NCube/papyrus huggingface dataset:
Finite population inference for population with a large number of zero-valued observations

In business surveys, we are often interested in estimating population means or totals of variables which, by nature, will often take a value of zero. In the presence of a large proportion of zero-valued observations, the customary estimators may be unstable. In this thesis, we study the properties of commonly used estimators for populations exhibiting a large proportion of zero-valued observations. In a model-based framework, we present some robust predictors in the presence of influential units. Finally, we perform simulation studies to evaluate the performance of several estimators in terms of bias and efficiency.

Keyphrases: Robustness, Influential units, Model-based inference, Design-based inference, Conditional bias
La détection automatique multilingue d’énoncés biaisés dans Wikipédia

We propose a multilingual method for the extraction of biased sentences from Wikipedia, and use it to create corpora in Bulgarian, French and English. Sifting through the revision history of the articles that at some point had been considered biased and later corrected, we retrieve the last tagged and the first untagged revisions as the before/after snapshots of what was deemed a violation of Wikipedia’s neutral point of view policy. We extract the sentences that were removed or rewritten in that edit. The approach yields sufficient data even in the case of relatively small Wikipedias, such as the Bulgarian one, where 62k articles produced 5 thousand biased sentences. We evaluate our method by manually annotating 520 sentences for Bulgarian and French, and 744 for English. We assess the level of noise and analyze its sources. Finally, we exploit the data with well-known classification methods to detect biased sentences.

Keyphrases: Classification, Bias, Neutrality, Multilingual, Corpora
Deep reinforcement learning for multi-modal embodied navigation

This work focuses on an Outdoor Micro-Navigation (OMN) task in which the goal is to navigate to a specified street address using multiple modalities including images, scene-text, and GPS. This task is a significant challenge to many Blind and Visually Impaired (BVI) people, which we demonstrate through interviews and market research. To investigate the feasibility of solving this task with Deep Reinforcement Learning (DRL), we first introduce two partially observable grid-worlds, Grid-Street and Grid City, containing houses, street numbers, and navigable regions. In these environments, we train an agent to find specific houses using local observations under a variety of training procedures. We parameterize our agent with a neural network and train using reinforcement learning methods. Next, we introduce the Sidewalk Environment for Visual Navigation (SEVN), which contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. In SEVN, we train another neural network model using Proximal Policy Optimization (PPO) to fuse multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data, and to use this representation to navigate to goal doors. Our best model used all available modalities and was able to navigate to over 100 goals with an 85% success rate. We found that models with access to only a subset of these modalities performed significantly worse, supporting the need for a multi-modal approach to the OMN task. We hope that this thesis provides a foundation for further research into the creation of agents to assist members of the BVI community to safely navigate.

Keyphrases: Embodied navigation, Neural networks, Reinforcement learning, Multimodal representations, Assistive technology, Blind and visually impaired, Navigation incarnée
Data-efficient reinforcement learning with self-predictive representations

Data efficiency remains a key challenge in deep reinforcement learning.  Although modern techniques have been shown to be capable of attaining high performance in extremely complex tasks, including strategy games such as StarCraft, Chess, Shogi, and Go as well as in challenging visual domains such as Atari games, doing so generally requires enormous amounts of interactional data, limiting how broadly reinforcement learning can be applied.  In this thesis, we propose SPR, a method drawing from recent advances in self-supervised representation learning designed to enhance the data efficiency of deep reinforcement learning agents.  We evaluate this method on the Atari Learning Environment, and show that it dramatically improves performance with limited computational overhead.  When given roughly the same amount of learning time as human testers, a reinforcement learning agent augmented with SPR achieves super-human performance on 7 out of 26 games, an increase of 350% over the previous state of the art, while also strongly improving mean and median performance.  We also evaluate this method on a set of continuous control tasks, showing substantial improvements over previous methods.  Chapter 1 introduces concepts necessary to understand the work presented, including overviews of Deep Reinforcement Learning and Self-Supervised Representation learning. Chapter 2 contains a detailed description of our contributions towards leveraging self-supervised representation learning to improve data-efficiency in reinforcement learning. Chapter 3 provides some conclusions drawn from this work, including a number of proposals for future work.

