This thesis aims to identify the factors influencing practitioners' continuance intention toward Agile methodology usage. The study also examines the influence of identified factors on the continuance intention of Agile methodology usage and proposes a model in the context of Agile methodology. The model was verified with the reliability tests, Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling. By utilizing Structural Equation Modeling, the influencing factors and the relationships among these factors were analyzed and the final model is proposed.

Date: 05.09.2024 / 09:45 Place: A-108

This thesis explores the identification and prioritization of Critical Success Factors (CSFs) in data analytics projects. Through a systematic literature review and semi-structured interviews with data professionals, a comprehensive list of CSFs was developed, structured hierarchically, and refined based on expert feedback. The study addresses gaps in existing literature by providing a cross-disciplinary CSF framework applicable to various fields like AI, big data, and business intelligence. Additionally, the research prioritizes these factors through the semi-structured interviews based on organizational contexts such as company size, project complexity, and technological maturity.

Date: 05.09.2024 / 11:00 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: 04.09.2024 / 10:00 Place: II-06

This thesis explores the use of small to medium-sized language models (LLMs) for multi-hop question answering, focusing on overcoming their limitations in knowledge and reasoning compared to larger models like GPT-4. The research explores using knowledge graphs to enhance these models' performance, specifically by integrating entity-relation triplets extracted from text. Techniques like supervised fine-tuning and reinforcement learning are evaluated for improving entity-relation extraction. A new prompting technique, Connect-the-Entities (CTE), is introduced to improve question answering performance with lower computational costs.

Date: 29.08.2024 / 10:00 Place: A-212

There are two goals of the thesis: (i) to understand the descriptive properties of the so-called negative dependencies in Turkish, which are expressions that basically require the presence of a negative-like element or environment to be well-formed; and (ii) to understand the so-called intervention effects on negative dependencies, where the term refers to the anti-licensing of negative dependencies in an otherwise adequate structure due to the presence of some elements (e.g. conjunction, universal quantifier, etc.).

Date: 06.09.2024 / 14:00 Place: A-212

In the literature, it is widely accepted that there is a direct relationship between the aspectual interpretation of the predicate and the internal argument of the verb. For languages such as Turkish and Finnish, it is argued that the accusative case marker on the direct object functions as a measurer and elicits a delimited event interpretation. However, in this thesis, we will argue that the correlation between the accusative case and the delimited interpretation of events is not a result of the supposed direct relationship between case and aspect, and show that the said relationship is only indirect following MacDonald’s (2006) work. 

Date: 06.09.2024 / 12:00 Place: A-212

In software development, the metaphor of technical debt (TD) describes the cost of additional work caused by choosing a non-ideal solution. This study aims to identify how cultural dynamics affect TD accumulation and management. Using both quantitative and qualitative methods, responses from 30 software industry practitioners across six different domains revealed that organizations with clan and market cultures tend to accumulate higher levels of TD. These findings demonstrate that organizational culture has a significant impact on TD outcomes and emphasize the importance of management strategies tailored to specific cultural dynamics within organizations.

Date: 16.08.2024 / 13:30 Place: A-212

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

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