The EUROSCIENTIA project aims at combining searching academics from various knowledge areas around a French-German knot. It is devoted to the study of state knowledge building and institutionalization as it developed in many different contexts between 1750 and 1850. "State Knowledge" is here to be understood as multiple knowings, which were conceived and reshaped both for and by states and there active brains. The main goal is to reach a better understanding of how the Republic of letters converted into a national and transnational (as international) web. The spatial metaphor leads as commonly to an abstract topology (dedicated to knowledge areas insofar as they remain contiguous and dependent) and to an objective geography addressing to localities, to flows and webs intertwining capital cities and territories. A particular attention will be granted to the impact of territorial metamorphoses which occured from revolutionary wars and French first Empire manoeuvres. As a French-German development, this very project will allow various working stances including both distance and proximity : distance between institutions and knowledge practices, territorial and political proximity. Furthermore, knowledge pay-backs from non european worlds will not be outcasted. Working days, seminars and a doctoral workshop will punctuate this program. An open-access web-site will bring to the academic community the team main achievements and a dynamic mapping dedicated to state knowledges in Europe. As a closing point, a collective work will be published (in English-language).

<< Objectives >>This project will lead to positive results for our target groups. Within this project it is aimed to improve tests for determining and then to develop modules for alleviating the learning losses of immigrant students on STEAM approach through a problem-based learning. Besides, it is aimed to support their social inclusion, to build capacity of less experienced and newcomer organizations, to attract and widen access for them to the programme and work on a new e-scoter in an eco-friendly way.<< Implementation >>The activities that will be as below; - Preparation of Project Management Tools. -Preparation of pre-test, post-test and achievement tests to determine learning losses of immigrant students on STEAM approach. – Analysis of the obtained data.-Determination of learning losses of immigrant students. -Developing of Modules on STEAM approach. –Training of trainers.–Pilot training for immigrant students through Project Based Learning methodology.- Practicing on a eco-friendly scooter.<< Results >>The expected results are to encourage national and international communication and coordination, to increase the human resources and institutional capacity of less experienced and newcomers, to develop tests to determine the learning losses of immigrant students on STEAM, to uptake innovative approach (PBL) and digital technologies for the trainers, to develop modules, to train immigrant students, to practice on an eco-friendly scooter, to build bridges between the school and the environment.

Engaging children with ASC (Autism Spectrum Conditions) in communication centred activities during educational therapy is one of the cardinal challenges by ASC and contributes to its poor outcome. To this end, therapists recently started using humanoid robots (e.g., NAO) as assistive tools. However, this technology lacks the ability to autonomously engage with children, which is the key for improving the therapy and, thus, learning opportunities. Existing approaches typically use machine learning algorithms to estimate the engagement of children with ASC from their head-pose or eye-gaze inferred from face-videos. These approaches are rather limited for modeling atypical behavioral displays of engagement of children with ASC, which can vary considerably across the children. The first objective of EngageME is to bring novel machine learning models that can for the first time effectively leverage multi-modal behavioural cues, including facial expressions, head pose, vocal and physiological cues, to realize fully automated context-sensitive estimation of engagement levels of children with ASC. These models build upon dynamic graph models for multi-modal ordinal data, based on state-of-the-art machine learning approaches to sequence classification and domain adaptation, which can adapt to each child, while still being able to generalize across children and cultures. To realize this, the second objective of EngageME is to provide the candidate with the cutting-edge training aimed at expanding his current expertise in visual processing with expertise in wearable/physiological, and audio technologies, from leading experts in these fields. EngageME is expected to bring novel technology/models for endowing assistive robots with ability to accurately ‘sense’ engagement levels of children with ASC during robot-assisted therapy, while providing the candidate with a set of skills needed to become one of the frontiers in the emerging field of affect-sensitive assistive technology.