Engaging new digital locals with interactive urban screens to collaboratively improve the city

Local governments struggle to engage time poor and seemingly apathetic citizens, as well as the city’s young digital natives, the digital locals. This project aims at providing a lightweight, technological contribution towards removing the hierarchy between those who build the city and those who use it. We aim to narrow this gap by enhancing people’s experience of physical spaces with digital, civic technologies that are directly accessible within that space. This paper presents the findings of a design trial allowing users to interact with a public screen via their mobile phones. The screen facilitated a feedback platform about a concrete urban planning project by promoting specific questions and encouraging direct, in-situ, real-time responses via SMS and twitter. This new mechanism offers additional benefits for civic participation as it gives voice to residents who otherwise would not be heard. It also promotes a positive attitude towards local governments and gathers information different from more traditional public engagement tools.

Timed Probabilistic Automaton : a bridge between Raven and Song Scope for automatic species recognition

Raven and Song Scope are two automated sound anal-ysis tools based on machine learning technique for en-vironmental monitoring. Many research works have been conducted upon them, however, no or rare explo-ration mentions about the performance and comparison between them. This paper investigates the comparisons from six aspects: theory, software interface, ease of use, detection targets, detection accuracy, and potential application. Through deep exploration one critical gap is identified that there is a lack of approach to detect both syllables and call structures, since Raven only aims to detect syllables while Song Scope targets call structures. Therefore, a Timed Probabilistic Automata (TPA) system is proposed which separates syllables first and clusters them into complex structures after.

Physical activity recognition from accelerometer data using a multi-scale ensemble method

Accurate and detailed measurement of an individual's physical activity is a key requirement for helping researchers understand the relationship between physical activity and health. Accelerometers have become the method of choice for measuring physical activity due to their small size, low cost, convenience and their ability to provide objective information about physical activity. However, interpreting accelerometer data once it has been collected can be challenging. In this work, we applied machine learning algorithms to the task of physical activity recognition from triaxial accelerometer data. We employed a simple but effective approach of dividing the accelerometer data into short non-overlapping windows, converting each window into a feature vector, and treating each feature vector as an i.i.d training instance for a supervised learning algorithm. In addition, we improved on this simple approach with a multi-scale ensemble method that did not need to commit to a single window size and was able to leverage the fact that physical activities produced time series with repetitive patterns and discriminative features for physical activity occurred at different temporal scales.