Research News

M.S. Thesis
İbrahim Ethem Deveci, Transformer Models for Translating Natural Language Sentences into Formal Logical Expressions

Translating natural language sentences into logical expressions has been challenging due to contextual information and the variational complexity of sentences. In recent years, a new deep learning architecture, namely the Transformer architecture, has provided 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: 17.04.2024 / 09:30 Place: B-116

Ph.D. Thesis
Fatih Ömrüuzun, A Novel Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery

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

Ph.D. Thesis
Hatice Gonca Bulur, Analyzing Decision Making Behaviour Under Risk and Uncertainty with The Help of Computational Cognitive Modeling and Neuroscience Perspectives

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

M.S. Thesis
Barış Fındık, Using Topological Features of Microservice Call Graphs to Predict the Response Time Variation

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

Ph.D. Thesis
Utku Civelek, The Conceptual Design and Implementation of a Knowledge Management System for Collaborative Data Science

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