Graduate School of Informatics

Microservices are increasingly gaining popularity in software design. It is essential for microservice architectures to have low response time variation to design testable and predictable systems. In this study, the aim is to predict the response time variation of microservice call graphs by using their topological features. Following the prediction processes with machine learning models, feature explanations methods are used to investigate which topological features are influential in the machine learning models' outputs regarding response time variation and how these features influence model outputs.

Date: 19.01.2024 / 09:00 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

Organizations strive to improve their digital transformation (DX) maturity for market success, utilizing maturity structures such as maturity index. However, these structures face limitations, revealing a research gap. Therefore, this thesis introduces a novel self-diagnostic tool called the DX maturity index (DX-MI) using design science research. DX-MI assists organizations in measuring and advancing their DX maturity. It has a hierarchical structure that includes dimensions, sub-dimensions, and metrics, all underpinned by an assessment approach grounded in evidence or objective quantifiable metrics. Multiple case studies were conducted to check the applicability and usability of the DX-MI, confirming its effectiveness and practicality.

Date: 22.01.2024 / 14:30 Place: II-06

Compounding is one of the known ways of word formation. It is also a productive way of word-formation in German (Neef, 2009). Compounding in German makes use of some markers, mostly called linking elements, between the constituents, and this phenomenon is highly common. Whether these markers have any meaning or what primary functions they have are seemingly highly controversial. In this study, we suggest that the close relation between meaning and reference on the one hand and categorization on the other can be explored computationally in distributional properties of these markers which are difficult to identify analytically.

Date: 22.01.2024 / 14:00 Place: A-212

This study aims to investigate the potential outcomes when children are exposed to a hypothetical English, i.e. ergative English, rather than accusative English in the language acquisition process by using a child-directed speech data set. Based on the data set, the English grammar is constructed with both syntactic and semantic structures. Subsequently, some parts are modified for the hypothetical English. Following this, a model is trained to generate sentences with their corresponding syntactic and semantic structures. After the training process, a comparative analysis is conducted to determine the predominant category—accusative or ergative—in the acquisition of language by children.

Date: 22.01.2024 Place: A-212

This study investigates the global discourse organization of Turkish TEDx talks. 70 TEDx Talks in Turkish with, reliable human-generated transcriptions were chosen to be annotated. These were collected as subtitle files and manually annotated to map out significant discourse segments. For every talk, a number of features such as the number of total words, specific transition words, duration (second-wise), speed, average embedding and the ending percentile of each sentence were used for training Machine Learning (ML) models. The results indicate that the transitions between motivational discourse segments can be predicted with an F1-score of 0.78.

Date: 22.01.2024 / 13:00 Place: B-116

Translating natural language sentences into logical expressions has been a challenging task due to contextual information and the variational complexity of sentences. In recent years, a new deep learning architecture, namely the Transformer architecture, has been providing new ways to handle what was hard or seemed impossible in natural language processing tasks. The Transformer architecture and language models that are based on it revolutionized the artificial intelligence field of research and changed how we approach natural language processing tasks. In this thesis, we conduct experiments to see whether successful results can be achieved using Transformer models in translating sentences into first-order logic expressions.

Date: 23.01.2024 / 11:00 Place: B-116

Steel production serves as the backbone of countless infrastructure projects and industrial applications worldwide. In order to maintain and improve its productivity, quality and environmental sustainability, hyperspectral imaging is a promising technology for steel industry.  A novel, non-destructive approach is presented to quantify the free lime content in steel slag by utilizing an integrated algorithm applied to hyperspectral images. This method includes spectral unmixing for mixture component quantification and endmember extraction of mixture. Methodology involved various experiments with both fresh and six-month-aged steel slag, demonstrating its accuracy compared to the Rietveld Analysis of X-ray Diffraction patterns.

Date: 11.01.2024 / 09:30 Place: B-116

State-of-the-art omics technologies use network-based contextualization methods to give molecular information about different biological contexts, like disease states, patients, and drug changes. In the beginning, this thesis identified challenging issues such as missing points in contextualization, hidden knowledge in omics datasets, bias in reference networks, and noisy interactions with highly connected nodes or hubs. Subsequently, to address these challenges, we developed pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omics data integratioN). Also, a novel tool, pyPARAGON, contextualized patient datasets by inferring patient-specific networks and complex diseases by constructing disease models, namely breast cancer and autism spectrum disorders.

Date: 22.01.2024 / 14:00 Place: A-108