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检索条件"机构=Computer Science and Methods of Teaching Computer Science"

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Direct and Inverse Optimization Problems in a Gas Deposit Model 15

Direct and Inverse Optimization Problems in a Gas Deposit Mo...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Skiba, Alexander Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Design Methods for Developing Systems Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

A continuous dynamic model for the development of a gas field is studied. Two mathematical problems are posed, solved and analyzed. In the direct problem, we are looking for the maximum accumulated profit on a set of temporary functions of a certain type. In the inverse optimization problem, we define the entire set of planning horizons. We subject the assigned tasks to a comprehensive analysis. © 2022 IEEE.

关键词： Profitability

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Transformative Applications and Key Challenges of Generative AI 7

Transformative Applications and Key Challenges of Generative...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kovari, Attila Katona, Jozsef Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary University of Dunaújváros Department of Institute of Engineering Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI. © 2024 IEEE.

关键词： Generative adversarial networks

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Using an Online Data Mining Method for Frequent Pattern Analysis of Invented Spelling 23

Using an Online Data Mining Method for Frequent Pattern Anal...

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8th International Conference on Distance Education and Learning, ICDEL 2023

作者： Wang, Ling Li, Jiang Department of Teaching and Learning Austin Peay State University United States Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9798400700422

Invented spelling is an important indicator of children's phonetic development and their knowledge of phoneme-morpheme relationships. Research has revealed that students who extensively use invented spellings in their reading and writing were superior to the others on measures of word decoding. This study applies an online association rule learning tool to discover the frequent patterns in various stages of invented spelling of English learners. The rules generalized from these patterns may help teachers to track the progress of a student's phonetic development and create corresponding intervention instructions. © 2023 ACM.

关键词： Data mining

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RIGID PROTEIN-PROTEIN DOCKING VIA EQUIVARIANT ELLIPTIC-PARABOLOID INTERFACE PREDICTION 12

RIGID PROTEIN-PROTEIN DOCKING VIA EQUIVARIANT ELLIPTIC-PARAB...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Yu, Ziyang Huang, Wenbing Liu, Yang Department of Computer Science Tsinghua University China Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

The study of rigid protein-protein docking plays an essential role in a variety of tasks such as drug design and protein engineering. Recently, several learning-based methods have been proposed, exhibiting much faster docking speed than those computational methods. In this paper, we propose a novel learning-based method called ElliDock, which predicts an elliptic paraboloid to represent the protein-protein docking interface. To be specific, our model estimates elliptic paraboloid interfaces for the two input proteins respectively, and obtains the roto-translation transformation for docking by making two interfaces coincide. By its design, ElliDock is independently equivariant with respect to arbitrary rotations/translations of the proteins, which is an indispensable property to ensure the generalization of the docking process. Experimental evaluations show that ElliDock achieves the fastest inference time among all compared methods and is strongly competitive with current state-of-the-art learning-based models such as DiffDock-PP and Multimer particularly for antibody-antigen docking. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Proteins

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teaching in student-centred active learning spaces: How relational, pedagogical, spatial, and technological aspects intertwine and affect the learning environment 51

Teaching in student-centred active learning spaces: How rela...

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51st Annual Conference of the European Society for Engineering Education, SEFI 2023

作者： Korpås, G.S. Andersen, T.H. Kahrs, M.S. Hansen, G. Røren, K.A.F. Department of Physics Norwegian University of Science and Technology Trondheim Norway Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Section for Teaching and Learning Support Education Quality Division Norwegian University of Science and Technology Trondheim Norway

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Large Language Model-based Human-Agent Collaboration for Complex Task Solving

Large Language Model-based Human-Agent Collaboration for Com...

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2024 Conference on Empirical methods in Natural Language Processing, EMNLP 2024

作者： Feng, Xueyang Chen, Zhi-Yuan Qin, Yujia Lin, Yankai Chen, Xu Liu, Zhiyuan Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798891761681

In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs. In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential. To tackle the problem, we propose a Reinforcement Learning-based HumanAgent Collaboration method, ReHAC, which trains a policy model designed to determine the most opportune stages for human intervention within the task-solving process. We conduct experiments under real and simulated human-agent collaboration scenarios. Experimental results demonstrate that the synergistic efforts of humans and LLM-based agents significantly improve performance in complex tasks, primarily through well-planned, limited human intervention. Datasets and code are available at: https://***/XueyangFeng/ReHAC/. © 2024 Association for Computational Linguistics.

关键词： Autonomous agents

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THE BLESSING OF RANDOMNESS: SDE BEATS ODE IN GENERAL DIFFUSION-BASED IMAGE EDITING 12

THE BLESSING OF RANDOMNESS: SDE BEATS ODE IN GENERAL DIFFUSI...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Nie, Shen Guo, Hanzhong Allan Lu, Cheng Zhou, Yuhao Zheng, Chenyu Li, Chongxuan Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

We present a unified probabilistic formulation for diffusion-based image editing, where a latent variable is edited in a task-specific manner and generally deviates from the corresponding marginal distribution induced by the original stochastic or ordinary differential equation (SDE or ODE). Instead, it defines a corresponding SDE or ODE for editing. In the formulation, we prove that the Kullback-Leibler divergence between the marginal distributions of the two SDEs gradually decreases while that for the ODEs remains as the time approaches zero, which shows the promise of SDE in image editing. Inspired by it, we provide the SDE counterparts for widely used ODE baselines in various tasks including inpainting and image-to-image translation, where SDE shows a consistent and substantial improvement. Moreover, we propose SDE-Drag - a simple yet effective method built upon the SDE formulation for point-based content dragging. We build a challenging benchmark (termed DragBench) with open-set natural, art, and AI-generated images for evaluation. A user study on DragBench indicates that SDE-Drag significantly outperforms our ODE baseline, existing diffusion-based methods, and the renowned DragGAN. Our results demonstrate the superiority and versatility of SDE in image editing and push the boundary of diffusion-based editing methods. See the project page https://***/SDE-Drag-demo/for the code and DragBench dataset. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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Predicting Students Performance in Exams using Machine Learning Techniques 12

Predicting Students Performance in Exams using Machine Learn...

