Graduate School of Informatics

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

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