Graduate School of Informatics

This dissertation presents ID-SDM, a computational framework utilizing Influence Diagrams to model Shared Decision-Making (SDM) based on the Three-Talk Model. By representing clinicians and patients through separate IDs, the model simulates information flow via three node operations: decision alternative transfer, chance node transfer, and preference transfer. Applied to Graves’ Disease, results show that SDM achieves the perfect-information-sharing model’s optimal decision.more efficiently than other decision models. The SDM process reaches consensus in less time with upfront information sharing from both sides. When the clinician attributes greater importance to the patient's utility criteria, the clinician's decision shifts to the perfect-information-sharing model’s optimal decision.

Date: 13.01.2026 / 13:30 Place: A-212

This thesis proposes a unified framework for few-shot vision-language adaptation. Addressing the limitations of holistic image matching in models like CLIP, we introduce a dual-branch architecture combining global prompts with a locality-aware pathway. This local branch utilizes Value-Value (V-V) attention and Optimal Transport (OT) to enforce balanced, discriminative alignments between fine-grained image patches and class-specific prompts. Extensive evaluation on 11 benchmarks demonstrates state-of-the-art average accuracy. Furthermore, the framework exhibits superior Out-of-Distribution (OOD) robustness, offering a configurable trade-off between task specialization and generalized robustness.

Date: 05.01.2026 / 13:00 Place: A-212

This thesis study investigates the adoption of Generative Artificial Intelligence (GenAI) technologies among small and medium-sized IT enterprises in Türkiye. Using the Delphi method and Interpretive Structural Modeling (ISM), the research identifies key barriers and examines their interrelationships to reveal hierarchical influences. The study explores technological, organizational, and environmental determinants shaping GenAI adoption. Findings provide an empirically grounded framework that supports strategic decision-making for SMEs aiming to integrate generative AI effectively. The proposed model offers both theoretical insight and practical guidance for advancing AI-driven digital transformation.

Date: 22.12.2025 / 10:00 Place: B-116

This thesis proposes the Data Governance Capability Maturity Model (DG-CMM), a structured assessment model based on ISO/IEC 330xx standards for evaluating organizational data governance maturity. The model examines maturity across four core process areas: Data, Organization, Strategy, and Technology. DG-CMM was developed using a Design Science Research methodology in line with Becker et al. (2009), incorporating an extensive literature review, a Modified Delphi approach with domain experts, and empirical case-based validation. The model offers organizations a standardized and actionable framework to systematically identify maturity gaps, prioritize improvements, and strengthen data-driven decision-making and strategic alignment through effective data governance practices.

Date: 16.01.2026 / 09:00 Place: A-212

Addressing the replication crisis in Terror Management Theory, this dissertation investigates the inconsistent link between Mortality Salience and risk-taking. It moves beyond self-reports by integrating three methodologies: causal modeling via a systematic literature review (ESC-DAG), objective neural measurement using EEG and the Balloon Analogue Risk Task (BART), and Bayesian cognitive modeling. By synthesizing causal, neural, and computational evidence, the study aims to clarify the cognitive mechanisms driving risk behavior under mortality salience, ultimately contributing to resolving the field's reproducibility challenges.

Date: 14.01.2026 / 10:30 Place: A-212

This thesis addresses the inherent complexity of discourse parsing in Natural Language Processing (NLP) by developing a multilingual framework implemented for Penn Discourse TreeBank (PDTB) datasets. Leveraging the advancements of Large Language Models (LLMs) and transformer architectures, the thesis proposes a hybrid methodology that integrates fine-tuned BERT models for Discourse Connective (DC) detection and argument span labeling with in-context learning strategies for Discourse Relation Recognition (DRR). The study bridges the gap between isolated sub-tasks and end-to-end processing by defining interconnected modules that link detection, labeling, and recognition phases. Evaluating this pipeline across seven datasets in English, Portuguese, and Turkish, the framework achieves performance on par with state-of-the-art models. Additionally, the thesis contributes a novel lightweight DC detection model and introduces a method to enhance implicit discourse relation recognition using machine translation techniques, demonstrating the efficacy of these approaches in both high- and low-resource linguistic contexts.

Date: 23.12.2025 / 15:00 Place: A-212