activity
20152026
most citedApplication of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review

48 citations · 535 across the 164 of their papers we have counts for

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Showing 2021Show all

23 papers · 1 filter

cs.SE2021★ 1 cited

Machine Learning Application Development: Practitioners' Insights

Md Saidur Rahman, Foutse Khomh, Alaleh Hamidi +3

Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world problems, thanks to recent breakthroughs in…

cs.SE2021★ 6 cited

Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow

Florian Tambon, Amin Nikanjam, Le An +2

Deep Learning (DL) frameworks are now widely used, simplifying the creation of complex models as well as their integration to various applications even to non DL experts. However,…

cs.LG2021

On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods

Paulina Stevia Nouwou Mindom, Amin Nikanjam, Foutse Khomh +1

The increasing adoption of Reinforcement Learning in safety-critical systems domains such as autonomous vehicles, health, and aviation raises the need for ensuring their safety. Ex…

cs.SE2021

An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets

Gias Uddin, Yann-Gael Gueheneuc, Foutse Khomh +1

Sentiment analysis in software engineering (SE) has shown promise to analyze and support diverse development activities. We report the results of an empirical study that we conduct…

cs.SE2021

Reputation Gaming in Stack Overflow

Iren Mazloomzadeh, Gias Uddin, Foutse Khomh +1

Stack Overflow incentive system awards users with reputation scores to ensure quality. The decentralized nature of the forum may make the incentive system prone to manipulation. Th…

cs.LG2021★ 2 cited

Partial Order in Chaos: Consensus on Feature Attributions in the Rashomon Set

Gabriel Laberge, Yann Pequignot, Alexandre Mathieu +2

Post-hoc global/local feature attribution methods are progressively being employed to understand the decisions of complex machine learning models. Yet, because of limited amounts o…