Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Deep Learning for Motion Recognition

Automatic analysis and interpretation of human motion from visual data has been one of the most significant computer vision challenges since 1970. In recent years, deep learning has fueled the rapid advancement of computer vision topics. In particular, human motion analysis has drawn substantial attention due to its practical importance in many applications in a variety of domain including social behavior studies, medical assistance, robotics, sport analytics, and more. Human motion is one of the key parts of human social behavior and a rich source of information. We move our whole body involving head, shoulders, hands, trunk, legs, and limbs combined with facial expressions flavored with our individualized style to transmit social signals. A number of studies have suggested the existence of unique motion signatures of individuals by analyzing data obtained from KinectTM devices, and Electromyography (EMG) electrodes attached to muscles. Meaning that when we move and communicate, we tend to use our characteristic style of motion. These distinct motion patterns are attributed to behavioral and anatomical di↵erences between individuals as well as their di↵erent muscle activation strategies. This research aims at establishing a fully-automated framework to push the envelope of understanding information hidden in human motions from visual inputs and its potential applications on a set of fundamental tasks including classification, identification, and user authentication. For this purpose, we propose a number of deep learning approaches and try to tackle the problem from a data-driven perspective and figure out to what extend we would be able to model human motion signatures and see if it is possible to authenticate or identify people based on their movement pattern. Our results demonstrate an accuracy of 94.04% for human authentication and 92.62% for human identification among 10 subjects confirming that human motion conveys information regarding their identity and can be considered as practical biometric cues. Considering particular applications and their limitations, we further propose a generative biometric model that efficiently learns task-relevant features in data and integrate them into a probabilistic authentication setting based on limited amount of data. The proposed framework is able to authenticate the correct subject 86.11% of times

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described