References of "Cordy, Maxime 50027892"
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See detailA Formal Framework of Software Product Line Analyses
Castro, Thiago; Teixeira, Leopoldo; Alves, Vander et al

in ACM Transactions on Software Engineering and Methodology (in press)

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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 (in press)

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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 (in press)

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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 detailTackling the equivalent mutant problem in real-time systems: the 12 commandments of model-based mutation testing
Basile, Davide; Ter Beek, Maurice; Cordy, Maxime UL et al

in SOFTWARE PRODUCT LINE CONFERENCE (2020, October)

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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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See detailStatistical Model Checking for Variability-Intensive Systems
Cordy, Maxime UL; Papadakis, Mike UL; Legay, Axel

in FUNDAMENTAL APPROACHES TO SOFTWARE ENGINEERING, Dublin 22-25 April 2020 (2020, April)

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See detailPreventing Overloading Incidents on Smart Grids: A Multiobjective Combinatorial Optimization Approach
Antoniadis, Nikolaos UL; Cordy, Maxime UL; Sifaleras, Angelo et al

in Communications in Computer and Information Science (2020, February 15)

Cable overloading is one of the most critical disturbances that may occur in smart grids, as it can cause damage to the distribution power lines. Therefore, the circuits are protected by fuses so that ... [more ▼]

Cable overloading is one of the most critical disturbances that may occur in smart grids, as it can cause damage to the distribution power lines. Therefore, the circuits are protected by fuses so that, the overload could trip the fuse, opening the circuit, and stopping the flow and heating. However, sustained overloads, even if they are below the safety limits, could also damage the wires. To prevent overload, smart grid operators can switch the fuses on or off to protect the circuits, or remotely curtail the over-producing/over-consuming users. Nevertheless, making the most appropriate decision is a daunting decision-making task, notably due to contractual and technical obligations. In this paper, we define and formulate the overloading prevention problem as a Multiobjective Mixed Integer Quadratically Constrained Program. We also suggest a solution method using a combinatorial optimization approach with a state-of-the-art exact solver. We evaluate this approach for this real-world problem together with Creos Luxembourg S.A., the leading grid operator in Luxembourg, and show that our method can suggest optimal countermeasures to operators facing potential overloading incidents. [less ▲]

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See detailFeatureNET: Diversity-driven Generation of Deep Learning Models
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

in International Conference on Software Engineering (ICSE) (2020)

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See detailAdversarial Embedding: A robust and elusive Steganography and Watermarking technique
Ghamizi, Salah UL; Cordy, Maxime UL; Papadakis, Mike UL et al

Scientific Conference (2020)

We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image ... [more ▼]

We propose adversarial embedding, a new steganography and watermarking technique that embeds secret information within images. The key idea of our method is to use deep neural networks for image classification and adversarial attacks to embed secret information within images. Thus, we use the attacks to embed an encoding of the message within images and the related deep neural network outputs to extract it. The key properties of adversarial attacks (invisible perturbations, nontransferability, resilience to tampering) offer guarantees regarding the confidentiality and the integrity of the hidden messages. We empirically evaluate adversarial embedding using more than 100 models and 1,000 messages. Our results confirm that our embedding passes unnoticed by both humans and steganalysis methods, while at the same time impedes illicit retrieval of the message (less than 13% recovery rate when the interceptor has some knowledge about our model), and is resilient to soft and (to some extent) aggressive image tampering (up to 100% recovery rate under jpeg compression). We further develop our method by proposing a new type of adversarial attack which improves the embedding density (amount of hidden information) of our method to up to 10 bits per pixel. [less ▲]

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See detailPandemic Simulation and Forecasting of exit strategies:Convergence of Machine Learning and EpidemiologicalModels
Ghamizi, Salah UL; Rwemalika, Renaud UL; Cordy, Maxime UL et al

Report (2020)

The COVID-19 pandemic has created a public health emergency unprecedented in this century. The lack ofaccurate knowledge regarding the outcomes of the virus has made it challenging for policymakers to ... [more ▼]

The COVID-19 pandemic has created a public health emergency unprecedented in this century. The lack ofaccurate knowledge regarding the outcomes of the virus has made it challenging for policymakers to decideon appropriate countermeasures to mitigate its impact on society, in particular the public health and the veryhealthcare system.While the mitigation strategies (including the lockdown) are getting lifted, understanding the current im-pacts of the outbreak remains challenging. This impedes any analysis and scheduling of measures requiredfor the different countries to recover from the pandemic without risking a new outbreak.Therefore, we propose a novel approach to build realistic data-driven pandemic simulation and forecastingmodels to support policymakers. Our models allow the investigation of mitigation/recovery measures andtheir impact. Thereby, they enable appropriate planning of those measures, with the aim to optimize theirsocietal benefits.Our approach relies on a combination of machine learning and classical epidemiological models, circum-venting the respective limitations of these techniques to allow a policy-making based on established knowl-edge, yet driven by factual data, and tailored to each country’s specific context. [less ▲]

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See detailSearch-based adversarial testing and improvement of constrained credit scoring systems
Ghamizi, Salah UL; Cordy, Maxime UL; Gubri, Martin UL et al

in ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE '20), November 8-13, 2020 (2020)

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See detailVerification and abstraction of real-time variability-intensive systems
Cordy, Maxime UL; Legay, Axel

in International Journal on Software Tools for Technology Transfer (2019), 21(6), 635-649

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See detailAutomated evaluation of embedded-system design alternatives
Cordy, Maxime UL; Lazreg, Sami

in Proceedings of the 23rd International Systems and Software Product Line Conference, SPLC 2019, Volume A, Paris, France, September 9-13, 2019 (2019, September)

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See detailTowards context-aware automated writing evaluation systems
Patout, Pierre-André; Cordy, Maxime UL

in Proceedings of the 1st ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, EASEAI@ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 26, 2019 (2019, August 26)

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See detailTowards sampling and simulation-based analysis of featured weighted automata
Cordy, Maxime UL; Legay, Axel; Lazreg, Sami et al

in Proceedings of the 7th International Workshop on Formal Methods in Software Engineering (2019, May)

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See detailMultifaceted automated analyses for variability-intensive embedded systems
Lazreg, Sami; Cordy, Maxime UL; Collet, Philippe et al

in Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, Montreal, QC, Canada, May 25-31, 2019 (2019, May)

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See detailTowards Learning-Aided Configuration in 3D Printing: Feasibility Study and Application to Defect Prediction
Amand, Benoit; Cordy, Maxime UL; Heymans, Patrick et al

in Proceedings of the 13th International Workshop on Variability Modelling of Software-Intensive Systems, VAMOS 2019, Leuven, Belgium, February 06-08, 2019 (2019, February)

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See detailProceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation
Arcelli, Francesca; Walter, Bartosz; Ampatzoglou, Apostolos et al

Book published by ACM (2019)

Detailed reference viewed: 23 (2 UL)