Now showing 1 - 8 of 8
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    Rapid Aspect-Oriented Assessment of Relational Database Design Assignments
    (ACM, 2022-09-21)
    Ajanovski, Vangel V.
    It can be argued that assignments in a Databases course, where students are required to build a database instance based on specifications provided in the form of Entity-Relationship Diagrams and descriptions, are one of the best ways to evaluate students’ practical knowledge because they simultaneously cover a variety of topics and cognitive levels. Organizing an exam with such assignments is a time-consuming process, even more so in a large course. This work proposes a rapid aspect-oriented assessment process and presents a software system that automates most of the tasks needed to administer and assess an exam that includes such assignments.
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    dbLearn*: Open-Source System and a Set of Practices for Conducting Iterative Exercises and Exams in a Databases Course
    (ACM, 2021-10-06)
    The author's approach in teaching databases, focuses on acquiring competences needed to use and develop systems of realistic complexity, typical for a micro or small company. Each student works towards gaining experience with various database designs and implementations, querying and development of relational schemas, and building applications. An open-source system is introduced, intended to help teachers organize such courses at a scale, and effectively guide the students in an iterative process of acquiring competencies, at the level of smaller groups or even individuals. The main goal of the system is speed-up of administrative tasks at all levels. The system implements the core process of organizing exams in a partial and piece-wise manner, and implements sets of tools and practices for semi-automated assessment processes.
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    Classification of Companies using Graph Neural Networks
    (IEEE, 2024-05-20)
    Manchev, Jovan
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    Classification of companies into GICS categories can be addressed using Graph Neural Networks (GNN), by utilizing the different types of relationship between companies such as customer, supplier, partner, competitor, and investor. We use the Relato business graph data and compare the performances of several GNNs and a large language model like BERT that is trained only on the descriptions of the companies. Our goal is company classification into its corresponding category within the four tiers of the GICS hierarchy. Several architectures of GNNs are explored such as GCN, GraphSAGE and GAT, but also RGCN and RGAT that consider the edge type, or relationship between the companies. The main purpose is to reveal what kind of relationship between the companies is most valuable when determining the category of the company. The findings indicate that Graph Neural Networks (GNNs) enhance both classification performance and the understanding of collaboration patterns among companies, providing valuable insights for determining the industry in which these companies operate. This contrasts with the classification based solely on company descriptions using BERT.
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    Estimation of Blood Pressure from Arterial Blood Pressure using PPG Signals
    (Faculty of Computer Science and Engineering, Skopje, North Macedonia, 2023-08)
    Mladenovska, Teodora
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    Madevska Bogdanova, Ana
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    Predicting Blood pressure from Photoplethysmography (PPG) signals is an active area of research and there have been many studies exploring the feasibility of this approach. This paper elaborates on a technique for the estimation of continuous Arterial blood pressure (ABP) waveform using PPG signals as inputs in a developed deep-learning model. The ultimate goal is estimating the Blood pressure, but unlike the standard regression models for predicting Blood pressure by systolic BP (SBP) and Diastolic BP (DBP), this approach calculates SBP and DBP from the estimated ABP waveform, which enables further analysis to enhance the BP estimation. The best-obtained results are an MAE of 8.40mmHg, and an MAE of 11.1mmHg and 7mmHg for SBP and DBP respectively. The promising prediction of SBP and DBP using our proposed machine learning model has the potential to improve clinical decision-making and resource allocation process in emergency situations.
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    Inclusive Higher Education during the Covid-19 Pandemic
    (Croatian Society for Information, Communication and Electronic Technology - MIPRO, 2021-09)
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    Krasniqi, Venera
    The Covid-19 pandemic caused a sudden shift towards online teaching, learning and assessment, which was troublesome for both teachers and students. The most affected were the students with various disabilities, whose inclusive options provided in the classroom were no longer available at home. Many socially responsible universities managed to support the education for differently abled students enabling various forms of alternative activities that partially or completely bypass their problems. This paper reviews the accessibility options of operating systems, learning management systems and especially assistive technologies that facilitate the education of impaired students. They cover the add-ons and tools for vision and hearing impairment. Teaching, learning and assessment are carefully examined for both disabilities. Particular attention is paid to practical work, which is a compulsory part of many higher education courses. The paper concludes with the major barriers of online higher education, which should during Covid-19 pandemic enable an equal right to all the students, regardless of their physical and mental disabilities.
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    Fog Computing for Personal Health: Case Study for Sleep Apnea Detection
    (The 13-th conference on Information Systems and Grid Technologie, 2020-05-29)
    Ace Dimitrievski
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    Snezana Savoska
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    The recent trends in healthcare as e-health and electronic hospital health services pushed healthcare systems to a patient-centric concept, collecting a large amount of data in Electronic or Personal Health Records, providing evidence-based medicine and data analysis. This concept, together with the pervasive health care environments, can generate recommendations and suggestions for preventive intervention, depending on some measured parameters of the patient at home. This can improve the healthcare service from home, based on the health conditions, disease history, and data gained from vital sign sensors according to the Internet of Things Smart living concept. From the technical point of view, a remote monitoring system can provide remote consultation as a part of Assistive technology trends. We used cloud and fog computing for experiment with noninvasive sensors that can follow humans’ sleeping activities towards detecting sleep apnea, to demonstrate the fog-based data processing. With this case study, we have shown the applicability of fog computing and ability trough preprocessing to accomplish computational and bandwidth savings, protecting sensitive data privacy.
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    Full-mesh VPN performance evaluation for a secure edge-cloud continuum
    (John Wiley & Sons Ltd., 2024-03-11)
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    Gilly, Katja
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    The recent introduction of full-mesh virtual private network (VPN) solutions which offer near native performance, coupled with modern encryption algorithms and easy scalability as a result of a central control plane have a strong potential to enable the implementation of a seamless edge-cloud continuum. To test the performance of existing solutions in this domain, we present a framework consisted of both essential and optional features that full-mesh VPN solutions need to support before they can be used for interconnecting geographically dispersed compute nodes. We then apply this framework on existing offerings and select three VPN solutions for further tests: Headscale, Netbird, and ZeroTier. We evaluate their features in the context of establishing an underlay network on top of which a Kubernetes overlay network can be created. We test pod-to-pod TCP and UDP throughput as well as Kubernetes application programming interface (API) response times, in multiple scenarios, accounting for adverse network conditions such as packet loss or packet delay. Based on the obtained measurement results and through analysis of the underlying strengths and weaknesses of the individual implementations, we draw conclusions on the preferred VPN solution depending on the use-case at hand, striking a balance between usability and performance.