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: 2022Has files: No

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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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    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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    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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    Detection of Premature Heartbeats
    (IEEE, 2022-05-23)
    Premature heartbeats are those that appear earlier than the regular ones due to contractions not originating from the Sinus Atrial Node, out of the normal heart rhythm. Although one might think this is a trivial task to detect, the distribution of premature heartbeats in the benchmark electrocardiograms shows it is not the case. We aim at finding the optimal method to detect premature heartbeats, threshold value and context level for temporal based approach. Methodology: Several methods are specified to calculate the relation of the premature heartbeat to a set of several previous instances and conduct experiments to present which averaging method (arithmetic, geometric and harmonic mean) delivers the best solution. Then, we calculate the deviation ratio to the average of these beats that affect the prematurity condition. Particularly, the goal is to find an average AV G of previous k beat-to-beat intervals RR, such that the ratio between the difference dRR of the analyzed RR and AV G versus AV G is more than a specific threshold T hr. Data: The comprehensive MIT-BIH Arrhythmia Electrocardiogram benchmark database is used in our evaluation. The analysis is conducted on the array of beat-to-beat intervals for the heartbeats preceding the premature one. Conclusion: The results show that the optimal method is based on the arithmetic mean AM We found that the larger k, the better performance is achieved. A sufficient performance with F1 score over 85% is achieved for k = 5 and T hr = 15%.
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    Comparing AWS Streaming Services: A Use Case on ECG Data Streams
    (IEEE, 2022-05-23)
    Velickovska, Marija
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    The goal of this research paper is to compare Amazon Kinesis Data Streams and SQS via a practical use case for processing electrocardiogram medical data. Both services are tested on a large number of concurrent data streams up to 10.000 concurrent data streams, measuring and evaluating their performance. In both cases, the producer is a programmed data generator that supplies the data to the services. The motivation of the paper is the streaming of textual files with 125-500 samples per second written as integers, representing electrocardiograms from sensors. We set a research hypothesis that Amazon Kinesis will outperform SQS service for streaming medical data collected from sensors. The pros and cons are listed for each of the Amazon services taking into account the price, use cases when each of the services will be more applicable, and the scalability. The concluding results from this experiment are that both of the services provide great performance but they will mainly provide better results if each of them is applied to a specific use case.
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    Network Traffic Impact on Cloud Usage at Different Providers
    (IEEE, 2022-05-23)
    Bidikov, V.
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    Markozanov, V.
    The research question focuses on finding an optimal cost-less and efficient solution, analyzing the differences between uploading and downloading data between different cloud solutions. The conducted experiments compare AWS cloud platform offered by Amazon and use two of its products: computational cloud platform EC2 and storage cloud platform S3. We explore three different versions of S3 storage and S3 Standard, each of them with its own purposes, characteristics and costs. Additionally we test the data transfer and computational capabilities of a t2.micro instance of the EC2 virtual machine. Then, we compare the experiment on the European Cloud provider - Scaleway Elements and use two of its products - Virtual Instances based on the Stardust instances for computations and Object Storage a S3 compatible storage platform.We conclude that the network connection between Amazon nodes is far superior than any other, both in performance and costs. Fetching results from an AWS computational service (EC2) is much cheaper and faster when using an AWS platform as an intermediary (S3) compared to a direct transfer to the local machine. We observe a similar superior network performance in the comparison of Scaleway Cloud Infrastructure.
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    Detecting Ventricular Beats with Machine Learning Models
    (IEEE, 2022-05-23)
    Tudjarski, Stojancho
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    Stankovski, Aleksandar
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    This paper aims at modeling a classifier of Ventricular heartbeats by experimenting with the most advanced classic binary classifiers in different scenarios for feature engineering. Methodology: The results were acquired based on experimenting with XGBoost and Random Forest algorithms, as two of the most advanced classifiers not based on neural networks. Although the annotated ECG data sets contain records with several heartbeat classes, we focus on a model that would distinguish V from others (Non-V heartbeats). Considering that we are dealing with a highly imbalanced data set, we applied the SMOTE algorithm for data enrichment to provide a better-balanced data set for training the model. To acquire better results, we added new calculated features, with and without feature selection. For feature selection, we used the Fisher Selector algorithm. Data: We used MIT-BIH Arrhythmia benchmark database, with train/test split according to the patient-oriented splitting approach that separates the original dataset into two subsets with approximately equal sizes and distribution of heartbeat types. Conclusion: The best results are achieved with XGBoost algorithm with original feature set. We achieved precision of 91.36%, recall of 88.31% and F1 score of 89.81%. Results showed that oversampling does not provide significantly better overall model performance. Still, we would recommend this approach since in practice, when dealing with imbalanced data sets, this leads to more robust models that perform better with data outside the training and test sets, such as when the model is used in production.
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    Parallelization of Dijkstra’s Algorithm for Robot Motion Planning: Is It Worth Increasing Speed Without Losing Accuracy?
    (IEEE, 2022-11-15)
    Josifoski, Darko
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    This paper examines the problem of parallelizing Dijkstra’s algorithm, as an algorithm for robot motion planning was a challenge to twist the algorithm in such a way so it can be executed in parallel, rather than sequentially. In this paper, we test the validity of a research hypothesis that the speed of Dijkstra’s algorithm can be increased by parallelizing and keeping the same accuracy. The developed use-case of Dijkstra’s algorithm as a robot motion planning algorithm was tested against performance, both in execution speed and accuracy. Finally, we elaborate on the benefits of this parallelizing approach.
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    CardioHPC: Serverless Approaches for Real-Time Heart Monitoring of Thousands of Patients
    (IEEE, 2022-11)
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    Amza, Andrei
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    Hohenegger, Armin
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    Prodan, Radu
    We analyze a heart monitoring center for patients wearing electrocardiogram sensors outside hospitals. This prevents serious heart damages and increases life expectancy and health-care efficiency. In this paper, we address a problem to provide a scalable infrastructure for the real-time processing scenario for at least 10,000 patients simultaneously, and efficient fast processing architecture for the postponed scenario when patients upload data after realized measurements. CardioHPC is a project to realize a simulation of these two scenarios using digital signal processing algorithms and artificial intelligence-based detection and classification software for automated reporting and alerting. We elaborate the challenges we met in experimenting with different serverless implementations: 1) container-based on Google Cloud Run, and 2) Function-as-a-Service (FaaS) on AWS Lambda. Experimental results present the effect of overhead in the request and transfer time, and speedup achieved by analyzing the response time and throughput on both container-based and FaaS implementations as serverless workflows.