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Abstract: This paper delineates the contemporary landscape, challenges, and prospective developments in human-centred artificial intelligence (AI) within the ambit of smart farming, a pivotal element of the emergent Agriculture 5.0, supplanting Agriculture 4.0. Analogous to Industry 4.0, agriculture has witnessed a trend towards comprehensive automation, often marginalizing human involvement. However, this approach has encountered limitations in agricultural contexts for various reasons. While AI’s capacity to assume human tasks is acknowledged, the inclusion of human expertise and experiential knowledge (human-in-the-loop) often proves indispensable, corroborated by the Moravec’s Paradox: tasks simple for humans are complex for AI. Furthermore, social, ethical, and legal imperatives necessitate human oversight of AI, a stance strongly reflected in the European Union’s regulatory framework. Consequently, this paper explores the advancements in human-centred AI focusing on their application in agricultural processes. These technological strides aim to enhance crop yields, minimize labor and resource wastage, and optimize the farm-to-consumer supply chain. The potential of AI to augment human decision-making, thereby fostering a sustainable, efficient, and resilient agri-food sector, is a focal point of this discussion - motivated by the current worldwide extreme weather events. Finally, a framework for Agriculture 5.0 is presented, which balances technological prowess with the needs, capabilities, and contexts of human stakeholders. Such an approach, emphasizing accessible, intuitive AI systems that meaningfully complement human activities, is crucial for the successful realization of future Agriculture 5.0.
Keywords: human-centered AI, smart framing, agriculture 5.0, digital transformation, artificial intelligence
Published in DKUM: 23.04.2025; Views: 0; Downloads: 3
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Abstract: 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.
Keywords: digital language resources, native language, language improvement, perceived usefulness, perceived ease of use
Published in DKUM: 27.03.2025; Views: 0; Downloads: 2
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Abstract: Since the recent introduction of several viable vaccines for SARS-CoV-2, vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. We argue that game theory and social network models should be used to guide decisions pertaining to vaccination programmes for the best possible results. In the months following the introduction of vaccines, their availability and the human resources needed to run the vaccination programmes have been scarce in many countries. Vaccine hesitancy is also being encountered from some sections of the general public. We emphasize that decision-making under uncertainty and imperfect information, and with only conditionally optimal outcomes, is a unique forte of established game-theoretic modelling. Therefore, we can use this approach to obtain the best framework for modelling and simulating vaccination prioritization and uptake that will be readily available to inform important policy decisions for the optimal control of the COVID-19 pandemic.
Keywords: COVID-19, evolutionary game theory, digital epidemiology, vaccination, social network, public goods game, social physics
Published in DKUM: 28.02.2025; Views: 0; Downloads: 3
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Abstract: Background: While anxiety disorders are one of the most prevalent mental diseases, they are often overlooked due to shortcomings of the existing diagnostic procedures, which predominantly rely on self-reporting. Due to recent technological advances, this source of information could be complemented by the so-called observable cues – indicators that are displayed spontaneously through individuals’ physiological responses or behaviour and can be detected by modern devices. However, while there are several individual studies on such cues, this research area lacks a synthesis. In line with this, our scoping review aimed to identify observable cues that offer meaningful insight into individuals’ anxiety and to determine how these cues can be measured. Methods: We followed the PRISMA guidelines for scoping reviews. The search string containing terms related to anxiety and observable cues was entered into four databases (Web of Science, MEDLINE, ERIC, IEEE). While the search – limited to English peer-reviewed records published from 2012 onwards – initially yielded 2311 records, only 33 articles fit our selection criteria and were included in the final synthesis. Results: The scoping review unravelled various categories of observable cues of anxiety, specifically those related to facial expressions, speech and language, breathing, skin, heart, cognitive control, sleep, activity and motion, location data and smartphone use. Moreover, we identified various approaches for measuring these cues, including wearable devices, and analysing smartphone usage and social media activity. Conclusions: Our scoping review points to several physiological and behavioural cues associated with anxiety and highlights how these can be measured. These novel insights may be helpful for healthcare practitioners and fuel future research and technology development. However, as many cues were investigated only in a single study, more evidence is needed to generalise these findings and implement them into practice with greater confidence.
Keywords: anxiety, observable cues, digital biomarkers, scoping review, physiological cues, behavioural cues
Published in DKUM: 07.02.2025; Views: 0; Downloads: 5
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Abstract: Purpose:The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods. Design/methodology/approach: The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model. Findings: Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts. Research limitations/implications: The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians. Practical implications: The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases. Social implications: The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable. Originality/value: These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.
Keywords: digital library, artificial intelligence, machine learning, text classification, older texts, Universal Decimal Classification
Published in DKUM: 28.01.2025; Views: 0; Downloads: 5
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