Is three the magic number? The role of ergonomic principles in cross country comprehension of road traffic signs

Road sign comprehension plays an important part in road safety management, particularly for those drivers who are travelling in an unfamiliar country. Previous research has established that comprehension can be improved if signs are designed to adhere to ergonomic principles. However, it may be difficult for sign designers to incorporate all the principles into a single sign and may thus have to make a judgement as to the most effective ones. This study surveyed drivers in three countries to ascertain their understanding of a range of road signs, each of which conformed in varying degrees and combinations to the ergonomic principles. We found that using three of the principles was the most effective and that the most important one was that relating to standardisation; the colours and shapes used were key to comprehension. Other concepts which related to physical and spatial characteristics were less important, whilst conceptual compatibility did not aid comprehension at all. Practitioner Summary: This study explores how road sign comprehension can be improved using ergonomic principles, with particular reference to cross-border drivers. It was found that comprehension can be improved significantly if standardisation is adhered to and if at least three principles are used

Genetic Programming is Naturally Suited to Evolve Bagging Ensembles

Learning ensembles by bagging can substantially improve the generalization performance of low-bias, high-variance estimators, including those evolved by Genetic Programming (GP). To be efficient, modern GP algorithms for evolving (bagging) ensembles typically rely on several (often inter-connected) mechanisms and respective hyper-parameters, ultimately compromising ease of use. In this paper, we provide experimental evidence that such complexity might not be warranted. We show that minor changes to fitness evaluation and selection are sufficient to make a simple and otherwise-traditional GP algorithm evolve ensembles efficiently. The key to our proposal is to exploit the way bagging works to compute, for each individual in the population, multiple fitness values (instead of one) at a cost that is only marginally higher than the one of a normal fitness evaluation. Experimental comparisons on classification and regression tasks taken and reproduced from prior studies show that our algorithm fares very well against state-of-the-art ensemble and non-ensemble GP algorithms. We further provide insights into the proposed approach by (i) scaling the ensemble size, (ii) ablating the changes to selection, (iii) observing the evolvability induced by traditional subtree variation. Code: https://github.com/marcovirgolin/2SEGP.Comment: Added interquartile range in tables 1, 2, and 3; improved Fig. 3 and its analysis, improved experiment design of section 7.

Variable interactions in risk factors for dementia

Current estimates predict 1 in 3 people born today will develop dementia, suggesting a major impact on future population health. As such, research needs to connect specialist clinicians, data scientists and the general public. The In-MINDD project seeks to address this through the provision of a Profiler, a socio-technical information system connecting all three groups. The public interact, providing raw data; data scientists develop and refine prediction algorithms; and clinicians use in-built services to inform decisions. Common across these groups are Risk Factors, used for dementia-free survival prediction. Risk interactions could greatly inform prediction but determining these interactions is a problem underpinned by massive numbers of possible combinations. Our research employs a machine learning approach to automatically select best performing hyperparameters for prediction and learns variable interactions in a non-linear survival-analysis paradigm. Demonstrating effectiveness, we evaluate this approach using longitudinal data with a relatively small sample size

Patterns of change in children with autism spectrum disorders who received community based comprehensive interventions in their pre-school years: a seven year follow-up study

10.1016/j.rasd.2010.11.007Research in Autism Spectrum Disorders531016-102

CASP-DM: Context Aware Standard Process for Data Mining

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs

Campaniforme o no Campaniforme: una perspectiva sobre las cerámicas ‘pellizcadas’ en vasos con perfil en ‘s’ del Calcolítico en la Península Ibérica

The Bell Beaker phenomenon is the sum of several regional answers. Those are diluted into a reality with several shared characteristics. Nevertheless, and although being one of the most studied expressions of the European Recent Prehistory, more specific adaptations are still to be understood. This is the case of the paired fingernail imprints, or pinched motifs, that due to their scarceness are mostly unnoticed in Iberia. However, one was able to highpoint a scarceness of these standardised motifs in funerary contexts and a concentration in contexts dated from the last quarter of the IIIrd millennium BC, in the precise period of transition in the way of life of the peninsular human groups. Also, the regression in the communicative ability of the vessels, but at the same time dear links with other European Bell Beaker contexts seems to strengthen the hypothesis that this large-scale style must be understood as another agent in the ongoing identarian and social processes acting, as such, in the transition to the beginning of the Peninsular Bronze Age.info:eu-repo/semantics/publishedVersio

Geographical Origin Prediction of Folk Music Recordings from the United Kingdom

Field recordings from ethnomusicological research since the beginning of the 20th century are available today in large digitised music archives. The application of music information retrieval and data mining technologies can aid large-scale data processing leading to a better understanding of the history of cultural exchange. In this paper we focus on folk and traditional music from the United Kingdom and study the correlation between spatial origins and musical characteristics. In particular, we investigate whether the geographical location of music recordings can be predicted solely from the content of the audio signal. We build a neural network that takes as input a feature vector capturing musical aspects of the audio signal and predicts the latitude and longitude of the origins of the music recording. We explore the performance of the model for different sets of features and compare the prediction accuracy between geographical regions of the UK. Our model predicts the geographical coordinates of music recordings with an average error of less than 120 km. The model can be used in a similar manner to identify the origins of recordings in large unlabelled music collections and reveal patterns of similarity in music from around the world