M.S. Thesis

M.S. Thesis

Mert Türedioğlu, Model-Based Route Planning and Difficulty Estimation of Indoor Bouldering Problems

Bouldering is a subdiscipline of climbing that challenges both problem-solving skills and physical abilities. This study focuses on the decision-making processes of climbers when solving boulder problems in an artificial and standardized climbing wall called MoonBoard. This study aims to build a goal-based AI agent that learns from previous solutions to plan the sequence of actions for novel boulder problems it encounters. We evaluate the agent's cost estimates and climbing solutions by comparing it to the difficulty estimations and solutions provided by expert climbers.

Date: 26.01.2023 / 09:30 Place: A-212

English

Beyza Eren, The Acquisition of Turkish Causal Connectives: An Experimental Study on Content and Epistemic Domains

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

English

Şükrü Alataş, A Deep Neural Network Based Product Metadata Validation Approach for Online Marketplaces

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

English

Mehmet İlteriş Bozkurt, Analyzing Counterfactual Statements in Turkish: A Framework That Combines Linguistics and Causal Modeling

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

English

Gülşah Kargın Aslım, Assessment for Identifying Skills Gaps in Higher Education High Performance Computing Related Programs

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

English

Cansu Alptekin Gökbender, Visual Aids for Interpreting Predictive Probability Distributions Obtained From Bayesian Network Models

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

English

Mehmet Can Baytekin, Dimension Decoupled Region Proposal Network for Object Detection

In this thesis, we proposed new region proposal network to eliminate the traditional region proposal network’s disadvantages. Region Proposal Networks are used for generating object candidate boxes for two stage object detection algorithms to detect objects with higher accuracy rate on Deep Learning area. With this proposed method, the accuracy of the classical methods is passed on MS-COCO dataset.

Date: 19.01.2023 / 13:30 Place: -

English

Ömer Öztürk, Analysis of Industrial 4.0 Technologies’ Adaptation Using Interpretive Structural Modelling: Empirical Findings From Manufacturing Sector in Turkey

The emerging destructive Technologies such as big data, internet of things (IoT), cloud have revealed the Industry 4.0 revolution. Although Industry 4.0 technologies have very important benefits to the manufacturing sector, various obstacles may arise against the application of these technologies. The aim of this thesis is to make detailed studies on the Industry 4.0 revolution and to reveal the obstacles that may arise during the application of Industry 4.0 technologies to the Turkish manufacturing sector. Interpretive Structural Modeling (ISM) technique was used while establishing the relationship between the barriers in front of IE 4.0 adaptation. ISM Framework was obtained with the findings obtained as a result of the study.

Date: 25.01.2023 / 10:30 Place: A212

English

Kerem Nazlıel, Data Science Technology Selection: Development of a Decision-Making Approach

Recent developments in IT and Analytics have created a multitude of technologies for analytics professionals to use. This technology variety complicates the technology selection process. When improper technologies are selected, analytics teams face problems, inefficiencies, and technical debt. To solve this problem, this thesis proposes a systematic technology selection approach and tests it on a case study.

Date: 29.12.2022 / 14:00 Place: A-108

English

Salih Samet Akar, Development And Validation Of A Cloud Framework For A Small And Medium Enterprise In Defense Industry

With the widespread use of cloud computing in software development processes, many industries have started to use cloud computing. Within the scope of this study, a framework has been created to enable the use of cloud computing in the defense industry. While creating the framework, all the constraints required by the defense industry were complied with and the appropriate cloud structure was selected. At the end of the study, the use of the framework was confirmed by the defense industry employees.

Date: 19.01.2023 / 10:00 Place: ODTÜ Enformatik Enstitüsü

English

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