575 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    On the Significance of Distance in Machine Learning

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    Avstandsbegrepet er grunnleggende i maskinlæring. Hvordan vi velger å måle avstand har betydning, men det er ofte utfordrende å finne et passende avstandsmål. Metrisk læring kan brukes til å lære funksjoner som implementerer avstand eller avstandslignende mål. Vanlige dyplæringsmodeller er sårbare for modifikasjoner av input som har til hensikt å lure modellen (adversarial examples, motstridende eksempler). Konstruksjon av modeller som er robuste mot denne typen angrep er av stor betydning for å kunne utnytte maskinlæringsmodeller i større skala, og et passende avstandsmål kan brukes til å studere slik motstandsdyktighet. Ofte eksisterer det hierarkiske relasjoner blant klasser, og disse relasjonene kan da representeres av den hierarkiske avstanden til klasser. I klassifiseringsproblemer som må ta i betraktning disse klasserelasjonene, kan hierarkiinformert klassifisering brukes. Jeg har utviklet en metode kalt /distance-ratio/-basert (DR) metrisk læring. I motsetning til den formuleringen som normalt anvendes har DR-formuleringen to gunstige egenskaper. For det første er det skala-invariant med hensyn til rommet det projiseres til. For det andre har optimale klassekonfidensverdier på klasserepresentantene. Dersom rommet for å konstruere modifikasjoner er tilstrekklig stort, vil man med standard adversarial accuracy (SAA, standard motstridende nøyaktighet) risikere at naturlige datapunkter blir betraktet som motstridende eksempler. Dette kan være en årsak til SAA ofte går på bekostning av nøyaktighet. For å løse dette problemet har jeg utviklet en ny definisjon på motstridende nøyaktighet kalt Voronoi-epsilon adversarial accuracy (VAA, Voronoi-epsilon motstridende nøyaktighet). VAA utvider studiet av lokal robusthet til global robusthet. Klassehierarkisk informasjon er ikke tilgjengelig for alle datasett. For å håndtere denne utfordringen har jeg undersøkt om klassifikasjonsbaserte metriske læringsmodeller kan brukes til å utlede klassehierarkiet. Videre har jeg undersøkt de mulige effektene av robusthet på feature space (egenskapsrom). Jeg fant da at avstandsstrukturen til et egenskapsrom trent for robusthet har større likhet med avstandsstrukturen i rådata enn et egenskapsrom trent uten robusthet.The notion of distance is fundamental in machine learning. The choice of distance matters, but it is often challenging to find an appropriate distance. Metric learning can be used for learning distance(-like) functions. Common deep learning models are vulnerable to the adversarial modification of inputs. Devising adversarially robust models is of immense importance for the wide deployment of machine learning models, and distance can be used for the study of adversarial robustness. Often, hierarchical relationships exist among classes, and these relationships can be represented by the hierarchical distance of classes. For classification problems that must take these class relationships into account, hierarchy-informed classification can be used. I propose distance-ratio-based (DR) formulation for metric learning. In contrast to the commonly used formulation, DR formulation has two favorable properties. First, it is invariant of the scale of an embedding. Secondly, it has optimal class confidence values on class representatives. For a large perturbation budget, standard adversarial accuracy (SAA) allows natural data points to be considered as adversarial examples. This could be a reason for the tradeoff between accuracy and SAA. To resolve the issue, I proposed a new definition of adversarial accuracy named Voronoi-epsilon adversarial accuracy (VAA). VAA extends the study of local robustness to global robustness. Class hierarchical information is not available for all datasets. To handle this challenge, I investigated whether classification-based metric learning models can be used to infer class hierarchy. Furthermore, I explored the possible effects of adversarial robustness on feature space. I found that the distance structure of robustly trained feature space resembles that of input space to a greater extent than does standard trained feature space.Doktorgradsavhandlin

    HM 32: New Interpretations in Naval History

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    Selected papers from the twenty-first McMullen Naval History Symposium held at the U.S. Naval Academy, 19–20 September 2019.https://digital-commons.usnwc.edu/usnwc-historical-monographs/1031/thumbnail.jp

