1. Human-centered ai in smart farming : toward agriculture 5.0Andreas Holzinger, Iztok Fister, Iztok Fister, Peter Kaul, Senthold Asseng, 2024, original scientific article 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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2. Sensors and artificial intelligence methods and algorithms for human - computer intelligent interaction: a systematic mapping studyBoštjan Šumak, Saša Brdnik, Maja Pušnik, 2022, original scientific article Abstract: To equip computers with human communication skills and to enable natural interaction
between the computer and a human, intelligent solutions are required based on artificial intelligence
(AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing
the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer
intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence,
and identify potential directions for future research. We conduct a systematic mapping study of the
HCII body of research. Four hundred fifty-four studies published in various journals and conferences
between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have
primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using
sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and
others. Researchers most often apply deep-learning and instance-based AI methods and algorithms.
The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition,
primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN)
is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture
recognition solutions.
Keywords: human–computer intelligent interaction, intelligent user interfaces, IUI, sensors, artificial intelligence
Published in DKUM: 31.03.2025; Views: 0; Downloads: 3
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3. Influence of highly inflected word forms and acoustic background on the robustness of automatic speech recognition for human–computer interactionAndrej Žgank, 2022, original scientific article Abstract: Automatic speech recognition is essential for establishing natural communication with
a human–computer interface. Speech recognition accuracy strongly depends on the complexity of
language. Highly inflected word forms are a type of unit present in some languages. The acoustic
background presents an additional important degradation factor influencing speech recognition
accuracy. While the acoustic background has been studied extensively, the highly inflected word
forms and their combined influence still present a major research challenge. Thus, a novel type of
analysis is proposed, where a dedicated speech database comprised solely of highly inflected word
forms is constructed and used for tests. Dedicated test sets with various acoustic backgrounds were
generated and evaluated with the Slovenian UMB BN speech recognition system. The baseline word
accuracy of 93.88% and 98.53% was reduced to as low as 23.58% and 15.14% for the various acoustic
backgrounds. The analysis shows that the word accuracy degradation depends on and changes
with the acoustic background type and level. The highly inflected word forms’ test sets without
background decreased word accuracy from 93.3% to only 63.3% in the worst case. The impact of
highly inflected word forms on speech recognition accuracy was reduced with the increased levels of
acoustic background and was, in these cases, similar to the non-highly inflected test sets. The results
indicate that alternative methods in constructing speech databases, particularly for low-resourced
Slovenian language, could be beneficial.
Keywords: human–computer interaction, automatic speech recognition, acoustic modeling, highly inflected word forms, acoustic background
Published in DKUM: 28.03.2025; Views: 0; Downloads: 2
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4. Is the Hitchcock story really true? Public opinion on Hooded crows in cities as input to managementLászló Kövér, Petra Paládi, Isma Benmazouz, Andrej Šorgo, Natalija Špur, Lajos Juhász, Peter Czine, Péter Balogh, Szabolcs Lengyel, 2022, original scientific article Abstract: In recent years, the Hooded crow (Corvus cornix) has become one of the most successful wild bird species in urban environments across Europe. Hooded crows can cause several problems in cities, including trash scattering, noise disturbance, and aggressive behavior toward humans or pets, and they can be potential vectors of pathogens. To find effective solutions, the public has to be involved in the decision-making process in urban planning management, managed by the city administration. In this study, we surveyed the attitude of people in Hungary towards crows and crow management by collecting information using an online questionnaire containing 65 questions published in 14 Facebook groups. We found that many people were familiar with corvid species and had personal experience with them. In most cases, these experiences were not negative, so the crows were not or only rarely perceived to cause problems to people, such as aggressive behavior, damage to cars or stealing something. Most respondents recognized that the presence of large numbers of hooded crows is a problem to be solved and acknowledged that they do not know how to resolve it. The majority of people expressed their interest in raising public awareness of crows but not in their management actions, which they believe should be implemented by experts. Most respondents preferred passive, harmless methods. More direct methods such as egg/chick removal from the nest, control by trapping, poisoned baits or firearms, or oral contraceptives were the least acceptable. These results express the difficulty in identifying a control method for managing hooded crow populations that is both acceptable to most people and effective at the same time. This study demonstrates the importance of involving public opinion in wildlife management and providing more information to citizens to reduce human-crow conflicts.
