Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
Estimating Expected Calibration Errors
Uncertainty in probabilistic classifiers predictions is a key concern when models are used to support human decision making, in broader probabilistic pipelines or when sensitive automatic decisions have to be taken.
Studies have shown that most models are not intrinsically well calibrated, meaning that their decision scores are not consistent.
10 Papers in 2021
To answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. Find out the impacts of our latest published papers.
A Framework Using Contrastive Learning for Classification with Noisy Labels
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labelling, sample selection with Gaussian Mixture models, and weighted supervised contrastive learning have been combined into a fine-tuning phase following the pre-training.
AMU-EURANOVA at CASE 2021 Task 1: Assessing the stability of multilingual BERT
This paper explains our participation in task 1of the CASE 2021 shared task. This task is about multilingual event extraction from the news. We focused on sub-task 4, event information extraction. This sub-task has a small training dataset, and we fine-tuned a multilingual BERT to solve this sub-task.
Our Research Director Invited as PC Member at IEEE Big Data
We are very proud of our research director Sabri Skhiri for joining the program committee of IEEE Big Data 2021!
He will be the only Belgian and one of the few Europeans to be on the program committee of this top tier research conference in Big Data.
Congratulation Sabri!
A Combined Rule-Based and Machine Learning Approach for Automated GDPR Compliance Checking
The General Data Protection Regulation (GDPR) requires data controllers to implement end-to-end compliance. Controllers must therefore ensure that the terms agreed with the data subject and their own obligations under GDPR are respected in the data flows from data subject to controllers, processors and sub-processors (i.e. data supply chain).
Our Research Director Is Co-chair at DEBS 2021 [Call for Paper]
Congratulations to our research director Sabri Skhiri on his appointment as industry co-chair of the international conference on distributed and event-based systems.
DMMM: Data Management Maturity Model
The assessment of the digital transformation progress is essential to understand and undertake in order to evaluate the level of maturity of data-driven companies in terms of data capabilities and to plan for improvement actions.
A Survey of Maturity Models in Data Management
Maturity models are helpful business tools that refine and develop how organizations conduct their businesses and benchmark their maturity status against a scale or with industry peers. They serve to prioritize the actions for improvement better and control the progress in reaching the target maturity stage.
MIC: Multi-view Image Classifier using Generative Adversarial Networks for Missing Data Imputation
In this paper, we propose a framework for image classification tasks, named MIC, that takes as input multi-view images, such as RGB-T images for surveillance purposes. We combine auto-encoder and generative adversarial network architectures to ensure the multi-view embedding in a common latent space.
MDPI Data Journal, special issue – Paper Submission Opening
After the success of five international workshops co-located at IEEE Big Data, the MDPI Data Journal is dedicating a special issue to real-time stream analytics, stream mining, CER/CEP and stream data management in big data.
Towards a Continuous Evaluation of Calibration
For safety-critical systems involving AI components (such as in planes, cars, or healthcare), safety and associated certification tasks are one of the main challenges, which can become costly and difficult to address.
One key aspect is to ensure that the decisions a machine-learning classifier makes are properly calibrated.