Abstract
This paper discusses different models that can be used to construct composite indicators with both desirable and undesirable output indicators. Two approaches are considered. The first is an indirect approach, based on a traditional Data Envelopment Analysis model, requiring a prior transformation in the measurement scale of the undesirable outputs. The second is a direct approach, based on a directional distance function model. The use of a directional distance function allows for the accommodation of undesirable indicators in their original form. The main limitations of these approaches are discussed related to the data transformation in the case of the indirect approach and the possibility to obtain negative margin rates of substitution between the desirable and undesirable outputs in the case of the direct approach. These issues lead to the proposal of a new composite indicator model based on a directional distance function that overcomes the limitations associated with the existing approaches. The incorporation of information on the relative importance of individual indicators using weight restrictions is discussed. Proposed here is an enhanced formulation of weight restrictions, in the form of assurance regions type I, that reflects the relative importance of the indicators in percentage terms. The models are illustrated in the assessment of Brazilian hydropower plants and are suitable for any assessment involving the aggregation of key performance indicators whenever undesirable outputs are present.

Abstract
Urban mobility services generate massive amounts of raw data that are usually not explored in depth by the entities that own them. Visualisation techniques could improve knowledge extraction and decision-making, as well as support the reengineering of those services. Some studies in Information Visualisation provide a domain-independent classification for visualisations based on their own characteristics and the data they support, although independent of their context of use. We propose a classification for visualisations of urban mobility data according to their context of use and their characteristics. Our first results are encouraging and are supported by a user-centred design process carried with urban mobility experts, in which we developed and evaluated a set of visualisation prototypes. The conclusions form a first effort towards a conceptual framework proposal for classifying visualisations of this domain, and are expected to guide researchers and practitioners searching for adequate ways to visually represent their data.

Abstract
The adoption of smart cards in urban public transport has fundamentally changed how transport providers manage and plan their networks. Traveller information services, in particular, have leveraged this contextual data for targeting passengers and providing relevant information. Thus, it becomes increasingly relevant for the next generation of services to obtain on-time contextual passenger information, to support the development of intelligent information services. In this paper an adaptation of the Top-K algorithm is proposed for predicting journey destination, applied to different scenarios in public transport. The performance and efficiency are analysed and compared to a decision tree classifier. Finally, the feasibility and potential of applying the proposed methods to large-scale systems in a real-world environment is discussed.

Abstract
This study develops a tool to assess livability in European cities covering two components of livability: human wellbeing and environmental impact. First, we propose a conceptual model to assess cities' livability, that extends the concept of urban livability to include a component related to environmental sustainability. Second, we address the measurement of cities' livability. For this purpose, a new composite indicator was constructed based on a data envelopment analysis model specified using a directional distance function. In addition to assigning a summary measure of performance for each city, the composite indicator can be used to guide improvements concerning different livability objectives. One of the innovative features of the model proposed is to enable, by the specification of different directional vectors, focusing separately on each component of livability (e.g., human wellbeing and environmental impact) or alternatively considering cities' potential for improvement in all indicators simultaneously. In addition, it is possible to incorporate a new type of weight restrictions, specified as assurance regions type I, that reflect the relative importance of both desirable and undesirable outputs in percentage terms. Finally, this paper approached the assessment of the evolution of cities' performance over time using the Luenberger productivity indicator.

Abstract
In this paper we present the results from an experiment designed to characterize the Leap Motion movement in 2D pointing tasks and compare it to a mouse and touchpad. We used the ISO 9241-9 multi-directional tapping test for comparing the devices, and we analyse the results using standard throughput and error rate measures as well as additional accuracy measures such as target re-entry, task axis crossing, movement direction change, orthogonal direction change, movement variability, movement offset, and movement error. We also present the results from the ISO 9241-9 assessment of comfort questionnaire, and our observations from the participant's postures when using the Leap Motion device. Results indicate that the Leap Motion performs poorly in these tasks when compared to a mouse or touchpad.

Abstract
Recommender systems have radically changed the way people find products, services and information. They are a precious tool in e-commerce and other online services and have slowly been clawing their way into the real-world stage. Location is one of the variables that can be useful in this new situation. While this particular area has been the subject of some research, it can go even further with the exploration of mobility. In this work, we analyze the integration of mobility in a recommender system with real mobility data from a public transportation network. We developed an algorithm that incorporates location and frequency in a conventional recommender system. Our results show successful recommendations of items adapted to users' mobility patterns.