Publications

Are Multi-language Design Smells Fault-prone? An Empirical Study
Mouna Abidi
Md Saidur Rahman
Moses Openja
Foutse Khomh
Nowadays, modern applications are developed using components written in different programming languages. These systems introduce several adv… (voir plus)antages. However, as the number of languages increases, so does the challenges related to the development and maintenance of these systems. In such situations, developers may introduce design smells (i.e., anti-patterns and code smells) which are symptoms of poor design and implementation choices. Design smells are defined as poor design and coding choices that can negatively impact the quality of a software program despite satisfying functional requirements. Studies on mono-language systems suggest that the presence of design smells affects code comprehension, thus making systems harder to maintain. However, these studies target only mono-language systems and do not consider the interaction between different programming languages. In this paper, we present an approach to detect multi-language design smells in the context of JNI systems. We then investigate the prevalence of those design smells. Specifically, we detect 15 design smells in 98 releases of nine open-source JNI projects. Our results show that the design smells are prevalent in the selected projects and persist throughout the releases of the systems. We observe that in the analyzed systems, 33.95% of the files involving communications between Java and C/C++ contains occurrences of multi-language design smells. Some kinds of smells are more prevalent than others, e.g., Unused Parameters, Too Much Scattering, Unused Method Declaration. Our results suggest that files with multi-language design smells can often be more associated with bugs than files without these smells, and that specific smells are more correlated to fault-proneness than others.
2021-02-10
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
arxiv.org
Analyzing the Contribution of Ethical Charters to Building the Future of Artificial Intelligence Governance
Lyse Langlois
Catherine Régis
2021-02-06
Reflections on Artificial Intelligence for Humanity (publié)
doi.org
Structured Sparsity Inducing Adaptive Optimizers for Deep Learning
Tristan Deleu
Yoshua Bengio
The parameters of a neural network are naturally organized in groups, some of which might not contribute to its overall performance. To prun… (voir plus)e out unimportant groups of parameters, we can include some non-differentiable penalty to the objective function, and minimize it using proximal gradient methods. In this paper, we derive the weighted proximal operator, which is a necessary component of these proximal methods, of two structured sparsity inducing penalties. Moreover, they can be approximated efficiently with a numerical solver, and despite this approximation, we prove that existing convergence guarantees are preserved when these operators are integrated as part of a generic adaptive proximal method. Finally, we show that this adaptive method, together with the weighted proximal operators derived here, is indeed capable of finding solutions with structure in their sparsity patterns, on representative examples from computer vision and natural language processing.
2021-02-06
ArXiv (prépublication)
arxiv.org
arxiv.org
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy
Léo Milecki
Jonathan Porée
Hatim Belgharbi
Chloé Bourquin
Rafat Damseh
Patrick Delafontaine-Martel
Frédéric Lesage
Maxime Gasse
Jean Provost
Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubb… (voir plus)le contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images. To overcome the fundamental limit of diffraction and achieve a dense reconstruction of the network, low microbubble concentrations must be used, which leads to acquisitions lasting several minutes. Conventional processing pipelines are currently unable to deal with interference from multiple nearby microbubbles, further reducing achievable concentrations. This work overcomes this problem by proposing a Deep Learning approach to recover dense vascular networks from ultrasound acquisitions with high microbubble concentrations. A realistic mouse brain microvascular network, segmented from 2-photon microscopy, was used to train a three-dimensional convolutional neural network (CNN) based on a V-net architecture. Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles. The 3D-CNN approach was validated in silico using a subset of the data and in vivo in a rat brain. In silico, the CNN reconstructed vascular networks with higher precision (81%) than a conventional ULM framework (70%). In vivo, the CNN could resolve micro vessels as small as 10 μ m with an improvement in resolution when compared against a conventional approach.
2021-02-02
IEEE Transactions on Medical Imaging (publié)
doi.org
arxiv.org
Enabling Secure Trustworthiness Assessment and Privacy Protection in Integrating Data for Trading Person-Specific Information
Rashid Hussain Khokhar
Farkhund Iqbal
Benjamin C. M. Fung
Jamal Bentahar
With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders de… (voir plus)mand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey–Clarke–Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.
