LiDAR Domain Adaptation - Automotive 3D Scene Understanding

Umgebungswahrnehmung und Szeneverständnis spielen bei autonomen Fahrzeugen eine wesentliche Rolle. Ein Fahrzeug muss sich der Geometrie und Semantik seiner Umgebung bewusst sein, um das Verhalten anderer Verkehrsteilnehmer:innen vorherzusagen und sich selbst im fahrbaren Raum zu lokalisieren, um somit richtig zu navigieren. Heutzutage verwenden praktisch alle modernen Wahrnehmungssysteme für das automatisierte Fahren tiefe neuronale Netze. Um diese zu trainieren, werden enorme Datenmengen mit passenden Annotationen benötigt. Die Beschaffung der Daten ist relativ unaufwendig, da nur ein mit den richtigen Sensoren ausgestattetes Fahrzeug herumfahren muss. Die Erstellung von Annotationen ist jedoch ein sehr zeitaufwändiger und teurer Prozess. Erschwerend kommt hinzu, dass autonome Fahrzeuge praktisch überall (z.B. Europa und Asien, auf dem Land und in der Stadt) und zu jeder Zeit (z.B. Tag und Nacht, Sommer und Winter, Regen und Nebel) eingesetzt werden müssen. Dies erfordert, dass die Daten eine noch größere Anzahl unterschiedlicher Szenarien und Domänen abdecken. Es ist nicht praktikabel, Daten für eine solche Vielzahl von Domänen zu sammeln und zu annotieren. Wenn jedoch nur mit Daten aus einer Domäne trainiert wird, führt dies aufgrund von Unterschieden in den Daten zu einer schlechten Leistung in einer anderen Zieldomäne. Für eine sicherheitskritische Anwendung ist dies nicht akzeptabel. Das Gebiet der sogenannten Domänenanpassung führt Methoden ein, die helfen, diese Domänenlücken ohne die Verwendung von Annotationen aus der Zieldomäne zu schließen und somit auf die Entwicklung skalierbarer Wahrnehmungssysteme hinzuarbeiten. Die Mehrzahl der Arbeiten zur Domänenanpassung konzentriert sich auf die zweidimensionale Kamerawahrnehmung. In autonomen Fahrzeugen ist jedoch das dreidimensionale Verständnis der Szene essentiell, wofür heutzutage häufig LiDAR-Sensoren verwendet werden. Diese Dissertation befasst sich mit der Domänenanpassung für LiDAR-Wahrnehmung unter mehreren Aspekten. Zunächst wird eine Reihe von Techniken vorgestellt, die die Leistung und die Laufzeit von semantischen Segmentierungssystemen verbessern. Die gewonnenen Erkenntnisse werden in das Wahrnehmungsmodell integriert, das in dieser Dissertation verwendet wird, um die Wirksamkeit der vorgeschlagenen Domänenanpassungsansätze zu bewerten. Zweitens werden bestehende Ansätze diskutiert und Forschungslücken durch die Formulierung von offenen Forschungsfragen aufgezeigt. Um einige dieser Fragen zu beantworten, wird in dieser Dissertation eine neuartige quantitative Metrik vorgestellt. Diese Metrik erlaubt es, den Realismus von LiDAR-Daten abzuschätzen, der für die Leistung eines Wahrnehmungssystems entscheidend ist. So wird die Metrik zur Bewertung der Qualität von LiDAR-Punktwolken verwendet, die zum Zweck des Domänenmappings erzeugt werden, bei dem Daten von einer Domäne in eine anderen übertragen werden. Dies ermöglicht die Wiederverwendung von Annotationen aus einer Quelldomäne in der Zieldomäne. In einem weiteren Feld der Domänenanpassung wird in dieser Dissertation eine neuartige Methode vorgeschlagen, die die Geometrie der Szene nutzt, um domäneninvariante Merkmale zu lernen. Die geometrischen Informationen helfen dabei, die Domänenanpassungsfähigkeiten des Segmentierungsmodells zu verbessern und ohne zusätzlichen Mehraufwand bei der Inferenz die beste Leistung zu erzielen. Schließlich wird eine neuartige Methode zur Erzeugung semantisch sinnvoller Objektformen aus kontinuierlichen Beschreibungen vorgeschlagen, die – mit zusätzlicher Arbeit – zur Erweiterung von Szenen verwendet werden kann, um die Erkennungsfähigkeiten der Modelle zu verbessern. Zusammenfassend stellt diese Dissertation ein umfassendes System für die Domänenanpassung und semantische Segmentierung von LiDAR-Punktwolken im Kontext des autonomen Fahrens vor

