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SusTrainable: Promoting Sustainability as a Fundamental Driver in Software Development Training and Education Teacher Training November 1–5, Nijmegen, The Netherlands Revised lecture notes

arXiv

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arXiv 2022年

作者： Koopman, Pieter Lubbers, Mart Fernandes, João Paulo Korečko, Štefan Paquete, Luís Porkoláb, Zoltán Szalay, Richárd Mauša, Goran Oprescu, Ana Koedijk, Lukas van Oostveen, Sander Kok, Stephan Li, Jianhao Zsók, Viktória Grbac, Tihana Galinac Grbac, Neven Valkanov, Vladimir Petrov, Mihail Radboud University Nijmegen Netherlands University of Porto Portugal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia CISUC Department of Informatics Engineering University of Coimbra Portugal Department of Programming Languages and Compilers Institute of Computer Science Faculty of Informatics Eötvös Lorád University Budapest Hungary University of Rijeka Faculty of Engineering Vukovarska 58 Rijeka51000 Croatia Complex Cyber Infrastructure University of Amsterdam Netherlands KPMG Software Engineering Master University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Juraj Dobrila Univeristy of Pula Zagrebačka 30 PulaHR-52100 Croatia University Of Plovdiv "Paisii Hilendarski" Faculty of Mathematics and Informatics 24 Bulgaria Blvd. Plovdiv4027 Bulgaria

These are the proceedings of the first teacher training of the Erasmus+ project No. 2020-1-PT01-KA203-078646 -- Sustrainable. The full title of this project is Promoting Sustainability as a Fundamental Driver in Software Development Training and Education and the interested reader may know more about it at: https://*** The flagship contribution is the organization of two summer schools on sustainable software production. The first summer school is moved due to the Corona pandemic. It is now scheduled for July 2022 in Rijeka, Croatia. This summer school will consist of lectures and practical work for master and PhD students in the area of computing science and closely related fields. There will be contributions from Plovdiv University, University of Ko\v{s}ice, University of Coimbra, University of Minho, E\"otv\"os Lor\'and University, University of Rijeka, Radboud University Nijmegen, University of Pula, University of Amsterdam and Babe\c{s}-Bolyai University. To prepare and streamline this summer school, the consortium organized a teacher training in Nijmegen, the Netherlands. This was an event of five full days on November 1-5, 2022 organized by Ingrid Berenbroek, Mart Lubbers and Pieter Koopman on the campus of the Radboud University Nijmegen, The Netherlands. Sustainability is one of the themes within the strategy of the Radboud University called `A Significant Impact', see https://***/sustainability/organisation/radboud-sustainable/. Sustainability plays an important role in education, research, and management. The contributions were reviewed and give a good overview of what can be expected from the summer school. Based on these papers and the very positive and constructive atmosphere, we expect a high quality and pleasant summer school. We are looking forward to contributing to a sustainable future. © 2022, CC BY-NC-ND.

关键词： Personnel training

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Identification of the computer System State Based on Multidimensional Discriminant Analysis 29

Identification of the Computer System State Based on Multidi...

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29th International Scientific Symposium "Metrology and Metrology Assurance", MMA 2019

作者： Gavrylenko, Svetlana Chelak, Viktor Hornostal, Oleksii Gornostal, Stella National Technical University Kharkiv Polytechnic Institute Department of Computer Engineering and Programming Kharkiv Ukraine National University of Civil Defence of Ukraine Department of Fire Prevention in Settlements Kharkiv Ukraine

ISBN: (纸本)9781728122137

This paper describes the process of identifying the state of a computer system based on tracking changes in the file system under the condition that there is no reliable information about the state of objects in the external environment and the state of the system itself. In this case, the performance indicators of a computer system are random variables with unknown distribution laws. A method for identifying the state of the computer system based on tracking changes in the file system using classical discriminative analysis, taking into account the uncertainty of the input data, is proposed. For this purpose, a special method was developed for solving a fuzzy system of linear algebraic equations. The software was developed for tracking changes in the computer file system: creating, modifying, renaming, and deleting files and directories. The program also performs state classification based on a fuzzy discriminant classifier. © 2019 IEEE.

