Abstract
This paper provides a review of the literature on applications of the Driving forces, Pressure, State, Impact, Response (DPSIR) framework to fisheries. The interpretation given to each DPSIR category differs in existing studies, and as a result the indicators used to support fisheries management also vary considerably. This impairs comparisons concerning the state of different fishery systems, and does not provide a common base of knowledge concerning potential management measures that can be adopted in a given context. This paper clarifies the interpretation of each DPSIR category and proposes a set of indicators that can be applied in fishery contexts. The set of indicators proposed is also classified according to sustainability dimensions. It is argued that organising the indicators according to the DPSIR framework and sustainability dimensions (ecologic, economic, social and governance) is a positive contribution to serve as a guideline for future applications to adopt standardized indicators and improve fisheries management.

Abstract
Schematic Maps are mainly used for depicting transportation networks. They are generated through a schematization process where irrelevant details are eliminated and important details are emphasized. This process, being manually performed by teams of expert designers, is expensive and time consuming. Such manual execution is unsuitable for the production of schematic maps for location-based services or on-demand schematic maps, as near real-time and user-centered properties are needed. This work proposes GeneX, a framework that can support the automated generation of schematic maps. The framework and a new algorithms developed were able to completely eliminate erroneous map point placement, and to decrease by 33% the contention for map point placement, producing schematic maps without human intervention in soft real time.

Abstract
In recent years, mass adoption of increasingly powerful mobile devices and ubiquitous communication networks have paved the way to smart environments. Such environments allow for the collection of user and environment data with the final goal to improve users' experience. In this context a number of opportunities and challenges are presented to Human Computer Interaction. This poster explores Quality of Experience, a subjective aspect of interaction, informally defined as the degree to which a system meets users' expectations. Furthermore, a mobile application was developed for the collection of user and environment data and delivery of personalised services in the context of Public Transport. This application will be used in a real-world environment, to further investigate the factors that have an influence on User eXperience, as well as the delivery of relevant services with the potential to enhance users' journeys while in transit.

Abstract
In recent years the mass adoption of mobile devices and increasingly ubiquitous connectivity have contributed to a radical change in the way people interact with computer systems. Moreover cloud computing infrastructures have paved the way for the development of smart systems in such settings, whose goal is to provide a service to enhance user experience based on environment and user sensed data. In this context, there is a clear disconnection between the two streams that flow continuously between user and cloud-based systems. On the one hand, user- and environment-generated data is being, for the most part, disregarded by service providers. On the other hand, services offered do not address users' specific needs and preferences. In addition, service discovery is a cognitive demanding process and it may have detrimental consequences in user experience. In this paper we propose a user-centric framework that addresses the disconnection between these two streams: Cloud2Bubble. The framework facilitates the design and development of smart systems. It aims at leveraging existing technology, such as environment sensors and personal devices, to aggregate localised user-related data - defined as a bubble - into the cloud. This aggregation later supports the delivery of personalised services, contextually relevant to users. The delivery of services with such characteristics has the potential to enhance quality of experience and influence user behaviour. A first iteration of the platform was developed and an evaluation in a simulated environment was performed with encouraging results. Thus, the platform will be further expanded for instantiation and evaluation in the context of urban public transports. We intend to investigate the effects of relevant service delivery in terms of enhancement of quality of experience and influencing user behaviour. The delivery of a service with these characteristics presents benefits for both users and service providers. © 2012 ACM.

Abstract
Retaining customers has been considered one of the most critical challenges among those included in Customer Relationship Management (CRM), particularly in the grocery retail sector. In this context, an accurate prediction whether or not a customer will leave the company, i.e. churn prediction, is crucial for companies to conduct effective retention campaigns. This paper proposes to include in partial churn detection models the succession of first products' categories purchased as a proxy of the state of trust and demand maturity of a customer towards a company in grocery retailing. Motivated by the importance of the first impressions and risks experienced recently on the current state of the relationship, we model the first purchase succession in chronological order as well as in reverse order, respectively. Due to the variable relevance of the first customer-company interactions and of the most recent interactions, these two variables are modeled by considering a variable length of the sequence. In this study we use logistic regression as the classification technique. A real sample of approximately 75,000 new customers taken from the data warehouse of a European retail company is used to test the proposed models. The area under the receiver operating characteristic curve and 1%, 5% and 10% percentiles lift are used to assess the performance of the partial-churn prediction models. The empirical results reveal that both proposed models outperform the standard RFM model.

Abstract
Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products' first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95,000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products' sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests.