Introducing the EnSCAN framework, we propose a pioneering algorithm to consolidate selected variants even across distinct platforms, thereby prioritizing candidate causative loci and enhancing ML outcomes through combining prior information captured from each multi-model of each dataset. The proposed ensemble algorithm utilizes chromosomal locations of SNVs by mapping to cytogenetic bands, along with the proximities between pairs and multi-model via Random Forest validations to prioritize SNVs and candidate causative genes for Alzheimer Disease. The scoring method is scalable and can be applied to any multi-platform genotyping study. We present how the proposed EnSCAN scoring algorithm prioritizes the candidate causative variants related to LOAD among three GWAS datasets.

Date: 06.09.2024 / 16:00 Place: B-116

This thesis investigates cross-linguistic differences in realizing discourse relations, centered on the TED-MDB corpus. By developing a framework for aligning discourse relation annotations in parallel corpora, the study explores variations in discourse relation realization, semantic shifts, and inter-sentential encoding patterns across languages. Key findings highlight the importance of discourse relation linking, revealing differences in the translation of discourse connectives. Also, this study develops method for bilingual lexicon induction from aligned data, supporting pragmatic studies and natural language processing systems. Future work includes adapting discourse relation-aligned data to Linked Language Open Data (LLOD) standards for better accessibility and interoperability.

Date: 04.09.2024 / 10:00 Place: A-212

Humans have long been curious about other minds; a fascination rooted in ancient philosophy that shapes key debates in modern cognitive science. As artificial intelligences become more widespread, the human tendency to attribute mental states—known as mind perception—to non-human entities has found relevance in human-robot interaction. This thesis explores the dynamics of mind perception in this context, focusing on determinants related to both the perceiver and the perceived entity. In multiple-step experiments involving 160 participants from four generations, we examined how agent type (human vs. robot), action type (communicative vs. noncommunicative), individual traits, and generational differences influence mental capacity attributions. We measured mind perception along Agency (ability to do) and Experience (ability to feel) dimensions. Our methodology combined implicit and explicit tasks in a real-time, naturalistic lab setting with live actors, enhancing ecological validity while maintaining experimental control. We collected both behavioral measurements and self-report answers, addressing the recent discussions in the field. Findings revealed that the human was consistently attributed higher mental capacities than the robot. Action type effects were varied and context-dependent. Young participants were more likely to attribute mental states to the robot, while individual traits showed weak influences. Explicit measures aligned with implicit ones in showing higher mind attribution to the human, while implicit measures revealed subtler effects, particularly for action type and agency. Response times and mouse trajectories captured nuances and interactions that were not apparent in explicit ratings. This thesis highlights the significance of considering both perceiver- and perceived-related factors and using implicit and explicit assessment methods to reveal layered interactions among determinants by uncovering distinctions between these two measurement types.

Date: 03.09.2024 / 11:00 Place: A-212

This study is about narrative comprehension. Narrative comprehension involves how the characters and events described in narratives are represented in readers’ mind, what kinds of inferences are activated while reading narratives, and what text factors affect comprehension of narratives. In particular, our research will concentrate on the comprehension of spatial cues and protagonist’s perspective in narratives. Several studies in the literature suggest that readers are sensitive to the spatial cues and can, under some conditions, adopt the perspective of the protagonist in narratives. For this thesis, we will investigate this issue further, by testing some new research questions.

Date: 06.09.2024 Place: II-06

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