This volume recaps selected empirical projects from the research group Empirical Research and User Experience (ERUX) at the Institute for Information Systems (iisys) at Hof University from 2023 to 2024. The focus is on the usability of features inside the vehicle, specifically driver assistance systems, climate control options, and situational awareness interfaces, which are designed to provide drivers with orientation and safety when transitioning from manual to automated driving modes. While the first volume of this series included a large-scale survey and primarily relied on apparatus observation using eye tracking, the research projects referenced in the current volume employ a more qualitative empirical method spectrum, ranging from expert ratings to the analysis of use cases and concept testing with numerically limited user groups. As in the first volume, a conjoint study is also included. In the automotive sector, the focus on "market" or "customer orientation" has made little noticeable progress in recent decades, and customer satisfaction with automobiles is declining. Over time, the cost-saving wave and, later, the demands and preferences of software development have determined HMI in the vehicle interior much more strongly than usability from the perspective of the drivers, who ultimately pay for the final product. Furthermore, at some OEMs, powerful design departments still have the final say on all surfaces that the driver interacts with. As a conclusion from a variety of detailed findings, it can be stated that there are still many areas for the consideration of user requirements in development. At the same time, the studies reported here show that users' ability to provide meaningful feedback on future driving situations and associated requirements is limited. This volume does not advocate for the one-sided dominance of "marketing" or "marketing research" in the development process. Rather, what has been known for decades remains true: It is essential to strike a balance between engineering-based innovations, creative design concepts, customer expectations of value, and market-driven cost requirements. The expertise required for the ongoing coordination process is what ultimately determines market success.

Die Möglichkeit, Gaststätten online zu bewerten, hat viele Leute zu „Restaurantkritikern“ gemacht. Das sind die am besten beurteilten Lokale im Hofer Land. Und: Was Gastwirte und ein Experte zu den Sternebewertungen sagen.

Wer ein Restaurant sucht, findet auf Google auch Bewertungen der Lokale. Wie aussagekräftig sind diese?

In der digitalen Marketinglandschaft entwickelt sich künstliche Intelligenz zum Game-Changer für Kundenbindungsstrategien. Wie Andreas Wagener in seinem Fachbeitrag "Wie KI das Loyalty Marketing verändert" aufzeigt, haben Unternehmen wie Starbucks, Sephora und Nike bereits innovative KI-gestützte Loyalty-Konzepte etabliert. Grundsätzlich werden 4 innovative Lösungsansätze durch KI identifiziert:

• Tiefere Personalisierung: KI-Systeme können Kundenpräferenzen präziser analysieren und maßgeschneiderte Angebote entwickeln
• Präzise Kundenvorhersagen: Frühzeitige Erkennung von Abwanderungsrisiken und automatisierte Rückgewinnungsmaßnahmen
• Effizientere Kommunikation: Optimale Ansprache zum richtigen Zeitpunkt über den bevorzugten Kanal
• Gamification: Spielerische Elemente erhöhen das Engagement und die emotionale Bindung

Für das KI-Briefing erläutert der Experte, welche spezifischen Potenziale sich daraus für Verlage ergeben. 

This study explores the integration of Industry 5.0 principles into existing Industry 4.0 configurations to address excessive heat dissipation, energy efficiency and promote carbon neutral manufacturing. Industry 5.0 represents a paradigm shift, emphasizing human-centered approaches, energy efficiency and environmental sustainability alongside traditional productivity goals. The case study analyzes energy consumption in an Industry 4.0 setup, quantifies heat waste and applies innovative solutions to optimize energy use. Considering digital manufacturing through the Industrial Internet of Things (IIoT), big data analytics, smart automation technologies in cyber-physical production systems and sustainable practices, the study aims to mitigate and minimize energy waste and carbon emissions. Through quantitative analysis, it identified energy inputs, mapped energy transformations, and assessed energy waste hotspots. Despite all the benefits of Industry 4.0, it showed that responsible practices and new initiatives are required to reuse and capitalize on that wasted energy or heat, underscoring the importance of integrating Industry 5.0 principles for sustainable manufacturing.

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.

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.

Map matching is a widely used technology for assigning tracks recorded by Global Navigation Satellite Systems (GNSS) to existing road networks. Due to the measurement uncertainty of GNSS positions, the biggest challenge is to map them accurately. To develop and verify suitable algorithms for map matching, ground truth, i.e., the traveled routes in the road network, is required as a reference. However, GNSS recorded tracks naturally lack the ground truth routes. Providing this data is time-consuming and costly in these cases, as it requires manual correction of the routes based on human memorization. This is not practical on a large scale, e.g., with floating car data (FCD). This is why there exist only a few isolated ground truth data sets that were created in this way for map matching. To close this gap, we introduce and evaluate in this work a new open source tool-chain for artificially generating large amounts of simulated ground truth routes for map matching. Based on these routes, we generate simulated FCD and we apply comparably authentic and parameterizable artificial GNSS noise with varying noise characteristics. The generated data allows to thoroughly evaluate and improve the performance of existing map matching algorithms and facilitates in future research the development of novel algorithms based on sufficiently large and diverse labeled data. In this work, we evaluate different scenarios of varying noise characteristics of our artificially generated ground truth data to compare the robustness, individual strengths, and weaknesses of existing open source map matching implementations. Our new approach of artificially generating ground truth data for map matching addresses the existing lack of sufficient available reference data for ongoing map matching research.

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. (This work is a part of the project (M4-SKI) has been supported and funded by the European Regional Development Fund (ERDF)).

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