Search Results - "Riveill, Michel"

What Do You Think About Your Company's Leaks? A Survey on End-Users Perception Toward Data Leakage Mechanisms by Bertrand, Yoann, Boudaoud, Karima, Riveill, Michel

“…Data leakage can lead to severe issues for a company, including financial loss, damage of goodwill, reputation, lawsuits and loss of future sales. To prevent…”
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An Architecture to Support the Collection of Big Data in the Internet of Things by Cecchinel, Cyril, Jimenez, Matthieu, Mosser, Sebastien, Riveill, Michel

“…The Internet of Things (IoT) relies on physical objects interconnected between each others, creating a mesh of devices producing information. In this context,…”
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A delta‐oriented approach to support the safe reuse of black‐box code rewriters by Benni, Benjamin, Mosser, Sébastien, Moha, Naouel, Riveill, Michel

“…Large‐scale corrective and perfective maintenance is often automated thanks to rewriting rules using tools such as Python2to3, Spoon, or Coccinelle. Such tools…”
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At the Cross-Roads between Human-Computer Interaction and Model Driven Engineering by Calvary, Gaëlle, Déry-Pinna, Anne-Marie, Occello, Audrey, Renevier, Philippe, Riveill, Michel

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Scalable Architectures to Support Sustainable Advanced Information Technologies by Carrillo, Oscar, Hernandez, Carlos Jaime Barrios, Le Mouel, Frederic, Barrera, Harold E. Castro, Denneulin, Yves, Hernandez, Jose Tiberio, Vargas, Fernando Jimenez, Rozo, Lola Xiomara Bautista, Roncancio, Claudia, Riveill, Michel

“…Advanced computing resources are requested by different interest groups, with the acceleration of digital transformation generated both by the pandemic and by…”
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FLCAP: Federated Learning with Clustered Adaptive Pruning for Heterogeneous and Scalable Systems by Miralles, Hugo, Tosic, Tamara, Riveill, Michel

“…In this paper, we address the challenge of efficient local training on heterogeneous data in Federated Learning, where devices have limited computation and…”
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AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling by Mayaki, Mansour Zoubeirou A, Riveill, Michel

“…Anomaly detection or more generally outliers detection is one of the most popular and challenging subject in theoretical and applied machine learning. The main…”
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Human action recognition based on 3D skeleton part-based pose estimation and temporal multi-resolution analysis by Halim, A. Aly, Dartigues-Pallez, C., Precioso, F., Riveill, M., Benslimane, A., Ghoneim, S.

“…Human action recognition is a challenging field that have been addressed with many different classification techniques such as SVM or Random Decision Forests…”
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Domain-specific long text classification from sparse relevant information by D'Cruz, Célia, Bereder, Jean-Marc, Precioso, Frédéric, Riveill, Michel

“…Large Language Models have undoubtedly revolutionized the Natural Language Processing field, the current trend being to promote one-model-for-all tasks…”
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Managing a Software Ecosystem Using a Multiple Software Product Line: A Case Study on Digital Signage Systems by Urli, Simon, Blay-Fornarino, Mireille, Collet, Philippe, Mosser, Sebastien, Riveill, Michel

“…With the advent of Web 2.0, the growth of developer teams and user communities increases the number of software ecosystems: software platforms developed and…”
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Multiple Inputs Neural Networks for Medicare fraud Detection by Mayaki, Mansour Zoubeirou A, Riveill, Michel

“…Medicare fraud results in considerable losses for governments and insurance companies and results in higher premiums from clients. Medicare fraud costs around…”
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Autoregressive based Drift Detection Method by Mayaki, Mansour Zoubeirou A, Riveill, Michel

“…In the classic machine learning framework, models are trained on historical data and used to predict future values. It is assumed that the data distribution…”
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WComp middleware for ubiquitous computing: Aspects and composite event-based Web services by Tigli, Jean-Yves, Lavirotte, Stéphane, Rey, Gaëtan, Hourdin, Vincent, Cheung-Foo-Wo, Daniel, Callegari, Eric, Riveill, Michel

“…After a survey of the specific features of ubiquitous computing applications and corresponding middleware requirements, we list the various paradigms used in…”
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Autoregressive based Drift Detection Method by Mayaki, Mansour Zoubeirou A, Riveill, Michel

“…In the classic machine learning framework, models are trained on historical data and used to predict future values. It is assumed that the data distribution…”
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Cloud computing, security and data concealment by Delettre, C., Boudaoud, K., Riveill, M.

“…Cloud computing is a new paradigm providing software and hardware resources according to the customers' needs. However, it introduces new security risks such…”
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Machinery Anomaly Detection using artificial neural networks and signature feature extraction by Mayaki, Mansour Zoubeirou A, Riveill, Michel

“…Machine learning models are increasingly being used in predictive maintenance. However, due to the complexity of vibration and audio signals used in fault…”
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Low response time context awareness through extensible parameter adaptation with ORCA : Ubiquitous networked robots by TIGLI, Jean-Yves, LAVIROTTE, Stéphane, REY, Gaëtan, HOURDIN, Vincent, FERRY, Nicolas, VERGONI, Christophe, RIVEILL, Michel

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Adaptation Mechanism based on Service-Context Distance for Ubiquitous Computing by Cremene, M., Riveill, Michel, Rarau, A., Miron, C., Iulian, B., Todica, V.

“…Service adaptation is one of the main research subjects in Ubiquitous Computing. Dynamic service adaptation, at runtime, is necessary for services that cannot…”
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Low response time context awareness through extensible parameter adaptation with ORCA by Tigli, Jean-Yves, Lavirotte, Stéphane, Rey, Gaëtan, Hourdin, Vincent, Ferry, Nicolas, Vergoni, Christophe, Riveill, Michel

“…Ubiquitous computing applications or widespread robots interactions execute in unforeseen environments and need to adapt to changeful available services, user…”
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Multiple Inputs Neural Networks for Fraud Detection by Mayaki, Mansour Zoubeirou A, Riveill, Michel

“…This study aims to use artificial neural network based classifiers to predict fraud, particularly that related to health insurance. Medicare fraud results in…”
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