31. Sprachspiele als Motivationsfaktor im DaF-Unterricht mit jugendlichen und jungen Erwachsenen : Einsatzmöglichkeiten von Sprachelementspielen im WortschatzerwerbBrigita Kacjan, 2007, doktorska disertacija Opis: Ausgehend von einem interdisziplinären Studium eng miteinander verwobener wissenschaftlicher Disziplinen wird in der vorliegenden Dissertation theoretisch untersucht und belegt, wie der Wortschatzerwerb bzw. das Wortschatzlemen bei Jugendlichen und jungen Erwachsenen verläuft und wie Sprachelementspiele sinnvoll in diesen Prozess integriert werden können. Die so eruierten Paradigmen bilden die Grundlage fUr die Entwicklung einer Sprachspieltypologie, die sich speziell mit dem institutionellen Wortschatzerwerb hei Jugendlichen und jungen Erwachsenen beschäftigt, Anhand einer empirischen Fallstudie wird die Effizienz einiger ausgewählter Sprachelementspiele in der Praxis mit jugendlichen Deutschlernem überprüft. Vor dem Hintergrund der theoretischen und empirischen Erkenntnisse werden die aufgestellten Hypothesen im Bezug auf den motivationalen Charakter von Sprachspielen bzw. Sprachelementspielen auf ihre Gilltigkeit überprüft. Schließlich werden noch ein Fragenkatalog und die entwickelte Sprachspieltypologie als Werkzeuge angeboten, die es einer DaF-Lehrkraft ermöglichen, Sprachelementspiele sinnvoll und zielgerichtet in ihrem DaF-Unterricht mit Jugendlichen und jungen Erwachsenen einzusetzen. Dies alles wird durch die genau beschriebene, erklärte und begründete Schrittabfolge des Wortschatzerwerbs, den ausgearbeiteten Fragenkatalog und die entwickelte Sprachspieltypologie ermöglicht.
Ključne besede: German, foreign languages, didactics, vocabulary, language games, motivation, cognition, learning, adolescents, dissertations
Objavljeno v DKUM: 27.05.2025; Ogledov: 0; Prenosov: 8
 Celotno besedilo (9,70 MB) |
32. The OpenScience Slovenia metadata datasetMladen Borovič, Marko Ferme, Janez Brezovnik, Sandi Majninger, Albin Bregant, Goran Hrovat, Milan Ojsteršek, 2020, drugi znanstveni članki 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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33. LLM in the loop: a framework for contextualizing counterfactual segment perturbations in point cloudsVeljka Kočić, Niko Lukač, Dzemail Rozajac, Stefan Schweng, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Javier Del Ser, Andreas Holzinger, 2025, izvirni znanstveni članek 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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34. A brief review on benchmarking for large language models evaluation in healthcareLeona Cilar Budler, Hongyu Chen, Aokun Chen, Maxim Topaz, Wilson Tam, Jiang Bian, Gregor Štiglic, 2025, pregledni znanstveni članek Opis: This paper reviews benchmarking methods for evaluating large language models (LLMs) in healthcare settings. It highlights the importance of rigorous benchmarking to ensure LLMs' safety, accuracy, and effectiveness in clinical applications. The review also discusses the challenges of developing standardized benchmarks and metrics tailored to healthcare-specific tasks such as medical text generation, disease diagnosis, and patient management. Ethical considerations, including privacy, data security, and bias, are also addressed, underscoring the need for multidisciplinary collaboration to establish robust benchmarking frameworks that facilitate LLMs' reliable and ethical use in healthcare. Evaluation of LLMs remains challenging due to the lack of standardized healthcare-specific benchmarks and comprehensive datasets. Key concerns include patient safety, data privacy, model bias, and better explainability, all of which impact the overall trustworthiness of LLMs in clinical settings.
Ključne besede: artificial intelligence, benchmarking, chatbots, healthcare, large language models, natural language processing
Objavljeno v DKUM: 12.05.2025; Ogledov: 0; Prenosov: 3
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35. Analysis and synthesis of theoretical and practical implications of case management model and notationMateja Bule, Gregor Polančič, 2025, izvirni znanstveni članek Opis: Case Management Model and Notation (CMMN) is a graphical notation used to model less predictable, highly flexible processes that may behave differently in each instance. It uses an event-centred approach and expands on what can be modelled with procedural modelling notations. Nearly a decade since the occurrence of CMMN, its practical use is questionable. We performed this research to identify possible reasons for this and to classify the potential advantages and disadvantages of CMMN. With the aforementioned objectives, we conducted a systematic literature review, which provided a broad insight into the state of the investigated object along with techniques for analysing qualitative data, coding, and successive approximation. From an initial set of 942 articles, 43 remain relevant. The results of the analysis and synthesis of the obtained data from relevant articles were generalised codes, which were used to explicitly answer the research questions. The results indicate that CMMN has good foundations in the declarative modelling approach and within the Case Management paradigm. Nevertheless, some issues were identified with the notation and elements of CMMN and with its complement—Business Process Model and Notation (BPMN).
