Bo Jiang
8 Papers
4 Citations
Bo Jiang is an academic researcher. The author has contributed to research in topics: Computer science & Inpainting. The author has an hindex of 2, co-authored 6 publications.
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Papers
Online At-Risk Student Identification using RNN-GRU Joint Neural Networks
TL;DR: A novel recurrent neural network-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.
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Experiment Information System Based on an Online Virtual Laboratory
TL;DR: In this paper, the authors introduce virtual technology into an electronic circuit experiment course and explore its teaching strategy, thereby realizing the informatization of experiment teaching, which can promote the development of students abilities in active learning, reflective thinking, and creativity.
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Experience of Online Learning from COVID-19: Preparing for the Future of Digital Transformation in Education
TL;DR: In this article , the authors examined the different characteristics and challenges that virtual tools brought to online education in the pre-pandemic and pandemic era, with the aim of providing experience of how virtual tools supported purely online learning during a health crisis.
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Adaptive Slicing-Aided Hyper Inference for Small Object Detection in High-Resolution Remote Sensing Images
TL;DR: Adaptive Slicing Aided Hyper Inference (ASHI) as discussed by the authors adaptively adjusts the slicing size to control the number of slices according to the image resolution, which can dramatically reduce redundant computation using an adaptive slicing size.
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SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting
TL;DR: Zhang et al. as discussed by the authors proposed a general deep learning framework for high-resolution image inpainting, which first hallucinates a semantically continuous blurred result using low-resolution in-painting and then reconstructs high-frequency details with original resolution using super-resolution refinement.
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