Network simulators/emulators are a useful option to create virtual network environments for distributed applications on a single machine. In general, network emulators offer more realistic environments compared to simulators, however in terms of scalability and reproducibility of the experiments network simulators tend to have an advantage. The goal of this project or bachelor thesis is to compare the feature sets and usability of different network emulators and simulators using one or multiple representative demo applications.

This project will focus on improving the trustworthiness of generated information through the fine-tuning of the Llama 3 8b model using the Unsloth training performance optimization library. The primary goal is to enhance the reliability and accuracy of AI-generated content by leveraging advanced training techniques. The research will involve evaluating the performance of the Llama 3 8b model before and after fine-tuning, analyzing improvements in trustworthiness metrics, and developing new methodologies to further optimize the model鈥檚 performance.

This project will investigate the performance differences between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) in the context of image classification tasks. Kolmogorov-Arnold Networks offer a novel approach to neural network architecture based on mathematical foundations that differ from traditional MLPs. The primary goal of this research is to empirically compare these two types of neural networks to evaluate their classification accuracy. The outcome of this research may provide insights into the potential advantages of KANs over conventional MLPs in practical applications.

The alps see a surge of mountain biking as a recreational activity. This leads to frequent encounters of hikers and bikers on shared trails, but also to crashes between bikers due to, bad visibility in curves. In our previous work, we have investigated various scenarios and solutions, for example, a biker-to-hiker warning system, or a collision warning system for bike parks. Essential elements for these systems include localization of bikers in alpine environments, communication with near-range radio technologies like WiFi or BLE, but also suitable design of user interfaces and many more. Based on such earlier works (documented in theses and publications), we already identified various open challenges and possible future work that you can contribute to through a thesis or project. Please contact us to identify and define a suitable topic definition fitting your interests and previous experience. The overall project is collaboration between 午夜探花 and University of Trento.

in.Crease ist ein geplantes Informationssystem f眉r Studierende. In Vorarbeiten wurde bereits ein Modul entworfen, um Personen und Gremien zu verwalten und geeignet darszustellen. Ziel dieses Projekts ist es die Anforderungen an die Darstellung von Personen und Gremien zu 眉berarbeiten und neu zusammen zu stellen. Auch das Datenmodell soll auf Vollst盲ndigkeit und Konsistenz abgeklopft werden. Im Anschluss sollen UI-Elemente sowie entsprechende Berechtigungen f眉r die einzelnen Use-Cases implementiert werden - je nach Umfang eventuell nur eine Teilmenge von wichtigen Use-Cases. Zu den Use-Cases geh枚ren nicht nur lesende Zugriffe in Form von geeigneten Anzeigeelementen sondern auch Editier-, Anlege- und L枚schfunktionen. Die Arbeit hat damit einen konzeptionellen Anteil sowie einen Implementierungsanteil, der mit TypeScript und Angular in Verbindung mit einem Redux-Store.

Intel SGX is a technology that allows to launch tamper-proof enclaves in main memory, which isolate parts of applications that deal with sensitive data. There is a broad spectrum of application scenarios, ranging from fault-tolerant systems to privacy-preserving machine learning approaches. Intel provides a native SDK that can be used to derive low-level wrapper functions from a function definitions provided in a DSL, which are then used to interact with the protected parts of the application applications. However, the SDK requires special care during the design process as well as C/C++ programming skills, in order to create a bulletproof interface to the enclave. The Gramine project promises to simplify the SGX application development process by providing functionality to wrap unmodified linux applications in Intel SGX enclaves. Since this approach trades in performance for usability, the goal of this project is to conduct a performance evaluation for different applications launched natively and wrapped with Gramine.

The Vehicular Reference Misbehavior (VeReMi) dataset is a dataset is a dataset for evaluationg of misbehavior detection mechanisms for V2X networks. The dataset consists of message logs generated from a simulation environment. The dataset contains malicious messages which the single misbehavior detectors of a misbehavior detection system (MBD) intend to detect. The VeReMi dataset serves as a baseline to compare different MBDs. However, the existing VeReMi dataset lacks some information, so that not all existing misbehavior detectors of an MBD system receive the necessary information to work accordingly. In this project, the existing VeReMi dataset should be extended with the necessary information so that further misbehavior detectors receive the necessary information to work accordingly.

This project will focus on improving the trustworthiness of generated information through the fine-tuning of the Llama 3 8b model using the Unsloth training performance optimization library. The primary goal is to enhance the reliability and accuracy of AI-generated content by leveraging advanced training techniques. The research will involve evaluating the performance of the Llama 3 8b model before and after fine-tuning, analyzing improvements in trustworthiness metrics, and developing new methodologies to further optimize the model鈥檚 performance.

This project will investigate the performance differences between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) in the context of image classification tasks. Kolmogorov-Arnold Networks offer a novel approach to neural network architecture based on mathematical foundations that differ from traditional MLPs. The primary goal of this research is to empirically compare these two types of neural networks to evaluate their classification accuracy. The outcome of this research may provide insights into the potential advantages of KANs over conventional MLPs in practical applications.