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: 2020

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Now showing 1 - 10 of 1013
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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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    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.
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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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    The Ability of Word Embeddings to Capture Word Similarities
    (Academy and Industry Research Collaboration Center (AIRCC), 2020-06-30)
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    Kalajdjieski, Jovan
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    Stojanovska, Frosina
    Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embedding. Determining the most qualitative word embedding is of crucial importance for such models. However, selecting the appropriate word embedding is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
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    Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability
    (Wiley, 2020-01-06)
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    Spasevski, Gjoko
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    Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.
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    A Programmatic Approach for Development of the ViewHRV Service Platform with Accurate and Reliable Results
    (IEEE, 2020-09-28)
    Shaqiri, E.
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    There are dozens of available packages and libraries that claim to calculate HRV. This paper aims at comparing the results from the calculation of a single array of intervals between normal beats, including the most popular open source Python HRV measurement packages available on GitHub. Furthermore, the same array was ran through the Kubios software and compared to the previous results. In order to compare the accuracy of the results, as a benchmark we used the C programs provided by Physionet. The results showed a huge difference in the results with almost all the indices, in fact the simplest measurement that of Standard Deviation of NN Intervals showed to be incorrect in Kubios and in most of the Python packages. Results like these are the reason we decided to develop our own package to calculate HRV. Finally, the goal of this paper is to present details on developing a publicly available web service platform ViewHRV with guaranteed precision obtaining accurate and reliable results.