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This thesis investigates the integration of Model-Based Systems Engineering (MBSE) and Product Line Engineering (PLE) into Model-Based Product Line Engineering (MBPLE) for systematic variability management. It develops a robust MBPLE methodology by adapting variability management techniques, enhancing visualization, and ensuring traceability across abstraction levels. The methodology is applied to a case study and validated against established requirements, aiming to optimize business processes, improve product quality, and reduce engineering efforts within the aerospace industry.
Date: 06.08.2024 /10:00 Place: A-212
This study introduces a testing framework for Deep Neural Network (DNN) models to identify fault-revealing data and understand the causes of failures. We prioritized test inputs based on model uncertainty, and with the proposed meta-model-based approach, we enhanced the effectiveness of test data prioritization. Moreover, distribution-aware test datasets are generated by initially focusing on in-distribution data and subsequently including out-of-distribution data. Finally, we employed post-hoc explainability methods to pinpoint the causes of incorrect predictions after test executions. Evaluations in the image classification domain show that uncertainty-based test selection significantly improves the detection rate of DNN model failures.
Date: 10.07.2024 / 15:30 Place: A-212
Brain-computer interfaces (BCIs) offer solutions for motor impairments and enhance human-computer interaction in virtual reality and cognitive augmentation. Adoption is hindered by user fatigue and the unnatural feel of visual stimuli, necessitating comfortable, intuitive paradigms. This study develops an imperceptible steady-state motion visual evoked potential (SSMVEP) stimulus for multi-directional BCI control. Using sinusoidal gratings with high-frequency motion, the first experiment shows robust cortical responses with reduced discomfort. The second experiment combines eye-tracking, EEG, and advanced machine learning to decode attentional responses accurately. Findings support naturalistic, high-performance BCIs for assistive technologies and human-computer interaction.
Date: 05.07.2024 / 14:00 Place: A-212
We employ the XGBoost algorithm to predict driver non-coding mutations based on multiple engineered features, augmented with features from existing annotation and effect prediction tools. The resulting dataset is passed through a feature selection and engineering pipeline and then trained to predict driver versus passenger non-coding mutations. We also use this model within the architecture of a known driver discovery model from existing literature. We then use non-coding driver mutations found in previous studies and predict their driver-ness using our models. Furthermore, we use Explainable AI methodologies to perform an in-depth analysis of the generated predictions.
Date: 07.06.2024 / 10:00 Place: A-212
This thesis explores attribution mechanisms in Turkish discourse through the adaptation of the Penn Discourse TreeBank (PDTB) framework, resulting in the Turkish Discourse Bank (TDB 1.2). Utilizing insights from lexical control and eventuality specific to Turkish, a custom annotation scheme was developed, facilitating robust data annotation. Analysis shows the predominance of communicative verbs in attribution instances, highlighting novels and news as rich domains for study. Achieving high inter-annotator agreement, this work advances the field by enriching the TDB and laying groundwork for future automated text analysis in Turkish.
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