References of "Le Traon, Yves 50002182"
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See detailSelecting Fault Revealing Mutants
Titcheu Chekam, Thierry UL; Papadakis, Mike UL; Bissyande, Tegawendé François D Assise UL et al

in Empirical Software Engineering (in press)

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See detailTowards Generalizable Machine Learning for Chest X-ray Diagnosis with Multi-task learning
Ghamizi, Salah UL; Garcia Santa Cruz, Beatriz UL; Temple, Paul et al

E-print/Working paper (2022)

Clinicians use chest radiography (CXR) to diagnose common pathologies. Automated classification of these diseases can expedite analysis workflow, scale to growing numbers of patients and reduce healthcare ... [more ▼]

Clinicians use chest radiography (CXR) to diagnose common pathologies. Automated classification of these diseases can expedite analysis workflow, scale to growing numbers of patients and reduce healthcare costs. While research has produced classification models that perform well on a given dataset, the same models lack generalization on different datasets. This reduces confidence that these models can be reliably deployed across various clinical settings. We propose an approach based on multitask learning to improve model generalization. We demonstrate that learning a (main) pathology together with an auxiliary pathology can significantly impact generalization performance (between -10% and +15% AUC-ROC). A careful choice of auxiliary pathology even yields competitive performance with state-of-the-art models that rely on fine-tuning or ensemble learning, using between 6% and 34% of the training data that these models required. We, further, provide a method to determine what is the best auxiliary task to choose without access to the target dataset. Ultimately, our work makes a big step towards the creation of CXR diagnosis models applicable in the real world, through the evidence that multitask learning can drastically improve generalization. [less ▲]

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See detailAn Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement
Hu, Qiang UL; Guo, Yuejun UL; Cordy, Maxime UL et al

in ACM Transactions on Software Engineering and Methodology (2022)

Similar to traditional software that is constantly under evolution, deep neural networks (DNNs) need to evolve upon the rapid growth of test data for continuous enhancement, e.g., adapting to distribution ... [more ▼]

Similar to traditional software that is constantly under evolution, deep neural networks (DNNs) need to evolve upon the rapid growth of test data for continuous enhancement, e.g., adapting to distribution shift in a new environment for deployment. However, it is labor-intensive to manually label all the collected test data. Test selection solves this problem by strategically choosing a small set to label. Via retraining with the selected set, DNNs will achieve competitive accuracy. Unfortunately, existing selection metrics involve three main limitations: 1) using different retraining processes; 2) ignoring data distribution shifts; 3) being insufficiently evaluated. To fill this gap, we first conduct a systemically empirical study to reveal the impact of the retraining process and data distribution on model enhancement. Then based on our findings, we propose a novel distribution-aware test (DAT) selection metric. Experimental results reveal that retraining using both the training and selected data outperforms using only the selected data. None of the selection metrics perform the best under various data distributions. By contrast, DAT effectively alleviates the impact of distribution shifts and outperforms the compared metrics by up to 5 times and 30.09% accuracy improvement for model enhancement on simulated and in-the-wild distribution shift scenarios, respectively. [less ▲]

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See detailEvasion Attack STeganography: Turning Vulnerability Of Machine Learning ToAdversarial Attacks Into A Real-world Application
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in Proceedings of International Conference on Computer Vision 2021 (2021)

Evasion Attacks have been commonly seen as a weakness of Deep Neural Networks. In this paper, we flip the paradigm and envision this vulnerability as a useful application. We propose EAST, a new ... [more ▼]

Evasion Attacks have been commonly seen as a weakness of Deep Neural Networks. In this paper, we flip the paradigm and envision this vulnerability as a useful application. We propose EAST, a new steganography and watermarking technique based on multi-label targeted evasion attacks. Our results confirm that our embedding is elusive; it not only passes unnoticed by humans, steganalysis methods, and machine-learning detectors. In addition, our embedding is resilient to soft and aggressive image tampering (87% recovery rate under jpeg compression). EAST outperforms existing deep-learning-based steganography approaches with images that are 70% denser and 73% more robust and supports multiple datasets and architectures. [less ▲]

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See detailRequirements And Threat Models of Adversarial Attacks and Robustness of Chest X-ray classification
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

E-print/Working paper (2021)

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little ... [more ▼]

