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International computer sciences and Information Technologies (CSIT)

作者： Yuliia Prokop Natalia Nestoruk Pavlo Chykunov Olena Trofymenko Department of Computer Science Czech Technical University in Prague Prague Czech Republic Department of Pedagogy and Teaching Methods Donbas State Pedagogical University Dnipro Ukraine Department of Information Technologies National University “Odessa Law Academy” Odesa Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

The study of the educational process of university students has revealed their need for more engagement in class. They seem unable to absorb material that does not require a certain level of mental tension, negatively affecting their academic performance and competence. Therefore, there is a need to develop pedagogical technology to optimize the educational process. We analyzed the educational process at the National University “Odesa Law Academy” in 2020-2023. We evaluated the effectiveness of ways and means of enhancing the academic and cognitive skills of 150 full-time IT students during their study of professional-oriented courses. We have improved students' educational and mental activities by optimizing the educational process through personalized problem-based learning, utilizing professionally oriented problem situations, and providing favorable motivational and emotional support. We confirmed the reliability of the research results using descriptive statistics.

关键词： Training computer science Games Search problems Teamwork Reliability Information technology Information systems

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About Digital Twins, agents, and multiagent systems: a cross-fertilisation journey

arXiv

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arXiv 2022年

作者： Mariani, Stefano Picone, Marco Ricci, Alessandro Department of Sciences and Methods of Engineering University of Modena and Reggio Emilia Italy Department of Computer Science and Engineering University of Bologna Italy

Digital Twins (DTs) are rapidly emerging as a fundamental brick of engineering cyber-physical systems, but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or application domains (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and cyber-physical systems, and the former could both fill in some gaps in agent-oriented engineering and benefit from an agent-oriented interpretation—in a cross-fertilisation journey. © 2022, CC BY.

关键词： Multi agent systems

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Deep Learning and Holt-Trend Algorithms for Predicting Covid-19 Pandemic

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computers, Materials & Continua 2021年第5期67卷 2141-2160页

作者： Theyazn H.H.Aldhyani Melfi Alrasheed Mosleh Hmoud Al-Adaileh Ahmed Abdullah Alqarni Mohammed Y.Alzahrani Ahmed H.Alahmadi Community College of Abqaiq King Faisal UniversityAl HofufSaudi Arabia Department of Quantitative Methods School of BusinessKing Faisal UniversityAl HofufSaudi Arabia Deanship of E-Learning and Distance Education King Faisal UniversityAl HofufSaudi Arabia Department of Computer Sciences and Information Technology Albaha UniversityAl BahahSaudi Arabia Department of Computer Science and Information Taibah UniversityMadinahKingdom of Saudi Arabia

The Covid-19 epidemic poses a serious public health threat to the world,where people with little or no pre-existing human immunity can be more vulnerable to its ***,developing surveillance systems for predicting the Covid-19 pandemic at an early stage could save millions of *** this study,a deep learning algorithm and a Holt-trend model are proposed to predict the *** Long-Short Term Memory(LSTM)and Holttrend algorithms were applied to predict confirmed numbers and death *** real time data used has been collected from theWorld Health Organization(WHO).In the proposed research,we have considered three countries to test the proposed model,namely Saudi Arabia,Spain and *** results suggest that the LSTM models show better performance in predicting the cases of coronavirus *** measure performance Mean squared Error(MSE),Root Mean Squared Error(RMSE),Mean error and correlation are employed to estimate the results of the proposed *** empirical results of the LSTM,using the correlation metrics,are 99.94%,99.94%and 99.91%in predicting the number of confirmed cases in the three *** far as the results of the LSTM model in predicting the number of death of Covid-19,they are 99.86%,98.876%and 99.16%with respect to Saudi Arabia,Italy and Spain ***,the experiment’s results of the Holt-Trend model in predicting the number of confirmed cases of Covid-19,using the correlation metrics,are 99.06%,99.96%and 99.94%,whereas the results of the Holt-Trend model in predicting the number of death cases are 99.80%,99.96%and 99.94%with respect to the Saudi Arabia,Italy and Spain *** empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of Covid-19 in these *** findings provide better insights regarding the future of Covid-19 this pandemic in *** results were obtained by applying time series models,which

