Editorial of the special issue from WorldCIST'20

The world, as we know it, is changing every day thanks to intensive research in fields such as expert systems, and the unexpected discoveries that influence our daily life and habits. Researchers develop more and more innovative concepts and novel paradigms, outstanding technologies that become mature and are launched on the market ‘in a blink of an eye’ with applications in a large number of fields of activities. Smart devices with these novel technologies embedded have become inseparable partners with humans. The knowledge incorporated in advanced programs assist humans in solving difficult problems and taking fast and smart decisions with applications in many areas, starting from industry, healthcare, agriculture, education and many more. In this special issue, we present a range of papers covering some of the subareas of expert systems such as intelligent and decision support systems, ethics, computers, linear regression and big data analytics. This special issue comprises six research papers. All manuscripts are extended versions of selected papers fromWorldCIST'20—8thWorld Conference on Information Systems and Technologies, held in Budva, Montenegro between 7 and 10 April 2020. The WorldCIST conference is already a well-known global forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences and concerns in several areas of Information Systems and Technologies, as well as computer science in general. The six selected papers in this special section include a study that identifies the main factors that can explain the number of patent filing requests made by residents in Brazil, the United States and Europe, detect if an information visualization can be potentially confusing and misunderstood based on the analytic task, a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth to train it or not, a novel orca cultural algorithm, introduces the paradigm of machine culture as an extension to machine intelligence, and a decision model to take care of the water requirement of sensitive crops of agriculture industry. Sousa et al. (2020) present a study that identifies the main factors that can explain the number of patent filing requests made by residents in Brazil, the United States, Europe and triadic patent families. The methods used in the research are quantitative, using big data from private and public investments in Science and Technology, and about patent deposit numbers in Brazil from 2000 to 2017. A model of linear regression was performed and explains how these investments in Science and Technology influence patent deposit numbers. The results of this research study point towards the importance of universities, up and beyond the traditional training and education. The importance of public and private innovation investments is also shown to be important. This study shows that the patent registrations in the different regions under analysis are affected by different factors. There is thus no single formula towards the creation of innovation output and governments would do well to continue to invest in higher education while also investing in public research and development activities. Additionally, and not least important, private entities should be continually encouraged to make innovation investments and favourable government policies need to thus exist for this to happen. Finally, the low numbers regarding patent filings in Brazil may be linked to institutional deficiencies in the country. Patent breaches may be difficult to punish, and the judicial system may be slow and untrustworthy, compared with the United States and Europe—leading to diminished patent registrations in Brazil. Vázquez-Ingelmo et al. (2020), using the machine learning technique, search to find the possibility to detect if an information visualization can be potentially confusing and misunderstood based on the analytic task it tries to support. This approach is supported by fine-grained features identified through domain engineering and meta modelling on the information visualization and dashboards domain. Data visualizations encode data through different visual features, which have been captured and structured through a meta-modelling approach. The identified features were employed to automatically generate a set of parameterized visualizations that were subsequently discussed through a tagging process to obtain a training dataset of ‘helpful/not helpful’ information visualizations. Finally, the resulting dataset was employed to train ML algorithms that classify information visualizations as helpful or not helpful given their features and supported analytic task. The experiment shows promising results as the viability of the approach has been tested through a proof-of-concept in the domain of visualizations that display tri-variate datasets with the goal of identifying correlation among their variables. Although some limitations were identified, this experiment can set the foundations for subsequent research on this domain. Carneiro et al. (2020) present in this paper a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth to train it or not. To see if the model proposed holds significantly better results than the current one, the authors propose the use of metalearning. Therefore, two different meta-models are evaluated: one built for a specific machine learning problem, and another one built based on many different problems, meant to be a generic meta-model, applicable to virtually any problem. The focus is on the prediction of the root mean square error (RMSE). Results show that it is possible to accurately predict the RMSE of future models, event in streaming scenarios. Moreover, results also show that it is possible to reduce the need for re-training models between 60% and 98%, depending on the problem and on the threshold used. Drias et al. (2020) introduce in this article the paradigm of machine culture as an extension to machine intelligence. This new concept is modelled based on animal intelligence and culture. The example of orca intelligence and culture is considered as orcas possess in addition to skills DOI: 10.1111/exsy.13164