April 2023 – Laboratoire Informatique d’Avignon

PhD defense of Paul-Gauthier Noé – 26 April 2023

26 April 2023

Date: 26th of April at 2:30pm. Place: Centre d’Enseignement et de Recherche en Informatique (Ada Lovelace auditorium) The jury will consist of: Title: Representing evidence for attribute privacy: Bayesian updating, compositional evidence and calibration. Abstract: Attribute privacy in multimedia technology aims to hide only one or a few personal characteristics, or attributes, of an individual rather than the full identity. To give a few examples, these attributes can be the sex, nationality, or health state of the individual. When the attribute to hide is discrete with a finite number of possible values, the attacker’s belief about the attribute is represented by a discrete probability distribution over the set of possible values. The Bayes’ rule is known as an information acquisition paradigm and tells how the likelihood function is changing the prior belief into a posterior belief. In the binary case—i.e. when there are only two possible values for the attribute—the likelihood function can be written in the form of a Log-Likelihood-Ratio (LLR). This has been known as the weight-of-evidence and is considered a good candidate to inform which hypothesis the data is supporting and how strong. The Bayes’ rule can be written as a sum between the LLR and the log-ratio of Plus d'infos

Cornet Seminar – Shane Mannion – 05/04/2023

5 April 2023

In the context of team Cornet’s seminars, Shane Mannion (University of Limerick) will present his research work on Correlations on complex networks and their degree distributions, on April 5, 2023, at 11:35 in the meeting room. Abstract: First we look at long range correlations in complex networks. The assortativity of a network, that is, the correlation between properties of neighboring nodes can have important practical implications. For example, a targeted vaccination program will be less effective in an assortative social network (where high-degree people mix with others of high degree). We are concerned with whether these correlations between nodes extend to nodes that are separated by more than a single edge. In this talk I will discuss how the correlation between properties of connected nodes in a social network changes as the distances between those nodes increases. This lead us to research on fitting degree distributions, where we introduce a method for fitting to the degree distributions of complex network datasets, such that the most appropriate distribution from a set of candidate distributions is chosen while maximizing the portion of the distribution to which the model is fit. Current methods for fitting to degree distributions in the literature are inconsistent Plus d'infos