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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Tetiana Hovorushchenko Alla Herts Yelyzaveta Hnatchuk Computer Engineering & System Programming Department Khmelnytskyi National University Khmelnytskyi Ukraine Civil Law and Legal Proceedings Department Ivan Franko National University of Lviv Lviv Ukraine

ISBN: (数字)9781728199573

ISBN: (纸本)9781728199580

Currently, there is a need in Ukraine for the design and development of multidisciplinary decision support systems (DSSs) for the medical and other related spheres - for example, the medical law sector, in particular, the DSS for the surrogacy's legal regulation field. This DSS will be able to protect doctors from potentially wrong decisions by taking into account all available information when making the decisions. For design such DSS, it's necessary to conduct the modelling of the decision-making process on surrogacy's legal conducting, that is the goal of this research. In this paper, there are the theoretical foundations of using the ontologies for the decision-making process on surrogacy's legal conducting, a model of base ontology with the necessary requirements and recommendations for conducting the surrogate motherhood, and ontology-based model of the decision-making process on surrogacy's legal conducting. The conducted modelling is a theoretical basis for the development of methods and design of DSS in the field of legal regulation of surrogate motherhood.

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Synchronous composition of quasi-complete and quasi-deterministic FSMs

Synchronous composition of quasi-complete and quasi-determin...

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International Conference on Actual Problems of systems and Software Engineering

作者： Igor Burdonov Alexandr Kossachev Nina Yevtushenko Tiziano Villa Software Engineering department Ivannikov Institute for System Programming of RAS Software engineering deparment Ivannikov Institute for System Programming Computer Science Department University of Verona

ISBN: (纸本)9781728160627

In this paper, we consider the synchronous composition of Finite State Machines (FSMs) that corresponds to instantaneous communication of hardware components. Such FSMs can be deterministic or nondeterministic, partial or complete. We first extend the existing synchronous composition operator from a pair of components to a collection of components with multiple input and output ports, and provide a procedure to compute the composition working directly on the collection of transition tables of the component FSMs. Then, based on the notion of input-output Moore pair (the output is not sensitive to the input), we prove the following sufficient condition: if the composition has the property that every cycle of ports has a component with a Moore pair, then the class of quasi-complete and quasi-deterministic FSMs is closed under the proposed synchronous composition.

关键词： Finite state machine (FSM) Quasi-deterministic FSM Quasi-complete FSM Moore pair Synchronous composition Pairing multi-input output ports harbors finite state machines deterministic

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Modeling and forecasting of wheat of South Asian region countries and role in food security

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Advances in Computational Intelligence 2021年第6期1卷 1-8页

作者： Yonar, Aynur Yonar, Harun Mishra, Pradeep Kumari, Binita Abotaleb, Mostafa Badr, Amr Department of Statistics Faculty of Science Selçuk University Konya Turkey Department of Biostatistics Faculty of Veterinary Medicine Selçuk University Konya Turkey College of Agriculture J.N.K.V.V. Powarkheda India Department of Agricultural Economics Rashtriya Kisan (PG) College (affiliated to Chaudhary Charan Singh University Meerut) Shamli India Department of System Programming South Ural State University Chelyabinsk Russia Faculty of Science School of Science and Technology University of New England Armidale Australia

Wheat is the most important source of food on earth and vital for food safety. It contains 75–80% carbohydrates, 9–18% protein, fiber, many vitamins (especially B vitamins), calcium, iron, and many macronutrients and micronutrients. According to data from the International Grain Council (IGC), wheat has continued to be the most important food grain source for humans in the world. Therefore, determining wheat production behavior has a very important role in food security. In this study, we have modeled and forecasted the production of wheat for 6 years from 2020 to 2025 using ARIMA and Holt’s linear trend models in Afghanistan, Bangladesh, Bhutan, China, India, Nepal, and Pakistan, which are all countries in the South Asian region. Since there is an expectation of a decrease in wheat production in some of these countries, this study can provide these countries with the information they need to take appropriate decisions to prevent the occurrence of food problems in the future and to help deal with food security. Moreover, this projection helps with policy implications and planning.

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Prospects for Generative - Adversarial Networks in Network Traffic Classification Tasks

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： G D Asyaev Lecturer Department of system programming FSAEIHE SUSU (NRU) 76 Lenin prospect 454080 Chelyabinsk Russia

The paper presents an approach that allows increasing the training sample and reducing class imbalance for traffic classification problems. The basic principles and architecture of generative adversarial networks are considered. The mathematical model of network traffic classification is described. The training sample taken to solve the problem has been analyzed. The data proprocessing is carried out and justified. An architecture of the generative-adversarial network is constructed and an algorithm for generating new features is developed. Machine learning models for traffic classification problem were considered and built: Logistic regression, k Nearest Neighbors, Decision tree, Random forest. A comparative analysis of the results of machine learning models without and with the generation of new features is conducted. The obtained results can be applied both in the tasks of network traffic classification, and in general cases of multiclass classification and exclusion of unbalanced features.

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Prospects for the Application of Reinforcement Learning to Network Traffic Classification Tasks

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： G D Asyaev Lecturer Department of system programming FSAEIHE SUSU (NRU) 76 Lenin prospect 454080 Chelyabinsk Russia

The basic principles and methods of reinforcement learning are reviewed. The problems and approaches for applying a model based on reinforcement learning in the framework of attack prevention are described. The model is built and the hyperparameters of machine learning for the task of classifying network traffic are selected, and its performance on the test data set is evaluated by such quality metrics as accuracy and completeness. The dataset used to implement an agent for selecting the optimal defense strategy for a particular attack has been finalized. Developed an algorithm for using a reinforcement learning neural network for the traffic classification task. A table of rules and rewards for the problem is generated. An agent has been developed and trained to interact with the system. We describe the application of reinforcement learning to the traffic classification task.

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Deriving adaptive homing sequences for weakly initialized nondeterministic FSMs

Deriving adaptive homing sequences for weakly initialized no...

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2019 IEEE East-West Design and Test Symposium, EWDTS 2019

作者： Vinarskii, Evgenii Tvardovskii, Aleksandr Evtushenko, Larisa Yevtushenko, Nina Computer Science Department Lomonosov Moscow State University Moscow Russia Radiophysical Department Tomsk State University Tomsk Russia Software Engineering Deparment Ivannikov Institute for System Programming Moscow Russia

ISBN: (纸本)9781728110035

State identification sequences, such as homing and distinguishing sequences (HS and DS), are widely used in FSM (Finite State Machine) based testing in order to reduce the size of a returned complete test suite as well as minimize checking efforts in passive testing. Preset HS are known to always exist for deterministic complete reduced FSMs but it is not the case for nondeterministic FSMs. It is also known that in this case, adaptive HS exist more often and usually are shorter than the preset. Nowadays, a number of specifications are represented by nondeterministic FSMs and thus, a deeper study of such sequences is required. There exist sufficient and necessary conditions for the existence of an adaptive HS for complete nondeterministic FSMs when each state can be an initial state but those conditions become only sufficient for weakly initialized FSMs where only some states are initial. In this paper, we propose sufficient and necessary conditions for a weakly initialized FSM to have an adaptive homing sequence, possibly up to given length, which are based on deriving an appropriate so-called homing FSM. The experimental evaluation of the existence of adaptive and preset HS is performed for randomly generated FSMs. © 2019 IEEE.

关键词： Well testing

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Analysing the impact of COVID-19 outbreak and economic policy uncertainty on stock markets in major affected economies

Analysing the impact of COVID-19 outbreak and economic polic...

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6th Smart Cities Symposium (SCS 2022)

作者： A. Matuka S. S. Asafo G. O. Eweke P. Mishra S. Ray M. Abotaleb T. Makarovskikh A.A. Alhussan A.A. Abdelhamid E.M. El-Kenawy P. Dutta S. Chowdhury University of Macerata Department of Economics and Law Macerata Italy Department of Banking & Finance Niger Delta University Amassoma Nigeria College of Agriculture J.N.K.V.V. Powarkheda India Centurion University of Technology and Management Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog School of Engineering and Technology Amity University Kolkata Kolkata India MCA Department Sri Venkateshwara College of Engineering and Technology Chittoor India

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.

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Method of Activity of Intelligent Agent for Semantic Analysis of Software Requirements

Method of Activity of Intelligent Agent for Semantic Analysi...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems

作者： Olga Pavlova Tetiana Hovorushchenko Artem Boyarchuk System Programming Department of Khmelnytsky National University Computer Engineering & System Programming Department of Khmelnytsky National University Lecturer of Computer Systems Networks and Cybersecurity Department of National Aerospace University "Kharkiv Aviation Institute"

ISBN: (纸本)9781728140704

The paper deals with the development of the method of activity and the realization of the intelligent agent (IA) for semantic analysis of the natural language software requirements. This IA performs the analysis of the requirements, determines the number and percentage of missing indicators for determining the metrics of complexity and quality of software, displays which indicators are missing for each metric, and also forms the real ontology of the domain "Software Engineering" (part "Quality and complexity of software. Metric analysis"). During an experiment, which is conducted with the help of the realized IA, the semantic analysis of requirements for the Internet of Things (IoT)-system was performed. Such analysis provides an increase of the volume and ensures of the sufficiency of metric information in the software requirements, which, in turn, provides an improvement in the quality of the software of IoT-system.

关键词： Natural language software requirements Semantic analysis Intelligent agent (IA) Ontology-based intelligent agent (OBIA) Internet of things (IoT)-system software requirements Intelligent agents Semantics Metronidazole activity INFORMATION ASSURANCE Metric computer software

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On 'artificial intelligence-user interface' approach

On 'artificial intelligence-user interface' approach

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2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019

作者： Latipova, Alina Gasenko, Aleksei Sim, Gavin School of Electric Engineering and Computer Science South Ural State University System Programming Department Chelyabinsk Russia School of Physical Sciences and Computing University of Central Lancashire Preston United Kingdom

This paper considers the basic concepts of automated design and implementation of application graphical user interface supported by artificial neural networks and application of this approach to child computer interac... 详细信息

ISBN: (纸本)9781728144016

关键词： Neural networks

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Development the Numerical Method to Solve the Inverse Initial Value Problem for the Thermal Conductivity Equation of Composite Materials

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Journal of Physics: Conference Series 2021年第3期1879卷

作者： H K Al-Mahdawi Department of System Programming School of Electronic Engineering and Computer Science South Ural State University Lenina 76 Chelyabinsk 454080 Russia

In this paper, the heat conduction equation for composite materials posed and solved. This problem is known as an inverse initial value problem for the heat conduction equation. In order to solve and formulate this inverse problem, the function spaces must be defined and represented. By studying and solving the direct problem for the heat equation in composite materials, it is possible to determine the function spaces and solve the inverse initial value problem. Scientific methods used: the separation of variables method used to solve the direct problem for the heat equation. It found that method separation of variables does not completely lead to the solution of the inverse initial value problem, since this method leads to a divergent series of solutions and rather massive errors. The heat conduction problem can be formulated as Fredholm integral first kind equations. The discretization algorithm applied to reformulated the problem as linear operator problem as matrix and vectors form. Then, Tikhonov's regularization inversion method has been used to find an approximation solution. Finally, as shown in the numerical example the regularized approximate solutions obtained.

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