In an attempt to understand the semantics of –(y)Iş better, this thesis aims to systematically distinguish imperfect -(y)Iş nominals from perfect -(y)Iş nominals, manner-denoting -(y)Iş nominals from eventuality-denoting ones, identify the types of eventuality denoted, and identify the factivity status of eventuality-denoting -(y)Iş nominals. We suggested sets of tests for each category, and we applied these tests to a sample set of data to demonstrate that they can be used to reliably and accurately make these distinctions.

Date: 17.11.2022 / 11:00 Place: A212

An outcome of taking poor decisions or choosing easier solutions for faster code delivery is technical debt. There is a gap in the field to assist managing technical debt for software development companies that work with software as a service applications. This thesis aims to specify and categorize the technical debt present in organizations using software as a service applications, by studying the processes for categorizing technical debt, and specifying the causes and issues arising due to this debt.

Date: 28.11.2022 / 09:30 Place: A212

When choosing a route in orienteering, it is important to combine physical endurance with mental processes and the ability to adapt to the environment and optimize them correctly. In this study, the components affecting route selection were investigated. For this purpose, the data obtained from athletes through GPS containing watches were examined with quantitative and qualitative research methods. Then, a model based on spatial data was created to find the shortest paths and to compare the compatibility with the behaviors of athletes, and the relation of route selection decisions with some specified cognitive paradigms was questioned.

Date: 30.01.2020 / 10:00 Place: A-108

Perceptual sound field reconstruction (PSR) is a spatial audio recording and repro-duction method based on the application of stereophonic panning laws in microphone array design. 

Date: 08.01.2020 Place: SPARG LAB. (Modsimmer Building)

In this study, we propose to integrate large-scale gene/protein annotation data by using non-negative matrix factorization (NMF). Using NMF, the ultimate aim here is to predict the unknown binary relationships between these biological entities; and to represent these entities (i.e., proteins, functions and disease entries) as informative and non-redundant quantitative feature vectors (using the low-rank feature matrices generated by the factorization process), which can be used in diverse data mining and machine learning tasks in the future, such as the automated annotations of proteins or the construction of biological knowledge graphs.

Date: 30.01.2020 / 15:30 Place: A-212

Studies showed that single nucleotide variations that alter the protein sequence, structure and function are associated with many diseases in humans. However, the current rate of manually annotating reported nsSNPs cannot catch up with the rate of producing new sequencing data. To aid this process, automated computational approaches are being developed and applied on the unknown data. In this study, we propose a new methodology to collect and organize the information related to the effects of nsSNPs at the amino acid sequence level from various biological databases and to utilize this information in a supervised machine-learning based system to predict the function disrupting capacities of mutations with unknown consequences.

Date: 30.01.2020 / 14:00 Place: A-212

Sorafenib is one of FDA approved targeted agents in HCC treatment. PI3K/AKT pathway is altered in 50% of hepatoma, hence understanding how Sorafenib and PI3K/AKT pathway inhibitors act at signalling level is crucial for targeted therapies and to reveal their off-target effects. In this work, we use gene expression profiles of HCC cells treated with seven different drugs/inhibitors and combination. Our aim is to reveal the important targets and modulators in a drug treatment by inferring the dysregulation of Interactome. In other words, we search for the mechanism of action of drugs in a network context beyond gene list.

Date: 13.01.2020 / 14:00 Place: A-108

This thesis consists of three eye tracking applications. Main purpose of the applications is to develop a communication system for users by tracking eye movements.

Date: 14.01.2020 / 16:00 Place: A-212

This study is about using a persuasive game as a tool to raise awareness about trolling behavior. A game about online trolling behavior is designed and implemented. After exploring how the toxic behaviors that are considered as trolling in the context of online gaming are perceived, this study observes if the persuasive game has an influence in raising awareness and knowledge about these behaviors.

Date: 09.12.2019 / 10:00 Place: A-212

Features extracted from speech are widely used for problems such as biometric speaker identification, but the use of speech data raises concerns about privacy. We propose a method for speech and posture classification using only breath data. The acoustical information was extracted from breath instances using the Hilbert-Huang transform and fed into our CNN-RNN network for classification. We also created our publicly available dataset, BreathBase, which contains more than 5000 breath instances of 20 participants in 5 different postures with 4 different microphones. Using this data, 85% speaker classification and 98% posture classification accuracy is obtained.

Date: 27.11.2019 / 15.00 Place: A-212