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Concept

Expérience

Résumé

L'expérience est la connaissance acquise à travers l'interaction avec l'environnement. Une expérience est un enchaînement d'événements dont on peut tirer une leçon par un retour d'expérience. La connaissance issue de l'expérience s'oppose à celle qui relève d'une élaboration théorique. L'expérience d'une personne est l'ensemble des savoirs qu'elle a acquis par la pratique, et non seulement par un enseignement formel. Lorsque cette connaissance est socialement partagée et construite dans l'intersubjectivité, il s'agit d'expérience sociale. En philosophie, l'expérience est un concept central du pragmatisme, par opposition aux doctrines idéalistes qui supposent une connaissance a priori. Une expérience scientifique est une interaction avec l'environnement destinée à vérifier une hypothèse dans le cadre d'une théorie réfutable. L'observateur définit et note précisément les conditions de réalisation afin qu'elle soit reproductible. La méthode scientifique codifie des règles d'expérimen

Source officielle

https://fr.wikipedia.org/wiki/Exp%C3%A9rience

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La philosophie, du grec ancien (composé de , « aimer », et de , « sagesse, savoir »), signifiant littéralement « amour du savoir » et communément « amour de la sagesse », est une démarche qui vise

Lépistémologie (du grec ancien « connaissance vraie, science » et / « discours ») est d'abord l'étude de la connaissance scientifique. Au sens actuel, l'épistémologie désigne également l'étude criti

thumb|right|200px|Représentation des facultés de l'esprit humain (de Robertus de Fluctibus, 1619) L'esprit est la totalité des phénomènes et des facultés mentales : perception, affectivité, intuiti

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Institutional ELN/LIMS deployment Highly customizable ELN/LIMS platform as a cornerstone of digital transformation for life sciences research institutes

Nicolas Argento

The systematic recording and management of experimental data in academic life science research remains an open problem. EPFL engaged in a program of ELN/LIMS deployment 6 years ago, encountering a host of fundamental questions at the institutional level and in each single laboratory. Here, based on our experience we aim to share with research institute managers, PIs, and any scientists involved in an ELN/LIMS deployment, helpful tips and tools to surround yourself with the right people and the right software at the right time. In this article we describe the resources used, the challenges, key success factors, and the results obtained at each phase of our project. Finally, we discuss the current and next challenges and how our experience leads us to support the creation of a new position in the research groups: the laboratory data manager.

2020

Chargement

Preventing Breaks in Embodiment in Immersive Virtual Reality

Thibault Serge Mario Porssut

Virtual reality (VR) is immersive not only because of visual integration, but because we can act and perform in a virtual environment (VE). Beyond the mere fact that a VR system provides images computed directly from the users' viewpoint in stereoscopy, it can also provide a natural way to interact within the VE using the entire body. Full-body motion capture allows the user's movement to be mapped to a virtual body. Such a mapping may let users feel in control of (sense of agency), own (sense of body ownership) and locate themselves inside this virtual body (sense of self-location), thus leading them to feel embodied in this virtual body whenever these conditions are met (sense of embodiment (SoE)). Hence the integration of a full body avatar in VR is essential, as it brings even more; experiencing being someone else, train to recover mobility or having superhuman abilities. Most importantly, the avatar mediates the virtual nature of the VE to make it seamless for the user. Thus, this thesis focuses on helping the users execute complex movements in VR by applying a distortion to their movements. However, such a distortion is efficient only if users, when assisted, do not experience a break in embodiment (BiE), i.e., do not abruptly lose their sense of embodiment. Moreover, the way the virtual body is perceived is not consistent between users and some users might accept or reject a virtual body more easily than others. Therefore, this thesis explores how to prevent a BiE while distorting users' movement and adapting the VR experience to each individual. To this end, we designed a system combining brain-computer interface and machine learning to detect when users experience a BiE, compute users' preferences, and adapt the VR application's parameters. We first identified the user`s threshold for perceiving a movement distortion in some critical contexts, such as articular limits. We designed a new distortion to help users execute a complex movement. We introduced a method based on machine learning to find users' preferences when using our new distortion. We also established a link between the brain's error monitoring mechanism and the cognitive process of embodiment. This link exposed a new implicit EEG marker indicating when users experience a BiE. Finally, we designed a system to calibrate the distortion implicitly based on users' preferences with all these elements. This system first consists of detecting a BiE during a continuous movement thanks to state-of-the-art brain-computer interface algorithms. Our machine learning method then computes users' preferences, and the distortion is adapted dynamically. This original approach demonstrates the use of an implicit feedback loop to help users while preventing any BiE. Our framework opens an exciting perspective for personalized and self-adapting embodiment systems.

EPFL2021

Chargement

Google AI Experiments [3] is a set of simple experiments that showcase innova- tive uses of AI in UX. As part of this collection, Google shared Quick Draw, where user draws simple sketches and intelligent agent tries to guess what it depicts, and Sketch- RNN interactive demo, an experience where user starts a sketch and recurrent neural network model completes it [4]. These experiences provide fun and engaging drawing activities which have potential to be used in more educational context. This report describes CoPainter - an interactive AI drawing experience which aims to provide an educational activity based on learning by teaching paradigm - the application asks the user to draw a painting of some object in cooperation with an intel- ligent agent (which can either be a robot or an abstract entity). The activity is based on Sketch-RNN model mentioned above. Main contributions of this project are porting the Sketch-RNN web app experience to Qt, which enables integration with CoWriter code- base and makes it possible to collect more data on user activities. An additional draw in turns game mode is also introduced. The activity may possibly be extended to enable it to be used in diagnosing and treating dysgraphia, which is briefly mentioned in this re- port and is aim of further research on this project.

2019

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Preventing Breaks in Embodiment in Immersive Virtual Reality

Thibault Serge Mario Porssut

EPFL2021