Learning Contextualized Music Semantics from Tags via a Siamese Network

Music information retrieval faces a challenge in modeling contextualized musical concepts formulated by a set of co-occurring tags. In this paper, we investigate the suitability of our recently proposed approach based on a Siamese neural network in fighting off this challenge. By means of tag features and probabilistic topic models, the network captures contextualized semantics from tags via unsupervised learning. This leads to a distributed semantics space and a potential solution to the out of vocabulary problem which has yet to be sufficiently addressed. We explore the nature of the resultant music-based semantics and address computational needs. We conduct experiments on three public music tag collections -namely, CAL500, MagTag5K and Million Song Dataset- and compare our approach to a number of state-of-the-art semantics learning approaches. Comparative results suggest that this approach outperforms previous approaches in terms of semantic priming and music tag completion.Comment: 20 pages. To appear in ACM TIST: Intelligent Music Systems and Application

Collaborative Health Care Plan Support

This paper envisions a multi-agent system that assists patients and their health care providers. This system would support a diverse, evolving team in formulating, monitoring and revising a shared "care plan" that operates on multiple time scales in uncertain environments. It would also enhance communication of health information within this planning framework. The coordination of care for children with complex conditions (CCC), which is a compelling societal need, is presented as a model environment in which to develop and assess such systems. The design of algorithms and techniques needed to realize this vision would yield agents capable of being collaborative partners in health care delivery broadly as well as in other environments with similar properties such as rescue and rebuilding after natural disasters. This paper describes the key characteristics of collaborative health care plan support, defines a set of essential capabilities for autonomous "care-augmenting software agents", and discusses three major multi-agents systems research challenges that building such agents raises: evolving long-term plan management, enhancing team interactions, and leveraging human computation for care plan customization.Engineering and Applied Science

Working with the parents and families of children with developmental language disorders: An international perspective

Background The relationship between parental input and child language development has had a complex history. It has become clear that indirect parent training for the parents of children with delayed language development is an important feature of interventions offered by speech and language therapists in the anglophone countries. Yet we know less about how this type of approach is realised in other countries. Methods In this paper we report the results of a survey of practice undertaken as part of the work of COST Action IS1406, a European Union (EU) funded research network. The focus of this paper is specifically on parent-related questions and responses referring to children under the age of twelve. The survey was devised by members of the Action and circulated electronically during the summer of 2017. In all, 4024 practitioners responded from 60 countries, the majority of whom came from EU member countries. Findings Respondents to the survey indicated that indirect therapy is commonly carried out via the parent in the early years and via teachers later. A range of professional groups, in addition to speech and language therapists, is likely to adopt this approach; including teachers, pedagogues and psychologists. A variety of interventions is reported, some of which have a reasonable evidence-base underpinning them. It is interesting to see the widespread involvement of fathers and other family members in interventions. Finally, the fact that practitioner characteristics (age, experience, location of practice etc.) are not related to the use of indirect techniques points to the universal recognition of the value of these approaches. Conclusions Despite the very different traditions in the practice of intervention across countries, there is clearly a widespread recognition of the importance of indirect approaches to intervention and specifically those focusing on parents. The mixture of family members being involved in interventions is a very promising indication of the role sharing commonly associated with the contemporary family. Yet the number of specific intervention approaches identified is relatively small given the number of respondents. There is a need for a better understanding of what exactly practitioners are doing when they involve parents in intervention or carry out parent-child interaction interventions and how well these interventions work in routine practice. This also has implications for the application of evidence-based practice and the precise nature of the interventions concerned (advice to parents, video interaction training etc.).COST (European Cooperation in Science and Technology)Unión Europea Horizonte 2020 Marie Sklodowska-Curie grant agreement No. 70504

Identifying Functions and Behaviours of Social Robots for In-Class Learning Activities: Teachers’ Perspective

International audienceWith advances in artificial intelligence, research is increasingly exploring the potential functions that social robots can play in education. As teachers are a critical stakeholder in the use and application of educational technologies, we conducted a study to understand teachers' perspectives on how a social robot could support a variety of learning activities in the classroom. Through interviews, robot puppeteering, and group brainstorming sessions with five elementary and middle school teachers, we take a sociotechnical perspective to conceptualize possible robot functions and behaviours, and the effects they may have on the current way learning activities are designed, planned, and executed. Using activity theory to analyze learning activities as an activity system illustrated a number of tensions that currently exist between the components of the system. Overall, the teachers responded positively to the idea of introducing a social robot as a technological tool for learning activities, envisioning differences in usage for teacher-robot and student-robot interactions. We discuss the fine-grained functions and behaviours envisioned by teachers, and how they address the current tensions-providing suggestions for improving the design of social robots for learning activities

