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An efficient iterative approach to explainable feature learning
Dino Vlahek, Domen Mongus, 2023, original scientific article

Keywords: data classification, explainable artificial intelligence, feature learning, knowledge discovery
Published in DKUM: 13.06.2024; Views: 14; Downloads: 0
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Categorisation of open government data literature
Aljaž Ferencek, Mirjana Kljajić Borštnar, Ajda Pretnar Žagar, 2022, review article

Abstract: Background: Due to the emerging global interest in Open Government Data, research papers on various topics in this area have increased. Objectives: This paper aims to categorise Open government data research. Methods/Approach: A literature review was conducted to provide a complete overview and classification of open government data research. Hierarchical clustering, a cluster analysis method, was used, and a hierarchy of clusters on selected data sets emerged. Results: The results of this study suggest that there are two distinct clusters of research, which either focus on government perspectives and policies on OGD, initiatives, and portals or focus on regional studies, adoption of OGD, platforms, and barriers to implementation. Further findings suggest that research gaps could be segmented into many thematic areas, focusing on success factors, best practices, the impact of open government data, barriers/challenges in implementing open government data, etc. Conclusions: The extension of the paper, which was first presented at the Entrenova conference, provides a comprehensive overview of research to date on the implementation of OGD and points out that this topic has already received research attention, which focuses on specific segments of the phenomenon and signifies in which direction new research should be made.
Keywords: open government data, open government data research, hierarchical clustering, OGD classification, OGD literature overview
Published in DKUM: 12.06.2024; Views: 30; Downloads: 0
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Cyber (in)security of personal data and information in times of digitization
Miha Dvojmoč, Mojca Tancer Verboten, 2022, original scientific article

Abstract: In an employment relationship, work and pay are no longer the only important aspects, as importance is increasingly shifting toward obligations concerning the protection of personal data and privacy arising from the prohibition of causing harm to the employer and the duty of loyalty to the employer. The article deals with the constitutionally protected right to privacy and the protection of personal data from the point of view of ensuring cyber security at the employer. The employer is obligated to protect the right to privacy by legal provisions, whereas from the point of view of ensuring the protection of privacy and information, the employer must protect, first and foremost, the personal data of employees. The main purpose of the legal protection of personal data is the lawful and fair processing of the personal data of individuals. Employers are thus facing an increasing number of risks related to the safety of employees and the security of business processes, and it is therefore important to establish comprehensive corporate security to ensure adequate security across all levels.
Keywords: cyber security, protection of personal data, protection of privacy, cyber threats, protection of business secrets
Published in DKUM: 11.06.2024; Views: 33; Downloads: 0
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Neo4j graph dataset of cycling paths in Slovenia
Alen Rajšp, Iztok Fister, 2023, other scientific articles

Keywords: data mining, geographical data, graph database, OpenStreetMap, route generation, sports training
Published in DKUM: 10.06.2024; Views: 50; Downloads: 0
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A review of federated learning in agriculture
Krista Rizman Žalik, Mitja Žalik, 2023, review article

Keywords: federated learning, agriculture, architecture, data partitioning, federation scal, aggregation algorithms, communication bottleneck
Published in DKUM: 05.06.2024; Views: 59; Downloads: 1
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Simulated and experimental HDEMG signals of biceps brachii muscle for analysis of motor unit merging
Aleš Holobar, Jakob Škarabot, Dario Farina, 2024, complete scientific database of research data

Abstract: This dataset contains a collection of simulated and experimental surface HDEMG recordings of the biceps brachii muscle during the isometric elbow flexion. Simulated data contains 50 recordings: 5 subjects and 5 excitation levels, each with and without added noise. Experimental data contains 16 recordings: 2 subjects with 4 excitation levels and 2 repetitions of each level. Synthetic data was simulated using the cylindrical volume conductor model [1] and the motor unit recruitment and firing modulation model proposed in [2]. Each recording is 20 seconds in length with 90 HDEMG channels sampled at 2048 Hz and is stored as a 2D matrix of raw EMG values in Matlab’s MAT format. Experimental surface EMG data was recorded on two volunteers during isometric contractions at constant force level. Each recording is 25 seconds in length with 64 HDEMG channels sampled at 2048 Hz and is also stored as a 2D matrix of raw EMG values in Matlab’s MAT format. The dataset is approximately 1.5 GB in size.
Keywords: surface high density electromyogram (HDEMG), motor unit, spike train, motor unit merging, simulated data, experimental data, biceps brachii, dataset
Published in DKUM: 30.05.2024; Views: 86; Downloads: 4
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Age-related changes in lipid and glucose levels associated with drug use and mortality : an observational study
Rene Markovič, Vladimir Grubelnik, Helena Blažun Vošner, Peter Kokol, Matej Završnik, Karmen Janša, Marjeta Zupet, Jernej Završnik, Marko Marhl, 2022, original scientific article

