Hochschule Bonn-Rhein-Sieg
Publikationsserver der Hochschule Bonn-Rhein-Sieg - pub H-BRSNot a member yet
7939 research outputs found
Sort by
A5/1 is in the Air: Passive Detection of 2G (GSM) Ciphering Algorithms
No full textThis paper investigates the ongoing use of the A5/1 ciphering algorithm within 2G GSM networks. Despite its known vulnerabilities and the gradual phasing out of GSM technology by some operators, GSM security remains relevant due to potential downgrade attacks from 4G/5G networks and its use in IoT applications. We present a comprehensive overview of a historical weakness associated with the A5 family of cryptographic algorithms. Building on this, our main contribution is the design of a measurement approach using low-cost, off-the-shelf hardware to passively monitor Cipher Mode Command messages transmitted by base transceiver stations (BTS). We collected over 500,000 samples at 10 different locations, focusing on the three largest mobile network operators in Germany. Our findings reveal significant variations in algorithm usage among these providers. One operator favors A5/3, while another surprisingly retains a high reliance on the compromised A5/1. The third provider shows a marked preference for A5/3 and A5/4, indicating a shift towards more secure ciphering algorithms in GSM networks
Covalent binding of thioredoxin to TXNIP is required for diet-induced insulin resistance in the liver
Get PDFHepatic insulin resistance is an important pathophysiology in type 2 diabetes, and the mechanisms by which high-caloric diets induce insulin resistance are unclear. Among vertebrate animals, mammals have retained a unique molecular change that allows an intracellular arrestin domain-containing protein called Thioredoxin-Interacting Protein (TXNIP) to bind covalently to thioredoxin, allowing TXNIP to "sense" oxidative stress(1). Here, we show that a single cysteine in TXNIP mediates the development of hepatic insulin resistance in the setting of a high-fat diet (HFD). Mice with an exchange of TXNIP Cysteine 247 for Serine (C247S) showed improved whole-body and hepatic insulin sensitivity compared to wild-type (WT) controls following an 8-week HFD. HFD-fed TXNIP C247S mouse livers also showed improved insulin signaling. The Transmembrane 7 superfamily member 2 (Tm7sf2) gene encodes for a sterol reductase involved in the process of cholesterol biosynthesis (2). We identified TM7SF2 as a potential mediator of enhanced insulin signaling in HFD-fed TXNIP C247S mouse livers. TM7SF2 increased Akt phosphorylation and suppressed gluconeogenic markers PCK1 and G6Pc specifically under oxidative-stress-induced conditions in HepG2 cells. We also present data suggesting that a heterozygous variant of TXNIP C247 is well-tolerated in humans. Thus, mammals have a single redox-sensitive amino acid in TXNIP that mediates insulin resistance in the setting of a HFD. Our results reveal an evolutionarily conserved mechanism for hepatic insulin resistance in obesity. Hepatic insulin resistance is an important pathophysiology in type 2 diabetes, and the mechanisms by which high-caloric diets induce insulin resistance are unclear. Among vertebrate animals, mammals have retained a unique molecular change that allows an intracellular arrestin domain-containing protein called Thioredoxin-Interacting Protein (TXNIP) to bind covalently to thioredoxin, allowing TXNIP to "sense" oxidative stress. Here, we show that a single cysteine in TXNIP mediates the development of hepatic insulin resistance in the setting of a high-fat diet (HFD). Mice with an exchange of TXNIP Cysteine 247 for Serine (C247S) showed improved whole-body and hepatic insulin sensitivity compared with WT controls following an 8-week HFD. HFD-fed TXNIP C247S mouse livers also showed improved insulin signaling. The Transmembrane 7 Superfamily Member 2 (Tm7sf2) gene encodes for a sterol reductase involved in the process of cholesterol biosynthesis. We identified TM7SF2 as a potential mediator of enhanced insulin signaling in HFD-fed TXNIP C247S mouse livers. TM7SF2 increased Akt phosphorylation and suppressed gluconeogenic markers PCK1 and G6Pc specifically under oxidative stress-induced conditions in HepG2 cells. We also present data suggesting that a heterozygous variant of TXNIP C247 is well tolerated in humans. Thus, mammals have a single redox-sensitive amino acid in TXNIP that mediates insulin resistance in the setting of an HFD. Our results reveal an evolutionarily conserved mechanism for hepatic insulin resistance in obesity
The industry known as ‘media development’
Get PDFFor cosmopolitan communication studies, media development is a field of utmost relevance. Whenever organizations—traditionally from the Global North—intervene in the media systems and journalism cultures of transformation or (post-)conflict societies, different concepts and norms of news and news making collide. Mostly “Western” actors—state- or privately funded, from major global actors like the NGO Internews to small but highly visible foundations like the Swiss-based Fondation Hirondelle—address media practitioners and newsrooms that act in profoundly different political and economic contexts—even if they share normative concepts of journalism with donors
Arm's Platform Security Architecture (PSA) Attestation Token
No full textArm's Platform Security Architecture (PSA) is a family of hardware and firmware security specifications, along with open-source reference implementations, aimed at helping device makers and chip manufacturers integrate best-practice security into their products. Devices that comply with PSA can generate attestation tokens as described in this document, which serve as the foundation for various protocols, including secure provisioning and network access control. This document specifies the structure and semantics of the PSA attestation token.
