1. Digital twins in sport : concepts, taxonomies, challenges and practical potentialsTilen Hliš, Iztok Fister, Iztok Fister, 2024, pregledni znanstveni članek Opis: Digital twins belong to ten of the strategic technology trends according to the Gartner list from 2019, and have encountered a big expansion, especially with the introduction of Industry 4.0. Sport, on the other hand, has become a constant companion of the modern human suffering a lack of a healthy way of life. The application of digital twins in sport has brought dramatic changes not only in the domain of sport training, but also in managing athletes during competitions, searching for strategical solutions before and tactical solutions during the games by coaches. In this paper, the domain of digital twins in sport is reviewed based on papers which have emerged in this area. At first, the concept of a digital twin is discussed in general. Then, taxonomies of digital twins are appointed. According to these taxonomies, the collection of relevant papers is analyzed, and some real examples of digital twins are exposed. The review finishes with a discussion about how the digital twins affect changes in the modern sport disciplines, and what challenges and opportunities await the digital twins in the future.
Ključne besede: artificial intelligence, digital twin, machine learning, optimization, sports, sport science
Objavljeno v DKUM: 04.09.2024; Ogledov: 52; Prenosov: 2
 Celotno besedilo (4,08 MB) |
2. Commit-level software change intent classification using a pre-trained transformer-based code modelTjaša Heričko, Boštjan Šumak, Sašo Karakatič, 2024, izvirni znanstveni članek Opis: Software evolution is driven by changes made during software development and maintenance. While source control systems effectively manage these changes at the commit level, the intent behind them are often inadequately documented, making understanding their rationale challenging. Existing commit intent classification approaches, largely reliant on commit messages, only partially capture the underlying intent, predominantly due to the messages’ inadequate content and neglect of the semantic nuances in code changes. This paper presents a novel method for extracting semantic features from commits based on modifications in the source code, where each commit is represented by one or more fine-grained conjoint code changes, e.g., file-level or hunk-level changes. To address the unstructured nature of code, the method leverages a pre-trained transformer-based code model, further trained through task-adaptive pre-training and fine-tuning on the downstream task of intent classification. This fine-tuned task-adapted pre-trained code model is then utilized to embed fine-grained conjoint changes in a commit, which are aggregated into a unified commit-level vector representation. The proposed method was evaluated using two BERT-based code models, i.e., CodeBERT and GraphCodeBERT, and various aggregation techniques on data from open-source Java software projects. The results show that the proposed method can be used to effectively extract commit embeddings as features for commit intent classification and outperform current state-of-the-art methods of code commit representation for intent categorization in terms of software maintenance activities undertaken by commits.
Ključne besede: software maintenance, code commit, mining software repositories, adaptive pre-training, fine-tuning, semantic code embedding, CodeBERT, GraphCodeBERT, classification, code intelligence
Objavljeno v DKUM: 14.08.2024; Ogledov: 82; Prenosov: 5
Celotno besedilo (1,65 MB) |
3. Computer science education in ChatGPT Era: experiences from an experiment in a programming course for novice programmersTomaž Kosar, Dragana Ostojić, Yu David Liu, Marjan Mernik, 2024, izvirni znanstveni članek Opis: The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students’ performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.
Ključne besede: large language models, ChatGPT, artificial intelligence, controlled experiment, object-oriented programming, software engineering education
Objavljeno v DKUM: 12.08.2024; Ogledov: 56; Prenosov: 2
Celotno besedilo (492,37 KB) |
4. New challenges in scientific publications : referencing, artificial intelligence and ChatGPTIgor Švab, Zalika Klemenc-Ketiš, Saša Zupanič, 2023, drugi znanstveni članki Opis: The COVID-19 pandemic has led to a surge in scientific publications, some of which have bypassed the usual peer-review processes, leading to an increase in unsupported claims being referenced. Therefore, the need for references in scientific articles is increasingly being questioned. The practice of relying solely on quantitative measures, such as impact factor, is also considered inadequate by many experts. This can lead to researchers choosing research ideas that are likely to generate favourable metrics instead of interesting and important topics. Evaluating the quality and scientific value of articles requires a rethinking of current approaches, with a move away from purely quantitative methods. Artificial intelligence (AI)-based tools are making scientific writing easier and less time-consuming, which is likely to further increase the number of scientific publications, potentially leading to higher quality articles. AI tools for searching, analysing, synthesizing, evaluating and writing scientific literature are increasingly being developed. These tools deeply analyse the content of articles, consider their scientific impact, and prioritize the retrieved literature based on this information, presenting it in simple visual graphs. They also help authors to quickly and easily analyse and synthesize knowledge from the literature, prepare summaries of key information, aid in organizing references, and improve manuscript language. The language model ChatGPT has already greatly changed the way people communicate with computers, bringing it closer to human communication. However, while AI tools are helpful, they must be used carefully and ethically.In summary, AI has already changed the way we write articles, and its use in scientific publishing will continue to enhance and streamline the process.
