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Friedrich-Alexander-Universität Lehrstuhl für Wirtschaftsinformatik IT-Management
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Friedrich-Alexander-Universität Lehrstuhl für Wirtschaftsinformatik IT-Management
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  • Offene Abschlussarbeiten

Offene Abschlussarbeiten

Google AI Blog

Thema Betreuer Typ Partner
Testing and Evaluation of Explainability in AI

Description: The growing adoption of artificial intelligence (AI) has created a need for models that are not only accurate but also interpretable. Explainable AI (XAI) aims to provide insights into how AI systems make decisions, which is critical for enhancing transparency, accountability, and trustworthiness. However, measuring and evaluating the explainability of AI models remains a challenge. This thesis will focus on exploring the testing and evaluation of explainability in AI. The goal is to develop a systematic approach to assess the degree of explainability of different AI models and identify best practices for designing explainable AI systems. The thesis will involve conducting a comprehensive literature review, designing and conducting experiments to evaluate the explainability of AI models, and proposing metrics to quantify the interpretability of models.

Tobias Clement Bachelor/Master
Explainable AI for Image Segmentation and Quality Inspection

Description: Image segmentation and quality inspection are crucial tasks in various domains, such as manufacturing, healthcare, and autonomous vehicles. Deep learning-based approaches have shown remarkable performance in these tasks; however, they often lack interpretability, making it challenging to understand how the models make decisions. Explainable AI (XAI) can help address this issue by providing insights into the decision-making process of AI models. This thesis will focus on developing an XAI approach for image segmentation and quality inspection tasks. The goal is to design and implement an XAI system that can accurately segment images and inspect their quality while providing interpretability into the model’s decision-making process. The thesis will involve conducting a comprehensive literature review, designing and implementing a deep learning-based image segmentation and quality inspection model, developing an XAI approach to explain the model’s decision-making process, and evaluating the effectiveness of the XAI system in a given use case based on the TTPLA dataset. The thesis will also involve exploring the potential of integrating different XAI techniques, such as attention mechanisms and visualization, to enhance the interpretability of the model. This research will contribute to the advancement of XAI and help organizations develop more interpretable and trustworthy image segmentation and quality inspection systems.

 

Tobias Clement Master
Developing an Approach for a Generalizable Explainable AI Platform

Description: As AI systems are increasingly integrated into various domains, there is a growing need for interpretable AI models that can explain their decision-making processes. However, designing and implementing explainable AI (XAI) systems can be a complex and time-consuming process. Therefore, the development of a generalizable XAI platform can significantly accelerate the deployment of interpretable AI models across different domains. This thesis will focus on developing an approach for a generalizable XAI platform. The goal is to design and/or implement an XAI platform that can be used across multiple domains and scenarios, enabling developers to integrate explainable AI models into their systems more easily. The thesis will involve conducting a comprehensive literature review, identifying common challenges and requirements for XAI platforms, designing and/or implementing a prototype platform, and evaluating its usability and effectiveness in different use cases. The thesis will also involve exploring the potential of integrating different XAI techniques, such as rule-based models, decision trees, and visualizations, into the platform. This research will contribute to the advancement of XAI and help organizations develop more interpretable and trustworthy AI systems.

Tobias Clement Master
How trustworthy is Chat-GPT?

Context: Large Language Models like GPT-3 or BERT have become increasingly popular within the last years. Due to the strong improvements in natural language understanding and generation they have a huge potential to be used in real-world applications within  a broad spectrum of application fields. However, it was shown that they can adapt unethical behavior, e.g. by using inappropriate language or stating wrong information.

Task: Develop a framework to systematically assess the trustworthiness of Large language models.

Please apply with brief motivation letter and resume.

Nils Kemmerzell Master –
Development of a Framework to Assess Trustworthiness for Text/Tabular Data

Context: Trustworthy AI refers to the development and deployment of artificial intelligence systems that are reliable, ethical, transparent, and secure. It aims to ensure that AI systems are developed and used in a way that respects human rights and values, is free from bias and discrimination, and can be trusted by individuals and society as a whole.

