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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Bain, A.M. Portnov, E.M. Sin, Si Thu Thant Institute of System and Software Engineering and Information Technologies National Research University Miet Moscow Zelenograd Russia

In this paper, we developed a method to calculate the time parameters for interfacing with code message sources (CMS). This method enables the evaluation of how the collection and delivery time of information from CMS to the automated control system of the energy supply control system dispatcher varies based on the communication channel speed. Additionally, it accounts for the different numbers of CMS connected to a single control point. by analyzing these factors, the method provides insights into optimizing information transmission efficiency and ensures reliable data flow from CMS to the dispatcher's automated control system, thereby enhancing the overall performance and responsiveness of the energy supply control system. This development is crucial for improving the management and coordination within energy supply systems, ensuring timely and accurate information delivery, and ultimately contributing to more effective energy resource management. © 2024 IEEE.

关键词： Resource allocation

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Processing of Nonlinear Heterogeneous Data in information and Control systems

Processing of Nonlinear Heterogeneous Data in Information an...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Shevnina, Yu.S. Tsarapkin, S.F. Sin, Si Thu Thant Institute of System and Software Engineering and Information Technologies National Research University Miet Moscow Zelenograd Russia

ISBN: (纸本)9798350395044

The article presents a method for cluster analysis of heterogeneous data using the provisions of fuzzy logic. Heterogeneous are data of various formats and collected from various sources. In addition, such data is usually incomplete and inaccurate, which makes it difficult to process and cluster them. The article presents mathematical models for representing a candidate for a vacant position, which is characterized by heterogeneous data. The apparatus of algebraic systems was used to develop mathematical models. A method for determining the membership function of fuzzy sets using a probabilistic approach, as the most effective when working with heterogeneous data, is described in detail. An example of the formation of a base of logical rules for the formation and selection of classification features in a set of heterogeneous data of the personnel reserve of a manufacturing enterprise is given. The selected classification features allow for further accurate and efficient verification and evaluation of information about candidates for a vacant position. In addition, the proposed method of cluster analysis of heterogeneous data can be applied in various subject areas that involve the use of incomplete and inaccurate data, for example, socio-economic, technical, biological systems. © 2024 IEEE.

关键词： Membership functions

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Modeling of a Digital Twin of an Automated Production Process

Modeling of a Digital Twin of an Automated Production Proces...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Shevnina, Yu.S. Gagarina, L.G. Konyuhov, E.V. Institute of System and Software Engineering and Information Technologies National Research University Miet Moscow Zelenograd Russia

ISBN: (纸本)9798350395044

The article discusses the methodology for modeling a digital twin of an automated production process. The basis of the methodology is the method of intellectual analysis and information processing, which involves identifying patterns in the features, characteristics and parameters of the production process, forming classifiers for them according to various criteria in order to determine and predict the behavior of the process. The proposed digital twin modeling technique allows us to take into account hidden connections between the characteristics of the production process and their combinations, which increases the accuracy of forecasting changes in process behavior and, as a result, increases the efficiency of its management. As an example, the use of the proposed approaches for modeling a digital twin of automated assembly operations is considered. © 2024 IEEE.

关键词： Classification (of information)

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Development of a Method for Detecting Fruit Maturity Based on the Use of Neural Network technologies

Development of a Method for Detecting Fruit Maturity Based o...

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2024 Conference of Young Researchers in Electrical and Electronic engineering, ElCon 2024

作者： Zaw, Hein Htet Win, Khant Zin Portnov, Evgeniy M. National Research University of Electronic Technology Zelenograd Institute of System and Software Engineering and Information Technologies Moscow Russia

ISBN: (纸本)9798350360646

The article substantiates the need to use neural network algorithms to search for mature crops and optimize harvesting equipment routes based on the data obtained. A technique for detecting fruit maturity based on the use of neural network technologies has been developed. The experiments have shown that the accuracy of the neural network algorithm developed in this dissertation work significantly exceeds the standard YOLOv4 neural networks. © 2024 IEEE.

关键词： image preprocessing maturity determination neural network algorithm

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Identifying and Documenting Best Practices in Digital Transformation 28

Identifying and Documenting Best Practices in Digital Transf...

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28th European Conference on Pattern Languages of Programs, EuroPLoP 2023

作者： Momand, Mohammad Yusuf Vranic, Valentino Institute of Informatics Information System and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9798400700408

Digital transformation allows organizations to maintain sustainable development and address ongoing challenges. The current digital transformation and advanced technology mega-trend significantly impact society and organizations, making digital technology crucial for public and private organizations, universities, and daily life. Despite the rising demand, there is often a lack of standard policies governing the use of digital technology. Practical experience and literature reveal key challenges in transitioning from traditional to digital systems, including limited public awareness, mindset, ICT skills, government support, and collaboration between organizations and software engineering societies. Governments must establish digital transformation policies and offer software development guidelines for public organizations to digitalize their systems. Additionally, they should provide or support research projects funded by governmental and nongovernmental organizations to identify infrastructure and regulatory issues within Afghanistan’s digital transformation context. This study documents six organizational patterns observed during the transformation of traditional systems into digital systems, illustrating their sequence and explaining the development of a pattern language from these patterns. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： software design

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Development of Mathematical Methods and Algorithms for Filtering Images Obtained from Unmanned Aerial Vehicle Camera

Development of Mathematical Methods and Algorithms for Filte...

