ABSTRACT. The objective of this paper is to provide a comprehensive study of statistical and machine learning methods for predicting daily and weekly volatility of the following four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Monero. Several models and forecasting methods are compared in terms of their forecasting accuracy, i.e., HAR, ARFIMA, GARCH, LASSO, RR, SVR, MLP, FNM, RF, and LSTM. Our experimental results demonstrate that there is no single best method for forecasting volatility of each cryptocurrency, and different models may perform better depending on the specific cryptocurrency, choice of the error metric and forecast horizon. For daily forecasts, the method that is always found in a set of best models is linear SVR, while for weekly forecasts, there are two such methods, namely FNM and RR. Furthermore, we show that simple linear models such as HAR and ridge regression, perform not worse than more complex models like LSTM and RF.

ABSTRACT. We examined modeling errors in two types of modeling diagram, a class diagram (CD) and an entity-relationship diagram (ERD), to find out whether learning the notations of one diagram (CD) leads to fewer errors in the next (ERD). Although previous gender research showed that females are better at reading comprehension, other studies indicate a lower tendency in females to choose science, technology, engineering, and mathematics (STEM) professions. Hence, we were interested in finding out whether the reading advantage would manifest within a STEM activity of database modeling. We conducted an experiment with college students, using a within-subject design and type of modeling task (CD/ERD), and observed the types and number of modeling errors. Overall, we found that there was a learning effect from one model to the other and that male students made more errors. In addition, we discovered that females had much more significant learning between models.

ABSTRACT. In computed tomography (CT) imaging, the Hounsfield Unit (HU) scale quantifies radiodensity, but its nonlinear nature across organs and lesions complicates machine learning analysis. This paper introduces an automated method for adaptive HU scale windowing in deep learning-based CT liver segmentation. We propose a new neural network layer that optimizes HU scale window parameters during training. Experiments on the Liver Tumor Segmentation Benchmark show that the learned window parameters often converge to a range encompassing clinically used windows but wider, suggesting that adjacent data may contain useful information for machine learning. This layer may enhance model efficiency with just 2 additional parameters.

ABSTRACT. The objective of this work was to provide an app that can automatically recognize hand gestures from the American Sign Language (ASL) on mobile devices. The app employs a model based on Convolutional Neural Network (CNN) for gesture classification. Various CNN architectures and optimization strategies suitable for devices with limited resources were examined. InceptionV3 and VGG-19 models exhibited negligibly higher accuracy than our own model, but they also had more complicated architectures. The best method for network optimization became Layer Decomposition which achieved the lowest inference time in classification effectiveness. Each optimization method reduced the inference time of our model at the small expense of classification accuracy. The accelerators with the shortest inference time were GPU and CPU in a configuration of 5 threads. For the purpose of loading the trained models, running and testing their effectiveness under different hardware configurations a prototype of the mobile application was developed: [removed].

ABSTRACT. This research in progress aims to investigate the factors that influence students' academic performance by using educational Social Networking Sites (SNSs) in Kuwait. The updated DeLone and McLean IS model (2003) is utilized with an additional factor ‘Student Emotional Engagement' found to be a significant variable in the relevant literature. This research has applied quantitative methods by distributing a survey to a sample of students who use educational SNSs in the Public Authority for Applied Education and Training (PAAET) colleges. Using Structural Equation Modelling, valuable results may be obtained to identify the most important factors that could influence students' academic performance using educational SNSs. The findings may provide useful insights for stakeholders including students and teachers on how to invest in the use of Information and Communication Technologies in the education sector, which may ultimately help in increasing the effectiveness of the use of educational SNSs for all targeted stakeholders.

ABSTRACT. The study aims to verify whether, from the perspective of lecturers, there have been changes with regard to Generation Z in terms of the need to diversify classes by implementing a variety of technological tools and applications to activate students. The use of technology and perceptions of artificial intelligence in the teaching process were examined. Methodology: employing a quantitative research framework, the study used a questionnaire to gather data from a sample of 789 university teachers across 27 European countries. The questionnaire probes into various facets of teaching methodologies, including preparation for active learning, utilization of teaching tools and applications. Results: the findings reveal that there has been a significant positive change in the need for technological tools and applications in the education of Generation Z, which implies the need for differentiated instructional design, activating students in it.

