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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Item type:Publication, Preventing Academic Dishonesty Originating from Large Language Models(Springer Nature Switzerland, 2025); After the creation of ChatGPT, many students were tempted to appropriate AI-generated texts and present them as own original contribution. Therefore, professors all around the world are skeptical of integrating large language models into their courses because they fear that they will additionally increase academic dishonesty. After the professors of the Computer Ethics course, whose goal is, among other things, to raise the ethical standards of students and increase their academic integrity, noted massive cheating in academic writing at the end of 2022, they prepared a strategy for the realization and delivery of assignments that explicitly shows where and how used large language models. Students applied this approach for producing two group essay assignments during the winter semester of this academic year. This paper explains the approach in detail and, based on the experience with a group of over 150 students, evaluates its impact on essay writing, stimulating the responsible use of technology and improving the quality of delivered assignments. Based on extensive observations of the use of artificial intelligence in writing and personal impressions of students, this paper offers recommendations on how various applications of large language models can be used to improve student outcomes without encouraging academic dishonesty. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applications of Quasigroups in Cryptography and Coding Theory(Springer International Publishing, 2023); ; ; ; This survey article discusses some applications of quasigroups in cryptography and coding theory. Here mainly results obtained by the authors of this article are considered and obtained in the last quarter of the century. Not all of their results are presented; emphasis is given to those that were interested for the wider community. Security of the modern world is dependent on the many cryptographic products like block ciphers, stream ciphers, digital signatures and encryption schemes, hash functions, pseudo-random number generators, ... These products are mainly produced by using associative structures (number theory, group and finite field theory, Boolean algebras, etc.) The development of quantum computers questioned security based on associative structures. So, nowadays, the use of quasigroups for building cryptographic products is becoming more important. This short survey presents how quasigroups can be exploited for building suitable cryptographic primitives. For that aim, we define some types of quasigroups that are suitable for that purpose, we give the definitions of several kinds of quasigroup transformations, and we explain the constructions of some types of cryptographic primitives obtained by quasigroup transformations. (We notice that cryptographic properties are not discussed in this survey. The efficiency and security of the crypto products based on quasigroups is an open research problem for cryptographers and cryptanalysts.) The quasigroups are also suitable algebraic structures for building error detecting and error correcting code. We give one type of error detecting code based on quasigroups. Error correcting codes resistant to an intruder attack, so called RCBQ (Random Codes Based on Quasigroups) are given in details, as well as some of their applications in processing images and audio signals. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, On the General Decay Stability of Coupled System of Stochastic Neural Networks with Impulses, Markovian Switching and Node and Interconnection Delays(Springer International Publishing, 2024); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stability of Coupled Systems of Stochastic Cohen-Grossberg Neural Networks with Time Delays, Impulses and Markovian Switching(Springer International Publishing, 2021); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Shift: Assessment of Mental States Through Passive Mobile Sensing(Springer International Publishing, 2022) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applications of Quasigroups in Cryptography and Coding Theory(Springer, 2023); ; ; ; Popovska-Mitrovikj, Aleksandra - Some of the metrics are blocked by yourconsent settings
Item type:Publication, When Evolutionary Computing Meets Astro- and Geoinformatics(Elsevier, 2020) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, StegIm: Image in Image Steganography(Springer Nature Switzerland, 2022); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, StegYou: Model for Hiding, Retrieving and Detecting Digital Data in Images(Springer International Publishing, 2022-10-13) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Learning Robust Food Ontology Alignment(IEEE, 2022-12-17); ; In today’s knowledge society, large number of information systems use many different individual schemes to represent data. Ontologies are a promising approach for formal knowledge representation and their number is growing rapidly. The semantic linking of these ontologies is a necessary prerequisite for establishing interoperability between the large number of services that structure the data with these ontologies. Consequently, the alignment of ontologies becomes a central issue when building a worldwide Semantic Web. There is a need to develop automatic or at least semi-automatic techniques to reduce the burden of manually creating and maintaining alignments. Ontologies are seen as a solution to data heterogeneity on the Web. However, the available ontologies are themselves a source of heterogeneity. On the Web, there are multiple ontologies that refer to the same domain, and with that comes the challenge of a given graph-based system using multiple ontologies whose taxonomy is different, but the semantics are the same. This can be overcome by aligning the ontologies or by finding the correspondence between their components.In this paper, we propose a method for indexing ontologies as a support to a solution for ontology alignment based on a neural network. In this process, for each semantic resource we combine the graph based representations from the RDF2vec model, together with the text representation from the BERT model in order to capture the semantic and structural features. This methodology is evaluated using the FoodOn and OntoFood ontologies, based on the Food Onto Map alignment dataset, which contains 155 unique and validly aligned resources. Using these limited resources, we managed to obtain accuracy of 74% and F1 score of 75% on the test set, which is a promising result that can be further improved in future. Furthermore, the methodology presented in this paper is both robust and ontology-agnostic. It can be applied to any ontology, regardless of the domain.