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1.
Data management and academic integrity
Milan Ojsteršek, 2024, drugo učno gradivo

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: 2
.pptx Celotno besedilo (16,66 MB)

2.
A hierarchical universal algorithm for geometric objects’ reflection symmetry detection
Borut Žalik, Damjan Strnad, Štefan Kohek, Ivana Kolingerová, Andrej Nerat, Niko Lukač, David Podgorelec, 2022, izvirni znanstveni članek

Opis: A new algorithm is presented for detecting the global reflection symmetry of geometric objects. The algorithm works for 2D and 3D objects which may be open or closed and may or may not contain holes. The algorithm accepts a point cloud obtained by sampling the object’s surface at the input. The points are inserted into a uniform grid and so-called boundary cells are identified. The centroid of the boundary cells is determined, and a testing symmetry axis/plane is set through it. In this way, the boundary cells are split into two parts and they are faced with the symmetry estimation function. If the function estimates the symmetric case, the boundary cells are further split until a given threshold is reached or a non-symmetric result is obtained. The new testing axis/plane is then derived and tested by rotation around the centroid. This paper introduces three techniques to accelerate the computation. Competitive results were obtained when the algorithm was compared against the state of the art.
Ključne besede: computer science, computational geometry, uniform subdivision, centroids
Objavljeno v DKUM: 01.04.2025; Ogledov: 0; Prenosov: 4
.pdf Celotno besedilo (2,99 MB)
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3.
Application of machine learning to reduce casting defects from bentonite sand mixture
Žiga Breznikar, Marko Bojinović, Miran Brezočnik, 2024, izvirni znanstveni članek

Opis: One of the largest Slovenian foundries (referred to as Company X) primarily focuses on casting moulds for the glass industry. In collaboration with Pro Labor d.o.o., Company X has been systematically gathering defect data since 2021. The analysis revealed that the majority of scrap caused by technological issues is attributed to sand defects. The initial dataset included information on defect occurrences, technological parameters of sand mixture and chemical properties of the cast material. This raw data was refined using data science techniques and statistical methods to support classification. Multiple binary classification models were developed, using sand mixture parameters as inputs, to distinguish between good casting and scrap, with the k-nearest neighbours algorithm. Their performances were evaluated using various classification metrics. Additionally, recommendations were made for development of a real-time industrial application to optimize and regulate pouring temperature in the foundry process. This is based on simulating different pouring temperatures while keeping the other parameters fixed, selecting the temperature that maximizes the likelihood of successful casting
Ključne besede: gravity casting, machine learning, defects, classifier, data science
Objavljeno v DKUM: 11.03.2025; Ogledov: 0; Prenosov: 9
.pdf Celotno besedilo (518,07 KB)
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4.
Dissimilarity-driven behavior and cooperation in the spatial public goods game
Yinhai Fang, Tina Perc Benko, Matjaž Perc, Haiyan Xu, 2019, izvirni znanstveni članek

Opis: In this paper, we explore the impact of four different types of dissimilarity-driven behavior on the evolution of cooperation in the spatial public goods game. While it is commonly assumed that individuals adapt their strategy by imitating one of their more successful neighbors, in reality only very few will be awarded the highest payoffs. Many have equity or equality preferences, and they have to make do with an average or even with a low payoff. To account for this, we divide the population into two categories. One consists of payoff-driven players, while the other consists of dissimilarity-driven players. The later imitate the minority strategy in their group based on four different dissimilaritydriven behaviors. The rule that most effectively promotes cooperation, and this regardless of the multiplication factor of the public goods game, is when individuals adopt the minority strategy only when their payoff is better than that of their neighbors. If the dissimilarity-driven players adopt the minority strategy regardless of the payoffs of others, or if their payoff is the same, the population typically evolves towards a neutral state where cooperators and defectors are equally common. This may be beneficial when the multiplication factor is low, when defectors would otherwise dominate. However, if the dissimilarity-driven players adopt the minority strategy only when their payoff is worse than that of their neighbors, then cooperation is not promoted at all in comparison to the baseline case in the absence of dissimilarity-driven behavior. We explore the pattern formation behind these results, and we discuss their wider implications for the better understanding of cooperative behavior in social groups.
Ključne besede: theoretical biology, evolution, agent-based modeling, complex system, network science, evolutionary game theory
Objavljeno v DKUM: 26.02.2025; Ogledov: 0; Prenosov: 3
.pdf Celotno besedilo (5,13 MB)
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5.
Identification of influential invaders in evolutionary populations
Guoli Yang, Tina Perc Benko, Matteo Cavaliere, Jincai Huang, Matjaž Perc, 2019, izvirni znanstveni članek

Opis: The identification of the most influential nodes has been a vibrant subject of research across the whole of network science. Here we map this problem to structured evolutionary populations, where strategies and the interaction network are both subject to change over time based on social inheritance. We study cooperative communities, which cheaters can invade because they avoid the cost of contributions that are associated with cooperation. The question that we seek to answer is at which nodes cheaters invade most successfully. We propose the weighted degree decomposition to identify and rank the most influential invaders. More specifically, we distinguish two kinds of ranking based on the weighted degree decomposition. We show that a ranking strategy based on negative-weighted degree allows to successfully identify the most influential invaders in the case of weak selection, while a ranking strategy based on positive-weighted degree performs better when the selection is strong. Our research thus reveals how to identify the most influential invaders based on statistical measures in dynamically evolving cooperative communities.
Ključne besede: theoretical biology, evolution, agent-based modeling, complex system, network science, evolutionary game theory
Objavljeno v DKUM: 26.02.2025; Ogledov: 0; Prenosov: 4
.pdf Celotno besedilo (3,95 MB)
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6.
A case study on entropy-aware block-based linear transforms for lossless image compression
Borut Žalik, David Podgorelec, Ivana Kolingerová, Damjan Strnad, Štefan Kohek, 2024, izvirni znanstveni članek

Opis: Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented within , including prediction methods, string transformation methods, and inverse distance weighting, as a representative of interpolation methods. Thirty-two different greyscale raster images with varying resolutions and contents were used in the experiments. It was shown that reduces information entropy better than the popular JPEG LS and CALIC predictors. The additional information associated with each block in is then evaluated. It was confirmed that, despite this additional cost, the estimated size in bytes is smaller in comparison to the sizes achieved by the JPEG LS and CALIC predictors.
Ključne besede: computer science, information entropy, prediction, inverse distance transform, string transformations
Objavljeno v DKUM: 07.01.2025; Ogledov: 0; Prenosov: 9
.pdf Celotno besedilo (5,13 MB)

7.
Advanced tools for education : ChatGPT-based learning preparations
Dejan Zemljak, 2023, izvirni znanstveni članek

Opis: Artificial intelligence (AI) is increasingly permeating our daily lives, and the field of education is no exception. Technology already plays a significant role in education, and AI is rapidly advancing. Chatbots, for instance, have been used as a valuable tool in schools for decades. With the emergence of tools like ChatGPT, their usage has expanded even further. The presence of such tools can be highly beneficial for teachers in the educational setting. The study focused on the fact that ChatGPT can serve as an excellent support for teachers in lesson planning. The usefulness of the tool and the challenges that teachers may encounter when using it to create lesson plans were explored. The results of the study, based on the analysis of 58 lesson plans created using ChatGPT, revealed certain limitations. Therefore, it is crucial to empower teachers to make prudent use of this tool.
Ključne besede: artificial intelligence, learning preparation, technology and engineering, natural science
Objavljeno v DKUM: 10.12.2024; Ogledov: 0; Prenosov: 24
.pdf Celotno besedilo (410,59 KB)
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8.
Cross-sectional personal network analysis of adult smoking in rural areas
Bianca-Elena Mihǎilǎ, Marian-Gabriel Hâncean, Matjaž Perc, Jürgen Lerner, Iulian Oană, Marius Geanta, José Luis Molina González, Cosmina Cioroboiu, 2024, izvirni znanstveni članek

Opis: Research on smoking behaviour has primarily focused on adolescents, with less attention given to middle-aged and older adults in rural settings. This study examines the influence of personal networks and sociodemographic factors on smoking behaviour in a rural Romanian community. We analysed data from 76 participants, collected through face-to-face interviews, including smoking status (non-smokers, current and former smokers), social ties and demographic details. Multilevel regression models were used to predict smoking status. The results indicate that social networks are essential in shaping smoking habits. Current smokers were more likely to have smoking family members, reinforcing smoking within familial networks, while non-smokers were typically embedded in non-smoking environments. Gender and age patterns show that women were less likely to smoke, and older adults were more likely to have quit smoking. These findings suggest that targeted interventions should focus not only on individuals but also on their social networks. In rural areas, family-based approaches may be particularly effective due to the strong influence of familial ties. Additionally, encouraging connections with non-smokers and former smokers could help disrupt smoking clusters, supporting smoking cessation efforts.
Ključne besede: network science, human behaviour, data science, smoking, social physics
Objavljeno v DKUM: 03.12.2024; Ogledov: 0; Prenosov: 3
.pdf Celotno besedilo (1,07 MB)
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9.
The role of teacher education in the science literacy development
Eva Klemenčič, Mateja Ploj Virtič, Janja Majer Kovačič, 2023, izvirni znanstveni članek

Opis: One of the aims of education is to produce wise and responsible citizens who are aware of their impact on the environment and can address and solve daily life problems. From this point of view, science education leading to science literacy is helpful for all students, regardless of their future careers. In this paper, we first review the definitions of science literacy in the literature and present the strategies for its development. In Slovenia, we refer to the ongoing national project NA-MA POTI. Most of the strategies studied focus on primary and secondary schools. However, for the development of science literacy in primary and secondary education, teachers themselves must achieve a sufficient level of science literacy. The research was conducted with a small group of prospective teachers, focusing on three components of science literacy: asking research questions, making hypotheses, and designing an experiment. In addition, we analysed the curricula of the science didactics courses in the teacher education program. The findings show a great need for a systematic change in the curricula. Finally, proposals and ideas for improving the curricula for the didactics of science and the syllabus of the Subject teacher study program are presented.
Ključne besede: science literacy, teacher education, didactics of science, didactics of technics & technology, curricula
Objavljeno v DKUM: 02.12.2024; Ogledov: 0; Prenosov: 12
.pdf Celotno besedilo (369,52 KB)
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10.
The role of computerized laboratory exercises in development of key competences
Andrej Šorgo, Vida Lang, 2023, objavljeni znanstveni prispevek na konferenci

Opis: Over the past 20 years, the first author and numerous collaborators have attempted to introduce computer-based laboratory exercises in science, particularly in biology classes. While working with secondary and post-secondary students, it was realised that it was possible to simultaneously develop cross-cutting competencies that bridged several key competences of the European framework of eight key competences for lifelong learning. These were (a) Collecting, analysing, and organising information; (b) Communication of ideas; (c) Planning and organising activities; (d) Working with others in teams; (e) The use of mathematical ideas and techniques; (f) Problem solving; and (g) The use of technology. When inquiry and problem-solving strategies are used, student achievement is much higher compared to explanatory and expository labs.
Ključne besede: biology education, computer based laboratory, key competences, laboratory work, science education
Objavljeno v DKUM: 25.09.2024; Ogledov: 0; Prenosov: 2
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