关键词： Discriminant analysis

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Modern Information Technologies in Scientific Research and Educational Activities

arXiv

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arXiv 2024年

作者： Malakhov, Kyrylo Kaverinskiy, Vladislav Ivanova, Liliia Romanyuk, Oleksandr Romaniuk, Oksana Voinova, Svitlana Kotlyk, Sergii Sokolova, Oksana V. M. Glushkov Institute of Cybernetics The National Academy of Sciences of Ukraine Kyiv Ukraine Department of Wear-Resistant and Corrosion-Resistant Powder Construction Materials Frantsevic Institute for Problems in Material Science The National Academy of Sciences of Ukraine Kyiv Ukraine Separated Structural Subdivision «Odesa Technical Professional College of Odesa National University of Technology» Odessa Ukraine Vinnytsia National Technical university Vinnytsia Ukraine Vinnytsia National Technical university Vinnytsia Ukraine Odesa National University of Technology Odessa Ukraine The Institute of Computer Engineering Automation Robotics and Programming Odesa National University of Technology Odessa Ukraine Odesa National University of Technology Odessa Ukraine

This article presents a comprehensive methodology for enhancing the capabilities of conversational AI systems, focusing on ChatGPT, through the integration of ontology-driven structured prompts and meta-learning techniques. The proposed approach, exemplified by the RDWE OntoChatGPT system, demonstrates significant potential for improving productivity and expanding knowledge bases in specific subject domains and languages. By formalizing methods for creating and configuring structured prompts and context ontologies, the system enables ChatGPT to provide specific information using selected contexts, thereby enhancing its performance in handling data across diverse linguistic and domain contexts. Through empirical testing, the study evaluates the system’s effectiveness in providing accurate responses while also highlighting areas for further refinement. The methodology’s adaptability to other conversational AI systems based on large language models underscores its potential for broader application in natural language processing and dialogue systems. Future enhancements aim to refine structured prompts and incorporate additional input data to further improve system performance and accuracy. Overall, this research contributes to advancing the capabilities of conversational AI systems, paving the way for more sophisticated and effective human-computer interactions in various domains. © 2024, CC BY-SA.

关键词： Ontology

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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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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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Special Processors Simulation for Forming and Digital Processing of Error-Correcting Codes in computer Systems

Special Processors Simulation for Forming and Digital Proces...

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International Conference on Modern Problems of Radio engineering, Telecommunications and computer Science

作者： Taras Grynchyshyn Volodymyr Hryha Artur Voronych Mykhaylo Ilko Department of Programming and Information Technologies National Transport University Nadvirna UKRAINE Department of Computer Engineering and Electronics Vasyl Stefanyk Precarpathian National University Ivano-Frankivsk UKRAINE Department of Computer System and Networks Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk UKRAINE

ISBN: (数字)9781728155661

ISBN: (纸本)9781728155678

The methods of digital and pulse signal modulation in modern computer systems of data transmission are analysed. Process of forming non-redundant coding that use the errors detection and correction in the Galois basis are offered. Structures of special processors for forming signal correcting codes are built. Working algorithms for proposed coding methods are developed and their software implementation are offered. Circuit design of Galois code sequence generator is implemented. Work simulation and synthesis on FPGA of Xilinx are performed.