ABSTRACT. Traditional repositories, such as university knowledge resources, present challenges for students accustomed to instant answers. To improve information delivery at university, we propose an AI-Driven System for University Information Provision (UnIPro). By analyzing 4551 student posts over 18 months, we classified 20 key categories of student questions. These insights formed the basis for developing a system that assists students in accessing relevant information. The system will be helpful in meeting genuine student information needs and could yield functional and operational improvements within universities, facilitating communication between students and administrative bodies.

ABSTRACT. In recent months, students, teachers, and researchers have become equally impressed by Generative AI (GenAI) tools, with ChatGPT at the top. However, numerous concerns about the GenAI-related threats to academic integrity and the validity of learning outcomes are emerging. This problem is also vivid in Human-Computer Interaction (HCI) education since students can use GenAI tools to rapidly generate ideas, user interface templates, screen graphics and mock-ups, or entire user research programmes. This paper presents the results of a small-scale survey performed with a group of HCI students regarding their experiences and expectations regarding the use of GenAI tools in their current HCI course, as well as expected GenAI-relevant university policies. Conclusions from this study can be informative for HCI teachers considering the potential use of GenAI tools in their classes and for university managers in the broader context of engineering university education, regarding computer science in particular.

ABSTRACT. The paper presents developing a virtual reality-based escape room to teach mathematical concepts. The goal was to create an immersive game to engage students in actively solving math puzzles. The research team built the application for use in the Immersive 3D Visualization Lab at the Gdańsk University of Technology. The escape room comprises an introductory room followed by three themed rooms with 13 puzzles total that involve mathematical thinking. To assess the tool’s educational impact, the team prepared surveys and planed an experiment with students. Key outcomes delivered were the completed application configured for the target lab, plus the surveys to quantitatively measure math comprehension before and after students use the escape room. Overall this project combined virtual reality and game design concepts to create an innovative approach for engaging students in learning math concepts in an interactive, visually stimulating setting.

ABSTRACT. IT specialists in the business environment work in teams according to the established methodology and using the established toolkit. From the university's point of view, preparing IT students to work in such an environment is a challenging task, as it requires either cooperation with business or the simulation of similar conditions in the university environment. Participation of students in real projects can provide them with the necessary practical skills. The aim of this paper is to present the experience gained in running real-life, long-term projects in academia, and to provide guidelines on how to involve students in running these projects to the benefit of students.

ABSTRACT. Open online courses serve as a disruptive force in education, challenging traditional educational paradigms. While classroom teaching remains prevalent, advancements in digitization, technology, and artificial intelligence are catalysts prompting a reevaluation of educational models and the necessity for new global frameworks. These developments prompt a reexamination of how we should design for online learning in the age of AI. In this emergent research paper, we share initial insights from an online survey on the roles of humans in open online course design. Recognizing the complex nature of education, we envision AI's future in education through collaborative relationships between humans and AI, leveraging their combined strengths. Thus, we provide a conceptual model for desiging online learning environments, utlizing human-centered design principles and human-centered AI frameworks aimed at prioritizing human interests rather than replacing human roles.

ABSTRACT. This paper examines the challenges and opportunities in Poland’s Information Systems Develop- ment (ISD) and Artificial Intelligence (AI) sectors in the post-COVID-19 era. Highlighting the shift towards hybrid and remote work models, it addresses the significant skills gap in the Polish IT industry, emphasizing the need for enhanced education and training. The analysis reveals a critical shortage of IT specialists, intensified by an inadequate development of competencies alongside technical skills. The paper advocates for strategic educational reforms and corporate training programs tailored to meet the demands of advanced AI technologies and interdisci- plinary collaboration, and argues for the necessity of continuous professional development to maintain competitiveness and adaptability in a rapidly evolving digital landscape, incorporating modern pedagogy.

ABSTRACT. Drawing from the higher education teachers experience from Poland and Ukraine, this qualitative study addresses key issues of distance learning and its future prospects. While it confirms other research in terms of the perceived flexibility of distance learning and issues related to the lack of suitability of some modules to be conducted in distance mode, as well as problems with keeping student engagement, it sheds a new perspective in relation to future possible perceptions of traditional education as more prestigious when conducted by renowned universities. Polish and Ukrainian respondents in general revealed similar concerns, e.g. in relation to blurring of personal and professional boundaries. However, Ukrainian respondents face some unique challenges related to the ongoing war, including mandatory implementation of distance education and more prominent difficulties in keeping students engaged.

