1. Achieving Environmental sustainability through theadoption of industry 4.0 : an exploratory case study withinthe information technology industryMohamed El Merroun, Istvan Janos Bartók, Osama Alkhlaifat, 2024, original scientific article Abstract: In the present-day competitive business landscape, integrating Industry 4.0 has transitioned from a choice to a necessity for companies striving to maintain their edge. Given the automation functions of IoT, the data management and transformation capabilities of AI, and the traceability benefits provided by Blockchain, this imperative is now more evident than ever. While widespread interest in Industry 4.0 is prevalent, the uncertainties surrounding the implementation process pose notable challenges. For this reason, in this paper, we present a single case study of a firm that operates in the information technology market to showcase the implementation process and how they overcome the challenges of digital transformation. Furthermore, the effect of this implementation on environmental sustainability experienced by the company and three of its customers was discussed.
Keywords: industry 4.0, artificial intelligence, environmental sustainability, digital transformation
Published in DKUM: 01.10.2024; Views: 0; Downloads: 2
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2. Metallurgical and geometric properties controlling of additively manufactured products using artificial intelligenceSnehashis Pal, Igor Drstvenšek, 2021, original scientific article Abstract: This article has presented a technical concept for producing precisely desired Additive
Manufactured (AM) metallic products using Artificial Intelligence (AI). Due to the stochastic
nature of the metallic AM process, which causes a greater variance in product properties
compared to traditional manufacturing processes, significant inaccuracies in metallurgical
properties, as well as geometry, occur. The physics behind these phenomena are related to
the melting process, bonding, cooling rate, shrinkage, support condition, part orientation.
However, by controlling these phenomena, a wide range of product features can be achieved
using the fabricating parameters. A variety of fabricating parameters are involved in the
metal AM process, but an appropriate combination of these parameters for a given material
is required to obtain an accurate and desired product. Zero defect product can be achieved
by controlling these parameters by implementing Knowledge-Based System (KBS). A suitable
combination of manufacturing parameters can be determined using mathematical tools with
AI, considering the manufacturing time and cost. The knowledge required to integrate AM
manufacturing characteristics and constraints into the design and fabricating process is beyond
the capabilities of any single engineer. Concurrent Engineering enables the integration of design
and manufacturing to enable trades based not only on product performance, but also on other
criteria that are not easily evaluated, such as production capability and support. A decision
support system or KBS that can guide manufacturing issues during the preliminary design
process would be an invaluable tool for system designers. The main objective of this paper is to
clearly describe the metal AM manufacturing process problem and show how to develop a KBS
for manufacturing process determination.
Keywords: metallurgical properties, geometry, additive manufacturing, artificial intelligence, knowledge-based system
Published in DKUM: 25.09.2024; Views: 0; Downloads: 5
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3. Abstracts of the 10th Student Computing Research Symposium (SCORES’24)2024, proceedings Abstract: The 2024 Student Computing Research Symposium (SCORES 2024), organized by the Faculty of Electrical Engineering and Computer Science at the University of Maribor (UM FERI) in collaboration with the University of Ljubljana and the University of Primorska, showcases innovative student research in computer science. This year’s symposium highlights advancements in fields such as artificial intelligence, data science, machine learning algorithms, computational problem-solving, and healthcare data analysis. The primary goal of SCORES 2024 is to provide a platform for students to present their research, fostering early engagement in academic inquiry. Beyond research presentations, the symposium seeks to create an environment where students from different institutions can meet, exchange ideas, and build lasting connections. It aims to cultivate friendships and future research collaborations among emerging scholars. Additionally, the conference offers an opportunity for students to interact with senior researchers from institutions beyond their own, promoting mentorship and broader academic networking.
Keywords: student conference, computer and information science, artificial intelligence, data science, data mining
Published in DKUM: 18.09.2024; Views: 0; Downloads: 19
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4. Digital twins in sport : concepts, taxonomies, challenges and practical potentialsTilen Hliš, Iztok Fister, Iztok Fister, 2024, review article Abstract: 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.
Keywords: artificial intelligence, digital twin, machine learning, optimization, sports, sport science
Published in DKUM: 04.09.2024; Views: 53; Downloads: 7
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5. Commit-level software change intent classification using a pre-trained transformer-based code modelTjaša Heričko, Boštjan Šumak, Sašo Karakatič, 2024, original scientific article Abstract: 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.
Keywords: software maintenance, code commit, mining software repositories, adaptive pre-training, fine-tuning, semantic code embedding, CodeBERT, GraphCodeBERT, classification, code intelligence
Published in DKUM: 14.08.2024; Views: 87; Downloads: 7
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6. 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, original scientific article Abstract: 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.
Keywords: large language models, ChatGPT, artificial intelligence, controlled experiment, object-oriented programming, software engineering education
Published in DKUM: 12.08.2024; Views: 59; Downloads: 3
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7. New challenges in scientific publications : referencing, artificial intelligence and ChatGPTIgor Švab, Zalika Klemenc-Ketiš, Saša Zupanič, 2023, other scientific articles Abstract: 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.
Keywords: scientific articles, referencing, artificial intelligence, ChatGPT, peer review, research assessment
Published in DKUM: 15.07.2024; Views: 85; Downloads: 9
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8. Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprisesMaja Rožman, Dijana Oreški, Polona Tominc, 2022, original scientific article Abstract: 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.
Keywords: artificial intelligence, talent management, employees, employee engagement, performance of the company
Published in DKUM: 03.07.2024; Views: 128; Downloads: 11
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