Un modèle d’innovation pour l’enseignement public : le Cyber Home Learning System coréen

Depuis 2004, le ministère de l’Éducation et du développement des ressources humaines, le KERIS (Korea Education & Research Information Service) et seize bureaux de l’éducation métropolitains et provinciaux ont participé ensemble à la mise en œuvre du Cyber Home Learning System, un système d’apprentissage en ligne à l’échelle de toute la Corée du Sud. Le Cyber Home Learning System permet aux élèves d’utiliser un système d’apprentissage personnalisé. Il est constitué d’un Learning Management Sy..

Unsupervised training of denoisers for low-dose CT reconstruction without full-dose ground truth

Department of Electrical EngineeringRecently, deep neural network (DNN) based methods for low-dose CT have been investigated to achieve excellent performance in both image quality and compu- tational speed. However, almost all methods using DNNs for low-dose CT require clean ground truth data with full radiation dose to train the DNNs. In this work, we attempt to train DNNs for low-dose CT reconstructions with reduced tube current by investigating unsupervised training of DNNs for denoising sensor measurements or sinograms without full-dose ground truth images. In other words, our proposed methods allow training of DNNs with only noisy low-dose CT measurements. First, the Poisson Unbiased Risk Estimator (PURE) is investigated to train a DNN for denoising CT measurements, and a method is proposed for reconstructing the CT image using filtered back-projection (FBP) and the DNN trained with PURE. Then, the CT forward model-based Weighted Stein???s Unbiased Risk Estimator (WSURE) is proposed to train a DNN for denoising CT sinograms and to subsequently re- construct the CT image using FBP. Our proposed methods achieve excellent per- formance in both fast computation and reconstructed image quality, which is more comparable to the results of the DNNs trained with full-dose ground truth data than other state-of-the-art denoising methods such as the BM3D, Deep Image Prior, and Deep Decoder.clos

ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts

Recent success of large-scale Contrastive Language-Image Pre-training (CLIP) has led to great promise in zero-shot semantic segmentation by transferring image-text aligned knowledge to pixel-level classification. However, existing methods usually require an additional image encoder or retraining/tuning the CLIP module. Here, we propose a novel Zero-shot segmentation with Optimal Transport (ZegOT) method that matches multiple text prompts with frozen image embeddings through optimal transport. In particular, we introduce a novel Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an optimal mapping between multiple text prompts and visual feature maps of the frozen image encoder hidden layers. This unique mapping method facilitates each of the multiple text prompts to effectively focus on distinct visual semantic attributes. Through extensive experiments on benchmark datasets, we show that our method achieves the state-of-the-art (SOTA) performance over existing Zero-shot Semantic Segmentation (ZS3) approaches.Comment: 18pages, 8 figure

Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge

Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. Although diffusion models have achieved remarkable progress in recent years, they have limitations in the unpaired image-to-image translation tasks due to the Gaussian prior assumption. Schr\"odinger Bridge (SB), which learns an SDE to translate between two arbitrary distributions, have risen as an attractive solution to this problem. However, none of SB models so far have been successful at unpaired translation between high-resolution images. In this work, we propose the Unpaired Neural Schr\"odinger Bridge (UNSB), which combines SB with adversarial training and regularization to learn a SB between unpaired data. We demonstrate that UNSB is scalable, and that it successfully solves various unpaired image-to-image translation tasks. Code: \url{https://github.com/cyclomon/UNSB

