Generalised Regression Hypothesis Induction for Energy Consumption Forecasting

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.This work was supported by the Spanish Government (research project TIN201564776-C3-1-R). M. Molina-Solana was funded by European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 743623

2D-Tasks for Cognitive Rehabilitation

Neuropsychological Rehabilitation is a complex clinic process which tries to restore or compensate cognitive and behavioral disorders in people suffering from a central nervous system injury. Information and Communication Technologies (ICTs) in Biomedical Engineering play an essential role in this field, allowing improvement and expansion of present rehabilitation programs. This paper presents a set of cognitive rehabilitation 2D-Tasks for patients with Acquired Brain Injury (ABI). These tasks allow a high degree of personalization and individualization in therapies, based on the opportunities offered by new technologies

Discourses of conflict and collaboration and institutional context in the implementation of forest conservation policies in Soria, Spain

This article examines the emergence of conflict and collaboration in the implementation of forest conservation policies in Soria, Spain. We draw insights from discursive institutionalism and use a comparative case study approach to analyse and compare a situation of social conflict over the Natural Park declaration in the Sierra de Urbión, and a civil society led collaborative process to develop management plans for the “Sierra de Cabrejas” in Soria. The implementation of the EU Habitats Directive generated different outcomes in these two cases, which unfolded in the context of the same nature conservation legislation and national and provincial administrative structures but differed in terms of types of forests involved, property rights arrangements and forest use histories. We critically examine the influence of the institutional context and dominant discourses on the emergence of outcomes: conflict emerged where local institutions and discourses were threatened by the EU directive, while collaboration was possible where local institutions and counter-discourses were weak. We find that the institutional context plays an important part in determining local discourses in the implementation of forest conservation policies. Yet local counter-discourses have limited influence in the implementation and policy processes in the face of contestation by the discourses of regional civil servants conservation activists

Do the shuffle: Exploring reasons for music listening through shuffled play

Adults listen to music for an average of 18 hours a week (with some people reaching more than double that). With rapidly changing technology, music collections have become overwhelmingly digital ushering in changes in listening habits, especially when it comes to listening on personal devices. By using interactive visualizations, descriptive analysis and thematic analysis, this project aims to explore why people download and listen to music and which aspects of the music listening experience are prioritized when people talk about tracks on their device. Using a newly developed data collection method, Shuffled Play, 397 participants answered open-ended and closed research questions through a short online questionnaire after shuffling their music library and playing two pieces as prompts for reflections. The findings of this study highlight that when talking about tracks on their personal devices, people prioritise characterizing them using sound and musical features and associating them with the informational context around them (artist, album, and genre) over their emotional responses to them. The results also highlight that people listen to and download music because they like it-a straightforward but important observation that is sometimes glossed over in previous research. These findings have implications for future work in understanding music, its uses and its functions in peoples' everyday lives

Variational Gaussian process for optimal sensor placement

Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used to provide observations. When sensor location is optimally selected, the predictive model can greatly reduce its internal errors. A greedy-selection algorithm is used for locating these optimal spatial locations from a numerical embedded space. A novel architecture for solving this big data problem is proposed, relying on a variational Gaussian process. The generalisation of the model is further improved via the preconditioning of its inputs: Masked Autoregressive Flows are implemented to learn nonlinear, invertible transformations of the conditionally modelled spatial features. Finally, a global optimisation strategy extending the Mutual Information-based optimisation and fine-tuning of the selected optimal location is proposed. The methodology is parallelised to speed up the computational time, making these tools very fast despite the high complexity associated with both spatial modelling and placement tasks. The model is applied to a real three-dimensional test case considering a room within the Clarence Centre building located in Elephant and Castle, London, UK

Automatic performer identification in celtic violin audio recordings

We present a machine learning approach to the problem of identifying performers from their interpretative styles. In particular, we investigate how violinists express their view of the musical content in audio recordings and feed this information to a number of machine learning techniques in order to induce classifiers capable of identifying the interpreters. We apply sound analysis techniques based on spectral models for extracting expressive features such as pitch, timing, and amplitude representing both note characteristics and the musical context in which they appear. Our results indicate that the features extracted contain sufficient information to distinguish the considered performers, and the explored machine learning methods are capable of learning the expressive patterns that characterize each of the interpreters.This work was supported by the Spanish Ministry of Science and Innovation under grant TIN2009-14247- C02-01 DRIMS Project

Suicidal ideation in a community-derived sample of Spanish adolescents

Introduction: Suicide is a current public health problem and among the main causes of mortality in adolescents and young adults. The main goal of this study was to analyse suicidal ideation in a representative sample of Spanish adolescents. Specifically, the prevalence rates of suicide ideation, the psychometric properties of the Paykel Suicide Scale (PSS) scores, and the socio-emotional adjustment of adolescents at risk for suicide were analysed. Material and methods: The sample consisted of 1,664 participants (M = 16.12 years, SD = 1.36, range 14-19 years), selected by stratified sampling by clusters. The instruments used were the PSS, the Strengths and Difficulties Questionnaire, the Personal Wellbeing Index-School Children, and the Oviedo Infrequency Scale. Results: The results showed that 4.1% of the sample indicated that they had tried to commit suicide in the previous year. Statistically significant differences were found according to gender but not according to age in the PSS mean scores. The analysis of the internal structure of the PSS showed that the one-dimensional model presented excellent goodness of fit indexes. This model showed measurement invariance across gender. The reliability of the scores, estimated with ordinal alpha, was 0.93. Participants who reported suicide ideation showed poorer mental health status and lower life satisfaction compared to the non-suicide ideation group. Conclusions: Suicidal ideation is present during adolescence and is associated with poor subjective well-being and increased emotional and behavioural problems. PSS seems to show adequate psychometric behaviour to assess suicidal ideation in adolescents. These findings have clear implications, both in health and education systems, to improve the promotion of emotional well-being and prevention of psychological and psychiatric problems in this sector of the population. © 2017 SEP y SEPB