Design, development and field evaluation of a Spanish into sign language translation system

This paper describes the design, development and field evaluation of a machine translation system from Spanish to Spanish Sign Language (LSE: Lengua de Signos Española). The developed system focuses on helping Deaf people when they want to renew their Driver’s License. The system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs). For the natural language translator, three technological approaches have been implemented and evaluated: an example-based strategy, a rule-based translation method and a statistical translator. For the final version, the implemented language translator combines all the alternatives into a hierarchical structure. This paper includes a detailed description of the field evaluation. This evaluation was carried out in the Local Traffic Office in Toledo involving real government employees and Deaf people. The evaluation includes objective measurements from the system and subjective information from questionnaires. The paper details the main problems found and a discussion on how to solve them (some of them specific for LSE)

A comprehensive fracture prevention strategy in older adults : The European union geriatric medicine society (EUGMS) statement

Published also in Aging Clinical and Experimental Research, Vol.28, No.4, WOS: 000379034800030Prevention of fragility fractures in older people has become a public health priority, although the most appropriate and cost-effective strategy remains unclear. In the present statement, the Interest group on falls and fracture prevention of the European union geriatric medicine society (EUGMS), in collaboration with the International association of gerontology and geriatrics for the European region (IAGG-ER), the European union of medical specialists (EUMS), the Fragility fracture network (FFN), the International osteoporosis foundation (IOF) - European society for clinical and economic aspects of osteoporosis and osteoarthritis (ECCEO), outlines its views on the main points in the current debate in relation to the primary and secondary prevention of falls, the diagnosis and treatment of bone fragility, and the place of combined falls and fracture liaison services for fracture prevention in older people. (C) 2016 Published by Elsevier Masson SAS.Peer reviewe

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction.

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children's social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a "mental model" of the robot, tailoring the tutoring to the robot's performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot's bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance

Do parents’ collectivistic tendency and attitudes toward filial piety facilitate autonomous motivation among young Chinese adolescents?

The present study investigates the association of Chinese parents' collectivistic tendency, attitudes towards filial piety (i.e., children respecting and caring for parents (RCP) and children protecting and upholding honor for parents (PUHP)), parenting behaviors (i.e., autonomy granting (AG) and psychological control (PC)) with young adolescents' autonomous motivation. Participants were 321 Chinese parents and their eighth-grade children who independently completed a set of surveys. Results showed that parents' collectivistic tendency indirectly and positively contributes to children's autonomous motivation through the mediation of AG and PC, respectively. Parents' attitude toward RCP has an indirect and positive contribution to children's autonomy motivation through the mediation of AG while parents' attitude toward PUHP shows an indirect and negative contribution to children's autonomous motivation through the mediation of PC. The findings suggest that different cultural emphases in collectivist-based societies play different roles in adolescents' autonomy development. The implications of the findings are discussed. © 2013 Springer Science+Business Media New York

A comprehensive fracture prevention strategy in older adults: The European union geriatric medicine society (EUGMS) statement

Prevention of fragility fractures in older people has become a public health priority, although the most appropriate and cost-effective strategy remains unclear. In the present statement, the Interest group on falls and fracture prevention of the European union geriatric medicine society (EUGMS), in collaboration with the International association of gerontology and geriatrics for the European region (IAGG-ER), the European union of medical specialists (EUMS), the Fragility fracture network (FFN), the International osteoporosis foundation (IOF) – European society for clinical and economic aspects of osteoporosis and osteoarthritis (ECCEO), outlines its views on the main points in the current debate in relation to the primary and secondary prevention of falls, the diagnosis and treatment of bone fragility, and the place of combined falls and fracture liaison services for fracture prevention in older people

Joint action modulates motor system involvement during action observation in 3-year-olds

When we are engaged in a joint action, we need to integrate our partner’s actions with our own actions. Previous research has shown that in adults the involvement of one’s own motor system is enhanced during observation of an action partner as compared to during observation of an individual actor. The aim of this study was to investigate whether similar motor system involvement is present at early stages of joint action development and whether it is related to joint action performance. In an EEG experiment with 3-year-old children, we assessed the children’s brain activity and performance during a joint game with an adult experimenter. We used a simple button-pressing game in which the two players acted in turns. Power in the mu- and beta-frequency bands was compared when children were not actively moving but observing the experimenter’s actions when (1) they were engaged in the joint action game and (2) when they were not engaged. Enhanced motor involvement during action observation as indicated by attenuated sensorimotor mu- and beta-power was found when the 3-year-olds were engaged in the joint action. This enhanced motor activation during action observation was associated with better joint action performance. The findings suggest that already in early childhood the motor system is differentially activated during action observation depending on the involvement in a joint action. This motor system involvement might play an important role for children’s joint action performance

Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

International audienceMany real-life large-scale datasets are open-ended and dynamic: new images are continuously added to existing classes, new classes appear over time, and the semantics of existing classes might evolve too. Therefore, we study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers. Since the performance of distance-based classifiers heavily depends on the used distance function, we cast the problem into one of learning a low-rank metric, which is shared across all classes. For the NCM classifier we introduce a new metric learning approach, and we also introduce an extension to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over one million training images of thousand classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art, while being orders of magnitude faster

Early Social Cognition: Alternatives to Implicit Mindreading

According to the BD-model of mindreading, we primarily understand others in terms of beliefs and desires. In this article we review a number of objections against explicit versions of the BD-model, and discuss the prospects of using its implicit counterpart as an explanatory model of early emerging socio-cognitive abilities. Focusing on recent findings on so-called ‘implicit’ false belief understanding, we put forward a number of considerations against the adoption of an implicit BD-model. Finally, we explore a different way to make sense of implicit false belief understanding in terms of keeping track of affordances

Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation

This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech coefficients. The constraint imposed by dynamic features (i.e., the time derivatives of the speech coefficients) are used to enhance the smoothness of predicted coefficient trajectories in two ways. One is to obtain the enhanced speech coefficients with a least square estimation from the coefficients and dynamic features predicted by DNN. The other is to incorporate the constraint of dynamic features directly into the DNN training process using a sequential cost function. In the linear feature adaptation approach, a sparse linear transform, called cross transform, is used to transform multiple frames of speech coefficients to a new feature space. The transform is estimated to maximize the likelihood of the transformed coefficients given a model of clean speech coefficients. Unlike the DNN approach, no parallel corpus is used and no assumption on distortion types is made. The two approaches are evaluated on the REVERB Challenge 2014 tasks. Both speech enhancement and automatic speech recognition (ASR) results show that the DNN-based mappings significantly reduce the reverberation in speech and improve both speech quality and ASR performance. For the speech enhancement task, the proposed dynamic feature constraint help to improve cepstral distance, frequency-weighted segmental signal-to-noise ratio (SNR), and log likelihood ratio metrics while moderately degrades the speech-to-reverberation modulation energy ratio. In addition, the cross transform feature adaptation improves the ASR performance significantly for clean-condition trained acoustic models.Published versio