Managing Learner’s Affective States in Intelligent Tutoring Systems

Abstract. Recent works in Computer Science, Neurosciences, Education, and Psychology have shown that emotions play an important role in learning. Learner’s cognitive ability depends on his emotions. We will point out the role of emotions in learning, distinguishing the different types and models of emotions which have been considered until now. We will address an important issue con-cerning the different means to detect emotions and introduce recent approaches to measure brain activity using Electroencephalograms (EEG). Knowing the influ-ence of emotional events on learning it becomes important to induce specific emo-tions so that the learner can be in a more adequate state for better learning or memorization. To this end, we will introduce the main components of an emotion-ally intelligent tutoring system able to recognize, interpret and influence learner’s emotions. We will talk about specific virtual agents that can influence learner’s emotions to motivate and encourage him and involve a more cooperative work, particularly in narrative learning environments. Pushing further this paradigm, we will present the advantages and perspectives of subliminal learning which inter

Épidémiologie et caractérisation des atteintes dermatologiques au cours du syndrome de Gougerot-Sjögren primitif à partir des données de trois cohortes (TEARS, ASSESS, diapSS)

International audienc

EPIDEMIOLOGY OF CUTANEOUS INVOLVEMENT IN SJOGREN'S SYNDROME: DATA FROM THREE FRENCH POPULATIONS OF PSS (TEARS, ASSESS, DIAPSS)

Annual European Congress of Rheumatology (EULAR), ELECTR NETWORK, JUN 03, 2020International audienc

Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network

© 2019, Springer Nature Switzerland AG. In this paper, we present R-CNN, Fast R-CNN and Faster R-CNN methods to automatically detect and recognise the predators in underwater videos. We compare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faster R-CNN, then evaluate them on a test dataset compose of challenging objects that had not been seen during training. We perform evaluation on GPU, acquiring information about the AP and IOU for each model and network based on various proposal numbers as well as runtime speeds. Based on the results, we found that the best model of predator detection using visual deep learning models is Faster R-CNN with 2000 proposals

Épidémiologie des atteintes dermatologiques dans le syndrome de Gougerot-Sjögren : données provenant de trois populations françaises de syndrome de Gougerot-Sjögren primitif (TEARS, ASSESS, DiapSS)

International audienc