Driving simulators are essential for the development of vehicle sys-tems, as they enable safe and efficient user engagement. Their validity determines the extent to which the results from empirical user studies obtained in driving simulators can be transferred to real-world driving situations.

This study examines the gaze behavior of participants in a within-subject de-sign, both in a real vehicle and in a driving simulator with a digital road replica. Using gaze-point plots and expert ratings, we compare fixation patterns across three road sections (City Drive, Rural Drive, Highway). The results visually in-dicate a moderate to high similarity in gaze distributions, suggesting consistent fixation patterns in both environments, with some notable exceptions on an indi-vidual level and generally highest matches in the City Drive.

However, further statistical analyses are necessary to quantitatively confirm similarities and assess systematic differences.

This study examines how Large Language Models (LLMs) can reduce biases in text-to-image generation systems by modifying user prompts. We define bias as a model's unfair deviation from population statistics given neutral prompts. Our experiments with Stable Diffusion XL, 3.5 and Flux demonstrate that LLM-modified prompts significantly increase image diversity and reduce bias without the need to change the image generators themselves. While occasionally producing results that diverge from original user intent for elaborate prompts, this approach generally provides more varied interpretations of underspecified requests rather than superficial variations. The method works particularly well for less advanced image generators, though limitations persist for certain contexts like disability representation. All prompts and generated images are available at https://iisys-hof.github.io/llm-prompt-img-gen/

This paper considers three fundamental approaches to software development, namely manual coding, modeldriven software engineering, and code generation by large language models. All of these approaches have their individual pros and cons, motivating the desire for an integrated approach. We present MoProCo, a technical solution to integrate the three approaches into a single tool chain, allowing the developer to split a software engineering task into modeling, prompting or coding sub-tasks. From a single input file consisting of static model structure, natural language prompts and/or source code fragments, Java source code is generated using a two-stage approach. A case study demonstrates that the MoProCo approach combines the desirable properties of the three development approaches by offering the appropriate level of abstraction, determinism, and dynamism for each specific software engineering sub-task.

Similar to LLMs, the development of vision language models is mainly driven by English datasets and models trained in English and Chinese language, whereas support for other languages, even those considered high-resource languages such as German, remains significantly weaker. In this work we present an analysis of open-weight VLMs on factual knowledge in the German and English language. We disentangle the image-related aspects from the textual ones by analyzing accuracy with jury-as-a-judge in both prompt languages and images from German and international contexts. We found that for celebrities and sights, VLMs struggle because they are lacking visual cognition of German image contents. For animals and plants, the tested models can often correctly identify the image contents according to the scientific name or English common name but fail in German language. Cars and supermarket products were identified equally well in English and German images across both prompt languages.

Nach einer Studie des spanischen Center of the Governance of Change würde ein Viertel der Befragten Europäer es bevorzugen, dass politische Entscheidungen eher von einer KI als von Politikern aus Fleisch und Blut getroffen werden würden. Damit würde der Endpunkt einer Entwicklung zunehmender Mechanisierung gesellschaftlicher Prozesse beschrieben, die vor allem durch den Rückgriff auf Technologien wie Maschinelles Lernen und Blockchain möglich wird. Führt dies zu mehr in Verwaltung und Staat oder befinden wir uns damit auf dem Weg in eine Dystopie?

HP_sim&app25 - Carnot User Meeting 2025, Bayreuth, Germany

HP_sim&app25 - Carnot User Meeting 2025, Bayreuth

KI eröffnet für das Marketing und die Mediaplanung neue Möglichkeiten. Marken, die die analytische und operative Effizienz intelligenter Systeme nutzen wollen, sollten aber einiges beachten. Was, das erläutert Andreas Wagener, Professor für Digitales Marketing an der Hochschule Hof.