Student:

Namespace in the wiki: FIXME

The goal of the project: Get out of the SWI-Prolog from the Loki. Review current graph bases engines (triplestores), select the most promising one and move the whole knowledge to the selected triplestore.​

Technology: PHP, Semantic Web

Description: Semantic wiki Loki is a DokuWiki system with a set of plugins for putting knowledge within wiki pages. It can be then easily extracted/processed. Now, the whole knowledge processing is done via plain files and SWI-Prolog, which is interesting approach, but not the optimal one, as there are many triplestores (dedicated graph base engines) available.

Student:

Namespace in the wiki: FIXME

The goal of the project: Exploration of existing semantic catalogues for the manuscript collections

Technology: Semantic Web (RDF, SPARQL, ontologies), scientific papers reading/mail writing

Description: A survey of the systems used for the storage and management of manuscripts should be prepared (use the links below as a starting point) and then compared by presenting the content/assumptions/purpose, the semantic model underpinning the project and the practical possibilities for inferring/filtering/displaying collection items.
There will probably be no way to get to the details of the catalogue directly from the website, so one will need to look for scientific papers describing the systems, and if this still does not provide an answer then the people responsible for these projects should be contacted.

Student: Kacper Panczykowski

Namespace in the wiki: faceapis

The goal of the project: Comparison of the effectiveness of off-the-shelf APIs for emotion recognition in non-trivial images

Technology: Any scripting/programming language (to call the APIs)

Description: The facial expression recognition tools are trained and evaluated on benchmark datasets that contain many expressions generated 'at the request' of the expressor and photographed en face. This does not match the reality, where expressions are not so strong and where the face is not always facing the camera. The project should: (a) identify a catalogue of situations that may frequently arise when interacting with camera-based systems (e.g., tilt/turn of the head, various quality and resolution of the image), (b) prepare a database of images expressing facial expressions in a natural (non-forced) way in different situations, © identify existing APIs for recognising emotions from facial expressions, (d) evaluate the found APIs on the prepared set, (e) summarise the results by indicating the strengths and weaknesses (supported situations) for each API.

Student: Łukasz Wójcik

Namespace in the wiki: metafaceapis

The goal of the project: Preparation of meta-classificator for results from face recognition API

Technology: Python, machine learning

Description: There are off-the-shelf tools for recognising emotions from facial images (e.g. provided by Microsoft as part of Azure Cognitive Services). Unfortunately - trained on benchmark datasets - they do not work properly 'in the real world', where no one sitting in front of a computer has such a broad smile or such a definite expression of sadness. The aim of the project will be to explore this topic - is it possible to improve these existing models/services (appropriate pre-processing of input/post-processing of output) to make them more effective?

Student:

Namespace in the wiki: FIXME

The goal of the project: Add image modality to the explanation mechanism based on LUX

Technology: Python, Explainable AI, DeepNeuralNetworks,

Student:

Namespace in the wiki: FIXME

The goal of the project: Add image modality to the explanation mechanism based on LUX

Technology: Python, Explainable AI, DeepNeurralNetworks, AnomalyDetection,

Student:

Namespace in the wiki: FIXME

The goal of the project: Comaprison of LUX wrt. various XAI metrics

Technology: Python, Explainable AI

Student: Adrian Domagała

Namespace in the wiki: problogcounterfactuals

The goal of the project: Create Counterfactual explanations of DeepNeuralNetworks with usage of probabilistic declarative programming

Technology: DeepProbLog, Python

Description: The goal of the project is to create counterfactual explanations (i.e. What I need to change in the input of the instance to change the prediction of the classifier to desired output. For instance, if the system predicts I cannot get a loan, I want to know what should I do (how change my customer profile) to get a loan. There are several methods fro that, but they most often are “blond” searchers. Therefore you may get a suggestion to change your gender, or your date of birth. To prevent that, we want to modulate the action the counterfactual search can perform in order to find the best one. The goal is to use probabilistic programming, where we can define a meta-procedure of generating counterfactual and allow the system to induce the concrete procedure itself (similar to inductive programming).

Student:

Namespace in the wiki: FIXME

The goal of the project: Prepare a script that will build meta-learnign dataset out of OpenML logs

Technology: Python, OpenML API

Description: The main goal of the project is to create a script that will fetch all of the runs/pipelines and dataset from OpenML platform and create a dataset out of it. The challenge is to transform pipeline definitions which are code snippets into logical components of machine-learning pipeline (including deep neural networks). Such a dataset will serve as a learn-to-learn dataset for meta-learning solutions.

Student: Illia Andrieiev

Namespace in the wiki: anomalydetection

The goal of the project: Build auto-encoder for time series that will successfully predict anomalies

Technology: Python, PyTorch, TSAI

Description: The goal of the project is to build auto-encoder and test it on three real datasets. The goal is to detect failures in the machinery by analyzing reconstruction error of auto-encoder (when failure occurs, the reconstruction error is high due to the abnormal characteristics of the failure/anomaly).

Student:

Namespace in the wiki: FIXME

The goal of the project: Contribute to the InXAI framework by extending it with image modality

Technology: Python

Student: Sandra Rudnicka

Namespace in the wiki: phonetics

The goal of the project: produce a language agnostic or multilingual model for narrow phonetic transcription of human speech into International Phonetic Alphabet

Technology: Python, Word2Vec2

Description: find labelled datasets (phonetically transcribed speech; segmented or not), unify phonetic transcription, (segment speech into phones), check/train/improve existing models, implement retraining for expanded IPA symbol sets, data collection and analysis of historical and modern population-scale speech samples,

Student:

Namespace in the wiki: FIXME

The goal of the project:

Technology:

Description: