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While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects’ health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject’s movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better.
In der Schriftenreihe „Voneinander Lehren lernen“ publiziert das LearningCenter der Hochschule Osnabrück anwendungsbezogene Beiträge zur Qualitätsentwicklung in Studium und Lehre. Die Schriftenreihe ist an das Konzept des „Scholarship of Teaching and Learning“ (SoTL) angelehnt. Demnach soll sie insbesondere den Fachlehrenden verschiedener Studiengänge als Plattform dienen, um ihre eigenen Erfahrungen, Ideen und Konzepte zur Lehr- und Studiengangentwicklung systematisch zu reflektieren und entsprechende Erkenntnisse für andere nutzbar zu machen. Ziel ist es, den Diskurs über hochschuldidaktische Themen in die Fächer zu tragen und so die Qualität der Lehr-Lernprozesse in den Studiengängen zu fördern. In diesem fünften Band der Schriftenreihe werden Projekte der Hochschule Osnabrück beschrieben, deren Umsetzung durch verschiedene Förderlinien oder durch Studienqualitätsmittel unterstützt wurde. Die Textbeiträge sind sowohl inhaltlich als auch didaktisch-methodisch sehr vielschichtig. Eine Gemeinsamkeit liegt jedoch darin, dass sie jeweils eine konstruktive hochschuldidaktische Reaktion auf zukunftsbezogene Trends und daraus resultierende Kompetenz-Anforderungen an Hochschulabsolvent*innen widerspiegeln. Der Terminologie des Zukunftsinstituts folgend sind es primär die Megatrends „Konnektivität“, „New Work“, „Gesundheit“, „Wissenskultur“ und „Globalisierung“, die in den Beiträgen implizit oder explizit thematisiert werden.
Studies on nutrition have historically concentrated on food-shortages and over-nutrition. The physiological states of feeling hungry or being satiated and its dynamics in food choices, dietary patterns, and nutritional behavior, have not been the focus of many studies. Currently, visual analytic using easy-to-use tooling offers applicability in a wide-range of disciplines. In this interdisciplinary pilot-study we tested a novel visual analytic software to assess dietary patterns and food choices for greater understanding of nutritional behavior when hungry and when satiated. We developed software toolchain and tested the hypotheses that there is no difference between visual search patterns of dishes 1) when hungry and when satiated and 2) in being vegetarian and non-vegetarian. Results indicate that food choices can be deviant from dietary patterns but correlate slightly with dish-gazing. Further, scene perception probably could vary between being hungry and satiated. Understanding t he complicated relationship between scene perception and nutritional behavioral patterns and scaling up this pilot-study to a full-study using our introduced software approaches is indispensable.
In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.
Compliance of agricultural AI systems : app-based legal verification throughout the development
(2024)
Significant advances in artificial intelligence (AI) have been achieved; however, practical implementation in agriculture remains limited. Compliance with emerging regulations, such as the EU AI Act and GDPR, is now vital, even for non-critical AI systems. Developers need tools to assess legal compliance, which is complex, often requiring full legal advice. To address this issue, we are developing a support app that simplifies the legal aspects of AI system development, covering the entire lifecycle, from conception to distribution. The current app, which covers the key legal area of copyright and will soon include GDPR and the AI Act, aims to bridge the gap between AI research and agriculture. An evaluation of our app by experts from both the legal and the IT domains shows that the app assists the developers so that they make legally correct statements. Consequently, it promotes legal compliance and awareness among developers, contributing to the seamless integration of AI into agriculture. The need for compliant AI systems in various industries, including agriculture, will only increase as regulations evolve.
Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data
problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.
Over 200 georeferenced registered rephotographic compilations of the Faroe Islands are provided in this dataset. The position of each compilation is georeferenced and thus locatable on a map. Each compilation consists of a historical and a corresponding contemporary image showing the same scene. With steady object features, these two images of the same geolocation are aligned pixel accurately. In the summer of 2022, all contemporary images were photographed by A. Schaffland, while historical images were retrieved from the National Museum of Denmark collections.
Images show Faroese landscape and cultural heritage sites, focusing on relevant areas when the historical images were taken, e.g., Kirkjubøur, Tórshavn, and Saksun. Historic images date from the end of the 19th century to the middle of the 20th century. The historical images were taken by scientists, surveyors, archaeologists, and painters.
All historical images are in the public domain, have no known rights, or are shared under a CC license. The contemporary images by A. Schaffland are released under CC BY-NC-SA 4.0.
The dataset is organized as a GIS project. Historic images, not already georeferenced, were referenced with street view services. All historical images were added to the GIS database, containing camera position, viewing direction, etc. Each compilation can be displayed as an arrow from the camera position along the view direction on a map. Contemporary images were registered to historical images using a specialized tool. None or only a suboptimal rephotograph could be taken for some historical images. These historical images are still added to the database together with all other original images, providing additional data for improvements in rephotography methods in the upcoming years.
The resulting image pairs can be used in image registration, landscape change, urban development, and cultural heritage research. Further, the database can be used for public engagement in heritage and as a benchmark for further rephotography and time-series projects.
Guaranteeing and improving crop yields is a major challenge to securing the worldwide food supply. Farmers need to be aware of the soil structure to produce crops under drought and improve the resilience of soils towards extreme weather events. Therefore, this paper introduces a high-fidelity smartphone app prototype for simple soil structure analysis. The primary motivation of such an app is to enable farmers to perform soil structure assessments without the need for expensive sensors and advise the farmers on which actions lead to an optimal soil structure. The app can further be used to build a database of soil structure images, enabling computer vision-based artificial intelligence to automate soil structure analysis in the future. Understanding the soil will help to reach the goal of zero hunger formulated by the UN's Sustainable Development Goal (SDG) 2, and can make agriculture more sustainable.