Brain-computer interfaces (BCIs) offer solutions for motor impairments and enhance human-computer interaction in virtual reality and cognitive augmentation. Adoption is hindered by user fatigue and the unnatural feel of visual stimuli, necessitating comfortable, intuitive paradigms. This study develops an imperceptible steady-state motion visual evoked potential (SSMVEP) stimulus for multi-directional BCI control. Using sinusoidal gratings with high-frequency motion, the first experiment shows robust cortical responses with reduced discomfort. The second experiment combines eye-tracking, EEG, and advanced machine learning to decode attentional responses accurately. Findings support naturalistic, high-performance BCIs for assistive technologies and human-computer interaction.

Date: 05.07.2024 / 14:00 Place: A-212

We employ the XGBoost algorithm to predict driver non-coding mutations based on multiple engineered features, augmented with features from existing annotation and effect prediction tools. The resulting dataset is passed through a feature selection and engineering pipeline and then trained to predict driver versus passenger non-coding mutations. We also use this model within the architecture of a known driver discovery model from existing literature. We then use non-coding driver mutations found in previous studies and predict their driver-ness using our models. Furthermore, we use Explainable AI methodologies to perform an in-depth analysis of the generated predictions.

Date: 07.06.2024 / 10:00 Place: A-212

This thesis explores attribution mechanisms in Turkish discourse through the adaptation of the Penn Discourse TreeBank (PDTB) framework, resulting in the Turkish Discourse Bank (TDB 1.2). Utilizing insights from lexical control and eventuality specific to Turkish, a custom annotation scheme was developed, facilitating robust data annotation. Analysis shows the predominance of communicative verbs in attribution instances, highlighting novels and news as rich domains for study. Achieving high inter-annotator agreement, this work advances the field by enriching the TDB and laying groundwork for future automated text analysis in Turkish.

Date: 04.09.2024 / 10:00 Place: B-116

This study investigates the association between SNPs and COVID-19 susceptibility in the Turkish population, focusing on patients from Hacettepe University Hospital. Using NGS, we analyzed SNP data from various scientific publications, performing variant calling, linkage analysis, and statistical comparisons with non-Finnish European allele frequencies. Key findings indicate that certain variants have different frequencies compared to the European population, suggesting genetic predispositions affecting disease susceptibility in the Turkish population. Linkage disequilibrium analysis revealed strong correlations between specific genetic loci.

Date: 23.07.2024 / 15:00 Place: A-212

This study examines infrastructure issues and system malfunctions in Cloud Computing systems exacerbated by the COVID-19 pandemic, which acts as a stress test due to increased demand. It is argued that task scheduling algorithms are the main source of these problems. Post-pandemic Cloud Computing task scheduling algorithms were systematically reviewed and analyzed using the Meta-Synthesis method. A global categorization schema for these algorithms was presented, comparing their advantages, disadvantages, applications and vulnerabilities. Current task scheduling approaches and trends in Cloud Computing were analyzed comparatively.

Date: 27.05.2024 / 13:30 Place: B-116

This thesis aims to make experimental studies on malware family classification using deep learning algorithms. A new dataset called MamMalware which is publicly available and has 450K labeled malware was created within this study. Samples in dataset were translated into gray-scale image files, and the opcode sequences were also extracted. Image files and opcode sequences were used as input. Then 2 and 3 layered Convolutional Neural Networks (CNN) experiments were applied on MamMalware dataset. In addition, experiments using the transfer learning methods with ResNet152 and VGG19 pretrained models were conducted. As a result, the transfer learning models obtained the best results with 94% test accuracy.

Date: 17.05.2024 / 11:00 Place: A-212

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: 22.04.2024 / 09:00 Place: B-116

This study investigates the potential outcomes when children are exposed to 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, English grammar is constructed with 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, a comparative analysis is conducted to determine the predominant category—accusative or ergative—in children's language acquisition.

Date: 22.04.2024 Place: B-116

This thesis delves into a fresh approach within the domain of digital music composition, offering an extensive model that replicates the complex social interactions among composers, broadcasters, and synthetic audiences. Utilizing sophisticated machine learning techniques, the research examines the development of compositions within a dynamic environment where composers iteratively adjust their styles in response to feedback from artificial audiences.

Date: 22.04.2024 / 10:00 Place: B-116

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