Conveying Audience Emotions through Humanoid Robot Gestures to an Orchestra during a Live Musical Exhibition

In the last twenty years, robotics have been applied in many heterogeneous contexts. Among them, the use of humanoid robots during musical concerts have been proposed and investigated by many authors. In this paper, we propose a contribution in the area of robotics application in music, consisting of a system for conveying audience emotions during a live musical exhibition, by means of a humanoid robot. In particular, we provide all spectators with a mobile app, by means of which they can select a specific color while listening to a piece of music (act). Each color is mapped to an emotion, and the audience preferences are then processed in order to select the next act to be played. This decision, based on the overall emotion felt by the audience, is then communicated by the robot through body gestures to the orchestra. Our first results show that spectators enjoy such kind of interactive musical performance, and are encouraging for further investigations

Mapping Robots to Therapy and Educational Objectives for Children with Autism Spectrum Disorder

The aim of this study was to increase knowledge on therapy and educational objectives professionals work on with children with autism spectrum disorder (ASD) and to identify corresponding state of the art robots. Focus group sessions (n = 9) with ASD professionals (n = 53) from nine organisations were carried out to create an objectives overview, followed by a systematic literature study to identify state of the art robots matching these objectives. Professionals identified many ASD objectives (n = 74) in 9 different domains. State of the art robots addressed 24 of these objectives in 8 domains. Robots can potentially be applied to a large scope of objectives for children with ASD. This objectives overview functions as a base to guide development of robot interventions for these children

Making New "New AI" Friends : Designing a Social Robot for Diabetic Children from an Embodied AI Perspective

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Robin is a cognitively and motivationally autonomous affective robot toddler with "robot diabetes" that we have developed to support perceived self-efficacy and emotional wellbeing in children with diabetes by providing them with positive mastery experiences of diabetes management in a playful but realistic and natural interaction context. Underlying the design of Robin is an "Embodied" (formerly also known as "New") Artificial Intelligence approach to robotics. In this paper we discuss the rationale behind the design of Robin to meet the needs of our intended end users (both children and medical staff), and how "New AI" provides a suitable approach to developing a friendly companion that fulfills the therapeutic and affective requirements of our end users beyond other approaches commonly used in assistive robotics and child-robot interaction. Finally, we discuss how our approach permitted our robot to interact with and provide suitable experiences of diabetes management to children with very different social interaction styles.Peer reviewedFinal Published versio

Update on the correlation of the highest energy cosmic rays with nearby extragalactic matter

Data collected by the Pierre Auger Observatory through 31 August 2007 showed evidence for anisotropy in the arrival directions of cosmic rays above the Greisen-Zatsepin-Kuz'min energy threshold, \nobreak{$6\times 10^{19}$eV}. The anisotropy was measured by the fraction of arrival directions that are less than $3.1^\circ$ from the position of an active galactic nucleus within 75 Mpc (using the V\'eron-Cetty and V\'eron $12^{\rm th}$ catalog). An updated measurement of this fraction is reported here using the arrival directions of cosmic rays recorded above the same energy threshold through 31 December 2009. The number of arrival directions has increased from 27 to 69, allowing a more precise measurement. The correlating fraction is $(38^{+7}_{-6})$, compared with $21$ expected for isotropic cosmic rays. This is down from the early estimate of $(69^{+11}_{-13})$. The enlarged set of arrival directions is examined also in relation to other populations of nearby extragalactic objects: galaxies in the 2 Microns All Sky Survey and active galactic nuclei detected in hard X-rays by the Swift Burst Alert Telescope. A celestial region around the position of the radiogalaxy Cen A has the largest excess of arrival directions relative to isotropic expectations. The 2-point autocorrelation function is shown for the enlarged set of arrival directions and compared to the isotropic expectation.Comment: Accepted for publication in Astroparticle Physics on 31 August 201

Multimodal Detection of Engagement in Groups of Children Using Rank Learning

In collaborative play, children exhibit different levels of engagement. Some children are engaged with other children while some play alone. In this study, we investigated multimodal detection of individual levels of engagement using a ranking method and non-verbal features: turn-taking and body movement. Firstly, we automatically extracted turn-taking and body movement features in naturalistic and challenging settings. Secondly, we used an ordinal annotation scheme and employed a ranking method considering the great heterogeneity and temporal dynamics of engagement that exist in interactions. We showed that levels of engagement can be characterised by relative levels between children. In particular, a ranking method, Ranking SVM, outperformed a conventional method, SVM classification. While either turn-taking or body movement features alone did not achieve promising results, combining the two features yielded significant error reduction, showing their complementary power

