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Kategorie: ‘Theses’

Supervised fine-tuning LLMs for cybersecurity playbook translation

April 5th, 2024 | by

Prompt Engineering for Translating unstructured playbooks into CACAO standard with the help of Large Language Models

April 2nd, 2024 | by

A Mixed Reality Agent as a Navigation Guide

March 7th, 2024 | by

Mixed reality agents are simulated humans which can be viewed and interacted with on mixed reality technologies. For conveying content in educational settings, mixed reality agents have a series of advantages. They are part of a mixture of virtual elements with the real world, which provides opportunities where the agent can refer to points and locations in the real surroundings. Moreover, they are viewable in 3D from any angle so that an observer can freely choose the perspective to watch the agent. This is, e.g., not possible with videos where the viewpoint was chosen previously by the cameraman. The agents underlying behavior can also be made configurable and so, content can quickly be produced and changed as opposed to instructional videos where changes to the content require re-recording the video. In the educational sector, the combination of these factors means that mixed reality agents have a high potential to act as navigation guides. One issue that freshmen in universities face in the beginning is that they are unsure how to find the correct course room and are generally unaware of the building’s layout. Here, mixed reality agents can act as initial guides which inform students about their location and guide them around, e.g., pointing out where to find rooms. Additionally, they can add interesting information, e.g., about the organizational aspects, e.g., where important offices are or successful research projects of the different university chairs as a help in finding interests.

Extending Data Stream Processors with Interoperable User-Defined Functions in WebAssembly

March 5th, 2024 | by

Interoperable Data Exchange between Data Stream Processing Platforms

March 5th, 2024 | by

Frequent Learner Errors: NLP Insights into Automated Grading

February 29th, 2024 | by

This thesis aims to explore the realm of Natural Language Processing (NLP) in the context of automated grading systems, focusing specifically on identifying and analyzing frequent learner mistakes. With the increasing integration of technology in education, automated grading systems have gained prominence, but they often lack nuanced understanding and feedback provision. This research endeavors to enhance the efficacy of such a system in use at the institute by employing NLP techniques to dissect learner errors and provide more tailored feedback.

Automated Data Processing and Knowledge Discovery for Time Series Data utilizing Large Language Models

February 14th, 2024 | by

Large language models (LLMs) have proven the ability to assist diverse users in conducting a variety of individual tasks via intuitive and natural conversations. This thesis discusses a utilization of LLMs to perform (semi-)automated data processing and analyses on time series data. One major goal is to reduce expertise-related dependencies, allowing more people to manipulate data and gain beneficial insights.

Explainable Data – Trust, Transparency and Bias Mitigation in ML

February 8th, 2024 | by

This bachelor thesis aims to delve into the critical intersection of trust, transparency, and bias mitigation in machine learning (ML) systems through the lens of explainable data. The proliferation of ML algorithms across various domains has underscored the importance of understanding how these systems make decisions, especially when they impact individuals or societal outcomes.

Adaptive Semantics for Generic Latex Expressions in the Context of Educational Resources

January 29th, 2024 | by

This thesis aims at developing a pipeline for automatic translation of generic latex expression into domain specific, processable, languages.

Need for Speed: Evaluating Feedback Latency in Psychomotor Learning

November 30th, 2023 | by

The bachelor thesis aims to contribute to the ongoing research project MILKI-PSY, which is centered around advancing Self-Regulated Learning (SRL) in psychomotor training through multimodal immersive mentoring. In the context of the project, we explore how various calculation mechanisms and hardware decision impact the latency of feedback in psychomotor learning.