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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    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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    ANTI-VIRUS TOOLS ANALYSIS USING DEEP WEB MALWARES
    (AIRCC Publication Corporation, 2018-12-22)
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    Šćepanović, Sanja
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    Knowledge about the strength of the anti-virus engines (i.e. tools) to detect malware files on the Deep web is important for people and companies to devise proper security polices and to choose the proper tool in order to be more secure. In this study, using malware file set crawled from the Deep web we detect similarities and possible groupings between plethora of anti-virus tools (AVTs) that exist on the market. Moreover, using graph theory, data science and visualization we find which of the existing AVTs has greater advantage in detecting malware over the other AVTs, in a sense that the AVT detects many unique. Finally, we propose a solution, for the given malware set, what is the best strategy for a company to defend against malwares if it uses a multi-scanning approach.
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    Interplay Between Spreading and Random Walk Processes in Multiplex Networks
    (Institute of Electrical and Electronics Engineers (IEEE), 2017-10)
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    Scepanovic, Sanja
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    Real networks in our surrounding are usually complex and composite by nature and they consist of many interwoven layers. The commutation of agents (nodes) across layers in these composite multiplex networks heavily influences the underlying dynamical processes, such as information, idea and disease spreading, synchronization, consensus, etc. In order to understand how the agents' dynamics and the compositeness of multiplex networks influence the spreading dynamics, we develop a susceptible-infected-susceptible-based model on the top of these networks, which is integrated with the transition of agents across layers. Moreover, we analytically obtain a critical infection rate for which an epidemic dies out in a multiplex network, and latter show that this rate can be higher compared with the isolated networks. Finally, using numerical simulations we confirm the epidemic threshold and we show some interesting insights into the epidemic onset and the spreading dynamics in several real and generic multiplex networks.
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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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    Simulating the error-detecting capability of the error-detecting code
    (IEEE, 2018-05)
    Using simulations, we analyze an error-detecting code from the aspect of the number of errors that the code surely detects. In order to conclude whether and how the order of the quasigroup used for coding affects the number of errors that the code surely detects, we use quasigroups of different orders for coding. Also, we code input blocks of different lengths in order to conclude whether the number of errors that the code surely detects depends on the length of the input block, i.e., the length of the code word.
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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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    Improving the QRS detection for one-channel ECG sensor
    (SAGE Publications, 2019-11-07)
    Domazet, Ervin
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    We analyzed several QRS detection algorithms in order to build a quality industrial beat detector, intended for a small, wearable, one channel electrocardiogram sensor with a sampling rate of 125 Hz, and analog-to-digital conversion of 10 bits. The research was a lengthy process that included building several hundred rules to cope with the QRS detection problems and finding an optimal threshold value for several parameters. We obtained 99.90% QRS sensitivity and 99.90% QRS positive predictive rate measured on the first channel of rescaled and resampled MIT-BIH Arrhythmia ECG database. Even more so, our solution works better than the algorithms for the original signals with a sampling rate of 360 Hz and analog-to-digital conversion of 11 bits.