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Noel Crespi

Researcher at Telecom SudParis

Publications -  391
Citations -  6315

Noel Crespi is an academic researcher from Telecom SudParis. The author has contributed to research in topics: Service (systems architecture) & Computer science. The author has an hindex of 31, co-authored 360 publications receiving 4696 citations. Previous affiliations of Noel Crespi include Orange S.A. & Institut Mines-Télécom.

Papers
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Journal ArticleDOI

The Cluster Between Internet of Things and Social Networks: Review and Research Challenges

TL;DR: This paper explores the novel paradigm for ubiquitous computing beyond IoT, denoted by Social Internet of Things (SIoT), and proposes a generic SIoT architecture and presents a discussion about enabling technologies, research challenges, and open issues.
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Internet of Things-Aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

TL;DR: A comprehensive survey on the IoT-aided smart grid systems is presented in this article, which includes the existing architectures, applications, and prototypes of the IoTaided SG systems.
Journal ArticleDOI

Wireless Sensor Network Virtualization: A Survey

TL;DR: The basics of WSN virtualization are introduced and motivate its pertinence with carefully selected scenarios and existing works are presented in detail and critically evaluated using a set of requirements derived from the scenarios.
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Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

TL;DR: This article analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process.
Book ChapterDOI

A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

TL;DR: This study introduces a novel transfer learning approach based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and investigates the ability of BERT at capturing hateful context within social media content by using new fine-tuning methods based on transfer learning.