NCube/papyrus huggingface dataset:
Le débat opposant les conceptions naturalistes et les conceptions politiques des droits humains au sujet de leur fondation philosophique

This thesis, which is in the field of ethics of human rights doctrine, seeks to explain the debate on the philosophical foundations of human rights, on their justification and on their content. More precisely, to demonstrate that this debate is not sterile despite two opposing views and at first sight irreconcilable positions. To do this, an analysis of the different interpretations of the concept of dignity makes it possible to select the one that best ensures rigorous philosophical support for contemporary human rights doctrine. After the selection of this concept of dignity, an assessment of naturalistic and political conceptions of human rights is necessary in order to philosophically anchor the concept of human dignity in human rights doctrine. This assessment then makes it possible to reject natural law and as well as political conceptions of human rights as the only conception necessary for the philosophical foundation of the doctrine of human rights. Each conception presents significant problems to the justification of human rights content. Finally, a possible solution to end this debate is proposed. It is about the possibility of understanding naturalistic and political conceptions of human rights not as opposing views but rather as complementary conceptions making it possible to justify and define the content of human rights.

Keyphrases: philosophy, dignity, human rights, ethics of international relations, natural law, political conseption of human rights
L'implémentation des relatives dans un réalisateur profond

This Master’s thesis is about the implementation of French relative clauses in the multilingual deep realizer GenDR. Surface realizers (SimpleNLG, JSReal or RealPro) generate relative clauses, but in deep realizers (MARQUIS, Forge or GenDR) their handling remains rudimentary. In a French corpus of 21,461 sentences, 4,505 contain a relative, i.e. about one in five sentences. Thus, it is a core linguistic phenomenon that should be handled by GenDR. Our theoretical framework is the Meaning-Text theory. Relative clause is relevant in the semantics-syntax interface. We offer a typology of relative clauses. The relative clause is defined, and it is divided into two main categories: direct and indirect. Our definition of relative pronouns is based on Riegel et al. (2018). We used GREW to analyze a French corpus in SUD. There are more direct (≈78%) than indirect (≈22%) relatives. The most frequent pronouns are qui (58.8%), que (13.8%), dont (10.2%) and où (10%), then a preposition followed by lequel (5.7%), a preposition followed by qui (0.7%), lequel (0.4%), and a preposition followed by quoi (0.1%). The most frequent function of the modified noun is direct object. We implemented in GenDR the rules for direct relative, indirect relative, and relative pronouns qui, que, dont, a preposition followed by qui, and a preposition followed by lequel. Our implementation covers the most common types of relatives. The phenomena that are not well handled by our rules are the generation of the pronouns lequel, a preposition followed by quoi, où and object qui, the treatment of modal verbs and the generation of sentences with an infinitive verb after a modal verb, the treatment of support verbs and other collocations. Our implementation is for French, but it can be easily adapted to other languages.

Keyphrases: proposition relative, relative clause, natural language generation, text realizer, Meaning-Text theory
L'acquisition de la coréférence chez les enfants ayant un trouble développemental du langage : revue méta-analytique des facteurs influençant ce phénomène

This project aims to understand the difficulties related to the acquisition of coreference of French speaking children with developmental language disorder (DLD) and to determine the various factors influencing its acquisition and mastery. The current definition of DLD indicates that it is a spoken language difficulty that affects both comprehension and expression. It is a neurodevelopmental disorder characterized by highly variable developmental delays in one or more language spheres. Two theories are seen in detail: the computational grammatical complexity (CGC) hypothesis (van der Lely and Stollwerck, 1997) and the working memory–based account (Montgomery and Evans, 2009). The CGC theory proposes that the difficulties of using coreference are due to the innate representation of the syntax which would be immature for DLD children and particularly the principle B which would not be acquired. In contrast, Montgomery and Evans argue that this difficulty in understanding and using anaphoras stems from a limitation in working memory and specifically with the allocation and capacity of attentional resources. Different factors influencing the acquisition of coreference in children with are discussed. First, the child must have lexical and semantic knowledge related to pronouns and anaphoras. Second, the child must acquire syntactic knowledge in order to know the possible antecedents for pronouns and anaphoras as well as the rules connecting them. Finally, working memory and the allocation and capacity of mental resources play an important role in the resolution of anaphoras. It is thus clear, according to us, that the theories are not totally sufficient to explain the deficit in coreference, but they partly explain other types of problems which are necessary for the resolution of the anaphora. The resolution of anaphoras is an important phenomenon for a good understanding of developmental language disorder, especially in French speaking children, since it is a clinical marker of the disorder in French.

Keyphrases: Developmental language disorder, DLD, Acquisition, Coreference, Dysphasie
Self-disclosure model for classifying & predicting text-based online disclosure

Social media and social networking sites have evolved into digital billboards for internet users due to their rapid expansion. As these sites encourage consumers to expose personal information via profiles and postings, increased use of social media has generated privacy concerns. There have been notable efforts from researchers to detect self-disclosure using Information extraction (IE) techniques. Recent research on machine learning and natural language processing methods shows that understanding the contextual meaning of the words can result in better accuracy than traditional data extraction methods. Driven by the facts mentioned earlier, users are often ignorant of the quantity of personal information published in online forums, there is a need to detect various disclosures in natural language and give them a choice to test the possibility of disclosure before posting. For this purpose, this work proposes "SD_ELECTRA," a context-specific language model to detect Interest, Personal, Education and Work, Relationship, Personality, Residence, Travel plan, and Hospitality disclosures in social media data. The goal is to create a context-specific language model on a social media platform that performs better than the general language models. Moreover, recent advancements in transformer models paved the way to train language models from scratch and achieve higher scores. Experimental results show that SD_ELECTRA has outperformed the base model in all considered metrics for the standard text classification method. In addition, the results also show that training a language model with a smaller pre-training context-specific corpus on a single GPU can improve its performance. An illustrative web application designed allows users to test the disclosure possibilities in their social media posts. As a result, by utilizing the efficiency of the suggested model, users would be able to get real-time learning on self-disclosure.

Keyphrases: Self-disclosure, Natural Language Processing,, Information extraction, Transformers, Privacy on social media, User interface
Privacy evaluation of fairness-enhancing pre-processing techniques

The prevalence of decision-making algorithms, based on increasingly powerful patternrecognition machine learning algorithms, has brought a growing wave of concern about dis-crimination and fairness of those algorithm predictions as well as their impacts on equity andtreatment of minority or under-represented groups. This in turn has fuelled the developmentof new techniques to mitigate those issues and helped outline challenges related to such issues.  n this work, we analyse recent advances in fairness enhancing pre-processing techniques,evaluate how they control the fairness-utility trade-off and the dataset’s ability to be usedsuccessfully in downstream tasks.  We focus on three techniques that attempt to hide asensitive attribute in a dataset, two based onGenerative Adversarial Networksarchitectures(LAFTR [67] and GANSan [6]), and one deterministic transformation of dataset relyingon density functions (Disparate Impact Remover [33]). First we analyse the control overthe fairness-utility trade-off each of these techniques offer.  We then attempt to revertthe transformation on the data each of these techniques applied using a variation of anauto-encoder built specifically for this purpose, which we calledreconstructor. Lastly wesee that even though these techniques offer practical guarantees of specific fairness metrics,basic machine learning classifiers are often able to successfully predict the sensitive attributefrom the transformed data, effectively enabling discrimination. This creates what we believeis a major issue in fairness-enhancing technique research that is in large part due to intricaterelationship between fairness and privacy.

Keyphrases: Fairness, Privacy, Machine Learning, Generative Adversarial Network
A data-driven discrete elastic rod model for shells and solids

Rod-like structures are ubiquitous in the world today. Henceforth accurately predicting their behavior for engineering and virtual environments are indispensable for many industries including computer graphics, computer animation, and computational design. In this thesis we explore a new computational model for elastic rods that leverages simulation data to reproduce shell and solid-like effects present in rods that break the assumptions of the classical Kirchhoff rod theory, thus presenting a possible improvement avenue to many states-of-the-art techniques.   Our approach consists of taking a data set of simulations from both volumetric solids or shells to train a novel high-order polynomial positive-definite energy model for an elastic rod. This new energy increases the range of material behaviors that can be modeled for the rod, thus allowing for a larger range of phenomena to be captured. In order to propose and test this model, we design an experimental pipeline to test the limits of the linear theory of rods and investigate the interface geometries between the Shell-Rod and Volume-Shell cases to observe the effects of a nonlinear material model and a non-elliptical cross-section in the rod deformation. We also investigate the relation between rod curvature and deformation of the cross-section and curvature to introduce a modification on the bending term of the energy. This allows us to reproduce both the asymmetric bending behavior present in thin volumetric solid and shell beams with non-convex cross-sections. Suggestions for further improvements in models and experimental techniques are also given.