Task: Development of a Framework to systematically assess the trustworthiness of a ML learning model based on text or tabular data.

Please apply with brief motivation letter and resume.

Nils Kemmerzell Master –
Requirements Engineering for Trustworthy AI

Context: Trustworthy AI refers to the development and deployment of artificial intelligence systems that are reliable, ethical, transparent, and secure. It aims to ensure that AI systems are developed and used in a way that respects human rights and values, is free from bias and discrimination, and can be trusted by individuals and society as a whole.

Task: Development of a concept for requirements engineering for Trustworthy AI.

Please apply with brief motivation letter and resume.

Nils Kemmerzell Master –
Literature Review on Synthetic Data

Context: Synthetic data is computer-generated data that can be used to supplement or replace real-world data in applications like machine learning and data analysis. It can help overcome limitations in available data and protect privacy.

Task: Systematic Literature Review on Techniques to generate synthetic Data

Please apply with brief motivation letter and resume.

Nils Kemmerzell Bachelor –
Abschlussarbeit zu Cloud-Architekturen und Design Pattern bei der XITASO GmbH

Anwendungen können auf vielfältige Weise entwickelt und betrieben werden. Von Monolithen On-Premises bis zu Serverless und Cloud-native Microservices ist vieles möglich, wenn auch nicht immer alles sinnvoll. Jedes Pattern hat seine eigenen Herausforderungen bezüglich:

  • Kosten
  • Komplexität
  • Performance
  • Skalierbarkeit
  • Security
  • Deployment
  • Testing und vielen weiteren

Das Ziel der Abschlussarbeit ist die Analyse dieser Faktoren am konkreten Anwendungsfall GovRadar. Dieses im Frühjahr 2020 gegründete Tech-Startup treibt die Digitalisierung Deutschlands voran, indem es modernste Technologien einsetzt, um etablierte Prozesse in Behörden zu automatisieren oder komplett abzulösen. Aktuell wird u.a. eine innovative, datengetriebene Beschaffungsplattform für den öffentlichen Sektor entwickelt.

Wir suchen stets engagierte Kolleginnen und Kollegen mit agilem Mindset, die dafür brennen, herausragende Softwarelösungen zu entwickeln und die mit uns den XITASO Weg mitgestalten möchten. Bei XITASO kannst Du Dein im Studium erworbenes Wissen direkt praktisch anwenden. Die Einarbeitung in das Thema verschafft Dir sowohl Grundlagen- als auch vertiefte Kenntnisse aus der Praxis. Zur Ausschreibung: https://xitaso.com/karriere/jobs/abschlussarbeit-cloud-architekturen-und-design-patterns/

Bewerbungen bitte mit kurzem Anschreiben (z.B. Erfahrungen im Bereich Softwareentwicklung) und Lebenslauf.

Annika Schreiner Bachelor/ Master XITASO GmbH
Abschlussarbeiten im Kontext Vertrauenswürdige KI, insbesondere Fairness und Robustheit

Methoden des Maschinellen Lernens (ML) werden heutzutage in zahlreichen Anwendungsgebieten eingesetzt. In Anbetracht der realen (gesellschaftlichen) Auswirkungen besteht ein hoher Anspruch an die Vertrauenswürdigkeit von ML-basierten Systemen. Das Forschungsgebiet „Vertrauenswürdige KI“ (engl. Trustworthy AI) untersucht, wie ein sicherer, transparenter und verantwortungsvoller Einsatz von KI bzw. ML gestaltet werden kann. Dabei sind verschiedene Anforderungen zu erfüllen. Ein wichtiger Aspekt ist, dass Vorhersagen von ML-Systemen fair sind, d.h. keine bestimmten Personen(gruppen) diskriminiert werden. Zum anderen sollen ML-Systeme robust gegenüber Abweichungen realer Daten von Trainingsdaten sein (engl. „Distribution Shift“, z.B. verschiedene Lichtverhältnisse in Bildern). Mögliche Themen:

  • Definition und Evaluation von Robustheit: Distribution Shift, Reliability, Uncertainty
  • Umfragen oder Experteninterviews zum Stand in KI entwickelnden Unternehmen bzgl. Umgang mit Bias, Distribution Shift in ML-Anwendungen

Bewerbungen bitte mit Themeninteresse/ kurzer Motivation und Lebenslauf.

