Publications

2024

• The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code
  
  • Casanueva Annalí
  • Rossi Davide
  • Zacchiroli Stefano
  • Zimmermann Théo
  Empirical Software Engineering, Springer Verlag, 2024. Despite its promise of openness and inclusiveness, the development of free and open source software (FOSS) remains significantly unbalanced in terms of gender representation among contributors. To assist open source project maintainers and communities in addressing this imbalance, it is crucial to understand the causes of this inequality. In this study, we aim to establish how the COVID-19 pandemic has influenced the ability of women to contribute to public code. To do so, we use the Software Heritage archive, which holds the largest dataset of commits to public code, and the difference in differences (DID) methodology from econometrics that enables the derivation of causality from historical data. Our findings show that the COVID-19 pandemic has disproportionately impacted women's ability to contribute to the development of public code, relatively to men. Further, our observations of specific contributor subgroups indicate that COVID-19 particularly affected women hobbyists, identified using contribution patterns and email address domains. (10.1007/s10664-024-10552-7)
  DOI : 10.1007/s10664-024-10552-7
• Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey
  
  • Suchanek Fabian M.
  • Razniewski Simon
  • Arnaout Hiba
  • Ghosh Shrestha
  ACM Computing Surveys, Association for Computing Machinery, 2024. General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from web sources, and are thus far from complete. This poses challenges for the consumption as well as the curation of their content. While several surveys target the problem of completing incomplete KBs, the first problem is arguably to know whether and where the KB is incomplete in the first place, and to which degree. In this survey, we discuss how knowledge about completeness, recall, and negation in KBs can be expressed, extracted, and inferred. We cover (i) the logical foundations of knowledge representation and querying under partial closed-world semantics; (ii) the estimation of this information via statistical patterns; (iii) the extraction of information about recall from KBs and text; (iv) the identification of interesting negative statements; and (v) relaxed notions of relative recall. This survey is targeted at two types of audiences: (1) practitioners who are interested in tracking KB quality, focusing extraction efforts, and building quality-aware downstream applications; and (2) data management, knowledge base and semantic web researchers who wish to understand the state of the art of knowledge bases beyond the open-world assumption. Consequently, our survey presents both fundamental methodologies and the results that they have produced, and gives practice-oriented recommendations on how to choose between different approaches for a problem at hand. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Knowledge representation and reasoning; Artificial intelligence.
• Exploring the potential of representation and transfer learning for anatomical neuroimaging: application to psychiatry
  
  • Dufumier Benoit
  • Gori Pietro
  • Petiton Sara
  • Louiset Robin
  • Mangin Jean-François
  • Grigis Antoine
  • Duchesnay Edouard
  NeuroImage, Elsevier, 2024. The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimagingbased prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modelling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging dataset (N ≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N ≤ 1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity of learning meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry.
• Self-Supervised Learning of Multi-level Audio Representations for Music Segmentation
  
  • Buisson Morgan
  • Mcfee Brian
  • Essid Slim
  • Crayencour Hélène
  IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2024, pp.1-13. The task of music structure analysis refers to automatically identifying the location and the nature of musical sections within a song. In the supervised scenario, structural annotations generally result from exhaustive data collection processes, which represents one of the main challenges of this task. Moreover, both the subjectivity of music structure and the hierarchical characteristics it exhibits make the obtained structural annotations not fully reliable, in the sense that they do not convey a "universal ground-truth" unlike other tasks in music information retrieval. On the other hand, the quickly growing quantity of available music data has enabled weakly supervised and self-supervised approaches to achieve impressive results on a wide range of music-related problems. In this work, a self-supervised learning method is proposed to learn robust multi-level music representations prior to structural segmentation using contrastive learning. To this end, sets of frames sampled at different levels of detail are used to train a deep neural network in a disentangled manner. The proposed method is evaluated on both flat and multi-level segmentation. We show that each distinct sub-region of the output embeddings can efficiently account for structural similarity at their own targeted level of detail, which ultimately improves performance of downstream flat and multi-level segmentation. Finally, complementary experiments are carried out to study how the obtained representations can be further adapted to specific datasets using a supervised fine-tuning objective in order to facilitate structure retrieval in domains where human annotations remain scarce. (10.1109/TASLP.2024.3379894)
  DOI : 10.1109/TASLP.2024.3379894
• The Non-Cancelling Intersections Conjecture
  
  • Amarilli Antoine
  • Monet Mikaël
  • Suciu Dan
  , 2024. In this note, we present a conjecture on intersections of set families, and a rephrasing of the conjecture in terms of principal downsets of Boolean lattices. The conjecture informally states that, whenever we can express the measure of a union of sets in terms of the measure of some of their intersections using the inclusion-exclusion formula, then we can express the union as a set from these same intersections via the set operations of disjoint union and subset complement. We also present a partial result towards establishing the conjecture.
• Reducing the Silicon Area Overhead of Counter-Based Rowhammer Mitigations
  
  • France Loïc
  • Bruguier Florent
  • Novo David
  • Mushtaq Maria
  • Benoit Pascal
  IEEE Computer Architecture Letters, Institute of Electrical and Electronics Engineers, 2024, 23 (1), pp.61-64. Modern computer memories have shown to have reliability issues. The main memory is the target of a security threat called Rowhammer, which causes bit flips in adjacent victim cells of aggressor rows. Numerous countermeasures have been proposed, some of the most efficient ones relying on row access counters, with different techniques to reduce the impact on performance, energy consumption and silicon area. In these proposals, the number of counters is calculated using the maximum number of row activations that can be issued to the protected bank. As reducing the number of counters results in lower silicon area and energy overheads, this can have a direct impact on the production and usage costs. In this work, we demonstrate that two of the most efficient countermeasures can have their silicon area overhead reduced by approximately 50% without impacting the protection level by changing their counting granularity. (10.1109/LCA.2023.3328824)
  DOI : 10.1109/LCA.2023.3328824