With the increasing use of digital technologies in the automotive sector, the traditional automobile is undergoing a structural transformation, requiring new technologies and enabling innovative mobility concepts. In particular, the ability to drive automatically or even fully autonomously, update control software, and remain connected to the environment allows attackers to infiltrate highly critical vehicle systems and take control without adequate protection. Once not only individual vehicles but entire fleets are dominated by software, cyberattacks could disrupt a significant portion of the infrastructure and expose passengers to substantial risks. This work follows a holistic approach to protecting highly automated software-defined vehicles from cyberattacks by designing and implementing security concepts in the main phases of a vehicle's lifecycle. We use SAE level 4 prototype vehicles to evaluate our proposed techniques. We start with a systematic security requirement analysis using the ISA-62443 standard series, demonstrating how threats can be identified in a collaborative, hierarchical process and how the resulting security risks impact the software and hardware architecture of a self-driving vehicle. We show how this analysis process results in concrete requirements whose consideration reduces the overall security risk to a tolerable level. Subsequently, we develop technical solutions for selected requirements. We begin by securing the CAN and FlexRay legacy protocols, which we foresee being used in specific areas of SDV in a transitional period despite technological changes. To enable vehicle-wide security management, we address the management and distribution of cryptographic keys within such networks, mainly focusing on resource-constrained devices. We propose using lightweight implicit certificates for deriving cryptographic group keys that can be used in CAN networks. Additionally, we demonstrate how the slot-based frame structure of the FlexRay protocol allows for efficient "multi-slot" authentication, for which we calculate cryptographic keys using hash-based key chains. SDV use Ethernet-based communication protocols and custom middleware stacks to transmit large amounts of data in real-time. We develop a three-stage security process for the novel ASOA, which enables the development and central orchestration of system-agnostic functional software components on embedded systems and HPC platforms. After the central specification of the security architecture at the data flow level, security tokens are automatically calculated and distributed for runtime protection of the service-oriented, DDS-based data transmission. Our process ensures the strict separation of function and system knowledge, allowing for cost-effective and adaptable security architecture management. The evaluation in four self-driving, software-defined vehicles demonstrates an average runtime overhead of approximately 5.71%. As the initial risk analysis and actual cyberattacks have shown, protective measures against the compromise of control units must be taken alongside communication security. To address this, we develop a method for verifying and validating the software integrity of control units. A governmental third party confirms a measurement through a digital certificate, proving the examined vehicle's trustworthiness and suitability for participation in automated traffic. In the final step of this work, we present an assessment scheme that allows software-defined vehicles to evaluate security incidents during operation in terms of their maximum expected damage and initiate appropriate countermeasures. We follow the ISO/SAE 21434 standard and model attack paths using a graph representing dependencies among internal vehicle assets to account for the propagation effects of cyberattacks. The assessment of a security incident considers not only the probability of individual attack paths but also the vehicle context. Our practical evaluation demonstrates that we can detect, report, and assess security incidents below the human reaction time in the earlier mentioned prototype vehicles.

Vanadium redox-flow batteries (VRFBs) have played a significant role in hybrid energy storage systems (HESSs) over the last few decades owing to their unique characteristics and advantages. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as they are indispensable for incorporating the distinctive features of energy storage systems and control algorithms within embedded energy architectures. In this work, we propose a novel approach that combines model-based and data-driven techniques to predict battery state variables, i.e., the state of charge (SoC), voltage, and current. Our proposal leverages enhanced deep reinforcement learning techniques, specifically deep q-learning (DQN), by combining q-learning with neural networks to optimize the VRFB-specific parameters, ensuring a robust fit between the real and simulated data. Our proposed method outperforms the existing approach in voltage prediction. Subsequently, we enhance the proposed approach by incorporating a second deep RL algorithm—dueling DQN—which is an improvement of DQN, resulting in a 10% improvement in the results, especially in terms of voltage prediction. The proposed approach results in an accurate VFRB model that can be generalized to several types of redox-flow batteries.

ChatGPT and similar generative AI models have attracted hundreds of millions of users and have become part of the public discourse. Many believe that such models will disrupt society and lead to significant changes in the education system and information generation. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models—both lack scientific rigor. We systematically assess the quality of AI-generated content through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays. We use essays that were rated by a large number of human experts (teachers). We augment the analysis by considering a set of linguistic characteristics of the generated essays. Our results demonstrate that ChatGPT generates essays that are rated higher regarding quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays. Since the technology is readily available, we believe that educators must act immediately. We must re-invent homework and develop teaching concepts that utilize these AI models in the same way as math utilizes the calculator: teach the general concepts first and then use AI tools to free up time for other learning objectives.

Understanding of financial data has always been a point of interest for market participants to make better informed decisions. Recently, different cutting edge technologies have been addressed in the Financial Technology (FinTech) domain, including numeracy understanding, opinion mining and financial ocument processing. In this thesis, we are interested in analyzing the arguments of financial experts with the goal of supporting investment decisions. Although various business studies confirm the crucial role of argumentation in financial communications, no work has addressed this problem as a computational argumentation task. In other words, the automatic analysis of arguments. In this regard, this thesis presents contributions in the three essential axes of theory, data, and evaluation to fill the gap between argument mining and financial text. First, we propose a method for determining the structure of the arguments stated by company representatives during the public announcement of their quarterly results and future estimations through earnings conference calls. The proposed scheme is derived from argumentation theory at the micro-structure level of discourse. We further conducted the corresponding annotation study and published the first financial dataset annotated with arguments: FinArg. Moreover, we investigate the question of evaluating the quality of arguments in this financial genre of text. To tackle this challenge, we suggest using two levels of quality metrics, considering both the Natural Language Processing (NLP) literature of argument quality assessment and the financial era peculiarities. Hence, we have also enriched the FinArg data with our quality dimensions to produce the FinArgQuality dataset. In terms of evaluation, we validate the principle of ensemble learning on the argument identification and argument unit classification tasks. We show that combining a traditional machine learning model along with a deep learning one, via an integration model (stacking), improves the overall performance, especially in small dataset settings. In addition, despite the fact that argument mining is mainly a domain dependent task, to this date, the number of studies that tackle the generalization of argument mining models is still relatively small. Therefore, using our stacking approach and in comparison to the transfer learning model of DistilBert, we address and analyze three real-world scenarios concerning the model robustness over completely unseen domains and unseen topics. Furthermore, with the aim of the automatic assessment of argument strength, we have investigated and compared different (refined) versions of Bert-based models that incorporate external knowledge in the decision layer. Consequently, our method outperforms the baseline model by 13 ± 2% in terms of F1-score through integrating Bert with encoded categorical features. Beyond our theoretical and methodological proposals, our model of argument quality assessment, annotated corpora, and evaluation approaches are publicly available, and can serve as strong baselines for future work in both FinNLP and computational argumentation domains. Hence, directly exploiting this thesis, we proposed to the community, a new task/challenge related to the analysis of financial arguments: FinArg-1, within the framework of the NTCIR-17 conference. We also used our proposals to react to the Touché challenge at the CLEF 2021 conference. Our contribution was selected among the «Best of Labs».

After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.