Efficient server management is essential for modern IT infrastructures, particularly as organizations increasingly adopt distributed and virtualized environments. Traditional command-line-based server administration, while powerful, poses challenges for scalability, usability, and monitoring. To address these issues, web-based graphical interfaces such as Cockpit, Webmin, Portainer, and Virt-Manager have emerged, providing centralized, real-time system management with enhanced accessibility. However, existing solutions often lack tailored features for specific institutional needs. In response, we developed a custom server management platform at the Faculty of Computer Science, designed to streamline system administration while addressing gaps in usability, extensibility, and integration. Our platform builds on Cockpit’s principles but introduces enhanced automation, role-based access control, and improved monitoring capabilities tailored for academic and research environments. This paper presents a comparative analysis of these platforms, applying a weighted scoring methodology that incorporates industry best practices, user feedback, and academic benchmarking. Findings highlight Cockpit’s advantage in real-time monitoring and multi-server management, Webmin’s extensive modular capabilities, Portainer’s focus on containerized environments, and Virt-Manager’s virtualization support. By aligning the capabilities of these tools with the requirements of our internally developed platform, this research provides a foundation for optimizing server management in institutional settings.
The work of Gödel is the Summa Mathematica of a process of understanding of linguistic and mathematical paradoxes, which spanned over millennia. Gödel has shown—together with Cantor, Turing and other great minds—that mathematical truth does not exist, and any model having as objective to formally represent reality is doomed to fail. The circle of Copenhagen and the interpretation of quantum mechanics have further disillusioned and distanced us from an anthropocentric view. We understand how not even the physical reality can be captured by our fallible senses. In a context of disillusion from anthropocentrism, how we can reflect on Artificial Intelligence in our everyday life? This article aims at opening a discussion between the modern concept of Artificial Intelligence and the established idea that humans are capable of producing indelible expressions of their passage that can stay for millennia. We understand little of both processes of creation, artificial and natural. Could one ever replace the other or should they synergistically coexist? Different points of view are discussed. In particular, Jung seems to suggest that purely algorithmic based analyses of data (no matter how large) may not be able to give rise to what we understand as intelligence, and even less so as intuition. In this sense, Machine Learning may be not be suitable tool to reach Artificial Intuition and Intelligence in a broader sense. More recent positions see Deep Learning as a kind of artificial intuition. Being both positions speculative, the topic deserves more reflection.
Urban mobility faces significant challenges such as congestion, environmental impact, and inefficiencies in transportation systems. Traditional transportation models struggle to adapt to the increasing demand for efficient and sustainable urban mobility solutions.
Large language models (LLMs) are a type of deep learning model characterized by an exceptionally high number of parameters. Their significant progress across a wide array of tasks has enabled them to become valuable and versatile tools for a diverse range of applications. Their capabilities present promising opportunities for business process management, although these potential benefits have yet to be thoroughly explored. In this research, we explore the use of LLMs to enhance various aspects of business process management (BPM). The primary focus is on process mining as a subset of BPM. To answer research questions “How are LLMs being integrated with process mining?” and “What are the challenges of integrating LLMs with process mining” we conducted a literature review focused on positioning of LLMs in process mining. In our research we present areas of integration of LLMs in process mining, together with limitations and challenges, and future directions.
