically classify ten-modulation formats correctly. the suggested CNN network is constructed mainly of six-Convolutional layers. the overall number of trainable parameters in the proposed network is 1,757,962. thus, the network accuracy, which was achieved during the training reached more than 90%. Python programming packages have been used to implement the suggested approach using *** platform, which provides wide-range of facilities.

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Issues and Challenges for Teaching Successful programming Courses at National Secondary Schools of Malaysia 7th

Issues and Challenges for Teaching Successful Programming Co...

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7th international conference on Computational science and Technology, ICCST 2020

作者： Salleh, Faridah Hani Mohamed Dewi, Deshinta Arrova Liyana, Nurul Azlin Department of Computing College of Computing & Informatics Universit Tenaga Nasional Jalan IKRAM-UNITEN Kajang Selangor Malaysia INTI International University Persiaran Perdana BBN Putra Nilai Nilai Negeri Sembilan71800 Malaysia College of Graduate Studies Universiti Tenaga Nasional Jalan IKRAM-UNITEN Kajang Selangor Malaysia

ISBN: (纸本)9789813340688

Undoubtedly, the initiative of the Malaysia education ministry to introduce coding in school curricula is a very good effort with lots of advantages. Able to code will be an advantage and a necessity when the students join the workforce. After not small amount of budget and time has been spent by the country for this mission, there are several issues that need to be considered and worked on to ensure coding lessons in schools achieves the target. this paper presents six issues and the recommended solutions that do not require a change to the current educations system as frequent changes in national education policy will burden teachers, parents and students. the identified issues are from the perspective of language of communication, implementation and execution, digital divide, quality tools, assessments and teaching and learning time. this works suggests to create a self-learning system built specifically for the national secondary schools’ syllabus, short-term job exemption for teachers and programming skill test to replace project as part of assessments to increase the rate of teaching effectiveness of Computer science subjects, especially programming. this study will be of great important to educational planners, school authorities, educational researchers and the governments. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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