Semantische Suche

Extracting meaningful insights from the compared corpora requires various linguistic metrics, statistical analyses and visualization techniques. By identifying patterns, trends and linguistic changes we can uncover valuable information on language usage evolution over time. This thesis provides a comprehensive framework for comparing the GBNC to other corpora, showcasing the development of a database application that enables not only valuable linguistic analyses but also shed light on the composition of the GBNC by highlighting linguistic similarities and differences.

Daher wird dieser Arbeit ein Konzept für eine automatische Konsistenzhaltung vier verschiedener Modelle umgesetzt, welche als Eingabemodelle für eine statische Sicherheitsanalyse dienen. Diese vier Modelle sind ein Architekturmodell, dessen Quelltext und jeweils dazu passende Annotationen für eine statische Sicherheitsanalyse. Es wird zunächst ein Konzept für die Konsistenzhaltung zwischen diesen vier Modelltypen entwickelt. Für das entwickelte Konzept wurde anhand einer Fallstudie mit vier konkreten Metamodellen in dem Framework Vitruvius eine Konsistenzhaltung implementiert. Für diese wurde auf einer existierenden Konsistenzhaltung zwischen dem Quelltext und der Architekturmodellierung aufgebaut. Diese Implementierung wurde anhand eines Testmodells evaluiert. Diese hat ergeben, dass es machbar ist, anhand des in dieser Arbeit vorgestellten Konzeptes eine Konsistenzhaltung für die Eingabemodelle zu implementieren. Jedoch ist die Implementierung der Regeln aufwändig bei komplexen Abbildungen zwischen den Elementen.

We propose using a GAN architecture with feature ensemble learning to address hyperparameter sensibility and architectural dependency. This follows the success of feature ensembling in mitigating these problems inside other areas of Deep Learning. This thesis will study the optimization problem, training, and tuning of feature ensemble GANs in an unsupervised scenario, comparing it to other deep generative methods in a similar setting.

With many algorithms to choose from, each requiring proper parameterization, making an informed decision becomes difficult. Moreover, the selected algorithm is typically applied uniformly across the entire dataset, regardless of the specific data analysis task, often leading to suboptimal results.

In this thesis, we evaluate a novel attention-based method for automating the selection of log parsing algorithms, aiming to improve data analysis outcomes. We build on the success of a recent Master Thesis, which introduced this attention-based method and demonstrated its promising results for a specific log parsing algorithm and dataset. The primary objective of our work is to evaluate the effectiveness of this approach across different algorithms and datasets.

We propose a GNN-based model that can predict the fill times and physical properties of the mold filling process. We model the mold geometry as a static graph and encode the process information into node, edge, and global features. We employ a self-attention mechanism to enhance the learning of the direction and magnitude of the fluid flow. To further enforce the physical constraints and behaviors of the process, we leverage domain knowledge to construct features and loss functions. We train our model on simulation data, using a multi-step loss to capture the temporal dependencies and enable it to iteratively predict the filling for unseen molds. Thereby, we compare our models with different distance-based heuristics and conventional machine learning models as baselines in terms of predictive performance, computational efficiency, and generalization ability. We evaluate our architectural and training choices, and discuss both the potential applications and challenges of using GNNs for surrogate modeling of injection molding.