Asterios Leonidis

Asterios Leonidis, Maria Korozi, Vassilis Kouroumalis et al.

This work aims to investigate how the amenities offered by Intelligent Environments can be used to shape new types of useful, exciting and fulfilling experiences while watching sports or movies. Towards this direction, two ambient media players were developed aspiring to offer live access to secondary information via the available displays of an Intelligent Living Room, and to appropriately exploit the technological equipment so as to support natural interaction. Expert-based evaluation experiments revealed some factors that can influence the overall experience significantly, without hindering the viewers' immersion to the main media.

Georgios Th. Papadopoulos, Asterios Leonidis, Margherita Antona et al.

Learning from Demonstration (LfD) has been established as the dominant paradigm for efficiently transferring skills from human teachers to robots. In this context, the Federated Learning (FL) conceptualization has very recently been introduced for developing large-scale human-robot collaborative environments, targeting to robustly address, among others, the critical challenges of multi-agent learning and long-term autonomy. In the current work, the latter scheme is further extended and enhanced, by designing and integrating a novel user profile formulation for providing a fine-grained representation of the exhibited human behavior, adopting a Deep Learning (DL)-based formalism. In particular, a hierarchically organized set of key information sources is considered, including: a) User attributes (e.g. demographic, anthropomorphic, educational, etc.), b) User state (e.g. fatigue detection, stress detection, emotion recognition, etc.) and c) Psychophysiological measurements (e.g. gaze, electrodermal activity, heart rate, etc.) related data. Then, a combination of Long Short-Term Memory (LSTM) and stacked autoencoders, with appropriately defined neural network architectures, is employed for the modelling step. The overall designed scheme enables both short- and long-term analysis/interpretation of the human behavior (as observed during the feedback capturing sessions), so as to adaptively adjust the importance of the collected feedback samples when aggregating information originating from the same and different human teachers, respectively.