Abstract
In an automatic music playlist generator, such as an automated online radio channel, how should the system react when a user hits the skip button? Can we use this type of negative feedback to improve the list of songs we will playback for the user next? We propose SkipAwareRec, a next-item recommendation system based on reinforcement learning. SkipAwareRec recommends the best next music categories, considering positive feedback consisting of normal listening behaviour, and negative feedback in the form of song skips. Since SkipAwareRec recommends broad categories, it needs to be coupled with a model able to choose the best individual items. To do this, we propose Hybrid SkipAwareRec. This hybrid model combines the SkipAwareRec with an incremental Matrix Factorisation (MF) algorithm that selects specific songs within the recommended categories. Our experiments with Spotify's Sequential Skip Prediction Challenge dataset show that Hybrid SkipAwareRec has the potential to improve recommendations by a considerable amount with respect to the skip-agnostic MF algorithm. This strongly suggests that reformulating the next recommendations based on skips improves the quality of automatic playlists. Although in this work we focus on sequential music recommendation, our proposal can be applied to other sequential content recommendation domains, such as health for user engagement.

Abstract
Rich Internet Applications (RIA) technology leads to new opportunities in Web development, adding a new level of usability and overcoming several limitations of traditional Web applications. However, there are still a few gaps in RIA development, namely the lack of methodologies enabling direct evolution of traditionally developed Web applications into this new concept. This paper describes a methodology to adapt User Interfaces (UI) from Web 1.0 into Web 2.0 introducing RIA capabilities, maintaining the business logic and data access layers legacy. The main system architecture is maintained and the new concept is achieved by adding a new Web services encapsulation layer responsible to intermediate the business logic layer and the new UI layer, providing a seamless and dynamic integration. For validating this methodology, a prototype was developed by applying and evaluating the suggested methodology. The results obtained validate that this methodology is appropriate for the given problem. © 2009 IADIS.

Abstract
Spatial data infrastructures (SDI) are extremely important in order to combine the technical and organizational elements required to promote the use of territory-based information in an interoperable way. In fact, geographic information has unique intrinsic features, which makes it a natural indexing mechanism for spatial data. Due to its high cost and upgrade dynamics, this information should be available for reuse and should be managed and maintained by those responsible for producing it as part of management interventions in the territory. Therefore, it is important that different spatial data infrastructures, developed at different hierarchical levels, coexist and communicate in an interoperable way so that they can constitute a spatial basis of reference that facilitates the integration with other sectorial applications. This ongoing work proposes the implementation of a regional spatial data infrastructure based on free software, in compliance with the principles of the EU INSPIRE Directive and with the OGC standards.

Abstract
Several developments have been observed recently in areas such as Web development, social networks, interface design, recommendation systems and Geographic Information Systems (GIS). The integration of these developments can provide a superior experience, greater than the sum of their individual contributions, regarding user satisfaction. This paper proposes an integration of all these innovations in e-tourism, more specifically by the development of a Web based geographic information platform adaptable to any tourist region. As a case study, we also show how this platform was adapted to the Douro region, in Portugal. The Web platform developed as a proof of concept combines geospatial information from diverse and heterogeneous data sources, encompassing events, news, routes and points of interest (POI). This platform provides also a recommendation engine and features the possibility that users can contribute with content as part of the community, thus emerging a mini social network. © 2011 AISTI.

Abstract
The impact of climate change on global temperature and precipitation patterns can lead to an increase in extreme environmental events. These events can create favourable conditions for the spread of plant pests and diseases, leading to significant production losses in agriculture. To mitigate these losses, early detection of pests is crucial in order to implement effective and safe control management strategies, to protect the crops, public health and the environment. Our work focuses on the development of a computer vision framework to detect and classify the olive fruit fly, also known as Bactrocera oleae, from images, which is a serious concern to the EU’s olive tree industry. The images of the olive fruit fly were obtained from traps placed throughout olive orchards located in Greece. The approach entails augmenting the dataset and fine-tuning the YOLOv7 model to improve the model performance, in identifying and classifying olive fruit flies. A Portuguese dataset was also used to further perform detection. To assess the model, a set of metrics were calculated, and the experimental results indicated that the model can precisely identify the positive class, which is the olive fruit fly.