Publications

Optimization and Simplification of PCPA Decoder for Reed-Muller Codes
Jiajie Li
Warren J. Gross
The collapsed projection-aggregation (CPA) decoder reduces the computational complexity of the recursive projection-aggregation (RPA) decode… (voir plus)r by removing the recursive structure. From simulations, the CPA decoder has similar error-correction performance as the RPA decoder, when decoding Reed-Muller (RM) (7, 3) and (8, 2) codes. The computational complexity can be further reduced by only selecting a subset of sub-spaces, which is achieved by pruning CPA decoders. In this work, optimization methods are proposed to find the pruned CPA (PCPA) decoder with small performance loss. Furthermore, the min-sum approximation is used to replace non-linear projection and aggregation functions, and a simplified list decoder based on the syndrome check is proposed. Under the same complexity, the optimized PCPA decoder has less performance loss than randomly constructed PCPA decoders in most case. The min-sum approximation incurs less than 0.15 dB performance loss at a target frame error rate of 10−4, and the simplified list decoder does not have noticeable performance loss.
2022-05-31
IEEE Communications Letters (publié)
doi.org
Parametric Scattering Networks
Shanel Gauthier
Benjamin Therien
Laurent Alsène-Racicot
Muawiz Chaudhary
Irina Rish
Eugene Belilovsky
Michael Eickenberg
Guy Wolf
The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yi… (voir plus)eld more discriminative representations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is optimal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scattering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective representations.
2022-05-31
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)
doi.org
arxiv.org
The use of artificial intelligence and virtual reality in doctor-patient risk communication: A scoping review.
Ryan Antel
S. A. Rahimi
Elena Guadagno
Jason M. Harley
Dan Poenaru
2022-05-31
Patient Education and Counseling (publié)
doi.org
Towards an AAK Theory Approach to Approximate Minimization in the Multi-Letter Case
Clara Lacroce
Prakash Panangaden
Guillaume Rabusseau
We study the approximate minimization problem of weighted finite automata (WFAs): given a WFA, we want to compute its optimal approximation … (voir plus)when restricted to a given size. We reformulate the problem as a rank-minimization task in the spectral norm, and propose a framework to apply Adamyan-Arov-Krein (AAK) theory to the approximation problem. This approach has already been successfully applied to the case of WFAs and language modelling black boxes over one-letter alphabets \citep{AAK-WFA,AAK-RNN}. Extending the result to multi-letter alphabets requires solving the following two steps. First, we need to reformulate the approximation problem in terms of noncommutative Hankel operators and noncommutative functions, in order to apply results from multivariable operator theory. Secondly, to obtain the optimal approximation we need a version of noncommutative AAK theory that is constructive. In this paper, we successfully tackle the first step, while the second challenge remains open.
2022-05-31
ArXiv (prépublication)
doi.org
arxiv.org
Evolving Domain Generalization
Wei Wang
Gezheng Xu
Ruizhi Pu
Jiaqi Li
Fan Zhou
Changjian Shui
Charles Ling
Christian Gagné
Boyu Wang
2022-05-30
ArXiv (prépublication)
doi.org
arxiv.org
JARV1S: Phenotype Clone Search for Rapid Zero-Day Malware Triage and Functional Decomposition for Cyber Threat Intelligence
Christopher Molloy
Philippe Charland
Steven H. H. Ding
Benjamin C. M. Fung
Cyber threat intelligence (CTI) has become a critical component of the defense of organizations against the steady surge of cyber attacks. M… (voir plus)alware is one of the most challenging problems for CTI, due to its prevalence, the massive number of variants, and the constantly changing threat actor behaviors. Currently, Malpedia has indexed 2,390 unique malware families, while the AVTEST Institute has recorded more than 166 million new unique malware samples in 2021. There exists a vast number of variants per malware family. Consequently, the signature-based representation of patterns and knowledge of legacy systems can no longer be generalized to detect future malware attacks. Machine learning-based solutions can match more variants. However, as a black-box approach, they lack the explainability and maintainability required by incident response teams.There is thus an urgent need for a data-driven system that can abstract a future-proof, human-friendly, systematic, actionable, and dependable knowledge representation from software artifacts from the past for more effective and insightful malware triage. In this paper, we present the first phenotype-based malware decomposition system for quick malware triage that is effective against malware variants. We define phenotypes as directly observable characteristics such as code fragments, constants, functions, and strings. Malware development rarely starts from scratch, and there are many reused components and code fragments. The target under investigation is decomposed into known phenotypes that are mapped to known malware families, malware behaviors, and Advanced Persistent Threat (APT) groups. The implemented system provides visualizable phenotypes through an interactive tree map, helping the cyber analysts to navigate through the decomposition results. We evaluated our system on 200,000 malware samples, 100,000 benign samples, and a malware family with over 27,284 variants. The results indicate our system is scalable, efficient, and effective against zero-day malware and new variants of known families.
2022-05-30
2022 14th International Conference on Cyber Conflict: Keep Moving! (CyCon) (publié)
doi.org
Agnostic Physics-Driven Deep Learning
Benjamin Scellier
Siddhartha Mishra
Yoshua Bengio
Yann Ollivier
2022-05-29
ArXiv (prépublication)
doi.org
arxiv.org
Multi-language design smells: a backstage perspective
Mouna Abidi
Md Saidur Rahman
Moses Openja
Foutse Khomh
2022-05-29
Empirical Software Engineering (publié)
doi.org
Works for Me! Cannot Reproduce – A Large Scale Empirical Study of Non-reproducible Bugs
Mohammad Masudur Rahman
Foutse Khomh
Marco Castelluccio
2022-05-29
Empirical Software Engineering (publié)
doi.org
Adaptive Confidence Calibration
David Meger
Jonathan W. Pearce
2022-05-26
Applied Informatics (publié)
doi.org
Contextual bandit optimization of super-resolution microscopy
Anthony Bilodeau
Renaud Bernatchez
Albert Michaud-Gagnon
Flavie Lavoie-Cardinal
Audrey Durand
2022-05-26
Applied Informatics (publié)
doi.org
Efficient Fine-Tuning of BERT Models on the Edge
Danilo Vucetic
Mohammadreza Tayaranian
Maryam Ziaeefard
James J. Clark
Brett H. Meyer
Warren J. Gross
Resource-constrained devices are increasingly the deployment targets of machine learning applications. Static models, however, do not always… (voir plus) suffice for dynamic environments. On-device training of models allows for quick adaptability to new scenarios. With the increasing size of deep neural networks, as noted with the likes of BERT and other natural language processing models, comes increased resource requirements, namely memory, computation, energy, and time. Furthermore, training is far more resource intensive than inference. Resource-constrained on-device learning is thus doubly difficult, especially with large BERT-like models. By reducing the memory usage of fine-tuning, pre-trained BERT models can become efficient enough to fine-tune on resource-constrained devices. We propose Freeze And Reconfigure (FAR), a memory-efficient training regime for BERT-like models that reduces the memory usage of activation maps during fine-tuning by avoiding unnecessary parameter updates. FAR reduces fine-tuning time on the DistilBERT model and CoLA dataset by 30%, and time spent on memory operations by 47%. More broadly, reductions in metric performance on the GLUE and SQuAD datasets are around 1% on average.
2022-05-26
2022 IEEE International Symposium on Circuits and Systems (ISCAS) (publié)
doi.org
arxiv.org