Announcements
Research News
This thesis develops a reproducible methodology for classifying mental workload using EEG signals across multiple sessions. Guidelines for reproducible research are established and a thorough review of existing EEG-based workload classification studies is conducted to assess their reproducibility status. Graph neural networks are employed for classification. Domain adaptation with optimal transport is explored for improved generalization across sessions. Subject-specific evaluations using diverse metrics are performed to assess model performance. The outcomes aim to enhance the robustness and generalizability of mental workload classification for brain-computer interfaces and other cognitive workload applications.
Date: 20.12.2024 / 13:00 Place: A-212
This thesis aims to understand the applicability of virtual reality or mixed reality environments to perform model-based systems engineering and develop a prototype for a framework for such uses. By conducting user tests with people from systems engineering and interactive application and game development background, identifies the primary advantages and disadvantages of using these environments compared to desktop environment. The outcomes serve as a strong baseline for possible future research and established that the virtual reality or mixed reality environments can be suitable for model-based systems engineering.
Date: 26.11.2024 Place: A-212
This thesis presents a method of comparing the cost-effectiveness of 3D mesh simplification algorithms using the McGurk effect, where visual and auditory cues are combined to create an illusion. The study involves designing a human head mesh, animating mouth movements, and recording certain syllable sounds to produce a virtual scene. Using this virtual scene and applying three different mesh simplification algorithms on the animated head, a user study was conducted to test and measure the effectiveness of each algorithm for each different syllable in medium and high difficulty levels. Results highlight the balance between computational efficiency and perceptual accuracy, providing insights for 3D modeling and virtual reality applications.
Date: 29.11.2024 / 10:00 Place: A-212
Active Directory (AD) is one of the most widely used directory services today, playing a key role in organizing and managing network resources within an organization. A robust security strategy is crucial to prevent and detect AD attacks, which can be difficult to detect due to their blend in with normal network traffic. One such attack is the Kerberoasting attack, which exploits weaknesses in the Kerberos authentication protocol. To detect these attacks, supervised machine learning algorithms are being proposed. And also publicly available dataset to measure the efficiency of these algorithms for Kerberoasting attacks was created and shared.
Date: 22.11.2024 / 14:00 Place: A-108
Introducing the EnSCAN framework, we propose a pioneering algorithm to consolidate selected variants even across distinct platforms, thereby prioritizing candidate causative loci and enhancing ML outcomes through combining prior information captured from each multi-model of each dataset. The proposed ensemble algorithm utilizes chromosomal locations of SNVs by mapping to cytogenetic bands, along with the proximities between pairs and multi-model via Random Forest validations to prioritize SNVs and candidate causative genes for Alzheimer Disease. The scoring method is scalable and can be applied to any multi-platform genotyping study. We present how the proposed EnSCAN scoring algorithm prioritizes the candidate causative variants related to LOAD among three GWAS datasets.
Date: 06.09.2024 / 16:00 Place: B-116
