Abstract
Nowadays, the use of creativity in business has been increasing drastically because it has been perceived to be important for the market to come up with new ways, focused on answers to the problems proposed by the users. Several different creativity techniques can be used in a myriad of contexts. One of the most important techniques is the SCAMPER technique, which is based on reorganizing, modifying, adding, and eliminating information. An automated system will provide answers and solutions to creativity problems and contribute to minimizing the cost of innovation in companies. The aim of this paper is, therefore, to design an architecture system for a creative information system based on the SCAMPER creativity technique, thus building an automated system of this technique. © 2020, Springer Nature Switzerland AG.

Abstract

Abstract
Under the premise of rationality, politicians behave to maximize re-election probability. Favorable macroeconomic contexts, alignment with central governance, and balanced public finance will be rewarded leading to re-election. Logit estimations applied to Portuguese municipalities in the period 2002-2017 fail to empirically support these theoretical effects, providing no incentive-controlled policy actions. Local voters do not punish mayors for the adverse economic performance, staying loyal to ideological voting geographically and over time. Only turnout punishes incumbents over the entire period. The introduction of the law of limitation of terms did not change the incentives towards wise governance; therefore, lack of electoral punishments to undesirable policy actions withstands the potential misconduct of incumbents. Existing evidence points to the need of reforms in what concerns electoral participation as when we compel voters to express their democratic rights, they become more critical to undesirable achievements.

Abstract
In celebration of the 10th anniversary of the International Conference on Exploring Service Science (IESS), this paper takes a historical look at the papers that have been published in the IESS proceedings. The analysis is focused on the development and evolution of the IESS community and of the main research topics covered by the published papers over time. The IESS community is portrayed in terms of authors, their affiliations and co-authoring network, while the topics are analyzed according to the papers' keywords. Moreover, this paper analyzes the impact of the papers published in this decade, in terms of citations. These results are then discussed in light of the observed trends and of the evolution of the service science field, to guide the future development of the IESS conference and of research on service science.

Abstract
Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Servicos Distritais de Actividades Economicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.

Abstract
Human epidermal growth factor receptor 2 (HER2) evaluation commonly requires immunohistochemistry (IHC) tests on breast cancer tissue, in addition to the standard haematoxylin and eosin (H&E) staining tests. Additional costs and time spent on further testing might be avoided if HER2 overexpression could be effectively inferred from H&E stained slides, as a preliminary indication of the IHC result. In this paper, we propose the first method that aims to achieve this goal. The proposed method is based on multiple instance learning (MIL), using a convolutional neural network (CNN) that separately processes H&E stained slide tiles and outputs an IHC label. This CNN is pretrained on IHC stained slide tiles but does not use these data during inference/testing. H&E tiles are extracted from invasive tumour areas segmented with the HASHI algorithm. The individual tile labels are then combined to obtain a single label for the whole slide. The network was trained on slides from the HER2 Scoring Contest dataset (HER2SC) and tested on two disjoint subsets of slides from the HER2SC database and the TCGA-TCIA-BRCA (BRCA) collection. The proposed method attained83.3%classification accuracy on the HER2SC test set and 53.8% on the BRCA test set. Although further efforts should be devoted to achieving improved performance, the obtained results are promising, suggesting that it is possible to perform HER2 overexpression classification on H&E stained tissue slides.