Graduate School of Informatics

This study aims to gain an understanding of the content and the epistemic causal connective acquisition process of children aged 6;5- 8 in Turkish. For this purpose, to test whether there are connectives that children use specific to domains of causality as adults do (Çokal, Zeyrek, & Sanders, 2020); child and adult participants are given both descriptive (biased for content relations) and argumentative (biased for epistemic relations) tasks. Data that is collected from these tasks are annotated and statistically analyzed.

Date: 26.01.2023 / 13:30 Place: A-212

This research proposes a new AI-based approach to improve the user experience on online marketplaces by validating product images and metadata in an automated fashion. As e-commerce has become popular, online marketplaces have seen a surge in merchants offering various products and maintaining data quality of product metadata and images can be challenging. Our approach offers several advantages over traditional methods, including handling complex and noisy data and adapting to various challenging product categories, such as fashion items. The effectiveness of this approach is demonstrated through comparisons with traditional methods and in different settings.

Date: 23.01.2023 / 13:00 Place: A-212

Counterfactual statements are conditional statements with false or unrealized antecedents. This thesis aims to analyze counterfactuals in Turkish using linguistic and causality perspectives, which will shed new light on their nature. The research will examine the antecedent’s need to be false or unrealized, the use of a complex suffix in Turkish and its relation to counterfactual interpretation, the difference between -DIysA and -sAydI and the role of pragmatics in the interpretation of a counterfactual statement. Additionally, this thesis is the first to have a clear focus on Turkish counterfactuals and use directed acyclic graphs to graphically represent counterfactual scenarios.

Date: 26.01.2023 / 11:00 Place: A-212

The goal of this thesis is to evaluate the skills mismatch in the curricula of HPC MSc's programs for some of the most relevant positions in the HPC areas in light of the ESCO database criteria and industry requirements. The four separate profiles "Data Science, Computer Architecture, Parallel Programming, and DevOps" that are thought to be crucial in HPC are the focus of this research. The methodology of this contribution explicitly examines the key responsibilities for the aforementioned positions and conducts a gap analysis based on Natural Language Processing (NLP) techniques for the competencies required for each in the MSc degree curriculum. The goal of applying NLP is to determine the degree to which the occupational skills stated in the ESCO and the HPC graduate courses’ syllabuses are semantically similar, as well as the level at which they overlap.

Date: 16.01.2023 / 13:00 Place: B-116

This thesis demonstrates research towards innovating a novel framework that (i) could be trained by supervised learning of discourse parsing from annotated and parsed relations, (ii) makes it easy to build end-to-end shallow discourse parsing system, (iii) forms a discourse relation detector benchmark for low resourced languages. Our modern neural network approach, integrated with contextualized text embedding, produced by pretrained language models, accomplished two sub-tasks of shallow discourse parsing, namely, identification of discourse relation realization types and the sense classification of explicit and implicit relations. The effect of multilingual data aggregation on the classification of discourse relation type through Cross-lingual Transfer Learning experiments is researched too.

Date: 25.01.2023 / 10:00 Place: A-108

Building on an extensive review of the psycholinguistics literature and Turkish Derivational Morphology (DM), we propose a novel structure for representing DM in three hierarchical layers: segmentation, lexical selection and derivation. This proposal involves laying a conventionalized structure over the traditional morphological structure of DM. We develop a computational model of morphology processing based on this structure using Bayesian Belief Networks (BBN). We present an algorithmic implementation for this model that learns and accurately represents new lexical items, recognizes affixes and tracks the salience of each item probabilistically. We carry out trials on this model with realistic observation lists and observe that model predictions are in line with the findings in studies in psycholinguistics.

Date: 25.01.2023 / 12:00 Place: A-108

Musical conducting is the art of making real time manipulations on a musical piece as if the instruments are the musicians themselves. Orchestra responses affect the conductor’s actions while a musical piece is being played. Therefore there is a significant information flow on the stage. In this research, we focused on the causality relations between the conductor, orchestra and the musical scores. We collected data from an actual orchestral practice and processed the collected data for causality measurements using the Transfer Entropy method. We utilized the method also for unsupervised detection of conducting commands.

Date: 20.01.2023 / 10:00 Place: A-108

In Türkiye, Social Security Institution is the primary healthcare insurer. Turkish citizens are registered under General Medicare Insurance coverage. In 2003, Ministry of Health (MoH) has initiated the “Health Transformation Program”, and implemented the interoperable health data exchange standards. The MoH is focusing on collecting medical data in a coded, structured, and electronic format, generated at all healthcare providers. Contrarily, genetic test results are exchanged in narrative, unstructured form among governmental and private health care providers. In this dissertation, we lay out the bottlenecks and put forward a conceptual model for meaningful genomic data exchange for Turkish Electronic Health Records.

Date: 18.01.2023 / 15:00 Place: B-116

Communicating uncertainty is a challenging task. Personal differences such as culture, cognitive load and even feelings of the user can impact the interpretation of an uncertain situation. Decision support models such as Bayesian networks can aid dealing with uncertainty. However, the outputs of these models are probability distributions, their interpretation can be challenging and visualisations can help with this task. The aim of the study is to investigate how effective visual aids communicating BN predictions are and users preferences regarding these visual aids. Model’s prediction and performance are needed to communicate BN predictions. Hence, in this study both model’s prediction and performance are investigated. BN model that has been developed to make predictions on a medical condition namely, Trauma Induced Coagulopathy (TIC BN) are used as a case study.

Date: 16.01.2023 / 14:00 Place: A212