Graduate School of Informatics

The software industry is playing a significant role in development of economies all over the world. It is mainly made up of small and medium software enterprises (SMEs). These companies aim to benefit from Software Process Improvements (SPI) to increase product quality and productivity. As SPI require organizational change and adaptation to new work practices; organizations have to handle with several challenges emerged from this change. The objective of this study is to identify the factors that influence the success and the acceptance of SPI models used in SMEs. Moreover, it is aimed to analyze the attitude of SME employees towards the SPI models in Turkey.

Date: 25.01.2023 / 14:00 Place: B-116

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

This research aims to enhance the perceptual quality of adversarial attacks, regardless of the type of attack used. To achieve this, the study proposes an integration of two seperate attack independent techniques. The first technique, called Normalized Variance Weighting, applies a variance map to intensify the perturbations in high variance areas. The second technique, called the Minimization Method, minimizes the perceptual distance between the benign and adversarial example iteratively. Integrated Method, with the first method applied during the attack and the second method applied after the attack has quantifically the best visual quality.

Date: 23.12.2022 / 10:00 Place: A108

This thesis solves the 3D mesh segmentation problem from a different perspective. This perspective contains a data-centric approach with unsupervised learning. This means that 3D mesh segmentation mapping plays a key role in terms of a data-centric approach. We provide a mapping architecture using the node2vec model that also solves the curse of dimension and transforms 3D mesh data into an embedding vector. Segmentation was obtained using the K-Means clustering algorithm using this embedding vector. Additionally, we provide a new strategy that evaluates the graph embedding vector and a new inertia method calculated on 3D mesh data, geodesic inertia.

Date: 02.12.2022 / 14:00 Place: Computer Engineering - A105