Keyphrases: Deep learning, Reinforcement learning, Self-supervised learning, Representation learning
Modélisation des réseaux de régulation de l’expression des gènes par les microARN

MicroRNAs are small non-coding RNAs of approximately 22 nucleotide long involved in the regulation of gene expression. They target complementary regions to the RNA transcripts molecules that these genes encode and adjust the concentration according to the needs of the cell.  As microRNAs and their RNA targets binds each other with imperfect complementarity, these two groups actively compete to form regulatory interactions. Consequently, attempting to quantitatively predict their equilibrium concentrations is a task that must take several factors into account, including the affinity for hybridization, the ability to catalyze the target, cooperation, and RNA accessibility.  In the model we propose, miRBooking 2.0, each possible interaction between a microRNA and a binding site on a target RNA is characterized by an enzymatic reaction. A reaction of this type operates in two phases: a reversible formation of an enzyme-substrate complex, the microRNA-RNA duplex, and an irreversible conversion of the substrate in an RNA degradation product that restores the enzyme which can subsequently participate to other reactions.  We show that the stationary state of this system, which can include up to 10 million equations in practice, has a very shallow Jacobian, allowing its efficient resolution using a sparse linear solver. This solution allows us to characterize precisely the mechanism of regulation and to study the role of microRNAs in a given cellular context. Predictions obtained on a HeLa S3 cell model correlate significantly with a set of experimental data obtained experimentally and can remarkably explain the expression threshold effects of genes. Using this solution as an initial condition and an explicit method of numerical integration, we simulate in real time the response of the system to changes of experimental conditions.  We apply this model to target elements involved in the Epithelio-Mesenchymal Transition (EMT), an important mechanism of tumours proliferation. By identifying differentially expressed elements between the two conditions, we design synthetic microRNAs to interfere with the transition. To do so, we propose a method based on a parallel greedy best-first search to efficiently crawl the sequence space of the microRNA and present preliminary results on known EMT markers.

Keyphrases: MicroRNA, Mathematical modelling, Michaelis-Menten enzyme kinetics, Multidimensional root-finding, Numerical integration, Numerical integration, High-throughput sequencing data analysis
Estimating the probability of a fleet vehicle accident : a deep learning approach using conditional variational auto-encoders

Risk is the possibility of a negative or undesired outcome. In our work, we evaluate the risk of a fleet vehicle accident using the 1998 and 1999 records from the files of the Societe d'assurance automobiles du Quebec (SAAQ), where each observation in the data set corresponds to a truck carrier of merchandise, and where the number of accidents during the following year it had. For each vehicle, we have useful information such as the number and type of violations it had, as well as some of its characteristics like the number of axles or the number of cylinders. With our objective in mind, we propose a new approach using conditional variational auto-encoders (CVAE) considering two distributional assumptions, Negative Binomial and Poisson, to model the distribution of a fleet vehicle accident. Our main motivation for using a CVAE is to capture the joint distribution between the number of accidents of a fleet vehicle and the predictor variables of such accidents, and to extract latent features that help reconstruct the distribution of the number of fleet vehicle accidents. We compare the CVAE with other probabilistic methods, such as a simple MLP model that learns the distribution of the number of fleet vehicle accidents without extracting meaningful latent representations. We found that the CVAE marginally outperforms the MLP model, which suggests that a model able to learn latent features has added value over one that does not. We also compared the CVAE with another basic probabilistic model, the generalized linear model (GLM), as well as with classification models. We found that the CVAE and GLM using the Negative Binomial distribution tend to show better results. Moreover, we provide a feature engineering scheme that incorporates features related to the whole fleet in addition to individual features for each vehicle that translates into improved performances of all the models implemented in our work used to evaluate the probability of a fleet vehicle accident.

Keyphrases: Assurance, Insurance, Deep learning, Conditional variational auto-encoders
Evolution of domain-specific languages depending on external libraries

Model-driven engineering (MDE) is an approach that relies on abstraction to further express domain concepts. Hence, language engineers develop domain-specific languages (DSLs) that encapsulates the domain structure, constraints, and behavior.  Like any software, DSLs evolve regularly. This evolution can occur when one of its components or the domain evolves. The domain evolution as well as the evolution of DSL components and the impact of such evolution on them has been widely investigated. However, a DSL may also rely on external dependencies that are not modeled. As a result, the evolution of these external dependencies affects the DSL and its components. This evolution problem has yet to be addressed. Currently, language engineers must manually evolve the DSL when the external dependencies evolve. In this thesis, we focus on the evolution of external libraries. More specifically, our goal is to assist language engineers in the task of evolution. To this end, we propose an approach that automatically integrates the changes of the external libraries into the DSL. In addition, we offer a DSL that supports the evolution of the Arduino libraries. We also evaluate our approach by evolving an interactive modeling editor that depends on a DSL. This study allows us to demonstrate the feasibility and usefulness of our approach.

