La critique sociale de Raymond Ruyer (1902-1987) fait partie de son œuvre , surtout connue par les ouvrages de philosophie des sciences et la réflexion sur la biologie dont Georges Canguilhem avait souligné l’originalité dès 1947. Nous montrons que la critique sociale existe dès la thèse complémentaire de 1930, "L’Humanité de l’avenir d’après Cournot" se poursuit avec la réflexion sur l’utopie. L’articulation de la critique sociale avec la philosophie unie à la science que propose Ruyer se trouve dans les valeurs et l’axiologie originale qu’il développe dès 1948. Ce serait une erreur, selon nous, de négliger la critique sociale de Ruyer et nous présentons ici, pour la première fois, une vue d’ensemble des idées de Ruyer sur les sociétés humaines et leur avenir. / Raymond Ruyer’s social critic, French philosopher, is a part of his works, mainly known for philosophy of science and philosophy of biology books, whose originality was underlined as early as 1947. We point out that social critic originates with his Doctorate essay "The Future of Humanity according to Cournot", in 1930, carries on with the studies on utopias. His philosophy unified to science is articulated to the social critic thanks to his original philosophy of values unfolded since 1948. It would be a mistake, in our opinion, to ignore Ruyer’s social critic and we submit here for the first time a survey of Ruyer’s ideas on human societies and their development.

This report will guide you through a journey of a socio-technological research where questions evolved from the intertwining of technology and social structures will be answered. During spring 2019, this research project has taken part as a pilot study of a research project called Gendered sustainability: Norm-Critical explorations of energy practices for everyday transitions. The research project has the purpose to show the understanding of climate, energy and equality issues as interlinked, and to explore power structures embedded in present relations between practices, products and environments. Both the UN’s global sustainability goal, and Swedish national goal regarding energy politics and equality, explicitly mention a need to raise social and ecological perspectives as integrated. The chosen focus for this pilot study is a practice that is a human right but has become a luxury – this study will focus on practices related to hygiene. The purpose of this study is to create a new narrative of practices related to hygiene, thus encouraging a more sustainable manner to perform these practices. To create a new narrative, a design process has been performed to generate discursive design artefacts which tells the new story. These artefacts are a part of the research method and aims to explore, not explain a problem definition or become of commercial interest. By understanding that present narratives need to be questioned, not only from a material perspective, but also from a conventional and temporal perspective – a change could be possible with design. To explore and visualise norms, behaviours and social structures behind the practice, the research and design process is based on theories of practice as a unit of design and norm-critical design. These theories highlight the importance of empirical user insights, which is explored with human centred design methods, interviews, and user analysis. Based on the insights gained a generative design process took place to create the discursive design objects. The objects aim to be means of communication and to trig discussions of how the new narrative takes place. The new narrative consists of three artefacts contributing to the story telling; the Mistwall challenges the cleanliness norm by providing a concept without flowing water; the Skinbrush that challenges the norm of gendered products by putting the human body as genderless in focus; and the Meter that takes advantage of norms related to the high beliefs in science and technology to question how cleanliness is estimated. Together they tell a new, more sustainable, narrative of performing hygiene practices / Följande rapport tar dig med på en resa igenom en socio-teknologisk forskning, där frågor kring hur teknologi och sociala strukturer som sammanlänkande besvaras. Under våren 2019 utfördes detta forskningsarbete som en pilotstudie till forskningsprojektet Könad Hållbarhet. Forskningen har som syfte att visa förståelse kring hur klimat, energi och jämställdhet är sammanlänkande, och undersöka hur maktstrukturer är inbäddade i nuvarande relationer mellan praktiker, produkter och miljöer. Både FN:s globala hållbarhetsmål och svenska nationella mål gällande energipolitik och jämställdhet nämner ett behov av att lyfta sociala och ekologiska perspektiv som integrerade. Det valda fokuset för denna pilotstudie är en praktik som är en mänsklig rättighet, men som har kommit att bli en lyx. Denna studie kommer fokusera på praktiker relaterade till hygien. Syftet med projektet är att skapa en ny berättelse om praktiker relaterade till hygien, detta för att uppmuntra ett mer hållbart sätt att utföra dessa praktiker på. För att skapa en ny berättelse utförs en designprocess som genererar diskursiva designartefakter som ska kunna bidra till den nya berättelsen. Dessa artefakt är en del av forskningen och har i syfte att undersöka och utforska, de har inte i syfte att svara mot en problemdefinition och bli kommersiella. Genom att förstå att nuvarande berättelser måste ifrågasättas, inte bara från ett materiellt perspektiv utan också från ett konventionellt och progressivt perspektiv, så skapas möjlighet för förändring genom design. Forskningen och designprocessen baseras på