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural Networks. While most of the studies focus on natural images with standardized benchmarks like ImageNet and CIFAR, little research has considered real world applications, in particular in the medical domain. Our research shows that, contrary to previous claims, robustness of chest x-ray classification is much harder to evaluate and leads to very different assessments based on the dataset, the architecture and robustness metric. We argue that previous studies did not take into account the peculiarity of medical diagnosis, like the co-occurrence of diseases, the disagreement of labellers (domain experts), the threat model of the attacks and the risk implications for each successful attack. In this paper, we discuss the methodological foundations, review the pitfalls and best practices, and suggest new methodological considerations for evaluating the robustness of chest xray classification models. Our evaluation on 3 datasets, 7 models, and 18 diseases is the largest evaluation of robustness of chest x-ray classification models. We believe our findings will provide reliable guidelines for realistic evaluation and improvement of the robustness of machine learning models for medical diagnosis. [less ▲]

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See detailBlockPerf: A Hybrid Blockchain Emulator/Simulator Framework
Polge, Julien UL; Ghatpande, Sankalp UL; Kubler, Sylvain et al

in IEEE Access (2021), 9

Blockchain is increasingly used for registering, authenticating and validating digital assets (financial assets, real estate, etc.) and transactions, governing interactions, recording data and managing ... [more ▼]

Blockchain is increasingly used for registering, authenticating and validating digital assets (financial assets, real estate, etc.) and transactions, governing interactions, recording data and managing identification among multiple parties in a trusted, decentralized, and secure manner. Today, a large variety of blockchain technologies is expanding in order to fulfill technical and non-technical needs and requirements. Within this context, determining and most importantly evaluating the characteristics/performance of a given blockchain platform is crucial for system designers before deploying it. A number of blockchain simulators have been proposed in the literature over the past few years, as reviewed in this paper, but are often limited in several respects (lack of extensibility, do not allow for evaluating all aspects of a blockchain...). This paper extends and improves a state-of-the-art simulator (BlockSim) into a new simulator called ‘‘BlockPerf’’ to overcome those limitations. Both simulators are compared based on a real-life (benchmarking) Bitcoin scenario, whose results show that BlockPerf provides more realistic results than BlockSim, improving by around ≈50% (in average) the outcomes. [less ▲]

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See detailA Replication Study on the Usability of Code Vocabulary in Predicting Flaky Tests
Haben, Guillaume UL; Habchi, Sarra UL; Papadakis, Mike UL et al

in 18th International Conference on Mining Software Repositories (2021, May)

Abstract—Industrial reports indicate that flaky tests are one of the primary concerns of software testing mainly due to the false signals they provide. To deal with this issue, researchers have developed ... [more ▼]

Abstract—Industrial reports indicate that flaky tests are one of the primary concerns of software testing mainly due to the false signals they provide. To deal with this issue, researchers have developed tools and techniques aiming at (automatically) identifying flaky tests with encouraging results. However, to reach industrial adoption and practice, these techniques need to be replicated and evaluated extensively on multiple datasets, occasions and settings. In view of this, we perform a replication study of a recently proposed method that predicts flaky tests based on their vocabulary. We thus replicate the original study on three different dimensions. First we replicate the approach on the same subjects as in the original study but using a different evaluation methodology, i.e., we adopt a time-sensitive selection of training and test sets to better reflect the envisioned use case. Second, we consolidate the findings of the initial study by building a new dataset of 837 flaky tests from 9 projects in a different programming language, i.e., Python while the original study was in Java, which comforts the generalisability of the results. Third, we propose an extension to the original approach by experimenting with different features extracted from the Code Under Test. Our results demonstrate that a more robust validation has a consistent negative impact on the reported results of the original study, but, fortunately, these do not invalidate the key conclusions of the study. We also find re-assuring results that the vocabulary-based models can also be used to predict test flakiness in Python and that the information lying in the Code Under Test has a limited impact in the performance of the vocabulary-based models [less ▲]

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See detailEfficient and Transferable Adversarial Examples from Bayesian Neural Networks
Gubri, Martin UL; Cordy, Maxime UL; Papadakis, Mike UL et al

E-print/Working paper (2021)

An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on a surrogate ensemble model to increase diversity. We argue that transferability is ... [more ▼]

An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on a surrogate ensemble model to increase diversity. We argue that transferability is fundamentally related to epistemic uncertainty. Based on a state-of-the-art Bayesian Deep Learning technique, we propose a new method to efficiently build a surrogate by sampling approximately from the posterior distribution of neural network weights, which represents the belief about the value of each parameter. Our extensive experiments on ImageNet and CIFAR-10 show that our approach improves the transfer rates of four state-of-the-art attacks significantly (up to 62.1 percentage points), in both intra-architecture and inter-architecture cases. On ImageNet, our approach can reach 94% of transfer rate while reducing training computations from 11.6 to 2.4 exaflops, compared to an ensemble of independently trained DNNs. Our vanilla surrogate achieves 87.5% of the time higher transferability than 3 test-time techniques designed for this purpose. Our work demonstrates that the way to train a surrogate has been overlooked although it is an important element of transfer-based attacks. We are, therefore, the first to review the effectiveness of several training methods in increasing transferability. We provide new directions to better understand the transferability phenomenon and offer a simple but strong baseline for future work. [less ▲]