Ključne besede: scientific articles, referencing, artificial intelligence, ChatGPT, peer review, research assessment
Objavljeno v DKUM: 15.07.2024; Ogledov: 85; Prenosov: 8
Celotno besedilo (211,38 KB)
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5. Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprisesMaja Rožman, Dijana Oreški, Polona Tominc, 2022, izvirni znanstveni članek Opis: The purpose of the paper is to create a multidimensional talent management model with embedded aspects of artificial intelligence in the human resource processes to increase employees' engagement and performance of the enterprise. The research was implemented on a sample of 317 managers/owners in Slovenian enterprises. Multidimensional constructs of the model include several aspects of artificial intelligence implementation in the organization's activities related to human resource management in the field of talent management, especially in the process of acquiring and retaining talented employees, appropriate training and development of employees, organizational culture, leadership, and reducing the workload of employees, employee engagement and performance of the enterprise. The results show that AI supported acquiring and retaining a talented employees, AI supported appropriate training and development of employees, appropriate teams, AI supported organizational culture, AI supported leadership, reducing the workload of employees with AI have a positive effect on performance of the enterprise and employee engagement. The results will help managers or owners create a successful work environment by implementing artificial intelligence in the enterprise, leading to increased employee engagement and performance of the enterprise. Namely, our results contribute to the efficient implementation of artificial intelligence into an enterprise and give owners or top managers a broad insight into the various aspects that must be taken into account in business management in order to increase employee engagement and enterprise’s competitive advantage.
Ključne besede: artificial intelligence, talent management, employees, employee engagement, performance of the company
Objavljeno v DKUM: 03.07.2024; Ogledov: 123; Prenosov: 8
Celotno besedilo (1,63 MB)
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8. Science teachers’ approach to contemporary assessment with a reading literacy emphasisMaja Kerneža, Dejan Zemljak, 2023, izvirni znanstveni članek Opis: In a sample of 1215 teachers, this study examined the readiness of science educators for assessment in the rapidly evolving landscape of artificial intelligence in education. Participants responded to an online questionnaire during the emergency remote teaching phase, offering insights into the frequency and nature of assessment methods utilized. The research draws a connection between assessment techniques during remote teaching and the emergence of AI in education. The results show that the selected assessment methods vary across teachers, with some specific differences observed in the assessment practices of science teachers. The study underscores the critical role of reading literacy in enhancing student engagement in contemporary learning environments. Moreover, the findings suggest that continuous professional development significantly improves the readiness of (science) teachers for AI-enhanced assessment. Drawing from these insights, recommendations for subsequent research are delineated.
Ključne besede: artificial intelligence, assessment, reading literacy, science teachers, teacher training
Objavljeno v DKUM: 08.05.2024; Ogledov: 180; Prenosov: 12
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9. Tool condition monitoring using machine tool spindle current and long short-term memory neural network model analysisNiko Turšič, Simon Klančnik, 2024, izvirni znanstveni članek Opis: In cutting processes, tool condition affects the quality of the manufactured parts. As such, an essential component to prevent unplanned downtime and to assure machining quality is having information about the state of the cutting tool. The primary function of it is to alert the operator that the tool has reached or is reaching a level of wear beyond which behaviour is unreliable. In this paper, the tool condition is being monitored by analysing the electric current on the main spindle via an artificial intelligence model utilising an LSTM neural network. In the current study, the tool is monitored while working on a cylindrical raw piece made of AA6013 aluminium alloy with a custom polycrystalline diamond tool for the purposes of monitoring the wear of these tools. Spindle current characteristics were obtained using external measuring equipment to not influence the operation of the machine included in a larger production line. As a novel approach, an artificial intelligence model based on an LSTM neural network is utilised for the analysis of the spindle current obtained during a manufacturing cycle and assessing the tool wear range in real time. The neural network was designed and trained to notice significant characteristics of the captured current signal. The conducted research serves as a proof of concept for the use of an LSTM neural network-based model as a method of monitoring the condition of cutting tools.
Ključne besede: tool condition monitoring, artificial intelligence, LSTM neural network
Objavljeno v DKUM: 22.04.2024; Ogledov: 180; Prenosov: 13
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10. Understanding conversational interaction in multiparty conversations: the EVA CorpusIzidor Mlakar, Darinka Verdonik, Simona Majhenič, Matej Rojc, 2023, izvirni znanstveni članek Ključne besede: corpora and language resources, speech corpus, multimodal corpus, pragmatics, conversational intelligence
Objavljeno v DKUM: 10.04.2024; Ogledov: 285; Prenosov: 9
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