Axe SIC

Chargée de mission Axe SIC (Systèmes Interactifs et Cognitifs) du LIG avec Renaud Blanch (2016-2020)

Axe SIC - Wiki

Sondage thèmes et applications ouvert jusqu'au 10 janvier 2019

Séminaires passés

• Svitlana Galeshchuk (Ternopil National University in Economics). Mardi 20 Septembre 2017, 10h, IMAG 306. Details. Deep Learning for Exchange-Rates Prediction

• Jaroslaw Kozlak (AGH University of Science and Technology, Krakow, Pologne). Lundi 5 septembre 2017 à 16h en salle 306. Analysis of social media and international news using social network analysis and data mining methods.

• Journée de suivi des doctorants de 1e année le 8 juin 2017. Inscription ici  (fermée)

• Andres Diaz-Pace, Professeur UNICEN, Tandil (Argentine). Contact mailto:adiaz@exa.unicen.edu.ar. 8 Juin 2016, 13h30, salle de séminaire du nouveau bâtiment IMAG. Agent-based Negotiation Techniques for improving Tradeoffs in Group Decision Making.

• Falk Scholer, Associate Professor au RMIT University, Melbourne (Australie). 12 Avril 2016, 13h30, amphi MJK. Evaluating Information Retrieval Systems. Détails : ici

• Jean Vanderdonckt, Professeur à l'Université Catholique de Louvain (Belgique). 14 Avril 2016, 13h, amphi 022, UFR IM2AG. Organisatrice : Sophie Dupuy-Chessa. Titre : Machine Learning for Improving Adaptive User Interfaces. Résumé : Although adaptive user interfaces are aimed at optimizing the end user's performance and/or preference, they are known as suffering from a series of shortcomings: user cognitive disruption (the end user is disrupted by the adaptation), lack of predictability (the end user does not know when and how a user interface will be adapted by the system), lack of explanation (the system rarely provides the end user with some explanation on why this adaptive process took place), and the lack of user involvement (the end user is rarely given the opportunity to intervene in the adaptivity process). In order to address these challenges, machine learning techniques, combined with some end-user development, offer a promising opportunity for improving the whole process, but also introduces new challenges. This presentation will review open issues in the domain and demonstrates two software applying machine learning techniques for intelligent widget selection and adaptive layout of graphical user interfaces based on task model.