Publications

Technology-enhanced trauma training in low-resource settings: A scoping review and feasibility analysis of educational technologies.
Minahil Khan
Fabio Botelho
Laura Pinkham
Elena Guadagno
Dan Poenaru
2022-12-31
Journal of Pediatric Surgery (publié)
doi.org
Temporal Abstraction in Reinforcement Learning with the Successor Representation
Marlos C. Machado
Andre Barreto
Doina Precup
Michael Bowling
2022-12-31
J. Mach. Learn. Res. (publié)
arxiv.org
arxiv.org
Test-time Defense against Adversarial Attacks: Detection and Reconstruction of Adversarial Examples via Masked Autoencoder
Yun-Yun Tsai
Ju-Chin Chao
Albert Wen
Zhaoyuan Yang
Chengzhi Mao
Tapan Shah
Junfeng Yang
Existing defense methods against adversarial attacks can be categorized into training time and test time defenses. Training time defense, i.… (voir plus)e., adversarial training, requires a significant amount of extra time for training and is often not able to be generalized to unseen attacks. On the other hand, test time defense by test time weight adaptation requires access to perform gradient descent on (part of) the model weights, which could be infeasible for models with frozen weights. To address these challenges, we propose DRAM, a novel defense method to Detect and Reconstruct the multiple types of Adversarial attacks via Masked autoencoder (MAE). We demonstrate how to use MAE losses to build a KS-test to detect adversarial attacks. Moreover, the MAE losses can be used to repair adversarial samples from unseen attack types. In this sense, DRAM neither requires model weight updates in test time nor augments the training set with more adversarial samples. Evaluating DRAM on the large-scale ImageNet data, we achieve the best detection rate of 82% on average on eight types of adversarial attacks compared with other detection baselines. For reconstruction, DRAM improves the robust accuracy by 6% ∼ 41% for Standard ResNet50 and 3% ∼ 8% for Robust ResNet50 compared with other self-supervision tasks, such as rotation prediction and contrastive learning.
2022-12-31
(publié)
www.semanticscholar.org
The Age of Ransomware: A Survey on the Evolution, Taxonomy, and Research Directions
Salwa Razaulla
Claude Fachkha
Christine Markarian
Amjad Gawanmeh
Wathiq Mansoor
Benjamin C. M. Fung
Chadi Assi
The proliferation of ransomware has become a significant threat to cybersecurity in recent years, causing significant financial, reputationa… (voir plus)l, and operational damage to individuals and organizations. This paper aims to provide a comprehensive overview of the evolution of ransomware, its taxonomy, and its state-of-the-art research contributions. We begin by tracing the origins of ransomware and its evolution over time, highlighting the key milestones and major trends. Next, we propose a taxonomy of ransomware that categorizes different types of ransomware based on their characteristics and behavior. Subsequently, we review the existing research over several years in regard to detection, prevention, mitigation, and prediction techniques. Our extensive analysis, based on more than 150 references, has revealed that significant research, specifically 72.8%, has focused on detecting ransomware. However, a lack of emphasis has been placed on predicting ransomware. Additionally, of the studies focused on ransomware detection, a significant portion, 70%, have utilized Machine Learning methods. This study uncovers a range of shortcomings in research pertaining to real-time protection and identifying zero-day ransomware, and two issues specific to Machine Learning models. Adversarial machine learning exploitation and concept drift have been identified as under-researched areas in the field. This survey is a constructive roadmap for researchers interested in ransomware research matters.
2022-12-31
IEEE Access (publié)
doi.org
On the (Im)Possibility of Estimating Various Notions of Differential Privacy (short paper)
Daniele Gorla
Louis Jalouzot
Federica Granese
Catuscia Palamidessi
Pablo Piantanida
We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism … (voir plus)is a priori unknown to them (the so-called “black-box" scenario). In particular, we delve into the investigation of two notions of local differential privacy (LDP), namely 𝜀 -LDP and Rényi LDP. On one hand, we prove that, without any assumption on the underlying distributions, it is not possible to have an algorithm able to infer the level of data protection with provable guarantees. On the other hand, we demonstrate that, under reasonable assumptions (namely, Lipschitzness of the involved densities on a closed interval), such guarantees exist and can be achieved by a simple histogram-based estimator.
2022-12-31
ICTCS (publié)
dblp.uni-trier.de
The KITMUS Test: Evaluating Knowledge Integration from Multiple Sources
Akshatha Arodi
Martin Pömsl
Kaheer Suleman
Adam Trischler
Alexandra Olteanu
Jackie Chi Kit Cheung
Akshatha Arodi, Martin Pömsl, Kaheer Suleman, Adam Trischler, Alexandra Olteanu, Jackie Chi Kit Cheung. Proceedings of the 61st Annual Meet… (voir plus)ing of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
2022-12-31
Association for Computational Linguistics (publié)
doi.org
arxiv.org
On the Limitations of Elo: Real-World Games, are Transitive, not Additive
Quentin Bertrand
Wojciech M. Czarnecki
Gauthier Gidel
Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these g… (voir plus)ames are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate the challenge of identifying the strength of the transitive component in games. First, we show that Elo models can fail to extract this transitive component, even in elementary transitive games. Then, based on this observation, we propose an extension of the Elo score: we end up with a disc ranking system that assigns each player two scores, which we refer to as skill and consistency. Finally, we propose an empirical validation on payoff matrices coming from real-world games played by bots and humans.
