Axe SIC
Chargée de mission Axe SIC (Systèmes Interactifs et Cognitifs) du LIG avec Renaud Blanch (2016-2020)
Sondage thèmes et applications ouvert jusqu'au 10 janvier 2019
Prochains séminaires d'axe
Aucun séminaire prévu pour l'instant...
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)
8 Juin 2016, 13h30, salle de séminaire du nouveau bâtiment IMAG
Andres Diaz-Pace, Professeur UNICEN, Tandil (Argentine). Contact mailto:adiaz@exa.unicen.edu.ar
Organisatrice : Carole Adam
Titre : Agent-based Negotiation Techniques for improving Tradeoffs in Group Decision Making.
12 Avril 2016, 13h30, amphi MJK
Falk Scholer, Associate Professor au RMIT University, Melbourne (Australie)
Organisatrice : Carole Adam
Titre : Evaluating Information Retrieval Systems
Détails : ici
14 Avril 2016, 13h, amphi 022, UFR IM2AG.
Jean Vanderdonckt, Professeur à l'Université Catholique de Louvain (Belgique)
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.