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Due to the current events related to Covid-19, the conference originally planned for July 2020 has been postponed to April 7-9, 2021.The conference will be organized virtually by videoconference. ORAL; International audience; In this paper, a study of UWB receivers in terms of detection theory is presented. The UWB radar which is presented in many works previously [1]-[3] has many applications. For road UWB radar application, the receiver based on correlation is the optimum receiver [4]. In fact, it maximizes the probability of detection. We will consider, in this study, a correlator receiver based on a threshold detection method. As in narrowband [5] [6], we will describe the theoretical study that evaluates the performance of the UWB receiver based on correlation in terms of detection and false alarm probabilities. Then a study of curves showing threshold receiver operating characteristics (ROC system), based on correlation and destined to be used for a UWB radar is presented. The study is original because it is presented for the first time in a UWB radar system.

Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V Lecture Notes in Computer Science, 12370 ISBN:978-3-030-58594-5 ISBN:978-3-030-58595-2 ISSN:0302-9743 ISSN:1611-3349

International audience; The Robots for Learning workshop series aims at advancing the research topics related to the use of social robots in educational contexts. This year's half-day workshop follows on previous events in Human-Robot Interaction conferences focusing on efforts to discuss potential benchmarks in design, methodology and evaluation of new robotics systems that help learners. In this 6th edition of the workshop, we will be investigating in particular methods from technologies for education and online learning. Since the past few months, online and remote learning has been put in place in several countries to cope with the health and safety measures due to the Covid-19 pandemic. In this workshop, we aim to discuss strategies to design robotics system able to provide embodied assistance to the remote learners and to demonstrate long-term learning effects.

Our human community face a pandemic, which has lead us to lockdown half of world inhabitants. We are so disarmed to deal with the coronavirus (SARS-CoV-2) that lockdown seems us the only countermeasure suitable to stop the pandemic, whatever the resulting economic cost could be. First publications focus on disease characteristics, on disease infectivity, on available treatments, on ICU medical care, on lockdown but not on medical countermeasure simulation. In order to fill this gap, we propose a flow model, which calculates by period the people in different disease stages and under medical treatments and which propagates flow of people between periods. We use a discrete representation of the SIR model over a horizon of six months. Cplex solver has been chosen as a simulator. Notre communauté fait face à une pandémie, qui nous a conduit à confiner la moitié des habitants de notre planète. Nous sommes désarmés face au coronavirus (SRAS-CoV-2) et le confinement semble être la seule contre-mesure apte à stopper la pandémie, quel que soit le coût économique qui en résulte. Les premières publications portent sur les caractéristiques de la maladie, sur la contagiosité de la maladie, sur les traitements de première intention disponibles, sur les soins médicaux en unités de soins intensifs et sur le confinement. Nous n'avons pas trouvé d'articles traitant de la simulation de la pandémie de COVID-19 afin d'évaluer et de dimensionner les contre-mesures médicales.

International audience; The Faculty of Education and Lifelong Learning of the University of Strasbourg implemented a competency-based approach during the academic year of 2020-2021. This study presents empirical research on students' perspectives of their self-regulation and self-direction in learning as one of the first feedbacks on this project. This research was conducted in an online course organised during the Covid-19 pandemic. It was hypothesised that the competency-based approach positively impacted students' self-direction within the course and their self-regulation in an online learning context. Anonymous preand post-surveys were conducted. The results confirmed the research hypothesis. The design of the course presented could serve as a valuable input to define appropriate learning scenario to increase students' self-regulation and self-direction in elearning.

