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检索条件"任意字段=Proceedings of the 7th International Conference on Algorithms, Computing and Systems"

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7th international conference on Smart Cities and Green ICT systems, SMARTGREENS 2018 and 4th international conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2018

7th International Conference on Smart Cities and Green ICT S...

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7th international conference on Smart Cities and Green ICT systems, SMARTGREENS 2018 and 4th international conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2018

ISBN: (纸本)9783030266325

the proceedings contain 18 papers. the special focus in this conference is on Smart Cities and Green ICT systems. the topics include: Real-time overtaking vehicle detection based on optical flow and convolutional neural network;evaluation of iterative calibration of vehicle cameras using reference information from traffic signs;implementation and investigation of a weather- and jam density-tuned network perimeter controller;city-level agent-based multi-modal modeling of transportation networks: Model development and preliminary testing;distributed quality of information-aware decision-making in vehicular networks;enhanced cellular bandwidth prediction for highly automated driving;a probabilistic travel time modeling approach based on spatiotemporal speed variations;Survey of passengers’ origin-destination in public transportation networks using WI-FI;medical freezers as flexible load for demand response in a business park microgrid with local solar power generation;A MPC based peak shaving application for a household with photovoltaic battery system;***: A decentralised IoT architecture to control electrical appliances in households;a SaaS implementation of a new generic crypto-classifier service for secure energy efficiency in smart cities;identification of environment- and context-specific key factors influencing the user’s thermal comfort;information system development for seamless mobility;car accident detection and reconstruction through sound analysis with crashzam;A multi-domain co-simulation ecosystem for fully virtual rapid ADAS prototyping.

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7th international conference on Operations Research and Enterprise systems, ICORES 2018

7th International Conference on Operations Research and Ente...

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7th international conference on Operations Research and Enterprise systems, ICORES 2018

ISBN: (纸本)9783030160340

the proceedings contain 12 papers. the special focus in this conference is on Operations Research and Enterprise systems. the topics include: Computational Study of Emergency Service System Reengineering Under Generalized Disutility;dealing with Scheduling Fairness in Local Search: Lessons Learned from Case Studies;dynamic Pricing Competition with Unobservable Inventory Levels: A Hidden Markov Model Approach;a Bayesian Inference Analysis of Supply Chain Enablers, Supply Chain Management Practices, and Performance;operations Administration and Maintenance Constraints in Fiber Cables Network Design;impact of Iterated Local Search Heuristic Hybridization on Vehicle Routing Problems: Application to the Capacitated Profitable Tour Problem;an Efficient Heuristic for Pooled Repair Shop Designs;A Marginal Allocation Approach to Resource Management for a System of Multiclass Multiserver Queues Using Abandonment and CVaR QoS Measures;an Efficient Application of Goal Programming to Tackle Multiobjective Problems with Recurring Fitness Landscapes;An Efficient Algorithm Based Tabu Search for the Robust Sparse CARP Under Travel Costs Uncertainty.

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RWRel: A fast training framework for random walk-based knowledge graph embedding 21

RWRel: A fast training framework for random walk-based knowl...

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proceedings of the 2021 4th international conference on algorithms, computing and Artificial Intelligence

作者： Hui Zhang Da-Gao Duan Qiu-Li Zhang Department of Computer Science and Engineering Beijing Technology and Business University China Department of International Economics and Management Beijing Technology and Business University China

ISBN: (纸本)9781450385053

With the development of typical graph data scenarios such as social networks and recommender systems, the size of knowledge graphs is growing. In recent years, large-scale knowledge graphs have posed a challenge in terms of fast training of knowledge graph representation model when applied. the article proposes a fast training framework for random walk-based embedding of knowledge graphs (RWRel), which contains a random walk strategy based on relational paths and a relational encoding that introduces subject-object embeddings to model the subject-object semantics of relations in knowledge graphs. the experimental results show that the RWRel framework is effective in improving the speed and maintaining the performance of the representation learning method.

