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Abstract: In light of digitalisation, we are witnessing an increased volume of collected data and data generation and exchange acceleration. Therefore, the European Union (EU) has introduced the General Data Protection Regulation (GDPR) as a new framework for data protection on the European level. However, GDPR allows the member states to change some parts of the regulation, and the member states can always build on top of the GDPR. An example is the collection of biometric data with electronic signatures. This paper aims to compare the legislation on data protection topics in the various EU member states. The findings show that the member states included in the study generally do not have many additional/specific laws (only in 29.4% of the cases). However, almost all have other/additional legislation to the GDPR on at least one topic. The most additional legislation is on the topics of video surveillance, biometry, genetic data and health data. We also introduce a dynamic map that allows for quick navigating between different information categories and comparisons of the EU member states at a glance.
Keywords: data protection, personal data, data privacy, GDPR legislation, heterogeneity, legislation on data protection, European Union
Published in DKUM: 16.06.2025; Views: 0; Downloads: 0
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Abstract: Aims and Objective: The aim of this study was to explore the feasibility, usability and acceptance of integrating Clinical Decision Support Systems with Socially Assistive Robots into hospital grand rounds. Background: Adopting Clinical Decision Support Systems in healthcare faces challenges such as complexity, poor integration with workflows, and concerns about data privacy and quality. Issues such as too many alerts, confusing errors, and difficulty using the technology in front of patients make adoption challenging and prevent it from fitting into daily workflows. Making Clinical Decision Support System simple, intuitive and user-friendly is essential to enable its use in daily practice to improve patient care and decision-making. Methods: This six-month pilot study had two participant groups, with total of 40 participants: a longitudinal intervention group (n =8) and a single-session evaluation group (n=32). Participants were medical doctors at the University Clinical Center Maribor. The intervention involved implementing a Clinical Decision Support System delivered via a Socially Assistive Robot during hospital grand rounds. We developed a system that employed the HL7 FHIR standard for integrating data from hospital monitors, electronic health records, and patient-reported outcomes into a single dashboard. A Pepper-based SAR provided patient specific recommendations through a voice and SAR tablet enabled interface. Key evaluation metrics were assessed using the System Usability Scale (SUS) and the Unified Theory of Acceptance, Use of Technology (UTAUT2) questionnaire, including Effort Expectancy, Performance Expectancy and open ended questions. The longitudinal group used the system for 6 months and completed the assessments twice, after one week and at the end of the study. The single-session group completed the assessment once, immediately after the experiment. Qualitative data were gathered through open-ended questions. Data analysis included descriptive statistics, paired t-tests, and thematic analysis. Results: System usability was rated highly across both groups, with the longitudinal group reporting consistently excellent scores (M =82.08 at final evaluation) compared to the acceptable scores of the single-session group (M =68.96). Extended exposure improved user engagement, reflected in significant increases in Effort Expectancy and Habit over time. Participants found the system enjoyable to use, and while no significant changes were seen in Performance Expectancy, feedback emphasized its efficiency in saving time and improving access to clinical data, supporting its feasibility and acceptability. Conclusions: This research supports the potential of robotic technologies to transform CDSS into more interactive, efficient, and user-friendly tools for healthcare professionals. The paper also suggests further research directions and technical improvements to maximize the impact of innovative technologies in healthcare.
Keywords: clinical decision support systems, clinical decision-making, hospital grand rounds, patient data integration, perceived quality of care, socially assistive robots, usability and familiarity, user experience questionnaire, workload reduction
Published in DKUM: 30.05.2025; Views: 0; Downloads: 2
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Abstract: This article presents a high-performance-computing differential-evolution-based hyperparameter optimization automated workflow (AutoDEHypO), which is deployed on a petascale supercomputer and utilizes multiple GPUs to execute a specialized fitness function for machine learning (ML). The workflow is designed for operational analytics of energy efficiency. In this differential evolution (DE) optimization use case, we analyze how energy efficiently the DE algorithm performs with different DE strategies and ML models. The workflow analysis considers key factors such as DE strategies and automated use case configurations, such as an ML model architecture and dataset, while monitoring both the achieved accuracy and the utilization of computing resources, such as the elapsed time and consumed energy. While the efficiency of a chosen DE strategy is assessed based on a multi-label supervised ML accuracy, operational data about the consumption of resources of individual completed jobs obtained from a Slurm database are reported. To demonstrate the impact on energy efficiency, using our analysis workflow, we visualize the obtained operational data and aggregate them with statistical tests that compare and group the energy efficiency of the DE strategies applied in the ML models.
Keywords: high-performance computing, operational data analytics, energy efficiency, machine learning, AutoML, differential avolution, optimization
Published in DKUM: 29.05.2025; Views: 0; Downloads: 2
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Abstract: Higher education (HE) is a significant factor in a country’s economic prosperity and plays a vital role in addressing sustainability issues and actively promoting sustainable development. While many EU member countries have well-developed education systems in terms of rankings and SDGs’ attainment, little is known about the progress of other European countries. The goal of this research is to estimate the efficiency of higher education in the attainment of Sustainable Development Goals (SDGs) in 40 European countries. The method used to estimate efficiency is Data Envelopment Analysis (DEA) with output-orientation and variable returns to scale approach. In the final model specification, two input variables and one output variable are used. Results indicate that the average technical efficiency of the 40 European countries is relatively high and equal to 0.94. Nine countries emerge as fully efficient in achieving SDG 4 with a coefficient equal to 1. The four largest higher education systems achieved an aboveaverage efficiency score of 0.97 or higher. Six countries are recognized as the worst performing. However, more analysis is necessary to examine the sources of inefficiency in the worst-performing countries. Due to specific data limitations indicated in this research, it remains a challenge to evaluate the precise impact of higher education and its contribution to SDGs.
Keywords: sustainable development goals (SDGs), higher education, data envelopment analysis, SDG4, European countries
Published in DKUM: 28.05.2025; Views: 0; Downloads: 1
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Abstract: The OpenScience Slovenia metadata dataset contains metadata entries for Slovenian public domain academic documents which include undergraduate and postgraduate theses, research and professional articles, along with other academic document types. The data within the dataset was collected as a part of the establishment of the Slovenian Open-Access Infrastructure which defined a unified document collection process and cataloguing for universities in Slovenia within the infrastructure repositories. The data was collected from several already established but separate library systems in Slovenia and merged into a single metadata scheme using metadata deduplication and merging techniques. It consists of text and numerical fields, representing attributes that describe documents. These attributes include document titles, keywords, abstracts, typologies, authors, issue years and other identifiers such as URL and UDC. The potential of this dataset lies especially in text mining and text classification tasks and can also be used in development or benchmarking of content-based recommender systems on real-world data.
Keywords: metadata, real world data, text data, text mining, text identification, natural language processing
Published in DKUM: 22.05.2025; Views: 0; Downloads: 6
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