Thesis Topics

This list includes topics for potential bachelor or master theses, guided research, projects, seminars, and other activities. Search with Ctrl+F for desired keywords, e.g. ‘machine learning’ or others.

PLEASE NOTE: If you are interested in any of these topics, click the respective supervisor link to send a message with a simple CV, grade sheet, and topic ideas (if any). We will answer shortly.

Of course, your own ideas are always welcome!


Bisecting Debugger for SWI-Prolog

Type of Work:

  • Bachelor
  • Project

Keywords:

  • debugging
  • IDE
  • program transformation
  • Prolog

Description:

Although it is hard to admit: most of the time we are actually not writing code but instead debug existing. Due to its good backtracking and tracing abilities, debugging in Prolog could benefit from a bisecting debugger as described by Michael Hendricks at Strange Loop 2014. In this work we want to enhance SWI-Prolog’s built-in debugger with this functionality.


Anomaly detection in time-series

Type of Work:

  • Master
  • Project

Keywords:

  • cnn
  • explainability

Description:

Working on deep neural networks for making the time-series anomaly detection process more robust. An important aspect of this process is explainability of the decision taken by a network.


Understanding and enhancing model robustness against adversarial attacks

Type of Work:

  • Master

Keywords:

  • adversarial training
  • cnn
  • deep learning
  • robust models
  • visual recognition

Description:

None


Constraint Handling Rules as a Library with Delimited Control

Type of Work:

  • Bachelor
  • Master
  • Project

Keywords:

  • CHR
  • compiler construction
  • declarative programming
  • Prolog

Description:

Constraint Handling Rules (CHR) is a declarative, rule-based programming language. Its syntax consists of just three committed-choice multi-set transformation rules and is thus fairly simple. There are implementations and embeddings for many programming languages such as C, Java, Haskell, and JavaScript. However, CHR’s most popular host language remains Prolog. Current implementations of CHR in Prolog rely on Prolog’s implicit execution stack, others use attributed variables. In this work, we want to implement a new compilation scheme to Prolog based on delimited continuations. This takes up the idea of an explicit stack, which is a common optimisation technique when compiling CHR to imperative programming languages. Delimited continuations have already been successfully used to efficiently implement tabling in Prolog – so it might be worth considering them for CHR, too.


XPCE Compatibility for SWISH

Type of Work:

  • Bachelor
  • Master
  • Project

Keywords:

  • GUI
  • program transformation
  • Prolog

Description:

XPCE was the way to go for graphical user interfaces with SWI-Prolog. In recent years, with SWISH, SWI-Prolog moved to the browser as the most important GUI, leaving XPCE unsupported and almost deprecated. We want to examine typical XPCE applications and provide mechanisms to convert them to SWISH applications.


Context-Aware Recommender Systems for Personal Knowledge Assistants

Type of Work:

  • Bachelor
  • Guided Research
  • Master
  • Project
  • Seminar

Keywords:

  • context-aware recommender systems
  • graph embedding
  • information retrieval
  • knowledge worker actions and scenarios detection
  • personal information management
  • re-finding agents

Description:

One of the ways to assist knowledge workers in the daily tasks and improve their productivity is by providing them with relevant helpful information based on their current context. There are many challenges in developing such a recommender system which could be capable of recommending the right information at the right time to the right person. Understanding the contextual state of the users as precisely as possible, along with detecting their activities and information need play a significant role in enhancing the context-aware recommender systems for personal knowledge assistants. A mixture of knowledge and skills in various fields such as information retrieval, graph embedding, and machine learning is needed to tackle the existing challenges in this area.


Self-supervised Video Object Segmentation

Type of Work:

  • Master

Keywords:

  • self-supervision
  • video object segmentation

Description:

Exploring the potential advantages of integrating global context for self-supervised video object segmentation.


