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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 examines clozapine effects on cognitive function and brain connectivity in schizophrenia with the M1 muscarinic receptor polymorphism (rs2067477). Differences in cortical activity and connectivity between genotypes were assessed using an N-back working memory task and functional near infrared spectroscopy (fNIRS). Wild-type individuals exhibited higher cortical activation during the task, but had lower functional connectivity in the frontotemporal network compared to non-wild-types. The findings suggest different compensatory mechanisms by highlighting the genetic effects of clozapine-related neural responses and provide valuable information for personalized treatment approaches in schizophrenia.
Date: 10.01.2025 / 10:00 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 develops a reproducible methodology for classifying mental workload using EEG signals across multiple sessions. Guidelines for reproducible research are established and a thorough review of existing EEG-based workload classification studies is conducted to assess their reproducibility status. Graph neural networks are employed for classification. Domain adaptation with optimal transport is explored for improved generalization across sessions. Subject-specific evaluations using diverse metrics are performed to assess model performance. The outcomes aim to enhance the robustness and generalizability of mental workload classification for brain-computer interfaces and other cognitive workload applications.
Date: 20.12.2024 / 13:00 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