    Subversive Semantics in Political and Cultural Discourse: The Production of Popular Knowledge

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    The large-scale use of semantic transfer and inversion as rhetorical tactics is particularly prevalent in right-wing discourses and populist "alternative knowledge" production. The contributors to this volume analyze processes of re-semanticizing received meanings, effectually re-coding those meanings. They investigate to what extent rhetorical maneuvers serve to establish new and powerful belief systems beyond rational and democratic control. In addition to the contemporary rightwing and conspiracy narratives, the contributions examine the discursive fields around conceptions of human nature and the deep past, population politics, gender conceptions, use of land, identity politics, nationhood, and cultural heritage

    The Freedom of Influencing

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    Social media stars and the Federal Trade Commission (“FTC”) Act are clashing. Influencer marketing is a preferred way for entertainers, pundits, and everyday people to monetize their audiences and popularity. Manufacturers, service providers, retailers, and advertising agencies leverage influencers to reach into millions or even billions of consumer devices, capturing minutes or seconds of the market’s fleeting attention. FTC enforcement actions and private lawsuits have targeted influencers for failing to disclose the nature of a sponsorship relationship with a manufacturer, marketer, or service provider. Such a failure to disclose payments prominently is very common in Hollywood films and on radio and television, however. The Code of Federal Regulations, FTC notices, and press releases contain exemptions tailored to such legacy media. This Article addresses whether the disparate treatment of social media influencers and certain legacy media formats may amount to a content-based regulation of speech that violates the freedom of speech. Drawing on intellectual property law, consumer law, and securities law precedents, it argues that the more intense focus on disclosures by social media influencers infringes the freedom of influencing. It is irrational and discriminatory to impose greater obligations on influencers who are paid to mention or use products or services than on legacy media formats whose actors or directors mention or use similar products or services

    Executive Pay Clawbacks and Their Taxation

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    Executive pay clawback provisions require executives to repay previ¬ously received compensation under certain circumstances, such as a downward adjustment to the financial results upon which their incen¬tive pay was predicated. The use of these provisions is on the rise, and the SEC is expected to soon finalize rules implementing a mandatory, no-fault clawback requirement enacted as part of the Dodd-Frank leg¬islation. The tax issue raised by clawbacks is this: should executives be allowed to recover taxes previously paid on compensation that is returned to the company as a result of a clawback provision? This Arti¬cle argues that a full tax offset regime is most in keeping with the evolving rationales for clawbacks, with consistent treatment of execu¬tives subject to clawbacks, with encouraging even-handed implemen¬tation of clawbacks, and with minimizing clawback-induced distortions and other unintended consequences associated with a tax regime that would not provide full offsets. But the tax treatment of clawback pay-ments has been uncertain, and the enactment of the Tax Cuts and Jobs Act adds to that uncertainty. Meanwhile, adoption of legislation to ensure that executives are fully compensated for taxes previously paid on recouped compensation is probably a political non-starter. Given that, this Article argues that the IRS and courts should interpret the relevant tax laws liberally to maximize recovery of taxes paid on clawed back compensation

    Evaluating Adversarial Robustness of Detection-based Defenses against Adversarial Examples

