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computer science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Joel Mandlazi Ashwini Jadhav Ritesh Ajoodha School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Science Teaching and Learning Unit Faculty of Science University of the Witwatersrand Johannesburg South Africa

ISBN: (纸本)9781665495530

One of the key priorities in all instructional environments is to ensure that students recognise their learning mechanisms and pathways. Knowledge Tracing (KT), the task of modelling student knowledge from their learning history, is an important problem in the field of Artificial Intelligence in Education (AIEd) and has numerous applications in the development of interactive and adaptive learning technologies. KT can be utilised to understand each student’s distinct learning style, particular needs, and ability levels. We trained and evaluated the performance of Knowledge Tracing models on the ASSISTments dataset and EdNet-KT1 dataset. This study revealed that deep learning models for knowledge tracing (Deep Knowledge Tracing (DKT), Dynamic Key-Value Memory Network (DKVMN), and Attentive Knowledge Tracing (AKT)) outperform the Markov process model (Bayesian Knowledge Tracing). We also observed that AKT and DKT go hand in hand with predicting whether or not the following question will be answered correctly or incorrectly by the student.

关键词： Knowledge engineering Deep learning Learning systems Adaptation models Learning (artificial intelligence) Predictive models Markov processes

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Use of digital platforms for enhancing academic performance. Systematic Review

Use of digital platforms for enhancing academic performance....

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Cristina Anamaria Costescu Mălina Șogor Sabina Pălimariu Jozsef Katona Ioana Tufar Carla Maria Avram Special Education Department Babeș-Bolyai University Cluj-Napoca Romania Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros 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 Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills.

关键词： Performance evaluation Power engineering Databases Personal digital devices Tablet computers Libraries

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Closing the Performance Gap with Modern C++

arXiv

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arXiv 2022年

作者： Heller, Thomas Kaiser, Hartmut Diehl, Patrick Fey, Dietmar Schweitzer, Marc Alexander Computer Science 3 Computer Architectures Friedrich-Alexander-University Germany Center for Computation and Technology Louisiana State University United States Institute for Numerical Simulation University of Bonn Germany Meshfree Multiscale Methods Fraunhofer SCAI Schloss Birlinghoven Germany The STEllAR Group United States

On the way to Exascale, programmers face the increasing challenge of having to support multiple hardware architectures from the same code base. At the same time, portability of code and performance are increasingly difficult to achieve as hardware architectures are becoming more and more diverse. Today's heterogeneous systems often include two or more completely distinct and incompatible hardware execution models, such as GPGPU's, SIMD vector units, and general purpose cores which conventionally have to be programmed using separate tool chains representing non-overlapping programming models. The recent revival of interest in the industry and the wider community for the C++ language has spurred a remarkable amount of standardization proposals and technical specifications in the arena of concurrency and parallelism. This recently includes an increasing amount of discussion around the need for a uniform, higher-level abstraction and programming model for parallelism in the C++ standard targeting heterogeneous and distributed computing. Such an abstraction should perfectly blend with existing, already standardized language and library features, but should also be generic enough to support future hardware developments. In this paper, we present the results from developing such a higher-level programming abstraction for parallelism in C++ which aims at enabling code and performance portability over a wide range of architectures and for various types of parallelism. We present and compare performance data obtained from running the well-known STREAM benchmark ported to our higher level C++ abstraction with the corresponding results from running it natively. We show that our abstractions enable performance at least as good as the comparable base-line benchmarks while providing a uniform programming API on all compared target architectures. Copyright © 2022, The Authors. All rights reserved.

关键词： C++ (programming language)

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Educational data mining: using knowledge tracing as a tool for student success

Educational data mining: using knowledge tracing as a tool f...

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International Multidisciplinary Information Technology and Engineering Conference (IMITEC)

ISBN: (纸本)9781665417501

Student success is important to institutions of learning. Learning institutions offer academic support to their students to ensure success. Knowledge Tracing (KT), the task of modelling student knowledge based on their learning history is a significant topic in the field of Educational Data Mining and has numerous applications in the development of interactive and adaptive learning technology. KT can be used to understand each student’s unique learning behaviour, individual needs, and skill-levels. Machine learning classification models were implemented to trace the knowledge state of students. The Support Vector Machine outperformed all other models in this article, with Random Forest performing the worst, with accuracy of 76.8% and 70.76%, respectively. One important goal of KT is to personalise study materials in order to assist student’s understand learning concepts more efficiently.

