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Towards a Catalogue of Self-Sovereign Identity Design Patterns
Špela Čučko, Vid Keršič, Muhamed Turkanović, 2023, original scientific article

Abstract: Self-sovereign identity (SSI) is a user-centric, decentralised identity approach that provides a means for identification, authentication, and authorisation without the involvement of external entities, responsible for identity provisioning and management in current centralised and federated approaches. In general, the basic building blocks of an SSI system include decentralised identifiers, verifiable credentials, identity wallets, a verifiable data registry, and three main actors: issuer, identity holder, and verifier. Even though the SSI field is dominated by proposals, SSI systems can be implemented in different ways, which is reflected in the absence of a well-defined architecture. Thus, the best implementation is still a matter of research, the requirements of the individual system, and its field of application. However, well-designed and implemented systems are crucial to avoiding failures, speeding up the development process, ensuring high quality, and the broader adoption of SSI solutions. Hence, the main objective of this study was to identify design patterns and good practices of the SSI ecosystems by reviewing and analysing the literature, technical documentation, and existing SSI implementations. Therefore, the study is built on existing knowledge, and presents a comprehensive catalogue of thirty-five SSI design patterns that can serve as a starting point for a possible SSI system design.
Keywords: self-sovereign identity, decentralised identity, design patterns, decentralised identifiers, verifiable credentials, blockchain, verifiable data registry, identity wallets
Published in DKUM: 05.12.2023; Views: 8; Downloads: 0
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Towards trusted data sharing and exchange in agro-food supply chains: design principles for agricultural data spaces
Martina Šestak, Daniel Copot, original scientific article

Abstract: In the modern agricultural landscape, realizing data’s full potential requires a unified infrastructure where stakeholders collaborate and share their data to gain insights and create business value. The agricultural data ecosystem (ADE) serves as a crucial socio-technical infrastructure, aggregating diverse data from various platforms and, thus, advertising sustainable agriculture and digitalization. Establishing trustworthy data sharing and exchange in agro-food value chains involves socioeconomic and technological elements addressed by the agricultural data space (ADS) and its trust principles. This paper outlines key challenges to data sharing in agro-food chains impeding ADE establishment based on the review of 27 studies in scientific literature. Challenges mainly arise from stakeholders’ mistrust in the data-sharing process, inadequate data access and use policies, and unclear data ownership agreements. In the ADE context, interoperability is a particularly challenging topic for ensuring the long-term sustainability of the system. Considering these challenges and data space principles and building blocks, we propose a set of design principles for ADS design and implementation that aim to mitigate the adverse impact of these challenges and facilitate agricultural data sharing and exchange.
Keywords: data sharing and exchange, agro-food supply chain, design principles, agricultural data space, agricultural data ecosystem
Published in DKUM: 30.11.2023; Views: 39; Downloads: 1
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Is the essential facilities doctrine fit for access to data cases? The data protection aspect
Rok Dacar, 2022, original scientific article

Abstract: Personal data can be of great economic value for companies as it is an essential input for the offering of a wide array of services. One way for a company to obtain access to essential personal data controlled by another company is by demanding mandatory access on the grounds of the essential facilities doctrine. Such access, however, can violate the right to the protection of personal data of the data subjects if it is not based on one of the legitimate grounds for the processing of personal data set by the GDPR. Two of these grounds are especially likely to be applicable to the access to personal data mandated using the essential facilities doctrine: the interpretation of the Commission decision or the judgment of the Court of Justice ordering the granting of access as a legal obligation and the legitimate interest of the company requesting access, for such access. The anonymisation of personal data is not a viable option for the circumvention of the rules of the GDPR as anonymised personal data loses most of its economic relevance for companies.
Keywords: essential facilities doctrine, right to protection of personal data, grounds for processing personal data, anonymisation of personal data, General Data Protection Regulation
Published in DKUM: 26.09.2023; Views: 104; Downloads: 8
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The impact of macroprudential policy on credit growth in nine euro area economies
Eva Lorenčič, Robert Volčjak, Mejra Festić, 2023, original scientific article

Abstract: In this paper, we investigate the impact of macroprudential policy measures (bundled together into a macroprudential policy index, MPI) on the nonfinancial corporate sector credit and household credit growth using a one-step system GMM empirical research method. The goal of our paper is to test whether contractionary macroprudential policy stymies credit growth rate and whether expansionary macroprudential policy spurs credit growth rate in selected Euro Area economies (Austria, Belgium, Finland, Germany, Ireland, Italy, Netherlands, Slovenia, and Spain) over the period 2008Q4–2018Q4. We test two hypotheses: H1: The tightening of macroprudential policy measures reduces the non-financial corporate sector credit growth rate, and H2: The tightening of macroprudential policy measures reduces the growth rate of household credit. Based on our empirical results, we can confirm the first hypothesis. In contrast, the second hypothesis can be neither confirmed nor rejected since the explanatory variable of interest (MPI) is statistically insignificant in the second model.
Keywords: macroprudential policy, systemic risk, financial stability, dynamic panel data, one-step system GMM
Published in DKUM: 05.09.2023; Views: 82; Downloads: 3
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The impact of COVID-19 on bank efficiency in the Western Balkans : a DEA approach
Katerina Fotova Čiković, Violeta Cvetkoska, Damira Keček, 2023, original scientific article

