Research background: Export activities are crucial for SMEs' growth and income since they enable businesses to expand abroad. However, SMEs encounter some export impediments, including legislative, tax-related, and cultural-linguistic differences, which increase their export risk. Moreover, since different legislative, tax-related, and cultural conditions affect SMEs' export activities, SMEs' perceptions regarding export obstacles might also differ. Purpose of the article: This paper aims to determine whether the SMEs' perceptions of export barriers in selected Visegrad countries differ. Methods: The researchers employ a random sampling method to create the research sample and create an Internet-mediated questionnaire to collect the re-search data, including 408 SMEs from the Czech Republic, Slovakia, and Hungary. The researchers ran ANOVA analyses with the Gabriel Post Hoc test to find differences between those SMEs. Findings & value added: The results reveal that the perceptions of Czech and Slovak SMEs differ regarding legislative and tax-related export barriers. On the other hand, the perceptions of SMEs from various countries do not differ concerning cultural-linguistic export barriers. Unfortunately, there is a lack of studies comparing the perceptions of SMEs from Visegrad countries regarding legislative, tax-related, and cultural barriers. Thus, evaluating this topic from an international perspective brings novel findings and fills this research gap. Therefore, policymakers, SMEs, governments, public institutions, and academicians might gain benefits from the results of this unique research.

Currently, there are a lot of hotel booking systems. However, the quality of the systems and interconnection with another system is very different. In this article, the fuzzy approach and expert system for hotel booking system is proposed. The proposed approach is based on evaluating hotel services for different kinds of hotel guests. The output of the expert system is a proposal of suitable hotel services for hotel guests during the process of hotel booking.

This data article presents responses from a comprehensive, multi-year survey conducted between 2022 and 2024 at several universities in Central and Eastern Europe, focusing on students' artificial intelligence (AI) literacy, attitudes, and readiness. The data collection was part of a longitudinal project within the FITPED consortium, which built upon previous EU-funded initiatives to support digital education. A total of 1146 university students participated, representing a diverse range of study programs, academic years, and countries, including Slovakia, Poland, the Czech Republic, Lithuania, Indonesia, Turkey, France, and Ukraine. The structured questionnaire was based on validated instruments and included constructs such as AI literacy, AI readiness, AI anxiety, behavioural intention, satisfaction, confidence, perceived relevance of AI, and social goods. Items were rated on a 5-point Likert scale, and demographic information, including gender, age, year of study, field of study, and previous experience with AI-related courses, was collected. The survey was administered anonymously via Google Forms and the Moodle LMS in multiple languages, ensuring accessibility across disciplines. The dataset supports cross-disciplines and longitudinal comparisons and is suitable for quantitative analytical methods such as factor analysis and structural equation modeling. This openly shared dataset provides a foundation for tracking trends in AI readiness and perceptions in higher education. It allows for its reuse for comparative international research, curriculum development, and targeted educational interventions that promote inclusive and context-aware AI literacy.

Despite suggestions that the global ratio of unsolicited e-mail messages (SPAM) has been decreasing recently, the SPAM ratio values measured in individual SMTP servers have not confirmed this trend. Blacklisting can eliminate a part of SPAM even before their delivery. A multilevel anti-SPAM mechanism is described and the behavior and efficiency of blacklisting using DNSBL is analyzed. Drawbacks of DNSBL blacklisting and the potential of their elimination are discussed, too. The efficiency of DNSBL application in a specific mail server and its comparison to the efficiency of other SPAM blocking techniques is also presented.

This paper describes a novel approach in the area of evaluating suitable job applicants for various job positions, and specifies typical areas of requirement and their usage. Requirements for this decision-support system are defined in order to be used in middle-size companies. Suitable tools chosen were fuzzy expert systems, primarily the inference system Takagi-Sugeno type, which were then supplied with implementation of methods of variant multi-criteria analysis. The resulting system is a variable tool with the possibility to simply set the importance of individual selection criteria so that it can be used in various situations, primarily in repeated selection procedures for similar job positions. A strong emphasis is devoted to the explanatory module, which enables the results of the expert system to be used easily. Verification of the system on real data in cooperation with a collaborating company has proved that the system is easily usable.