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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Start date: 2025

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Now showing 1 - 10 of 127
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    Observable and Reproducible Sandbox Environment for Cybersecurity Research
    (IEEE, 2026-07)
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    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.
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    A STUDENT-CENTERED APPROACH FOR FOCUS ENHANCING BY SUPPORTING SELF-REGULATED LEARNING THROUGH A MOBILE APPLICATION
    (IATED, 2026-07-25)
    Stefanija Filipashikj
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    Madevska Bogdanova, Ana
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    MetriKG: Profiling Static and Evolving Knowledge Graphs
    (ACM, 2026-05-28)
    Günes, Hasan H.
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    Hose, Katja
    Knowledge 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.
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    MetriKG: Profiling Static and Evolving Knowledge Graphs
    (ACM, 2026-05-28)
    Günes, Hasan H.
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    Hose, Katja
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    Privacy preserving synchronization of directed dynamical networks with periodic data-sampling
    (Elsevier BV, 2025-01)
    Jia, Qiang
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    Yao, Xinyi
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    Data privacy has become a key issue in networked systems, but few effort was devoted to privacy preservation in synchronization of nonlinear dynamical networks when data sampling is involved. This work focuses on the privacy preserving synchronization in a type of nonlinear dynamical network with sampled data. In order to preserve their private initial states, the nodes conceal the sampled data via certain deterministic perturbation, and exchange the masked data with their neighbors via the communication network. A novel privacy-preserving protocols with sampled data is developed, which differs from existing designs with continuous data, and a commonly used restriction on the nodes’ neighbor sets is unnecessary herein. By establishing a new Halanay-type inequality with decaying perturbation, some sufficient criteria are derived to guarantee synchronization without disclosing the nodes’ privacy, revealing how the decaying rate of the masking functions, the topology and the sampling period influence synchronization. Furthermore, in order to reduce the control update, the analogue of the above design with event-trigger is also given, leading to another useful condition for privacy preserving synchronization. Some numerical examples are finally given to validate the theoretical results and demonstrate the effectiveness of the proposed designs.
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    VulnerSec: A Flexible, Automated and Open-Source Cybersecurity Framework
    (Faculty of Computer Science and Engineering, 2025)
    Krajchevska, Evgenija
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    A Comparison of GEC Tools for Grammatical Error Correction in English
    (IEEE, 2025-06-02)
    Virtanen, Johanna
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    Using the Building Educational Application (BEA) benchmark11https://codalab.lisn.upsaclay.fr/competitions/4057, this study compares the capabilities of Google Gemini22https://gemini.google.com/, ChatGPT33https://openai.com/chatgpt, DeepSeek44https://www.deepseek.com/en, and the builtin grammar checkers in Google Docs and Microsoft Word for grammatical error correction (GEC). These tools correct a variety of errors, some of which overlap. Based on the BEA benchmark evaluation results, Google Gemini and the Google Docs grammar checker achieve the best F0.5 scores of 60.2 and 65.86, respectively. Google Docs grammar checker is easy to use and, according to this evaluation, performs well, thus proving to be a viable option for GEC. However, standard grammar checkers are not typically designed for rewriting text to the same extent as GenAI tools; hence, it may be advisable especially for non-native speakers to combine traditional and GenAI grammar correction for the best possible results. However, it is necessary to check the grammatical corrections of LLMs, since generative AI tools suffer from hallucinations, which refers to their tendency to generate information that can be factually incorrect [1].
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    Improving Atrial Fibrillation Detection with Machine Learning Models
    (IEEE, 2025-06-02)
    Tudjarski, Stojancho
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    Madevska Bogdanova, Ana
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    Stankovski, Aleksandar
    In this paper, we focus on feature engineering to detect Atrial Fibrillation by determining whether the heart rhythm has irregularities without patterns. We experiment with a broad spectrum of features derived from the duration of heartbeat-to-heartbeat intervals in the benchmark electrocardiogram databases MIT-BIH Arrhythmia Database, MIT-BIH Atrial Fibrillation Database, and Long Term AF Database. The experiments included position-based features, fluctuation indices, standard deviations, mean average values, Shannon entropy, and statistical measures, such as compressed time series data length. The research questions are to detect the most influential features that result in the best-performing model and the impact of the training dataset. Our approach is to evaluate the model on a completely different dataset from the one it was trained on. Achieved F1 scores vary between 62,32% and 85.08%. The results prove that positional features increase the model's performance.
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    Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs
    (IEEE, 2025-06-02)
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    Petrovski, Nikola
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    Training large language models requires extensive processing, made possible by many high-performance computing resources. This study compares multi-node and multi-GPU environments for training large language models using electrocardiogram data. It provides a detailed mapping of current frameworks for distributed deep learning in multi-node and multi-GPU settings, including Horovod from Uber, DeepSpeed from Microsoft, and the built-in distributed capabilities of PyTorch and TensorFlow. We compare various multi-GPU setups for different dataset configurations, utilizing multiple HPC nodes independently and focusing on scalability, speedup, efficiency, and overhead. The analysis leverages HPC infrastructure with SLURM, Apptainer (Singularity) containers, CUDA, PyTorch, and shell scripts to support training workflows and automation. We achieved a sub-linear speedup when scaling the number of GPUs, with values of 1.6x for two GPUs and 1.9x for four GPUs.