Evaluation of GraphRAG Strategies for Efficient Information Retrieval
Traditional RAG systems struggle to capture relationships and cross-references between different sources unless explicitly mentioned. This challenge is common in real-world scenarios, where information is often distributed and interlinked, making graphs a more effective representation. Our work provides a technical contribution through a comparative evaluation of retrieval strategies within GraphRAG.
Flight Load Factor Predictions based on Analysis of Ticket Prices and other Factors
The ability to forecast traffic and to size the operation accordingly is a determining factor, for airports. However, to realise its full potential, it needs to be considered as part of a holistic approach, closely linked to airport planning and operations. To ensure airport resources are used efficiently, accurate information about passenger numbers and their effects on the operation is essential. Therefore, this study explores machine learning capabilities enabling predictions of aircraft load factors.
Investigating a Feature Unlearning Bias Mitigation Technique for Cancer-type Bias in AutoPet Dataset
We proposed a feature unlearning technique to reduce cancer-type bias, which improved segmentation accuracy while promoting fairness across sub-groups, even with limited data.
Muppet: A Modular and Constructive Decomposition for Perturbation-based Explanation Methods
The topic of explainable AI has recently received attention driven by a growing awareness of the need for transparent and accountable AI. In this paper, we propose a novel methodology to decompose any state-of-the-art perturbation-based explainability approach into four blocks. In addition, we provide Muppet: an open-source Python library for explainable AI.
Development & Evaluation of Automated Tumour Monitoring by Image Registration Based on 3D (PET/CT) Images
Tumor tracking in PET/CT is essential for monitoring cancer progression and guiding treatment strategies. Traditionally, nuclear physicians manually track tumors, focusing on the five largest ones (PERCIST criteria), which is both time-consuming and imprecise. Automated tumor tracking can allow matching of the numerous metastatic lesions across scans, enhancing tumor change monitoring.
Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings
In this paper, we study graph representation learning and show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis.
SANGEA: Scalable and Attributed Network Generation
In this paper, we present SANGEA, a sizeable synthetic graph generation framework that extends the applicability of any SGG to large graphs. By first splitting the large graph into communities, SANGEA trains one SGG per community, then links the community graphs back together to create a synthetic large graph.
The Building Blocks of a Responsible AI Practice: An Outlook on the Current Landscape
Responsible AI comes with the challenge of implementation. This survey aims to bridge the gap between principles and practice through a study of different approaches taken in the literature and the proposition of a foundational framework.
Tech Insights from the PET Summit 23
In March 2023, our research director Sabri Skhiri travelled to London to attend the Privacy Enhancing Technologies Summit 2023, dedicated to PETs and their uses (enhance data security, facilitate compliance, and create value).
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol
Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets. The most popular metrics used to compare performances are F1-score, AUC and AVPR...
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.
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).
2Be3-Net : Combining 2D and 3D convolutional neural networks for 3D PET scans predictions
Radiomics is the main approach used to develop predictive models based on 3D Positron Emission Tomography (PET) scans of patients suffering from cancer. We propose a deep learning architecture associating a 2D feature extractor to a 3D CNN predictor.
Privacy Policy Classification with XLNet
The popularisation of privacy policies has become an attractive subject of research in recent years, notably after the General Data Protection Regulation came into force in the European Union. While GDPR gives Data Subjects more rights and control over the use of their personal data, length and complexity of privacy policies can still prevent them from exercising those rights. An accepted way to improve the interpretability of privacy policies is…
Towards Privacy Policy Conceptual Modeling
After GDPR enforcement in May 2018, the problem of implementing privacy by design and staying compliant with regulations has been more prominent than ever for businesses of all sizes, which is evident from frequent cases against companies and significant fines paid due to non-compliance. Consequently, numerous research works have been emerging in this area….
Thirty-Fourth AAAI Conference On Artificial Intelligence
In 2020, research engineers Hounaida Zemzem and Rania Saidi attended the 34th AAAI Conference on Artificial Intelligence in New York. They engaged in technical sessions at this forum designed to promote AI research and scientific exchange among global experts.
Schloss Dagstuhl: where computer science meets
Which direction stream and complex event processing is going to take? Last week, the world’s best-known international researchers met in Schloss Dagstuhl.
Throwback to 2019
At EURANOVA, we believe technology is a catalyst for change. Let's look back at what happen in 2019!
STRASS: A Light and Effective Method for Extractive Summarization
This paper introduces STRASS: Summarization by TRAnsformation Selection and Scoring. It is an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. Our method creates an extractive summary by selecting the sentences with the closest embeddings to the document embedding. The model learns a transformation of the document embedding to […]
An analytics-aware conceptual model for evolving graphs
Graphs are ubiquitous data structures commonly used to represent highly connected data. Many real-world applications, such as social and biological networks, are modeled as graphs. To answer the surge
The captivating AROM of distributed processing
Last month we have had the opportunity to present AROM at the 4th IEEE International Conference on Cloud Computing Technology and Science (CLOUDCOM) in Taipei, the beautiful capital of Taiwan. For the people who are just getting on train, I will quickly here recall what AROM is about, where one can find it and how its taste […]
Trust-based Recommendation: an empirical analysis
The use of trust in recommender systems has been shown to improve the accuracy of rating predictions, especially in the case where a user’s rating significantly differs from the average. Different techniques have been used to incorporate trust into recommender systems, each showing encouraging results. However, the lack of trust information available in public datasets […]
Towards trust inference from bipartite social networks
The emergence of trust as a key link between users in social networks has provided an effective means of enhancing the personalization of online user content.