Abstract
The Business School of the Polytechnic of Porto, Portugal aiming at following the demands of the region decided to make available a master's degree program in e-business. This paper describes the study held ascertain the most relevant skills to be considered in the master program. In order to obtain relevant feedback, an interview was conducted to professionals working in the field. Also, a questionnaire was applied to the students attending the last year of undergraduate after working programs, since they have already professional experience in related fields. The most relevant skills were identified, and curriculum was defined for the master's degree according to the analysis of the results of these activities.

Abstract
The purpose of this study was to measure the subject's sense of presence while they performed a task (riding a bicycle downhill) in a virtual reality (VR) environment and to compare it by body position (standing vs. sitting) and gender. The sample consisted of 35 subjects (19 male and 16 female) between 17 and 33 years of age. A translated and validated Portuguese version of the lgroup Presence Questionnaire (IPQp) and the Reflexive Motor Acts (RMAs), based on direct observation, were used as metrics. The results showed significant differences between body position at the level of Experienced Realism, Spatial Presence and Overall Sense of Presence. When measuring RMAs, it was demonstrated that people in the sitting position presented a higher frequency. We concluded that body position influences perceptions of credibility, which has an impact on the sense of presence. No differences were identified between the genders.

Abstract
Procedural modeling techniques reduce the effort of creating large virtual cities. However, current methodologies do not allow direct user control over the generated models. Associated with this problem, we face the additional problem related to intrinsic ambiguity existing in user selections. In this paper, we propose to address this problem by using a genetic algorithm to generalize user-provided point-and-click selections of building elements. From a few user-selected elements, the system infers new sets of elements that potentially correspond to the user's intention, including the ones manually selected. These sets are obtained by queries over the shape trees generated by the procedural rules, thus exploiting shape semantics, hierarchy and geometric properties. Our system also provides a complete selection-action paradigm that allows users to edit procedurally generated buildings without necessarily explicitly writing queries. The pairs of user selections and procedural operations (the actions) are stored in a tree-like structure, which is easily evaluated. Results show that the selection inference is capable of generating sets of shapes that closely match the user intention and queries are able to perform complex selections that would be difficult to achieve in other systems. User studies confirm this result.

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Abstract
Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable. Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts. We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running.  The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.