448 results on '"Learning data"'
Search Results
2. Neural-Network-Assisted Finite Difference Discretization for Numerical Solution of Partial Differential Equations.
- Author
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Izsák, Ferenc and Izsák, Rudolf
- Subjects
- *
NUMERICAL solutions to partial differential equations , *POISSON'S equation , *FINITE differences , *LAPLACIAN operator , *NONLINEAR equations , *LAPLACE'S equation - Abstract
A neural-network-assisted numerical method is proposed for the solution of Laplace and Poisson problems. Finite differences are applied to approximate the spatial Laplacian operator on nonuniform grids. For this, a neural network is trained to compute the corresponding coefficients for general quadrilateral meshes. Depending on the position of a given grid point x 0 and its neighbors, we face with a nonlinear optimization problem to obtain the finite difference coefficients in x 0 . This computing step is executed with an artificial neural network. In this way, for any geometric setup of the neighboring grid points, we immediately obtain the corresponding coefficients. The construction of an appropriate training data set is also discussed, which is based on the solution of overdetermined linear systems. The method was experimentally validated on a number of numerical tests. As expected, it delivers a fast and reliable algorithm for solving Poisson problems. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
3. Prediction of Students' Academic Learning Performance Based on Big Learning Data
- Author
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Sun, Jing, Li, Kan, Editor-in-Chief, Li, Qingyong, Associate Editor, Fournier-Viger, Philippe, Series Editor, Hong, Wei-Chiang, Series Editor, Liang, Xun, Series Editor, Wang, Long, Series Editor, Xu, Xuesong, Series Editor, Fox, Bob, editor, Zhao, Chuan, editor, and Anthony, Marcus T., editor
- Published
- 2023
- Full Text
- View/download PDF
4. “数字徽章+AI”平台助推过程性评价:技术框架 与实施路径.
- Author
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颜荆京 and 李钦芳
- Abstract
Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
5. Program Evaluation and Triangulation: What Three Data Sets Show about How Participants Respond to the Course Teaching Academic English with the TOEFL iBT® Test.
- Author
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Janssen, Gerriet, Ohta, Renka, Lee, Jeremy, and Suhan, Michael
- Subjects
TEST of English as a Foreign Language ,TEACHER training ,COURSE content (Education) ,TEACHING aids ,QUALITATIVE research - Abstract
Drawing from a larger CIPP program evaluation (Context, Input, Processes, Products) comprised of 11 analyses of the ETS online teacher training course titled Teaching Academic English with the TOEFL iBT® Test (TAE), this paper triangulates three qualitatively different data sets and seeks to understand how participants responded to the TAE course. The research team considered self-reported data (closed- and open-answer survey questions), engagement data, and learning data. The self-reported data were strongly positive and illustrated the value participants reported having about the course content, especially ETS-created materials. Some gaps were indicated: calls were made for more materials--especially additional resources participants could use when teaching--and for increased interactions during the course workshop. In partial juxtaposition, the engagement data indicated that participants were engaging the course less than anticipated. The learning data simultaneously indicated that some assessments were quite easy for participants while elsewhere participants did not have the desired uptake of some topics. Viewing the three data sets together, the research team arrived at a more nuanced conclusion: despite participant satisfaction, development processes should continue, to ensure the highest quality teacher training program possible, in terms of its content, resources, and interactions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
6. Cibernética. Realidades y falacias.
- Author
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SADOSKY, MANUEL
- Subjects
- *
ARTIFICIAL intelligence , *THEORY of knowledge , *ETHICS , *TOTALITARIANISM , *AUTHORITARIANISM - Abstract
The article focuses on disturbing context in which so much is said about the so-called "artificial intelligence" and problematization of the question and his attempt to define the fallacies that, already in the fifties of the last century. It mentions sixty years impacts its epistemological relevance and its potential ethical warning and there is a serious risk of falling imperceptibly into a techno-totalitarian status quo and disturbing context so-called "artificial intelligence."
- Published
- 2023
7. Learning Data for Neural-Network-Based Numerical Solution of PDEs: Application to Dirichlet-to-Neumann Problems.
- Author
-
Izsák, Ferenc and Djebbar, Taki Eddine
- Subjects
- *
ELECTRICAL impedance tomography , *BOUNDARY value problems - Abstract
We propose neural-network-based algorithms for the numerical solution of boundary-value problems for the Laplace equation. Such a numerical solution is inherently mesh-free, and in the approximation process, stochastic algorithms are employed. The chief challenge in the solution framework is to generate appropriate learning data in the absence of the solution. Our main idea was to use fundamental solutions for this purpose and make a link with the so-called method of fundamental solutions. In this way, beyond the classical boundary-value problems, Dirichlet-to-Neumann operators can also be approximated. This problem was investigated in detail. Moreover, for this complex problem, low-rank approximations were constructed. Such efficient solution algorithms can serve as a basis for computational electrical impedance tomography. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. Practices, Policies, and Problems in the Management of Learning Data: A Survey of Libraries' Use of Digital Learning Objects and the Data They Create.
- Author
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Sherriff, Graham, Benson, Daisy, and Atwood, Gary S.
- Subjects
- *
DISTANCE education , *LIBRARY administration , *STANDARDIZATION , *EDUCATIONAL technology , *SUBSTANTIAL compliance doctrine (Law) - Abstract
Abstract This study analyzed libraries' management of the data generated by library digital learning objects (DLOs) such as forms, surveys, quizzes, and tutorials. A substantial proportion of respondents reported having a policy relevant to learning data, typically a campus-level policy, but most did not. Other problems included a lack of access to library learning data, concerns about student privacy, inadequate granularity or standardization, and a lack of knowledge about colleagues' practices. We propose more dialogue on learning data within libraries, between libraries and administrators, and across the library profession. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
9. Crawling Low Appearance Frequency Character Images for Early-Modern Japanese Printed Character Recognition
- Author
-
Fujisaki, Nanami, Ishikawa, Yu, Takata, Masami, Joe, Kazuki, Arabnia, Hamid, Series Editor, Arabnia, Hamid R., editor, Deligiannidis, Leonidas, editor, Grimaila, Michael R., editor, Hodson, Douglas D., editor, Joe, Kazuki, editor, Sekijima, Masakazu, editor, and Tinetti, Fernando G., editor
- Published
- 2021
- Full Text
- View/download PDF
10. Neural-Network-Assisted Finite Difference Discretization for Numerical Solution of Partial Differential Equations
- Author
-
Ferenc Izsák and Rudolf Izsák
- Subjects
boundary value problems ,Laplacian ,Poisson problem ,neural networks ,finite difference discretization ,learning data ,Industrial engineering. Management engineering ,T55.4-60.8 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
A neural-network-assisted numerical method is proposed for the solution of Laplace and Poisson problems. Finite differences are applied to approximate the spatial Laplacian operator on nonuniform grids. For this, a neural network is trained to compute the corresponding coefficients for general quadrilateral meshes. Depending on the position of a given grid point x0 and its neighbors, we face with a nonlinear optimization problem to obtain the finite difference coefficients in x0. This computing step is executed with an artificial neural network. In this way, for any geometric setup of the neighboring grid points, we immediately obtain the corresponding coefficients. The construction of an appropriate training data set is also discussed, which is based on the solution of overdetermined linear systems. The method was experimentally validated on a number of numerical tests. As expected, it delivers a fast and reliable algorithm for solving Poisson problems.
- Published
- 2023
- Full Text
- View/download PDF
11. Use of Knowledge Acquisition Surface to Monitor and Assess Students' Success.
- Author
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Daugule, Iveta, Kapenieks, Atis, and Timšāns, Žanis
- Subjects
KNOWLEDGE acquisition (Expert systems) ,ACADEMIC achievement ,LEARNING ,ACQUISITION of data - Abstract
The aim of the Knowledge Acquisition Monitoring (KAM) approach created is to teach faster and/or more in a particular time frame. The KAM Question Framework that was designed according to the KAM approach ensures the active involvement of the students in the study process and provides them with relevant and updated content in real-time. The obtained data enables understanding the nature of knowledge acquisition - to improve the content, and enhance the efficiency of the learning process. 3-D coordinates were used to design the Knowledge Acquisition Surface (KAS). This surface describes the relevance of the e-course content to the needs of a student or group of students. The course suitability evaluation is performed considering the area in which the obtained knowledge acquisition data point is located. The approach provides the teacher with insight into the specific needs of students, and facilitates e-course improvement. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
12. Developing a Learning Data Collection Platform for Learning Analytics in Online Education.
- Author
-
Pak, JuGeon, Lee, JooHwa, and Lee, MyungSuk
- Subjects
ONLINE education ,DATA collection platforms ,ENGINEERING education ,COVID-19 pandemic ,DIGITAL learning - Abstract
During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
13. Development of a DNN model using calibrated simulation for the optimal control of HVAC systems.
- Author
-
Noh, Jeong-Hyun and Lee, Je-Hyeon
- Abstract
Acquiring actual data for developing data-based prediction models for the optimization of control setpoints (hereinafter referred to as optimal control (OC)) in heating, ventilation, and air conditioning (HVAC) systems is challenging, especially when changing control setpoints while considering all possible boundary conditions. Thus, considering the scalability of OC, it is a promising solution to generate sufficient learning data according to the control settings using a highly accurate simulation model, build a source model, and expand the learning model based on the actual data of the target system. This is the first study to improve the scalability of OC aiming to develop a deep neural network (DNN) model to predict the power consumption of a heat source system. First, this study will construct a calibrated heat source system simulation model and then use the generated learning data to train a DNN model. The actual operation conditions of the target building and heating system located in Vietnam were analyzed using operation data. A physics-based simulation model of the target heat source system was developed and calibrated using actual operation data to improve accuracy. By using the calibrated simulation, sufficient learning data was generated for all control settings allowing for the development of a learning model based on DNN that predicted the power consumption of the heat source system. In addition, a highly accurate prediction model through hyperparameter optimization was secured. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
14. Learning Data for Neural-Network-Based Numerical Solution of PDEs: Application to Dirichlet-to-Neumann Problems
- Author
-
Ferenc Izsák and Taki Eddine Djebbar
- Subjects
boundary-value problems ,neural networks ,Dirichlet-to-Neumann problem ,learning data ,method of fundamental solutions ,Industrial engineering. Management engineering ,T55.4-60.8 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
We propose neural-network-based algorithms for the numerical solution of boundary-value problems for the Laplace equation. Such a numerical solution is inherently mesh-free, and in the approximation process, stochastic algorithms are employed. The chief challenge in the solution framework is to generate appropriate learning data in the absence of the solution. Our main idea was to use fundamental solutions for this purpose and make a link with the so-called method of fundamental solutions. In this way, beyond the classical boundary-value problems, Dirichlet-to-Neumann operators can also be approximated. This problem was investigated in detail. Moreover, for this complex problem, low-rank approximations were constructed. Such efficient solution algorithms can serve as a basis for computational electrical impedance tomography.
