Offenburg University of Applied Sciences
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Analyse von Cybervorfällen
No full textDer Crowdstrike-Vorfall im Juli 2024 hat in Erinnerung gerufen, dass die Geschäftswelt eingebettet in digitalwirtschaftliche Ökosysteme hyperkonnektiv mit anderen Akteuren verbunden ist. Die daraus resultierenden Effekte machen deutlich, dass Lektionen gelernt und umgesetzt werden müssen, was eine Kenntnis der Zwischenfälle erfordert. In dieser dreiteiligen Beitragsserie wird ein Risikomodell entworfen, das zusammen mit einer Ursachenanalyse tiefere Einblicke ermöglicht. Cybervorfälle analysieren zu können, hat den Vorteil, dass zum einen die Analysen entweder selbst durchgeführt oder verfügbare Analysen angemessen eingeschätzt werden können. Im ersten Teil geht es um die Bedrohungen, die mittels der Praxisberichte der ENISA und dem MITRE Att@ck Modell näher konkretisiert werden
The Dark Side of Social Media: Systematization and Overview
Get PDFSocial media is associated with many positive aspects, such as sharing information and media with friends and relatives worldwide. However, in recent years studies have identified a large number of negative effects in connection with social media. This article presents a systematization of this dark side of social media and provides an overview of the identified dark aspects. Furthermore, the authors outline measures that can be taken to counteract these dark aspects
Wireless Fine Time Measurement Implementation and Analysis in Indoor Factory Environments
No full textPositioning and accurate time delay measurement techniques have been used with the Internet of Things (IoT) and embedded systems due to their importance in providing location information for the communicating nodes. In the last two decades, positioning techniques were introduced using the Received Signal Strength Indicator (RSSI) of Wi-Fi signals and time-based techniques. Fine Timing Measurement (FTM) is the most important time-based technique, which relies on the captured timestamps during the messaging between nodes. Thus, in addition to its originally intended application for wireless localization, it can be used for the future of Wi-Fi Time Sensitive Networking (WTSN), where low latency, low jitter, and precise time synchronization play an important role in Industrial IoT (IIOT)-oriented applications.The presented work considers FTM’s behavior and performance measurements, especially in a factory environment with different room sizes. An Automated Physical Test Bed (APTB) and emulated multipath propagation model based on ITU-R radio wave propagation standard for the factory environment are considered in the work. The results show that the FTM performance is noticeably affected by multipath signal propagation, thus increasing RTT, delay, and fluctuation in jitter and resulting in a noticeable degradation in RSSI. In contrast, the total number of correctly received frames is not affected, indicating the efficiency and reliability of the Wi-Fi FTM technique
Kinetic requirements of sudden slip-like anteroposterior perturbation recovery: a joint level perspective
No full textA Performance Scoring Approach for QoS Optimization in Private 5G Networks
No full textPerformance benchmarking is crucial for optimizing networks, including 5G Non-Public Networks (5G-NPN). Since one of the major advantages of 5G-NPN is to guarantee Quality of Service (QoS), ensuring optimal performance for their diverse applications is critical. This requires adjustments and testing of various radio-related parameters. Also, performance analysis and benchmarking need to be done based on the evaluation of relevant Key Parameter Indicators (KPIs) in order to identify the parameter set for the optimum performance for each application’s requirements. Many published results on performance benchmarking often lack transparency in their scoring methods. Additionally, QoS benchmarking evaluation for 5G-NPN use cases needs further steps due to the varying ranges of their KPIs. For example Block Error Rate (BLER) is mostly represented by percentage, while Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) are negative values.3GPP in TR 103.559 outlines practices for benchmarking network QoS, with a focus on Speech and multimedia Transmission Quality (STQ). This paper extends these outlines to evaluate 5G-NPN performance by defining a multi-objective function. We select a specific 5G-NPN use case as an example and apply four tests with varying network configurations. After each test, we collect most relevant 5G KPIs. To facilitate comparison, all KPIs are rescaled to a common scale. Additionally, we assign weights to each KPI based on its significance in the chosen use case. By combining rescaling and weight assignments, we propose a single metric that effectively characterizes the overall network performance for 5G-NPNs based on their specific use case requirements
Challenges in Inventive Design Problem Solving with Generative AI: Interactive Problem Definition, Multi-directional Prompting, and Concept Development
No full textThis paper explores the application of generative AI for systematic and inventive problem-solving in engineering design. Utilizing a multi-directional prompting approach, the study investigates the ability of AI chatbots to generate and evaluate innovative concepts based on numerous elementary solution principles. The research involved two sets of experiments with graduate and undergraduate students solving seven engineering design problems. The findings indicate that while generative AI can quickly generate a large number of ideas, it often overestimates the feasibility and usefulness of its solutions and tends to create overly complex designs. Comparisons of AI evaluations with those conducted by human participants revealed significant differences, highlighting the need for human oversight to ensure practical and contextually relevant outcomes. The experiments also revealed performance differences among various AI models, confirming a bias in self-assessment. Despite these challenges, integrating generative AI with multidirectional prompting using elementary inventive principles, primarily based on TRIZ methodology, proved effective in fostering innovative solutions. The study highlights the potential of simultaneously applying different AI models alongside human expertise to leverage the strengths of both large language models and human technical creativity
Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets
No full textThe widespread adoption of generative image models has highlighted the urgent need to detect artificial content, which is a crucial step in combating widespread manipulation and misinformation. Consequently, numerous detectors and associated datasets have emerged. However, many of these datasets inadvertently introduce undesirable biases, thereby impacting the effectiveness and evaluation of detectors.