Ključne besede: CMMN, case management, BPMN, declarative modelling, flexibility, visual language
Objavljeno v DKUM: 23.04.2025; Ogledov: 0; Prenosov: 3
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36. On the use of morpho-syntactic description tags in neural machine translation with small and large training corporaGregor Donaj, Mirjam Sepesy Maučec, 2022, izvirni znanstveni članek Opis: With the transition to neural architectures, machine translation achieves very good quality for several resource-rich languages. However, the results are still much worse for languages
with complex morphology, especially if they are low-resource languages. This paper reports the
results of a systematic analysis of adding morphological information into neural machine translation
system training. Translation systems presented and compared in this research exploit morphological
information from corpora in different formats. Some formats join semantic and grammatical information and others separate these two types of information. Semantic information is modeled using
lemmas and grammatical information using Morpho-Syntactic Description (MSD) tags. Experiments
were performed on corpora of different sizes for the English–Slovene language pair. The conclusions
were drawn for a domain-specific translation system and for a translation system for the general
domain. With MSD tags, we improved the performance by up to 1.40 and 1.68 BLEU points in the
two translation directions. We found that systems with training corpora in different formats improve
the performance differently depending on the translation direction and corpora size.
Ključne besede: neural machine translation, POS tags, MSD tags, inflected language, data sparsity, corpora size
Objavljeno v DKUM: 28.03.2025; Ogledov: 0; Prenosov: 11
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37. Evolution of domain-specific modeling language: an example of an industrial case study on an RT-sequencerTomaž Kos, Marjan Mernik, Tomaž Kosar, 2022, izvirni znanstveni članek Opis: Model-driven engineering is a well-established software development methodology that
uses models to develop applications where the end-users with visual elements model abstractions
from a specific domain. These models are based on domain-specific modeling language (DSML),
which is particular to the problem domain. During DSML use, new ideas emerge and DSMLs evolve.
However, reports on DSML evolution are rare. This study presents a new DSML called RT-Sequencer
that evolved from our DSML Sequencer to support, in addition to the Data Acquisition domain,
also a new domain—Real-Time Control (RTC) systems. The process of defining models with a new
language RT-Sequencer has changed in a way that new end-users were introduced—advanced endusers, which use general-purpose language (GPL) and advanced programming concepts to define
modeling environments for the RT-Sequencer end-users. More specifically, an industrial experience
with the RT-Sequencer is presented, where DSML was opened for extension so that a GPL code
could be inserted into the model to create new visual blocks for the end-user, and the possibility to
adapt and optimize the execution code for a particular task. Our experience shows the specific case
of DSML evolution supporting another problem domain, and the implementation effort needed to
extend domain-specific modeling language with GPL support.
Ključne besede: model-driven engineering, domain-specific modeling languages, measurement systems, Real-Time Control systems, data acquisition, language evolution, experience report
Objavljeno v DKUM: 27.03.2025; Ogledov: 0; Prenosov: 15
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38. Analysis of it solutions to improve the inclusiveness of foreign language speakers : master's thesisNikola Vilar Jordanovski, 2025, magistrsko delo Opis: This thesis explores how information technologies can enhance linguistic inclusivity in a
globalized society, where language barriers are increasingly evident due to greater
mobility and cultural interconnectedness. The empirical section compares ten IT
solutions, five offline and five using artificial intelligence, by translating ten common
email messages into five languages. The analysis focuses on translation accuracy,
reliability, and user experience. User surveys provided additional insights into challenges
and desired functionalities for greater inclusivity.
Findings indicate that AI-based solutions like ChatGPT and DeepL achieve better
contextual accuracy. The thesis suggests that future development of these solutions
should emphasize cultural nuances and contextual precision to improve accessibility and
inclusivity for speakers of different languages.
Ključne besede: inclusiveness, foreign language speakers, IT solutions for inclusiveness, multilingualism
Objavljeno v DKUM: 27.03.2025; Ogledov: 0; Prenosov: 30
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39. Usefulness of digital language resources in improving native language among adultsSuzana Žilič Fišer, Jani Pavlič, Ines Kožuh, 2022, izvirni znanstveni članek Opis: Important keys to effective communication are language competences, which can be
supported by using digital language resources. These usually assist the acquisition of a second
language, despite their potential for improving one’s native language. Our study was, thus, aimed at
raising awareness about the possibilities of improving the native language of an adult population
by using digital language resources for the Slovenian language. We conducted workshops, a survey
and, partly, semi-structured interviews with 124 participants. We examined whether the perceived
usefulness and ease of using digital language resources depends on age, education, self-assessed
language proficiency, and experience with language training. The analysis revealed that self-initiative
use of analogue language resources is related positively to using digital ones for seeking information,
improving language use, as well as for study or work. Moreover, self-assessed proficiency in language
was found to affect the perceived ease of using digital language resources. These findings may help
language professionals support developing language skills by using digital language resources and
preserving language in an adult population.
Ključne besede: digital language resources, native language, language improvement, perceived usefulness, perceived ease of use
Objavljeno v DKUM: 27.03.2025; Ogledov: 0; Prenosov: 6
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