- Published
- 2023
- Full Text
- View/download PDF
15. Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design.
- Author
-
Yan, Hongxin, Lin, Fuhua, and Kinshuk
- Subjects
INTELLIGENT tutoring systems ,ONLINE education ,EDUCATIONAL technology ,ACADEMIC achievement ,DIGITAL learning - Abstract
Online education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as learning awareness and academic intervention. These barriers can affect the academic performance of online learners. Recently, learning analytics has been shown to have great potential in removing these barriers. However, it is challenging to achieve the full potential of learning analytics with the traditional online course learning design model. Thus, focusing on SPOL, this study proposes that learning analytics should be included in the course learning design loop to ensure data collection and pedagogical connection. We propose a novel learning design-analytics model in which course learning design and learning analytics can support each other to increase learning success. Based on the proposed model, a set of online course design strategies are recommended for online educators who wish to use learning analytics to mitigate the learning barriers in SPOL. These strategies and technologies are inspired by Jim Greer's work on student modelling. By following these recommended design strategies, a computer science course is used as an example to show our initial practices of including learning analytics in the course learning design loop. Finally, future work on how to develop and evaluate learning analytics enabled learning systems is outlined. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
16. Development of AR Information System Based on Deep Learning and Gamification
- Author
-
Ogi, Tetsuro, Takesue, Yusuke, Lukosch, Stephan, Xhafa, Fatos, Series Editor, Barolli, Leonard, editor, Kryvinska, Natalia, editor, Enokido, Tomoya, editor, and Takizawa, Makoto, editor
- Published
- 2019
- Full Text
- View/download PDF
17. LEARNING ANALYTICS TOOL FOR BULGARIAN SCHOOL EDUCATION.
- Author
-
Gaftandzhieva, Silvia, Doneva, Rositsa, Pashev, George, and Docheva, Mariya
- Subjects
- *
LEARNING Management System , *INFORMATION superhighway , *PARENTS , *LEARNING , *ACCOUNTING software , *LIBRARY cooperation , *FINANCIAL management - Abstract
Nowadays, schools use many information systems to automate their activities for different stakeholders’ groups – learning management systems, student diary, library systems, digital repositories, financial management and accounting systems, document processing systems, etc. The huge amount of data generated by the users of these systems, led to increased interest in the collection and analysis of data to encourage students to achieve higher results, teachers to provide personalized support and school managers to make data-driven decisions at all levels of school, and stimulates research into the application of Learning Analytics (LA) in schools. The paper presents a LA model and a software prototype of the LA tool designed for the needs of Bulgarian school education from the perspective of different stakeholder groups (students, teachers, class teachers, parents, school managers, inspectors from evaluation agencies), aiming to improve school methods of approaching and analyzing learning data. The tool allows stakeholders to track data for students’ learning or training for different purposes, e.g. monitoring, analysis, forecast, intervention, recommendations, etc., but finally to improve the quality of learning and teaching processes. Research and experiments with the model and the LA tool under consideration are conducted based on the information infrastructure of a typical Bulgarian school. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
18. Developing a Learning Data Collection Platform for Learning Analytics in Online Education
- Author
-
JuGeon Pak, JooHwa Lee, and MyungSuk Lee
- Subjects
online education ,learning analytics ,programming education ,data collection ,platform ,learning data ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
During the COVID-19 pandemic, most education has been conducted through online classes. While feedback and interaction between students and instructors are significant in programming education or engineering practice, online education today cannot satisfy these aspects of learning. Therefore, this study proposes a learning support system for programming education and presents the results of designing and implementing this system. The proposed system consists of an online development environment module, a learning monitoring module, and a learning support module. It also provides a web-based programming environment, real-time chat and code mirroring, error guide messages and related lectures, e-learning quizzes, and learning activity analysis features. The system standardizes the development environment between the instructor and students, helps students take the initiative in solving errors, and enables code-oriented interactions between the instructor and students. It also collects data from all learning situations in the database. Conducting a big data analysis with the collected data will enable individual guidance for students by finding errors that frequently occur in programming and recommending learning materials to solve them.
- Published
- 2022
- Full Text
- View/download PDF
19. The EMMA Experience. Emerging Patterns and Factors for Success
- Author
-
De Rosa, Rosanna, Ferrari, Chiara, Kerr, Ruth, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Delgado Kloos, Carlos, editor, Jermann, Patrick, editor, Pérez-Sanagustín, Mar, editor, Seaton, Daniel T., editor, and White, Su, editor
- Published
- 2017
- Full Text
- View/download PDF
20. Quantitative Analysis of Learning Data in a Programming Course
- Author
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Bai, Yu, Chen, Liqian, Yin, Gang, Mao, Xinjun, Deng, Ye, Wang, Tao, Lu, Yao, Wang, Huaimin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bao, Zhifeng, editor, Trajcevski, Goce, editor, Chang, Lijun, editor, and Hua, Wen, editor
- Published
- 2017
- Full Text
- View/download PDF
21. A comprehensive approach to learning analytics in Bulgarian school education.
- Author
-
Gaftandzhieva, Silvia, Docheva, Mariya, and Doneva, Rositsa
- Subjects
INFORMATION storage & retrieval systems ,BULGARIAN schools abroad ,SECONDARY education ,STAKEHOLDERS ,STUDENT attitudes - Abstract
Many educational institutions use a large number of information systems to automate their activities for different stakeholders' groups – learning management systems, student diary, library system, digital repositories, management system, etc. This leads to a significant increase in the volume and variety of data that can be captured, stored, and harnessed to improve student learning and school effectiveness. Educational institutions are realising that, with the help of technology, they are collecting data that could be very valuable when properly analysed, aligned with learning outcomes, and integrated into a tighter feedback loop with stakeholders. In addition, the analysis of data can help their managers to take data-driven decision making at all levels of educational institutions. The paper presents a comprehensive approach to Learning Analytics in the field of Secondary Education from the perspective of all different stakeholders, which aims to improve its methods of approaching and analysing learning data. On the basis of a literature review in the field and an investigation of requirements for quality evaluation of learning in school education, the corresponding stakeholder groups are identified - students, teachers, class teachers, managers, parents, inspectors and 6 models (1 model per each stakeholder group) for data collection and personalized and meaningful analysis are proposed for the needs of Learning Analytics. Each model consists of measurable indicators allowing the relevant stakeholder to track data for students' learning or training for different purposes, e.g. monitoring, analysis, forecast, intervention, recommendations, etc., but finally to improve the quality of learning and teaching processes. The proposed models are evaluated by the representatives of 4 stakeholder groups – students, teachers, class teachers, parents. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
22. Deep visual unsupervised domain adaptation for classification tasks: a survey.
- Author
-
Madadi, Yeganeh, Seydi, Vahid, Nasrollahi, Kamal, Hosseini, Reshad, and Moeslund, Thomas B.
- Abstract
Learning methods are challenged when there is not enough labelled data. It gets worse when the existing learning data have different distributions in different domains. To deal with such situations, deep unsupervised domain adaptation techniques have newly been widely used. This study surveys such domain adaptation methods that have been used for classification tasks in computer vision. The survey includes the very recent papers on this topic that have not been included in the previous surveys and introduces a taxonomy by grouping methods published on unsupervised domain adaptation into five groups of discrepancy‐, adversarial‐, reconstruction‐, representation‐, and attention‐based methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
23. An artificial immune system-based algorithm for abnormal pattern in medical domain.
- Author
-
Sharmila, L. and Sakthi, U.
- Subjects
- *
IMMUNOCOMPUTERS , *IMMUNE system , *INFORMATION design , *ALGORITHMS , *PATTERN matching - Abstract
In general, medical pattern is a collection of characters formed using the characters such as 'a', 'c', 'g' and 't'. The length of the pattern varies from one disease to another disease, and the pattern also seems to be different for different patients. Identifying an unusual pattern from the information design is troublesome a decade ago. In this paper, with a specific end goal to enhance the precision of the anomalous distinguishing pattern, an Artificial Immune System (AIS) is framed. In the present study, AIS is used to obtain the abnormal pattern by learning the characteristics of the entire data set. Due to the powerful and adaptive nature of AIS, detecting and identifying abnormal pattern is more accurate. This proposed idea is implemented in MATLAB software and experimented on DNA/RNA dataset and the performance is verified. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
