Automated motif identification: Analysing Flickr images to identify popular viewpoints in Europe’s protected areas

Visiting landscapes and appreciating them from specific viewpoints is not a new phenomenon. Such so-called motifs were popularised by travel guides and art in the romantic era, and find their contemporary digital twins through images captured in social media. We developed and implemented a conceptual model of motifs, based around spatial clustering, image similarity and the appreciation of a motif by multiple individuals. We identified 119 motifs across Europe, using 2146176 georeferenced Creative Commons Flickr images found in Natura 2000 protected areas. About 65% of motifs contain cultural elements such as castles or bridges. The remaining 35% are natural features, and biased towards coastal elements such as cliffs. Characterisation and localisation of motifs could allow identification of locations subject to increased pressure, and thus disturbance, especially since the visual characteristics of motifs allow managers to explore why sites are being visited. Future work will include methods of calculating image similarity using tags, explore different algorithms for assessing content similarity and study the behaviour of motifs through time

A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data

Finding a good predictive model for a high-dimensional data set can be challenging. For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable. This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial. We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features. As it is unclear which measure is best for evaluating the stability, we first compare a variety of stability measures. We conclude that the Pearson correlation has the best theoretical and empirical properties. Also, we find that for the stability assessment behaviour it is most important that a measure contains a correction for chance or large numbers of chosen features. Then, we analyse Pareto fronts and conclude that it is possible to find models with a stable selection of few features without losing much predictive accuracy

Aperfeiçoamento da integração de sensores de dispositivos móveis na domótica

A Internet das Coisas (IoT) veio para nos facilitar a vida. Na visão de um mundo da IoT, todas as coisas estarão ligadas à Internet, ou seja, estarão ubiquamente acessíveis e poder-se-á interagir com as mesmas de múltiplas formas, como seja enviando comandos para estas desempenharem uma determinada ação ou para se receber notificações. Atualmente existem soluções que utilizam smartphones para controlarem dispositivos IoT em casas inteligentes, mas são soluções incompletas. As mais comuns utilizam aplicações proprietárias onde se interage apenas através de botões e não exploram todas as potencialidades dos muitos sensores disponíveis. Imagine-se se fosse possível utilizar na sua plenitude os sensores do seu smartphone, como por exemplo a câmara, o sensor de luminosidade, de proximidade, giroscópio, etc., para gerar ações nos dispositivos IoT sejam estes luzes, televisões ou até uma torradeira. Poder-se-ia assim melhorar a experiência de utilização dos smartphones na domótica. Neste contexto, este estudo propõe uma arquitetura para uma melhor integração dos smartphones, considerando os seus múltiplos sensores disponíveis, numa casa inteligente. Com a solução aqui apresentada, foi desenvolvido um protótipo que permite testar parte da arquitetura, em que utilizadores podem navegar com um smartphone pela habitação e interagir com os diversos aparelhos, com os quais se vão cruzando, usando de uma forma mais integrada os sensores disponíveis. Por exemplo, a câmara do smartphone é usada para o reconhecimento dos objetos. Após o seu reconhecimento, o utilizador pode interagir com estes inclinando ou balançando o dispositivo. Esta solução de baixo custo, que utiliza ferramentas open-source, foi testada e validada, com recurso a testes funcionais e testes de desempenho. Provou-se que é uma solução eficaz, cumprindo os objetivos propostos, fiável, e apresenta um desempenho que permite uma boa usabilidade. Esta solução é inovadora, pois maximiza a utilização dos sensores disponíveis num smartphone, e aponta para um caminho onde os dispositivos moveis estarão integrados de uma forma plena em soluções de domótica.The Internet of Things (IoT), in which every device is connected to the Internet, will make our everyday lives easier by enabling interaction with them. This interaction will take many forms, perhaps the most common being commanding devices to carry out certain actions or receive notifications. Most current solutions that use smartphones to control IoT devices in smart homes are incomplete and commonly use proprietary applications which only interact with buttons and do not exploit the full potential of the many sensors available. Being able to fully exploit smartphone sensors, such as the camera, brightness sensor, proximity sensor, gyroscope etc., to command actions on IoT devices such as lights, televisions or even toasters will greatly improve the experience of using smartphones in home automation. In this context, this work proposes an architecture for the integration of smartphones and their sensors with the smart home. To prove the solution and its architecture a prototype was developed enabling users to navigate around a house using a smartphone and interacting with a range of IoT devices using the smartphone sensors. An example of this interaction is object recognition; the smartphone camera is used to recognize an object, once recognized the user is able to interact with the device by tilting or shaking the smartphone. This low-cost solution, using open source tools, has been tested and validated using functional and performance testing. It has been proven to be an effective solution, meeting the proposed objectives, shown to be reliable, and has performance permitting good usability. This solution is innovative as it maximizes the use of sensors available on a smartphone, and points to a future in which mobile devices will be fully integrated with home automation solutions