Miroslav Hudec

Rapid artificial intelligence (AI) penetration into university education presents opportunities and challenges. One of the issues is how the study materials and general support correlate with the student's perceptions of AI. Teachers should also know in which aspects (or applications fields) of AI students are uneasy and feel fear or danger. In this regard, a survey has been conducted in three countries, Iceland, Slovakia, and Serbia, covering three different European regions and different but intertwined study programs. Our results in the survey opinion mining are intriguing in several ways. For example, it has been shown that business students are more likely to support mandatory AI courses than business informatics students. Next, students feel the danger of applying AI in medicine, even though AI is usually learning from high-quality medical records. In this work, we explored the statistically significant differences in responses, proposed recommendations, and identified topics for future surveys. For example, why are the recognized application fields problematic from the students’ perspective? And handling hesitation in answers because, in a rapidly developing field of AI, it is difficult to express opinions clearly.

Public planning decisions affect the living conditions of diverse categories of people differently. Therefore, voters should express their support and/or rejection to each alternative. Since their opinions tend to be subjective, intensities of support and rejection should be collected and processed (rather than binary yes , no voting) to reveal whether an agreement is in favour or against each alternative. However, inconsistent responses (simultaneous high levels of support and rejection) for the same alternative represent a challenge. The next challenge is the different influence of alternatives to diverse citizens subgroups. To address these issues, this work proposes strengthening the consistent answers and weakening the contradictory responses by the convex combination of t-norm and t-conorm function. Next, the impact of coalitions (agreement) among subgroups is formalised by fuzzy measures and Choquet integral, because the impact is different when two of the most affected subgroups or two lightly affected subgroups agree on a specific alternative. In real-life problems through the Traffic Strategy Case Study in the Street of Unterdorf in Geuensee, Lucerne (Switzerland), 13 alternatives were evaluated by four subgroups of voters. Fuzzy measures or weights are assigned to each subgroup and their possible coalitions considering their features. In addition, the sensitivity analysis is performed by Monte Carlo simulation. Finally, topics for future work are outlined.

The COVID-19 pandemic impacted the educational process since the teaching process has been forced to go online in many countries. This enforced change revealed the weaknesses and strengths of the national educational systems and particular institutions. This article aims to analyse the impact of COVID-19 at selected European universities and assess the satisfaction of students, teachers, IT staff and management. This study is unique for its systematicity and complexity – it aggregates the opinions of all interested groups of stakeholders, distinguishes several time periods (before, during and after the pandemic), and allows the respondents to express hesitance in their evaluation. The evaluation model uses fuzzy sets to capture the uncertainty and to aggregate the opinions of different stakeholder groups. The empirical results show that most of the satisfaction development is the same or similar for all institutions examined. Then, the pandemic strongly influenced the satisfaction of all stakeholder groups at the universities examined. This impact was mostly negative, however, several lessons learnt have been revealed. Therefore, it was shown that it is highly beneficial to include these aspects to obtain a reliable picture of overall satisfaction.

The last decade has witnessed a rapid development of computational intelligence models in many scientific and industry fields. One of challenges is the lack of interpretability and explainability of the mechanism underlying the models. In this paper, we propose a methodology for evaluating and explaining influences by linear regression and by fuzzy logic techniques, which have been shown as promising approaches for the explainable computational intelligence. Precisely, fuzzy functional dependencies are able to verify regression result and explain intensities linguistically, whereas linguistic summaries can reveal intensities of relations among the subdomains of considered features. The proposed synergy is experimentally evaluated on the problem of explaining the influence of geographical features on the urban heat islands in the Bratislava downtown. Urban planning and development should consider environmental aspects. Thus, a support which is validated by three diverse approaches is more reliable.