24. TELECI Approach for e-Learning User Behavior Data Visualization and Learning Support Algorithm.
- Author
-
KAPENIEKS, Atis, DAUGULE, Iveta, KAPENIEKS, Kristaps, ZAGORSKIS, Viktors, KAPENIEKS Jr., Janis, TIMSANS, Zanis, and VITOLINA, Ieva
- Subjects
MACHINE learning ,DATA modeling ,KNOWLEDGE acquisition (Expert systems) ,BEHAVIOR ,PSYCHOLOGY of students ,LEARNING Management System ,MOBILE learning - Abstract
The user behavior data generated in the TELECI learning environment with additional short, easy-to-use multiple-choice questions before and after each content subunit are used for visualization and correlation analysis. Three user behavior data clusters were identified in data landscape. The student behavior change among the TELECI-clusters was used for TELECI learning support algorithm design. The student performance data before and after learning the e-content were used for knowledge acquisition model design. This model is based on the assumption that knowledge acquisition of real e-content can be quantified by superposition of the impact of learning “perfect†content, too easy content, and too complicated content. The learner knowledge acquisition surface is calculated on this assumption. The data of real course learner knowledge acquisition are located on this surface as “telecidesâ€. Telecides are the visualization of the appropriateness of an e-content unit for the needs of the specific learner or learners target group. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
25. Analysis of New Data Sources in Modern Teaching and Learning Processes in the Perspective of Personalized Recommendation
- Author
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Shivanagowda, G. M., Goudar, R. H., Kulkarni, U. P., Howlett, Robert J., Series editor, Jain, Lakhmi C., Series editor, Behera, Himansu Sekhar, editor, Mandal, Jyotsna Kumar, editor, and Mohapatra, Durga Prasad, editor
- Published
- 2015
- Full Text
- View/download PDF
26. Machine-Learning of Shape Names for the Game of Go
- Author
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Ikeda, Kokolo, Shishido, Takanari, Viennot, Simon, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Plaat, Aske, editor, van den Herik, Jaap, editor, and Kosters, Walter, editor
- Published
- 2015
- Full Text
- View/download PDF
27. Acquisition of meshing vibration data and image pictures of crack propagation at tooth root of plastic gears
- Author
-
Yunosuke ISHII, Daisuke IBA, Yusuke TSUTSUI, Nanako MIURA, Takashi IIZUKA, Arata MASUDA, Akira SONE, and Ichiro MORIWAKI
- Subjects
plastic gear ,automatic acquisition system ,learning data ,high-speed camera ,meshing vibration ,artificial intelligence ,Mechanical engineering and machinery ,TJ1-1570 ,Engineering machinery, tools, and implements ,TA213-215 - Abstract
The purpose of this study is to generate training data for failure detection by Neural Network, which estimates extent of damage from vibration data of meshing plastic gear pairs. Generally, measurement of meshing vibration with accelerometers installed on a gear system is not difficult. Therefore, one can automatically acquire vibration data under operation and obtain a large amount of vibration data. However, it is not easy to obtain a large amount of evaluation data of the damage extent. Evaluation of the damage extent is possible if the operation has stopped. It means that the procedure of this data acquisition requires additional work, and there is a need for acquiring data of the damage extent, automatically. This research addresses this need by developing an automatic data acquisition system of the meshing vibration and the extent of damage. In the developed system, an accelerometer installed on the housing of bearings is used for acquisition of vibration data, and a high-speed camera observes cracks occurring at the root of the target tooth. The measurement condition was discussed, and the effectiveness of the system was confirmed through experiments. Additionally, a damage index of the crack at the root was proposed, and the computed index was used as labels for training data. Finally, the measured meshing vibration data was visualized for generating training data of Convolutional Neural Network.
- Published
- 2019
- Full Text
- View/download PDF
28. Activity Worksheets for Teaching and Learning Data Visualization
- Author
-
Nicole Dwenger and Vetria Byrd
- Subjects
Motivation ,business.product_category ,Multimedia ,business.industry ,Computer science ,Process (engineering) ,Data Visualization ,ComputingMilieux_PERSONALCOMPUTING ,computer.software_genre ,Faculty ,Computer Graphics and Computer-Aided Design ,Learning data ,Visualization ,Data visualization ,ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION ,ComputingMilieux_COMPUTERSANDEDUCATION ,Humans ,Learning ,Curriculum ,business ,computer ,Software ,Worksheet - Abstract
In this work, the data visualization activity (DVA) worksheet method for teaching and learning data visualization is presented. The DVA worksheet method consists of a series of activity worksheets developed to guide novice instructors and students through the data visualization process. The activity worksheets help new faculty and visualization instructors, lacking formal training in pedagogy and data visualization, learn the data visualization process, design course work, and develop curriculum. The worksheets can be used for individual activities, or as a collection of activities to support data visualization capacity building. Each worksheet focuses on an individual step in the process, allowing the worksheets to be tailored to discipline-specific data visualization needs. We share the motivation and evolution of the worksheets from paper-based to the semi-automated process utilized in fall 2020. We conclude this work with a discussion and areas for improvement.
