This thesis explores the critical role of people and culture-related capabilities in the success of Advanced Data Analytics (ADA) projects, addressing a gap in current literature that predominantly focuses on technical aspects. By conducting a systematic literature review and semi-structured interviews, the study identifies and categorizes these capabilities, integrating them into structured processes tailored from the People Capability Maturity Model (Curtis et al., 2009). The research contributes actionable frameworks and practices to enhance workforce readiness, collaboration, and organizational culture, enabling businesses to align ADA initiatives with strategic goals and achieve sustainable success.

Date: 10.01.2025 / 13:30 Place: A-212

Somatic mutations in non-coding regions can disrupt transcription factor (TF)-DNA interactions, affecting gene regulation and contributing to cancer. This thesis introduces an in silico pipeline to assess the impact of these mutations on TF binding affinities. Using k-mer-based linear regression models trained on ChIP-seq and PBM data for 403 TFs, we analyzed somatic mutations in 560 breast cancer samples. Predicted TF binding changes were classified as gain or loss of function and linked to oncogene and tumor suppressor dysregulation using enhancer-target gene maps. Signature-specific and statistical analyses highlight distinct patterns, providing insights into the regulatory role of mutations in cancer.

Date: 07.01.2025 / 10:00 Place: A-212

This thesis introduces a methodology for training context-invariant autoencoders using unsupervised domain adaptation to enhance model generalizability under varying contexts. By employing domain-adversarial training and data augmentation, the approach extracts domain-invariant representations while disregarding contextual variations. Experiments utilize the CARLA simulator, generating diverse image datasets across weather conditions and times of day. The proposed framework improves reconstruction loss and feature robustness, demonstrating its efficacy in achieving reliable machine learning performance in dynamic environments. The study emphasizes the utility of domain adaptation techniques in addressing domain shifts, offering a foundation for robust applications in autonomous systems.

Date: 06.01.2025 / 14:30 Place: A-212

The thesis study "Investigating The Semantic Similarity Effect on Delayed Free Recall Using Word Embeddings," investigates how the semantic proximity effect, alongside the temporal proximity effect on delayed free recall. The current study uses fastText and word2vec for methodological purposes to outline the underlying cognitive mechanisms leading to the process of memory retrieval. By investigating the interplay between word meanings and memory performance, this study contributes to Cognitive Science and Psychology specifically in investigating language processing and human memory.

Date: 10.01.2025 / 13:00 Place: B-116

This thesis presents a Deepfake Detection System that leverages public blockchain and collective intelligence to address the growing threat of digital misinformation. Implemented on the Ethereum Sepolia testnet, the system combines human collaboration and decentralized technology to detect deepfakes independent of their generation methods. Using smart contracts ensure transparency, fairness, and scalability by automating voting processes and adjusting user credibility based on voting accuracy. The system builds trust and accuracy by normalizing user influence and promoting open participation. This study demonstrates the system’s robustness, scalability, and ability to combat misinformation, while laying the foundation for blockchain-based verification in other fields.

Date: 07.01.2025 / 14:00 Place: A-212

This thesis addresses the challenge of concept drift in machine learning, where evolving data patterns reduce model relevance and performance. This research proposes a dynamic system that detects and adapts to new concepts by developing tailored models for each concept. It includes leveraging ensemble strategies and mitigating class imbalances with synthetic data. By using detection techniques based on differences between datasets and performance metrics, and different prediction techniques that take account of the concept of the datasets that will be predicted this research aims to enhance model adaptability in dynamic environments, providing a comprehensive framework to tackle concept drift.

Date: 27.12.2024 / 14:00 Place: A-212

This thesis aims to understand the applicability of virtual reality or mixed reality environments to perform model-based systems engineering and develop a prototype for a framework for such uses. By conducting user tests with people from systems engineering and interactive application and game development background, identifies the primary advantages and disadvantages of using these environments compared to desktop environment. The outcomes serve as a strong baseline for possible future research and established that the virtual reality or mixed reality environments can be suitable for model-based systems engineering.

Date: 26.11.2024 Place: A-212

This thesis presents a method of comparing the cost-effectiveness of 3D mesh simplification algorithms using the McGurk effect, where visual and auditory cues are combined to create an illusion. The study involves designing a human head mesh, animating mouth movements, and recording certain syllable sounds to produce a virtual scene. Using this virtual scene and applying three different mesh simplification algorithms on the animated head, a user study was conducted to test and measure the effectiveness of each algorithm for each different syllable in medium and high difficulty levels. Results highlight the balance between computational efficiency and perceptual accuracy, providing insights for 3D modeling and virtual reality applications.

Date: 29.11.2024 / 10:00 Place: A-212

Active Directory (AD) is one of the most widely used directory services today, playing a key role in organizing and managing network resources within an organization. A robust security strategy is crucial to prevent and detect AD attacks, which can be difficult to detect due to their blend in with normal network traffic. One such attack is the Kerberoasting attack, which exploits weaknesses in the Kerberos authentication protocol. To detect these attacks, supervised machine learning algorithms are being proposed. And also publicly available dataset to measure the efficiency of these algorithms for Kerberoasting attacks was created and shared.

Date: 22.11.2024 / 14:00 Place: A-108

In the evolving landscape of open source software projects, effective issue management remains a pivotal aspect of sustaining project success. Issue reports provide valuable information as they are created for reporting bugs, requesting new features, or asking questions about a software product. The high number of issue reports, which vary widely in quality, requires accurate issue classification mechanisms to prioritize work and manage resources effectively. Properly assigned issue labels are crucial for effective project management and for the reliability of research conducted to improve issue management as they often assume the assigned issue labels as the ground truth. This study aims to assess the reliability of the assigned issue labels in open source software development projects to improve issue management processes. The research involves collecting two datasets of issue reports from open source software development projects hosted on GitHub. Experiments were conducted with the state-of-the-art large language models for issue label classification. Furthermore, a qualitative analysis was performed to evaluate the relevance of the assigned issue labels with respect to the content of the issues. The empirical study performed on issue reports revealed a significant mismatch between the assigned labels and the actual content of the issues. The study also demonstrated the effectiveness of the state-of-the-art large language models in classifying issue labels, highlighting concerns about the reliability of issue labels in open source software development projects.

Date: 06.09.2024 / 11:00 Place: A-108