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, 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, Exploring the Potential of Topological Data Analysis for Explainable Large Language Models: A Scoping Review(Zenodo, 2026); ; Large language models (LLMs) have become central to modern artificial intelligence, yet their internal decision-making processes remain difficult to interpret. As interest grows in making these models more transparent and reliable, topological data analysis (TDA) has emerged as a promising mathematical approach for exploring their structure. This scoping review maps the current landscape of research where TDA tools—such as persistent homology and Mapper—are used to examine LLM components like attention patterns, latent representations, and training dynamics. By analyzing topological features across layers and tasks, these methods provide new ways to understand how language models generalize, respond to unfamiliar inputs, and shift under fine-tuning. The review also considers how TDA-based techniques contribute to broader goals in interpretability and robustness, especially in detecting hallucinations, out-of-distribution behavior, and representational collapse. Overall, the findings suggest that TDA offers a rigorous and versatile framework for studying LLMs, helping researchers uncover deeper patterns in how these models learn and reason. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparing the Use of Simu5G and 5G‐Sim‐V2I/N Modules When Analysing the Edge Computing Resource Management Efficiency(Wiley, 2025-04-29); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis(MDPI AG, 2025-05-30); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, RDFGraphGen: An RDF Graph Generator Based on SHACL Shapes(Springer Nature (Singapore), 2026-04-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Small Prompts, Big Energy and CO2 Impact: Benchmarking Ollama LLMs on CPU and GPU(IEEE, 2025-11-25); Energy efficiency is a crucial challenge when deploying Large Language Models (LLMs). Electricity usage and related CO2 emissions can differ greatly depending on model architecture, parameter size, prompt length, and inference hardware. In this study, we evaluate 31 popular Ollama models across CPU and GPU inference, resulting in 60 testing scenarios. Energy and carbon metrics were gathered using the NVML and CodeCarbon libraries, providing insights into the environmental impact of LLM inference in data center settings. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Overview of Legal Artificial Intelligence Assistants Landscape(IEEE, 2025-11-25); ; ; This survey presents the current landscape of AI legal tools, serving both legal professionals and the general public. It compares existing solutions, while also addressing technological and business challenges that shape their development and use. The findings contribute to a clearer understanding of the role and potential of AI assistants in the legal domain, offering insights relevant to both practitioners and researchers. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Generating Synthetic EHR Knowledge Graphs — a Probabilistic Approach(GOBLIN COST Action, 2025-06-12); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applicability Assessment of Technologies for Predictive and Prescriptive Analytics of Nephrology Big Data(Wiley, 2025-05-27); ; ; ; The integration of big data into nephrology research will open new avenues for analyzing and understanding complex biological datasets, driving advances in personalized management of kidney diseases. This paper describes the multifaceted challenges and opportunities by incorporating big data in nephrology, emphasizing the importance of data standardization, advanced storage solutions, and advanced analytical methods. We discuss the role of data science workflows, including data collection, preprocessing, integration, and analysis, in facilitating comprehensive insights into disease mechanisms and patient outcomes. Furthermore, we highlight predictive and prescriptive analytics, as well as the application of large language models (LLMs) in improving clinical decision‐making and enhancing the accuracy of disease predictions. The use of high‐performance computing (HPC) is also examined, showcasing its role in processing large‐scale datasets and accelerating machine learning algorithms. Through this exploration, we aim to provide a comprehensive overview of the current state and future directions of big data analytics in nephrology, with a focus on enhancing patient care and advancing medical research. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Temperature induced self-healing properties of alginate gelatin hydrogels(Elsevier, 2024-05-15)