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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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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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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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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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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Comparison of ID-based blind signatures from pairings for e-voting protocols
(IEEE, 2014-05)
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Elliptic curves are gaining momentum as scientists are continuously proving their security and performance. Pairings over elliptic curves are relatively new in the world of cryptography. Researchers are coming with new cryptographic usage of pairings for over ten years. ID-based cryptography is also gaining popularity because of the certificate-less mode of work. Blind signatures are appropriate schemes when user anonymity is wanted as property. One possible type of blind signature is ID-based blind signature based on bilinear pairings. For easy computation we look into pairing-friendly elliptic curves for implementation of pairings. This paper will review state of the art ID-based blind signature schemes from pairings over elliptic curves which are suitable for building blind signatures as part of e-voting protocols. We give comparative results about the computation cost of arithmetic operations. In our knowledge, this is first paper which gives head-to-head bandwidth comparison of the interactive protocol in the signing algorithm of blind signature schemes. The results are easy to use when choosing appropriate blind signature scheme for e-voting protocols.
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Design specification of an ECG mobile application
(IEEE, 2017-11)
Domazet, Ervin
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The latest developments in IoT, have brought upon new innovations in that space. Electronic health solutions are among these innovations. Statistics indicate that cardiovascular diseases are the primary cause of deaths globally. Our knowledge about this type of diseases shows that specific types of heart attacks can be prevented. Real-time acquisition and processing using wearable biosensors can enable the detection of cardiac symptoms at an early stage. The concept of mHealth is defined as the combination of current state of the technology and the telecommunications infrastructure, in order to provide healthcare services. This paper contributes to such solutions by provisioning generic design specifications of an ECG mobile application.
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Forecasting Corporate Revenue by Using Deep-Learning Methodologies
(IEEE, 2019-05)
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Gjorgjevikj, Ana
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Vodenska, Irena
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Chitkushev, Ljubomir
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Souma, Wataru
In the past few years, deep learning evolved into a powerful machine learning technique, which uses multiple layers for feature representation to learn specific attitudes of the raw input data, in order to produce state of the art prediction results. Deep learning has become popular in many application domains which use rich variety of data. Large volumes of online business news provide an opportunity to explore various aspects of companies. Sentiment analysis of text establishes a new viewpoint of large scale data identifying, among other features, the tone of the author towards the subject of the text. Hence, the sentiment of news articles offers an insight into the internal state of the company, potential for revenue growth, and it can be useful for corporate decision making of the company. In this paper, we demonstrate a deep convolution LSTM neural network that uses a fusion of data including company stock price and sentiment of company-related news articles as time-series, in order to predict the revenue growth or decline of the companies belonging to the Dow Jones Industrial Average. Additionally, we present a method based on transfer learning for sentiment analysis of news articles related to finances, and compare this method with standard statistical sentiment analysis approaches.
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Corrigendum on “On a general decay stability of stochastic Cohen-Grossberg neural networks with time-varying delays” [Applied Mathematics and Computation 219 (2012) 2289–2302]
(Elsevier BV, 2013-01)
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Janković, Svetlana
In Lemma 2, as well as in Theorems 1 and 3 which proofs are based on Lemma 2, it is necessary to suppose for δ< 1 that functions a and b from C ([t 0,∞); R n) are increasing and decreasing, respectively, and that γ∗= γ (t 0). The proof of Lemma 2 in the paper holds only for δ⩾ 1 and must be similarly completed for δ< 1: since y (t)< kz (t) on [t 0, t 1) and y (t 1)= kz (t 1), k= const> 1, it follows straightforwardly that‖ y (t 1)‖< kz (t 1) δ γ∗ and D+(y (t 1)− kz (t 1))< 0, which completes the proof.

Faculty of Computer Science and Engineering

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