- Published
- 2021
29. What future for digital homework: Literature review
- Subjects
Reflection (computer programming) ,Coronavirus disease 2019 (COVID-19) ,Computer science ,media_common.quotation_subject ,Distance education ,General Medicine ,General Chemistry ,Task (project management) ,Learning data ,ComputingMilieux_COMPUTERSANDEDUCATION ,Mathematics education ,Technology integration ,Quality (business) ,Autonomy ,media_common - Abstract
Digital homework today is an observable fact of school practice, which resulted from the distance learning experience during the COVID-19 pandemic and the development of online platforms that provide EdTech products of this kind. Yet, not enough reflection of this issue has happened so far. The article attempts to summarise recent research on homework to answer the question: what kind of digital homework has the potential to improve teaching and learning? First, the article compares research findings on the effect of homework and homework-related teaching practices to outline deficiencies in three aspects of homework: the quality of homework, the quality of feedback, and autonomy support. Then it demonstrates digital homework variability using the SAMR model. Finally, the author suggests solutions for three named aspects of homework on different levels of the SAMR model. For homework quality, higher levels of technology integration bring new task designs and more complex learning objectives. For feedback quality, it means being able to collect and employ learning data which was impossible earlier. For autonomy support, it makes homework relevant to students’ goals and interests. Recommendations provided might be helpful for the design and development of new educational technologies, as well as for better use of digital homework in teaching practice.
- Published
- 2021
30. Analyzing and Visualizing Learning Data: A System Designer's Perspective
- Author
-
Radek Pelánek
- Subjects
Focus (computing) ,business.industry ,Computer science ,4. Education ,Interpretation (philosophy) ,Cheating ,Perspective (graphical) ,Learning analytics ,Data science ,Computer Science Applications ,Education ,Learning data ,Analytics ,Similarity (psychology) ,business - Abstract
In this work, we consider learning analytics for primary and secondary schools from the perspective of the designer of a learning system. We provide an overview of practically useful analytics techniques with descriptions of their applications and specific illustrations. We highlight data biases and caveats that complicate the analysis and its interpretation. Although we intentionally focus on techniques for internal use by designers, many of these techniques may inspire the development of dashboards for teachers or students. We also identify the consequences and challenges for research.
- Published
- 2021
31. Machine Learning, Ensemble Methods in
- Author
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Džeroski, Sašo, Panov, Panče, Ženko, Bernard, and Meyers, Robert A., editor
- Published
- 2012
- Full Text
- View/download PDF
32. Personalized Text Formatting for E-mail Messages
- Author
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Murata, Masaki, Ohno, Tomohiro, Matsubara, Shigeki, Watanabe, Toyohide, editor, Watada, Junzo, editor, Takahashi, Naohisa, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor
- Published
- 2012
- Full Text
- View/download PDF
33. THE NAÏVE BAYES ALGORITHM FOR LEARNING DATA ANALYTICS
- Author
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Tong Hung Anh, Tran Ngoc Viet, Hoang Le Minh, and Le Cong Hieu
- Subjects
Naive Bayes classifier ,General Computer Science ,Computer science ,business.industry ,Analytics ,Computational Mechanics ,Artificial intelligence ,Machine learning ,computer.software_genre ,business ,Engineering (miscellaneous) ,computer ,Learning data - Published
- 2021
34. Detecting Ddos Attack Using Adaptive Boosting with Software Defined Network in Cloud Computing Environment
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Mesay Mengstie, Sisay Wayu Tufa, Haftom Gebregziabher, and B. Ravindra Babu
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Boosting (machine learning) ,business.industry ,Computer science ,Distributed computing ,Server ,Scalability ,Cloud computing ,Denial-of-service attack ,Decision stump ,business ,Software-defined networking ,Learning data - Abstract
Cloud computing is most widely used platform in past decade for computational operations in the computers and which offers the cost effective system with the measurable results. Cloud computing and software defined network (SDN) combination gives the better environment which reduces the difficulties with the cloud network and improves the dynamism, programmability, scalability and manageability of the cloud. Several weaknesses are attacked on one side by changing the SDN pattern into the centralized architecture namely as Distribute denial of service (DDoS) attacks. So these interrupts are detected and then prevented in SDN with the technology. Detecting of DDoS attack by using anomaly-based adaptive boosting in SDN cloud environment is presented in this paper. By coordinating the SDN features with the adaptive boosting algorithm, the cloud environment of SDN system DDoS attacks are noticed and prevented. Decision stump is used to generate the learning data and serves huge data to the servers within less time which is used as the prediction data. Predictions are can be processed by the formation of learning data. The experimental test results show that the adaptive boosting algorithm with SDN features has effectiveness in detecting attacks with high accuracy and in the SDN the DDoS attacks are detected even in the low communication.
- Published
- 2021
35. Entornos tecnológicos y modelos educativos.
- Author
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Fernandes Gomes, Natália, Quitério Figueiredo, José, and Hernández Serrano, María José
- Abstract
Copyright of CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings is the property of Conferencia Iberica de Sistemas Tecnologia de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2017
36. Adaptive CWT‐based overcurrent protection for smart distribution grids considering CT saturation and high‐impedance fault.
