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

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

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

In the United States, institutional investors submit 13F reports to SEC every quarter, disclosing the number of shares they own. However, investors may delay filing these reports to conceal their strategies. In this study, data from 13F filings were enriched with additional information such as stock prices and industries to create a bipartite, dynamic, and rich-attributed graph. The changes in network metrics, edge counts, and motif counts were visualized, highlighting crises in the relevant time period. The study found that clustering coefficient, significant position changes, late-filed 13F reports, and specific motif counts of graph shows significant changes during market crashes.

Date: 18.01.2024 / 10:00 Place: A-212

Cancers of unknown primary (CUP) pose treatment challenges due to unidentified primary sites, affecting survival rates significantly. Traditional methods falter in pinpointing origins. Gene expression-based studies offer promise; machine learning models trained on annotated cancer data achieve remarkable accuracy, surpassing conventional methods. Our study, utilizing three machine learning models trained on TCGA data, attained competitive accuracy, around 96%.

Date: 18.01.2024 / 11:00 Place: A-108