Data Science, Digital Transformation, Risks & Complex Systems

@inproceedings{toufaily_2805,

title = {From start-ups to governments: challenges and opportunities of blockchain adoption in the MENA ecosystem},

author = {Elissar Toufaily and Tatiana ZALAN and Soumaya Ben Dhaou},

url = {https://dl.acm.org/doi/abs/10.1145/3368691.3368746},

issn = {978-1-4503-7284-8},

year  = {2019},

date = {2019-12-01},

booktitle = {DATA'19: International Conference on Data Science, E-learning and Information Systems},

address = {Dubai, UAE},

abstract = {In this paper, we investigate the realities, challenges and socio-economic implications of blockchain technology adoption from the ecosystem perspective, mainly the public and private sectors stakeholders, and entrepreneurs and start-ups. A holistic, theoretical framework is proposed as well as strategies to overcome the challenges and accelerate the pace of the adoption.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rigaux_888,

title = {Scalable Searching and Ranking for Melodic Pattern Queries},

author = {Philippe Rigaux and Nicolas Travers},

url = {https://ismir2019.ewi.tudelft.nl/},

year  = {2019},

date = {2019-11-01},

booktitle = {20th annual conference of the International Society for Music Information Retrieval},

address = {Delft, Netherlands},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dupuis_942,

title = {RTIM: a Real-Time Influence Maximization Strategy},

author = {David Dupuis and Cédric Du Mouza and Nicolas Travers and Gaël Chareyron},

url = {https://link.springer.com/book/10.1007/978-3-030-34223-4},

issn = {ISBN : 978-3-030-34222-7},

year  = {2019},

date = {2019-11-01},

booktitle = {International Conference on Web Information Systems Engineering},

volume = {11881},

pages = {pp 277-292},

address = {Hong-Kong, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{grossetti_944,

title = {Community-based Recommendations on Twitter: Avoiding The Filter Bubble},

author = {Quentin Grossetti and Cédric Du Mouza and Nicolas Travers},

url = {https://link.springer.com/chapter/10.1007/978-3-030-34223-4_14},

issn = {ISBN : 978-3-030-34222-7},

year  = {2019},

date = {2019-11-01},

booktitle = {Web Information Systems Engineering - WISE 2019},

volume = {11881},

pages = {pp 212-227},

address = {Hong-Kong, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ben_baccar_989,

title = {Tourist's Tour Prediction by Sequential Data Mining Approach},

author = {Lillia Ben Baccar and Sonia Djebali and Guillaume Guérard},

url = {https://link.springer.com/chapter/10.1007/978-3-030-35231-8_50},

issn = {ISBN 978-3-030-35230-1},

year  = {2019},

date = {2019-11-01},

booktitle = {Advanced Data Mining and Applications, 15th International Conference, ADMA 2019},

pages = {pp. 681-695},

address = {Dalian, China},

abstract = {This paper answers the problem of predicting future behaviour tourist based on past behaviour of an individual tourist. The individual behaviour is naturally an indicator of the behaviour of other tourists. The prediction of tourists movement has a crucial role in tourism marketing to create demand and assist tourists in decision-making. With advances in information and communication technology, social media platforms generate data from millions of people from different countries during their travel. The main objective of this paper is to consider sequential data-mining methods to predict tourist movement based on Instagram data. Rules emerge from those ones are exploited to predict future behaviors. The originality of this approach is a combination between pattern mining to reduce the size of data and the automata to condense the rules. The capital city of France, Paris is selected to demonstrate the utility of the proposed methodology.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dupuis_941,

title = {Influence Maximization in a Real-Time Bidding Environment},

author = {David Dupuis and Cédric Du Mouza and Nicolas Travers and Gaël Chareyron},

url = {https://bda.liris.cnrs.fr/list.html},

year  = {2019},

date = {2019-10-01},

booktitle = {35ème Conférence sur la Gestion de Données},

address = {Lyon, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lesort_2669,

title = {Deep unsupervised state representation learning with robotic priors: a robustness analysis},