Graph Structure and Node Label Prediction on Circuit Diagrams

Type of Work:

  • Master

Keywords:

  • Autoencoder
  • GAN
  • Graph Neural Network
  • LSTM

Description:

The objective of this master’s thesis is to examine the performance of deep (graph) neural networks for node labeling and structure prediction on graph structures representing electrical circuit diagrams. In the course of the thesis, the following model types should be evaluated: Classification Models should be used to aid the human user to get insights in the circuit’s functional principle. For example, all nodes of a circuit should be classified whether or not they contribute to the power supply of the described device. This part of the thesis is intended to compete (partly) against the existing rule-based approach. Transformative Models should be trained in a natural-language translator-like fashion to map one circuit to another, in which the entire graph structure is mapped to a coding space by an encode an subsequently unwrapped by a decoder model. The output graph should be an alternate version of the input graph which desirable properties. For example, the output graph should represent an energy- optimized version of the input device. Optionally, the process of turning the content of a coding space into a graph structure should be examined here. Biologically inspired approaches should be considered in which a single node divides, and differentiates under the exchange of signals like cells in embryonic tissue (mitosis, apoptosis, release and reception of cytokines). Generative Models (trained e.g. in GAN structures) should allow for the high-dimension interpolation between graphs. This should facilitate the generation of new devices like “a mixture of an AM and an FM radio”, “a slightly more toaster-ish backing oven.”


Graph Extraction/Generation from Diagrams

Type of Work:

  • Bachelor
  • Master
  • Project
  • Seminar

Keywords:

  • cnn
  • cv
  • dynamic system simulation

Description:

I am currently supervising students in multiple topics related to the extraction and simulation of Graph-Based Engineering Drawings from optical sources: Graph Extraction from Printed/Handdrawn Circuit Diagrams/Piping&Instumentation Diagrams, Generation of Hand-Drawn Diagrams, Interactive Circuit Detection and Simulation


A Style Linter for Prolog

Type of Work:

  • Bachelor
  • Master
  • Project

Keywords:

  • coding guidelines
  • continuous integration
  • Prolog

Description:

Although there are several style guides for Prolog, it is hard to enforce one in existing programs due to the lack of related tools and machine-readable representation. In other programming languages there are separate, flexible tools just to ensure a consistent coding style, for example Prettier or the JavaScript standard style. In this work we want to adapt these concepts and integrate a Prolog linter into CI workflows and IDEs, with respect to Prolog’s syntax, and based on our previous work.


Efficient & data type independent CUDA kernels

Type of Work:

  • Bachelor
  • Guided Research
  • Master

Keywords:

  • cuda
  • efficiency
  • gpu

Description:

With typical GPU code all operands must have the same data type. However, the speed of most operations on GPUs is limited not by computation, but by memory bandwidth and latency. In these cases using the smallest possible data types can greatly enhance performance. The standard approach of compiling one version of a kernel for each combination inputs is infeasible though, since the number of combinations, especially for 3 or more inputs, leads to exorbitantly large binaries and very long compilation times. Much more desirable is a flexible kernel that can load inputs of arbitrary data type and converts them to an intermediate representation that can be used to perform the actual computation.


Knowledge Graph Construction in Multiple Iterations

Type of Work:

  • Master
  • Project

Keywords:

  • etl
  • human-in-the-loop
  • information extraction
  • knowledge graph
  • rdf
  • semantic web

Description:

Building knowledge graphs from data from scratch is a non-trivial task, especially when we include user feedback in the building process. Because we are most likely not finished after the first iteration, multiple loops have to be performed, to construct the knowledge graph sufficiently. This topic is about a methodology that combines incremental data imports, automated bootstrapping, recurring user feedback loops and knowledge graph updates.


Knowledge-based Deep Learning

Type of Work:

  • Master
  • Project

Keywords:

  • gcnn
  • gnn
  • graph convolutional neural networks
  • graph embeddings
  • graph neural networks
  • natural language processing
  • nlp
  • word embeddings

Description:

None


OpenRuleBench Revised

Type of Work:

  • Bachelor
  • Master
  • Project

Keywords:

  • benchmarks
  • declarative programming
  • Prolog

Description:

OpenRuleBench is a suite of benchmarks for analysing the performance and scalability of different rule engines. The original benchmarks were run first in 2009, an updated report was published in 2011. Since then, the benchmarks have not been re-run. In this work, we want to create an experimental setup to run various tests on different systems, similar to our CHR Benchmark suite. It should allow to validate the original results, and to re-run the tests with modern rule engines. This could result in a public test server to compare existing and new logic programming systems and their strengths.


Innovative Knowledge Graph visualizations for end-users in corporate memories

Type of Work:

  • Bachelor
  • Master

Keywords:

  • corporate memory
  • knowledge graph
  • ontology

Description:

Topic just an example. If you are interested, just ping us, we may find a topic suting your expertise and interest in this field.