L’impact des TICE sur la formation des enseignants en Corée

Depuis la fin des années 90, la Corée a entrepris d’améliorer la qualité de l’enseignement en promouvant l’utilisation des TICE, vues comme donnant aux enseignants la possibilité d’améliorer leur maîtrise de l’information. À mesure que les technologies progressaient, la formation des enseignants s’est transformée pour adapter les nouvelles technologies à la situation de classe et pour développer les aptitudes des enseignants. Le « Plan TICE », depuis 2002, s’adresse à plus de 30 % des enseignants, ainsi qu’aux chefs d’établissements et autres personnels d’éducation. Les nouveaux programmes de formation aux TICE prennent en compte toute la durée de la carrière des enseignants et les nouvelles technologies.Since the end of the 1990s, Korea has been striving to improve teaching quality by promoting the use of educational ICT (EICT), seen as offering teachers the possibility to improve their mastery of their subjects. As technology has progressed, teacher training has been transformed in order to adapt such new technologies to classroom situations and to develop teachers’ skills. The “EICT Plan”, operational since 2002, concerns over 30% of teachers, heads of institutions and other education personnel. The new EICT training programmes take the teachers’ length of experience and the new technologies into account.Desde finales de los años 90, Corea se ha propuesto mejorar la calidad de la enseñanza promoviendo el uso de las TICE, considerando que ofrecen a los profesores la posibilidad de mejorar su dominio de la información. A medida que iban progresando las tecnologías, la formación de los profesores se ha ido transformando para adaptar las nuevas tecnologías a la situación de las clases y para desarrollar las aptitudes de los profesores. El “Plan TICE”, lanzado desde 2002, se dirige a más del 30% de los profesores, así como a los directores de establecimientos y a otros personales de la educación. Los nuevos programas de formación sobre las TICE tienen en cuenta todo el periodo de la carrera de los profesores y las nuevas tecnologías

Scientists and engineers in convergence technologies in Korea: where are they going and how do they collaborate?

Today convergence technologies have become a major issue in science policy. This paper describes the current state of scientific collaboration in convergence technologies among researchers in South Korea, by conducting survey and the Social Network Analysis (SNA) with a data set of 1,095 researchers who have involved in the development of the convergence technologies. The main research findings are fivefold. First, dominant numbers of researchers are involved in convergence technology with IT because IT is recognized as the most competitive technology in Korea. Second, mobility of researchers is active in convergence technologies. Third, it is found that the researchers in convergence technologies are more productive in terms of the number of research papers per capita than those in other scientific fields. Fourth, they, however, show limited research collaboration, compared with their high productivity. Finally, the members of the network in convergence technologies are closer to each other than those in other scientific fields, but most of their collaborative relationships remain bilateral rather than triangular. Only a few researchers act as hubs, revealing that collaborative research relationship in convergence technologies in Korea is highly concentrated. At the last part, some policy recommendations to promote research collaboration in convergence technologies are discussed

Map API-Based Evacuation Route Guidance System for Floods

Recently, human casualties and property damage caused by natural disasters have increased worldwide. Among these natural disasters, flood damage is affected by season. Depending on the concentration of precipitation in the summer, heavy rainfall can occur, thus resulting in typhoons, floods, and increased damage. To prevent such damages, the appropriate measures and research are being conducted in response to disasters. When a flash flood occurs, safe evacuation can be realized after detecting the situation and using announcements or laser indicators. However, these route guidance systems are typically used in fire or indoor environments, thus rendering them difficult to access outdoors. Therefore, we herein propose an evacuation route guidance system based on a map API that recognizes flood occurrences in forest areas, recreational forests, and parks. It calculates the route based on the map API and delivers the evacuation route to the nearest shelter to the user; and if there is a second problem on the moving evacuation route and it is difficult to proceed, the user’s current location is identified and the route to the next nearest shelter is provided. This will help you to evacuate safely

Efficient Module Based Single Image Super Resolution for Multiple Problems

Example based single image super resolution (SR) is a fundamental task in computer vision. It is challenging, but recently, there have been significant performance improve-ments using deep learning approaches. In this article, we propose efficient module based single image SR networks (EMBSR) and tackle multiple SR problems in NTIRE 2018 SR challenge by recycling trained networks. Our proposed EMBSR allowed us to reduce training time with effectively deeper networks, to use modular ensemble for improved performance, and to separate subproblems for better per-formance. We also proposed EDSR-PP, an improved ver-sion of previous ESDR by incorporating pyramid pooling so that global as well as local context information can be utilized. Lastly, we proposed a novel denoising / deblurring residual convolutional network (DnResNet) using residual block and batch normalization. Our proposed EMBSR with DnResNet and EDSR-PP demonstrated that multiple SR problems can be tackled efficiently and effectively by win-ning the 2nd place for Track 2 (??4 SR with mild adverse condition) and the 3rd place for Track 3 (??4 SR with diffi-cult adverse condition). Our proposed method with EDSR-PP also achieved the ninth place for Track 1 (??8 SR) with the fastest run time among top nine teams