Faculty of Computer Science and Engineering
Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5
The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest
faculties in that field in the region.
The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.
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Item type:Publication, Prime Alert Design - AI Supported Telemedicine System in High-Risk Environments(IEEE, 2026-09) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring Workforce-Informed Competency Pathways in Computing Education(ACM, 2026-07-09); Barr, Matthew - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Observable and Reproducible Sandbox Environment for Cybersecurity Research(IEEE, 2026-07); ; The availability of controlled sandbox environments from which relevant network traces and system logs can be obtained is a prerequisite for studying network attacks. Such data is also essential for the development of novel security tools as well as for evaluating the effectiveness of various incident response strategies. A persistent challenge faced by security researchers is how to set up these environments in a consistent manner, guaranteeing the integrity of the underlying infrastructure while still providing meaningful data. In this paper we present the design of a secure, reproducible, and observable sandbox in which purposefully vulnerable applications are deployed and exploited. Every interaction is monitored in real time using strategically placed network probes, performance monitoring agents, and log shippers. The use of modern configuration management tools eliminates configuration drift and entropy, guaranteeing reproducibility of the deployed scenarios. We describe practical approaches for implementing the sandbox, detailing the tools and methods required for real-world deployment. Using a recent sandbox instantiation as an example, we demonstrate how the acquired network data can be used to identify attack patterns, analyze defense strategies, and assess overall incident response effectiveness. The obtained dataset is open-sourced and can be reused to support further research. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A STUDENT-CENTERED APPROACH FOR FOCUS ENHANCING BY SUPPORTING SELF-REGULATED LEARNING THROUGH A MOBILE APPLICATION(IATED, 2026-07-25); Madevska Bogdanova, Ana - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comprehensive overview of quantum serverless: Elastic integration of quantum and classical computing(Elsevier BV, 2026-10); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MetriKG: Profiling Static and Evolving Knowledge Graphs(ACM, 2026-05-28); Hose, KatjaKnowledge graphs (KGs) are a foundational technology for representing and integrating information across heterogeneous domains. As some KGs evolve, understanding how their structural and semantic properties change over time is crucial for ensuring quality, consistency, and interpretability. Existing methods for KG evaluation often focus on static graphs or analyze evolution solely at the data level, leaving schema-level dynamics underexplored. To address this gap, we introduce MetriKG, a web-based application that computes a comprehensive set of metrics for both static and evolving KGs. MetriKG enables users to evaluate KGs provided as RDF files or through SPARQL endpoints, allowing for multi-dimensional analysis of aspects such as cohesion, connectivity, and inheritance depth. By supporting metric computation at both data and schema levels, MetriKG allows for systematic profiling, classification, and temporal monitoring of KGs. MetriKG is open-source and publicly available. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, MetriKG: Profiling Static and Evolving Knowledge Graphs(ACM, 2026-05-28); Hose, Katja - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Privacy preserving synchronization of directed dynamical networks with periodic data-sampling(Elsevier BV, 2025-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, VulnerSec: A Flexible, Automated and Open-Source Cybersecurity Framework(Faculty of Computer Science and Engineering, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Contribution to the Quasigroup Based Error-Detecting Code(IEEE, 2023-07-19)In the last years we have developed few error-detecting codes based on quasigroups. One of them is the code which is a subject of this paper. The previous analyses of the code shows that the code has very high probability of detecting transmission errors. We have previously identified so-called best class of quasigroups of order 4, with the highest probability of detecting errors when the coding process is performed with a quasigroups of order 4. But, there is one more class of quasigroups of order 4, second-best class, whose quasigroups give approximately equal probability of undetected errors as the quasigroups from the best one. Therefore, we proceeded with the examination of the code when it uses quasigroups from this second-best class of quasigroups of order 4. In this paper we will analytically obtain the second important parameter of every error-detecting code, i.e. the number of errors that the code surely detects when for coding it uses a quasigroup from this second-best class of quasigroups of order 4. At the end we will conclude whether the quasigroups from these top two classes have overall equal ability to detect errors with this code.