The Body in Motion – AIDOC Study Day

The symposium entitled “The Body in Motion” took place on September 10th, 2020 at Maison de la Recherche Germaine Tillion (MRGT) in the University of Angers (France). It was organized by an interdisciplinary organization mainly composed of Ph.D. candidates and young researchers called the AIDOC and chaired by Louise Couëffé at the time of the scholarly event. The latter was supported by various research units (3L.AM, ESO, TEMOS) and federative research structures (IRPaLL, SFR Confluences). As..

Enhancing Big Data Warehousing and Analytics for Spatio-Temporal Massive Data

The increasing amount of data generated by earth observation missions like Copernicus, NASA Earth Data, and climate stations is overwhelming. Every day, terabytes of data are collected from these resources for different environment applications. Thus, this massive amount of data should be effectively managed and processed to support decision-makers. In this paper, we propose an information system-based on a low latency spatio-temporal data warehouse which aims to improve drought monitoring analytics and to support the decision-making process. The proposed framework consists of 4 main modules: (1) data collection, (2) data preprocessing, (3) data loading and storage, and (4) the visualization and interpretation module. The used data are multi-source and heterogeneous collected from various sources like remote sensing sensors, biophysical sensors, and climate sensors. Hence, this allows us to study drought in different dimensions. Experiments were carried out on a real case of drought monitoring in China between 2000 and 2020

Naive possibilistic classifiers for imprecise or uncertain numerical data

International audienceIn real-world problems, input data may be pervaded with uncertainty. In this paper, we investigate the behavior of naive possibilistic classifiers, as a counterpart to naive Bayesian ones, for dealing with classification tasks in the presence of uncertainty. For this purpose, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. Here the possibility distributions that are used are supposed to encode the family of Gaussian probabilistic distributions that are compatible with the considered dataset. We consider two types of uncertainty: (i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and (ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. Moreover, the approach takes into account the uncertainty about the estimation of the Gaussian distribution parameters due to the limited amount of data available. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an algorithm based on the extension principle to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data

Possibilistic classifiers for numerical data

International audienceNaive Bayesian Classifiers, which rely on independence hypotheses, together with a normality assumption to estimate densities for numerical data, are known for their simplicity and their effectiveness. However, estimating densities, even under the normality assumption, may be problematic in case of poor data. In such a situation, possibility distributions may provide a more faithful representation of these data. Naive Possibilistic Classifiers (NPC), based on possibility theory, have been recently proposed as a counterpart of Bayesian classifiers to deal with classification tasks. There are only few works that treat possibilistic classification and most of existing NPC deal only with categorical attributes. This work focuses on the estimation of possibility distributions for continuous data. In this paper we investigate two kinds of possibilistic classifiers. The first one is derived from classical or flexible Bayesian classifiers by applying a probability–possibility transformation to Gaussian distributions, which introduces some further tolerance in the description of classes. The second one is based on a direct interpretation of data in possibilistic formats that exploit an idea of proximity between data values in different ways, which provides a less constrained representation of them. We show that possibilistic classifiers have a better capability to detect new instances for which the classification is ambiguous than Bayesian classifiers, where probabilities may be poorly estimated and illusorily precise. Moreover, we propose, in this case, an hybrid possibilistic classification approach based on a nearest-neighbour heuristics to improve the accuracy of the proposed possibilistic classifiers when the available information is insufficient to choose between classes. Possibilistic classifiers are compared with classical or flexible Bayesian classifiers on a collection of benchmarks databases. The experiments reported show the interest of possibilistic classifiers. In particular, flexible possibilistic classifiers perform well for data agreeing with the normality assumption, while proximity-based possibilistic classifiers outperform others in the other cases. The hybrid possibilistic classification exhibits a good ability for improving accuracy

Creating a Cinema of Boxing in Day of the Fight (Stanley Kubrick, 1951)

Stanley Kubrick's first movie is a boxing film. It is Day of the Fight, a 1951 short documentary on the Irish-American boxer and actor William Walter Cartier. Previously a photographer for Look Magazine (1945-1950), Stanley Kubrick develops in the film his cinematographic style sketched in a photojournalistic report for which he took more than 1200 photographs of William Walter Cartier. This boxing film contributes to the construction of his status as an author and Day of the Fight kicks off his singular aesthetic of violence. As a transitional work between two art forms, photography and cinema, the boxing film allows the unfolding of Stanley Kubrick's fascination for physical confrontation. The young director, inspired by film noir, exploits the transgressive nature of the sport, which has been present since the beginnings of cinema. Beyond the supposed violence of boxing, Day of the Fight captures the male body and the intimacy of the boxer through a non-conformist homoerotic gaze

From Bayesian classifiers to possibilistic classifiers for numerical data

International audienceNaïve Bayesian classifiers are well-known for their simplicity and efficiency. They rely on independence hypotheses, together with a normality assumption, which may be too demanding, when dealing with numerical data. Possibility distributions are more compatible with the representation of poor data. This paper investigates two kinds of possibilistic elicitation methods that will be embedded into possibilistic naïve classifiers. The first one is derived from a probability-possibility transformation of Gaussian distributions (or mixtures of them), which introduces some further tolerance. The second kind is based on a direct interpretation of data in fuzzy histogram or possibilistic formats that exploit an idea of proximity between attribute values in different ways. Besides, possibilistic classifiers may be allowed to leave the classification open between several classes in case of insufficient information for choosing one (which may be of interest when the number of classes is large). The experiments reported show the interest of possibilistic classifiers

A Possibilistic Rule-Based Classifier

International audienceRule induction algorithms have gained a high popularity among machine learning techniques due to the “intelligibility” of their output, when compared to other “black-box” classification methods. However, they suffer from two main drawbacks when classifying test examples: i) the multiple classification problem when many rules cover an example and are associated with different classes, and ii) the choice of a default class, which concerns the non-covering case. In this paper we propose a family of Possibilistic Rule-based Classifiers (PRCs) to deal with such problems which are an extension and a modification of the Frank and Witten’ PART algorithm. The PRCs keep the same rule learning step as PART, but differ in other respects. In particular, the PRCs learn fuzzy rules instead of crisp rules, consider weighted rules at deduction time in an unordered manner instead of rule lists. They also reduce the number of examples not covered by any rule, using a fuzzy rule set with large supports. The experiments reported show that the PRCs lead to improve the accuracy of the classical PART algorithm

Possibilistic Classifiers for Uncertain Numerical Data

PSerr&al004International audienceIn many real-world problems, input data may be pervaded with uncertainty. Naive possibilistic classifiers have been proposed as a counterpart to Bayesian classifiers to deal with classification tasks in presence of uncertainty. Following this line here, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. We consider two types of uncertainty: i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an extension principle-based algorithm to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data

Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks

Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods