The Republic of North Macedonia's Research Ranking Platform for Academic Staff and Universities

Author(s): Arbnor Rushiti,Artan Luma,Ylber Januzaj,Azir Aliu,Halil Snopce,Anis Sefidanoski,Mitko Trpkoski / Language(s): English Issue: 1/2024

In the present-day realm of higher education, the integration of open data and machine learning has become a crucial means of improving the evaluation and ranking of research publications by university professors. This research endeavours to address the pressing need for an effective computerized paradigm that combines the collection, analysis, and use of public data in higher education. The main objective is to provide recommendations in the form of informative reports for interested institutions and to rank university professors' scientific works on an open data platform. This research not only contributes to the advancement of educational evaluation methodologies but also responds to the growing importance of open data and machine learning in modern higher education. By automating the assessment of research publications, universities can better track and enhance their academic impact, ultimately fostering a culture of excellence in research and innovation.

Prediction of Errors in the Field of Additive Manufacturing Technology

Author(s): Martin Pollák,Peter Gabštur,Marek Kočiško / Language(s): English Issue: 2/2024

Additive manufacturing, also known as 3D printing, allows the formation of complex geometric structures layer by layer. Predicting errors in this process may help identify potential problems in a timely manner and minimise waste. A human may detect an additive manufacturing error, but cannot provide continuous monitoring or real-time correction. The article is focused on the design of a camera system design for online monitoring of the 3D printing process with the task of detecting process errors arising during 3D printing of objects. The article describes the methodology for tracking the occurrence of process errors in 3D printing, which are identified in the OctoPrint Nexus AI plug-in environment for the subsequent application of a suitable solution to minimize the occurrence of defects. The application of a real-time process monitoring system including the ability to correctly predict anomalous behaviour in the context of artificial intelligence has proven to be an appropriate solution to that particular problem.

Critical Concerns of Deploying Blockchain in Internet of Things Applications

Author(s): Yehia Ibrahim Alzoubi,Alok Mishra,Atik Kulakli / Language(s): English Issue: 2/2024

The Internet of Things (IoT) and blockchain are both recognized as cutting-edge, popular technologies. In contrast to IoT, which pertains to the spread of linked equipment via supplying information over the Internet, blockchain offers innovative data storage and management avenues. Even as blockchain demands real-time data application and IoT specifies mechanisms to securely store and handle data overflows, a combination of the two seems promising. Blockchain, a technology created with the cryptocurrency Bitcoin, could meet the needs of the IoT. However, combining blockchain with the IoT might present a multitude of issues due to the features of blockchain and IoT technology. Although many articles have been released on the blockchain and the IoT, the concerns with this combination are still vague and dispersed. In light of this, this study seeks to give an overview of the problems that have the biggest impact on blockchain-based IoT by reviewing the pertinent peer-reviewed articles. This article also addresses some suggestions for lessening the impact of these concerns. The study considered peer-reviewed articles published within the last five years, focusing on topics related to blockchain and IoT integration. We identified 44 articles for this review. The study's significance is that it not only expands the scholarly understanding of this complex intersection between blockchain and IoT but also provides actionable insights that can drive innovation and enhance the reliability and security of IoT ecosystems in practical settings.

Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

Author(s): Gabriel Elías Chanchí G.,Laura Sofía Chanchí P.,Manuel Alejandro Ospina A. / Language(s): English Issue: 2/2024

Mouse tracking tests play a crucial role in evaluating software usability, but their efficient image processing remains challenging, especially with high-resolution images and numerous participants. This article introduces a novel method, leveraging parallel computing, for the efficient analysis of interaction zones in mouse tracking test images. Following Pratt's iterative research pattern, the proposed method is implemented using Python libraries Dask and OpenCV, validated through a proof of concept on Eclipse software. Results demonstrate the parallel approach's remarkable efficiency, being 252 times faster than the sequential method across various executions. The method's potential impact is discussed, providing a valuable reference for developing tools in usability and other application contexts. The open-source tools employed, Dask and OpenCV, prove suitable for parallel image analysis, offering versatility for broader application in diverse fields. This work contributes to advancing the field of mouse tracking analysis by significantly improving processing efficiency and lays the groundwork for future tools and methodologies.

IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi

Author(s): Kemal Hajdarević / Language(s): English Issue: 2/2024

This paper presents a system that is able to detect physical intrusion in a specific space based on temperature and humidity change. This specific space was housing hardware components important for information security management infrastructure. Presented system is able to predict that two spaces are connected and that there is a physical breach in protected space. The presented prediction approach involves identifying patterns in historical data, where the subsequent outcomes are already known in advance, and validating these patterns using more recent data. System is implemented using k-Nearest Neighbours, Random Forest, and Support Vector Machine algorithms in Python programming language on Raspberry Pi. Real observed data to predict if specific temperature and humidity indicates intrusion were used. This approach can be used to detect intrusions in the room or in other closed space. More specifically thermal equilibrium phenomenon between two spaces after barrier between them are opened was monitored. Through process of supervised learning using labelled data, system was able to detect intrusion by using k-nearest neighbours, random forest, and support vector machine with different accuracy. Presented model shows better results using k-nearest neighbours and support vector machine with accuracy of 100% compared to random forest with accuracy of 95%. The system is low cost because of cheap Raspberry Pi controller and sensors.

Banana Plant Waste Rotary Dryer Design and Simulation Using 3D Computer Aided Design

Author(s): Ray H. Malonjao,Juvy B. Malonjao,Ronald M. Galindo / Language(s): English Issue: 2/2024

This research determines the optimal rotary dryer design for banana waste drying with Biot number. When banana waste is adequately dried, it can be repurposed into valuable resources. Based on the proposed banana plant waste rotary dryer, drying efficiency is significantly improved compared to conventional dryers as attributed to design calculations and simulation. Here, the design process involves the three-dimensional (3D) computer-aided design of individual parts, subassemblies, and main assembly, accompanied by detailed illustration drawings, and optimization of rotary dryer flight design. Biot number is crucial in the design calculations of the diameter and length of the dryer. The Design of Experiments (DoE) and computational fluid dynamics simulations are also used. Proper drying, facilitated by the proposed rotary dryer, enables efficient repurposing of banana waste into animal feed, biofuel, bioplastics, and other nanotechnology applications, contributing to sustainable waste management practices and a promising solution to environmental challenges.

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Author(s): Aekarat Saeliw,Watcharasuda Hualkasin,Supattra Puttinaovarat / Language(s): English Issue: 2/2024

One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

The Application of Fuzzy Cognitive Mapping in Education: Trend and Potential

Author(s): Chee Siong The,Ahmad Ridzuan Kudus / Language(s): English Issue: 2/2024

Fuzzy cognitive mapping (FCM) is a valuable tool for understanding complex issues due to its ability to express complex and uncertain knowledge domains with dynamic modelling capabilities. This study focuses on the application of FCM in education, using a bibliometric analysis and systematic review of publications in the Scopus database from 2000 to October 2023. Using keywords related to FCM and education, the study retrieved fifty-four publications for frequency analysis and citation metrics. This study showcases its results by leveraging standard bibliometric indicators, encompassing metrics such as publication growth, top-cited publications, country contributions, and preferred publication titles. Findings from the systematic review reveal the potential of FCM in providing explanatory, predictive, reflective, and strategic insights and solutions to numerous educational issues.

Multi-Criteria Decision-Making Methods: A Case of Software Vendor Selection

Author(s): Anshul Rani,Deepti Mishra,Aida Omerović / Language(s): English Issue: 2/2024

Various multi-criteria decision-making methods have been utilized in literature to address vendor selection challenges in various contexts. This study reflects on these decision-making techniques for vendor selection in the software ecosystem during software outsourcing. For the same, firstly, a requirement framework for the decision-making process for software vendor selection is proposed. Afterwards, five selected different multi-criteria decision-making techniques are compared and critically analysed against this requirement framework. This study supports software practitioners and decision-makers by providing information about which decision-making method to choose, considering the trade-off between the benefits and drawbacks of adopting each method or using hybrid approaches by combining two or more techniques with fuzzy logic. It is found that decision-making techniques generally lack in modelling the problem itself and handling the conflicts and uncertainties that may prevail during the decision-making process. Furthermore, it has been identified that artificial neural networks (ANN) and analytic network process (ANP) can support the dependencies between alternatives and criteria while handling complex interactions in the software ecosystem. On the other hand, due to its comparative ease of use, analytic hierarchy process (AHP) can serve better in scenarios where decision-makers are not experienced, or past data to train the ANN or ANP models is unavailable.