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    Machine Learning algorithms provide astonishing performance in a wide range of tasks, including sensitive and critical applications. On the other hand, it has been shown that they are vulnerable to adversarial attacks, a set of techniques that violate the integrity, confidentiality, or availability of such systems. In particular, one of the most studied phenomena concerns adversarial examples, i.e., input samples that are carefully manipulated to alter the model output. In the last decade, the research community put a strong effort into this field, proposing new evasion attacks and methods to defend against them. With this thesis, we propose different approaches that can be applied to Deep Neural Networks to detect and reject adversarial examples that present an anomalous distribution with respect to training data. The first leverages the domain knowledge of the relationships among the considered classes integrated through a framework in which first-order logic knowledge is converted into constraints and injected into a semi-supervised learning problem. Within this setting, the classifier is able to reject samples that violate the domain knowledge constraints. This approach can be applied in both single and multi-label classification settings. The second one is based on a Deep Neural Rejection (DNR) mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers. To this end, we exploit RBF SVM classifiers, which provide decreasing confidence values as samples move away from the training data distribution. Despite technical differences, this approach shares a common backbone structure with other proposed methods that we formalize in a unifying framework. As all of them require comparing input samples against an oversized number of reference prototypes, possibly at different representation layers, they suffer from the same drawback, i.e., high computational overhead and memory usage, that makes these approaches unusable in real applications. To overcome this limitation, we introduce FADER (Fast Adversarial Example Rejection), a technique for speeding up detection-based methods by employing RBF networks as detectors: by fixing the number of required prototypes, their runtime complexity can be controlled. All proposed methods are evaluated in both black-box and white-box settings, i.e., against an attacker unaware of the defense mechanism, and against an attacker who knows the defense and adapts the attack algorithm to bypass it, respectively. Our experimental evaluation shows that the proposed methods increase the robustness of the defended models and help detect adversarial examples effectively, especially when the attacker does not know the underlying detection system

    The Relationship between Christianity and Slavery: An Examination of the Defense of Slavery within Christian Thought, Practices and Methodologies from 1619-1865

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    Slavery in the Unitred States was supported by individual Christians who skillfully mastered how to manipulate the Bible as justification for enslaving Africans and their descendates. Therefore, the examination of the relationship between Christianity and slavery within the United States and the greater western civilizations-explores the impact of Christian institutions on African Americans-investages the influence of Christianis relationship with slavery on all the descendants of enslaved African culture as the plural societies within this relationship’s functions. So far, two perspectives have emerged in the study of the existence of such an connectiuon. The first, which may be termed as “Proslavery Christians” examines the stance in which many slaveholders and prominent defenders of slavery accepted slavery in the broadest sense of the term. Their experiences and outlooks may best be seen in their commitment to reducing the value of African Americans while holding Christian morals. Therefore, their rejection of Africa and its people as significant offers justification for their desire to utilize Christianity to support their treatment of enslaved Africans and their descendants. Additionally, their willingness to justify the cruelty of the peculiar institution of slavery has defined the experiences of Africans whether enslaved or free. The second school of thought, a term just as broadly, “Antislavery Christians” sees enslaved Africans and their descendants as valuable believers in the faith that has endured one of the coldest hand of bondage and have been able to fashion themselves into a culture of believers nonetheless

    Evolving Bitcoin Custody

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    The broad topic of this thesis is the design and analysis of Bitcoin custody systems. Both the technology and threat landscape are evolving constantly. Therefore, custody systems, defence strategies, and risk models should be adaptive too. We introduce Bitcoin custody by describing the different types, design principles, phases and functions of custody systems. We review the technology stack of these systems and focus on the fundamentals; key-management and privacy. We present a perspective we call the systems view. It is an attempt to capture the full complexity of a custody system, including technology, people, and processes. We review existing custody systems and standards. We explore Bitcoin covenants. This is a mechanism to enforce constraints on transaction sequences. Although previous work has proposed how to construct and apply Bitcoin covenants, these require modifying the consensus rules of Bitcoin, a notoriously difficult task. We introduce the first detailed exposition and security analysis of a deleted-key covenant protocol, which is compatible with current consensus rules. We demonstrate a range of security models for deleted-key covenants which seem practical, in particular, when applied in autonomous (user-controlled) custody systems. We conclude with a comparative analysis with previous proposals. Covenants are often proclaimed to be an important primitive for custody systems, but no complete design has been proposed to validate that claim. To address this, we propose an autonomous custody system called Ajolote which uses deleted-key covenants to enforce a vault sequence. We evaluate Ajolote with; a model of its state dynamics, a privacy analysis, and a risk model. We propose a threat model for custody systems which captures a realistic attacker for a system with offline devices and user-verification. We perform ceremony analysis to construct the risk model.Comment: PhD thesi
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