Research News

M.S. Thesis
Gökçe Komaç, A Study of Using a Persuasive Game as a Tool to Raise Awareness About Trolling Behavior

This study is about using a persuasive game as a tool to raise awareness about trolling behavior. A game about online trolling behavior is designed and implemented. After exploring how the toxic behaviors that are considered as trolling in the context of online gaming are perceived, this study observes if the persuasive game has an influence in raising awareness and knowledge about these behaviors.

Date: 09.12.2019 / 10:00 Place: A-212

M.S. Thesis
Atıl İlerialkan, Speaker and Posture Classification Using Instantaneous Acoustic Features of Breath Signals

Features extracted from speech are widely used for problems such as biometric speaker identification, but the use of speech data raises concerns about privacy. We propose a method for speech and posture classification using only breath data. The acoustical information was extracted from breath instances using the Hilbert-Huang transform and fed into our CNN-RNN network for classification. We also created our publicly available dataset, BreathBase, which contains more than 5000 breath instances of 20 participants in 5 different postures with 4 different microphones. Using this data, 85% speaker classification and 98% posture classification accuracy is obtained.

Date: 27.11.2019 / 15.00 PlaceA-212

M.S. Thesis
Müge Değirmenci Camcı, Synthesis of Realistic 3D Artifacts Using Flow Fields

There is a high demand for realistic computer aided imagery by many applicatiion areas such as games and movies. Due to the complicated characteristics of certain natural phenomena such as fire, smoke or mist, it is difficult to realistically mimic these effects. There are various approximation methods to visually synthesize lifelike 3D artifacts. The use of flow fields to guide the motion of particles creates a random but natural-looking effect. The aim of this study is to use flow fields to generate realistic 3D visual effects.

Date: 06.12.2019 / 13:00 Place: B-116

Ph.D. Thesis
Sarper Alkan, A computational model of the brain for decoding mental states from fMRI

In this study we present a brain model for decoding mental states that are captured using functional brain imaging (fMRI). We postulate that, the human brain processes information coming from the senses using specialized brain regions and the brain combines the activity of the specialized regions to come up with a coherent mental state. We model the postulated pattern of information processing in the brain as follows: First, we propose to capture the activity of specialized brain regions using homogenous voxel (volumetric pixel) groups: Supervoxels. Second, we combine the activity of supervoxels to decode the overall mental state using classifier ensembles: Brain Region Ensembles (BRE).

We test our model in three distinct fMRI datasets, where our model performs better, in terms of accuracy of classification of mental states, than the widely used brain decoding methods that rely on voxel selection. Also, we present how supervoxels can be used for the localization of the brain regions that are effective in discriminating the mental states under consideration regarding fMRI experiments.

Date: 07.10.2019 14:30 Place: Conference Hall 1

Sarper Alkan, A computational model of the brain for decoding mental states from fMRI

M.S. Thesis
Gizem Özen, The effect of kinemorphs on F-Formation shapes: an investigation on human robot interaction in virtual reality

In this study, firstly, the effect of kinemorphs on F-formation shapes are investigated by focusing on the differences between the virtual environment and the real life settings. Secondly, the role of one-to-one F-formation shapes on joining a dyadic interaction as a third interactant is also studied.

Date: 12.09.2019 / 15:00 Place: A-212

Gizem Özen, The effect of kinemorphs on F-Formation shapes: an investigation on human robot interaction in virtual reality