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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Enhancing Transformer-Based Rerankers with Synthetic Data and LLM-Based Supervision
(Incoma Ltd. Shoumen, BULGARIA, 2025)
Peshevski, Dimitar
;
Blazhevski, Kiril
;
Popovski, Martin
;
Effective document reranking is essential for improving search relevance across diverse applications. While Large Language Models (LLMs) excel at reranking due to their deep semantic understanding and reasoning, their high computational cost makes them impractical for many real-world deployments. Fine-tuning smaller, task-specific models is a more efficient alternative but typically depends on scarce, manually labeled data. To overcome this, we propose a novel pipeline that eliminates the need for human-labeled query-document pairs. Our method uses LLMs to generate synthetic queries from domain-specific corpora and employs an LLM-based classifier to label positive and hard-negative pairs. This synthetic dataset is then used to fine-tune a smaller transformer model with contrastive learning using Localized Contrastive Estimation (LCE) loss. Experiments on the MedQuAD dataset show that our approach significantly boosts in-domain performance and generalizes well to out-of-domain tasks. By using LLMs for data generation and supervision rather than inference, we reduce computational costs while maintaining strong reranking capabilities.
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Methodology for food prices forecasting
(IEEE, 2023-12-15)
Peshevski, Dimitar
;
Todorovska, Ana
;
Trajkovikj, Filip
;
Hristov, Nikola
;
Trajanoska, Milena
Fluctuations in food prices play a pivotal role in maintaining economic equilibrium and influencing the very fabric of our everyday lives. This paper presents a comprehensive framework for modeling and analyzing food price trends in 12 select European countries, spanning from January 2013 to January 2023, utilizing advanced state-of-the-art Machine Learning techniques. To achieve this objective, historical price data and technical indicators are incorporated into the proposed XGBoost model alongside a baseline model. The model results are assessed using various measures, and a benchmark is established. Notably, the average achieved R2 for predicting food prices within the time frame from January 2020 to January 2022 is 0.85 and 0.64 from January 2021 to January 2023. The findings reveal the efficacy of the proposed model, providing valuable insights into food price forecasting model interpretability and laying the groundwork for further research, including exploration into areas such as food fraud, food sustainability, and other pertinent topics in food economics.
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Preserving Macedonian Culinary Heritage: Fine-Tuning a Large Language Model for Recipe Generation in a Low-Resource Language
(IEEE, 2025-12-08)
Peshevski, Dimitar
;
Sasanski, Darko
;
;
We introduce the first fine-tuned large language model for recipe instruction generation in Macedonian. Building on VezilkaLLM-Instruct, a 4-billion parameter model, we fine-tune it using a curated dataset of 36,000 recipes with detailed cooking instructions. Our key contributions include: (1) the development of a domain-adapted language model for a low-resource language; (2) the demonstration that relatively small LLMs can be effectively adapted to specialized culinary tasks; and (3) the proposal of a dual evaluation framework that combines semantic similarity and verb overlap analyses to assess both content generalization and procedural accuracy. Fine-tuning results in a mean cosine similarity of 0.90 and significantly increases the overlap of domain-specific cooking verbs, indicating improved generation quality. These results highlight the potential of targeted fine-tuning approaches for domain-specific applications in underrepresented languages and provide a foundation for further research in computational culinary heritage.
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A comprehensive study of food prices and food fraud in the European Union
(IEEE, 2023-12-15)
Trajkovikj, Filip
;
Todorovska, Ana
;
Peshevski, Dimitar
;
Nakova, Lina
;
Trajanoska, Milena
This research delves into the intricate dynamics of food pricing and fraud within European Union member countries. We analyze the complex interplay between food categories and countries, unraveling unique pricing trends and potential anomalies. By computing inflation-adjusted expected prices and sourcing real prices, we gain a deep understanding of inflation’s impact on actual food costs. Our multi-level analyses, network-based approaches, and cluster maps provide a global perspective, revealing international correlations in food pricing and fraud. The significance of our findings lies in setting the groundwork for understanding food fraud, informing strategies for fraud prevention, consumer protection, and, ultimately, food sustainability. Our work serves as a crucial resource for policymakers, economists, and consumers, emphasizing the importance of data-driven decision-making and transparency in the ever-evolving landscape of the European food market.
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Parallel Near-Duplicate Document Detection Using General-Purpose GPU
(Central Library of the Slovak Academy of Sciences, 2024)
Peshevski, Dimitar
;
;
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A comprehensive study of food prices and food fraud in the European Union
(IEEE, 2023-12-15)
Trajkovikj, Filip
;
Todorovska, Ana
;
Peshevski, Dimitar
;
Nakova, Lina
;
Trajanoska, Milena
This research delves into the intricate dynamics of food pricing and fraud within European Union member countries. We analyze the complex interplay between food categories and countries, unraveling unique pricing trends and potential anomalies. By computing inflation-adjusted expected prices and sourcing real prices, we gain a deep understanding of inflation’s impact on actual food costs. Our multi-level analyses, network-based approaches, and cluster maps provide a global perspective, revealing international correlations in food pricing and fraud. The significance of our findings lies in setting the groundwork for understanding food fraud, informing strategies for fraud prevention, consumer protection, and, ultimately, food sustainability. Our work serves as a crucial resource for policymakers, economists, and consumers, emphasizing the importance of data-driven decision-making and transparency in the ever-evolving landscape of the European food market.
Some of the metrics are blocked by your
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Methodology for food prices forecasting
(IEEE, 2023-12-15)
Peshevski, Dimitar
;
Todorovska, Ana
;
Trajkovikj, Filip
;
Hristov, Nikola
;
Trajanoska, Milena
Fluctuations in food prices play a pivotal role in maintaining economic equilibrium and influencing the very fabric of our everyday lives. This paper presents a comprehensive framework for modeling and analyzing food price trends in 12 select European countries, spanning from January 2013 to January 2023, utilizing advanced state-of-the-art Machine Learning techniques. To achieve this objective, historical price data and technical indicators are incorporated into the proposed XGBoost model alongside a baseline model. The model results are assessed using various measures, and a benchmark is established. Notably, the average achieved R2 for predicting food prices within the time frame from January 2020 to January 2022 is 0.85 and 0.64 from January 2021 to January 2023. The findings reveal the efficacy of the proposed model, providing valuable insights into food price forecasting model interpretability and laying the groundwork for further research, including exploration into areas such as food fraud, food sustainability, and other pertinent topics in food economics.
Some of the metrics are blocked by your
consent settings
Methodology for food prices forecasting
(IEEE, 2023-12-15)
Peshevski, Dimitar
;
Todorovska, Ana
;
Trajkovikj, Filip
;
Hristov, Nikola
;
Trajanoska, Milena
Fluctuations in food prices play a pivotal role in maintaining economic equilibrium and influencing the very fabric of our everyday lives. This paper presents a comprehensive framework for modeling and analyzing food price trends in 12 select European countries, spanning from January 2013 to January 2023, utilizing advanced state-of-the-art Machine Learning techniques. To achieve this objective, historical price data and technical indicators are incorporated into the proposed XGBoost model alongside a baseline model. The model results are assessed using various measures, and a benchmark is established. Notably, the average achieved R2 for predicting food prices within the time frame from January 2020 to January 2022 is 0.85 and 0.64 from January 2021 to January 2023. The findings reveal the efficacy of the proposed model, providing valuable insights into food price forecasting model interpretability and laying the groundwork for further research, including exploration into areas such as food fraud, food sustainability, and other pertinent topics in food economics.

Faculty of Computer Science and Engineering

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