关键词： Deep learning algorithm holt-trend prediction Covid-19 machine learning

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Pareto-Optimal Estimation and Policy Learning on Short-Term and Long-Term Treatment Effects

SSRN

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SSRN 2024年

作者： Wang, Yingrong Wu, Anpeng Li, Haoxuan Liu, Weiming Miao, Qiaowei Xiong, Ruoxuan Wu, Fei Kuang, Kun College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310058 China Center for Data Science Peking University Beijing Beijing100091 China Department of Quantitative Theory and Methods Emory University AtlantaGA30322 United States

This paper focuses on developing Pareto-optimal estimation and policy learn- ing to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a higher dosage of medication might increase the speed of a patient’s recovery (short-term) but could also result in severe long-term side effects. Although recent works have investigated the problems about short-term or long-term effects or the both, how to trade-off between them to achieve optimal treatment remains an open challenge. Moreover, when multiple objectives are directly estimated using conventional causal repre- sentation learning, the optimization directions among various tasks can con- flict as well. In this paper, we systematically investigate these issues and introduce a Pareto-Efficient algorithm, comprising Pareto-Optimal Estima- tion (POE) and Pareto-Optimal Policy Learning (POPL), to tackle them. POE incorporates a continuous Pareto module with representation balanc- ing, enhancing estimation efficiency across multiple tasks. As for POPL, it involves deriving short-term and long-term outcomes linked with various treatment levels, facilitating an exploration of the Pareto frontier emanating from these outcomes. Results on both the synthetic and real-world datasets demonstrate the superiority of our method. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Cyber-Physical Integrated Transport and Warehouse Logistics System for Courier Delivery Service 11

Cyber-Physical Integrated Transport and Warehouse Logistics ...

引用

11th International Conference on Advanced computer Information Technologies, ACIT 2021

作者： Shevchuk, Ruslan Kohut, Ihor Chopyk, Pavlo Madiudia, Iryna Melnyk, Andriy West Ukrainian National University Department of Computer Science Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Physics and Methods of Its Teaching Ternopil Ukraine

The paper is devoted to the solution of urgent problem in the field of transport and warehouse logistics -development of cyber-physical integrated system of courier delivery services. The paper proposes the architecture and implements cyber-physical system which enables to optimize transportation and storage costs, and makes it possible to improve the quality of customer service for courier delivery services. Testing the developed system at the enterprise Deliver This confirmed its functional suitability. © 2021 IEEE.

关键词： Warehouses

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Algorithm for Solving Inverse Problems of Geoelectrical Prospecting for Vertical Electrical Sounding by the Indirect Near-Boundary Element Method

Algorithm for Solving Inverse Problems of Geoelectrical Pros...

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International computer sciences and Information Technologies (CSIT)

作者： Liubov Zhuravchak Nataliya Zabrodska Software Department of Institute of Computer Science and Information Technologies Lviv Polytechnic National University Lviv Ukraine Department of Geoelectromagnetic Methods of Carpathian Branch of S.I. Subbotin Institute of Geophysics National Academy of Sciences Lviv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

using the indirect near-boundary element method, an algorithm for recognizing the physical and geometric parameters of the inclusion in a piecewise homogeneous half-plane, whose components are in ideal contact, based on the potential field data measured at its boundary, is constructed. The direct and inverse two-dimensional problems of the potential theory in relation to electrical prospecting, in particular, the method of vertical electrical soundings, have been solved. A computational experiment was conducted in which initial approximations for the electrical conductivity of the inclusion, its center of mass, orientation, and dimensions were determined by the nature of the change in apparent resistivity. The inverse problem is solved using excess boundary conditions by a number of iterations while minimizing the functional.

关键词： Electric potential Inverse problems Shape Groundwater Contacts Conductivity Soil Rocks Water pollution Electromagnetics

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Method of Structural Identification the Interval Models of Static Objects 11

Method of Structural Identification the Interval Models of S...