- Author
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AsghariGovar, Saeed, Heidari, Saeid, Seyedi, Heresh, Ghasemzadeh, Saeid, and Pourghasem, Pouya
- Abstract
In this study, an adaptive continuous wavelet transform (CWT)‐based overcurrent protection for smart grids is proposed to enhance the overcurrent protection performance encountering high‐impedance fault (HIF) and current transformer (CT) saturation, which are extremely complex phenomena and their impacts often cause mis‐coordination or mal‐operation. The proposed algorithm samples three phase current waveforms and imports them to CWT to extract high frequency coefficients. Afterwards, the sum of absolute values of the coefficients, Scoef, is calculated for each sample during the last cycle. Meanwhile, several simulations related to HIFs with different impedances and CT saturations with different severities are executed and the fault currents and the sum of absolute values of the coefficients are achieved and saved in the relay memory as (X, Y) coordinates of points (IL‐fault, SL‐coef), named 'learning data'. Thereafter, each Scoef is imported to the X −Y plane and, consequently, the occurrence of HIF or CT saturation is detected and the real‐fault current is estimated by both non‐linear interpolation and extreme learning machine approaches. Subsequently, new time dial setting and Ipickup of the relays are computed and reloaded. Security, dependability, and sensitivity of the proposed adaptive protection method are confirmed by numerous simulation studies. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
37. Using Learning Analytics to Improve Instructional Support Design for Online Learning.
- Author
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Lin Zhong
- Subjects
INSTRUCTIONAL systems design ,ONLINE education ,MODULAR coordination (Architecture) ,LEARNING modules ,CLASSROOM environment - Abstract
Learning analytics has been demonstrated as a great tool to evaluate quality of instructional design. This study examined how instructors can utilize learning analytics to improve online learning support design. 128 students from a southern university in United States participated in this study. All learning data related to learners and online learning environment was collected and analyzed. Results showed that learning analytics results were very helpful for instructors to improve online instructional support timing, adjust learning module design, and enhance learning materials accessibility. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
38. An online authoring tool for creating activity-based learning objects.
- Author
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Ahn, Jeong, Mun, Gil, Han, Kyung, and Choi, Sook
- Subjects
ACTIVITY programs in education ,ONLINE authorship ,MOBILE learning ,EDUCATIONAL technology ,EXPERIMENTAL methods in education ,TECHNICAL education ,HIGHER education - Abstract
As higher education increasingly relies on e-learning, the need for tools that will allow teachers themselves to develop effective e-learning objects as simply and quickly as possible has also been increasingly recognized. This article discusses the design and development of a novel tool, Enook (Evolutionary note book), for creating activity-based learning objects (LOs). This tool has three general foci: 1) creation of various activity-based objects for experimental learning or learning by doing while simultaneously offering as many learning resources as a real e-book that services as both a textbook and a notebook, dual functions that paperback books can never provide; 2) management of all aspects of live instructor-led classroom training in a network environment, such the Web and or a mobile network; 3) facilitated use of data on students' progress to monitor their understanding of the material and to provide other meaningful information through learning analytics. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
39. Driving Mobile Robots using a Deep LSTM Architecture: An Experimental Approach
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Flavio de Barros Vidal, Carla Mc e Cavalcante Koike, M.V. Lamar, and Thulio Noslen S. Santos
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0209 industrial biotechnology ,General Computer Science ,business.industry ,Computer science ,Deep learning ,Term memory ,Process (computing) ,Mobile robot ,02 engineering and technology ,Learning data ,020901 industrial engineering & automation ,Human–computer interaction ,020204 information systems ,Path (graph theory) ,0202 electrical engineering, electronic engineering, information engineering ,Robot ,Artificial intelligence ,Electrical and Electronic Engineering ,Architecture ,business - Abstract
Driving a mobile robot consists in moving the robot to its goal position quickly, without colliding with obstacles, and with no deviations from the planned path. This process requires a skilled pilot, with expertise and well trained. Among the various techniques available to help pilots to drive mobile robots, many of them are based on automatic learning approaches, as deep learning. This work presents the proposal of a new approaching for applications using a deep Long-Short Term Memory (LSTM) architecture, to assist the driving activities in mobile robots, using data extracted from an expert pilot as the main source of learning data. The main contributions of this proposal are i) a new application of the architecture for deep LSTM; ii) a new information data fusion strategy in the guidance command stage; and iii) a large number of tests in scenarios using a real mobile robot.