teorier som Praktiker som designenheter och normkritisk design då de ger möjlighet att utforska och visualisera normer, beteenden och sociala strukturer bakom praktikerna. Det betyder att empiriska insikter, samt insikter från användare är av stor vikt i arbetet, därför utforskas detta genom litteraturstudier, användarcentrerade designmetoder, intervjuer och användaranalyser. Med förståelsen från dessa insikter så påbörjas en generativ designprocess för att skapa diskursiva designobjekt. Objekten har i syfte att vara verktyg för kommunikation och trigga diskussioner om hur den nya berättelsen kan berättas. Den nya berättelsen består av tre artefakter; en mist-vägg som utmanar renlighetsnormer genom att skapa en duschmiljö utan rinnande vatten; en hudborste som utmanar könade normer relaterade till hygienprodukter genom att fokusera på den mänskliga kroppen som könslös; samt en mätare som utnyttjar normer relaterade till tron på vetenskap och teknologi genom att ifrågasätta hur renhet kan värderas. Tillsammans formar de tre artefakten nya, mer hållbara, beteenden i badrummet.

With the recent advances in the field of unmanned aerial vehicles, many applications have been identified. In tasks that require high-payload-to-weight ratios, flight times in the order of days, reduced noise and/or hovering capabilities, lighter-than-air vehicles present themselves as a competitive platform compared to fixed-wing and rotor based vehicles. The limiting factor in their widespread use in autonomous applications comes from the complexity of the control task. The so-called airships are highly-susceptible to aerodynamic forces and pose complex nonlinear system dynamics that complicate their modeling and control. Model-free control lends itself well as a solution to this type of problem, as it derives its control policies using input-output data, and can therefore learn complex dynamics and handle uncertain or unknown parameters and disturbances. In this work, two multi-input multi-output algorithms are presented on the basis of optimal control theory. Leveraging results from reinforcement learning, a single layer, partially connected neural network is formulated as a value function appropriator in accordance with Weierstrass higher-order approximation theorem. The so-called critic-network is updated using gradient descent methods on the mean-squared error of the temporal difference equation. In the single-network controller, the control policy is formulated as a closed form equation that is parameterized on the weights of the critic-network. A second controller is proposed that uses a second single-layer partially connected neural network, the actor-network, to calculate the control action. The actor-network is also updated using gradient descent on the squared error of the temporal difference equation. The controllers are employed in a highly realistic simulation airship model in nominal conditions and in the presence of external disturbances in the form of turbulent wind. To verify the validity and test the sensitivity of the algorithms to design parameters (the initialization of certain terms), ablation studies are carried out with multiple initial parameters. Both of the proposed algorithms are able to track the desired waypoints in both the nominal and disturbed flight tests. Furthermore, the performance of the controllers is compared to a modern, state-of-the-art multi-input multi-output controller. The two proposed controllers outperform the comparison controller in all but one flight test, with up to four fold reduction in the integral absolute error and integral time absolute error metrics. On top of the quantitative improvements seen in the proposed controllers, both controllers demonstrate a reduction in system oscillation and actuator chattering with respect to the comparison algorithm.

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This can be considered as a multidisciplinary research where ideas from Operations Research, Data Science and Logic came together to solve an inconsistency handling problem in a special type of ontology. / High data quality is a prerequisite for accurate data analysis. However, data inconsistencies often arise in real data, leading to untrusted decision making downstream in the data analysis pipeline. In this research, we study the problem of inconsistency detection and repair of the Ontology Multi-dimensional Data Model (OMD). We propose a framework of data quality assessment, and repair for the OMD. We formally define a weight-based repair-by-deletion semantics, and present an automatic weight generation mechanism that considers multiple input criteria. Our methods are rooted in multi-criteria decision making that consider the correlation, contrast, and conflict that may exist among multiple criteria, and is often needed in the data cleaning domain. After weight generation we present a dynamic programming based Min-Sum algorithm to identify minimal weight solution. We then apply evolutionary optimization techniques and demonstrate improved performance using medical datasets, making it realizable in practice. / Thesis / Master of Computer Science (MCS) / Accurate data analysis requires high quality data as input. In this research, we study inconsistency in an ontology known as Ontology Multi-dimensional Data (OMD) Model and propose algorithms to repair them based on their automatically generated relative weights. We proposed two techniques to restore consistency, one provides optimal results but takes longer time compared to the other one, which produces sub-optimal results but fast enough for practical purposes, shown with experiments on datasets.