Keyphrases: Model-driven engineering, Domain-specific language, Automatic evolution, External libraries
Articulating design-time uncertainty with DRUIDE

Modellers often encounter uncertainty about how to design a particular software model. Existing research has shown how  modellers can work in the presence of this type of ''design-time uncertainty''. However, the process by which developers come to elicit and express their uncertainties remains unclear.   In this thesis, we take steps to address this gap by proposing to create an uncertainty modelling language and an approach for articulating design-time uncertainty. We illustrate our proposal on a worked example and evaluate it not only on two software engineering scenarios, but also on a real case study based on uncertainties caused by the COVID-19 pandemic. We also conduct a post-study questionnaire with the researchers who participated in the case study. In order to prove the feasibility of our approach, we provide two tool supports and discuss them. Finally, we highlight the benefits and discuss the limitations of our current work.

Keyphrases: Uncertainty-wise language, Modelling language, Model-driven engineering, Design-time decision, Mega-modelling
Apprentissage de modèles causaux par réseaux de neurones artificiels

In this thesis by articles, we study the learning of causal models from data. The goal of this entreprise is to gain a better understanding of data and to be able to predict the effect of a change on some variables of a given system. Since discovering causal relationships is fundamental in science, causal structure learning methods have applications in many fields that range from genomics, biology, and economy. We present two new methods that have the particularity of being non-linear methods learning causal models casted as a continuous optimization problem subject to a constraint. Previously, causal strutural methods addressed this search problem by using greedy search heuristics. Recently, a new continuous acyclity constraint has allowed to address the problem differently. In the first article, we present one of these non-linear method: GraN-DAG. Under some assumptions, GraN-DAG can learn a causal graph from observational data. Since the publi- cation of this first article, several alternatives methods have been proposed by the community by using the same continuous-constrained optimization formulation. However, none of these methods support interventional data. Nevertheless, interventions reduce the identifiability problem and allow the use of more expressive neural architectures. In the second article, we present another method, DCDI, that has the particularity to leverage data with several kinds of interventions. Since the identifiabiliy issue is less severe, one of the two instantia- tions of DCDI is a universal density approximator. For both methods, we show that these methods have really good performances on synthetic and real-world tasks comparatively to other classical methods.

Keyphrases: causal structure learning, causal inference, causal model, machine learning, neural network, deep learning
European day-ahead electricity price forecasting

In the context of the increase in the fraction of power generation coming from unpredictable renewable sources, electricity prices are as volatile as ever. This volatility makes forecasting future prices more difficult yet more valuable. In this research, a benchmark of 8 forecasting models is conducted on the task of predicting day-ahead wholesale electricity prices in France, Germany, Belgium and the Netherlands. The methodology used to produce the forecasts is explained in detail. The differences in forecast accuracy between the models are tested for statistical significance. Gradient boosting produced the most accurate forecasts, closely followed by an ensemble method.

Keyphrases: Electricity Price Forecasting
Towards using intelligent techniques to assist software specialists in their tasks

Automation and intelligence constitute a major preoccupation in the field of software engineering. With the great evolution of Artificial Intelligence, researchers and industry were steered to the use of Machine Learning and Deep Learning models to optimize tasks, automate pipelines, and build intelligent systems. The big capabilities of Artificial Intelligence make it possible to imitate and even outperform human intelligence in some cases as well as to automate manual tasks while rising accuracy, quality, and efficiency.  In fact, accomplishing software-related tasks requires specific knowledge and skills. Thanks to the powerful capabilities of Artificial Intelligence, we could infer that expertise from historical experience using machine learning techniques. This would alleviate the burden on software specialists and allow them to focus on valuable tasks.  In particular, Model-Driven Engineering is an evolving field that aims to raise the abstraction level of languages and to focus more on domain specificities. This allows shifting the effort put on the implementation and low-level programming to a higher point of view focused on design, architecture, and decision making. Thereby, this will increase the efficiency and productivity of creating applications.  For its part, the design of metamodels is a substantial task in Model-Driven Engineering. Accordingly, it is important to maintain a high-level quality of metamodels because they constitute a primary and fundamental artifact. However, the bad design choices as well as the repetitive design modifications, due to the evolution of requirements, could deteriorate the quality of the metamodel. The accumulation of bad design choices and quality degradation could imply negative outcomes in the long term. Thus, refactoring metamodels is a very important task. It aims to improve and maintain good quality characteristics of metamodels such as maintainability, reusability, extendibility, etc.  Moreover, the refactoring task of metamodels is complex, especially, when dealing with large designs. Therefore, automating and assisting architects in this task is advantageous since they could focus on more valuable tasks that require human intuition.  In this thesis, we propose a cartography of the potential tasks that we could either automate or improve using Artificial Intelligence techniques. Then, we select the metamodeling task and we tackle the problem of metamodel refactoring. We suggest two different approaches: A first approach that consists of using a genetic algorithm to optimize set quality attributes and recommend candidate metamodel refactoring solutions. A second approach based on mathematical logic that consists of defining the specification of an input metamodel, encoding the quality attributes and the absence of smells as a set of constraints and finally satisfying these constraints using Alloy.