Execution Efficiency of the use of Array and Linked-List Implementations of a Stack Abstract Data Types Containing Complex Numbers in Methods of an Android Application

Author(s): Igor Košťál,Martin Mišút / Language(s): English Issue: 2/2024

Abstract data types (ADTs) provide a way to define data structures and the operations allowed on them, independent of the specific implementation details. Choosing the appropriate data type is for many applications the most important step in their development that affects their performance. To investigate the most suitable stack implementation for evaluating arithmetic expressions with complex numbers, we developed an Android application. These expressions may contain a larger number of complex numbers, for example, 25, which can be enclosed in parentheses arbitrarily. The Android application uses an array and linked-list implementation of a stack ADT to evaluate these expressions in its methods, as well as a simple stack implementation that uses none ADT. We determined a more efficient implementation of a stack ADT and the most efficient implementation at all by analysing the execution times of these methods, which were evaluating the same arithmetic expressions with complex numbers. In the paper we also analyse the functioning of both these stack ADT implementations and a simple stack implementation that does not use an ADT.

The Performances of Sorting Algorithms by Using Assembly Language

Author(s): Victor Deian Balutoiu,Liviu Octavian Mafteiu-Scai / Language(s): English Issue: 2/2024

In many programs the data have to be sorted according to one or more criteria. Algorithmics offers us specific algorithms classified according to the order of complexity. But from this point to the realization of a concrete and efficient program for a specific set of data there is a long way. The choice between performance and flexibility of an application is not easy. This paper presents an experimental study that tracks the time performance of assembly language implementations in relation to C and Rust in cases of Bubble Sort, Insertion Sort and Quick Sort algorithms. Conclusions as well as future research directions are also included.

Project-Based Learning on Critical Thinking Skills in Science Learning: Meta-analysis

Author(s): Wulan Suci Ramadhani,Utiya Azizah,Harun Nasrudin / Language(s): English Issue: 2/2024

The development of information technology and the environment constitutes one of the most difficult challenges facing the 20th century. Quality education plays a crucial and strategic role in building quality human resources. We need to teach critical thinking to today's students. Critical thinking skills can be improved if the project-based learning model is applied to science education. The research method used was a literature review of relevant articles for five years (2019-2023). The systematic literature review process involves three stages: article search, research article filtering and selection analysis. Using Cohen's formula, the researchers have estimated the effect size. In the light of this study, the practical application of the project based learning method, Project Based Learning, aims to improve critical thinking skills in science education. Twenty research articles show that critical thinking skills essential to science education can be improved using Project Based Learning instruction.

Бугарски информациони конзорцијум: двадесет година сарадње међу библиотекама

Author(s): Nadya Terzieva / Language(s): Serbian Issue: 1/2024

The paper presents the Bulgarian Information Consortium over more than twenty years of its activities. A few other initiatives for library cooperation in Bulgaria during the first decade of the transition to modern society, economy, and education failed or were not effective. That is why the initiators and later founders of the Consortium started the process very carefully, paying attention to various factors – tough economic situation, lack of national traditions in cooperation, psychological and behavioural characteristics of librarians and library managers in the country, historical path dependency, state of librarian and economic knowledge in the field of collective action, etc. Finally, the Bulgarian Information Consortium was established following three basic concepts: a) strong leadership, b) polycentric governance, and c) private nature.Today, the success of the Consortium is evident. The way of the achievement of this success and practical implementation of the three theoretical concepts are presented and discussed in detail in this paper. In some respect, it is a unique Bulgarian way. However, the lessons learned along that way could be useful for other librarians initiating any form of library cooperation.

Софтвери отвореног кода за израду дигиталних репозиторијума са освртом на EPrints

Author(s): Jelena Banović,Aleksandra Bradić Martinović / Language(s): Serbian Issue: 1/2024

Digital repositories are essential to the preservation, distribution, and organisation of scholarly and cultural resources in the information age. As a result, there is a notable increase in the need for effective and useful software solutions for repository creation and management. In this situation, the demand for adaptable and user-friendly tools that permit repository development and operation without reliance on proprietary solutions gives rise to open source software. The basic features of open source software are discussed in this study in relation to the creation of digital repositories, and their application is examined with an emphasis on the evaluation and application of the EPrints software. The main characteristics and capabilities of one of the most well-liked and extensively used platforms for building digital repositories are examined in this study.

Evaluating Handwritten Character Recognition with Hu Moments and K-Nearest Neighbors Algorithm

Author(s): Bojan Despodov,Done Stojanov,Cveta Martinovska Bande / Language(s): English Issue: 3/2024

Handwritten character recognition remains a challenging task in various domains, including handwriting analysis and document digitization. This task poses a significant challenge because of the variability and complexity of handwriting. To address this, it is crucial to develop different methodologies that enhance accuracy and efficiency in recognition. This research evaluates the discrimination capacity of a classifier that combines together the Hu Moments features extraction technique and the k-nearest neighbors (KNN) algorithm for the purpose of handwritten character recognition. The authors worked with handwritten characters from the English alphabet where each character is represented in two handwritten forms. The average accuracy of recognition was 82.69%, that conforms the efficiency of the proposed classifier. This research contributes to the field of automated handwritten recognition and highlights the necessity for improved techniques in character analysis, which has implications for handwriting recognition in different contexts, linguistic studies, document digitization, and other applications where character recognition is crucial. This is the first study that aims to apply the combination of Hu Moments features extraction technique in combination with the KNN algorithm for purpose of handwritten character recognition.

Mobile Web Application for Durian Orchard Management and Geospatial Data Visualization Using Deep Learning

Author(s): Supattra Puttinaovarat,Aekarat Saeliw,Jinda Kongcharoen,Siwipa Pruitikanee,Pimlaphat Pengthorn,Athicha Ketkaew,Kanit Khaimook / Language(s): English Issue: 3/2024

Durian, a globally popular fruit, is primarily exported by Thailand, making it the foremost contributor to the world market. Nevertheless, there remains a notable absence of a comprehensive platform or application catering to both consumer tourists and businesses seeking domestic purchases. Prior research has highlighted several shortcomings, notably the inability of existing applications to provide location-based search functionality or automatically identify durian plantation plots from digital photographs. Consequently, this study proposes the development of a mobile web application aimed at managing, processing, and visualizing geospatial data pertaining to orchards and durian plantations. Through the integration of mobile technology, geospatial technology, and machine learning, the research endeavours to address these deficiencies. The findings indicate promising results, particularly in the accurate classification of durian plantations using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest, and k-nearest neighbor (KNN). Among these algorithms, CNN exhibited the highest accuracy, achieving a value of 95%, with precision, recall, and f-measure values of 95.55%, 94.44%, and 94.97%, respectively.

Multiple-base Logarithmic Quantization and Application in Reduced Precision AI Computations

Author(s): Vassil Dimitrov,Richard Ford,Laurent Imbert,Arjuna Madanayake,Nilan Udayanga,Will Wray / Language(s): English Issue: XIV/2024

The power of logarithmic quantizations and computations has been recognized as a useful tool in optimizing the performance of large ML models. There are plenty of applications of ML techniques in digital preservation. The accuracy of computations may play a crucial role in the corresponding algorithms. In this article, we provide results that demonstrate significantly better quantization signal-to-noise ratio performance thanks to multiple-base logarithmic number systems (MDLNS) in comparison with the floating point quantizations that use the same number of bits. On a hardware level, we present details about our Xilinx VCU-128 FPGA design for dot product and matrix vector computations. The MDLNS matrix-vector design significantly outperforms equivalent fixed-point binary designs in terms of area (A) and time (T) complexity and power consumption as evidenced by a 4× scaling of AT^2 metric for VLSI performance, and 57% increase in computational throughput per watt compared to fixed-point arithmetic.

Metafory w projektowaniu interfejsów informacyjnych

Author(s): Bożena Jaskowska / Language(s): Polish Issue: 1/2023

The purpose of this article is to provide an overview of the use of metaphors in information design, with a particular focus on interfaces and navigation. Types of metaphors are presented with examples, various metaphors in interface design over the years are discussed, and the current use of metaphors in UI and UX is examined, with a particular emphasis on spatial metaphors. Heuristics exploring metaphors in information systems are reviewed, recommendations for effective implementation of metaphors in digital environments are included, and arguments criticizing such approaches are presented. A literature review and case studies were used to analyze the subject matter.

Public service innovation as a source of risk: An explorative case study from Poland

Author(s): Mateusz Lewandowski,Aleksandra Moskała / Language(s): English Issue: 1-2/2023

The relationship between risk and innovation in the sector of public services has gained little attention and remains mainly only outlined theoretically with very few empirical insights. This paper presents a case-based exploration of cultural public service innovation as a source of risk. The collected evidence demonstrates the complexity of the relations between innovation and risks and the interrelations between risk types. As a result, a refined framework of risk management styles is proposed. The new framework outlined in this article merges the two former theoretical contributions known from the literature and offers possible practical implementations.

Introduction to Special Issue on The Russian-Ukrainian War: Effects on Global Cybersecurity and Digital Infrastructure

Author(s): Jacek Leśkow / Language(s): English Issue: 1/2024