The PSA attestation token is a profile of the Entity Attestation Token (EAT). This specification describes the claims used in an attestation token generated by PSA-compliant systems, how these claims are serialized for transmission, and how they are cryptographically protected.
This Informational document is published as an Independent Submission to improve interoperability with Arm's architecture. It is not a standard nor a product of the IETF
Natural Language Processing–Based Technologies Along the Customer Journey—A Systematic Review and Co‐Occurrence Analysis
Get PDFInteractions between consumers and companies are increasingly relying on technologies such as chatbots and voice assistants that are based on natural language processing (NLP) techniques. With the advent of more sophisticated technologies such as transformers and generative artificial intelligence, this trend will likely continue and further solidify. To our knowledge, this study is the first to systematically review the current scientific discourse on NLP-based technologies in the context of the customer journey and attempts to outline existing knowledge and identify gaps before the onset of a new era in NLP sophistication. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and co-occurrence analysis, we offer new and nuanced insights into the prevailing discourse. From a sample of 734 articles, 41 studies were selected and analyzed. Our findings shed light on the current research focus, exploring various technologies, concepts, and challenges. We also offer a starting point for how emerging NLP-based technologies could impact the customer journey, as well as future research directions
Are Learning-Based Approaches Ready for Real-World Indoor Navigation? A Case for Imitation Learning
No full textTraditional indoor robot navigation methods provide a reliable solution when adapted to constrained scenarios, but lack flexibility or require manual re-tuning when deployed in more complex settings. In contrast, learning-based approaches learn directly from sensor data and environmental interactions, enabling easier adaptability. While significant work has been presented in the context of learning navigation policies, learning-based methods are rarely compared to traditional navigation methods directly, which is a problem for their ultimate acceptance in general navigation contexts. In this work, we explore the viability of imitation learning (IL) for indoor navigation, using expert (joystick) demonstrations to train various navigation policy networks based on RGB images, LiDAR, and a combination of both, and we compare our IL approach to a traditional potential field-based navigation method. We evaluate the approach on a physical mobile robot platform equipped with a 2D LiDAR and a camera in an indoor university environment. Our multimodal model demonstrates superior navigation capabilities in most scenarios, but faces challenges in dynamic environments, likely due to limited diversity in the demonstrations. Nevertheless, the ability to learn directly from data and generalise across layouts suggests that IL can be a practical navigation approach, and potentially a useful initialisation strategy for subsequent lifelong learning
Efficacy of EA575 as an Antitussive and Mucoactive Agent in Preclinical In Vivo Models
No full textBackground: The efficacy of EA575 in the treatment of respiratory diseases is described in various clinical studies, improving patients’ disease-related symptoms. However, mechanistic in vivo data proving its beneficial effects are limited. Methods: Focusing on the treatment of acute airway inflammation and accompanying cough, this study aimed to elucidate antitussive and mucoactive properties of EA575, applying two animal models. Animals were treated orally twice daily for 7 days, resulting in 43, 215.2, or 430.5 mg/kg bw/d of EA575. Antitussive effects were investigated within an acute lung inflammation model of bleomycin-treated guinea pigs after citric acid exposure. Hereby, the number of coughs, enhanced pause (penH), and bronchoalveolar lavage fluid (BALF) were investigated. Mucoactivity of EA575 was assessed within a murine model, determining phenol red concentration in BALF. Results: EA575 treatment within the acute lung inflammation model reduced cough events up to 56% while reducing inflammatory cell influx in BALF dose-dependently, e.g., reducing neutrophils in BALF up to 70.9%. This suggests a strong connection between anti-inflammatory and antitussive properties of EA575. Furthermore, penH decreased in a dose-dependent manner, suggesting an ease in respiration. Mucoactivity was shown by a dose-dependent increase in phenol red concentration in BALF up to 38.9%. Notably, EA575/salbutamol co-administration resulted in enhanced phenol red secretion compared to respective single administrations. Conclusions: These data highlight the benefits of EA575 in treating cough-related respiratory diseases, particularly when accompanied by sputum, as EA575 has been shown to obtain mucoactivity. Furthermore, the combinatory effect of EA575/salbutamol treatment provides a foundation for future research in the treatment of chronic respiratory diseases