Ce mémoire vise à mettre à l'épreuve les contributions scientifiques des auteurs qui critiquent la médicalisation de l'enjeu suicidaire. Selon eux, l'idée d'une relation causale entre le suicide et la maladie mentale est devenue une évidence acceptée dans les milieux scientifiques et profanes. Cette conception dominante est critiquée car elle oriente les stratégies de prévention du suicide vers des interventions médicales, négligeant ainsi l'importance des facteurs structurels. Nous proposons d'explorer comment ces critiques résonnent avec le contexte québécois. Nous interrogeons également ces critiques en les replaçant au sein d'un large éventail d'approches concernant le suicide, y compris les expertises élaborées en sociologie, psychologie et épidémiologie. En étayant notre examen de la littérature par une démonstration de la complexité théorique du suicide, nous explorons la manière dont les experts intègrent les discours "médicalisés" parmi la diversité de ces connaissances. L'analyse proposée se concentre spécifiquement sur l'établissement d'une cartographie des représentations concernant les causes du suicide et les pratiques de prévention privilégiées. Pour mener cette analyse, nous étudierons les discours de douze professionnels québécois et analyserons le contenu de divers documents institutionnels rédigés par le gouvernement du Québec. / This master’s thesis aims to challenge the scientific contributions of authors who criticize the medicalization of concepts and treatments surrounding the topic of suicide. According to their arguments, the notion of a causal link between suicide and mental illness has become widely acknowledged within scientific and non-expert circles. This prevailing notion also plays a significant role in the public management of suicide, as it narrows down suicide prevention strategies to medical interventions. We intend to investigate how these critiques resonate within the context of Quebec. Furthermore, we also suggest interrogating these criticisms by situating them within a broad a broad range of approaches concerning suicide, including expertise from sociology, psychology, and epidemiology. By grounding our literature review in a demonstration of the intricate theoretical nature of suicide, we will delve into how professionals incorporate "medicalized" discourses within the scope of these competencies. The proposed analysis is specifically directed towards constructing a map of representations regarding the causes of suicide and prevention practices. To accomplish this, we will analyze the viewpoints of twelve Quebec-based professionals, as well as scrutinize the content of various institutional documents produced by the Quebec government.

[pt] Nesta dissertação, proponho reformular o conceito de soberania através de uma atitude de diagnóstica política. Para articular esta linha alternativa de investigação, sugiro que o trabalho de Georges Bataille sobre soberania e a crítica de Immanuel Kant quanto aos limites da representação política partilham da problemática da política moderna de subjetividade, de modo que permanecem especialmente relevantes para o nosso presente. A análise prossegue através de leituras de críticas à política internacional preocupadas com questões de mudança política. Jens Bartelson, Martti Koskenniemi e Nicholas Onuf nos fornecem análises sofisticadas sobre a relação entre política e direito nos processos produtivos dos limites da modernidade política. Por fim, acompanho os movimentos diagnósticos presentes nestes textos até o limite que os possibilita. Concluo argumentando que a natureza deste limite exige uma ligeira mudança na sua problematização, caso contrário corremos o risco de perder nuanças importantes na forma como autoridade política autoriza a si própria sob a condições contemporâneas que informam of problema da soberania. / [en] In this dissertation, I propose to re-cast the concept of sovereignty through a political diagnostic attitude. In order to articulate this alternative line of inquire, I suggest that Georges Bataille s work on sovereignty and Immanuel Kant s critique of the limits of political representation share the problematic of modern political subjectivity in ways that remain poignantly relevant to our present. The analysis proceeds through close textual readings of critiques of international politics concerned with questions of political change. Jens Bartelson, Martti Koskenniemi and Nicholas Onuf provide us with sophisticate analyses about the relationship between politics and law in the knowledgeable production of the limits of political modernity. Lastly, I follow the diagnostic movements present in those texts up to the limit that enables them. I conclude by arguing that the nature of this limit demands a slight shift in problematization, otherwise we risk missing important nuances in how political authority authorizes itself under the contemporary conditions that inform the problem of sovereignty.