关键词： representation learning random walk knowledge graphs

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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20th international conference on Distributed computing and Networking, ICDCN 2019

ISBN: (纸本)9781450360944

the proceedings contain 78 papers. the topics discussed include: the weakest failure detector to solve the mutual exclusion problem in an unknown dynamic environment;multi-tenant mobile offloading systems for real-time computer vision applications;distributed symmetry-breaking with improved vertex-averaged complexity;parallel algorithms for predicate detection;improving efficacy of concurrent internal binary search trees using local recovery;session guarantees with raft and hybrid logical clocks;reconfigurable dataflow graphs for processing-in-memory;on the hardness of the strongly dependent decision problem;and cache attacks on blockchain based information centric networks: an experimental evaluation.

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Successes and Failures in Exploring Biometric algorithms in NIST Open Source Software and Data 7

Successes and Failures in Exploring Biometric Algorithms in ...

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7th international conference on Software Defined systems, SDS 2020

作者： Razaque, Abdul Amsaad, Fathi Almiani, Muder Gulsezim, Duisen Almahameed, Mohammad Adnan Al-Dmour, Ayman Khan, Meer Jaro Ganda, Raouf International IT University Department of Computer Engineering and Information System Almaty Kazakhstan School of Information Security Eastern Michigan University United States Al-Hussein Bin Talal University College of Information Technology Jordan Applied Science University Department of Computer Science Manama Bahrain National Univ. of Modern Lang. Dept. of Computer Sci. Pakistan Yale University Yale School of Management United States

ISBN: (数字)9781728172194

ISBN: (纸本)9781728172194

With the emergence of advanced technology, the user authentication methods have also been improved. Authenticating the user, several secure and efficient approaches have been introduced, but the biometric authentication method is considered much safer as compared to password-driven methods. In this paper, we explore the risks, concerns, and methods by installing well-known open-source software used in Unibiometric analysis by the partners of the National Institute of Standards and Technology (NIST). Not only are the algorithms used all open source but it comes with test data and several internal open source utilities necessary to process biometric data. © 2020 IEEE.

关键词： Open source 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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Numerical Study of Laminar Flow Using Various Difference Schemes 23

Numerical Study of Laminar Flow Using Various Difference Sch...

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proceedings of the 7th international conference on Future Networks and Distributed systems

作者： Farrukh Nazarov Murodil Madaliev Rabim Fayziev Bakhtiyor Murodullaev Institute of Mechanics and Seismic Stability of Structures Academy of Sciences of the Republic of Uzbekistan Uzbekistan Institute of Mechanics and Seismic Stability of Structures Academy of Sciences of the Republic of Uzbekistan Uzbekistan Department of ICT and Digital Economy Tashkent State University of Economics Uzbekistan Tashkent International University of Education Uzbekistan

ISBN: (纸本)9798400709036

Mathematical modeling plays a key role in econometrics as it is a field where statistics and mathematics are used to analyze economic data and develop economic models. this paper presents a numerical study of laminar flow in a suddenly expanding channel using various computational schemes. the Na-vier-Stokes equation was used to numerically solve the laminar flow in the channel. To calculate the Navier-Stokes equation, an explicit scheme of the third order of accuracy QUICK (Quadratic Upstream Interpolation for Con-vective Kinematics) and a second order of accuracy implicit against flow US (Up-wind scheme) schemes were used. For the difference approximation of the initial equa-tions, the finite difference method was applied, and the rela-tionship between velocities and pressure was found using the SIMPLE pro-cedure. the paper presents a two-dimensional geometry of an expansion channel with an expansion ratio of 1:2. Based on this geometry, results were obtained with Reynolds numbers of 100, 400, 600, and 800. Velocity plots at Reynolds numbers of 100 and 400 were compared with experimental re-sults at different sections. Graphs of friction coefficients were obtained for various values of Reynolds numbers. Speed profiles were compared using QUICK and implicit schemes. In addition, longitudinal velocity isolines are present-ed for Reynolds numbers of 100, 400, 600, and 800. It is shown that the re-sults are practically the same for the two presented finite-difference schemes for small Reynolds numbers. At large values of the Reynolds number, the numerical results obtained by the implicit scheme showed better agreement with the experimental results than by the QUICK scheme. And also the pa-per presents the primary and secondary flow vortices as isolines.

关键词： Navier-Stokes equations

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Implicit-Explicit Error Indicator based on Approximation Order

Implicit-Explicit Error Indicator based on Approximation Ord...