Keywords: attitude, control, corvids, human-wildlife conflict, urban wildlife management
Published in DKUM: 27.03.2025; Views: 0; Downloads: 2
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5. 44th International Conference on Organizational Science Development : Human Being, Artificial Intelligence and Organization, Conference Proceedings2025, proceedings of peer-reviewed scientific conference contributions (international and foreign conferences) Abstract: The 44th International Scientific Conference on the Development of Organisational Science was focused on developing and advancing knowledge in the organisational sciences, with a focus on the contemporary challenges and opportunities of our time. On the one hand, it is humans who have woven the knowledge of organisations and will continue to enrich the knowledge of organisations in the future. On the other hand, we need to take into account the situational factors and the wider environment that are intrinsic to understanding organisations. The title of this year's conference is: Human being, Artificial Intelligence and the Organisation. The society we live in today is going through a period of great change in various areas of our lives. Although our pace sometimes stops, the forces of the environment do not. The pace of change often no longer surprises us. But the pillars of our action, the achievements of human society, are something of which we can be justly proud. Artificial intelligence is one of the forces that has entered our everyday lives in many places in recent times. Where are the opportunities and where are the dangers of artificial intelligence, where is human intelligence still a significant step ahead of artificial intelligence?
Keywords: organization, human being, artificial intelligence, changes, organizational development
Published in DKUM: 20.03.2025; Views: 0; Downloads: 28
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6. The NASA-TLX approach to understand workers workload in humanrobot collaborationAljaž Javernik, Borut Buchmeister, Robert Ojsteršek, 2023, original scientific article Abstract: Human-robot collaboration (HRC) is becoming increasingly widespread in today's production systems, as it can contribute to achieving more efficient and flexible production systems. Given the growing importance of HRC, this paper addresses the significance of human workload in HRC. To study workers workload an experiment was conducted using NASA-TLX questionnaire. The experiment featured two scenarios involving the same operation but varying robot motion parameters. Recognizing that individual differences contribute to success of collaboration, the experiment considered worker utilization in relation to robot motion parameters. To ensure the credibility of the experimental results, the robot motion parameters were adjusted to each individual in order to achieve the same conditions and utilization at all participants. Results revealed that worker utilization, in conjunction with robot motion parameters significantly influenced worker workload. The results highlight the need for personalized guidelines in collaborative workplaces that emphasize individual differences in abilities, skills and personalities to increase overall well-being and robot and worker productivity.
Keywords: human-robot collaboration, industry 5.0, collaborative workplace, NASA-TLX, safety awareness, worker well-being, worker workload
Published in DKUM: 10.03.2025; Views: 0; Downloads: 4
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7. Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactualsAndreas Holzinger, Niko Lukač, Dzemail Rozajac, Emil Johnston, Veljka Kocic, Bernhard Hoerl, Christoph Gollob, Arne Nothdurft, Karl Stampfer, Stefan Schweng, Javier Del Ser, 2025, original scientific article Abstract: This paper introduces a novel framework for augmenting explainability in the interpretation of point cloud data by fusing expert knowledge with counterfactual reasoning. Given the complexity and voluminous nature of point cloud datasets, derived predominantly from LiDAR and 3D scanning technologies, achieving interpretability remains a significant challenge, particularly in smart cities, smart agriculture, and smart forestry. This research posits that integrating expert knowledge with counterfactual explanations – speculative scenarios illustrating how altering input data points could lead to different outcomes – can significantly reduce the opacity of deep learning models processing point cloud data. The proposed optimization-driven framework utilizes expert-informed ad-hoc perturbation techniques to generate meaningful counterfactual scenarios when employing state-of-the-art deep learning architectures. The optimization process minimizes a multi-criteria objective comprising counterfactual metrics such as similarity, validity, and sparsity, which are specifically tailored for point cloud datasets. These metrics provide a quantitative lens for evaluating the interpretability of the counterfactuals. Furthermore, the proposed framework allows for the definition of explicit interpretable counterfactual perturbations at its core, thereby involving the audience of the model in the counterfactual generation pipeline and ultimately, improving their overall trust in the process. Results demonstrate a notable improvement in both the interpretability of the model’s decisions and the actionable insights delivered to end-users. Additionally, the study explores the role of counterfactual reasoning, coupled with expert input, in enhancing trustworthiness and enabling human-in-the-loop decision-making processes. By bridging the gap between complex data interpretations and user comprehension, this research advances the field of explainable AI, contributing to the development of transparent, accountable, and human-centered artificial intelligence systems.
