Publications

Multilevel Approaches for the Critical Node Problem
Andrea Baggio
Margarida Carvalho
Andrea Lodi
Andrea Tramontani
In recent years, a lot of effort has been dedicated to develop strategies to defend networks against possible cascade failures or malicious … (voir plus)viral attacks. In particular, many results rely on two different viewpoints. On the one hand, network safety is investigated from a preventive perspective. In this paradigm, for a given network, the goal is to modify its structure, in order to minimize the propagation of failures. On the other hand, blocking models have been proposed for scenarios where the attack has already taken place. In this case, a harmful spreading process is assumed to propagate through the network with particular dynamics, allowing some time for an effective defensive reaction. In this work, we combine these two perspectives. More precisely, following the framework Defender-AttackerDefender, we consider a model of prevention, attack, and damage containment using a three-stage, sequential game. Thus, we assume the defender not only to be able to adopt preventive strategies but also to defend the network after an attack takes place. Assuming that the attacker will act optimally, we want to chose a defensive strategy for the first stage that would minimize the total damage to the network in the end of the third stage. Our contribution consists of considering this problem as a trilevel Mixed-Integer Program and design an exact algorithm for it based on tools developed for multilevel programming.
2020-12-31
Operational Research (publié)
doi.org
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding
Dmitriy Serdyuk
Yongqiang Wang
Christian Fue-730
Anuj Kumar
Baiyang Liu
Yoshua Bengio
Edwin Simonnet
Sahar Ghannay
Nathalie Camelin
Tandem spoken language understanding 001 (SLU) systems suffer from the so-called 002 automatic speech recognition (ASR) error 003 propagatio… (voir plus)n problem. Additionally, as the 004 ASR is not optimized to extract semantics, but 005 solely the linguistic content, relevant semantic 006 cues might be left out of its transcripts. In 007 this work, we propose a multimodal language 008 understanding (MLU) architecture to mitigate 009 these problems. Our solution is based on 010 two compact unidirectional long short-term 011 memory (LSTM) models that encode speech 012 and text information. A fusion layer is also 013 used to fuse audio and text embeddings. 014 Two fusion strategies are explored: a simple 015 concatenation of these embeddings and a 016 cross-modal attention mechanism that learns 017 the contribution of each modality. The first 018 approach showed to be the optimal solution 019 to robustly extract semantic information from 020 audio-textual data. We found that attention 021 is less effective at testing time when the text 022 modality is corrupted. Our model is evaluated 023 on three SLU datasets and robustness is tested 024 using ASR outputs from three off-the-shelf 025 ASR engines. Results show that the proposed 026 approach effectively mitigates the ASR error 027 propagation problem for all datasets. 028
2020-12-31
(publié)
www.semanticscholar.org
Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen
Dinghuai Zhang
Michael U. Gutmann
Aaron Courville
Zhanxing Zhu
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation… (voir plus) of the likelihood function is intractable, but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representations of the data with the help of deep neural networks. The infomax learning procedure does not need to estimate any density or density ratio. We apply our approach to both traditional approximate Bayesian computation and recent neural likelihood methods, boosting their performance on a range of tasks.
2020-12-31
ICLR (publié)
doi.org
openreview.net
A Novel and Dedicated Machine Learning Model for Malware Classification
Miles Q. Li
Benjamin C. M. Fung
Philippe Charland
Steven H. H. Ding
: Malicious executables are comprised of functions that can be represented in assembly code. In the assembly code mining literature, many so… (voir plus)ftware reverse engineering tools have been created to disassemble executables, search function clones, and find vulnerabilities, among others. The development of a machine learning-based malware classification model that can simultaneously achieve excellent classification performance and provide insightful interpretation for the classification results remains to be a hot research topic. In this paper, we propose a novel and dedicated machine learning model for the research problem of malware classification. Our proposed model generates assembly code function clusters based on function representation learning and provides excellent interpretability for the classification results. It does not require a large or balanced dataset to train which meets the situation of real-life scenarios. Experiments show that our proposed approach outperforms previous state-of-the-art malware classification models and provides meaningful interpretation of classification results.