ABSTRACT. Proper operation of software requires increasingly larger memory resources and efficient IT equipment. Therefore, methods and technologies are being sought to optimize IT structures. One of the directions is virtualization and switching to cloud. These are methods of running multiple virtual computers or servers on a single hardware platform that supports multiple IT systems and applications. The implementation of these technologies means number of benefits, but also costs, including the need to purchase appropriate hardware, software and then maintain the system. Thus, a business decision to implement virtualization requires comprehensive supporting tools testing its financial and economical effectiveness. As an artifact of the research, a prototype of a calculator was developed to evaluate the effectiveness of virtualization, which can be measured using the TCO (Total Cost of Ownership) and ROI (Return On Investment) indicators. To achieve the research goal, the DSR (Design Science Research) method was used.

ABSTRACT. Expert interviews, a commonly employed qualitative data collection procedure in information systems research (IS), lack consistent conceptualization. This paper aims to address this gap by providing a conceptual framework and comprehensive guidelines for a rigorous implementation of expert interviews, supported by real-world examples. After a systematic selection of method-relevant literature, a thematic analysis of twenty-eight articles, books, and book chapters is conducted to elicit the distinctive characteristics and rigorous conduct of expert interviews. Validation is provided by analyzing nineteen studies published in important IS outlets that use expert interviews. A particular focus is on a subset of five studies that cover digital transformation topics. The analysis reveals expert interviews’ flexibility as data collection procedure and shows the wide range of application opportunities for IS researchers. Lastly, we discuss theoretical and practical implications of our findings to enhance the rigor, systematic use, and relevance of expert interviews in IS research.

ABSTRACT. The article presents findings from a questionnaire-based study conducted among finance and accounting students at two Polish universities from January to March 2024. A total of 305 valid responses were collected from a pool of 602 bachelor, MSc, and post-MSc students, resulting in a response rate of 51%. The primary aim of the study was to gauge students' perspectives on the influence of digital transformation (DT) on the accounting profession. A significant majority (79%) of respondents believe that accountants must continually enhance their digital competencies (DC). Conversely, skills like creative thinking, communication, and teamwork are less prioritized. Simplification and accelerated execution of accounting processes emerge as the most notable benefits of DT, whereas increasing costs associated with employee training appear as a significant drawback of DT. This article contributes to a better comprehension of the need to enhance higher education accounting curricula to meet the demands of insistent digitalization.

ABSTRACT. Crowd-based and data-intensive requirements engineering (RE) strategy is considered a promising approach. It enables the gathering and analyzing of information from the general public or the so-called crowd in order to derive validated user requirements. This study aims to conceptualize the process of analyzing information from a crowd to achieve the fulfillment of user requirements. The created model is based on the ADO framework (Antecedents-Decisions-Outcomes). In the empirical part, we chose the Instagram mobile app and user feedback on it as a source of data for the validation of our approach. For extracting antecedents from user feedback, we applied the Latent Dirichlet Allocation (LDA), and then sentiment analysis was performed for each topic to prioritize the most urgent tasks delegated by the crowd.

ABSTRACT. The paper presents a research methodology focused on generating a global attribute ranking, based on discrete variants of datasets, transformed by multiple algorithms. The approach enables to accumulate information on feature importance from such local sources and, when it is represented in the form of a global ranking, identify the features that are the most relevant for decision-making processes. The research procedure was validated by experiments, in which the rankings were used to control filtering decision rules induced by the classic rough set approach. In the vast majority of cases, it was possible to obtain noticeable attribute reduction, and predictions improved or comparable with the results obtained for local variants of the data.

ABSTRACT. User defined functions (UDFs) are frequent components of SQL queries and data processing workflows (DPWs). In both of these applications, UDFs are often available as black boxes, i.e., their semantics and performance characteristics are unknown (such functions are further called BBUDFs). This feature prevents from optimizing execution plans of queries and from optimizing the whole DPWs. Discovering the semantics of a BBUDF is often impossible due to high complexity of its code. On the contrary, discovering its performance model seems to be feasible with the support of machine learning. In this paper, we present a solution for classifying BBUDFs into performance classes. This way, if a performance class of a given BBUDF is known, it may allow to reason about some hidden features of the BBUDF. Our solution is supported by experimental evaluation, which reveals that our initial approach, in multiple cases, allows to classify BBUDFs to adequate performance classes.

