Modern DRAM is susceptible to fault attacks that undermine the entire system’s security. The most well-studied disturbance effect is Rowhammer, where an attacker repeatedly opens and closes (i.e., hammers) different rows, which can lead to bitflips in adjacent rows. Different hammering strategies include double-sided, hammering two rows sandwiching a victim row, and one-location, hammering a single row. One-location Rowhammer requires no physical address information, as any location in memory is mapped to a DRAM row, and no relation between rows is required for hammering. The recently discovered Rowpress differs from Rowhammer by not hammering rows but keeping them open longer, evident by a disjoint set of affected memory locations.

In this paper, we examine the differences between four attack variants: one-location Rowhammer, a one-location Rowpress variant we developed, double-sided Rowhammer, and double-sided Rowpress on a set of 12 DDR4 modules. Our methodology is to hammer and press the exact same set of physical memory locations in all attack variants. Surprisingly, our results show that on 4 out of 12 DDR4 modules, we were only able to reproduce double-sided Rowhammer but none of the other attack variants. On 2 DDR4 modules, we were able to reproduce all attack variants. We find that the number of unique bitflip locations ranges from 161 to 15 612, when hammering the exact same set of physical memory locations. Our one-location Rowhammer attack induces roughly the same amount of bitflips as double-sided Rowhammer, however, only 61.8 % of bitflip locations overlap. We explain this by one-location Rowhammer inducing bitflips due to the Rowhammer as well as the Rowpress effect, making the differentiation of both methods difficult, therefore, calling it Presshammer. Based on our observed bitflips, we develop the first end-to-end one-location Rowpress attack. One-location Rowpress requires only minimal physical address information that an attacker can acquire through a same-row same-bank side-channel attack. Our end-to- end attack escalates to kernel privileges within less than 10 minutes.

Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering in German and English language, which requires models to provide elaborate answers without external document retrieval (RAG). The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best English MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. The best German model also surpasses ChatGPT for the equivalent dataset. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.

This book summarizes studies conducted by the Empirical Research and User Experience (ERUX) research group at the Institute for Information Systems (iisys) at Hof University of Applied Sciences over the period 2019 to 2022. The main topic focuses on “The Human-Machine-Interface in Advanced Driver Assistance Systems towards Autonomous Driving”, investigated using the example of automatic parking assistant in cars. A broad spectrum of methods was used to ensure comprehensive analyses and valid results. Starting situation for the studies has been the industry's realization that the demand for parking assistants had fallen short of expectations. Moreover, they concluded that a host of driving assistant system owners´ do not use them. This indicates an unsatisfactory acceptance of new technologies on the part of car drivers. The aim of the studies was therefore to determine the reasons for this restraint, as well as to derive measures to increase acceptance of this systems among end customers.

The first study focused on operators and the restraint of German drivers towards driver assistance systems. The results of this quantitative study displayed that although the modernity of these systems has been identified, the functional enhancements they offer are insufficient. In order to overcome this discrepancy, manufacturers are advised to emphasize the functional advantages more strongly when communication with the end customer. Study also suggested that direct experience can significantly increase customers' willingness to purchase such a system. Therefore, it is of pivotal importance to introduce customers to the new technologies in a targeted manner.

Using conjoint measurement, a second study illustrates driver assistance systems already representing a significant benefit when purchasing a car, even though the trust in fully autonomous vehicles is still restricted. Drivers favor established car manufacturer in terms of the development of autonomous vehicles. The third study utilizes eye-tracking to examine operating patterns of parking assistance systems. Moreover, it derives potential for improvement in terms of intuitive design and user experience. Fourthly, alternative scenarios for the design of parking assistants are evaluated in a concept test. This indicated the vital importance of simple and acquainted operating options.

Across all methods, the customer actually being able to experience driving assistance systems is a decisive matter for the acceptance of these new technologies. Therefore, manufacturers should emphasize their focus on getting customers in touch with these systems, as well as offering them targeted usage options. In addition, the integration of customer feedback into development efforts is essential to produce intuitive, user-friendly user interfaces and thus promote the diffusion process of new technologies.