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See detailWhat You See is What it Means! Semantic Representation Learning of Code based on Visualization
Keller, Patrick UL; Kabore, Abdoul Kader UL; Plein, Laura et al

in ACM Transactions on Software Engineering and Methodology (2021)

Recent successes in training word embeddings for NLP tasks have encouraged a wave of research on representation learning for sourcecode, which builds on similar NLP methods. The overall objective is then ... [more ▼]

Recent successes in training word embeddings for NLP tasks have encouraged a wave of research on representation learning for sourcecode, which builds on similar NLP methods. The overall objective is then to produce code embeddings that capture the maximumof program semantics. State-of-the-art approaches invariably rely on a syntactic representation (i.e., raw lexical tokens, abstractsyntax trees, or intermediate representation tokens) to generate embeddings, which are criticized in the literature as non-robustor non-generalizable. In this work, we investigate a novel embedding approach based on the intuition that source code has visualpatterns of semantics. We further use these patterns to address the outstanding challenge of identifying semantic code clones. Wepropose theWySiWiM(“What You See Is What It Means”) approach where visual representations of source code are fed into powerfulpre-trained image classification neural networks from the field of computer vision to benefit from the practical advantages of transferlearning. We evaluate the proposed embedding approach on the task of vulnerable code prediction in source code and on two variationsof the task of semantic code clone identification: code clone detection (a binary classification problem), and code classification (amulti-classification problem). We show with experiments on the BigCloneBench (Java), Open Judge (C) that although simple, ourWySiWiMapproach performs as effectively as state of the art approaches such as ASTNN or TBCNN. We also showed with datafrom NVD and SARD thatWySiWiMrepresentation can be used to learn a vulnerable code detector with reasonable performance(accuracy∼90%). We further explore the influence of different steps in our approach, such as the choice of visual representations or theclassification algorithm, to eventually discuss the promises and limitations of this research direction. [less ▲]

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See detailTest Selection for Deep Learning Systems
Ma, Wei UL; Papadakis, Mike UL; Tsakmalis, Anestis et al

in ACM Transactions on Software Engineering and Methodology (2021), 30(2), 131--1322

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See detailTowards Exploring the Limitations of Active Learning: An Empirical Study
Hu, Qiang UL; Guo, Yuejun UL; Cordy, Maxime UL et al

in The 36th IEEE/ACM International Conference on Automated Software Engineering. (2021)

Deep neural networks (DNNs) are being increasingly deployed as integral parts of software systems. However, due to the complex interconnections among hidden layers and massive hyperparameters, DNNs ... [more ▼]

Deep neural networks (DNNs) are being increasingly deployed as integral parts of software systems. However, due to the complex interconnections among hidden layers and massive hyperparameters, DNNs require being trained using a large number of labeled inputs, which calls for extensive human effort for collecting and labeling data. Spontaneously, to alleviate this growing demand, a surge of state-of-the-art studies comes up with different metrics to select a small yet informative dataset for the model training. These research works have demonstrated that DNN models can achieve competitive performance using a carefully selected small set of data. However, the literature lacks proper investigation of the limitations of data selection metrics, which is crucial to apply them in practice. In this paper, we fill this gap and conduct an extensive empirical study to explore the limits of selection metrics. Our study involves 15 selection metrics evaluated over 5 datasets (2 image classification tasks and 3 text classification tasks), 10 DNN architectures, and 20 labeling budgets (ratio of training data being labeled). Our findings reveal that, while selection metrics are usually effective in producing accurate models, they may induce a loss of model robustness (against adversarial examples) and resilience to compression. Overall, we demonstrate the existence of a trade-off between labeling effort and different model qualities. This paves the way for future research in devising selection metrics considering multiple quality criteria. [less ▲]

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See detailCerebro: Static Subsuming Mutant Selection
Garg, Aayush UL; Ojdanic, Milos UL; Degiovanni, Renzo Gaston UL et al

E-print/Working paper (2021)