Annika Schreiner Bachelor/ Master –
State-of-the-Art einer ausgewählten Forschungsrichtung des machinellen Lernens

Maschinelles Lernen ist ein Thema von großem Forschungsinteresse.

Für aktuelle Trends siehe z.B. Google AI Blog.

Bitte nur mit einem eigenen Thema bzw. konkreter Forschungsfrage bewerben.

Beispiel: CO2 Fußabdruck von ML und energiesparende Maßnahmen

Stefan Arnold Bachelor/Master –
Evaluating Architectures for Computer Vision w.r.t. Spurious Correlations in Images: A Fairness Perspective

Computer Vision is a subfield of Machine Learning (ML) that typically uses Convolutional Neural Networks (CNNs) to process Images. Instead of using CNNs, Google recently suggested new archtectures (see e.g., Transformer and Mixer) to deal with spatial data. While the top-line generalization accuracy has been comparatevly to CNNs, the fairness (measured as the parity of accuracy) among subgropus remains an open question.

Using the publicly available CelebA dataset, the goal of this thesis is to compare ResNet18 (representative for CNNs), ViT, and MLPMixer in terms of their accuracy within ethnical subgroupus (i.e., Gender and Age). See Sagawa et al. (2020) for the methodological setup (also code snippets available upon request) and Hooker et al. (2020) for a rigorous assessment of algorithmic bias.

NVIDIA GPU or Google Colab Prenium recommended.

Stefan Arnold Master –
Analysis of Modern Data Platform Concepts – A Comparison of Data Mesh and Data Fabric (VERGEBEN)
Data Mesh is a new Term that was first introduced by Zhamak Dehgani in 2019. The Term encompases a socio-technical concept/architecture, that introduces Data as a product in the enterprise context. Here, the organization is divided by independent domain teams that make their operational, analyitical data and associated DS/ML models available throughout the enterprise via consumer-friendly APIs (Data as a Service).In contrast, the term Data Fabric has been around since the mid 2000s. Tackling the same problems as Data Mesh, it tries to break old, inefficient ETL-Pipelines and centralized governance of data by making data a product that is governed decentral. However, Data Fabric has more „strong defined“ technical concepts, such as a layered architecture, which many times works with a semantic middleware like Knowledge Graphs.In this work, the focus lies on comparing these two concepts with qualitative research methods. Among others, some of the results from the analysis are:- Criteria/recommendation for an enterprise when to adopt either of the concepts- A catalogue of the technology stack and/or solution providers for the building blocks
Mark Kram Bachelor/Master Thesis can be done in cooperation with a company
Making State-Of-The-Art Research Transparent – Towards a Database for the Comparison of Machine Learning Experiments (VERGEBEN)
The Machine Learning field represents one of the most active research communities, where each month a big amount of new papers are being published. With a high degree of interdisciplinarity, we can observe that researchers invent or extend algorithms for known problems.
Thus, keeping track of new algorithms and state-of-the-art solutions becomes more challenging. To tackle this problem, there have been efforts to make the comparison of state-of-the-art algorithms more transparent, one example is paperswithcode.com. However, only certain algorithms can be found there.In this thesis, we want to show an alternative to existing benchmarking websites. The goal is to transfer the experiment results for machine learning papers in the field of anomaly detection into a graph database (Neo4J) and to evaluate the solution with regards to comparability and transparency. For this, some existing prework has been done that can be used in the thesis.
Mark Kram Bachelor/Master
Towards a Knowledge Graph for the European Startup Ecosystem (VERGEBEN)
The Startup-Ecosystem is a high evolving one, which encompasses several actors and relationships between each other, such as startups, entrepreneurs, different kinds of investors, incubators and institutions, to name a few. Every year, startups are born, die or become highly valuated unicorns. Understanding and being up-to-date in this highly evolving field is not only critical for several actors such as companies and private investors, but also to several research communities, like innovation research.Knowledge Graphs, on the other side, have become a popular technology when managing highly connected data and complex domains. Thus, in this thesis the goal is to concept a Knowledge Graph (with the respective ontology) that represents the main actors and relationships in the European Startup Ecosystem. Depending on the focus of the thesis, a prototype can be implemented.Pre-requisites:- Knowledge about Ontologies and Knowledge Graphs- Interest for the Startup domain
Mark Kram Bachelor/Master
Relational Text Mining am Beispiel der Startup-Szene (VERGEBEN)
„Relation Extraction“ is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization“ (arXiv:1707.08866).Ziel der Arbeit ist es, anhand eines konkreten Anwendungsbeispiels aus der Domäne der Startupbranche, einen NLP-basierten Algorithmus oder Teile davon prototypisch zu implementieren. Hierbei sollen als Datengrundlage News-Artikel der Startupbranche als unstrukturierte Textdokumente genommen werden.Voraussetzungen:
  • Grundkenntnisse in Python oder anderen Programmiersprachen
  • Grundkenntnisse oder Interesse an ML-Modellen, insbesondere im Bereich Natural Language Processing