- Published
- 2021
40. Information Extraction from Semi-structured Web Documents
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Yun, Bo-Hyun, Seo, Chang-Ho, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Lang, Jérôme, editor, Lin, Fangzhen, editor, and Wang, Ju, editor
- Published
- 2006
- Full Text
- View/download PDF
41. An Improvement Approach for Word Tendency Using Decision Tree
- Author
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Atlam, El-Sayed, Ghada, Elmarhomy, Fuketa, Masao, Morita, Kazuhiro, Aoe, Jun-ichi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Khosla, Rajiv, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor
- Published
- 2005
- Full Text
- View/download PDF
42. Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption
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Peña, Jose M., Björkegren, Johan, Tegnér, Jesper, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, and Godo, Lluís, editor
- Published
- 2005
- Full Text
- View/download PDF
43. Recognition and Retrieval of Face Images by Semi-supervised Learning
- Author
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Inoue, Kohei, Urahama, Kiichi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Aizawa, Kiyoharu, editor, Nakamura, Yuichi, editor, and Satoh, Shin’ichi, editor
- Published
- 2005
- Full Text
- View/download PDF
44. A Self-Learning Data-Driven Development of Failure Criteria of Unknown Anisotropic Ductile Materials with Deep Learning Neural Network
- Author
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Gun Jin Yun and Kyungsuk Jang
- Subjects
Artificial neural network ,Ductile materials ,business.industry ,Computer science ,Deep learning ,Computer Science Applications ,Learning data ,Biomaterials ,Development (topology) ,Mechanics of Materials ,Modeling and Simulation ,Artificial intelligence ,Electrical and Electronic Engineering ,business - Published
- 2021
45. Expression of gear-tooth-flank deviations for Hobbing-Machine-Diagnosis system (Learning-data collection through hobbing simulation and their compression with network representation)
- Author
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Daisuke Iba, Ichiro Moriwaki, Koichiro Uriu, Hiroki Inoue, and Kunitoshi Kawano
- Subjects
Hobbing ,Flank ,Gear tooth ,Computer science ,business.industry ,Compression (functional analysis) ,Representation (systemics) ,Pattern recognition ,Artificial intelligence ,business ,Expression (mathematics) ,Learning data - Published
- 2021
46. Pengaruh Kecemasan Matematika Siswa Kelas VII Terhadap Hasil Belajar Di SMP Negeri 3 Kota Bengkulu
- Author
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Sintia Lorenza, Fatrima Santri Syafri, Lola Anggun Nopela, and Amelia Lestari
- Subjects
T57-57.97 ,education.field_of_study ,Applied mathematics. Quantitative methods ,Mathematics anxiety ,education ,Population ,General Medicine ,Test (assessment) ,Learning data ,Normality test ,Linearity test ,QA1-939 ,Mathematics education ,Psychology ,Mathematics - Abstract
This research is a study that discusses the influence of mathematics on grade VII students—learning outcomes at SMP NEGERI 3 Kota Bengkulu. This study's population was all students of class VII SMP NEGERI 3 Bengkulu City, which consisted of 130 famous students in 5 classes. Samples were taken using the random sampling technique. Then, a sample of class VII.2 was chosen, possibly 20 students. This study using a questionnaire and documentation. The data were obtained using a questionnaire, while the documentation in this study was used to obtain learning data obtained from teacher data in the form of daily math test scores. In the research analysis, prerequisite tests were used, such as the normality test, linearity test, and product moment test with SPSS version 22. The analysis results obtained a value of 0.754, which indicates that the relationship's level is healthy between the two variables. There is a positive sign indicating that the property is valuable. Positive and unidirectional. The results of the analysis using Pearson products when the significance value was obtained 0.000. The results of this study indicate that there is a significant relationship between the effect of mathematics on learning outcomes at SMP NEGERI 3 Kota Bengkulu.Keywords: Mathematics anxiety, learning outcomes
- Published
- 2020
47. Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data
- Author
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Gorzałczany, Marian B., Kacprzyk, Janusz, editor, and Gorzałczany, Marian B.
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- 2002
- Full Text
- View/download PDF
48. Fuzzy neural network for system modelling and control
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Gorzałczany, Marian B., Kacprzyk, Janusz, editor, and Gorzałczany, Marian B.
- Published
- 2002
- Full Text
- View/download PDF
49. Development and Implementation of Adaptive Learning to Engage Learners in Engineering Technology
- Author
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Florence Martin, Angela Claris, Nicole Barclay, and Carl D. Westine
- Subjects
Behavioral engagement ,Construction management ,Focus (computing) ,Computer science ,ComputingMilieux_COMPUTERSANDEDUCATION ,Mathematics education ,Educational technology ,Personalized learning ,Adaptive learning ,Student learning ,Learning data - Abstract
Adaptive learning uses computers to provide personalized learning pathways for students. This project explores the use of an adaptive learning module implemented in a sophomore level course for civil engineering technology and construction management students with an instructional focus on “Pumps.” The research goal of this case study is to examine student learning and behavioral engagement when an adaptive learning module is introduced. The adaptive learning module was designed to engage students in personalized instruction and was used as a supplement to the instructor’s in-class lectures on the topic. The researchers gathered and analyzed 42 students’ learning data on learning, performance, and user pathways on the adaptive learning platform Smart Sparrow. In total, 81% of students demonstrated mastery across all modules by successfully answering all assessment questions. Furthermore, 65% interacted with at least one adaptive learning module due to assessment, and 24% had more than one interaction, suggesting students were able to efficiently resolve uncertainty within the lesson. Additionally, correct responses for students viewing adaptive content were associated with increased time spent reviewing adaptive content, demonstrating the usefulness of an adaptive learning program. Student responses to a follow-up survey reflect an overall positive experience and also highlight opportunities to improve the module in future iterations.
- Published
- 2020
50. Development of Autonomous Vehicle Learning Data Generation System
- Author
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Hyungjoo Kim, Kyungil Lim, June Hong, Seungje Yoon, Jaehwan Kim, and Jiwon Jung
- Subjects
Development (topology) ,Human–computer interaction ,Computer science ,Learning data - Published
- 2020
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