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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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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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.
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A new model for cloud elastic services efficiency
(Informa UK Limited, 2018-02-09)
Ristov, Sasko
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Mathá, Roland
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Kimovski, Dragi
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Prodan, Radu
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The speedup measures the improvement in performance when the computational resources are being scaled. The efficiency, on the other side, provides the ratio between the achieved speedup and the number of scaled computational resources (processors). Both parameters (speedup and efficiency), which are defined according to Amdahl’s Law, provide very important information about performance of a computer system with scaled resources compared with a computer system with a single processor. However, as cloud elastic services’ load is variable, apart of the scaled resources, it is vital to analyse the load in order to determine which system is more effective and efficient. Unfortunately, both the speedup and efficiency are not sufficient enough for proper modeling of cloud elastic services, as the assumptions for both the speedup and efficiency are that the system’s resources are scaled, while the load is constant. In this paper, we extend the scaling of resources and define two additional scaled systems by (i) scaling the load and (ii) scaling both the load and resources. We introduce a model to determine the efficiency for each scaled system, which can be used to compare the efficiencies of all scaled systems, regardless if they are scaled in terms of load or resources. We have evaluated the model by using Windows Azure and the experimental results confirm the theoretical analysis. Although one can argue that web services are scalable and comply with Gustafson’s Law only, we provide a taxonomy that classifies scaled systems based on the compliance with both the Amdahl’s and Gustafson’s laws. For three different scaled systems (scaled resources R, scaled load L and combination RL), we introduce a model to determine the scaling efficiency. Our model extends the current definition of efficiency according to Amdahl’s Law, which assumes scaling the resources, and not the load.
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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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Dew Computing Architecture for Cyber-Physical Systems and IoT
(Elsevier BV, 2020-09)
The concept to be on the edge of the Internet network means that the analyzed devices and systems will work only as a part of a general common integrated system, such as in the case of cyber-physical systems and various devices that act as an Internet of connected Things. Although post-cloud architectures are most commonly associated with edge computing, a focus in this paper is set on dew computing architecture that extends this concept with a specific architecture out of the edge. The dew computing implementation in cyber-physical systems allows autonomous devices and smart systems, that can collaborate and exchange information with the environment, still be independent of other external systems or perform in a connected more complex cyber-physical system of systems. This paper aims at presenting an architecture of applying dew computing for cyber-physical systems, elaborating the new features and functionalities and comparing it to other similar architectures.
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A 12-Bit 20-kS/s 640-nW SAR ADC With a VCDL-Based Open-Loop Time-Domain Comparator
(Institute of Electrical and Electronics Engineers (IEEE), 2022-02)
Zhou, Xiaochuan
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Gui, Xiaoyan
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Zhang, Yanlong
This brief presents a 12-bit ultra-low-power asynchronous successive approximation register (SAR) analog-to-digital converter (ADC). A voltage-controlled delay line (VCDL) based open-loop time-domain comparator is proposed and analyzed, achieving low noise and ultra-low power performance. By employing the mixed switching scheme, the segmented capacitive digital-to-analog converter (CDAC) arrays as well as the synchronous data-weighted averaging (DWA) calibration block, the proposed SAR ADC can operate from 1.8 V down to 0.8 V at 20–200 kS/s. The designed ADC is fabricated in a 0.18- μm CMOS process and the measurement results show the proposed SAR ADC achieves an SNDR of 65-dB with power consumption of 647 nW from a 0.8 V power supply at 20 kS/s.
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Acquiring diagnostic experience in placenta pathology through HIPON web course
(Elsevier BV, 2014)
Konstantinidou, Anastasia Evangelia
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Vrasidas, Charalambos
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Brcik, Luka
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Smeets, Annemieke
Aim The correct evaluation of microscopic features, a mandatory step for the correct pathologic diagnosis, reflects professional experience well worth acquiring by trainees in Pathology. This is the target of HIPON, a project co-financed by the Lifelong Learning Program of the EACEA, EU Commission. Methods We present our educational strategy as already implemented on the HIPON online chapter of placenta pathology. The diagnoses and differential diagnoses of representative cases of vascular and inflammatory placenta diseases are analysed step by step through microscopic images. A virtual slide of a placenta section with typical maternal and fetal vascular changes is available to the user. Results After becoming familiar with the principles of placental pathology, the user learns to identify the basic entities related to vascular and inflammatory placental disease. This knowledge is consolidated by an image-based test, the resources, the links and the glossary of this chapter. Discussion The concept of HIPON is to follow mixed learning pathways, supported by a rich variety of components of the ICT System (virtual portfolio, e-modules, online game, histo-book) and a mass amount of case data, in order to provide practical experience, essentially bridging the worlds of education and professional practice.
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Prompt-to-Pill: Multi-Agent Drug Discovery and Clinical Simulation Pipeline
(Oxford University Press (OUP), 2025-12-23)
Vichentijevikj, Ivana
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Yamanishi, Yoshihiro
Summary This study presents a proof-of-concept, comprehensive, modular framework for AI-driven drug discovery (DD) and clinical trial simulation, spanning from target identification to virtual patient recruitment. Synthesized from a systematic analysis of 51 large language model (LLM)-based systems, the proposed Prompt-to-Pill architecture and corresponding implementation leverages a multi-agent system (MAS) divided into DD, preclinical and clinical phases, coordinated by a central Orchestrator. Each phase comprises specialized LLM for molecular generation, toxicity screening, docking, trial design, and patient matching. To demonstrate the full pipeline in practice, the well-characterized target Dipeptidyl Peptidase 4 (DPP4) was selected as a representative use case. The process begins with generative molecule creation and proceeds through ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation, structure-based docking, and lead optimization. Clinical-phase agents then simulate trial generation, patient eligibility screening using electronic health records (EHRs), and predict trial outcomes. By tightly integrating generative, predictive, and retrieval-based LLM components, this architecture bridges drug discovery and preclinical phase with virtual clinical development, offering a demonstration of how LLM-based agents can operationalize the drug development workflow in silico. Availability and implementation The implementation and code are available at: https://github.com/ChatMED/Prompt-to-Pill.

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