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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 ...

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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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And then there were three: GIS-based analysis of the new three-operator-only e-scooter sharing system of Rome, Italy

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Transportation Research Procedia 2025年 90卷 846-853页

作者： Antonella Nardin Stefano Carrese Marialisa Nigro Fabio D’Andreagiovanni Department of Civil Computer Science and Aeronautical Technologies Engineering Universitá degli Studi Roma Tre via Vito Volterra 62 00146 Roma Italy Department of Sciences and Methods for Engineering Universitá degli Studi di Modena e Reggio Emilia via Giovanni Amendola 2 42122 Reggio Emilia Italy

In recent years, electric scooters (e-scooters) have rapidly spread all over the world, becoming a new major paradigm of micro- mobility. Their success can be mainly attributed to their easiness of riding and parking, providing an agile, cheap and sustainable option for last-mile trips. Following this success, many e-scooter sharing companies started to operate in many cities. However, the rapid diffusion of these sharing services has not only brought evident benefits, but also critical issues: a substantial part of the users has soon shown a tendency to ride and park mindless of road rules, causing safety risks for riders and pedestrians and reducing urban decorum. Consequently, municipalities have introduced bans, fines and stricter regulations. In this work, we study the reformed e-sharing scooter system of Rome, the Italian capital city, following the introduction of new regulations in 2023, after the end of the experimental phase started in 2019. Major changes have been a) reducing the number of operators to only three, b) limiting the size of fleets (decreasing the total number of deployed e-scooter by 40%), c) extending services to outer districts of the city. All the changes were aimed to favour a closer control of the sharing services, safer rides and more well-ordered parking, plus a fairer access to services also in suburbs. In our GIS-based study, we focus on identifying and analyzing the service coverage in this new Rome setting, in terms of the geofences defined by the operators. After having spatially identified the service areas, we analyze their new features, especially in terms of the new fairer distribution over the territory. We also analyze how service areas and geofences have evolved over time, in order to reflect modifications and updates in the regulations passed by the municipality.

关键词： Micromobility Shared Mobility E-Scooters Urban Transport GIS Geofences Service Coverage

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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

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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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Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach

arXiv

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arXiv 2023年

作者： Ouaari, Sofiane Ünal, Ali Burak Akgün, Mete Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tuebingen Germany University of Tuebingen Germany Medical Data Privacy and Privacy Preserving Machine Learning University of Tuebingen Germany

Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around data ethics and privacy and growing awareness of the need for privacy-preserving machine learning (ppML). Current ppML techniques utilize methods that are either purely based on cryptography, such as homomorphic encryption, or that introduce noise into the input, such as differential privacy. The main criticism given to those techniques is the fact that they either are too slow or they trade off a model's performance for improved confidentiality. To address this performance reduction, we aim to leverage robust representation learning as a way of encoding our data while optimising the privacy-utility trade-off. Our method centers on training autoencoders in a multi-objective manner and then concatenating the latent and learned features from the encoding part as the encoded form of our data. Such a deep learning-powered encoding can then safely be sent to a third party for intensive training and hyperparameter tuning. With our proposed framework, we can share our data and use third party tools without being under the threat of revealing its original form. We empirically validate our results on unimodal and multimodal settings, the latter following a vertical splitting system and show improved performance over state-of-the-art. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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STEM course on circuit design accomplished to enhance scientific and technological capabilities of students 3

STEM course on circuit design accomplished to enhance scient...

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3rd International Conference on sciences and Technology Applications, ICoSTA 2021

作者： Vexler, V. Aibashev, M. Saipbekova, A. Fominykh, I. Bazhenov, R. Department of Information Systems and Technologies in Education Saratov State University 83 Astrakhanskaya Saratov410012 Russia Department of Accounting and Economic Analysis Osh State University 331 Lenina Osh723500 Kyrgyzstan Department of Applied Mathematics Computer Science Osh State University 331 Lenina Osh723500 Kyrgyzstan Department of Methods of Teaching Mathematics Computer Science and Natural Science Disciplines Mari State University 6 Eshkinina Yoshkar-Ola424033 Russia Department of Information Systems and Mathematics Sholom-Aleichem Priamursky State University 70A Shirokaya Birobidzhan679015 Russia

The aim of the research is to develop an interdisciplinary course of extracurricular activities integrating knowledge in science, technology, engineering and mathematics for students to develop their scientific and technological skills. 52 students from secondary schools of Engels, Yoshkar-Ola (Russia), Sholom-Aleichem Priamursky State University (Birobidzhan, Russia), Osh State University College (Osh, Kyrgyz Republic) took part in the educational experiment. The theme-based course Basic Concepts of Arduino Systems Engineering is developed based on the proprietary technology of simple projects of practical relevance. The projects render the general concept from knowledge to competence. The course consists of four modules: Start, Big Workshop/Hands-on Training, Go Ahead/Step forward, Group Design. Arduino applied in design modeling allows students to create electrical devices that would be productive in one or another area of life at a low cost. The course program is aimed at engaging students in innovative design technologies, computer programming and using robotic devices. The findings of the study were observed as part of an interview with students, educational achievement, subsequent STEM literacy test activities and the performance of game summarizing projects. The recommended method has shown the efficiency in mastering the required core set of skills of circuit design and computer programming. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence

关键词： Students

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