Our Corporate Memory CoMem uses knowledge graphs (KG) to represent personal and organizational knowledge such as documents, topics, appointments, contacts, projects, etc. The resulting KG are complex and currently can be browsed by end-users using “classic” views such as dashboards and widgets. In this thesis, new ways of interacting with the KG are sought with focus on innovative visualizations of the graphs. The usual generic graph visualizations will overwhelm users. Therefore, adaptive visualizations in different scenarios shall be investigated and implemented such as exploration of a user’s search process switching between graphs, lists, and tables, exploring a context, or focussing on dedicated questions to be answered in the graph visualization. An inspiration can be taken by the wikidata implementation at https://query.wikidata.org/ CoMem https://comem.ai


A Controlled Natural Language for COVID Regulations

Type of Work:

  • Bachelor
  • Master

Keywords:

  • controlled natural language
  • domain-specific language

Description:

In the past two years, the Coronavirus disease COVID-19 had a strong impact on most parts of our everyday life. People had to wear masks under some conditions, like the number of persons in a group or the size of the room. These conditions have been determined by the government in more than 30 regulations. In this work, we want to tailor the corpus of these regulations towards a more controlled natural language so they can be easily used by both humans and machines, e.g., to automatically identify inconsistencies. Because the Corona-Bekämpfungsverordnung (CoBeLVO) is in German, a good knowledge of German is required.


Time Series Forecasting Using transformer Networks

Type of Work:

  • Guided Research
  • Project

Keywords:

  • time series forecasting
  • transformer networks

Description:

Transformer networks have emerged as competent architecture for modeling sequences. This research will primarily focus on using transformer networks for forecasting time series (multivariate/ univariate) and may also involve fusing knowledge into the machine learning architecture.


Benchmarking CHR Systems

Type of Work:

  • Bachelor
  • Project

Keywords:

  • benchmarks
  • CHR
  • declarative programming

Description:

Constraint Handling Rules (CHR) is a very simple but powerful rule-based language. Besides its most popular implementation as a library for SWI-Prolog, there exist CHR systems in C, Haskell, and JavaScript. More than 30 years after the creation of CHR, the existing implementations should be compared regarding supported features and performance, extending our previous work.


Explainable methods for computer vision and classification

Type of Work:

  • Project
  • Seminar

Keywords:

  • cnn
  • cv
  • deep learning
  • ml
  • xai

Description:

Train or evaluate CNNs under different conditions to induce or measure high-level priors related to explainability.


Exploring Self Attention in Transformers

Type of Work:

  • Master
  • Project
  • Seminar

Keywords:

  • language generation
  • nlp
  • transformers

Description:

Transformers have shown a promising new direction in Language generation replacing recurrent neural networks. Different attention mechanisms have been attributed as possible cause for successful performance of these architectures. The goal of this topic would broadly be to develop on and improve the self attention mechanism used in the transformer architectures.


Attention for video object segmentation

Type of Work:

  • Master

Keywords:

  • reinforcement learning (rl)
  • video object segmentation

Description:

Exploring RL-based methods for temporal attention in video object segmentation


Optimising Definite Clause Grammars

Type of Work:

  • Master
  • Project

Keywords:

  • backtracking
  • compiler
  • context-free grammar
  • parser
  • program transformation
  • Prolog

Description:

Definite Clause Grammars (DCG) can be easily written by hand and are therefore a good alternative to parser implementations based on LL(k), LF or PEG. Nevertheless, such hand-written grammars tend to be not optimal: Several parts of the parse tree have to be evaluated several times for the same expression because of its backtracking nature. Using source-to-source transformation the DCG can be rewritten into an optimal version, which should be done in Prolog as part of this work.


Construction & Application of Enterprise Knowledge Graphs in the E-Invoicing Domain

Type of Work:

  • Bachelor
  • Guided Research Project
  • Master

Keywords:

  • knowledge graphs
  • knowledge services
  • linked data
  • semantic web

Description:

In recent years knowledge graphs received a lot of attention as well in industry as in science. Knowledge graphs consist of entities and relationships between them and allow integrating new knowledge arbitrarily. Famous instances in industry are knowledge graphs by Microsoft, Google, Facebook or IBM. But beyond these ones, knowledge graphs are also adopted in more domain specific scenarios such as in e-Procurement, e-Invoicing and purchase-to-pay processes. The objective in theses and projects is to explore particular aspects of constructing and/or applying knowledge graphs in the domain of purchase-to-pay processes and e-Invoicing.