author = {Timothée Lesort and Mathieu Seurin and Xinrui Li and Natalia Díaz-Rodríguez and David Filliat},

url = {https://ieeexplore.ieee.org/document/8852042},

issn = {Electronic ISBN:978-1-7281-1985-4},

year  = {2019},

date = {2019-07-01},

booktitle = {2019 International Joint Conference on Neural Networks (IJCNN)},

address = {Budapest, Hungary},

abstract = {Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation metric of the learned representation that uses nearest neighbors in the state space and allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{seurin_2686,

title = {"I'm sorry Dave, I'm afraid I can't do that" Deep Q-learning from forbidden action},

author = {Mathieu Seurin and Philippe Preux and Olivier Pietquin},

url = {https://ieeexplore.ieee.org/abstract/document/9207496},

issn = {978-1-7281-6926-2},

year  = {2019},

date = {2019-01-01},

booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)},

address = {Glasgow, UK},

abstract = {The use of Reinforcement Learning (RL) is still restricted to simulation or to enhance human-operated systems through recommendations. Real-world environments (e.g. industrial robots or power grids) are generally designed with safety constraints in mind implemented in the shape of valid actions masks or contingency controllers. For example, the range of motion and the angles of the motors of a robot can be limited to physical boundaries. Violating constraints thus results in rejected actions or entering in a safe mode driven by an external controller, making RL agents incapable of learning from their mistakes. In this paper, we propose a simple modification of a state-of-the-art deep RL algorithm (DQN), enabling learning from forbidden actions. To do so, the standard Q-learning update is enhanced with an extra safety loss inspired by structured classification. We empirically show that it reduces the number of hit constraints during the learning phase and accelerates convergence to near-optimal policies compared to using standard DQN. Experiments are done on a Visual Grid World Environment and the TextWorld domain.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{grossetti_563,

title = {Reducing Filter Bubbles With a Community Aware Model},

author = {Quentin Grossetti and Camélia Constantin and Cédric Du Mouza and Nicolas Travers},

url = {https://bda2018.ensea.fr/},

year  = {2018},

date = {2018-10-01},

booktitle = {34ème Conférence sur la Gestion de Données},

address = {Bucarest, Romania},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nyzam_521,

title = {Un outil modulaire pour le résumé automatique},

author = {Valentin Nyzam and Christophe Rodrigues and Aurélien Bossard},

url = {https://project.inria.fr/coriataln2018/fr/},

year  = {2018},

date = {2018-05-01},

booktitle = {TALN 2018},

address = {Rennes, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{raimbault_503,

title = {Universal-endpoint.com : une plateforme d'accès simple au Web des Données},

author = {Thomas Raimbault and Abdellah Sabry and Sonia Djebali},

url = {https://editions-rnti.fr/?procid=100174},

issn = {ISBN : 979-10-96289-07-3},

year  = {2018},

date = {2018-01-01},

booktitle = {EGC 2018},

volume = {RNTI-E-34},

pages = {475-478},

address = {Paris, France},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{strub_2685,

title = {Visual reasoning with multi-hop feature modulation},

author = {Florian Strub and Mathieu Seurin and Ethan Perez and Harm De Vries and Jeremie Mary and Philippe Preux and Aaron Courville and Olivier Pietquin},

url = {https://openaccess.thecvf.com/content_ECCV_2018/html/Florian_Strub_Visual_Reasoning_with_ECCV_2018_paper.html},

issn = {978-3030012304},

year  = {2018},

date = {2018-01-01},

booktitle = {Computer Vision - ECCV 2018  15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part I},

address = {Munich, Germany},

abstract = {Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue. For such tasks, one successful approach is to condition image-based convolutional network computation on language via Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and shifting. By alternating between attending to the language input and generating FiLM layer parameters, this approach is better able to scale to settings with longer input sequences such as dialogue. We demonstrate that multi-hop FiLM generation significantly outperforms prior state-of-the-art on the GuessWhat?! visual dialogue task and matches state-of-the art on the ReferIt object retrieval task, and we provide additional qualitative analysis.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}