Drones, Signals, and the Techno-Colonisation of Landscape

This research project is a cross-disciplinary, creative practice-led investigation that interrogates increasing military interest in the electromagnetic spectrum (EMS). The project’s central argument is that painted visualisations of normally invisible aspects of contemporary EMS-enabled warfare can reveal useful, novel, and speculative but informed perspectives that contribute to debates about war and technology. It pays particular attention to how visualising normally invisible signals reveals an insidious techno-colonisation of our extended environment from Earth to orbiting satellites

How We Use Stories and Why That Matters

How We Use Stories and Why That Matters guides the reader through the tangled undergrowth of communication and cultural expression towards a new understanding of the role of group-mediating stories at global and digital scale. It argues that media and networked systems perform and bind group identities, creating bordered fictions within which economic and political activities are made meaningful. Now that computational and global scale, big data, metadata and algorithms rule the roost even in culture, subjectivity and meaning, we need population-scale frameworks to understand individual, micro-scale sense-making practices. To achieve that, we need evolutionary and systems approaches to understand cultural performance and dynamics. The opposing universes of fact (science, knowledge, education) and fiction (entertainment, story and imagination) – so long separated into the contrasting disciplines of natural sciences and the humanities – can now be understood as part of one turbulent sphere of knowledge-production and innovation. Using striking examples and compelling analysis, the book shows what the New York Shakespeare Riots tell us about class struggle, what Death Cab for Cutie tells us about media, what Kate Moss’s wedding dress tells us about authorship, and how Westworld and Humans imagine very different futures for Artificial Intelligence: one based on slavery, the other on class. Together, these knowledge stories tell us about how intimate human communication is organised and used to stage organised conflict, to test the ‘fighting fitness’ of contending groups – provoking new stories, identities and classes along the way

How We Use Stories and Why That Matters

How We Use Stories and Why That Matters guides the reader through the tangled undergrowth of communication and cultural expression towards a new understanding of the role of group-mediating stories at global and digital scale. It argues that media and networked systems perform and bind group identities, creating bordered fictions within which economic and political activities are made meaningful. Now that computational and global scale, big data, metadata and algorithms rule the roost even in culture, subjectivity and meaning, we need population-scale frameworks to understand individual, micro-scale sense-making practices. To achieve that, we need evolutionary and systems approaches to understand cultural performance and dynamics. The opposing universes of fact (science, knowledge, education) and fiction (entertainment, story and imagination) – so long separated into the contrasting disciplines of natural sciences and the humanities – can now be understood as part of one turbulent sphere of knowledge-production and innovation. Using striking examples and compelling analysis, the book shows what the New York Shakespeare Riots tell us about class struggle, what Death Cab for Cutie tells us about media, what Kate Moss’s wedding dress tells us about authorship, and how Westworld and Humans imagine very different futures for Artificial Intelligence: one based on slavery, the other on class. Together, these knowledge stories tell us about how intimate human communication is organised and used to stage organised conflict, to test the ‘fighting fitness’ of contending groups – provoking new stories, identities and classes along the way

Cryptography Based on Correlated Data: Foundations and Practice

Correlated data can be very useful in cryptography. For instance, if a uniformly random key is available to Alice and Bob, it can be used as an one-time pad to transmit a message with perfect security. With more elaborate forms of correlated data, the parties can achieve even more complex cryptographic tasks, such as secure multiparty computation. This thesis explores (from both a theoretical and a practical point of view) the topic of cryptography based on correlated data

