Graduate School of Informatics

This thesis presents an integrated framework for AI-assisted BIM analytics and digital twin development to support indoor navigation and emergency evacuation. Revit-based BIM data are transformed into a unified pipeline by separating geometric representations from structured spatial information and integrated into a Unity-based digital twin. Real-time indoor pathfinding is enabled using the A* algorithm while considering physical evacuation constraints. A large language model (LLM) provides natural-language navigation instructions and supports context-aware user interaction. The system enables interactive visualization and testing and is evaluated through user studies, demonstrating a scalable and human-centred approach for BIM-based digital twin applications.

Date: 14.01.2026 / 14:15 Place: II-06

The problem of distinguishing legitimate domains from DGAs is an ongoing challenge. CTAs use DGAs to bypass traditional detection mechanisms like blacklisting or static signature-based solutions. Since blacklisting all DGA-generated domains is not possible, the automatic detection of DGAs remains a critical necessity. Despite years of research, modern malware continues to evolve, and this makes detection increasingly difficult. This thesis employes a hybrid framework that integrates CNN based n-gram learning and LSTM for automatic feature extraction and Logistic Regression (LR) for final classification. The model input is further enriched by Top-Level Domain (TLD) information in order to improve DGA detection.

Date: 16.01.2026 / 11:00 Place: Cisco Lab

This thesis presents a comparative empirical evaluation of AI-generated front-end code quality. Three contemporary AI coding tools—Cursor, Gemini CLI, and Windsurf—are assessed through controlled experiments involving React and TypeScript applications of varying complexity. The study evaluates both single-prompt and incremental development approaches using industry-standard metrics, including Lighthouse, SonarQube, and ESLint. The findings demonstrate that while AI tools can generate functionally correct applications, code quality outcomes vary significantly depending on tool choice, development methodology, and application complexity. The results provide evidence-based guidance for selecting AI tools and workflows in modern front-end development.

Date: 20.01.2026 / 13:00 Place: A-212

This thesis introduces the Adaptive Reasoning Curriculum (ARC), a standardized framework that uses Large Language Models (LLM) for Automated Curriculum Learning (ACL). Designed to improve generalization in complex Open-Ended Learning (OEL) environments like Neural MMO 2, ARC facilitates efficient skill acquisition rather than environment memorization. Experiments demonstrate that ARC significantly outperforms expert-curricula and existing ACL methods across average return and sample efficiency. By providing open-source adapters and auxiliary tools, ARC establishes a reproducible, accessible standard for integrating LLMs into multi-agent RL, fostering broader innovation in open-ended research.

Date: 14.01.2026 / 13:00 Place: A-212

This study proposes a framework for investigating the alignment between institutional reviewer guidelines and peer review practice. A dataset was constructed from the ICLR 2024 venue on OpenReview. Large Language Model (LLM) pipelines were utilized to extract reviewer inquiries, perform topic modeling, and apply soft classification. These outputs were compared against the ICLR 2024 reviewer guideline to quantify the guideline-practice gap. Generated topics are also mapped to the guidelines from the top ten AI venues. The proposed framework provides a data-driven approach to identify implicit evaluation norms for venue organizers and gives authors and reviewers a data-driven roadmap of implicit evaluation norms.

Date: 14.01.2026 / 14:30 Place: A-212

This research addresses a persistent paradox in the digital economy: While data is increasingly recognized as a strategic asset, data science projects designed to leverage its potential impact continue to suffer from high failure rates. As established in management theory, measurement is the prerequisite for improvement; without the ability to objectively assess success, organizations cannot effectively detect risks or optimize their initiatives. However, the current literature lacks a formalized, operationalizable success assessment model that accounts for distinct characteristics of data science projects and is applicable across diverse project types and contexts. To bridge this gap, this thesis develops the Data Science Projects Success Assessment Model (DS PRO-S). Adopting a Design Science Research (DSR) approach, the study constructs a holistic solution that functions as a meta-model, an instantiation toolkit, and a methodology to make project success explicit, measurable, and comparable. This architecture is supported by a rigorous mathematical formalization of measurement and evaluation, aligned with the ISO/IEC 15939 standard. By introducing evaluations at both project and phase levels and decoupling success (the achievement of objectives) from health (establishing the enabling conditions for success), DS PRO-S offers a modular and asynchronous assessment capability with operational flexibility. The applicability and usefulness of DS PRO-S were validated through expert interviews and multiple case studies.

Date: 21.01.2026 / 13:30 Place: A-212

Accurate identification of splice-altering genetic variants is critical for understanding disease mechanisms and improving clinical variant interpretation. Although deep learning–based splice prediction tools perform well for canonical splice-site variants, their ability to detect exonic splice-altering variants remains limited. This limitation is primarily due to the scarcity of experimentally validated exonic variants and model architectures optimized for canonical splice motifs rather than regulatory exonic regions. Overall, this study provides a comprehensive evaluation of current splice prediction tools, demonstrates the benefit of targeted retraining for exonic variant detection, and establishes a foundation for developing more accurate and clinically relevant splice-altering variant prediction models.

Date: 15.01.2026 / 11:00 Place: A-212

This thesis proposes a data-driven framework to predict the surface roughness (Ra) of additively manufactured parts before printing. The most influential printing parameters were identified, and an experimental design was developed to generate a new, comprehensive dataset that contributes to the literature. Machine-learning and deep-learning models were implemented, with a multilayer perceptron (MLP) as the primary model to capture nonlinear effects. Data augmentation via a Conditional Generative Adversarial Network (CGAN) was investigated, and its impact on generalization was quantified. Finally, a custom web-based graphical user interface (GUI) was implemented for 3D model upload, interactive orientation and parameter adjustment, and real-time Ra visualization.

Date: 06.01.2026 / 13:00 Place: A-212