引用

12th International Conference on Cloud Computing, Data science and Engineering, Confluence 2022

作者： Sixhaxa, Khanyisile Jadhav, Ashwini Ajoodha, Ritesh The University Of The Witwatersrand School Of Computer Science And Applied Mathematics Johannesburg South Africa The University Of The Witwatersrand Science Teaching And Learning Unit Faculty Of Science Johannesburg South Africa

ISBN: (纸本)9781665437011

Predicting students success has been a very popular study across different fields and with this study we will be focusing on how Machine Learning can aid us in giving us insight in how students will perform in their exams. In this paper, we propose a model that takes in Demographic, Academic and Behavioural attributes and investigate the how these attributes contribute a student's performance and also predict at risk students. For the analysis of the features we will use the Mutual Information algorithm, alongside five machine learning models that are a mixture of classification and regression classifiers. We will also use these models to predict our students performance. The five classifiers used in this study are as follows: Gaussian Naive Bayes, Support Vector Machine, Random Forest, K-Nearest Neighbour and Logistic Regression and we achieved prediction accuracy of 50.83%, 81.67%, 78.33%, 75.00% and 74.17% respectively. The results yielded revealed that there is a strong correlation between a student's behavioural characteristics and their academic performance. © 2022 IEEE.

关键词： Students

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Dynamics of a relativistic particle in the field of a Gaussian laser pulse in 3+1 dimensions 7

Dynamics of a relativistic particle in the field of a Gaussi...

引用

7th International Symposium on Laser Interaction with Matter, LIMIS 2024

作者： Akintsov, Nikolai S. Nevecheria, Artem P. Yang, Yongjie Research Center for Intelligent Information Technology Nantong University Nantong226019 China Department of Mathematical and Computer Methods Kuban State University Krasnodar350040 Russia School of Information Science and Technology Nantong University Nantong226019 China

ISBN: (数字)9781510688865

ISBN: (纸本)9781510688858

The investigation examines the behavior of a relativistic particle in the field of a Gaussian laser pulse in 3+1 dimensions through an approach based on the method of coupled parameters within Lobachevsky geometry. The dynamics of an electron in a modulated laser pulse (in terms of amplitude, phase, and polarization) are analyzed, and the optimal conditions for phase synchronization of particle acceleration in an intense laser field of 1022 W/cm2 are determined. The role of a constant homogeneous magnetic field in stabilizing trajectories and enhancing the directed radiation of particles, including polarization and synchrotron effects, is investigated. Special attention is given to the spectral characteristics of radiation and the angular distribution of power, paving the way for the development of efficient radiation sources in high-energy physics and laser technologies. Copyright © 2024 SPIE.

关键词： Synchrotrons

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Engineering Educators' Adoption and implementation of Project-Based Learning: Experiences from a South Indian University 13

Engineering Educators' Adoption and implementation of Projec...

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13th IEEE International Conference on teaching, Assessment and Learning for Engineering, TALE 2024

作者： Dijo, Anjal P Anantha, Krishnan K. P. Subheesh, N.P. John, Aby Anzar, S.M. Sachidananthan, Sajin Federal Institute of Science and Technology Department of Computer Science and Engineering India College of Engineering Department of Computer Science and Engineering Kerala India Srm University - Ap Teaching Learning Centre Andhra Pradesh 522502 India Rudn University Faculty of Philology Moscow Russia Tkm College of Engineering Department of Electronics and Communication Engineering Kollam India Curriculum Research and Development U-DNA Educational Foundation Andhra Pradesh Anantapur India

ISBN: (纸本)9798350376234

This research investigates the attitudes and perceptions of engineering educators in Kerala, India, towards the implementation of Project-Based Learning (PjBL) within the framework of APJ Abdul Kalam Technological University (APJAKTU), Kerala. Despite the growing emphasis on experiential learning, only limited research studies focus on understanding the attitudes and perceptions of engineering faculty members towards PjBL. Against this backdrop, the present study aims to critically examine the readiness of engineering educators in adopting and identifying PjBL strategies for its effective integration into engineering curricula. The paper explores various dimensions of engineering educators' engagement with PjBL, including faculty interest levels, satisfaction of implementation, perceived effectiveness, and career implications. The data were collected from engineering faculty members by distributing a comprehensive questionnaire titled Project-Based Learning in Engineering Education in Kerala (PLEEK), uniquely designed by the researchers. The research findings reveal that over 90% of the surveyed faculty members have shown keen interest in implementing PjBL, while the remaining respondents exhibited a neutral stance. This underscores a noteworthy receptivity to PjBL among engineering educators in Kerala. Suggestions from participants indicate that, if implemented correctly, PjBL would be highly effective, although time constraints pose a significant challenge. The results obtained are pivotal as they provide an explicit idea of the current status of the engineering education settings and the discrepancies between traditional theoretical instruction and project-based learning. © 2024 IEEE.

关键词： Engineering education

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