Abstract
In this paper, we propose a method that can identify challenging music samples for beat tracking without ground truth. Our method, motivated by the machine learning method "selective sampling," is based on the measurement of mutual agreement between beat sequences. In calculating this mutual agreement we show the critical influence of different evaluation measures. Using our approach we demonstrate how to compile a new evaluation dataset comprised of difficult excerpts for beat tracking and examine this difficulty in the context of perceptual and musical properties. Based on tag analysis we indicate the musical properties where future advances in beat tracking research would be most profitable and where beat tracking is too difficult to be attempted. Finally, we demonstrate how our mutual agreement method can be used to improve beat tracking accuracy on large music collections.

Abstract
In spite of several technical advances made in recent years by the automotive industry, the driver's behaviour still influences significantly the overall fuel consumption. With the rise of smartphones adoption, there are also new opportunities to increase the awareness to this issue. The main aim of this paper is to present a new smartphone application that will help drivers reduce the fuel consumption of their vehicles. This is accomplished by using the smartphone's sensors and the vehicle state to detect the driving pattern and suggest new behaviours in real time that will lead to a more efficient driving experience. The preliminary results show the potential for significant energy savings and their relevance for changing the drivers' behaviour.

Abstract

Abstract
The use of service-orientation principles in manufacturing systems is a promising solution to achieve modularity, flexibility, re-configurability and interoperability. Crucial issues in these service-oriented systems are the description and co-ordination of the execution of the services offered by the distributed entities. This paper introduces an integrated approach for the design, analysis, validation, simulation and process execution of service-oriented manufacturing systems, using the High-level Petri net formalism as the formal language to describe the system behaviour. The use of the proposed approach contributes to achieving an easier and faster development of these solutions and provides the basis to support modularity and re-configurability.

Abstract
Virtual communities and their benefits have been widely exploited to support patients, caregivers, families, and healthcare providers. The complexity of the social organization evolved the concept of virtual community to social networks, exploring the establishment of ties and relations between people. These technological platforms provide a way to keep up with one's connections network, through a set of communication and interaction tools. Games, as social interactive technologies, have great potential, ensuring a supportive community and thereby reducing social isolation. Serious social health games bring forward several research challenges. This article examines the potential benefits of the triad "health-serious games-social networks'' and discusses some research challenges and opportunities of the liaison of serious health games and social networks.

Abstract
A new probabilistic framework for beat tracking of musical audio is presented. The method estimates the time between consecutive beat events and exploits both beat and non-beat information by explicitly modeling non-beat states. In addition to the beat times, a measure of the expected accuracy of the estimated beats is provided. The quality of the observations used for beat tracking is measured and the reliability of the beats is automatically calculated. A k-nearest neighbor regression algorithm is proposed to predict the accuracy of the beat estimates. The performance of the beat tracking system is statistically evaluated using a database of 222 musical signals of various genres. We show that modeling non-beat states leads to a significant increase in performance. In addition, a large experiment where the parameters of the model are automatically learned has been completed. Results show that simple approximations for the parameters of the model can be used. Furthermore, the performance of the system is compared with existing algorithms. Finally, a new perspective for beat tracking evaluation is presented. We show how reliability information can be successfully used to increase the mean performance of the proposed algorithm and discuss how far automatic beat tracking is from human tapping.