Abstract: Background: The pathogenesis of type 2 diabetes mellitus is complex and still unclear in some details. The main feature of diabetes mellitus is high serum glucose, and the question arises of whether there are other statistically observable dysregulations in laboratory measurements before the state of hyperglycemia becomes severe. In the present study, we aim to examine glucose and lipid profiles in the context of age, sex, medication use, and mortality. Methods: We conducted an observational study by analyzing laboratory data from 506,083 anonymized laboratory tests from 63,606 different patients performed by a regional laboratory in Slovenia between 2008 and 2019. Laboratory data-based results were evaluated in the context of medication use and mortality. The medication use database contains anonymized records of 1,632,441 patients from 2013 to 2018, and mortality data were obtained for the entire Slovenian population. Results: We show that the highest percentage of the population with elevated glucose levels occurs approximately 20 years later than the highest percentage with lipid dysregulation. Remarkably, two distinct inflection points were observed in these laboratory results. The first inflection point occurs at ages 55 to 59 years, corresponding to the greatest increase in medication use, and the second coincides with the sharp increase in mortality at ages 75 to 79 years. Conclusions: Our results suggest that medications and mortality are important factors affecting population statistics and must be considered when studying metabolic disorders such as dyslipidemia and hyperglycemia using laboratory data.
Keywords: diabetes, metabolic syndrome, hematological data, aging
Published in DKUM: 21.05.2024; Views: 92; Downloads: 1
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Webinar: Data Management and Ethics
Milan Ojsteršek, Matjaž Divjak, 2024, scientific film, scientific sound or video publication

Abstract: Video recording and presentation slides of a lecture by Milan Ojsteršek and Matjaž Divjak for the HybridNeuro webinar "Data Management and Ethics", which was held online on April 8th 2024 at Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia. More info including additional materials (slides, example dataset): https://www.hybridneuro.feri.um.si/webinars/data_management_and_ethics.html The HybridNeuro project combines the expertise of leading European partners in the field of Neural Interfaces to set up new pathways of analyzing human motor system and human movements and transfer the academic research into clinical and industrial practice. Link: https://www.hybridneuro.feri.um.si/ This project has received funding from the Horizon Europe Research and Innovation Programme under GA No. 101079392, as well as UK Research and Innovation organisation (GA No. 10052152). This video is available under the Creative Commons Attribution 4.0 International licence (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).
Keywords: HybridNeuro project, webinar, presentation, data management, data annotation, open science, open access, data repository, FAIR data, metadata
Published in DKUM: 09.05.2024; Views: 119; Downloads: 4
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Self-organization of enzyme-catalyzed reactions studied by the maximum entropy production principle
Andrej Dobovišek, Marko Vitas, Tina Blaževič, Rene Markovič, Marko Marhl, Aleš Fajmut, 2023, original scientific article

Abstract: The self-organization of open reaction systems is closely related to specific mechanisms that allow the export of internally generated entropy from systems to their environment. According to the second law of thermodynamics, systems with effective entropy export to the environment are better internally organized. Therefore, they are in thermodynamic states with low entropy. In this context, we study how self-organization in enzymatic reactions depends on their kinetic reaction mechanisms. Enzymatic reactions in an open system are considered to operate in a non-equilibrium steady state, which is achieved by satisfying the principle of maximum entropy production (MEPP). The latter is a general theoretical framework for our theoretical analysis. Detailed theoretical studies and comparisons of the linear irreversible kinetic schemes of an enzyme reaction in two and three states are performed. In both cases, in the optimal and statistically most probable thermodynamic steady state, a diffusion-limited flux is predicted by MEPP. Several thermodynamic quantities and enzymatic kinetic parameters, such as the entropy production rate, the Shannon information entropy, reaction stability, sensitivity, and specificity constants, are predicted. Our results show that the optimal enzyme performance may strongly depend on the number of reaction steps when linear reaction mechanisms are considered. Simple reaction mechanisms with a smaller number of intermediate reaction steps could be better organized internally and could allow fast and stable catalysis. These could be features of the evolutionary mechanisms of highly specialized enzymes.
Keywords: enzymes, kinetic data analysis, steady state, self-organization, maximum entropy production
Published in DKUM: 08.05.2024; Views: 151; Downloads: 8
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