关键词： Support vector machines Knowledge engineering Adaptation models Uncertainty Machine learning algorithms Data models Data mining

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Profit Maximization in a Gas Field Model 14

Profit Maximization in a Gas Field Model

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14th International Conference Management of Large-Scale System Development, MLSD 2021

作者： Skiba, Alexander Skiba, Nadezhda Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Department of Mathematical Methods of Regional Programming Moscow Russia Kuban State Technological University Institute of Oil Gas and Energy Ministry of Science and Higher Education Department of Oil and Gas Business Named after Prof Vartumyan G. T. Krasnodar Russia

ISBN: (纸本)9781665412308

We consider a dynamic continuous gas production at a deposit. We set and solve the optimization control problem for maximum profit, taking into account the discount factor. The problem posed relates to some optimal control problems with a mixed constraint with a fixed time and an unbounded right endpoint. We use the principle of Pontryagin maximum in the Arrow form. © 2021 IEEE.

关键词： Deposits

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Collaboration and orchestration in a virtual teaching organization of environmental education 23

Collaboration and orchestration in a virtual teaching organi...

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Proceedings of the 15th International Conference on Education Technology and computers

作者： Fenghua Li Mei Wang Chunfang Zhou Maria Inmaculada Navarro Gonzalez Chong Han Yong Li PBL Teaching Innovation Research Center of Northeastern University Department of Resources and Environment School of metallurgy Northeastern University China Department of Mathematics and Computer Science University of Southern Denmark Denmark Faculty of Education National University of Distance Education Spain Department of Resources and Environment School of metallurgy Northeastern University China

ISBN: (纸本)9798400709111

In the information era, Virtual teaching Organizations (VTOs) development is an important probe of creative education. This paper explores an investigation on the inter-departmental VTO in a college-wide general elective course Environment and Sustainable Development (VTO-ESD) that involves teachers who come from six majors in four departments in a Chinese university. In order to achieve ground-breaking expectations for the teachers’ teaching and research capacity of working at the virtual community, it is critical to enhance their skills of team collaboration and communication during the development process in practice. The VTO-ESD is an example of a large curriculum teaching organization that may be organically split into divisions based on diverse factors such as environmental education research, textbooks, teaching reform, training in teaching methods, case studies, and so on. Problem-Based Cooperation has been recognized as a good way to improve trust between team members, boost organizational resilience through the synergistic impact, and accomplish sustainable development and collective progress through deliberate and in-depth communication and exchange.

关键词： Environmental education Orchestration Organization resilience Problem-Based Collaboration Virtual teaching Organization (VTO)

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Robust Graph Matching Using An Unbalanced Hierarchical Optimal Transport Framework

arXiv

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arXiv 2023年

作者： Cheng, Haoran Luo, Dixin Xu, Hongteng School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China Key Laboratory of Artificial Intelligence Ministry of Education China Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Graph matching is one of the most significant graph analytic tasks, which aims to find the node correspondence across different graphs. Most existing graph matching approaches mainly rely on topological information, whose performances are often sub-optimal and sensitive to data noise because of not fully leveraging the multi-modal information hidden in graphs, such as node attributes, subgraph structures, etc. In this study, we propose a novel and robust graph matching method based on an unbalanced hierarchical optimal transport (UHOT) framework, which, to our knowledge, makes the first attempt to exploit cross-modal alignment in graph matching. In principle, applying multilayer message passing, we represent each graph as layer-wise node embeddings corresponding to different modalities. Given two graphs, we align their node embeddings within the same modality and across different modalities, respectively. Then, we infer the node correspondence by the weighted average of all the alignment results. This method is implemented as computing the UHOT distance between the two graphs — each alignment is achieved by a node-level optimal transport plan between two sets of node embeddings, and the weights of all alignment results correspond to an unbalanced modality-level optimal transport plan. Experiments on various graph matching tasks demonstrate the superiority and robustness of our method compared to state-of-the-art approaches. Our implementation is available at https://***/Dixin-Lab/UHOT-GM. © 2023, CC BY.

关键词： Graph embeddings

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RECOVERY TYPE A POSTERIORI ERROR ESTIMATION OF AN ADAPTIVE FINITE ELEMENT METHOD FOR CAHN-HILLIARD EQUATION

arXiv

引用

arXiv 2023年

作者： Chen, Yaoyao Huang, Yunqing Yi, Nianyu Yin, Peimeng School of Mathematics and Statistics Anhui Normal University Wuhu241000 China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China Multiscale Methods and Dynamics Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

In this paper, we derive a novel recovery type a posteriori error estimation of the Crank-Nicolson finite element method for the Cahn-Hilliard equation. To achieve this, we employ both the elliptic reconstruction technique and a time reconstruction technique based on three time-level approximations, resulting in an optimal a posteriori error estimator. We propose a time-space adaptive algorithm that utilizes the derived a posteriori error estimator as error indicators. Numerical experiments are presented to validate the theoretical findings, including comparing with an adaptive finite element method based on a residual type a posteriori error estimator. © 2023, CC BY-NC-ND.

关键词： Recovery

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Equivariant graph hierarchy-based neural networks 22

Equivariant graph hierarchy-based neural networks

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiaqi Han Wenbing Huang Tingyang Xu Yu Rong Department of Computer Science and Technology Tsinghua University Gaoling School of Artificial Intelligence Renmin University of China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tencent AI Lab

ISBN: (纸本)9781713871088

Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems. Existing EGNs conduct flat message passing, which, yet, is unable to capture the spatial/dynamical hierarchy for complex systems particularly, limiting substructure discovery and global information fusion. In this paper, we propose Equivariant Hierarchy-based Graph Networks (EGHNs) which consist of the three key components: generalized Equivariant Matrix Message Passing (EMMP), E-Pool and E-UnPool. In particular, EMMP is able to improve the expressivity of conventional equivariant message passing, E-Pool assigns the quantities of the low-level nodes into high-level clusters, while E-UnPool leverages the high-level information to update the dynamics of the low- level nodes. As their names imply, both E-Pool and E-UnPool are guaranteed to be E(n)-equivariant to meet the physical symmetry. Considerable experimental evaluations verify the effectiveness of our EGHN on several applications including multi-object dynamics simulation, motion capture, and protein dynamics modeling.

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Navigation Learning Assessment Using EEG-based Multi-Time Scale Spatiotemporal Compound Model

TechRxiv

引用

TechRxiv 2023年

作者： Wang, Lingling Liu, Yixin Li, Yiqing Chen, Renxiang Liu, Xiaohong Fu, Li Wang, Yao Experimental Teaching Demonstration Center of Mechanical and Control Engineering Beihang University Beijing100191 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The School of Communication Science Beijing Language and Culture University Beijing100083 China Software Services Co. Ltd Beijing101100 China The School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China

This study presents a novel method to assess the learning effectiveness using Electroencephalography (EEG)-based Multi-Time Scale Spatiotemporal Compound Model (MTSC). Due to the evaluation of navigation learning, which is centered on the practice innovation ability development, it is difficult to assess the learning effectiveness in cultivating students’ ability objectively by questionnaire or other assessment methods. Research in the field of brain science has shown that innovation ability can be reflected from cognitive ability which can be embodied by EEG signal features. Therefore, a navigation learning assessment method was applied to detect the cognitive training traces by EEG-based MTSC. To verify the validity of the method, three navigation missions with increasing cognitive difficulty were designed, and 41 participants attended the experiment. The features of participants’ EEG signals were extracted and classified by MTSC. Comparing the results between experimental group and control group, it can be seen that MTSC can effectively distinguish the cognitive training traces of students in the navigation problem-solving process. In addition, MTSC achieved 89.2% accuracy and 0.883 F1-Score on features classification, which had an average improvement of 15.7% over other three traditional EEG classification models in accuracy. The results indicate that the navigation learning assessment method provides a useful evaluation approach for learning effectiveness using EEG-based MTSC. © 2023, CC BY.

关键词： Electroencephalography

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