Ein wesentliches Potential von VR als Mensch-Maschine-Schnittstelle liegt in der Möglichkeit, dem Nutzer die Illusion der Anwesenheit in der dargestellten Virtuellen Welt zu suggerieren. Ob und wie gut dies gelingt, ist nicht nur ein technisches Problem, sondern beruht auch auf Prozessen der menschlichen Wahrnehmung zur Interpretation der dargebotenen Sinnesreize. Zum besseren Verständnis der damit verbundenen Fragestellungen werden in diesem Kapitel grundlegende Kenntnisse aus dem Bereich der menschlichen Informationsverarbeitung behandelt. Von besonderem Interesse in einer Virtuellen Umgebung sind die Raumwahrnehmung und die Wahrnehmung von Bewegung, auf die spezifisch eingegangen wird. Basierend auf diesen Grundlagen werden VR-typische Phänomene und Probleme diskutiert, wie z. B. das Sehen von Doppelbildern oder Cybersickness. Dabei kann jeweils das Wissen um menschliche Wahrnehmungsprozesse sowohl zur Erklärung dieser Phänomene wie auch zur Ableitung von Lösungsstrategien genutzt werden. Schließlich wird in diesem Kapitel gezeigt, wie sich verschiedene Limitierungen der menschlichen Wahrnehmung ausnutzen lassen, um die Qualität und die Nutzererfahrung während einer VR-Session zu verbessern.
Automated Program Repair (APR) proposes bug fixes to aid developers in maintaining software. The state of the art in this domain focuses on LLMs, leveraging their strong capabilities to comprehend specifications in natural language and to generate program code. However, despite the APR community's research achievements and industry deployments, APR still cannot generalize broadly. In this work, we present an intensive empirical evaluation of LLMs' capabilities in APR. We evaluate a diverse set of 13 recent open and closed models. In particular, we explore language-agnostic repair by utilizing benchmarks for Java, JavaScript, Python, and PHP. Besides the generalization across languages and levels of patch complexity, we also investigate the effects of fault localization (FL). Our key results include: (1) Different LLMs tend to perform best for different languages, which makes it hard to develop cross-platform, single-LLM repair techniques. (2) Combining models by pooling repairs adds value with respect to uniquely fixed bugs, so a committee of expert models should be considered. (3) Under realistic assumptions of imperfect FL, we observe significant drops in accuracy from the usual practice of using perfect FL. Our insights will help develop reliable and generalizable APR techniques and evaluate them in realistic and fair environments.
Generative artificial intelligence has the potential to improve productivity and transform the production of creative content. However, existing research indicates that image generation models are significantly influenced by biases. This work investigates the inherent biases and language-induced biases present in text-to-image models within the context of occupation-related image generation, complementing established metrics with human preference feedback. We present a comprehensive evaluation of five current text-to-image models: Midjourney v6.1, Stable Diffusion 3 Medium, DALL-E 3, Playground v2.5, and FLUX.1-dev , focusing on gender and ethnicity bias, image quality, and prompt alignment. To facilitate this evaluation, we developed the "Battle-Arena for Fair Image Synthesis" (BAFIS), a platform designed to collect human feedback on bias in generated images. Furthermore, we created a dataset comprising 21,140 synthetic images generated using multilingual prompts, which serves as a basis for our analysis. We further place our results within a broader social context by comparing them to official statistics from the German Federal Employment Agency. Our findings reveal systematic biases in text-to-image models, with established evaluation metrics in partial correlation with subjective user ratings. Thus, our research emphasizes the need for including human preferences to develop fairer and more inclusive text-to-image models.