 

Mark Kram Bachelor/Master –
Application of Graph Neural Networks for the Detection of Cyberattacks (UNAVAILABLE)
The goal of this thesis is to apply an unsupervised Graph Neural Network on the Coburg Intrusion Detection Dataset  for Network Intrusion Detection and evaluate the solution with other State-Of-The-Art algorithms.More information upon request.Prerequisites:- Knowledge/Experience in Python, Pytorch or similar- Knowledge of Graph Neural Networks
Mark Kram (Bachelor)/Master
Systematic Analysis of Graph-Machine-Learning Use Cases (VERGEBEN)
The goal of this thesis is to identify from the literature the successful application of graph based machine learning approaches, such as Graph Neural Networks and other Graph Embedding techniques. Once the use cases are identified, a systematic analysis is applied, in order to derive meaningful factors that can help identifying graph based approches.More information upon request.Prerequisites:- Knowledge about graphs- Studies of Information Systems or related
Mark Kram (Bachelor)/Master
Graph Neural Networks meet Reinforcement Learning – A Systematic Literature Review (NOT AVAILABLE)

Graph Neural Networks (GNNs) have gained interest in the past years, as they provide a suitable neural networks based technique to process graph and network data. Typical graph data are social networks, used in platforms like Twitter and Facebook.Recently, there have been efforts to combine GNNs with Reinforcement Learning with promising results. Exemplary use cases are fraud detection and molecular property prediction (E.g. for medicinal drug discovery).The aim of this work is to provide a comprehensive systematic literature analysis for GNNs in combination with Reinforcement Learning.

 

Mark Kram Bachelor/Master

Neural Recommendation System for Academic Contents – A Graph Neural Network Based Approach at FAU (VERGEBEN)

Recommendation Systems have been successfuly used in many areas, like marketing and E-commerce. A reason is that this technology tackles the problem of information overload, which often occurs in decision processes.

For this thesis, the aim is to implement a prototype for a recommendation algorithm for the academic content at the FAU with the usage of Graph Neural Networks. These type of deep learning technique are used in many state of the art recommendation approaches (e.g. recommendations at pinterest: shorturl.at/hFNP1).  Data (Already preprocessed) and a first graph structure will be provided.

Prerequisites:

  • Experience in programming languages like python and machine learning frameworks
  • Interess or experience in graph based approaches
Mark Kram Bachelor/Master

 

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