This thesis introduces advanced methodologies for Land Use Mix (LUM) analysis in urban planning, GIS research, and disaster risk assessment. It addresses limitations in traditional approaches by leveraging point-based geospatial data and develops an open-source Python package, landusemix, for scalable and adaptable LUM calculation. The research extends LUM analysis to evaluate temporal variations in urban vulnerability, particularly concerning earthquake risk, offering insights for time-sensitive urban planning. This work enhances sustainable, resilient, and livable cities through innovative tools and approaches in urban studies.

Date: 03.09.2024 / 17:00 Place: B-116

In this thesis, a Bayesian cognitive model of Theory of Mind in communication is developed based on a prior experimental study on joint action and attention. Specifically, the model compares demonstrative utterances between individuals with high-functioning autism and typically developing, non-clinical controls. It applies the Rational Speech Act framework, incorporating visual (joint) attention measures obtained through dual eye-tracking. By parameterizing context-dependence, nestedness of inference, and the preference for different demonstrative systems, the model provides insights into group differences observed under conditions of high ecological validity.

Date: 05.09.2024 / 09:00 Place: B-116

In the present thesis, the social nature of learning in a verbal communication setting has been investigated by employing a multimodal hyperscanning method to correlate non-complex and replicable ocular and neural features with the socio-linguistic process of interlocutors establishing and sustaining conceptual common grounds. A dyadic interaction setting was formed with a dual-EEG, dual-fNIRS, and dual-eye tracking setup wherein experiment data were synchronized on the same time-domain to explicate these features. The results showed that replicable features for ocular, hemodynamic, and neuroelectric domains constituted both linear and non-linear relationships in between that correlate with linguistic behavioral data during dyadic verbal communication.

Date: 05.09.2024 / 15:00 Place: A-108

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

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

The most interactive field of digital transformation is data science, as it entails a longtime active collaboration among multiple partners. Data scientists seek domain expertise to understand the structure and environment of the data while business users take pains with concepts to exploit analytical solutions. This thesis presents the conceptual design and implementation of CoDS (Collaborative Data Science Framework) as a knowledge management system on which business and data details, modeling procedures, and deployment steps are shared. It mediates and scales ongoing projects, enriches knowledge transfer among stakeholders, facilitates ideation of new products, and supports the onboarding of new developers.

Date: 06.06.2024 / 11:00 Place: II-06

The study aimed to understand the impact of IT governance mechanisms on IT agility. Results indicated that relational IT governance mechanisms significantly enhance IT agility, unlike structures and processes, which were not significant. Relational mechanisms involve top management support, cross-functional training, and a clear IT role within the firm, fostering mutual understanding and integration between IT and business units. While structures and processes are essential for compliance, they may not independently achieve agility. Future research should investigate how these formal mechanisms enable or inhibit agility. Additionally, a hybrid IT governance model may balance centralization and decentralization, offering both efficiency and flexibility.

Date: 22.05.2024 / 13:00 Place: A-212

Due to the increased use of hyperspectral remote sensing payloads, there has been a rise in the number of hyperspectral remote sensing image archives, resulting in a massive amount of collected data. This highlights the need for a content-based image retrieval system that can manage and enable the use hyperspectral remote-sensing images efficiently. A novel CBHIR system is proposed that aims to define global hyperspectral image representations based on a semantic approach to differentiate background and foreground image content considering both spatial and spectral information. In this way, two spectral content dictionaries are used in the process of modeling hyperspectral images.

Date: 24.01.2024 / 14:00 Place: B-116

It is significant to comprehend the basics of decision making behaviour because people make decisions in their everyday lives. The purpose of this research is to understand individuals’ decision making behaviour under risk and uncertainty using computational cognitive modeling and neuroscience perspectives. Results related to behavioural and neural data analyses and computational cognitive modeling utilizing the collected data from experiments provide explanations for the mechanisms behind decision making under risk and uncertainty cases.

Date: 26.01.2024 / 11:00 Place: A-212

The most interactive field of digital transformation is data science, as it entails a longtime active collaboration among multiple partners. Data scientists seek domain expertise to understand the structure and environment of the data while business users take pains with concepts to exploit analytical solutions. This thesis presents the conceptual design and implementation of CoDS (Collaborative Data Science Framework) as a knowledge management system on which business and data details, modeling procedures, and deployment steps are shared. It mediates and scales ongoing projects, enriches knowledge transfer among stakeholders, facilitates ideation of new products, and supports the onboarding of new developers.

Date: 22.01.2024 / 13:00 Place: II-06