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As part of Thoth Station’s knowledge aggregation, there are several packages inside a continuous deployment infrastructure. The goal of this project is to enable automatic alerts and, depending on the level of the project, enable automatic upgrades within the system.

First steps:

  • Monitor python indices
  • Check whether any updates to the existing packages exist and
  • Check whether any of them have already been updated
After picking a set of modules and versions, the project would send a notification to a Kafka topic at which point a consumer would then react on it. Whether that consumer is within scope of the project, depends on whether this is taken on as part of a BSc or MSc.

This project helps you to:

  • Understand how modern continuous deployment infrastructure works in enterprise companies (Red Hat)
  • Contribute to open source software publicly and visibly on your GitHub profile
  • Get into research (writing papers, learning how to publish)

Prerequisites:

  • Python and Git
  • Linux
  • High level of motivation

Nice to have:

  • Rudimentary or advanced knowledge of AI/ML (not a requirement, but a bonus for yourself to get started quicker)

As part of Thoth Station’s overall infrastructure knowledge gathering, the aim of this project is to algorithmically find correlations (or prove their nonexistence) in a provided data set of inspections. These inspections include, but are not limited to:

  • Runtime errors

  • Installation errors

  • Performance indicators

  • General knowledge of software stacks

The inspections are run as pods via OpenShift. In order to find out more about Thoth and how this project contributes to building a knowledge graph and a recommendation system for application stacks based on the collected knowledge, read more at https://developers.redhat.com/blog/2019/10/28/microbenchmarks-for-ai-applications-using-red-hat-openshift-on-psi-in-project-thoth/.

This project helps you to:

  • Understand how modern continuous deployment infrastructure works in enterprise companies (Red Hat)
  • Contribute to open source software publicly and visibly on your GitHub profile
  • Get into research (writing papers, learning how to publish)

Prerequisites:

  • Python and Git
  • Linux
  • Basic knowledge about databases
  • Basic knowledge about virtualization and containers
  • High level of motivation

Nice to have:

  • Rudimentary or advanced knowledge of Data Science and/or Pattern Recognition (not a requirement, but a bonus for yourself to get started quicker)



Update your Network: Loop Free!

Correct routing is one of the fundamental properties a network should provide, after all, packets must be able to reach their destination. However, sometimes routes have to change due to maintenance, failures, congestion etc. Even though the old and the new route provide reachability, the inherent asynchrony in networks could mean that temporarily networks get lost in a loop. For an example, see the above figure, where the task is to update from routing Tree 1 to Tree 2. If node v updates too slowly, packets will enter a loop, and subsequently get dropped as the buffers overflow. Even though modern programmable networks offer great flexibility how to handle this problem, there are still many open questions. In this thesis, you will be able to investigate this problem from both a practical and a theoretical perspective, according to your background and strengths. For example, you could implement your algorithmic ideas and benchmark them - how and when can current techniques be improved?

First steps

  • Understand the problem and its motivation
  • Check related algorithms and their implementation

What we offer

  • The possibility to do cutting edge research
  • Getting into research: how to write papers, how to publish, how to provide implementations for the research community
  • The possibility to publish research results on workshops and conferences (We have already successfully published together with students)

Expected knowledge

  • Highly motivated students towards conducting research
  • Good programming skills in C++
  • Fundamental graph theory and related algorithms

Keywords: Algorithm engineering, graph algorithms, routing, consistency

If you are interested, contact us and we can discuss possible directions for your thesis/project. To get a feeling for some past research on these topics (non-exhaustive), please feel free to look at the following:

This topic is available at all levels.
For more information, please contact Kathrin Hanauer at kathrin.hanauer@univie.ac.at and/or Klaus-Tycho Förster at klaus-tycho.foerster@univie.ac.at

Thoth StationImage Removed

Checking for Upgrades in a Continuous Event-Emitting Deployment

For more information on Project Thoth and/or if you would like to take this project, please contact sanja.bonic@univie.ac.at.

Thoth StationImage Removed

Algorithmically Find Correlations in a Data Set of Runtime Environment Inspections

For more information on Project Thoth and/or if you would like to take this project, please contact sanja.bonic@univie.ac.at.

