Research News

M.S. Thesis
Ata Hüseyin Aksöz, A Meta Synthesis on Cloud Task Scheduling Algorithms: COVID-19 and Onwards

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

Ph.D. Thesis
Burçin Sarı, Exploring The Impact of IT Governance Mechanisms on IT Agility Capabilities and Consequences of Centralization of IT Governance

The study aimed to understand the impact of IT governance mechanisms on IT agility. Results indicated that relational IT governance mechanisms significantly enhance IT agility, unlike structures and processes, which were not significant. Relational mechanisms involve top management support, cross-functional training, and a clear IT role within the firm, fostering mutual understanding and integration between IT and business units. While structures and processes are essential for compliance, they may not independently achieve agility. Future research should investigate how these formal mechanisms enable or inhibit agility. Additionally, a hybrid IT governance model may balance centralization and decentralization, offering both efficiency and flexibility.

Date: 22.05.2024 / 13:00 Place: A-212

M.S. Thesis
Emre Mutlu, Image-Based Malware Family Classification with Deep Learning and A New Dataset

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

M.S. Thesis
Ali Eren Çetintaş, Meaning, Referentiality and Distribution: A Computational Investigation of Markers in German Compounding

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

M.S. Thesis
Seda Demirel, A Computational Study on Accusativity and Ergativity

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