A growing problem in network security stems from the fact that both attack methods and target systems constantly evolve. This problem makes it difficult for human operators to keep up and manage the security problem. To deal with this challenge, a promising approach is to use reinforcement learning to adapt security policies to a changing environment. However, a drawback of this approach is that traditional reinforcement learning methods require a large amount of data in order to learn effective policies, which can be both costly and difficult to obtain. To address this problem, this thesis investigates ways to incorporate prior knowledge in learning systems for network security. Our goal is to be able to learn security policies with less data compared to traditional reinforcement learning algorithms. To investigate this question, we take a Bayesian approach and consider Bayesian reinforcement learning methods as a complement to current algorithms in reinforcement learning. Specifically, in this work, we study the following algorithms: Bayesian Q-learning, Bayesian REINFORCE, and Bayesian Actor-Critic. To evaluate our approach, we have implemented the mentioned algorithms and techniques and applied them to different simulation scenarios of intrusion prevention. Our results demonstrate that the Bayesian reinforcement learning algorithms are able to learn more efficiently compared to their non-Bayesian counterparts but that the Bayesian approach is more computationally demanding. Further, we find that the choice of prior and the kernel function have a large impact on the performance of the algorithms. / Ett växande problem inom cybersäkerhet är att både attackmetoder samt system är i en konstant förändring och utveckling: å ena sidan så blir attackmetoder mer och mer sofistikerade, och å andra sidan så utvecklas system via innovationer samt uppgraderingar. Detta problem gör det svårt för mänskliga operatörer att hantera säkerhetsproblemet. En lovande metod för att hantera denna utmaning är förstärkningslärande. Med förstärkningslärande kan en autonom agent automatiskt lära sig att anpassa säkerhetsstrategier till en föränderlig miljö. En utmaning med detta tillvägagångsätt är dock att traditionella förstärkningsinlärningsmetoder kräver en stor mängd data för att lära sig effektiva strategier, vilket kan vara både kostsamt och svårt att erskaffa. För att lösa detta problem så undersöker denna avhandling Bayesiska metoder för att inkorporera förkunskaper i inlärningsalgoritmen, vilket kan möjliggöra lärande med mindre data. Specifikt så studerar vi följande Bayesiska algoritmer: Bayesian Q-learning, Bayesian REINFORCE och Bayesian Actor- Critic. För att utvärdera vårt tillvägagångssätt har vi implementerat de nämnda algoritmerna och utvärderat deras prestanda i olika simuleringsscenarier för intrångsförebyggande samt analyserat deras komplexitet. Våra resultat visar att de Bayesiska förstärkningsinlärningsalgoritmerna kan användas för att lära sig strategier med mindre data än vad som kravs vid användande av icke-Bayesiska motsvarigheter, men att den Bayesiska metoden är mer beräkningskrävande. Vidare finner vi att metoden för att inkorporera förkunskap i inlärningsalgoritmen, samt val av kernelfunktion, har stor inverkan på algoritmernas prestanda.

[pt] Com recentes avanços em poder computacional, o uso de novos modelos de controle complexos se tornou viável para realizar o controle de quadrotores. Um destes métodos é o aprendizado por reforço profundo (do inglês, Deep Reinforcement Learning, DRL), que pode produzir uma política de controle que atende melhor as não-linearidades presentes no modelo do quadrotor que um método de controle tradicional. Umas das não-linearidades importantes presentes em veículos aéreos transportadores de carga são as propriedades variantes no tempo, como tamanho e massa, causadas pela adição e remoção de carga. A abordagem geral e domínio-agnóstica de um controlador por DRL também o permite lidar com navegação visual, na qual a estimação de dados de posição é incerta. Neste trabalho, aplicamos um algorítmo de Soft Actor- Critic com o objeivo de projetar controladores para um quadrotor a fim de realizar tarefas que reproduzem os desafios citados em um ambiente virtual. Primeiramente, desenvolvemos dois controladores de condução por waypoint: um controlador de baixo nível que atua diretamente em comandos para o motor e um controlador de alto nível que interage em cascata com um controlador de velocidade PID. Os controladores são então avaliados quanto à tarefa proposta de coleta e alijamento de carga, que, dessa forma, introduz uma variável variante no tempo. Os controladores concebidos são capazes de superar o controlador clássico de posição PID com ganhos otimizados no curso proposto, enquanto permanece agnóstico em relação a um conjunto de parâmetros de simulação. Finalmente, aplicamos o mesmo algorítmo de DRL para desenvolver um controlador que se utiliza de dados visuais para completar um