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international Multidisciplinary conference on Computer and Energy Science (SpliTech)

作者： Mitja Janč ič Filip Strniš a Gregor Kosec Parallel and Distributed Systems Laboratory Joief Stefan Institute Joief Stefan International Postgraduate School Ljubljana Slovenia Parallel and Distributed Systems Laboratory Joief Stefan Institute Ljubljana Slovenia

ISBN: (纸本)9781665488280

With the immense computing power at our disposal, the numerical solution of partial differential equations (PDEs) is becoming a day-to-day task for modern computational scientists. However, the complexity of real-life problems is such that tractable solutions do not exist. this makes it difficult to validate the numerically obtained solution, so good error estimation is crucial in such cases. It allows the user to identify problematic areas in the computational domain that may affect the stability and accuracy of the numerical method. Such areas can then be remedied by either h- or p-adaptive procedures. In this paper, we propose to estimate the error of the numerical solution by solving the same governing problem implicitly and explicitly, using a different approximation order in each case. We demonstrate the newly proposed error indicator on the solution of a synthetic two-dimensional Poisson problem with tractable solution for easier validation. We show that the proposed error indicator has good potential for locating areas of high error.

关键词： Poisson equations Error analysis Complexity theory Task analysis Numerical stability

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4th BenchCouncil international Symposium on Intelligent Computers, algorithms, and Applications, IC 2024

4th BenchCouncil International Symposium on Intelligent Comp...

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4th BenchCouncil international Symposium on Intelligent Computers, algorithms, and Applications, IC 2024

ISBN: (纸本)9789819663095

the proceedings contain 16 papers. the special focus in this conference is on Intelligent Computers, algorithms, and Applications. the topics include: Artificial Intelligence Modelling Paths for Reasoning Argumentation Methods for Criminal Evidence;parallel Decomposition Method for Deep Learning Models Based on Improved Dual Population Genetic Algorithm;Integrating CNNs and Transformers for Mid-price Prediction in High-Frequency Trading;hierarchical Recurrent Network for Active Stereo Matching;Research on Multidimensional Evaluation Technology of Teachers’ Digital Literacy for LLM as a Judge;research on the Academic Rubric for Innovative Talents in Ordinary High Schools Based on Classroom Settings;explaining to a Computer: Enhancing Student Assessment with Programming and Simulating Execution;research on Intelligent Traffic Surveillance Video Compression Quality Assessment Method;evaluating the Performance of Complex Text Generated by Large Language Models;a Benchmark Dataset and Evaluation of Collaboration Network in Open Source Software Community;constructing Benchmarks for Open Source Ecosystems: A Stakeholder Needs-Driven Approach;evaluating Large Language Models on the Edge Based on Evaluatology;Parallel computing on RTEMS Operating System;mixSchedSim: A Simulator for Mixed Workload Scheduling in Heterogeneous systems.

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7th international conference on Big Data Analytics, BDA 2019

7th International Conference on Big Data Analytics, BDA 2019

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7th international conference on Big Data Analytics, BDA 2019

ISBN: (纸本)9783030371876

the proceedings contain 25 papers. the special focus in this conference is on international conference on Big Data Analytics . the topics include: Analyzing domain knowledge for big data analysis: A case study with urban tree type classification;market intelligence for agricultural commodities using forecasting and deep learning techniques;TKG: efficient mining of top-K frequent subgraphs;why multilayer networks instead of simple graphs? modeling effectiveness and analysis flexibility and efficiency!;gossip based distributed real time task scheduling with guaranteed performance on heterogeneous networks;data-driven optimization of public transit schedule;discovering spatial high utility frequent itemsets in spatiotemporal databases;efficient algorithms for flock detection in large spatio-temporal data;local temporal compression for (globally) evolving spatial surfaces;deep learning models for medical image analysis: challenges and future directions;an explicit relationship between sequential patterns and their concise representations;a novel approach to identify the determinants of online review helpfulness and predict the helpfulness score across product categories;analysis and recognition of hand-drawn images with effective data handling;real time static gesture detection using deep learning;interpreting context of images using scene graphs;deep learning in the domain of near-duplicate document detection;recent advances and challenges in design of non-goal-oriented dialogue systems;data cube is dead, long life to data cube in the age of web data;improving result diversity using query term proximity in exploratory search;segment-search vs knowledge graphs: making a key-word search engine for web documents;pairing users in social media via processing meta-data from conversational files.

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