Keyphrases: Rods, Shells, Solid Volumes, Slender Structures, Three-dimensional Elasticity, Data Set, Asymmetry, Interface, Physical simulation, Kirchhoff Rod, Non-linear, Cross-Section, Positive Definiteness, Volumes Solides, Interface
Personalized question-based cybersecurity recommendation systems

With the proliferation of the virtual universe and the multitude of services provided by the World Wide Web, a major concern arises: Security and privacy have never been more in jeopardy. Nowadays, with the Covid 19 pandemic, the world faces a new reality that pushed the majority of the workforce to telecommute. This thereby creates new vulnerabilities for cyber attackers to exploit. It’s important now more than ever, to educate and offer guidance towards good cybersecurity hygiene. In this context, a major effort has been dedicated by researchers, governments, and businesses alike to protect people online against hackers and cybercriminals. With a focus on strengthening the weakest link in the cybersecurity chain which is the human being, educational and awareness-raising tools have been put to use. However, most researchers focus on the “one size fits all” solutions which do not focus on the intricacies of individuals. This work aims to overcome that by contributing a personalized question-based recommender system. Named “Cyberhelper”, this work benefits from an existing mature body of research on recommender system algorithms along with recent research on non-user-specific question-based recommenders. The reported proof of concept holds potential for future work in adapting Cyberhelper as an everyday assistant for different types of users and different contexts.

Keyphrases: Personalized recommender system, Cybersecurity, User, Cyberattacks
Image forgery detection using textural features and deep learning

The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation.  Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models.  Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset.

Keyphrases: Image Splicing, Convolution Neural Networks (CNN), ResNet-50, U-Net, Local Binary Pattern (LBP)
Evolving‌ ‌artificial‌ ‌neural‌ ‌networks‌‌ ‌to‌ ‌imitate‌ ‌human‌ ‌behaviour‌‌ ‌in‌ ‌Shinobi‌ ‌III‌ ‌:‌ ‌return‌ ‌of‌ ‌the‌ ‌Ninja‌ ‌master‌

Our society is increasingly fond of computational tools. This phenomenon has greatly increased over the past decade following, among other factors, the emergence of a new Artificial Intelligence paradigm. Specifically, the coupling of two algorithmic techniques, Deep Neural Networks and Stochastic Gradient Descent, thrusted by an exponentially increasing computing capacity, has and is continuing to become a major asset in many modern technologies. However, as progress takes its course, some still wonder whether other methods could similarly or even more greatly benefit from these various hardware advances. In order to further this study, we delve in this thesis into Evolutionary Algorithms and their application to Dynamic Neural Networks, two techniques which despite enjoying many advantageous properties have yet to find their niche in contemporary Artificial Intelligence. We find that by elaborating new methods while exploiting strong computational resources, it becomes possible to develop strongly performing agents on a variety of benchmarks but also some other agents behaving very similarly to human subjects on the video game Shinobi III : Return of The Ninja Master, typical complex tasks previously out of reach for non-gradient-based optimization.

Keyphrases: Artificial‌ ‌Intelligence, ‌Evolutionary‌ ‌Algorithms, Artificial‌ ‌Neural‌ ‌Networks‌
Méthodologies pour la détection de diachronies sémantiques et leurs impacts

The meaning of a word is subject to variations over time. Many phenomena motivate these modifications such as the appearance of new objects or changes in habits. Thus, the same word can be assigned a new meaning, or have a meaning withdrawn, or remain stable between two dates.  The study of semantic diachrony is a field that focuses on these changes in meaning. Recent work on semantic diachrony proposes methodologies for the detection of diachronies. In order to do so, they rely on texts from several different temporal periods, and through which language models are trained. An alignment of the obtained representations, and a comparison of those of target words enables one to infer the change of meaning. Nevertheless, the absence of a reference dataset for the validation of these methods leads to the development of alternative validation methods, suggesting in particular to rely on the changes of meaning identified in traditional dictionaries.  The work carried out during my master's degree aims at presenting a reflection on the existing methods of diachrony detection.  Based on a corpus of newspapers covering the whole 20th century, we propose complementary methods thanks to which we demonstrate that the proposed evaluations are subject to ambiguities. These ambiguities do not allow us to ensure the quality of the methods. We then develop a methodology for the construction of a validation dataset. This methodology takes advantage of a disambiguation algorithm in order to associate a date of appearance in the course of time to all the senses of a word. We propose a dataset composed of 151 words allowing one to evaluate the identification of diachronies in French between 1910 and 1990.