2020-12-31
International Conference on Software and Data Technologies (publié)
doi.org
A Novel Neural Network-Based Malware Severity Classification System
Miles Q. Li
Benjamin C. M. Fung
2020-12-31
International Conference on Software and Data Technologies (publié)
doi.org
On-the-Fly Attention Modularization for Neural Generation
Yue Dong
Chandra Bhagavatula
Ximing Lu
Jena D. Hwang
Antoine Bosselut
Jackie CK Cheung
Yejin Choi
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from de generation: generated… (voir plus) text is repetitive, generic, self-inconsistent, and lacking commonsense. The empirical analyses on sentence-level attention patterns reveal that neural text degeneration may be associated with insufficient learning of inductive biases by the attention mechanism. Our findings motivate on-the-fly attention modularization, a simple but effective method for injecting inductive biases into attention computation during inference. The resulting text produced by the language model with attention modularization can yield enhanced diversity and commonsense reasoning while maintaining fluency and coherence.
2020-12-31
arXiv.org (prépublication)
dblp.uni-trier.de
Optimal Spectral-Norm Approximate Minimization of Weighted Finite Automata
Borja Balle
Clara Lacroce
Prakash Panangaden
Doina Precup
Guillaume Rabusseau
We address the approximate minimization problem for weighted finite automata (WFAs) with weights in …
2020-12-31
arXiv (prépublication)
doi.org
arxiv.org
Optimization of Artificial Neural Network Hyperparameters For Processing Retrospective Information
A. Rogachev
F. Scholle
Yann Lecun
Yoshua Bengio
I. L. Kashirin
M. Demchenko
. Justification of the selection of the architecture and hyperparameters of artificial neural networks (ANN), focused on solving various cla… (voir plus)sses of applied problems, is a scientific and methodological problem. Optimizing the selection of ANN hyperparameters allows you to improve the quality and speed of ANN training. Various methods of optimizing the selection of ANN hyper-parameters are known – the use of evolutionary calculations, genetic algorithms, etc., but they require the use of additional software. To optimize the process of selecting ANN hyperparameters, Google Research has developed the KerasTuner software tool. It is a platform for automated search of a set of optimal combinations of hyperparameters. In Kerastuner, you can use various methods - random search, Bayesian optimization, or Hyperband. In the numerical experiments conducted by the author, 14 hyperparameters were varied, including the number of blocks of convolutional layers and the filters forming them, the type of activation function, the parameters of the "dropout" layers, and others. The studied tools demonstrated high efficiency while simultaneously varying more than a dozen optimized parameters of the convolutional network. The calculation time on the Colaboratory platform for the various combined ANN architectures studied, including recurrent RNN networks, was several hours, even with the use of GPU graphics accelerators. For ANN, focused on the processing and recognition of retrospective information, an increase in the quality of recognition was achieved to 80 ... 95%.
2020-12-31
(publié)
www.semanticscholar.org
Overview of the TREC 2021 Fair Ranking Track
Asia J. Biega
Fernando Diaz
Michael D. Ekstrand
Sebastian Kohlmeier
The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide … (voir plus)a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents' topical content or authors. The 2021 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2021 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.
2020-12-31
TREC (publié)
doi.org
arxiv.org
Personalized Medicine for OSA Syndrome in a Nutshell: Conceptual Clarification for Integration.
Christophe Gauld
Marie Darrason
Guillaume Dumas
Jean‐Arthur Micoulaud‐Franchi
2020-12-31
Chest (publié)
doi.org
Post-Editing Extractive Summaries by Definiteness Prediction
Jad Kabbara
Jackie Chi Kit Cheung
Extractive summarization has been the mainstay of automatic summarization for decades. Despite all the progress, extractive summarizers stil… (voir plus)l suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight post-editing step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.
2020-12-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
Predicting Infectiousness for Proactive Contact Tracing
Yoshua Bengio
Prateek Gupta
Tegan Maharaj
Nasim Rahaman
Martin Weiss
Tristan Deleu
Eilif Muller
Meng Qu
Victor Schmidt
Pierre-Luc St. Charles
Hannah Alsdurf
Olexa Bilaniuk
David Buckeridge
Gaétan Marceau-Caron
Pierre-Luc Carrier
Joumana Ghosn
Satya Ortiz-Gagné
Chris Pal
Irina Rish
Bernhard Schölkopf … (voir 3 de plus)
Abhinav Sharma
Jian Tang
Andrew Williams
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.
2020-12-31
ICLR (publié)
doi.org
openreview.net