Abstract
The increasing amount of scholarly literature and the diversity of dissemination channels are challenging several fields and research communities. A continuous interplay between researchers and citizen scientists creates a vast set of possibilities to integrate hybrid, crowd-machine interaction features into crowd science projects for improving knowledge acquisition from large volumes of scientific data. This paper presents SciCrowd, an experimental crowd-powered system under development “from the ground up” to support data-driven research. The system combines automatic data indexing and crowd-based processing of data for detecting topic evolution by fostering a knowledge base of concepts, methods, and results categorized according to the particular needs of each field. We describe the prototype and discuss its main implications as a mixed-initiative approach for leveraging the analysis of academic literature.

Abstract

Abstract
Over the past few decades, the behavior of electricity consumption has been changing, especially because of improvements in the distributed generation segment and technological innovations presented by smart grids. The use of microgeneration and the availability of electricity pricing in real time allow consumers to control their consumption, or generation, according to market conditions. This new dynamic tends to increasingly change the price elasticity of electricity demand, by indicating the need to readjust load forecasting models. In this market environment, in addition to providing robust estimates for the planning and operation of electric power systems, load forecasting models have become fundamental in the context of demand management. Thus, this paper proposes to develop an artificial neural network and fuzzy logic for load forecasting to perform an efficiency analysis. This system is able to provide estimates of the elasticity of electricity demand behavior with more satisfactory results. To do so, improvements in the neural network with multilayer perceptron are proposed. In this case, the adaptation of parameters to correlate variations in consumption with the changes in electricity tariffs was developed. The addition of this new structure produced better results compared with the conventional neural network. Computer tests were conducted using historical data from the ISO New England Inc and PJM Interconnection. Price elasticity estimates of electricity demand showed a sharp increase of demand in relation to the elasticity behavior.

Abstract
Smart buildings will play a fundamental role in ensuring comfort while reducing the energy required. However, due to the lack of knowledge about the operation of the smart controllers, the occupants can unintentionally increase the energy spent. Nevertheless, there is evidence that the informed and motivated user will actually cooperate with the system. Some of the issues associated with researching control systems in the context of building automation are difficult to address, because of the chronic lack of effective laboratory settings for experimentation. In this paper, we describe a system representative of the usual complexity found in cyber-physical systems, whose purpose is to address the needs for experimenting with building automation, with a focus on control systems and gamification. Designed with pragmatic concerns, this system presents a unique set of challenges and opportunities to research a new generation of software control systems, and supporting interfaces, that leverage the occupants' behaviour.

Abstract
This study followed a mixed methodology - five semi-structured interviews were performed, and transcribed in full, with four senior lecturers and one administrative employee at the university; and a survey with 124 valid responses, answered by students of the university, was also conducted. The research question was the following: how does the University of Aveiro achieve its competitive advantage, in view of its competition in Porto, Coimbra and Lisbon, which are the three main geographical areas nearby? The University of Aveiro may also be seen, however, to be competing with other international universities, as the University of Aveiro is highly placed in the international rankings for young universities (the University of Aveiro was, in 2017, ranked 81st in the Times Higher Education Young University Rankings, for universities aged under fifty years). The resources, capabilities and key competences of the University of Aveiro, founded in 1973, were analysed. The results of the study indicate that 76% of the respondents of the survey chose the University of Aveiro as their first choice in their applications, which means that these students are following their first choice for their careers. The University has constructed its attractiveness around value innovation: its low price (not only for its annual fees, but also concerning the cost of living in the Aveiro area - in central Portugal), coupled to its superior and highly recognized teaching practices, its highly recognized research around its 18 research centres, its start-up incubator, its creative science park, its cooperation and knowledge transfer to society (via its many partnerships with different organizations), its multicultural environment (the University of Aveiro is a favourite of Erasmus students), its award-winning campus (in architectural terms), and its organizational culture geared towards the satisfaction of its students, as well as its organizational structure in a matrix format whereby the university is built around different and unique departments (for different functional areas of study) with each having a degree of autonomy, though being managed centrally by the university rectorate. The University of Aveiro is thus seen to be very entrepreneurial as it has remained flexible, dynamic, and principally open to new teaching methods.

Abstract
Nowadays, humanity generates and contributes to form large and complex datasets, going from documents published on media outlets, posts on social media or location-based information. The generated information tends to be complex, heterogeneous (texts, images, videos, etc.) and is growing at an incredible pace, with much of this data having a strong spatial and temporal focus. This steady increase in the availability of such a volume of information, forces the development of more effective user interfaces that would assist users in efficient visualization, analysis and exploration of the data. This half-day workshop on User Interfaces for Spatial and Temporal Data Analysis (UISTDA) held in conjunction with the IUI2018 conference on March 11th, aimed at sharing the latest progress and developments, current challenges and potential applications for exploiting large amounts of spatial and temporal data. In this paper we provide an overview of the workshop goals together with its main contributions. © 2018 Copyright for the individual papers remains with the authors.