In this paper, we emphasize that many datasets for AI-generated image detection contain biases related to JPEG compression and image size. Using the GenImage dataset, we demonstrate that detectors indeed learn from these undesired factors. Furthermore, we show that removing the named biases substantially increases robustness to JPEG compression and significantly alters the cross-generator performance of evaluated detectors. Specifically, it leads to more than 11 percentage points increase in cross-generator performance for ResNet50 and Swin-T detectors on the GenImage dataset, achieving state-of-the-art results
Same Same but Different: A Comparative Analysis of Static Type Checkers in Erlang
No full textErlang is a dynamically typed language with support for optional type annotations. Though Erlang’s type annotations were originally intended for documentation, static analysis tools soon utilized them for semantic checks. The most advanced and mature of these tools is Dialyzer, a success typing-based tool widely used in current projects. Attempts to retrofit a static type system employing the type annotations have so far remained in the realm of research prototypes. Recently, three further tools have been developed: Gradualizer, eqWAlizer, and Etylizer. But, due to a need for more semantic agreement on Erlang’s type annotations, their results differ in ways that can be challenging for users to interpret. In this paper, we cross-compare the state-of-the-art static checkers regarding their expressivity and performance on the union of their respective test suites. Unsurprisingly, we find that the tools perform best on their own test suites. While Gradualizer, Etylizer, and eqWAlizer disagree on 25% - 45% of test cases across all test suites, Dialyzer’s success-typing approach sets it apart in its interpretation of the type annotations. Our analysis emphasizes that the nature of Erlang’s type language remains challenging when it comes to develop a correct and efficient static type checker
Social Media im B2B-Marketing
No full textMut, Kreativität und Authentizität sind Schlüsselwörter, welche die B2B (Business-to-Business)-Kommunikation prägen. Die Grundlage dieses Kapitels bildet die Überzeugung, dass erfolgreiche Kommunikation im B2B mehr ist als die bloße Übermittlung von Fakten – sie schafft eine tiefe Verbindung zu den Bedürfnissen der Zielgruppe. Dies erfordert nicht nur die Fähigkeit zur präzisen Informationsübermittlung, sondern auch die geschickte Platzierung von Inhalten auf den passenden Social-Media-Plattformen. Eine Schlüsselrolle dabei spielt das Content-Marketing, denn Content muss unterhalten, informieren oder transaktionell sein – kurz gesagt: Storytelling. Diese Erzählweise ist ein mächtiges Instrument zur Beeinflussung der Wahrnehmung, besonders in einem Umfeld, in dem komplexe Produkte erklärt werden müssen. Sie ermöglicht nicht nur eine emotionale Bindung zur Zielgruppe, sondern schafft auch eine authentische Differenzierung vom Wettbewerb
Bridging Enterprise Knowledge Management and Natural Language Processing - Integration Framework and a Prototype
No full textDespite the rapid advances in AI, most organizations struggle to fully harness the potential that emerging technologies in the realm of Natural Language Processing (NLP) offer. This study deals with the particular challenge of using large language models (LLMs) to enhance the communication of organizational knowledge among employees and with external customers. Traditionally, companies rely on distributing knowledge via websites, internal documents or knowledge management systems, the use of which often proves tedious. In response, this work proposes an integration framework that helps organizations to connect the digital representations of their existing knowledge with LLMs. This integration enables intelligent retrieval and enhances semantic matching of questions and answers based on the knowledge base. Objectives for the framework are derived from insights gathered through interviews with organizations, emphasizing the practical relevance of the proposed solution, and demonstrate the utility of the framework with a prototypical implementation. This research not only represents a contribution to the ongoing research on the organizational applications of LLM-based digital technologies but also outlines the benefits and the limits of current LLM technologies for the enhancement of organizational knowledge management