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Update your Network: Loop Free! |
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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
What we offer
Expected knowledge
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:
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This topic is available at all levels. |
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. |
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. |
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)
For more information, please contactstefan_schmid@univie.ac.at or sanja.bonic@univie.ac.at.
Evaluate Composition in Images
As part of a larger ML project that will be open sourced, this part of the project is about evaluating the composition of 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.
For more information, please contactstefan_schmid@univie.ac.at or sanja.bonic@univie.ac.at.
Determine Position and Calculate 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 calculating 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)
For more information, please contactstefan_schmid@univie.ac.at or sanja.bonic@univie.ac.at.
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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