Dans un contexte d'efficience pour le patient, avec une durée moyenne de séjour qui diminue, l'interprofessionnalité est une priorité dans le parcours patient. Dans cet objectif, l'interfiliarité est initiée à l'IFSI/IFAS Jura Nord de Dole, dès l'entrée en formation, afin de développer la collaboration entre les infirmiers et les aides-soignants. Dans cette optique, depuis trois années, les étudiants infirmiers et les élèves aides-soignants de l'IFSI/IFAS Jura Nord de Dole se rencontrent afin d'échanger sur les représentations de leur profession, leurs activités respectives.
Jusqu'à ce jour ces échanges reposaient essentiellement sur des questions autour des transmissions. Ils étaient basés sur l'oralité, aucune trace écrite n'étant exigée. D'années en années, cette séquence a mis en exergue une interrogation autour de la toilette comme acte de soin.
Guest blog by Dr. Jeremy Roschelle, Digital Promise, @roschelle63 Summary: When integrated with curriculum and pedagogy, visual representations that change in time can improve students’ conceptual understanding of mathematics. To understand mathematics, students need to connect ideas. For example, the slope of a line is often given as a number — the m in y…
In this tutorial we look at the word2vec model by Mikolov et al. This model is used for learning vector representations of words, called "word embeddings".
Cambridge Journals Online (CJO) is the e-publishing service for over 230 journals published by Cambridge University Press and is entirely developed and hosted in-house. The platform's powerful capacity and reliable performance are maintained by a combination of our own expertise and a process of consultation with the library and research communities. With the help of these stakeholders, we maintain CJO as an industry-leading e-publishing service.
Classical knowledge representation methods traditionally work with established relations such as synonymy, hierarchy and unspecified associations. Recent developments like
ontologies and folksonomies show new forms of collaboration, indexing and knowledge representation and encourage the reconsideration of standard knowledge relationships. In a
summarizing overview we show which relations are currently utilized in elaborated knowledge representation methods and which may be inherently hidden in folksonomies and ontologies.
This paper presents a work in progress whose
purpose is to model the handled, acquired, correct and
erroneous knowledge of individual learners engaged in
learning activities through virtual learning environments.
This knowledge is represented according to a cognitivecomputational
model which also serves to represent the
domain knowledge via an authoring tool. The latter
generates structures that allow the tutor to provide an
effective feedback to improve significantly the cognitive
level of the learner.
Source vs. Resource Ontology The notion of a resource is fundamental in current networked information systems. The term "resource" is used often, specifically in relation the World Wide Web and the W3C's semantic web activity, in standards such as Resour
FreePharma is a software plug-in that analyzes drug prescription information expressed in free natural language (written or spoken) and structures it automatically for integration in host applications. FreePharma can derive the dose, route, frequency etc.
FreePharma is a software plug-in that analyzes drug prescription information expressed in free natural language (written or spoken) and structures it automatically for integration in host applications. FreePharma can derive the dose, route, frequency etc.
D. Schlör, J. Pfister, and A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), page 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
S. Straka, M. Koch, A. Carolus, M. Latoschik, and C. Wienrich. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, New York, NY, USA, Association for Computing Machinery, (2023)
A. Dockhorn. IEEE Computational Intelligence Magazine, 17 (4):
52-53(November 2022)This is an immersive article. Therefore, extended interactive ressources are provided at the publisher webpage. A pre-print can be found at: https://aiexplained.github.io/.
Q. Le, and T. Mikolov. Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research, page 1188--1196. Bejing, China, PMLR, (June 2014)
M. Ryabinin, S. Popov, L. Prokhorenkova, and E. Voita. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), page 7317--7331. Online, Association for Computational Linguistics, (November 2020)
Z. Zhang, X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, page 1441--1451. Florence, Italy, Association for Computational Linguistics, (July 2019)
Y. Liang, S. Ke, J. Zhang, X. Yi, и Y. Zheng. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence Organization, (July 2018)
J. Frey, и S. Hellmann. 13th International Conference on Semantic Systems Proceedings (SEMANTiCS 2017) - Posters & Demonstrations Track, (September 2017)