Dieses Buch rekapituliert Studien der Forschungsgruppe "Empirical Research and User Experience (ERUX)" am Institut für Informationssysteme (iisys) der Hochschule Hof über den Zeitraum 2019 bis 2022. Das zentrale Thema umfasst die Mensch- Maschine-Schnittstelle bei Fahrerassistenzsystemen auf dem Weg zum autonomen Fahren, untersucht am Beispiel des automatischen Einparkassistenten im Automobil. Zum Einsatz kam dabei ein breites Methodenspektrum, um eine umfassende Analyse und valide Ergebnisse zu gewährleisten.

Ausgangslage der Studien ist die Erkenntnis der Industrie, dass die Nachfrage nach Parkassistenten hinter den Erwartungen zurückgeblieben ist und viele Besitzer solcher Systeme diese nicht nutzen. Dies deutet auf eine unbefriedigende Akzeptanz neuer Technologien seitens der Autofahrer hin. Ziel der dargestellten Studien war es daher, die Gründe für diese Zurückhaltung zu ermitteln und Maßnahmen zur Steigerung der Akzeptanz bei den Endkunden abzuleiten.

Die erste Studie fokussiert sich auf Treiber und Vorbehalte deutscher Autofahrer gegenüber Fahrerassistenzsystemen. Im Ergebnis dieser quantitativen Studie zeigt sich, dass die Modernität dieser Systeme zwar wahrgenommen wird, die von ihnen ausgehenden funktionalen Verbesserungen jedoch unzureichend. Um diese Diskrepanz zu überwinden, empfiehlt sich eine stärkere Betonung funktionaler Vorteile in der Kommunikation seitens der Hersteller. Zudem zeigt sich, dass direkte Erfahrungen die Kaufbereitschaft der Kunden signifikant steigern können. Essentiell ist daher eine gezielte Heranführung der Kunden an die neuen Technologien.

Mittels Conjoint Measurement wird in einer zweiten Studie herausgearbeitet, dass Fahrerassistenzsysteme bereits einen erheblichen Nutzenbeitrag beim Autokauf darstellen, allerdings ist das Vertrauen in vollkommen autonome Fahrzeuge noch begrenzt. Bei der Entwicklung autonomer Fahrzeuge präferieren Autofahrer etablierte Automobilhersteller.

Die dritte Studie analysiert mittels Eye-Tracking Bedienmuster von Parkassistenz-systemen und leitet daraus Verbesserungspotenziale in Bezug auf intuitives Design und Nutzererfahrung ab. Zum Vierten werden in einem Konzepttest alternative Szenarien für die Gestaltung von Parkassistenten evaluiert. Hierbei zeigt sich die Wichtigkeit einfacher und vertrauter Bedienungsmöglichkeiten.

Methodenübergreifend ist die entscheidende Rolle realer Erfahrungen für die kundenseitige Akzeptanz neuer Technologien zu konstatieren. Hersteller sollten daher verstärkt auf das Erlebnis ihrer Kunden setzen und diesen gezielte Nutzungsmöglich-keiten offerieren. Zudem ist die Integration von Kundenfeedback in die Entwicklungsbemühungen essentiell, um intuitive, nutzerfreundliche User Interfaces zu produzieren und auf diese Weise den Diffusionsprozess der neuen Technologien zu fördern.

Our proposed visual dashboard aims to facilitate training progress evaluation in sports like skateboarding and foil pumping by offering real-time sensor and camera data processing through machine learning-based pose estimation. The web-based application, developed in HTML, CSS, and JavaScript utilizing p5js and Google Charts frameworks, enables athletes to visualize and analyze their movements for efficient and safe performance without the need for app installation on various devices. This paper will delve into the advantages and challenges of the web-based approach, detailing data structures, synchronization techniques, and plans for future enhancements like Web BLE connectivity and improved user experience across different devices. (This work is a part of the project (M4-SKI) has been supported and funded by the European Regional Development Fund (ERDF)).