Knowledge Graphs für das Immobilienmanagement

Type of Work:

  • Bachelor
  • Master

Keywords:

  • corporate memory
  • knowledge graph
  • ontologie

Description:

Das Management von Immobilien ist komplex und umfasst verschiedenste Informationsquellen und -objekte zur Durchführung der Prozesse. Ein Corporate Memory kann hier unterstützen in der Analyse und Abbildung des Informationsraums um Wissensdienste zu ermöglichen. Aufgabe ist es, eine Ontologie für das Immobilienmanagement zu entwerfen und beispielhaft ein Szenario zu entwickeln. Für die Materialien und Anwendungspartner sind gute Deutschkenntnisse erforderlich.


Fault and Efficiency Prediction in High Performance Computing

Type of Work:

  • Master Thesis

Keywords:

  • deep learning
  • event data modelling
  • survival modelling
  • time series

Description:

High use of resources are thought to be an indirect cause of failures in large cluster systems, but little work has systematically investigated the role of high resource usage on system failures, largely due to the lack of a comprehensive resource monitoring tool which resolves resource use by job and node. This project studies log data of the DFKI Kaiserslautern high performance cluster to consider the predictability of adverse events (node failure, GPU freeze), energy usage and identify the most relevant data within. The second supervisor for this work is Joachim Folz.

Data is available via Prometheus-compatible system:

Reference:

Feel free to reach out if the topic sounds interesting or if you have ideas related to this work. We can then brainstorm a specific research question together. Link to my personal website.


Statistische Jahresberichte auf Basis von Linked Open Data

Type of Work:

  • Bachelor
  • Master
  • Project

Keywords:

  • linked data
  • open data
  • semantic web
  • smart city
  • visualization

Description:

Viele Kommunen in Deutschland veröffentlichen jährlich einen statistischen Jahresbericht. Diese Statistiken beinhalten u.a. soziodemografische Informationen zur Bevölkerung (z.B. Alter, Familienstand), zur Wirtschaft (z.B. Gewerbesteuer, Insolvenzverfahren) und Verkehr (z.B. PKW-Neuzulassungen, Verkehrsunfälle), und zu Kultur und Sport (z.B. Bibliotheksbestand, Vereinsmitgliedschaften). Sie sind wertvolle Ratgeber für Planung, Handel und Politik. Trotz ihres großen Nutzens ist die Erstellung der oft mehrere hundert Seiten langen Jahresberichte aber noch weitestgehend Handarbeit: Statistiken werden mühevoll zusammengetragen und Visualisierungen erstellt – eine Tätigkeit, die sich durch Linked (Open) Data vollständig automatisieren ließe. Ziel dieser Arbeit ist es, am Beispiel der Stadt Kaiserslautern den statistischen Jahresbericht als Linked (Open) Data umzusetzen und eine interaktive Darstellung zu ermöglichen, die über die bisherige Druckfassung hinausgeht. Für die Materialien und Anwendungspartner sind gute Deutschkenntnisse erforderlich.


Learning Analytics in Education

Type of Work:

  • Master
  • Project

Keywords:

  • affective state
  • cognitive state
  • deep learning
  • machine learning

Description:

None


Exploring the potential of hebbian plasticity (meta-learning) to treat adversarial attacks

Type of Work:

  • Master

Keywords:

  • adversarial attacks
  • deep learning
  • meta learning
  • reinforcement learning
  • robust models

Description:

Adversarial attacks are one of the biggest problems in the application of neural networks in safety-critical areas. The aim of this thesis is to evaluate to what extent dynamic parameters based on hebbian plasticity are suitable for handling these attacks. The aim of this work is to adapt this approach, which was originally introduced for reinforcement learning, for this purpose.


Deep Self-organizing feature maps in time series analysis

Type of Work:

  • Guided Research
  • Master

Keywords:

  • deep learning
  • python
  • self-organizing feature maps
  • sofm
  • time series analysis
  • unsupervised training

Description:

Deep Self-Organising Feature Maps (DSOFM) have shown that they are capable of capturing high-dimensional topologies and are furthermore suitable for image classification on a limited scale. The aim of this project is to apply a stacked version of DSOFM to data from the time analysis domain and to test its suitability for anomaly detection. In addition, advantages and disadvantages, as well as possible improvements, are to be highlighted.

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