A grounded theory of how consumers determine the veracity of online user reviews
No full textConsumers use online reviews to decide which products to purchase. Cybercriminals produce fake reviews to influence unknowing consumers into buying products of lower quality, which can lead to financial, emotional and physical damage. However, there is still limited understanding of how consumers assess the veracity of online reviews, or incorporate online reviews into purchasing decisions, especially outside of laboratory settings. Therefore, this study uses a grounded theory approach to explore how consumers determine the veracity and trustworthiness of online user reviews. Twenty-five interviews with consumers were held to identify veracity cues, thought processes and other markers of online shopping behaviour. The results show that consumers use online reviews differently depending on context (e.g. product value, consumer knowledge). Our findings support the development of a theory suggesting that consumers evaluate reviews through a two-step process. First, consumers scan the review for relevance and then subsequently evaluate trustworthiness, credibility, and veracity. The different deception cues that are used by consumers are also identified and classified. These findings offer new insights of how consumers identify fake reviews online
Interpretable Deepfake Voice Detection: A Hybrid Deep-Learning Model and Explanation Evaluation
No full textWith the unprecedented advancement of Generative Artificial Intelligence (GenAI), the threat of voice scams using synthetic voices has become a serious concern across various sectors. Recent efforts have focused on identifying fake voices through handcrafted features, deep learning models, and hybrid approaches. However, most existing methods lack explainability, rendering their predictions non-transparent to users. This paper proposes a novel, interpretable, and transparent method for fake voice identification by introducing a hybrid deep learning model that leverages multiple extracted features. The hybrid model consists of two main components: the first component addresses heterogeneous feature spaces by employing deep convolutional sub-models tailored to individual features, while the second component, the terminus model, utilizes the concatenated representations from the final layers of each sub-model as input. The terminus model follows a typical multi-layer perceptron architecture, enabling effective integration and classification of the diverse feature representations. To enhance interpretability, we decompose the model’s decisions using Local Interpretable Model-agnostic Explanations (LIME), taking advantage of the identical feature representation before the concatenation layers to address challenges related to multidimensional feature representations. To evaluate the features and assess the quality of the generated explanations, we propose two metrics: importance and trust. Extensive experiments are conducted on the In-the-Wild dataset, which is designed to test the generalization capability of synthetic audio detection methods. The experimental results demonstrate that our approach achieves performance comparable to benchmark methods. Furthermore, the results based on our proposed metrics conclude that certain perceptible features demonstrate promise for generating explanations that are meaningful to general users. For reproducibility, the source code for these experiments is available in the following repository: https://github.com/jacoblarock/fake_voices_xa
Do the Explanations Make Sense? Explainable Fake Review Identification and Users’ Perspectives on Explanations
No full textCustomer reviews and feedback play a crucial role in shaping purchase decisions on e-commerce platforms like Amazon, Zalando, and eBay. However, a major concern is the prevalence of fake or spam reviews, often posted by sellers to deceive potential customers and manipulate product perceptions. Machine learning (ML) models are widely used to detect fraudulent reviews, but their decisions can be difficult to interpret due to their complexity—often functioning as black boxes. In this paper, we propose an explainable framework for fake review detection that not only achieves high precision in identifying fraudulent content but also provides interpretable explanations. To assess the effectiveness of these explanations, we conduct an empirical user evaluation to determine which information is most valuable in understanding model decisions. Initially, we develop fake review detection models using deep learning (DL) and transformer-based architectures, including XLNet and DistilBERT. Wethen apply Layer-wise Relevance Propagation (LRP) to generate explanations by mapping word contributions to the predicted class. Experimental results on two benchmark fake review detection datasets demonstrate that our models achieve state-of-the-art performance, outperforming several existing methods. Furthermore, we conduct a user study with 12 participants to evaluate the comprehensibility and usefulness of LRP-generated explanations. The findings from this study provide key insights into how explanations can be improved to enhance transparency and user trust in fake review detection systems