Keyphrases: Artificial Intelligence, Model-Driven Engineering, Software Engineering, Machine Learning, Multi-Objective Optimization
Estimation neuronale de l'information mutuelle.

We argue that the estimation of mutual information between high dimensional continuous random variables can be achieved by gradient descent over neural networks. We present a Mutual Information Neural Estimator (MINE) that is linearly scalable in dimensionality as well as in sample size, trainable through back-prop, and strongly consistent. We present a handful of applications on which MINE can be used to minimize or maximize mutual information. We apply MINE to improve adversarially trained generative models. We also use MINE to implement the Information Bottleneck, applying it to supervised classification; our results demonstrate substantial improvement in flexibility and performance in the settings.

Keyphrases: Artificial neural networks, Information theory, Generative model
Le superhéros dans le cinéma étatsunien : approche générique et mythologique

This essay examines the construction of a mythological discourse by analyzing superhero movies, a genre whose popularity exploded after the terrorist attacks of September 11, 2001. This essay traces back the origins of the superhero figure through the history of the comic book medium in the 20th century all the way to contemporary Hollywood cinema. We will demonstrate how the terrorist attacks have been central to the production of this type of movie, of which we will examine the most important characteristics: a strongly mythological content, supported by a representation of absolute evil, and an ideological function, mainly based on the principle of social cohesion. A case study on the character of Batman will be able to show the mythological content and the ideologies present in four movies, produced between 1943 and 2012. The writings of Gérard Bouchard on the social myth as well as the theories of Robert Jewett and John Shelton Lawrence on the American monomyth will be used to demonstrate the importance of this type of movie in the American culture since 2001.

Keyphrases: batman, superhero, mythology, myth, september 11, 2001, american politics
Agentivités des personnages féminins dans les jeux de rôle japonais : le cas de Tales of

In Japanese role-playing games (JRPGs), if the typical protagonist is a male brunet swordsman saving the world, the typical support character is a young good-looking woman, enamored with the protagonist, and whose fighting style focuses on supporting him rather than offense. From the observation that most occidental feminist analysis in game studies do not take into consideration the specific cultural context in which JRPGs are born, this master thesis builds a theoretical framework to allow a deeper understanding of the historical dynamics that have guided the evolution of female characters in these videogames. This thesis examines the uses of the concept of agency in both game studies and literary gender studies, in order to bridge gameplay and gender. It then integrates Japanese perspectives of female characters in otaku cultural objects, such as mangas and animes. The thesis ends by depicting of the evolution of female characters in the Tales of series, more specifically their combat roles and importance in the story.

Keyphrases: JRPG, otaku, Tales of, Videogames, Character, Feminism, Gameplay, Role-playing games, Japan
Serial killer / serial TV : l’adaptation du « slasher » dans Scream : the TV series (2015-)

Inspired by the resurgence in popularity of TV horror, this thesis offers an analysis of the adaptation process of the slasher in Scream: the TV Series (2015-, USA, MTV and Netflix) created by Jill Blotevogel. Navigating between the condensed cinematographic narrative and the long and episodic format of television, the appearance of the TV slasher is positioned in a rehandling of television writing. Already based around serial killers, is the slasher the perfect serial phenomenon? The problematic of this research questions the definition of the serial phenomenon that is the slasher, and how it is imported on the television format. Through the analysis of the serial dimension of Scream's original trilogy (Craven, 1996, 1997, 2000), we will see how the various cinematographic slasher conventions find themselves transformed by the specificities of television.

Keyphrases: narration, slasher, Scream, Scream: the TV Series