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Modeling of Decision-Making Processes in the Service Management System 18

Modeling of Decision-Making Processes in the Service Managem...

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18th IEEE International conference on computer science and Information Technologies, CSIT 2023

作者： Mulesa, Oksana Melnyk, Olena Horvat, Petro Tokar, Marian Peresoliak, Vladyslav Kumar, Hanna Uzhhorod National University Department of Software Systems Ukraine Uzhhorod National University Department of Software Systems Uzhhorod Ukraine Uzhhorod National University Department of Computer Systems and Networks 1 Uzhhorod Ukraine Uzhhorod National University Political Science and Public Administration Department Uzhhorod Ukraine Uzhhorod National University Geodesy Land Surveillance and Geoinformatics Department Uzhhorod Ukraine

ISBN: (纸本)9798350360462

The service management system considers the problem of finding the optimal distribution of social services between executors. A mathematical model of the problem in the form of single and multi-criteria optimization problems was built. The decomposition of the task into independent subtasks was performed. An iterative method of finding the optimal distribution of services between executors has been developed. Individual methods and steps are formulated as production rules, making them understandable and allowing one to gain new knowledge. © 2023 IEEE.

关键词： Decision making

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Electroencephalogram Processing Method for More Accurate Determination of Cognitive Process Indicators 25

Electroencephalogram Processing Method for More Accurate Det...

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25th IEEE International conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Karmanov, Vitaliy S. Sapina, Elena A. Karmanova, Alla A. Kriveckiy, Andrey A. Dept. of Theoretical and Applied Computer Science Novosibirsk State Technical University Novosibirsk Russia Federal Research Center for Basic and Translational Medicine Novosibirsk State University of Economics and Management Novosibirsk Russia Dept. Correspondence Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9798350389234

To study neurophysiological mechanisms of functioning of the nervous system and psyche, modern computer technologies and methods of mathematical data processing are used. One of the non-invasive methods of determining the functional state of the brain is electroencephalography. The use of the spectral singular analysis method, together with the traditional approach using Fourier analysis, is proposed to determine the indicators of cognitive processes. Computation of the singular spectrum is a rather computationally expensive process, but modern technologies allow to perform capacious computations with acceptable speed and accuracy, which allows to use new methods of data analysis. The proposed approach allows us to decompose the original signal into elementary components, which are most often described by quasi-harmonic functions. It is further proposed to perform nonparametric robust estimation of frequencies, based on which some elementary components can be grouped on the basis of close spectral characteristics. The results of calculations on the experimental data are presented, and the change of amplitude and frequency of alpha rhythm for the case of open and closed eyes of the subject is shown. As a result, the proposed method makes it possible to clarify the main characteristics of electroencephalogram signals. © 2024 IEEE.

关键词： Harmonic functions

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MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for mathematical Reasoning

MuMath-Code: Combining Tool-Use Large Language Models with M...

引用

2024 conference on Empirical methods in Natural Language Processing, EMNLP 2024

作者： Yin, Shuo You, Weihao Ji, Zhilong Zhong, Guoqiang Bai, Jinfeng Tomorrow Advancing Life China College of Computer Science and Technology Ocean University of China China

ISBN: (纸本)9798891761643

The tool-use Large Language Models (LLMs) that integrate with external Python interpreters have significantly enhanced mathematical reasoning capabilities for open-source LLMs, while tool-free methods chose another track: augmenting math reasoning data. However, a great method to integrate the above two research paths and combine their advantages remains to be explored. In this work, we firstly include new math questions via multi-perspective data augmenting methods and then synthesize code-nested solutions to them. The open LLMs (e.g., Llama-2) are finetuned on the augmented dataset to get the resulting models, MuMath-Code (µ-Math-Code). During the inference phase, our MuMath-Code generates code and interacts with the external python interpreter to get the execution results. Therefore, MuMath-Code leverages the advantages of both the external tool and data augmentation. To fully leverage the advantages of our augmented data, we propose a two-stage training strategy: In Stage-1, we finetune Llama-2 on pure CoT data to get an intermediate model, which then is trained on the code-nested data in Stage-2 to get the resulting MuMath-Code. Our MuMath-Code-7B achieves 83.8% on GSM8K and 52.4% on MATH, while MuMath-Code-70B model achieves new state-of-the-art performance among open methods-achieving 90.7% on GSM8K and 55.1% on MATH. Extensive experiments validate the combination of tool use and data augmentation, as well as our two-stage training strategy. We release the proposed dataset along with the associated code for public use: https://***/youweihao-tal/MuMath-Code. © 2024 Association for Computational Linguistics.

关键词： Python

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Learning Hamiltonian Systems with Mono-Implicit Runge-Kutta methods 1

引用

The 6th International conference on Geometric science of Information, GSI 2023

作者： Noren, Håkon Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9783031382710

ISBN: (纸本)9783031382703

Numerical integrators could be used to form interpolation conditions when training neural networks to approximate the vector field of an ordinary differential equation (ODE) from data. When numerical one-step schemes such as the Runge–Kutta methods are used to approximate the temporal discretization of an ODE with a known vector field, properties such as symmetry and stability are much studied. Here, we show that using mono-implicit Runge–Kutta methods of high order allows for accurate training of Hamiltonian neural networks on small datasets. This is demonstrated by numerical experiments where the Hamiltonian of the chaotic double pendulum in addition to the Fermi–Pasta–Ulam–Tsingou system is learned from data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Factor Graph Neural Network Meets Max-Sum: A Real-Time Route Planning Algorithm for Massive-Scale Trips 23

Factor Graph Neural Network Meets Max-Sum: A Real-Time Route...

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23rd International conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Li, Yixuan Wang, Wanyuan Xu, Weiyi Deng, Yanchen Wu, Weiwei Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Southeast University Nanjing China

Global route planning (GRP) is a typical combinatorial optimization problem that has been solved for a variety of industrial purposes, such as traffic flow management, network routing, and conflict prevention. The goal of the GRP is to find a route for each trip query such that all queries have a minimum global travel time. The GRP problem is NP-hard and computationally challenging, even for medium-sized instances. However, in real-world GRP applications, such as Google Maps-based vehicle route guidance systems, there are always massive-scale trips issued simultaneously, and real-time response is required. Existing mathematical programming-based exact methods and heuristics struggle to balance the extremes of optimality and scalability. Considering that many closed-related GRP instances must be solved repeatedly, this paper explores a deep learning approach to learn real-time and efficient solutions for GRP. This paper first proposes a novel route-query factor graph (RQ-FG) to model the GRP problem, where the message-passing damped Max-sum (DMS) algorithm can be exploited to generate high-quality approximate solutions. A hybrid pruning method is proposed to accelerate solving the DMS. We further devise a route-query factor graph neural network (RQ-FGNN) based on the RQ-FG, which has the ability to return solutions in milliseconds. Experiments demonstrate that our method can generate high-quality solutions in massive-scale GRP instances in real-time. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Message passing

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Research on auto insurance anti-fraud-based on predator-prey model 24

Research on auto insurance anti-fraud-based on predator-prey...

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2024 International conference on Mathematics and Machine Learning, ICMML 2024

作者： Sun, Jia Yan, Chun Wang, Ying Liu, Wei College of Mathematics and Systems Science Shandong University of Science and Technology Shandong Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao China

ISBN: (纸本)9798400711657

In recent years, alongside the rapid development of China's insurance industry, a large number of fraudulent behavior has been observed. Among them, automobile insurance fraud has become a"disaster area" of insurance fraud. Therefore, how to effectively reduce the probability of auto insurance fraud is crucial to the development of the insurance industry. In biomathematics, more and more mathematical theories and methods are used to construct the biomathematical system of relationships within or between biological populations, and the dynamics of the system can be analyzed to predict the specific changes in biological populations. Based on this, this paper selects the predator-prey model in biomathematics, uses nonlinear differential dynamics methods and theories, analyzes the existence and stability of the equilibrium point of the model, and uses numerical simulation to verify its effectiveness. It aims to study the influencing factors of the density of fraudulent customers in auto insurance as well as the density of regulatory frequency of regulatory agencies to predict the specific changes in the density of fraudulent customers and the density of regulatory frequency of regulatory agencies and provide theoretical support for the healthy development of the auto insurance industry. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Predator prey systems

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Specifications of Virtual Reminder System using Z 19

Specifications of Virtual Reminder System using Z

引用

19th International conference on Emerging Technologies, ICET 2024

作者： Naveed, J Aviria Ahmed, Irfan Mahmood, Jabar Batool, Khadija University of Sialkot Department of Computer Sciences Sialkot Pakistan School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou China

ISBN: (纸本)9798350379587

Formal methods (FM) uses mathematical notations to precisely and accurately define specifications, which helps detect errors and inconsistencies in software development. For mathematical notations, a proper mathematical framework is needed to describe and specify the behavior and structure of software systems. A few specification languages provide that framework;one is Z, which significantly enhances the quality of system specification, design, and verification. This paper introduces a formal specification for a virtual reminder system using Z schema, which signifies the importance of formal specifications that can also be used for voice assistant specifications. A virtual Reminder System (VRS) is an assistant based on speech recognition and speaker identification;a personal voice-controlled aid for the user to remind about event-based tasks that do not need some specific time to be completed but are event-based like reaching a location or any keyword/phrase spoken by the user related to some specific topic. The specified keyword/phrase can be anything, such as a person's name, place name, or phrase. Using FM reduced development time, verification efforts. and maintenance costs. © 2024 IEEE.

