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276 results on '"Michel Riveill"'

1. What Do You Think About Your Company’s Leaks? A Survey on End-Users Perception Toward Data Leakage Mechanisms

Author

Yoann Bertrand, Karima Boudaoud, and Michel Riveill

Subjects
security, data leakage, access control, transmission control, security policies, usability, Information technology, T58.5-58.64
Abstract

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 these problems, a company can use other mechanisms on top of traditional Access Control. These mechanisms include for instance Data Leak Prevention or Information Rights Management and can be referred as Transmission Control. However, such solutions can lack usability and can be intrusive for end-users employees. To have a better understanding of the perception and usage of such mechanisms within business infrastructures, we have conducted in this article an online survey on 150 employees. These employees come from different companies of different sizes and sectors of activity. The results show that whatever the size of the company or its sector of activity, security mechanisms such as access control and transmission control can be considered as quite intrusive and blocking for employees. Moreover, our survey also shows interesting results regarding more acceptable and user-friendly anti-data leakage mechanisms that could be used within companies.

Published
2020
Full Text
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43. Aspect-Based Sentiment Analysis with Deep Learning: A Multidomain and Multitask Approach

Author

Trang Uyen Tran, Ha Thanh Thi Hoang, Phuong Hoai Dang, Michel Riveill, Vietnam-Korea Friendship IT College [Da Nang], The university of Danang, Modèles et algorithmes pour l’intelligence artificielle (MAASAI), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Laboratoire Jean Alexandre Dieudonné (LJAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), and Springer

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Sentiment analysis aids in obtaining the opinion of the users towards a particular product, service or policy. Focusing on classifying the sentiment that corresponds to each aspect of the entity in the document will help to identify the sentiment more clearly. This is also the mission of aspect-based sentiment analysis (ABSA). The vast majority of prior studies in ABSA have implemented single-task execution models on single-domain datasets. This is inconvenient when it is necessary to perform the full range of tasks in ABSA and on domain-independent datasets. In this paper, we offer to operate the advanced arrangement of deep learning techniques for multidomain and multitask approach in ABSA. The main tasks in ABSA: aspect extraction, category identification, sentiment classification and domain classification are all finished by an integration framework of Convolutional Neural Network (CNN), Bidirectional Independent Long Short Term Memory (BiIndyLSTM) and Attention mechanism. In addition, we use a POS tag layer combined with GloVe in word embedding layer to get the morphological attributes of each token word from review sentences. Through the experimenting process in the Laptop_Restaurant_Hotel multidomain dataset, we found that our proposed model has achieved high precision in multitasking ABSA. With this approach, we hope our proposed model will lay the foundation for ensuring flexibility and multiutility compared to previous opinion analysis models.

Published
2022

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