ABSTRACT. This paper explores the concept of Process Debt (PD) in Agile Software Development (ASD) organizations. Drawing on the analogy with Technical Debt, PD is defined as the challenges that emerge from suboptimal or outdated processes, which can significantly hinder an organization's adaptability and software delivery effectiveness. The study proposes a survey instrument, designed to measure various types of PD based on existing research and expert interviews. Five types of PD are identified and operationalized: Process Unsuitability Debt, Synchronization Debt, Roles Debt, (Process) Documentation Debt, and Infrastructure Debt. The instrument's reliability and validity are assessed through a multi-stage process, culminating in a field survey within two ASD organizations. The findings significantly contribute to our understanding of PD and provide the first version of a validated tool for researchers and practitioners to identify and measure PD in their organizations.

ABSTRACT. Machine learning has emerged as a fundamental tool for numerous endeavors within health informatics, bioinformatics, and medicine. However, novices among biomedical researchers and IT developers frequently lack the requisite experience to effectively execute a machine learning project, thereby increasing the likelihood of adopting erroneous practices that may result in common pitfalls or overly optimistic predictions.

The paper presents an assessment of the significance of best practices in the implementation of R\&D projects supporting the medical diagnostic process. Based on the literature and authors' experiences, 27 good practices influencing three fundamental stages of project implementation were identified. The evaluation was based on the Analytic Hierarchy Process, which relies on subjective assessments from experts, whose credibility is expressed through the consensus of assessment.

Initially focusing on DevOps methodology, research integration, interdisciplinary information sharing were prioritized over automation. Furthermore, annotation tools and data / model quality control were identified as of significant importance.

ABSTRACT. Agility and discipline are often treated in a systems and software development literature as a contradictory concept. We demonstrated through critical analysis that this claim is grounded in human susceptibility to quick dichotomous inference and can be successfully overcame eliminating unnecessary biases. In response to this challenge, we propose the use of a process management framework as a foundation for integrating various technical, organizational, and social ideas to construct system and software in a smooth and agile manner while maintaining a sufficient level of control. The proposed organizational design is developed using a method called study of practices, commonly practiced in qualitative research.

ABSTRACT. The article presents the specificity of innovativeness in software development companies in Europe from the perspective of its connection with Industry 4.0/5.0 technologies and closed versus open innovation methods, as well as its role in innovation systems. Empirical research in 142 European computer programming companies shows that they are involved in the development of software supporting the digital transition. High innovation in collaboration with external partners as well as closed innovation are success factors for the software industry. Consistent strategic management and waste reduction are also important. Further promotion of the software industry as an intermediary in innovation systems is recommended. The methods used were literature review, web survey and statistical and econometric analysis.

ABSTRACT. This paper presents a circular digital twin for climate-smart soils supported by a community composting network within a UNESCO Geopark site. The research addresses the challenges of water use management and organic matter optimization, with the dual objective of promoting sustainable land management practices and enhancing carbon sequestration. Following the design science research paradigm, we develop a circular digital twin architecture, which is then demonstrated and evaluated in terms of technical risk and efficacy, as well as human risk and effectiveness. This work advances the digital twin body of knowledge by presenting a solution that closes the loop of the food supply chain, improving soil management. For practitioners, our work provides an accessible and detailed demonstration and evaluation framework for digital twins in climate-smart soils, revealing how carbon sequestration can be deployed at a regional scale.

ABSTRACT. The paper presents the results of a study that evaluates the impact of intelligent transportation systems (ITS) on green logistics practices and their effects on customer service performance in freight transport enterprises. Based on a selection of ten ITS applications for management processes assistance and eight ITS applications for vehicle use support, we assessed the level of knowledge on these applications among freight transport managers, as well as the level of maturity of implementation in their companies. This study involves two-stage research conducted in 2023 in March-May and August-October with 840 and 640 freight transport enterprises in the Visegrad Group countries. A quantitative survey based on an online questionnaire was defined, and the collected data was then analysed using descriptive statistics methods and the SEM-PLS (Structural Equation Modelling-Partial Least Squares) methodology.