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See detailKilling Stubborn Mutants with Symbolic Execution
Titcheu Chekam, Thierry UL; Papadakis, Mike UL; Cordy, Maxime UL et al

in ACM Transactions on Software Engineering and Methodology (2021), 30(2), 191--1923

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See detailCONFUZZION: A Java Virtual Machine Fuzzer for Type Confusion Vulnerabilities
Bonnaventure, William; Khanfir, Ahmed UL; Bartel, Alexandre et al

in IEEE International Conference on Software Quality, Reliability, and Security (QRS), 2021 (2021)

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See detailAdversarial Robustness in Multi-Task Learning: Promises and Illusions
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

E-print/Working paper (2021)

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research ... [more ▼]

Vulnerability to adversarial attacks is a well-known weakness of Deep Neural networks. While most of the studies focus on single-task neural networks with computer vision datasets, very little research has considered complex multi-task models that are common in real applications. In this paper, we evaluate the design choices that impact the robustness of multi-task deep learning networks. We provide evidence that blindly adding auxiliary tasks, or weighing the tasks provides a false sense of robustness. Thereby, we tone down the claim made by previous research and study the different factors which may affect robustness. In particular, we show that the choice of the task to incorporate in the loss function are important factors that can be leveraged to yield more robust models. [less ▲]

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See detailA Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0
Polge, Julien UL; Robert, Jérémy UL; Le Traon, Yves UL

in Sensors (2020), 20(24),

With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber ... [more ▼]

With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow for them to have access to potentially useful information in order to optimise the Overall Equipment Effectiveness (OEE); however, most European industries still rely on the Computer-Integrated Manufacturing (CIM) model, where the production systems run as independent systems (i.e., without any communication with the upper levels). Those production systems are controlled by a Programmable Logic Controller, in which a static and rigid program is implemented. This program is static and rigid in a sense that the programmed routines cannot evolve over the time unless a human modifies it. However, to go further in terms of flexibility, we are convinced that it requires moving away from the aforementioned old-fashioned and rigid automation to a ML-based automation, i.e., where the control itself is based on the decisions that were taken by ML algorithms. In order to verify this, we applied a time series classification method on a scale model of a factory using real industrial controllers, and widened the variety of parts the production line has to treat. This study shows that satisfactory results can be obtained only at the expense of the human expertise (i.e., in the industrial process and in the ML process). [less ▲]

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See detailLoad approximation for uncertain topologies in the low-voltage grid
Mouline, Ludovic UL; Cordy, Maxime UL; Le Traon, Yves UL

in INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS, 11-13 November 2020 (2020, November)

Smart grids allow operators to monitor the grid continuously, detect occurring incidents, and trigger corrective actions. To perform that, they require a deep understanding of the effective situation ... [more ▼]

Smart grids allow operators to monitor the grid continuously, detect occurring incidents, and trigger corrective actions. To perform that, they require a deep understanding of the effective situation within the grid. However, some parameters of the grid may not be known with absolute confidence. Reasoning over the grid despite uncertainty needs the consideration of all possible states. In this paper, we propose an approach to enumerate only valid potential grid states. Thereby, we allow discarding invalid assumptions that poison the results of a given computation procedure. We validate our approach based on a real-world topology from the power grid in Luxembourg. We show that the estimation of cable load is negatively affected by invalid fuse state combinations, in terms of computation time and accuracy. [less ▲]

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See detailPermissioned Blockchain Frameworks in the Industry: A Comparison
Polge, Julien UL; Robert, Jérémy UL; Le Traon, Yves UL

in ICT Express (2020)

Permissioned and private blockchain platforms are increasingly used in today’s industry. This paper provides a comprehensive and comparative study of the 5 major frameworks (Fabric, Ethereum, Quorum ... [more ▼]

Permissioned and private blockchain platforms are increasingly used in today’s industry. This paper provides a comprehensive and comparative study of the 5 major frameworks (Fabric, Ethereum, Quorum, MultiChain and R3 Corda) with regard to the community activities, performance, scalability, privacy and adoption criteria. Based on a literature review, this study shows that even if Fabric is promising, the final selection of a framework for a specific case-study is always a trade-off. Finally, lessons learnt are given for industrial practitioners and researchers. [less ▲]

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See detailData-driven simulation and optimization for covid-19 exit strategies
Ghamizi, Salah UL; Rwemalika, Renaud UL; Cordy, Maxime UL et al

in Ghamizi, Salah; Rwemalika, Renaud; Cordy, Maxime (Eds.) et al Data-driven simulation and optimization for covid-19 exit strategies (2020, August)

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive ... [more ▼]

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb.In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers.Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R² score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies. [less ▲]

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