Towards Measuring states of curiosity through Electroencephalography and body sensors responses

International audienceThe neurophysiological mechanisms underlying curiosity and intrinsic motivation are currently not well understood. However, being able to identify objectively, from neurophysiological signals, the curiosity level of a user, would bring a very useful tool both to neuroscientists and psychologists, to understand curiosity deeper, as well as to designers of human-computer interaction, in order to trigger curiosity or to adapt an interaction to the curiosity levels of its users. A first step to do that, is to collect neurophysiological signals during known states of curiosity, in order to develop signal processing/machine learning tools to recognize those states from such signals. We propose an experimental protocol, that has been designed but has not been tested so far, in order to measure both brain activity through Electroencephalography (EEG) and physiological responses (heart rate, skin conductance, Electrocardiogram) when subjects are induced into different states of curiosity. During the experiment, fun facts will be presented to subjects to induce different levels of curiosity. We obtained those fun facts using the Google functionality "I’m feeling curious" as well as crowdsourcing. A subject will be able to choose a fun fact that makes him curious, and push forward with a 4-to-10 questions chain on this theme. For each question on a given theme, a subject will be able to reveal the answer (interpreted as a curious state) or to skip it (interpreted as a non-curious state). Skipping an answer will automatically break the chain and will point the subject to the next fun fact. Neurophysiological signals will be collected between a question and the choice of revealing the answer. Then the subject will grade the question on a 1-to-7 curiosity level scale. Neurophysiological measures during these states of curiosity will be recorded and we expect to find biological markers of curiosity by analyzing such information

Towards measuring states of epistemic curiosity through electroencephalographic signals

International audienceUnderstanding the neurophysiological mechanisms underlying curiosity and therefore being able to identify the curiosity level of a person, would provide useful information for researchers and designers in numerous fields such as neuroscience, psychology, and computer science. A first step to uncovering the neural correlates of curiosity is to collect neurophysiological signals during states of curiosity, in order to develop signal processing and machine learning (ML) tools to recognize the curious states from the non-curious ones. Thus, we ran an experiment in which we used electroencephalography (EEG) to measure the brain activity of participants as they were induced into states of curiosity, using trivia question and answer chains. We used two ML algorithms, i.e. Filter Bank Common Spatial Pattern (FBCSP) coupled with a Linear Discriminant Algorithm (LDA), as well as a Filter Bank Tangent Space Classifier (FBTSC), to classify the curious EEG signals from the non-curious ones. Global results indicate that both algorithms obtained better performances in the 3-to-5s time windows, suggesting an optimal time window length of 4 seconds (63.09% classification accuracy for the FBTSC, 60.93% classification accuracy for the FBCSP+LDA) to go towards curiosity states estimation based on EEG signals

Expression of Curiosity in Social Robots: Design, Perception, and Effects on Behaviour

International audienceCuriosity -the intrinsic desire for new information-can enhance learning, memory, and exploration. Therefore, understanding how to elicit curiosity can inform the design of educational technologies. In this work, we investigate how a social peer robot's verbal expression of curiosity is perceived, whether it can aect the emotional feeling and behavioural expression of curiosity in students, and how it impacts learning. In a between-subjects experiment, 30 participants played the game LinkIt!, a game we designed for teaching rock classication, with a robot verbally expressing: curiosity, curiosity plus rationale, or no curiosity. Results indicate that participants could recognize the robot's curiosity and that curious robots produced both emotional and behavioural curiosity contagion eects in participants

Pedagogical Agents for Fostering Question-Asking Skills in Children

Question asking is an important tool for constructing academic knowledge, and a self-reinforcing driver of curiosity. However, research has found that question asking is infrequent in the classroom and children's questions are often superficial, lacking deep reasoning. In this work, we developed a pedagogical agent that encourages children to ask divergent-thinking questions, a more complex form of questions that is associated with curiosity. We conducted a study with 95 fifth grade students, who interacted with an agent that encourages either convergent-thinking or divergent-thinking questions. Results showed that both interventions increased the number of divergent-thinking questions and the fluency of question asking, while they did not significantly alter children's perception of curiosity despite their high intrinsic motivation scores. In addition, children's curiosity trait has a mediating effect on question asking under the divergent-thinking agent, suggesting that question-asking interventions must be personalized to each student based on their tendency to be curious.Comment: Accepted at CHI 202