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: 2021

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Now showing 1 - 10 of 794
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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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    Evaluation of Scalability and Multi-tenancy: A Use-Case
    (IEEE, 2021-11-23)
    Dzalev, Stefan
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    Flexibility and affordability in modern computing systems can be achieved through scalability and multi-tenancy of all involved devices. This paper presents a use-case of a multi-tenant software solution, where each tenant generates a stream of data that is subsequently processed. The processing output contains metrics to be presented on an analytical dashboard. The tenants are web applications developed using Ruby on Rails, the data streams ingestion and processing platform are developed using a Kafka cluster and KSQL, the analytical dashboard is developed using NodeJS. The use-case is evaluated by measuring the scalability through the response time from data stream creation to metrics display. The experiments are specified by changing the number of tenants and the workload for each tenant. The results show that the system can scale with regard to the number of tenants. The response time increases by 10% to 20%, when the number of tenants increases by 100% to 150%. With regard to the number of data items within the data stream, the response time is stable.
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    What makes Dew computing more than Edge computing for Internet of Things
    (IEEE, 2021-07)
    A lot of research and development activities have been referencing to edge and dew computing solutions for IoT applications, but without determining the deep distinction between these two architectural approaches. Furthermore, there is no clear explanation if dew computing is a special case of edge computing or the opposite. In this paper, we analyze the features of post-cloud architectures to build an IoT solution and clarify the main differences between dew and edge computing approaches. Although the provided analysis covers IoT solutions, still the same principles can be applied more generally.
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    Benchmarking Virtual Machine and Container-based Services for DNN Training
    (IEEE, 2021-11-23)
    Postolovski, Damjan
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    Kjirovski, Kiril
    Containerized infrastructure is becoming the leading strategy for deployment and operation of software components including trending neural networks. Training neural networks requires computational resources and complex software environments with a lot of dependencies. In this regard, containers can solve some of the software related challenges. We perform benchmarks to compare the performances of containers inside a virtual machine and a bare metal model for training three artificial neural networks on two leading cloud providers. The results show negligible performance drawback for using containers in the stack tested on Google Cloud and clear performance advantages in Amazon Web Services.