Abstract: This paper aims to assess and compare the relative efficiency of commercial banks in six Western Balkan developing countries (North Macedonia, Serbia, Montenegro, Bosnia and Herzegovina (B&H), Kosovo1, and Albania), using the leading nonparametric methodology Data Envelopment Analysis (DEA) for the period 2016-2020, and investigating the impact of the COVID-19 pandemics on the performance of banking systems in these countries. The outputoriented DEA model has been implemented with interest expenses and noninterest expenses as inputs and interest revenue and non-interest revenue as outputs. Our findings show that the average efficiency of the six Western Balkan banking systems differs, and Kosovo’s banking system has noted the highest average efficiency in the whole observed period (2016–2020). In contrast, the banking system in Bosnia and Herzegovina noted the lowest average efficiency. The COVID-19 pandemic decreased the relative efficiency of the banking sectors in all six of the Western Balkan countries observed, except Kosovo. However, additional research that includes all pandemic years is recommended to assess the impact of the COVID-19 pandemic on the banking systems in the Western Balkans. This study provides invaluable insights for academic members, banking management and regulatory bodies, governments, and the interested public. It is the first empirical study that includes a glimpse of the first impact of the COVID-19 pandemic on banking systems in the developing countries of the Western Balkans.
Keywords: banking sector, data envelopment analysis, COVID-19, Western Balkans
Published in DKUM: 05.09.2023; Views: 82; Downloads: 3
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Analyzing EEG signal with Machine Learning in Python
Evgenija Siljanovska, 2023, undergraduate thesis

Abstract: This thesis presents a comprehensive analysis of EEG data using Python libraries, MNE and machine learning techniques. The thesis focuses on utilizing these tools to extract valuable insights from EEG recordings. Our dataset consists of EEG data in the BrainVision format, acquired during a psychology experiment. The analysis involves preprocessing, filtering, segmentation, and visualization of the EEG data. Additionally, machine learning algorithms are employed to classify and predict patterns within the EEG signals. The findings showcase the effectiveness of Python, MNE, and machine learning in EEG analysis.
Keywords: EEG data, MNE, Machine learning, Analyzing
Published in DKUM: 17.08.2023; Views: 259; Downloads: 37
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Scoping review on the multimodal classification of depression and experimental study on existing multimodal models
Umut Arioz, Urška Smrke, Nejc Plohl, Izidor Mlakar, 2022, review article

Abstract: Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.
Keywords: multimodal depression classification, scoping review, real-world data, mental health
Published in DKUM: 11.08.2023; Views: 258; Downloads: 21
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Digital transformation of healthcare through the prism of trends, challenges and principles
Muhamed Turkanović, 2023, review article

Keywords: artificial intelligence, Data Spaces, digital health, digital transformation, digitalization, EHR
Published in DKUM: 18.07.2023; Views: 232; Downloads: 28
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Development and optimisation of a business intelligence system : master's thesis
Mark Žohar, 2023, master's thesis

Abstract: In the fast-paced business landscape, unexpected events can pose significant challenges for companies. A lot of times, business intelligence presents a solution for companies in dealing with changes and improving organizational efficiency. As part of this master’s thesis, we have developed a business intelligence system that assists in the planning of new releases of smartphone components. We have simulated data and created reports, which can help the stakeholders to plan and analyze important milestones easily. We have tested two different data structures and showed how star schema is the most beneficial data structure for our BI system. We achieved this by restructuring data into a star schema model at different stages of migration and then compared the size of data and responsiveness of operations.
Keywords: business intelligence, star schema, data modeling
Published in DKUM: 27.06.2023; Views: 184; Downloads: 35
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36th Bled eConference – Digital Economy and Society: The Balancing Act for Digital Innovation in Times of Instability : June 25 – 28, 2023, Bled, Slovenia, Conference Proceedings

Abstract: The Bled eConference, organised by the University of Maribor, Faculty of Organizational Sciences, has been shaping electronic interactions since 1988. The theme of the 36th conference is "Digital Economy and Society: The Balancing Act for Digital Innovation in Times of Instability". In times of instability, which include political, economic, resource, health, and environmental challenges on the one hand, and technological disruption on the other, it is critical to ensure that digital innovation continues to lead to the right and sustainable solutions that are tailored to the needs of all people, enterprises and society. It is very important to keep in mind the protection of our planet, including fauna and flora. These efforts include adopting appropriate regulatory frameworks, fostering digital literacy and skills development, promoting inclusive access to digital technologies, and addressing the ethical, social and environmental implications of digital transformation. The papers in this conference proceedings address digital transformation of enterprises, artificial intelligence and data science solutions, decision analytics for business and societal challenges, new, digital and data driven business models, digital consumer, digital education, digital health, digital ethics, restructured work and solutions for smart and sustainable cities. We continue to provide an open forum for academia, including students, industry, and policy makers where everyone can contribute to creating a better world.
Keywords: digital transformation, digital business, digital technologijes, innovations, digitalization, sustainable development, smart and sustainable cities and societies, digital health, artificial intelligence and data science, digital ethics, digital education, restructured work, digital consumer, smart and sustainable cities
Published in DKUM: 26.06.2023; Views: 382; Downloads: 79
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