关键词： Formal methods Formal Specification Reminder Assistant Voice Assistant Z Schema

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Numerical Modeling of Vertical Axis Wind Turbines Using ANSYS Fluent Software 1

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22nd International conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2022

作者： Hamdamov, Muzaffar Muhiddinovich Ishnazarov, Akram Ismoilovich Mamadaliev, Khusniddin Abdujalilovich Institute of Mechanics and Seismic Stability of Structures of the Academy of Sciences of the Republic of Uzbekistan Tashkent Uzbekistan Mathematical Methods in Economics Tashkent State University of Economics Tashkent Uzbekistan Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9783031302589

ISBN: (纸本)9783031302572

The main objective of this study is to perform numerical simulations to test the performance of a vertical axis wind turbine in the low wind speed state of the east coast of Uzbekistan as indicated elsewhere with an average annual wind at a cut-off wind speed of 5 m/s. The aim is to develop wind energy technologies that can maximize the power extracted from the turbine in low wind conditions and to accelerate the penetration of wind energy technologies in Uzbekistan. The main problem of mathematical modeling of vertical axis wind turbines within the framework of the ANSYS Fluent software package is the choice of a turbulence model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Kinetic energy

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Implementation of Anatomy Constrained Contrastive Learning for Heart Chamber Segmentation 15

Implementation of Anatomy Constrained Contrastive Learning f...

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15th International conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Ukibassov, B.M. Rakhmetulayeva, S.B. Zhanabekov, Zh.O. Bolshibayeva, A.K. Yasar, Ansar-Ul-Haque Information Systems Department International Information Technology University Almaty050040 Kazakhstan Mathematical Computer Modeling International Information Technology University Almaty050040 Kazakhstan Transportation Research Institute Hasselt University Hasselt3500 Belgium

The article covers the implementation of anatomy constrained contrastive (ACC) learning networks on a dataset of echocardiographic images with the purpose of segmenting heart chambers for further study. The dataset was partially synthetic, generated using slicing of 3D models of the heart. The images were generated using cycle-consistent generative adversarial networks (CycleGAN) and contrastive unpaired translation (CUT) approaches. By utilizing both CycleGAN and CUT approaches, the generated images captured a wide range of variations in heart chamber segmentation, allowing for a more comprehensive study of the different anatomical features. Proposed approach augments the implementation of ACC learning networks and partially solves the data scarcity problem when it comes to segmentation implementation. © 2024 Elsevier B.V.. All rights reserved.

关键词： Heart

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Service Mechanism and Queue Discipline in Multitasking UAV Systems 7

Service Mechanism and Queue Discipline in Multitasking UAV S...

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7th IEEE International conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Petryshyn, Lubomyr Petryshyn, Mykhailo AGH University of Krakow Dept. of Enterprise Management Krakow Poland Precarpathian National University Dept. of Computer Science adh Information Systems Ivano-Frankivsk Ukraine

ISBN: (纸本)9798331534141

Modern combat practices have revealed the high effectiveness of unmanned aerial vehicle systems and their swarms in striking enemy resources. Data collection, dynamic task execution planning, and their service on parallel unmanned aerial vehicle systems require a high-performance combat network, effective methods for servicing incoming task flows, and significant computational power. The relevance of this research is dictated by the need to explore potential methods and tools for application that meet these specified requirements. The aim of this work is to analyze and justify the use of the mathematical apparatus of queueing theory, and to develop algorithmic and software solutions for automated control of unmanned aerial vehicle systems. As demonstrated by the analysis of the efficiency of various methods for servicing incoming task flows, queueing theory is one of the probable approaches. Processing such a significant amount of data, making control decisions, and synchronizing unmanned aerial vehicles require substantial intellectual and computational resources, which necessitates the development of artificial intelligence control systems. The novelty of the research lies in the application of the mathematical apparatus of queueing theory and artificial intelligence tools. The practical significance lies in the subsequent development of algorithms and software for managing unmanned aerial vehicle systems. An analytical mechanism for servicing and forming the discipline of queueing for multitasking in UAV swarms is presented. © 2024 IEEE.

关键词： Queueing theory

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