Student Seminar 2019-07-01

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Laboratoire de Recherche et Développement de l’EPITA
Séminaire des étudiants-chercheurs
1 July 2019
13h00-17h00, Amphi Master
http://www.lrde.epita.fr
EPITA / LRDE
14-16 rue Voltaire
94276 Le Kremlin-Bicêtre


13h00 Modelling and Identifying Troll Farm Accounts on TwitterAntoine Sainson

There is evidence that many news items are covered by troll farm accounts (i.e. over biased opinion or “fake news” spreading accounts). By modelling techniques and finding patterns of those accounts, it would be possible to anticipate their actions, and inhibit the spread of rumors. Within this context, the goal is to build a model to flag such accounts on trending topics. To do so, we use an unsupervised approach based on a dataset full of trolls from different countries released by Twitter in 2019

13h30 Automatic troll farm account detection on TwitterHugo Linsenmaier

Social media have been extensively used in the attempt to influence users with biased information and rumors. Recently, a new set of accounts know as troll farms have emerged, lauching coordinated disinformation campains. In this regard, we aim to identify the creation of troll farms by studying how they differ from common users and how they attempt to influence them. By leveraging graph analysis techniques we will look into how trolls propagate information through Twitter. Our goal being to build a system capable of recognizing troll farm accounts on trending topics on Twitter.

14h00

14h30 Estimation of the noise level function in natural images using the tree of shapesBaptiste Esteban

Nowadays, a lot of applications need to know the noise level in an image. A method had already been developped to know the noise level function in an image using the detection of homogeneous blocks and has been extended to the detection of homogeneous shapes, using the tree of shapes, which fits better the image content. However, this method is limited to grayscale images. So we aim to extend this estimation to color images, the pixels being multivariate.

15h00 Detecting danger in marine environnementCharles Ginane

The goal of this project is to increase security during boat's navigation. Using an artificial intelligence which analyses images allows to identify different kinds of danger that a boat can encounter during its navigation. Artificial intelligence can detect a man at sea and alert the boat's crew to help him. To develop this artificial intelligence, a dataset of different marine videos is needed to assure the maximum of situations in the marine environment. Generate a lot of realistic videos by computer is mandatory to train this artificial intelligence.

15h30

16h00

16h30 Implementing Baker's SUBTYPEP decision procedureLeo Valais

The Common Lisp language provides a predicate functionSUBTYPEP, for instrospecting sub-type relationship. In some situations, and given the type system of this languageknowing whether a type is a sub-type of another would require enumerating all the element of that type, possibly an infinite number of them. Because of that, SUBTYPEP is allowed to return the two values (NIL NIL), indicating that it couldn't answer the question. Common Lisp implementations have a tendency to frequently not answereven in situations where they could. Such an abusive behavior prevents potential optimizations to occur, or even leads to violating the standard. In his paper entitled “A Decision Procedure for Common Lisp's SUBTYPEP Predicate”Henry Baker proposes an algorithm that he claims to be both more accurate and more efficient than the average SUBTYPEP implementation. We present here the current state the current state of our implementation and discuss one potential improvement based on R-trees of Baker's algorithm.