7,688 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Towards A Practical High-Assurance Systems Programming Language

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    Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation. Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code. To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process

    Sensing Collectives: Aesthetic and Political Practices Intertwined

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    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders

    The Politics of Platformization: Amsterdam Dialogues on Platform Theory

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    What is platformization and why is it a relevant category in the contemporary political landscape? How is it related to cybernetics and the history of computation? This book tries to answer such questions by engaging in multidisciplinary dialogues about the first ten years of the emerging fields of platform studies and platform theory. It deploys a narrative and playful approach that makes use of anecdotes, personal histories, etymologies, and futurable speculations to investigate both the fragmented genealogy that led to platformization and the organizational and economic trends that guide nowadays platform sociotechnical imaginaries

    Structured machine learning models for robustness against different factors of variability in robot control

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    An important feature of human sensorimotor skill is our ability to learn to reuse them across different environmental contexts, in part due to our understanding of attributes of variability in these environments. This thesis explores how the structure of models used within learning for robot control could similarly help autonomous robots cope with variability, hence achieving skill generalisation. The overarching approach is to develop modular architectures that judiciously combine different forms of inductive bias for learning. In particular, we consider how models and policies should be structured in order to achieve robust behaviour in the face of different factors of variation - in the environment, in objects and in other internal parameters of a policy - with the end goal of more robust, accurate and data-efficient skill acquisition and adaptation. At a high level, variability in skill is determined by variations in constraints presented by the external environment, and in task-specific perturbations that affect the specification of optimal action. A typical example of environmental perturbation would be variation in lighting and illumination, affecting the noise characteristics of perception. An example of task perturbations would be variation in object geometry, mass or friction, and in the specification of costs associated with speed or smoothness of execution. We counteract these factors of variation by exploring three forms of structuring: utilising separate data sets curated according to the relevant factor of variation, building neural network models that incorporate this factorisation into the very structure of the networks, and learning structured loss functions. The thesis is comprised of four projects exploring this theme within robotics planning and prediction tasks. Firstly, in the setting of trajectory prediction in crowded scenes, we explore a modular architecture for learning static and dynamic environmental structure. We show that factorising the prediction problem from the individual representations allows for robust and label efficient forward modelling, and relaxes the need for full model re-training in new environments. This modularity explicitly allows for a more flexible and interpretable adaptation of trajectory prediction models to using pre-trained state of the art models. We show that this results in more efficient motion prediction and allows for performance comparable to the state-of-the-art supervised 2D trajectory prediction. Next, in the domain of contact-rich robotic manipulation, we consider a modular architecture that combines model-free learning from demonstration, in particular dynamic movement primitives (DMP), with modern model-free reinforcement learning (RL), using both on-policy and off-policy approaches. We show that factorising the skill learning problem to skill acquisition and error correction through policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. Our empirical evaluation demonstrates how to best do this with DMPs and propose “residual Learning from Demonstration“ (rLfD), a framework that combines DMPs with RL to learn a residual correction policy. Our evaluations, performed both in simulation and on a physical system, suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs. Last but not least, our study shows that the extracted correction policies can be transferred to different geometries and frictions through few-shot task adaptation. Third, we employ meta learning to learn time-invariant reward functions, wherein both the objectives of a task (i.e., the reward functions) and the policy for performing that task optimally are learnt simultaneously. We propose a novel inverse reinforcement learning (IRL) formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks executed on a 7DoF Kuka IIWA arm. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks. Finally, we employ our observations to evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we study the effects of using robust optimisation in the source and target networks. This allows us to identify transfer learning strategies under which adversarial defences are successfully retained, in addition to revealing potential vulnerabilities. We study the extent to which adversarially robust features can preserve their defence properties against black and white-box attacks under three different transfer learning strategies. Our empirical evaluations give insights on how well adversarial robustness under transfer learning can generalise.

    Offene-Welt-Strukturen: Architektur, Stadt- und Naturlandschaft im Computerspiel

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    Welche Rolle spielen Algorithmen fĂŒr den Bildbau und die Darstellung von Welt und Wetter in Computerspielen? Wie beeinflusst die Gestaltung der RĂ€ume, Level und Topografien die Entscheidungen und das Verhalten der Spieler_innen? Ist der Brutalismus der erste genuine Architekturstil der Computerspiele? Welche Bedeutung haben LandschaftsgĂ€rten und Nationalparks im Strukturieren von Spielwelten? Wie wird Natur in Zeiten des Klimawandels dargestellt? Insbesondere in den letzten 20 Jahren adaptieren digitale Spielwelten akribischer denn je Merkmale der physisch-realen Welt. Durch aufwĂ€ndige Produktionsverfahren und komplexe Visualisierungsstrategien wird die Angleichung an unsere ĂŒbrige Alltagswelt stets in AbhĂ€ngigkeit von Spielmechanik und Weltlichkeit erzeugt. Wie sich spĂ€testens am Beispiel der Open-World-Spiele zeigt, fĂŒhrt die Übernahme bestimmter Weltbilder und Bildtraditionen zu ideologischen Implikationen, die weit ĂŒber die bisher im Fokus der Forschung stehenden, aus anderen Medienformaten transferierten ErzĂ€hlkonventionen hinausgehen. Mit seiner Theorie der Architektur als medialem Scharnier legt der Autor offen, dass digitale Spielwelten medienspezifische Eigenschaften aufweisen, die bisher nicht zu greifen waren und der Erforschung harrten. Durch VerschrĂ€nken von Konzepten aus u.a. Medienwissenschaft, Game Studies, Philosophie, Architekturtheorie, Humangeografie, Landschaftstheorie und Kunstgeschichte erarbeitet Bonner ein transdisziplinĂ€res Theoriemodell und ermöglicht anhand der daraus entwickelten analytischen Methoden erstmals, die komplexe Struktur heutiger Computerspiele - vom Indie Game bis zur AAA Open World - zu verstehen und zu benennen. Mit "Offene-Welt-Strukturen" wird die Architektonik digitaler Spielwelten umfassend zugĂ€nglich

    Collaborative worldbuilding: Examining identities, ideologies, and literacy practices in a youth role-playing community

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    Thesis advisor: Jon M. WargoRole-playing games (RPGs) are storytelling games that employ character generation, improvisational acting, and rule-based interactions to build worlds and coauthor narratives. Contemporary education research identifies RPGs as robust examples of school-based and extra- academic literacy practices. As sites of narrative possibility and precarity, RPGs are political projects that can resist and reify hegemonic ideologies of race, gender, and power. In this three- paper dissertation, I build upon game studies and literacy scholarship to nuance the ways six youth participants coauthored worlds, negotiated storytelling practices, and (re)produced Whiteness. In Paper 1, I highlight a phenomenon I call “liminal play” – moments of gameplay wherein the boundaries between players, characters, and texts converge. My findings illustrate how liminal moments of play forward social and compositional dimensions of collaborative storytelling. In Paper 2, I leverage conversation analysis to detail how participants’ play-based talk oscillated across two participation frames: the game (i.e., their character roles) and the metagame (i.e., their player roles). My analysis examines the nested and contested processes of narrative negotiation inherent in RPG interactions. Finally, in Paper 3, I interrogate how participants’ worldbuilding practices resisted and reified White racialized ideology. Oriented by critical Whiteness studies, I unmask how participants and I privileged Whiteness despite our efforts to resist hegemonic Dungeons & Dragons lore.Thesis (PhD) — Boston College, 2023.Submitted to: Boston College. Lynch School of Education.Discipline: Teacher Education, Special Education, Curriculum and Instruction
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