Diagnostic accuracy of contrast-enhanced MR angiography in severe carotid stenosis: meta-analysis with metaregression of different techniques

Contrast-enhanced magnetic resonance angiography (CE-MRA) has become a well-established noninvasive imaging method for the assessment of severe carotid stenosis (70–99% by NASCET criteria). However, CE-MRA is not a standardised technique, but encompasses different concurrent techniques. This review analyses possible differences. A bivariate random effects meta-analysis of 17 primary diagnostic accuracy studies confirmed a high pooled sensitivity of 94.3% and specificity of 93.0% for carotid CE-MRA in severe carotid stenosis. Sensitivity was fairly uniform among the studies, while specificity showed significant variation (I2 = 73%). Metaregressions found significant differences for specificity with two covariates: specificity was higher when using not only maximum intensity projection (MIP) images, but also three-dimensional (3D) images (P = 0.01). Specificity was also higher with electronic images than with hardcopies (P = 0.02). The timing technique (bolus-timed, fluoroscopically triggered or time-resolved) did not result in any significant differences in diagnostic accuracy. Some nonsignificant trends were found for the percentages of severe carotid disease, acquisition time and voxel size. In conclusion, in CE-MRA of severe carotid stenosis the three major timing techniques yield comparably high diagnostic accuracy, electronic images are more specific than hardcopies, and 3D images should be used in addition to MIP images to increase the specificity

Towards accurate and precise T1 and extracellular volume mapping in the myocardium: a guide to current pitfalls and their solutions

Mapping of the longitudinal relaxation time (T1) and extracellular volume (ECV) offers a means of identifying pathological changes in myocardial tissue, including diffuse changes that may be invisible to existing T1-weighted methods. This technique has recently shown strong clinical utility for pathologies such as Anderson- Fabry disease and amyloidosis and has generated clinical interest as a possible means of detecting small changes in diffuse fibrosis; however, scatter in T1 and ECV estimates offers challenges for detecting these changes, and bias limits comparisons between sites and vendors. There are several technical and physiological pitfalls that influence the accuracy (bias) and precision (repeatability) of T1 and ECV mapping methods. The goal of this review is to describe the most significant of these, and detail current solutions, in order to aid scientists and clinicians to maximise the utility of T1 mapping in their clinical or research setting. A detailed summary of technical and physiological factors, issues relating to contrast agents, and specific disease-related issues is provided, along with some considerations on the future directions of the field. Towards accurate and precise T1 and extracellular volume mapping in the myocardium: a guide to current pitfalls and their solutions. Available from: https://www.researchgate.net/publication/317548806_Towards_accurate_and_precise_T1_and_extracellular_volume_mapping_in_the_myocardium_a_guide_to_current_pitfalls_and_their_solutions [accessed Jun 13, 2017]

An emotional humanoid partner

In this paper we propose an emotional humanoid robot based on Latent Semantic Analysis, that exhibits an emotional behaviour in the interaction with human. Latent Semantic Analysis (LSA) paradigm is capable to encode the semantics of words using a statistical computation of a large corpus of text. We illustrate how the creation and the use of this emotional conceptual space allows the building of “Latent Semantic Behaviour” because it simulates the emotional associative capabilities of human beings. The presented approach integrates traditional knowledge representation and intuitive capabilities provided by geometric and sub-symbolic information modelling. To validate the effectiveness of the approach we implemented the system using the humanoid robot from Aldebaran, Nao

AN EMOTIONAL ROBOTIC PARTNER FOR ENTERTAINMENT PURPOSES

In this paper an emotional humanoid robot based on Latent Semantic Analysis is presented. The robot is capable of interacting and entertain human users through the exhibition of spontaneous and non-repetitive emotional behaviours. The Latent Semantic Analysis (LSA) paradigm, used to encode the semantics of words through a statistical analysis of a large corpus of text, is employed to build an emotional conceptual space that simulates the emotional associative capabilities of human beings, through “Latent Semantic Behaviours”. The LSA paradigm integrates traditional knowledge representation and intuitive capabilities provided by geometric and sub-symbolic information modelling. The effectiveness of the proposed approach has been validated by implementing the system with the humanoid robot from Aldebaran, Nao