Keyphrases: TAL, diachronie, validation, NLP, diachrony, dataset, Word sens disambiguation
Estimation de pose 2D par réseau convolutif

Magic: The Gathering} is an imperfect information, stochastic, collectible card game invented by Richard Garfield in 1993. The goal of this project is to propose a machine learning pipeline capable of detecting and localising \textit{Magic} cards within an image. This is a 2D pose problem with 4 degrees of freedom, namely translation in $x$ and $y$, rotation, and scale, in a context where cards can be superimposed on one another. We tackle this problem by relying on deep learning using a combination of two separate neural networks. Our final pipeline has the ability to tackle real-world images and gives, with a very good degree of precision, the poses of cards within an image. Through the course of this project, we have developped a method of realistic synthetic data generation to train both our models to tackle real world images. The results show that our pose subnetwork is able to predict position within half a pixel, rotation within one degree and scale within 2 percent.

Keyphrases: Computer Vision, Machine Learning, Deep Learning, Convolutional Neural Networks, Synthetic Datasets
PatchUp : a feature-space block-level regularization technique for convolutional neural networks

Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. And, in this case, very deep and wide networks have a tendency to memorize the samples, and therefore they might be vulnerable under a slight distribution shift at testing time. This problem yields poor generalization for data outside of the training data distribution. To overcome this issue some data-dependent and data-independent methods have been proposed. A recent class of successful methods to address this problem uses various ways to construct a new training sample by mixing a pair (or more) of training samples. In this thesis, we introduce PatchUp, a feature-space block-level data-dependent regularization that operates in the hidden space by masking out contiguous blocks of the feature map of a random pair of samples, and then either mixes (Soft PatchUp) or swaps (Hard PatchUp) these selected contiguous blocks. Our regularization method does not incur significant computational overhead for CNNs during training. Our approach improves the robustness of CNN models against the manifold intrusion problem that may occur in other state-of-the-art mixing approaches like Mixup and CutMix. Moreover, since we are mixing the contiguous block of features in the hidden space, which has more dimensions than the input space, we obtain more diverse samples for training towards different dimensions. Our experiments on CIFAR-10, CIFAR-100, SVHN, and Tiny-ImageNet datasets using ResNet architectures including PreActResnet18, PreActResnet34, WideResnet-28-10, ResNet101, and ResNet152 models show that PatchUp improves upon, or equals, the performance of current state-of-the-art regularizers for CNNs. We also show that PatchUp can provide a better generalization to affine transformations of samples and is more robust against adversarial attacks. PatchUp also helps a CNN model to produce a wider variety of features in the residual blocks compared to other state-of-the-art regularization methods for CNNs such as Mixup, Cutout, CutMix, ManifoldMixup, and Puzzle Mix.  Key words: Deep Learning, Convolutional Neural Network, Generalization, Regular-ization, Data-dependent and Data-independent Regularization Techniques, Robustness to Adversarial Attacks.

Keyphrases: Deep learning, Convolutional Neural Network (CNN), Generalization, Regularization, Data-dependent and Data-independent Regularization Techniques, Robustness to Adversarial Attacks
On quantifying the value of simulation for training and evaluating robotic agents

A common problem in robotics is reproducing results and claims made by researchers. The experiments done in robotics laboratories typically yield results that are specific to a complex setup and difficult or costly to reproduce and validate in other contexts. For this reason, it is arduous to compare the performance and robustness of various robotic controllers. Low-cost reproductions of physical environments are popular but induce a performance reduction when transferred to the target domain. This thesis present the results of our work toward improving benchmarking in robotics, specifically for autonomous driving.  We build a new platform, the Duckietown Autolabs, which allow researchers to evaluate autonomous driving algorithms in a standardized framework on low-cost hardware. The platform offers a simulated environment for easy access to annotated data and parallel evaluation of driving solutions in customizable environments. We use the platform to analyze the discrepancy between simulation and reality in the case of predictivity and quality of data generated. We supply two metrics to quantify the usefulness of a simulation and demonstrate how they can be used to optimize the value of a proxy environment.

Keyphrases: Simulation, Sim-to-real, Reality gap, Autonomous driving, Robotics, Benchmarking, Machine learning, Reproducible science, Imitation learning, Reality gap