curso de corrida em uma simulação. Com este controlador, o quadrotor é capaz de localizar portões utilizando uma câmera RGB-D e encontrar uma trajetória que o conduz a atravessar o máximo possível de portões presentes no percurso. / [en] With recent advances in computational power, the use of novel, complex control models has become viable for controlling quadrotors. One such method is Deep Reinforcement Learning (DRL), which can devise a control policy that better addresses non-linearities in the quadrotor model than traditional control methods. An important non-linearity present in payload carrying air vehicles are the inherent time-varying properties, such as size and mass, caused by the addition and removal of cargo. The general, domain-agnostic approach of the DRL controller also allows it to handle visual navigation, in which position estimation data is unreliable. In this work, we employ a Soft Actor-Critic algorithm to design controllers for a quadrotor to carry out tasks reproducing the mentioned challenges in a virtual environment. First, we develop two waypoint guidance controllers: a low-level controller that acts directly on motor commands and a high-level controller that interacts in cascade with a velocity PID controller. The controllers are then evaluated on the proposed payload pickup and drop task, thereby introducing a timevarying variable. The controllers conceived are able to outperform a traditional positional PID controller with optimized gains in the proposed course, while remaining agnostic to a set of simulation parameters. Finally, we employ the same DRL algorithm to develop a controller that can leverage visual data to complete a racing course in simulation. With this controller, the quadrotor is able to localize gates using an RGB-D camera and devise a trajectory that drives it to traverse as many gates in the racing course as possible.

A key challenge in the field of cloud computing is to automatically scale software containers in a way that accurately matches the demand for the services they run. To manage such components, container orchestrator tools such as Kubernetes are employed, and in the past few years, researchers have attempted to optimise its autoscaling mechanism with different approaches. Recent studies have showcased the potential of Actor-Critic Deep Reinforcement Learning (DRL) methods in container orchestration, demonstrating their effectiveness in various use cases. However, despite the availability of solutions that integrate multiple container performance metrics to evaluate autoscaling decisions, a critical gap exists in understanding how model-free DRL algorithms interact with a state space based on those metrics. Thus, the primary objective of this thesis is to investigate the impact of the state space definition on the performance of model-free DRL methods in the context of horizontal autoscaling within Kubernetes clusters. In particular, our findings reveal distinct behaviours associated with various sets of metrics. Notably, those sets that exclusively incorporate parameters present in the reward function demonstrate superior effectiveness. Furthermore, our results provide valuable insights when compared to related works, as our experiments demonstrate that a careful metric selection can lead to remarkable Service Level Agreement (SLA) compliance, with as low as 0.55% violations and even surpassing baseline performance in certain scenarios. / En viktig utmaning inom området molnberäkning är att automatiskt skala programvarubehållare på ett sätt som exakt matchar efterfrågan för de tjänster de driver. För att hantera sådana komponenter, container orkestratorverktyg som Kubernetes används, och i det förflutna några år har forskare försökt optimera dess autoskalning mekanism med olika tillvägagångssätt. Nyligen genomförda studier har visat potentialen hos Actor-Critic Deep Reinforcement Learning (DRL) metoder i containerorkestrering, som visar deras effektivitet i olika användningsfall. Men trots tillgången på lösningar som integrerar flera behållarprestandamått att utvärdera autoskalningsbeslut finns det ett kritiskt gap när det gäller att förstå hur modellfria DRLalgoritmer interagerar med ett tillståndsutrymme baserat på dessa mätvärden. Det primära syftet med denna avhandling är alltså att undersöka vilken inverkan statens rymddefinition har på prestandan av modellfria DRL-metoder i samband med horisontell autoskalning inom Kubernetes-kluster. I synnerhet visar våra resultat distinkta beteenden associerade med olika uppsättningar mätvärden. Särskilt de set som uteslutande innehåller parametrar som finns i belöningen funktion visar överlägsen effektivitet. Dessutom våra resultat ge värdefulla insikter jämfört med relaterade verk, som vår experiment visar att ett noggrant urval av mätvärden kan leda till anmärkningsvärt Service Level Agreement (SLA) efterlevnad, med så låg som 0, 55% överträdelser och till och med överträffande baslinjeprestanda i vissa scenarier.