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Draw Simplified Icon Representations of Nouns Given via Various Inputs

As part of a larger ML project that will be open sourced, this part of the project is about drawing several icon representations of nouns within one image. You will take as input a list of nouns and let the computer represent those nouns via icons/sketches in an image file.

First steps:

  • Decide the possible input sources (CLI, web, voice, ?)
  • Research useful libraries and decide how to do error handling (receive full sentences and extract just nouns via entity recognition or be sure to receive only nouns)
  • Decide whether to go for sketches or icons (various approaches and libraries available open source)
  • Decide how to combine representations into an image (positioning)

This project helps you to:

  • Get started or advance with AI/ML topics
  • Get creative
  • Contribute to open source software publicly and visibly on your GitHub profile
  • Get into research (writing papers, learning how to publish)

Prerequisites:

  • Solid programming skills
  • Git

Nice to have:

  • Rudimentary or advanced knowledge of AI/ML (not a requirement, but a bonus for yourself to get started quicker)
This topic is available at all levels.
For more information, please contactstefan_schmid@univie.ac.at or sanja.bonic@univie.ac.at.
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Determine Composition Value in Images

As part of a larger ML project that will be open sourced, this part of the project is about determining composition value in images. You will find images with a license that allows usage in research projects and find a way to determine their composition value. You will then feed this information to an ML algorithm that will be used in the subsequent parts of this overall project.

First steps:

  • Locate > 30,000 images with a suitable license
  • Research existing libraries for this topic and write a report that is part of your project
  • Decide on the classification scale you want to use (e.g. good/neutral/bad, 1-10, ?)
  • Classify the images

This project helps you to:

  • Get started or advance with AI/ML topics
  • Get creative
  • Contribute to open source software publicly and visibly on your GitHub profile
  • Get into research (writing papers, learning how to publish)

Prerequisites:

  • Solid programming skills
  • Git

Nice to have:

  • Rudimentary or advanced knowledge of AI/ML (not a requirement, but a bonus for yourself to get started quicker)
  • Photography knowledge

Reading starters:

  • Marcelo Siqueira, Longin Jan Latecki, and Jean Gallier "Making 3D binary digital images well-composed", Proc. SPIE 5675, Vision Geometry XIII, (17 January 2005); https://doi.org/10.1117/12.596447
  • Ngo, Phuc & Passat, Nicolas & Kenmochi, Yukiko & Talbot, Hugues. (2013). Well-composed images and rigid transformations. 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings. 3035-3039. 10.1109/ICIP.2013.6738625.
This topic is available at all levels.
For more information, please contactstefan_schmid@univie.ac.at or sanja.bonic@univie.ac.at.
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Determine Position and Evaluate Area Percentage in Images

As part of a larger ML project that will be open sourced, this part of the project is about recognizing the position of objects in images and defining the area percentage these objects take within the image. You will then feed this information to an ML algorithm that will be used in the subsequent parts of this overall project.


First steps:

  • Locate > 30,000 images with a suitable license
  • Research existing libraries for this topic and write a report that is part of your project
  • Decide whether you want to work with artificial images (e.g. banners used on company websites, blogs, magazines) or natural pictures (pictures where the original source can be assumed to be natural)

Research questions:

  • Which parts of a picture form a single object?
  • Which percentage of the image is taken up by each individual object?

This project helps you to:

  • Get started or advance with AI/ML topics
  • Get creative
  • Contribute to open source software publicly and visibly on your GitHub profile
  • Get into research (writing papers, learning how to publish)

Prerequisites:

  • Solid programming skills
  • Git

Nice to have:

  • Rudimentary or advanced knowledge of AI/ML (not a requirement, but a bonus for yourself to get started quicker)
This topic is available at all levels.
For more information, please contactstefan_schmid@univie.ac.at or sanja.bonic@univie.ac.at.



Your own project idea related to networks and communication technologies

Should you have your own idea for a potential thesis which you think might fit the research interests of our group, do not hesitate to contact us directly.

It can be related to the topics above, but also something completely different, as long as it roughly fits into our area.

This topic is available at all levels.
For more information, please contact Prof Stefan Schmid atstefan_schmid@univie.ac.at

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