Results 1 to 10 of about 60,104 (158)
Design features of the synthetic learning environment
Educational Technology Quarterly, 2021 The article considers the features of the learning transformation in the transition from the usual material-object environment to learning in the digital synthetic environment.
Oleksandr Burov
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Informatics, 2023 During the pandemic, universities were forced to convert their educational process online. Students had to adapt to new educational conditions and the proposed online environment.
Malinka Ivanova, Tsvetelina Petrova
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Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022 Typos corrected ...
Matthew Finlayson +3 more
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SYNTHETIC LEARNING ENVIRONMENT: DESIGN FEATURES
Інформаційні технології і засоби навчання, 2018 The article discusses the transformation of teaching in the transition from traditional material-object environment to learning in a digital synthetic environment. Attention is paid to the fact that, to date, students prefer online and mixed learning, in
Evgeniy P. Popechitelev +1 more
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Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment [PDF]
Journal of Clinical Medicine, 2020 We investigated the capability of a trained deep learning (DL) model with a convolutional neural network (CNN) in a different scanning environment in terms of ameliorating the quality of synthetic fluid-attenuated inversion recovery (FLAIR) images. The acquired data of 319 patients obtained from the retrospective review were used as test sets for the ...
Soo Buem Cho, Ji Young Ha, Hye Jin Baek
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Educational Technology Quarterly, 2023 Learning focused on assimilation of facts, availability of information, free access to knowledge bases and convenient navigation in local and global networks is not a sufficient condition for the formation of an educated personality, active cognitive ...
Oleksandr Yu. Burov, Olha P. Pinchuk
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Learning Dense Correspondence from Synthetic Environments
2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2022 Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing methods map manually labelled human pixels in real 2D images onto the 3D surface, which is prone to human error, and the sparsity of available annotated data often leads to ...
Mithun Lal +5 more
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Extended reality in digital learning: influence, opportunities and risks’ mitigation
Освітній вимір, 2021 The paper discusses AR/VR/MR/XR technologies in learning namely their influence/ opportunity and risks’ mitigation. Main aspects are as follows: methodology (factors influencing a student’s cybersickness in AR/VR/MR/XR, the improved model of the ...
Oleksandr Burov, Olga Pinchuk
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Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies
CoRR, 2021 This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as a neural network.
Fabio Ferreira +2 more
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Learning to Localize in New Environments from Synthetic Training Data [PDF]
2021 IEEE International Conference on Robotics and Automation (ICRA), 2021 Most existing approaches for visual localization either need a detailed 3D model of the environment or, in the case of learning-based methods, must be retrained for each new scene. This can either be very expensive or simply impossible for large, unknown environments, for example in search-and-rescue scenarios.
Winkelbauer, Dominik +2 more
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