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Zaw, Hein Htet Hein, Zaw Mikhailovich, Portnov E. National Research University of Electronic Technology Institute of System and Software Engineering and Information Technologies Zelenograd Moscow Russia

ISBN: (纸本)9781665475952

Recently, the use of unmanned aerial vehicles (UAVs) has become an interesting and active research topic in the field of aerial photography. Such an increase in popularity makes the task of finding optimal hardware and software configurations for UAVs, developing systems for compensating adverse environmental effects, and navigation systems in space, etc. The key factors behind this trend are the relatively low cost of implementing such projects and the speed of obtaining data. The main problems preventing full automation of data processing of UAV imagery are motion blur with a still camera, full image blur with camera movement, and camera focus blur. When shooting from a UAV in bad weather conditions, blurry video frames often occur that require image filter. This blurring can interfere with visual analysis and interpretation of data, cause errors, and reduce the accuracy of automatic photogrammetric processing algorithms. This article describes the proposed algorithm that performs element-by-element image filtering based on the use of neural-like structures. © 2023 IEEE.

关键词： Support vector machines

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG School of Journalism and Communication Sun Yat-sen University School of Software Technology Zhejiang University School of Software Engineering Sun Yat-sen University School of Software Dalian University of Technology School of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences School of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University School of Computer and Information Technology Beijing Jiaotong University School of Computer Science and Technology Harbin Institute of Technology School of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

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Attention Management: Extending in Progress 24

Attention Management: Extending in Progress

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24th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2024

作者： Brodniansky, Juraj Lang, Ján Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak Technical University in Bratislava Bratislava Slovakia

ISBN: (纸本)9798350353433

Attention management in software development proves to be a highly relevant issue. It offers various ways of managing the user’s attention using visual perception of the work environment. Awareness of the workplace provides a user with work context which may positively affect the productivity and workflow of a project. However, the work context may also provide the user with arbitrary information. Thus, regulations of incoming workplace information are required throughout the user’s work cycle. Furthermore, previous research supports the proposed extensions devised according to the lacking parts in the existing project management system. Furthermore, to stabilize the extensions, results from an experiment conducted on the proof-of-concept designed according to the proposed parts. © 2024 IEEE.

关键词： Project management

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UAV-Mounted Intelligent Reflecting Surface in Maritime Wireless Powered Communication Network

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China Communications 2025年第3期22卷 254-269页

作者： Liu Yuan Han Fengxia Zhao Shengjie School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan 250357China College of Electronic and Information Engineering Tongji UniversityShanghai 201804China School of Software Engineering Tongji UniversityShanghai 201804China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of EducationShanghai 200003China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804China

Intelligent reflecting surface(IRS)assisted with the wireless powered communication network(WPCN)can enhance the desired signal energy and carry out the power-sustaining problem in ocean monitoring *** this paper,we investigate a reliable communication structure where multiple buoys transmit data to a base station(BS)with the help of the unmanned aerial vehicle(UAV)-mounted IRS and harvest energy from the base station *** organically combine WPCN with maritime data collection scenario,a scheduling protocol that employs the time division multiple access(TDMA)is proposed to serve multiple buoys for uplink data ***,we compare the full-duplex(FD)and half-duplex(HD)mechanisms in the maritime data collection system to illustrate different performances under these two *** maximize the fair energy efficiency under the energy harvesting constraints,a joint optimization problem on user association,BS transmit power,UAV’s trajectory and IRS’s phase shift is *** solve the non-convex problem,the original problem is decoupled into several subproblems,and successive convex optimization and block coordinate descent(BCD)methods are employed obtain the near-optimal solutions *** results demonstrate that the UAV-mounted IRS can significantly improve energy efficiency in our considered system.

关键词： full-duplex IRS joint optimization TDMA UAV WPCN

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DiaSet: An Annotated Dataset of Arabic Conversations 30

DiaSet: An Annotated Dataset of Arabic Conversations

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Israeli, Abraham Naaman, Aviv Makhoul, Rawaa Maduel, Guy Ejmail, Amir Jubran, Julian Karain, Dana Lisnyansky, Dina Fine, Shai Bar, Kfir The Data Science Institute Reichman University Israel Department of Software and Information System Engineering Ben-Gurion University of the Negev Israel

ISBN: (纸本)9782493814104

We introduce DiaSet, a novel dataset of dialectical Arabic speech, manually transcribed and annotated for two specific downstream tasks: sentiment analysis and named entity recognition. The dataset encapsulates the Palestine dialect, predominantly spoken in Palestine, Israel, and Jordan. Our dataset incorporates authentic conversations between YouTube influencers and their respective guests. Furthermore, we have enriched the dataset with simulated conversations initiated by inviting participants from various locales within the said regions. The participants were encouraged to engage in dialogues with our interviewer. Overall, DiaSet consists of 644.8K tokens and 23.2K annotated instances. Uniform writing standards were upheld during the transcription process. Additionally, we established baseline models by leveraging some of the pre-existing Arabic BERT language models, showcasing the potential applications and efficiencies of our dataset. We make DiaSet publicly available for further research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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