Keywords: explainable AI, point cloud data, counterfactual reasoning, information fusion, interpretability, human-centered AI
Published in DKUM: 06.03.2025; Views: 0; Downloads: 4
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8. Cataract progression associated with modifications in calcium signaling in human lens epithelia as studied by mechanical stimulationMarko Gosak, Dajana Gojić, Elena Spasovska, Marko Hawlina, Sofija Andjelić, 2021, original scientific article Keywords: human eye lens, lens epithelial cells, calcium signaling, mechanical stimulation, mechanical stimulation, calcium waves, paracrine signaling
Published in DKUM: 14.02.2025; Views: 0; Downloads: 2
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9. Reasons for Facebook usage : data from 46 countriesMarta Kowal, Piotr Sorokowski, Agnieszka Sorokowska, Małgorzata Dobrowolska, Katarzyna Pisanski, Anna Oleszkiewicz, Toivo Aavik, Grace Akello, Charlotte Alm, Naumana Amjad, Maja Zupančič, Tina Kavčič, Bojan Musil, Nejc Plohl, Afifa Anjum, Kelly Asao, Chiemezie Atama, Derya Atamtürk Duyar, Richard Ayebare, Mons Bendixen, Aicha Bensafia, Boris Bizumic, Mahmoud Boussena, David M. Buss, Marina Butovskaya, Seda Can, Katarzyna Cantarero, Antonin Carrier, Hakan Cetinkaya, Daniel Conroy-Beam, Marco A. C. Varella, Rosa M. Cueto, Marcin Czub, Daria Dronova, Seda Dural, Izzet Duyar, Berna Ertugrul, Agustín Espinosa, Ignacio Estevan, Carla S. Esteves, Tomasz Frackowiak, Jorge Contreras-Graduño, Farida Guemaz, Ivana Hromatko, Chin-Ming Hui, Iskra Herak, Jas L. Jaafar, Feng Jiang, Konstantinos Kafetsios, Leif Edward Ottesen Kennair, Nicolas Kervyn, Nils C. Köbis, András Láng, Georgina R. Lennard, Ernesto León, Torun Lindholm, Giulia Lopez, Mohammad Madallh Alhabahba, Alvaro Mailhos, Zoi Manesi, Rocío Martínez, Sarah L. McKerchar, Norbert Meskó, Girishwar Misra, Hoang Moc Lan, Conal Monaghan, Emanuel C. Mora, Alba Moya Garófano, George Nizharadze, Elisabeth Oberzaucher, Mohd S. Omar Fauzee, Ike E. Onyishi, Baris Özener, Ariela F. Pagani, Vilmante Pakalniskiene, Miriam Parise, Farid Pazhoohi, Mariia Perun, Annette Pisanski, Camelia Popa, Pavol Prokop, Muhammad Rizwan, Mario Sainz, Svjetlana Salkicević, Ruta Sargautyte, Susanne Schmehl, Oksana Senyk, Rizwana Shaikh, Shivantika Sharad, Franco Simonetti, Meri Tadinac, Truong Thi Khanh Ha, Trinh Thi Linh, Karina Ugalde González, Nguyen Van Luot, Christin-Melanie Vauclair, Luis D. Vega, Gyesook Yoo, Stanislava Yordanova Stoyanova, Zainab F. Zadeh, 2020, other scientific articles Keywords: online social networks, Facebook, cross cultural psychology, cross cultural differences, human sex differences, age differences, motives
Published in DKUM: 27.01.2025; Views: 0; Downloads: 8
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10. Cross-sectional personal network analysis of adult smoking in rural areasBianca-Elena Mihǎilǎ, Marian-Gabriel Hâncean, Matjaž Perc, Jürgen Lerner, Iulian Oană, Marius Geanta, José Luis Molina González, Cosmina Cioroboiu, 2024, original scientific article Abstract: 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.
Keywords: network science, human behaviour, data science, smoking, social physics
Published in DKUM: 03.12.2024; Views: 0; Downloads: 3
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