Virtual reality has proven to be a valuable addition in the tool belt of teachers. Immersive learning environments are applied in various settings, including, but not limited to the medical and nursing domain. In this study we present “We care in VR”, a simulation for practicing nursing tasks for care at home, a part of nursing that is currently underrepresented in available VR applications. We investigate how realistic interactions are perceived by end users compared to consistent usage of buttons on the controllers and how they affect the ease of use of the simulation. We conduct an empirical study with 50 participants from three vocational schools of nursing and a university of applied sciences. Results suggest that our simulation already works quite well and is accepted by the target group, but still needs improvement regarding ease of use, especially for users without any previous experience with VR applications.

Die großen digitalen Plattformen bestimmen nicht nur die Regeln auf ihren Märkten, sondern wirken zunehmend auch auf die gesellschaftliche Sphäre ein, mit oft als äußerst schädlich empfundenen Resultaten, wie etwa der Schaffung von Filterblasen und „Rabbit Holes“ oder der unkontrollierten Verbreitung von Fake News. Immer stärker werden daher die Rufe nach einer Beschränkung der Plattformmacht, nach Plattformregulierung oder sogar Zerschlagung der dahinterstehenden Konzerne laut. Dabei stellt sich die Frage, ob entsprechende Maßnahmen nicht an anderer Stelle ansetzen sollten – etwa beim „Treibstoff“ der Plattformökonomie, den Daten.

Geht man davon aus, dass die Macht der großen Tech-Plattformen ihren Ursprung vor allem in der Sammlung von Daten und deren algorithmischer Auswertung hat, so sollte sich womöglich auch die Plattformregulierung entsprechend hieran orientieren. Während der erste Teil dieses Artikels sich mit den Grundlagen der Plattformmacht im „Überwachungskapitalismus“ beschäftigte, widmet sich dieser zweite Teil nun möglichen Alternativen zu den gegenwärtigen Regulierungsbemühungen. Diese setzen nicht bei den gegenwärtigen Marktverhältnissen, sondern bei den individuellen Datenverfügungsrechten an.

This paper investigates the practical implementation of models in the energy management of buildings for complex user behavior and the use of multiple heating technologies, focusing on the development of an accurate yet efficient model. The study is exemplified by the new Institute for Hydrogen and Energy Technology building at Hof University of Applied Sciences, designed as a research platform for innovative energy solutions. We address the integration of shading strategies and the subsequent model order reduction necessary for effective Model Predictive Control application. The research involves creating a simplified resistance-capacitance model of the building's thermal zones, including its heating systems and a dual façade with solar thermal collectors. This simplified model, generated using the BRCM Toolbox and validated against a detailed EnergyPlus model, accounts for dynamic discrepancies, particularly during periods of high solar radiation. Optimization techniques are applied to the simplified model across different seasons, revealing that season-specific optimizations are more effective for long-term simulations, while a combined optimization approach is suitable for short-term and year-round MPC applications. The results underscore the potential of advanced MPC strategies to enhance energy efficiency and sustainability in complex building systems with multiple renewable energy sources.

Es ist nahezu unmöglich, sich dem Hype um das Thema Künstliche Intelligenz (KI) zu entziehen. Insbesondere das Aufkommen der generativen KI, von Instrumenten wie ChatGPT oder Midjourney und der zugrundeliegenden großen Sprachmodelle, hat die Spielregeln für das Marketing neu definiert. Auch in der Mediaplanung, bei der Entwicklung und Umsetzung von Werbestrategien, entstehen neue Handlungsfelder, Chancen und Herausforderungen.

The density of memory cells in modern DRAM is so high that frequently accessing a memory row can flip bits in nearby rows. That effect is called Rowhammer, and an attacker can exploit this phenomenon to flip bits by rapidly accessing the contents of nearby memory rows. In recent years, researchers have developed sophisticated exploits based on this vulnerability, which enable privilege escalation on desktop computers, mobile devices, and even cloud systems without requiring any software vulnerability. However, rows are not equally vulnerable to Rowhammer. Therefore, an attacker has to massage the memory, for instance, with Page Table Entry (PTE) spraying, to increase the chance of successful exploitation. More bit flips mean the attacks become easier and faster to conduct. 

In this paper, we present Flipper, a Rowhammer amplification attack against DDR3, consisting of two components: cmpIST exploits the cmpsb and repe x86 instructions to get DRAM access with higher frequency. cmpP AR exploits the effect of hammering in multiple threads, which increases the number of bit flips found in a given time, as shown in previous work. As a result, we can increase the number of bit flips by a factor of 830 on the measured devices, even on systems featuring mitigation techniques, without using administrative privileges. We evaluate our technique on six DDR3 DIMMs. Although DDR3 memory has been superseded by DDR4 and DDR5 memory technologies, it is still widely used in devices that do not require frequent replacement, such as projectors, smart displays, servers, embedded devices, routers, and printers.

There is a huge demand for trying on clothing at home. Recent methods to capture your figure mostly work in a 2d image plane and despite recent improvements of available technology the simulation of clothing is still not satisfactory. Especially the rendering of different clothing sizes is still a major challenge, which is only addressed by COTTON [1]. We propose an improvement to this approach by adding more control over the image generation process. For this we employ a special type of conditional diffusion model, namely ControlNet, and take keypoints of the fashion as conditional input.

Wichtig ist, dass wir KI als Instrument zur Unterstützung begreifen. Der Einsatz von KI aus Marketingsicht und Möglichkeiten für den Bereich Pflege.

Weinbau dürfte zu den ältesten und traditionsreichsten Branchen der Welt gehören. Politische Umbrüche und – natürlich vor allem in jüngerer Zeit – klimatische und ökologische Veränderungen sorgen, genauso wie technische Innovationen, für stetigen Anpassungsdruck bei der Erzeugung und Vermarktung von Wein. Auch die Digitalisierung der Weinwirtschaft schreitet immer weiter voran. Künstliche Intelligenz (KI) spielt im Weinbau eine zunehmend wichtige Rolle, über alle Glieder der Wertschöpfungskette hinweg.

Große Sprachmodelle wie ChatGPT benötigen große und teure Server und viel Energie. Man kann sie aber quantisieren, sodass sie mit viel weniger Speicher und Strom auskommen und sogar lokal auf einem Smartphone laufen. Wir erklären, warum quantisierte Modelle viel schneller antworten und trotzdem fast so schlau sind wie die großen Originale.

Despite being a specific sustainable development goal (SDG), the role of consumers for sustainable consumption is still ambiguous. This is exemplified by a vast amount of research on the attitude-intention-behavior gap, which generally describes consumers’ failures to behave as sustainable as theoretically predicted. Recent reviews have prompted further investigations beyond the existing literature on factors influencing this gap. We contribute to this call by quantitatively investigating five antagonistic dimensions – both intrapsychic and situation-related – of smartphone usage and sustainable consumer behavior in Germany (n=800). Our results indicate two novel concepts. Emotional connection – i.e., consumers’ connections with the consumption experience – can either promote or prevent sustainable behavior, while exploration-driven consumerism – i.e., new purchases due to exploration tendencies – typically attenuates sustainable behavior. This illustrates how and when sustainability is outweighed by other consumer attitudes. We contextualize these results and conclude our study by highlighting limitations and further research opportunities.

Seien Sie dabei, wenn wir am Dienstag, den 17. Dezember, einen aufschlussreichen Webcast zum Thema „CX und KI - eine deutsche Perspektive“ anbieten! 🌟 Entdecken Sie die einzigartige Sichtweise auf Customer Experience (CX) und Künstliche Intelligenz (KI) aus Deutschland. Unsere Experten werden sich mit folgenden Themen befassen: Die deutsche CX-Perspektive und ihre Unterschiede zu Europa und den USA. Der deutsche KI-Ansatz mit Fokus auf Branchen, Datenoptimierung und Qualitätsmanagement. KI-Anwendungen aus der Praxis in Vertrieb, Marketing und dem öffentlichen Sektor. Die Landschaft der KI-Startups in Deutschland und der DACH-Region. Herausforderungen und Chancen der KI-Finanzierung, Regulierung und Nachhaltigkeit. Verpassen Sie nicht die Gelegenheit, wertvolle Einblicke zu gewinnen und sich mit Referenten und Experten der Branche auszutauschen! Join us for an insightful webcast on "CX and AI - A German Perspective" on Tuesday, December 17th! 🌟 Explore the unique viewpoints on Customer Experience (CX) and Artificial Intelligence (AI) from Germany. Our experts will delve into: The German CX perspective and its differences from Europe and the USA. The German AI approach, focusing on sectors, data optimization, and quality management. Real-world AI applications in sales, marketing, and the public sector. The landscape of AI startups in Germany and the DACH region. Challenges and opportunities in AI financing, regulation, and sustainability. Don't miss this opportunity to gain valuable insights and engage with industry speakers and experts!

Literature reviews play a crucial role in Information Systems (IS) research. However, scholars have expressed concerns regarding the reproducibility of their results and the quality of documentation. The involvement of human reproducers in these reviews is often hindered by the time-consuming nature of the procedures. The emergence of Large Language Models (LLMs) seems promising to support researchers and to enhance reproducibility. To explore this potential, we conducted experiments using various LLMs, focusing on abstract scanning, and have presented initial evidence suggesting that the application of LLMs in structured literature reviews could assist researchers in refining and formulating rules for abstract scanning. Based on our preliminary findings, we identify potential future research directions in this research in progress paper.

Angesichts des enormen Veränderungspotenzials, das ChatGPT, Perplexity & Co für die Arbeitswelt bergen, stellt sich die Frage, wie man diese Instrumente der generativen KI sinnvoll in den Arbeitsalltag integriert. Einen entsprechenden Analyseansatz bietet das Kategorisierungsmodell der „Cyborgs und Zentauren“.

As the output quality of neural networks in the fields of automatic speech recognition (ASR) and text-to-speech (TTS) continues to improve, new opportunities are becoming available to train models in a weakly supervised fashion, thus minimizing the manual effort required to annotate new audio data for supervised training. While weak supervision has recently shown very promising results in the domain of ASR, speech synthesis has not yet been thoroughly investigated regarding this technique despite requiring the equivalent training dataset structure of aligned audio-transcript pairs.
In this work, we compare the performance of TTS models trained using a well-curated and manually labeled training dataset to others trained on the same audio data with text labels generated using both grapheme- and phoneme-based ASR models. Phoneme-based approaches seem especially promising, since even for wrongly predicted phonemes, the resulting word is more likely to sound similar to the originally spoken word than for grapheme-based predictions.
For evaluation and ranking, we generate synthesized audio outputs from all previously trained models using input texts sourced from a selection of speech recognition datasets covering a wide range of application domains. These synthesized outputs are subsequently fed into multiple state-of-the-art ASR models with their output text predictions being compared to the initial TTS model input texts. This comparison enables an objective assessment of the intelligibility of the audio outputs from all TTS models, by utilizing metrics like word error rate and character error rate.
Our results not only show that models trained on data generated with weak supervision achieve comparable quality to models trained on manually labeled datasets, but can outperform the latter, even for small, well-curated speech datasets. These findings suggest that the future creation of labeled datasets for supervised training of TTS models may not require any manual annotation but can be fully automated.

There are many publications talking about the biases to be found in in generative AI solutions like large language models (LLMs, e.g., Mistral) or text-to-image models (T2IMs, e.g., Stable Diffusion). However, there is merely any publication to be found that questions what kind of behavior is actually desired, not only by a couple of researchers, but by society in general. Most researchers in this area seem to think that there would be a common agreement, but political debate in other areas shows that this is seldom the case, even for a single country. Climate change, for example, is an empirically well-proven scientific fact, 197 countries (including Germany) have declared to do their best to limit global warming to a maximum of 1.5°C in the Paris Agreement, but still renowned German scientists are calling LLMs biased if they state that there is human-made climate change and humanity is doing not enough to stop it. This trend is especially visible in Western individualistic societies that favor personal well-being over common good. In this article, we are exploring different aspects of biases found in LLMs and T2IMs, highlight potential divergence in the perception of ethically desirable outputs and discuss potential solutions with their advantages and drawbacks from the perspective of society. The analysis is carried out in an interdisciplinary manner with the authors coming from as diverse backgrounds as business information systems, political sciences, and law. Our contribution brings new insights to this debate and sheds light on an important aspect of the discussion that is largely ignored up to now.

Vergnügungsparks stehen vor der Herausforderung, sich in einer zunehmend digitalisierten Welt weiterzuentwickeln, um ihre Attraktivität für ein breites Publikum zu sichern. Digitale Technologien, insbesondere Virtual Reality (VR), eröffnen hier neue Perspektiven. Sie verändern nicht nur das Besuchererlebnis, sondern haben auch einen signifikanten Einfluss auf den Geschäftserfolg.