ABSTRACT. This paper presents a novel way to conduct machine learning analysis on graphs and empirically evaluates it. We can not perform such analysis on non-fixed length feature vectors, so first, we must find a way to represent graphs as such. We propose a graph kernel based on graph automorphisms, also known as graph symmetries. We then empirically evaluate the classification accuracy of three machine learning algorithms, SVM, Random Forest, and AdaBoost, using this novel graph kernel against two existing graph kernels and a naive baseline. The models reach a higher classification accuracy on some datasets using our Symmetry kernels than the graphlet kernel and Weisfeiler-Lehman kernel despite our kernel constructing far smaller feature vectors than the existing approaches.

ABSTRACT. This paper proposes a logic-based approach based on Interpolative Boolean Algebra (IBA) for multi-criteria evaluation of different priority and dispatching rules for production scheduling. Scheduling is crucial in optimizing operational activities, enabling efficient resource allocation within specific time constraints. While standard approaches to multi-criteria evaluation often use the weighted sum or weighted product method, they cannot capture logical and statistical relationships from the data. To address these limitations, we propose logical aggregation (LA) based on IBA, ensuring transparency and explainability in data aggregation. This paper evaluates the performance of six well-known priority and dispatching rules on 30 common benchmark instances of the job shop problem based on four scheduling criteria functions as input attributes. Analysis shows that the Critical Ratio rule performs the best, with Earliest Due Date also being a solid recommendation. This is a valuable insight for production managers unable to perform time-consuming simulations when facing tight deadlines.

ABSTRACT. In everyday life, we collect large amounts of data that describe different states: positive, neutral or negative. This data can describe redundancies, uncertain information and deficiencies.

So, gathering, analysing, and processing data have three-state forms, e.i.: true, false, and no information, which requires developing extensions of fuzzy sets that allow their description and processing. One such extension is balanced fuzzy sets. Their development resulted in studying the concept of new fuzzy operators and their various properties. Nevertheless, many operators that have been described for classical fuzzy sets can indeed be extended to the range $[-1,1]$. To support fuzzy operations' bipolarity, we propose using extended to interval $[-1, 1]$ uninorms as unions and nullnorms as intersections. The work also presents the relationships between these operations and fuzzy balanced norms and conorms.

ABSTRACT. This pilot study examined the validity of the UTAUT 2 model after adding variables to make it more appropriate for Kuwait’s educational context. 38 items that measure the variables are adapted from existing literature and modified to suit Kuwait context. The instruments passed through reliability and validity assessments to ensure completeness and clarity of the measures. 36 valid data were gathered from teachers who use educational Social Networking Sites (SNSs) at Kuwait higher education. The data analyzed using Jamovi. The results showed a sufficient level of reliability for all instruments except SNSs Conditions which scored a low and unacceptable level of internal consistency, and this variable was dropped. Confirmatory Factor Analysis to verify the validity of the items revealed that all items met the suggested criteria, except items BI4 and UB1 which were dropped. These assessments confirm the validity of the extended model for a full-scale study in Kuwait context.

ABSTRACT. In the realm of the development of information systems related to education, integrating multimedia technologies offers novel ways to enhance foreign language learning. This study investigates audio-video processing methods that leverage real-time speech rate adjustment and dynamic captioning to support English language acquisition. Through a mixed-methods analysis involving participants from a language school, we explore the impact of auditory, visual, and bimodal input enhancements on learning outcomes. Results reveal that visual enhancements, especially caption highlighting, significantly enhance vocabulary acquisition and pronunciation, whereas simultaneous auditory-visual modifications show less advantage. Overall findings suggest that multimedia-enhanced environments, particularly those emphasizing visual input, can substantially improve language learning efficiency. This research contributes to Information Systems (IS) development education by proposing practical tools and strategies for embedding multimedia content in e-learning, thereby addressing the diverse needs of students in the digital era.

ABSTRACT. This paper describes the results of a questionnaire study conducted among accounting students at the Faculty of Management at the University of Gdansk, Poland, in May 2023. The questionnaires were physically distributed in classes and filled out on paper, yielding the response rate at 92.4% and totaling 97 respondents. The aim of the study was to compare students’ perceptions of the usefulness and advantages of two digital educational platforms, namely Moodle and MS Teams. We found that, in the students’ opinion, MS Teams garnered substantially more appreciation than Moodle as a very supportive tool for university-level education. Although both platforms are perceived as more supportive for general-purpose courses than specifically for accounting courses, MS Teams ranked higher in all categories of usefulness and advantages.