Abstract Background Primary ciliary dyskinesia (PCD) is a rare congenital respiratory disorder characterized by abnormal ciliary motility leading to chronic airway infections. Qualitative evaluation of ciliary beat pattern based on digital high-speed videomicroscopy analysis has been proposed in the diagnosis process of PCD. Although this evaluation is easy in typical cases, it becomes difficult when ciliary beating is partially maintained. We postulated that a quantitative analysis of beat pattern would improve PCD diagnosis. We compared quantitative parameters with the qualitative evaluation of ciliary beat pattern in patients in whom the diagnosis of PCD was confirmed or excluded. Methods Nasal nitric oxide measurement, nasal brushings and biopsies were performed prospectively in 34 patients with suspected PCD. In combination with qualitative analysis, 12 quantitative parameters of ciliary beat pattern were determined on high-speed videomicroscopy recordings of beating ciliated edges. The combination of ciliary ultrastructural abnormalities on transmission electron microscopy analysis with low nasal nitric oxide levels was the “gold standard” used to establish the diagnosis of PCD. Results This “gold standard” excluded PCD in 15 patients (non-PCD patients), confirmed PCD in 10 patients (PCD patients) and was inconclusive in 9 patients. Among the 12 parameters, the distance traveled by the cilium tip weighted by the percentage of beating ciliated edges presented 96% sensitivity and 95% specificity. Qualitative evaluation and quantitative analysis were concordant in non-PCD patients. In 9/10 PCD patients, quantitative analysis was concordant with the “gold standard”, while the qualitative evaluation was discordant with the “gold standard” in 3/10 cases. Among the patients with an inconclusive “gold standard”, the use of quantitative parameters supported PCD diagnosis in 4/9 patients (confirmed by the identification of disease-causing mutations in one patient) and PCD exclusion in 2/9 patients. Conclusions When the beat pattern is normal or virtually immotile, the qualitative evaluation is adequate to study ciliary beating in patients suspected for PCD. However, when cilia are still beating but with moderate alterations (more than 40% of patients suspected for PCD), quantitative analysis is required to precise the diagnosis and can be proposed to select patients eligible for TEM.

International audience; En France, comme dans d’autres pays, la Covid-19 a entraîné l’urgente décision politique de fermer les écoles. Cet impératif nous a conduits à mener une enquête en ligne renseignée par les collégiens en juin 2020, en France et au Vietnam dans le but de faire des propositions concrètes pour améliorer le bien-être subjectif des élèves. Le bien-être à l’école est une évaluation cognitive et affective des expériences vécues à l’école, ce sont les élèves qui sont les mieux placés pour en rendre compte (Florin & Guimard, 2017). Les données recueillies auprès de 422 collégiens français et de 261 collégiens vietnamiens sont analysées de manière comparative et différentielle, afin de rendre compte des diversités des vécus et des représentations dans deux environnements distincts (collège et domicile), dans plusieurs départements français et deux pays de culture différente (France et Vietnam), et touchés différemment par la pandémie, en fonction des caractéristiques sociodémographiques, des conditions de vie des enfants. Les résultats mettent en évidence des différences significatives au niveau des relations sociales, mais également au niveau du rapport aux évaluations. Les élèves des deux pays sont formels, les évaluations sont préférables à domicile.

In this paper, the robustness of tessellation images segmentation using Convolutional Neural Network (CNN) is presented. Particularly, this paper aims to study the effect of the quality and number of the images used for training on the robustness of the edge segmentation and completion. Three kinds of image deterioration are considered: the absence of a part of the tessellation, the discontinuity of the edges constituting the tessellation and the presence of a background noise. Five CNNs are trained with several kinds of deteriorated images and the trained models are then compared according to the results obtained with four classes of images.

Este paper propõe uma metodologia de seleção de modelos epidemiológicos via critério da informação de Akaike trazendo como novidade a construção de uma função de verossimilhança baseada nos resultados de uma análise de sensibilidade global através dosíndices de Sobol obtidos usando expansão em polinômios caos. A ideia geralé incorporar a informação sobre a influência dos parâmetros na resposta para selecionar um modelo mais interessante dentro do conjunto de candidatos. A estratégiaé aplicada a um conjunto de modelos compartimentais compatíveis aos usados para analisar a pandemia de COVID-19 recente, permitindo compará-los sem a presença de dados experimentais. This paper propose a methodology for epidemiological model selection by using Akaike information criteria bringing as novelty the construction of a likelihood function based in the results of a global sensitivity analysis through the Sobol's indices obtained by using polynomial chaos expansion. The main ideia is to incorporate of the information about the influence of the parameters on the response to select a more interesting model inside of a set of candidates. The strategy is applied to a set of compartmental models compatible with those used to analyze the recent COVID-19 pandemic, allowing to compare them without the presence of experimental data.