2021-01-31
IEEE transactions on engineering management (publié)
doi.org
The neural correlates of ongoing conscious thought
Jonathan Smallwood
Adam Turnbull
Hao-Ting Wang
Nerissa S.P. Ho
Giulia L. Poerio
Theodoros Karapanagiotidis
Delali Konu
Brontë Mckeown
Meichao Zhang
Charlotte Murphy
Deniz Vatansever
Danilo Bzdok
Mahiko Konishi
Robert Leech
Paul Seli
Jonathan W. Schooler
Boris C Bernhardt
Daniel S. Margulies
Elizabeth Jefferies
2021-01-31
iScience (publié)
doi.org
Training neural networks to recognize speech increased their correspondence to the human auditory pathway but did not yield a shared hierarchy of acoustic features
Jessica A.F. Thompson
Yoshua Bengio
Elia Formisano
Marc Schönwiesner
Trained CNNs more similar to auditory fMRI activity than untrainedNo evidence of a shared representational hierarchy for acoustic featuresAl… (voir plus)l ROIs were most similar to the first fully-connected layerCNN performance on speech recognition task positively associated with fmri similarity
2021-01-26
bioRxiv (prépublication)
doi.org
A systematic analysis of ICSD-3 diagnostic criteria and proposal for further structured iteration.
Christophe Gauld
Régis Lopez
Pierre A. GEOFFROY
Charles Morin
Kelly Guichard
Elodie Giroux
Yves Dauvilliers
Guillaume Dumas
Pierre Philip
Jean‐Arthur Micoulaud‐Franchi
2021-01-25
Sleep Medicine Reviews (publié)
doi.org
Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
Sahand Rezaei-Shoshtari
David Meger
Inna Sharf
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted… (voir plus) application is of a robotic manipulator executing a complex environment interaction task, in particular, cutting a wooden object. We train two flavours of Variational Autoencoders---standard and Vector-Quantised---to learn the latent space which is then used to infer certain properties of the cutting operation, such as whether the robot is cutting or not, as well as, material and geometry of the object being cut. The two VAE models are evaluated with reconstruction, prediction and a combined reconstruction/prediction decoders. The results demonstrate the expressiveness of the latent space for robotic interaction inference and the competitive prediction performance against recurrent neural networks.
2021-01-23
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
arxiv.org
PresSense: Passive Respiration Sensing via Ambient WiFi Signals in Noisy Environments
Yi Tian Xu
X. T. Chen
Xue Liu
David Meger
Gregory Dudek
Passive sensing with ambient WiFi signals is a promising technique that will enable new types of human-robot interactions while preserving u… (voir plus)sers' privacy. Here, we present PresSense, a system for human respiration sensing in noisy environments. Unlike existing WiFi-based respiration sensors, we employ a human presence detector, improving the robustness in scenarios where no human is present in an Area Of Interest (AOI). We also integrate our novel feature, Peak Distance Histogram (PDH), with other classic WiFi features to achieve better accuracy when someone is present in the AOI. We tested our system using commodity WiFi devices in an office room. Our PresSense outperforms the state of the arts in both respiration rate estimation and presence detection.
2021-01-23
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
Author response: Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2021-01-20
(publié)
doi.org
Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases
Manik Kuchroo
Marcello DiStasio
Eda Calapkulu
Maryam Ige
Le Zhang
Amar H. Sheth
Madhvi Menon
Yu Xing
Scott Gigante
Jessie Huang
Rahul M. Dhodapkar
Bastian Rieck
Guy Wolf
Smita Krishnaswamy
Brian P. Hafler
1 A novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas… (voir plus) with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimer’s disease and multiple sclerosis. 2 Neurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimer’s disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called ‘diffusion condensation.’ By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.
2021-01-19
bioRxiv (prépublication)
doi.org