关键词： digital signal processing chips encoding error correction codes error detection codes field programmable gate arrays Galois fields logic design microprocessor chips Error correction codes Field programmable gate arrays digital signal processing chips error detection codes computer systems Galois fields Microprocessor chips Logic design coding Forming Galois sequence generator Digital signal processing

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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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Darknet traffic big-data analysis and network management to real-time automating the malicious intent detection process by a weight agnostic neural networks framework

arXiv

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arXiv 2021年

作者： Demertzis, Konstantinos Tsiknas, Konstantinos Takezis, Dimitrios Skianis, Charalabos Iliadis, Lazaros Laboratory of Complex Systems Department of Physics Faculty of Sciences International Hellenic University Kavala Campus St. Loukas65404 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Vas. Sofias 12 Xanthi67100 Greece Hellenic National Defence General Staff Stratopedo Papagou Mesogeion 227-231 Athens15561 Greece University of Aegean Karlovassi Samos83200 Greece School of Engineering Department of Civil Engineering Faculty of Mathematics Programming and General courses Democritus University of Thrace Kimmeria Xanthi Greece

Attackers are perpetually modifying their tactics to avoid detection and frequently leverage legitimate credentials with trusted tools already deployed in a network environment, making it difficult for organizations to proactively identify critical security risks. Network traffic analysis products have emerged in response to attackers’ relentless innovation, offering organizations a realistic path forward for combatting creative attackers. Additionally, thanks to the widespread adoption of cloud computing, Device Operators (DevOps) processes, and the Internet of Things (IoT), maintaining effective network visibility has become a highly complex and overwhelming process. What makes network traffic analysis technology particularly meaningful is its ability to combine its core capabilities to deliver malicious intent detection. In this paper, we propose a novel darknet traffic analysis and network management framework to real-time automating the malicious intent detection process, using a weight agnostic neural networks architecture. It is an effective and accurate computational intelligent forensics tool for network traffic analysis, the demystification of malware traffic, and encrypted traffic identification in real-time. Based on Weight Agnostic Neural Networks (WANNs) methodology, we propose an automated searching neural net architectures strategy that can perform various tasks such as identify zero-day attacks. By automating the malicious intent detection process from the darknet, the advanced proposed solution is reducing the skills and effort barrier that prevents many organizations from effectively protecting their most critical assets. Copyright © 2021, The Authors. All rights reserved.

关键词： Network management

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Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN

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International Journal of Intelligent Systems 2025年第1期2025卷

作者： Xueyuan Duan Kun Wang Yu Fu Taotao Liu Yihan Yu Jianqiao Xu Lu Wang College of Computer and Information Technology Xinyang Normal University Xinyang 464000 Henan China *** Department of Information Security Naval University of Engineering Wuhan 430033 Hubei China *** Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang 464000 China *** School of Information and Communication Engineering Xinyang Vocational and Technical College Xinyang 464000 Henan China *** Department of Operational Research and Programming Naval University of Engineering Wuhan 430033 China *** Department of Economic Crime Investigation Henan Police College Zhengzhou 450046 Henan China

In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.

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Detecting and Counting of Each Nested Curve on Spectrum with Gaussian Package

Detecting and Counting of Each Nested Curve on Spectrum with...

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International Conference on Electrical and Electronics engineering, ELECO

作者： Yasemin Poyraz Kocak Selcuk Sevgen Ruya Samli Computer Programming Department Istanbul University-Cerrahpasa Istanbul Turkey Computer Engineering Department Istanbul University-Cerrahpasa Istanbul Turkey

Spectral analysis plays an important role in the interpretation of astronomical data. The determination of the number and parameters of each nested Gaussian curve on spectrum of any object in the sky gives information about the physical and chemical structure of the object. In this study, determination of each nested Gaussian curve's number and parameters such as position, width and amplitude in a given frequency range are performed. Thus, we have knowledge of about, which molecules there are in the observation region. Autonomous Gaussian Decomposition algorithm is implemented with additional scipy, numpy, h5py, lmfit packages. The algorithm is tested on spectral data that comes from active star forming region called as Orion KL obtained by SMA (Submillimeter Array) devices are used. Finally, information is given about positive and negative aspects of these study and in the future what should we do to improve the accuracy.

关键词： Chirp Spectroscopy Clustering algorithms Laser theory Matching pursuit algorithms Chemicals

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