Graduate School of Informatics

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

In this thesis  the parameters of the successful transformation from traditional grid to smart grid in Turkey are determined and analyzed by a survey with the participation of 535 respondents from  Turkish electricity ecosystem. The dependent and independent variables of Turkish smart grid transformation are  determined with the literature review, delphi analysis, expert views and survey research method. Correlation, regression and anova  analyse, are implemented. This study is a multidimensional study including all dimensions of the smart grid transformation success in Turkey with the views of  Turkish electricity grid institutions, stakeholders, and experts.

Date: 28.08.2024 / 14:00 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

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