2022-12-31
AISTATS (publié)
doi.org
arxiv.org
The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
Dung Nguyen Manh
Nam Le Hai
Anh T. V. Dau
Anh Minh Nguyen
Khanh Nghiem
Jin Guo
Nghi D. Q. Bui
Dung Nguyen Manh, Nam Le Hai, Anh T. V. Dau, Anh Minh Nguyen, Khanh Nghiem, Jin Guo, Nghi D. Q. Bui. Proceedings of the 3rd Workshop for Nat… (voir plus)ural Language Processing Open Source Software (NLP-OSS 2023). 2023.
2022-12-31
Workshop for Natural Language Processing Open Source Software (publié)
doi.org
Time-inhomogeneous diffusion geometry and topology
Guillaume Huguet
Alexander Tong
Bastian Rieck
Jessie Huang
Manik Kuchroo
Matthew Hirn
Guy Wolf
Smita Krishnaswamy
Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstract… (voir plus)ions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensional data. Diffusion condensation is constructed as a time-inhomogeneous process where each step first computes and then applies a diffusion operator to the data. We theoretically analyze the convergence and evolution of this process from geometric, spectral, and topological perspectives. From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel. Our spectral results are of particular interest since most of the literature on data diffusion is focused on homogeneous processes. From a topological perspective, we show diffusion condensation generalizes centroid-based hierarchical clustering. We use this perspective to obtain a bound based on the number of data points, independent of their location. To understand the evolution of the data geometry beyond convergence, we use topological data analysis. We show that the condensation process itself defines an intrinsic condensation homology. We use this intrinsic topology as well as the ambient persistent homology of the condensation process to study how the data changes over diffusion time. We demonstrate both types of topological information in well-understood toy examples. Our work gives theoretical insights into the convergence of diffusion condensation, and shows that it provides a link between topological and geometric data analysis.
2022-12-31
SIAM Journal on Mathematics of Data Science (publié)
doi.org
arxiv.org
Toward computing attributions for dimensionality reduction techniques
Matthew Scicluna
Jean-Christophe Grenier
Raphaël Poujol
Sébastien Lemieux
Julie G. Hussin
We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well es… (voir plus)tablished within the context of supervised classification—using the gradients of target outputs with respect to the inputs—on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS-CoV-2 sequence dataset. Throughout, we provide a road map so that similar explanation methods could be applied to other dimensionality reduction techniques to rigorously analyze biological datasets. We have created a Python package that can be installed using the following command: pip install interpretable_tsne. All code used can be found at github.com/MattScicluna/interpretable_tsne.
2022-12-31
Bioinformatics Advances (publié)
doi.org
Transposable elements regulate thymus development and function 1
Jean-David Larouche
Céline M. Laumont
Assya Trofimov
Krystel Vincent
Leslie Hesnard
Sylvie Brochu
Caroline Coté
Juliette Humeau
Éric Bonneil
Joël Lanoix
Chantal Durette
Patrick Gendron
Jean-Philippe Laverdure
Ellen Rothman Richie
S. Lemieux
Pierre Thibault
Claude Perreault
21 Transposable elements (TE) are repetitive sequences representing ~45% of the human and mouse genomes 22 and are highly expressed by medul… (voir plus)lary thymic epithelial cells (mTEC). In this study, we investigated the 23 role of transposable elements (TE), which are highly expressed by medullary thymic epithelial cells 24 (mTEC), on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and 25 mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the 26 human thymus is high and shows extensive ageand cell lineage-related variations. TEs interact with 27 multiple transcription factors in all cell types of the human thymus. Two cell types express particularly 28 broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, TEs interact with 29 transcription factors essential for mTEC development and function (e.g., PAX1 and RELB) and generate 30 MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 31 regulate non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large 32 numbers of TEs that lead to the formation of dsRNA, triggering RIG-I and MDA5 signaling and 33 explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study illustrates the diversity of 34 interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic 35 cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. 36 Therefore, we propose that the orchestration of TE expression in thymic cells is critical to prevent 37 autoimmunity in vertebrates. 38
2022-12-31
(publié)
www.semanticscholar.org
Trophic interaction models predict interactions across space, not food webs.
Dominique Caron
Ulrich Brose
Miguel Lurgi
F. Guillaume Blanchet
Dominique Gravel
Laura J. Pollock
Aim: Trophic interactions are central to our understanding of essential ecosystem functions as well as their stability. Predicting these int… (voir plus)eractions has become increasingly common due to the lack of empirical data on trophic interactions for most taxa in most ecosystems. We aim to determine how far and accurately trophic interaction models extrapolate to new communities both in terms of pairwise predator-prey interactions and higher level food web attributes (i.e., species position, food web-level properties).
2022-12-31
(publié)
www.semanticscholar.org