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11th International Conference on Advanced computer Information Technologies, ACIT 2021

作者： Dyvak, Mykola Pukas, Andriy Porplytsya, Natalia Oliinyk, Iryna Basistyi, Pavlo West Ukrainian National University Department of Computer Science Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Physics and Methods of Its Teaching Ternopil Ukraine

The task of structural identification the interval models of static objects is considered. It is shown that this task at each iteration is a task the forming and solving interval systems of nonlinear algebraic equations. The method of structural identification the interval models of static objects based on the analysis of interval data is proposed and substantiated. It, unlike the existing ones, on procedures of self-organization and self-adaptation of computational procedures by analogy with foraging models of honey bee colony is based. Possibility of obtaining interval models of characteristic with a simpler structure compared to the known ones, by example of building the mathematical model of characteristics of an environmentally friendly power generation system are showed. © 2021 IEEE.

关键词： Hydroelectric power plants

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A memetic dynamic coral reef optimisation algorithm for simultaneous training, design, and optimisation of artificial neural networks (vol 14, 6961, 2024)

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Berchez-Moreno, Francisco Duran-Rosal, Antonio M. Martinez, Cesar Hervas Gutierrez, Pedro A. Fernandez, Juan C. Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Maimonides Biomedical Research Institute of Córdoba IMIBIC University of Córdoba 14071 Córdoba Spain Department of Quantitative Methods Universidad Loyola Andalucía Córdoba Spain

Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals’ biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance.

关键词： Artificial neural networks Classification Coral reef optimisation algorithm Local search Neuroevolution Robust estimators

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Integrating AI-based adaptive learning into the flipped classroom model to enhance engagement and learning outcomes

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computers and Education: Artificial Intelligence 2025年 8卷

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

This study explores the integration of the AI-powered adaptive learning system within the flipped classroom model and discusses programming education. The increasing demand for the acquisition of programming skills in the digital era generates the development of innovative pedagogical approaches to help students overcome the difficulties in mastering programming concepts. The study investigates how AI-based adaptive feedback influences student engagement, motivation, and learning outcomes. This study was conducted for 13 weeks, with two groups of undergraduate students. In one of the groups, tshe traditional flipped classroom model was applied, while in the other group, AI-driven adaptive learning tools were introduced. In this research methodology, a mixed-methods approach was adopted, integrating quantitative analyses with qualitative data. These showed that the experimental group demonstrated significant improvements in learning results and motivation, thereby realiszing the value of real-time, personalized feedback provided through the AI system. The qualitative findings revealed that with the AI system, there was an increase in student autonomy;thus, there is increased responsibility among students to collaborate in in-class participation, which helps enhance a more dynamic and cooperative learning environment. On the whole, these findings reveal important implications of the integration of AI into flipped classrooms, while at the same time, they reveal how to make the modernization of programming education achieve a better overall experience in student learning. © 2025 The Authors

关键词： Contrastive Learning

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Instrumental Variables in Causal Inference and Machine Learning: A Survey

arXiv

引用

arXiv 2022年

作者： Wu, Anpeng Kuang, Kun Xiong, Ruoxuan Wu, Fei The College of Computer Science and Technology Zhejiang University China The Department of Quantitative Theory & Methods Emory University United States

Causal inference is the process of using assumptions, study designs, and estimation strategies to draw conclusions about the causal relationships between variables based on data. This allows researchers to better understand the underlying mechanisms at work in complex systems and make more informed decisions. In many settings, we may not fully observe all the confounders that affect both the treatment and outcome variables, complicating the estimation of causal effects. To address this problem, a growing literature in both causal inference and machine learning proposes to use Instrumental Variables (IV). This paper serves as the first effort to systematically and comprehensively introduce and discuss the IV methods and their applications in both causal inference and machine learning. First, we provide the formal definition of IVs and discuss the identification problem of IV regression methods under different assumptions. Second, we categorize the existing work on IV methods into three streams according to the focus on the proposed methods, including two-stage least squares with IVs, control function with IVs, and evaluation of IVs. For each stream, we present both the classical causal inference methods, and recent developments in the machine learning literature. Then, we introduce a variety of applications of IV methods in real-world scenarios and provide a summary of the available datasets and algorithms. Finally, we summarize the literature, discuss the open problems and suggest promising future research directions for IV methods and their applications. We also develop a toolkit of IVs methods reviewed in this survey at https://***/causal-machine-learning-lab/mliv. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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