Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Extracting product development intelligence from web reviews

Product development managers are constantly challenged to learn what the consumer product experience really is, and to learn specifically how the product is performing in the field. Traditionally, they have utilized methods such as prototype testing, customer quality monitoring instruments, field testing methods with sample customers, and independent assessment companies. These methods are limited in that (i) the number of customer evaluations is small, and (ii) the methods are driven by a restrictive structured format. Today the web has created a new source of product intelligence; these are unsolicited reviews from actual product users that are posted across hundreds of websites. The basic hypothesis of this research is that web reviews contain significant amount of information that is of value to the product design community. This research developed the DFOC (Design - Feature - Opinion - Cause Relationship) method for integrating the evaluation of unstructured web reviews into the structured product design process. The key data element in this research is a Web review and its associated opinion polarity (positive, negative, or neutral). Hundreds of Web reviews are collected to form a review database representing a population of customers. The DFOC method (a) identifies a set of design features that are of interest to the product design community, (b) mines the Web review database to identify which features are of significance to customer evaluations, (c) extracts and estimates the sentiment or opinion of the set of significant features, and (d) identifies the likely cause of the customer opinion. To support the DFOC method we develop an association rule based opinion mining procedure for capturing and extracting noun-verb-adjective relationships in the Web review database. This procedure exploits existing opinion mining methods to deconstruct the Web reviews and capture feature-opinion pair polarity. A Design Level Information Quality (DLIQ) measure which evaluates three components (a) Content (b) Complexity and (c) Relevancy is introduced. DLIQ is indicative of the content, complexity and relevancy of the design contextual information that can be extracted from an analysis of Web reviews for a given product. Application of this measure confirms the hypothesis that significant levels of quality design information can be efficiently extracted from Web reviews for a wide variety of product types. Application of the DFOC method and the DLIQ measure to a wide variety of product classes (electronic, automobile, service domain) is demonstrated. Specifically Web review databases for ten products/services are created from real data. Validation occurs by analyzing and presenting the extracted product design information. Examples of extracted features and feature-cause associations for negative polarity opinions are shown along with the observed significance

Learning from Unstructured Data to Monitor Human Health

The integration of mobile devices into our daily lives has created unique opportunities to improve human health and well-being. Many of these devices such as smartphones and smartwatches allow the users to enter unstructured data such as speech. This research is focused on utilizing such data for health monitoring through development of computational algorithms and optimization strategies that process unstructured data, compute health-related markers, and provide recommendations for improved health. The applications of this research include nutrition monitoring, dietary recommendation, personality assessment, and commonsense reasoning. Diet is known as an important lifestyle factor in self-management and prevention of chronic diseases. Although mobile and wearable sensors have been used to estimate eating context, accurate monitoring of dietary intake has remained a challenging problem. New approaches based on mobile devices have been proposed to facilitate the process of food intake recording. These technologies require individuals to use mobile devices such as smartphones to record nutrition intake by either entering text or taking images of the food. These technologies are prone to measurement errors related to challenges of human memory and bias. In order to address these limitations, we introduced development and validation of two nutrition monitoring frameworks, Speech2Health and EZNutriPal, which use unstructured data along with speech processing, natural language processing (NLP), and text mining techniques to facilitate dietary assessment.Implementing strategies that improve dietary intake is also very important. A general diet behavior change framework for joint nutrition monitoring and diet planning allows continuous diet recommendations for achieving a diet goal. This research introduces a diet planning framework, called iTell-uEat, to provide diet recommendations continuously based on user's diet habits. Two approaches are proposed including a reinforcement-learning-based and a greedy-based diet planning. An optimization algorithm is proposed to construct a meaningful action space for training reinforcement learning algorithms. Moreover, a linear optimization approach is developed forgreedy diet planning. To demonstrate the potential of utilizing unstructured data in applications beyond dietary assessment, a computational framework is proposed to analyze human personality traits based on expressed texts and to use these personalities for behavior and commonsense reasoning analysis

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

A multimodal system for stress detection

Stress is the physiological or psychological response to internal or external factors, which can happen in short or long terms. Prolonged stress can be harmful since it affects the body, negatively, in several ways, thus contributing to mental and physical health problems. Although stress is not simple to properly identify, there are several studied approaches that solidify the existence of a correlation between stress and perceivable human features. In order to detect stress, there are several approaches that can be taken into consideration. However, this task is more difficult in uncontrolled environments and where non-invasive methods are required. Heart Rate Variability (HRV), facial expressions, eye blinks, pupil diameter and PERCLOS (percentage of eye closure) consist in non-invasive approaches, proved capable to accurately identify the mental stress present in people. For this project, the users’ physiological signals were collected by an external video-based application, in a non-invasive way. Moreover, data from a brief questionnaire was also used to complement the physiological data. After the proposed solution was implemented and tested, it was concluded that the best algorithm for stress detection was the random forest classifier, which managed to obtain a final result of 84.04% accuracy, with 94.89% recall and 87.88% f1 score. This solution uses HRV data, facial expressions, PERCLOS and some personal characteristics of the userO stress é a resposta fisiológica ou psicológica a fatores internos ou externos, o que pode acontecer a curto ou longo prazo. O stress prolongado pode ser prejudicial uma vez que afeta o corpo, negativamente, de várias formas, contribuindo assim para problemas de saúde mental e física. Embora o stress não seja simples de identificar corretamente, existem várias abordagens estudadas que solidificam a existência de uma correlação entre o stress e as características humanas percetíveis. De forma a detetar o stress, existem várias abordagens que podem ser tidas em consideração. No entanto, esta tarefa é mais difícil em ambientes não controlados e onde são necessários métodos não invasivos. A variabilidade da frequência cardíaca (HRV), expressões faciais, piscar de olhos e diâmetro da pupila e PERCLOS (fecho ocular percentual) consistem em abordagens não-invasivas, comprovadamente capazes de identificar o stress nas pessoas. Para este projeto, os dados fisiológicos dos utilizadores são recolhidos a partir de uma aplicação externa baseada em vídeo, de forma não invasiva. Além disso, serão também utilizados dados recolhidos a partir de um breve questionário para complementar os dados fisiológicos Após a implementação e teste da solução proposta, concluiu-se que o melhor algoritmo de deteção de stress foi o random forest classifier, que conseguiu obter um resultado final de 84,04% de precision, com 94,89% de recall e 87,88% de f1 score. Esta solução utiliza dados de HRV, expressões faciais, PERCLOS e certas características pessoais do utilizado