Suchergebnisse

instances of incompleteness was evaluated through a series of experiments using different datasets and various prompting techniques. The F1 score was employed

Elements (UMEs) in the model and Missing Model Elements (MMEs) in the text using the linkage information. ArDoCo’s approach shows promising results but has

Vortragende(r) Fabian Reinbold Titel Entity Recognition in Software Documentation Using Trace Links to Informal Diagrams Vortragstyp Bachelorarbeit Betreuer(in)

meta-data set by characterizing base features using meta-features and quantifying their respective importance using model-agnostic feature importance measures

solution is to create a basic pretrained model using self-supervised objectives and then to fine-tune it using a smaller document-specific annotated dataset

Vortrag bearbeiten

Vortrag bearbeiten

detector JPlag. We show that this prevents automatic plagiarism generation using statement insertion and reordering. Additionally, we confirm that JPlag’s

approach to link elements from different artifacts is to identify similar words using word similarity measures. ArDoCo is a tool that uses word similarity measures

Karaca Titel Validating Completeness in Software Requirements Specifications using Large Language Models Vortragstyp Bachelorarbeit Betreuer(in) Lars König

addition) using measures of displacement. We also investigate the stability of privacy measures toward load profile length and number of bins using specific

applications. A prototypical implementation of the adapted approach was evaluated using real-world Lua-based sensor applications from the SICK AppSpace ecosystem

Kurzfassung The goal of this bachelor thesis was to analyse classification results using a 2017 published method called shap. Explaining how an artificial neural

selection using meta-learning approaches. Meta-learning, also known as learning how to learn, serves as the primary tool for this study. First, by using the

The main goal knowledge discovery focusses is, an increase of knowledge using some set of data. In many cases it is crucial that results are human-comprehensible

Titel Development of an Active Learning Approach for One Class Classification using Bayesian Uncertainty Vortragstyp Proposal Betreuer(in) Bela Böhnke Vortragssprache

physically consistent results. Theory-Guided Data Science (TGDS) aims at using scientific knowledge to improve the effectiveness of Data Science models

considers vulnerability analysis in software architecture. The analysis is using access control policies together with the vulnerabilities and their combinations

Vortragende(r) Florian Leiser Titel Modelling Dynamical Systems using Transition Constraints Vortragstyp Masterarbeit Betreuer(in) Pawel Bielski

abstractions and explain their differences. We then evaluate these abstractions using multiple datasets. We show that different abstractions have pros and cons