Abstract

Abstract
Software testing can promote software quality. However, this activity is often performed at the end of projects where failures are most difficult to correct. Combining requirements specification activities with test design at an early stage of the software development process can be beneficial. One way to do this is to use a more structured requirements specification language. This allow to reduce typical problems such as ambiguity, inconsistency, and incorrectness in requirements and may allow the automatic generation of (parts of) acceptance test cases reducing the test design effort. In this paper we discuss an approach that promotes the practice of requirements specification combined with testing specification. This is a model-based approach that promotes the alignment between requirements and tests, namely, test cases and also low-level automated test scripts. To show the applicability of this approach, we integrate two complementary languages: (i) the ITLingo RSL (Requirements Specification Language) that is specially designed to support both requirements and tests rigorously and consistently specified; and (ii) the Robot language, which is a low-level keyword-based language for specifying test scripts. This approach includes model-to-model transformation processes, namely a transformation process from requirements (defined in RSL) into test cases (defined in RSL), and a second transformation process from test cases (in RSL) into test scripts (defined according the Robot framework). This approach was applied in a fictitious online store that illustrates the various phases of the proposal.

Abstract
Expression Atlas is EMBL-EBI's resource for gene and protein expression. It sources and compiles data on the abundance and localisation of RNA and proteins in various biological systems and contexts and provides open access to this data for the research community. With the increased availability of single cell RNA-Seq datasets in the public archives, we have now extended Expression Atlas with a new added-value service to display gene expression in single cells. Single Cell Expression Atlas was launched in 2018 and currently includes 123 single cell RNA-Seq studies from 12 species. The website can be searched by genes within or across species to reveal experiments, tissues and cell types where this gene is expressed or under which conditions it is a marker gene. Within each study, cells can be visualized using a pre-calculated t-SNE plot and can be coloured by different features or by cell clusters based on gene expression. Within each experiment, there are links to downloadable files, such as RNA quantification matrices, clustering results, reports on protocols and associated metadata, such as assigned cell types.

Abstract
The planning and design of sustainable and smart cities—cities of the future—should properly address the challenges that arise by the every day growth of the urban population. Mobility is an important issue considering social inclusion and the sustainable development of such cities. Thus, future mobility will have an increased importance when having to plan and design the cities of tomorrow. A key component of any future mobility and its metabolism is what is known as Mobility as a Service (MaaS), representing emerging opportunities from any type or mode of transportation in future cities. Through an empirical and explorative research methodology, this chapter presents the main issues and characteristics that any future MaaS should consider. Concluding, some features and trends are presented that should be considered in the development of future MaaS systems, allowing a more convenient provision of sustainable, versatile and attractive mobility services. © 2020, Springer Nature Switzerland AG.

Abstract
Wiki-based crowdsourced data sources generally lack reliability, as their provenance is not intrinsically marshalled. By using recommendation, one may arguably assess the reliability of wiki-based repositories in order to identify the most interesting articles for a given domain. In this commentary, we explore current trends in scalable modelling and recommendation methods based on side information such as the quality and popularity of wiki articles. The systematic parallelization of such profiling and recommendation algorithms allows the concurrent processing of distributed crowdsourced Wikidata repositories. These algorithms, which perform incremental updating, need further research to improve the performance and generate up-to-date high-quality recommendations. This article builds upon our previous work (Leal et al., 2019) by extending the literature review and identifying important trends and challenges pertaining to crowdsourcing platforms, particularly those of Wikidata provenance.

Abstract
The application of feature engineering in classification problems has been commonly used as a means to increase the classification algorithms performance. There are already many methods for constructing features, based on the combination of attributes but, to the best of our knowledge, none of these methods takes into account a particular characteristic found in many problems: causality. In many observational data sets, causal relationships can be found between the variables, meaning that it is possible to extract those relations from the data and use them to create new features. The main goal of this paper is to propose a framework for the creation of new supposed causal probabilistic features, that encode the inferred causal relationships between the target and the other variables. In this case, an improvement in the performance was achieved when applied to the Random Forest algorithm.