Iskanje po katalogu digitalne knjižnice

Opis: 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.
Ključne besede: sustainable development goals (SDGs), higher education, data envelopment analysis, SDG4, European countries
Objavljeno v DKUM: 28.05.2025; Ogledov: 0; Prenosov: 7
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Opis: 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.
Ključne besede: metadata, real world data, text data, text mining, text identification, natural language processing
Objavljeno v DKUM: 22.05.2025; Ogledov: 0; Prenosov: 8
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Opis: Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to realworld applications where explainability and robustness are paramount.
Ključne besede: explainable AI, point cloud data, counterfactual reasoning, LiDAR, 3D point cloud data, interpretability, human-centered AI, large language models, K-nearest neighbors
Objavljeno v DKUM: 19.05.2025; Ogledov: 0; Prenosov: 3
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Opis: Sensitive data requires careful consideration and adherence to best practices to ensure its confidentiality, integrity, and availability. Essential steps in handling sensitive data are identification and classification of sensitive data, implementation of data access control, encryption of sensitive data, secure storage and transmission, implementation of data breach response plan, backup and monitoring usage of data, complying with regulation, and disposing of data securely. Misconduct in handling sensitive data can compromise data confidentiality, integrity, and availability. These include data breaches (unauthorised access or disclosure, theft, insider threats, falsification, fabrication, imputation, and amputation of data), failure to comply with data protection regulations, inadequate data security practices, improper retention and disposal of data, and failure to report data breaches and incidents. In this presentation Milan Ojsteršek presents how to manage sensitive data, desensitise it, and which are the most common breaches in handling sensitive data incidents. This presentation was given at the 4th ENAI Academic Integrity Summer School 2024, 16th – 21th September 2024, University of Konstanz, Germany.
Ključne besede: open science, metadata, research data management, sensitive data, academic integrity, data management ethics, research misconduct, licensing of open data, FAIR, Slovenian open access infrastructure
Objavljeno v DKUM: 18.04.2025; Ogledov: 0; Prenosov: 3
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Opis: The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.
Ključne besede: tennis, training, data-based coaching, shot recognition, wearable device, workload, recovery
Objavljeno v DKUM: 31.03.2025; Ogledov: 0; Prenosov: 11
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