Unclonability and quantum cryptanalysis: from foundations to applications

The impossibility of creating perfect identical copies of unknown quantum systems is a fundamental concept in quantum theory and one of the main non-classical properties of quantum information. This limitation imposed by quantum mechanics, famously known as the no-cloning theorem, has played a central role in quantum cryptography as a key component in the security of quantum protocols. In this thesis, we look at \emph{Unclonability} in a broader context in physics and computer science and more specifically through the lens of cryptography, learnability and hardware assumptions. We introduce new notions of unclonability in the quantum world, namely \emph{quantum physical unclonability}, and study the relationship with cryptographic properties and assumptions such as unforgeability, randomness and pseudorandomness. The purpose of this study is to bring new insights into the field of quantum cryptanalysis and into the notion of unclonability itself. We also discuss applications of this new type of unclonability as a cryptographic resource for designing provably secure quantum protocols. First, we study the unclonability of quantum processes and unitaries in relation to their learnability and unpredictability. The instinctive idea of unpredictability from a cryptographic perspective is formally captured by the notion of \emph{unforgeability}. Intuitively, unforgeability means that an adversary should not be able to produce the output of an \emp{unknown} function or process from a limited number of input-output samples of it. Even though this notion is almost easily formalized in classical cryptography, translating it to the quantum world against a quantum adversary has been proven challenging. One of our contributions is to define a new unified framework to analyse the unforgeability property for both classical and quantum schemes in the quantum setting. This new framework is designed in such a way that can be readily related to the novel notions of unclonability that we will define in the following chapters. Another question that we try to address here is "What is the fundamental property that leads to unclonability?" In attempting to answer this question, we dig into the relationship between unforgeability and learnability, which motivates us to repurpose some learning tools as a new cryptanalysis toolkit. We introduce a new class of quantum attacks based on the concept of `emulation' and learning algorithms, breaking new ground for more sophisticated and complicated algorithms for quantum cryptanalysis. Second, we formally represent, for the first time, the notion of physical unclonability in the quantum world by introducing \emph{Quantum Physical Unclonable Functions (qPUF)} as the quantum analogue of Physical Unclonable Functions (PUF). PUF is a hardware assumption introduced previously in the literature of hardware security, as physical devices with unique behaviour, due to manufacturing imperfections and natural uncontrollable disturbances that make them essentially hard to reproduce. We deliver the mathematical model for qPUFs, and we formally study their main desired cryptographic property, namely unforgeability, using our previously defined unforgeability framework. In light of these new techniques, we show several possibility and impossibility results regarding the unforgeability of qPUFs. We will also discuss how the quantum version of physical unclonability relates to randomness and unknownness in the quantum world, exploring further the extended notion of unclonability. Third, we dive deeper into the connection between physical unclonability and related hardware assumptions with quantum pseudorandomness. Like unclonability in quantum information, pseudorandomness is also a fundamental concept in cryptography and complexity. We uncover a deep connection between Pseudorandom Unitaries (PRU) and quantum physical unclonable functions by proving that both qPUFs and the PRU can be constructed from each other. We also provide a novel route towards realising quantum pseudorandomness, distinct from computational assumptions. Next, we propose new applications of unclonability in quantum communication, using the notion of physical unclonability as a new resource to achieve provably secure quantum protocols against quantum adversaries. We propose several protocols for mutual entity identification in a client-server or quantum network setting. Authentication and identification are building-block tasks for quantum networks, and our protocols can provide new resource-efficient applications for quantum communications. The proposed protocols use different quantum and hybrid (quantum-classical) PUF constructions and quantum resources, which we compare and attempt in reducing, as much as possible throughout the various works we present. Specifically, our hybrid construction can provide quantum security using limited quantum communication resources that cause our protocols to be implementable and practical in the near term. Finally, we present a new practical cryptanalysis technique concerning the problem of approximate cloning of quantum states. We propose variational quantum cloning (\VQC), a quantum machine learning-based cryptanalysis algorithm which allows an adversary to obtain optimal (approximate) cloning strategies with short depth quantum circuits, trained using the hybrid classical-quantum technique. This approach enables the end-to-end discovery of hardware efficient quantum circuits to clone specific families of quantum states, which has applications in the foundations and cryptography. In particular, we use a cloning-based attack on two quantum coin-flipping protocols and show that our algorithm can improve near term attacks on these protocols, using approximate quantum cloning as a resource. Throughout this work, we demonstrate how the power of quantum learning tools as attacks on one hand, and the power of quantum unclonability as a security resource, on the other hand, fight against each other to break and ensure security in the near term quantum era

Using Data Mining for Facilitating User Contributions in the Social Semantic Web

This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems

Volume II Acquisition Research Creating Synergy for Informed Change, Thursday 19th Annual Acquisition Research Proceedings

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