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Salome Viljoen - One of the best experts on this subject based on the ideXlab platform.
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democratic Data a relational Theory for Data governance
2020Co-Authors: Salome ViljoenAbstract:Data governance law — the law regulating how Data about people is collected, processed, and used — is the subject of lively theorizing. Concerns over Datafication (the transformation of information or knowledge about people into a commodity) and its harmful personal and social effects have produced an abundance of proposals for reform. Different theories advance different legal interests in information, resulting in various individualist claims and remedies. Some seek to reassert individual control for Data subjects over the terms of their Datafication, while others aim to maximize Data subject financial gain. But these proposals share a common conceptual flaw: they miss the central importance of population-level relations among individuals for how Data collection produces both social value and social harm. The Data collection practices of the most powerful technology companies are primarily aimed at deriving population-level insights from Data subjects for population-level applicability, not individual-level insights specific to the Data subject in question. Put simply, the point of Data production is to put people into population-based relations with one another; this activity drives Data collection practices in the digital economy and results in some of the most pressing forms of social informational harm. Individualist Data subject rights cannot represent, let alone address, these population-level effects. Treating Data’s population-level effects as central to the task of Data governance opens up new terrain. The proper aim of Data governance is not to reassert individual control over the terms of one’s own Datafication or to maximize personal gain, but instead to develop the institutional responses necessary to represent the relevant population-level interests at stake in Data production. This shifts the task of reform from granting individuals rights to exit or payment, to securing recognition and standing to shape the purposes and conditions of Data production for those with interests at stake in such choices. From this reorientation, Data governance law may develop legal reforms capable of responding to the harms of Datafication without foreclosing socially beneficial forms of Data production. Part One describes the stakes and the status quo of Data governance. It documents the significance of Data processing for the digital economy. It then evaluates how the predominant legal regimes that govern Data collection and use — contract and privacy law — code Data as an individual medium. This conceptualization is referred to throughout the Article as “Data as individual medium” (DIM). DIM regimes apprehend Data’s capacity to cause individual harm as the legally relevant feature of Datafication; from this Theory of harm follows the tendency of DIM regimes to subject Data to private individual ordering. Part Two presents the core argument of the Article regarding the incentives and implications of Data social relations within the Data political economy. Data’s capacity to transmit social and relational meaning renders Data production especially capable of benefitting and harming others beyond the Data subject from whom Data is collected. It also results in population-level interests in Data production that are not reducible to the individual interests that generally feature in Data governance. Thus, Data’s relationality presents both a conceptual challenge for Data governance reform. Part Three evaluates two prominent legal reform proposals that have emerged in response to concerns over Datafication. Propertarian proposals respond to growing wealth inequality in the Data economy by formalizing individual propertarian rights over Data as a personal asset. Dignitarian reforms respond to how excessive Data extraction can erode individual autonomy by granting fundamental rights protections to Data as an extension of personal selfhood. While propertarian and dignitarian proposals differ on the theories of injustice underlying Datafication and accordingly provide different solutions, both resolve to individualist claims and remedies that do not represent, let alone address, the relational nature of Data collection and use. Part Four proposes an alternative approach: Data as a democratic medium (DDM). This alternative conceptual approach apprehends Data’s capacity to cause social harm as a fundamentally relevant feature of Datafication; from this follows a commitment to collective institutional forms of ordering. Conceiving of Data as a public resource subject to democratic ordering accounts for the importance of population-based relationality in the digital economy. This recognizes a greater number of relevant interests in Data production and recasts the subject of legal concern from interpersonal violation to the condition of population-level Data relations under which Data is produced and used. DDM therefore responds not only to salient forms of injustice identified by other Data governance reforms, but also to significant forms of injustice missed by individualist accounts. In doing so, DDM also provides a Theory of Data governance from which to defend forms of socially beneficial Data production that individualist accounts may foreclose. Part Four concludes by outlining some examples of what regimes that conceive of Data as democratic could look like in practice.
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democratic Data a relational Theory for Data governance
2020Co-Authors: Salome ViljoenAbstract:Data governance law—the legal regime that regulates how Data about people is collected, processed, and used—is a subject of lively theorizing and several proposed legislative reforms. Different theories advance different legal interests in information. Some seek to reassert individual control for Data subjects over the terms of their Datafication, while others aim to maximize Data subject financial gain. But these proposals share a common conceptual flaw. Put simply, they miss the point of Data production in a digital economy: to put people into population-based relations with one another. This relational aspect of Data production drives much of the social value as well as the social harm of Data production and use in a digital economy. In response, this Article advances a theoretical account of Data as social relations, constituted by both legal and technical systems. It shows how Data relations result in supra-individual legal interests, and properly representing and adjudicating among these interests necessitates far more public and collective (i.e., democratic) forms of governing Data production. This theoretical account offers two notable insights for Data governance law. First, this account better reflects the realities of how and why Data production produces economic value as well as social harm in a digital economy. The Data collection practices of the most powerful technology companies are primarily aimed at deriving population-level insights from Data subjects for population-level applicability, not individual-level insights specific to a Data subject. The value derived from this activity drives Data collection in the digital economy and results in some of the most pressing forms of social informational harm. Individualist Data subject rights cannot represent, let alone address, these population-level effects. Second, this account offers an alternative (and it argues, more precise) normative argument for what makes Datafication—the transformation of information about people into a commodity—wrongful. What makes Datafication wrong is not (only) that it erodes the capacity for subject self-formation, but also that it materializes unjust social relations: Data relations that enact or amplify social inequality. This egalitarian normative account indexes many of the most pressing forms of social informational harm that animate criticism of Data extraction yet fall outside typical accounts of informational harm. This account also offers a positive Theory for socially beneficial Data production. To address the inegalitarian harms of Datafication—and develop socially beneficial alternatives—will require democratizing Data social relations: moving from individual Data subject rights, to more democratic institutions of Data governance. Part One describes the stakes and the status quo of Data governance. It documents the significance of Data processing for the digital economy. It then evaluates how the predominant legal regimes that govern Data collection and use — contract and privacy law — code Data as an individual medium. This conceptualization is referred to throughout the Article as “Data as individual medium” (DIM). DIM regimes apprehend Data’s capacity to cause individual harm as the legally relevant feature of Datafication; from this Theory of harm follows the tendency of DIM regimes to subject Data to private individual ordering. Part Two presents the core argument of the Article regarding the incentives and implications of Data social relations within the Data political economy. Data’s capacity to transmit social and relational meaning renders Data production especially capable of benefitting and harming others beyond the Data subject from whom Data is collected. It also results in population-level interests in Data production that are not reducible to the individual interests that generally feature in Data governance. Part Three evaluates two prominent legal reform proposals that have emerged in response to concerns over Datafication. Propertarian proposals respond to growing wealth inequality in the Data economy by formalizing individual propertarian rights over Data as a personal asset. Dignitarian reforms respond to how excessive Data extraction can erode individual autonomy by granting fundamental rights protections to Data as an extension of personal selfhood. While propertarian and dignitarian proposals differ on the theories of injustice underlying Datafication (and therefore provide different solutions), both resolve to individualist claims and remedies that do not represent, let alone address, the relational nature of Data collection and use. Part Four proposes an alternative approach: Data as a democratic medium (DDM). This alternative conceptual approach apprehends Data’s capacity to cause social harm as a fundamentally relevant feature of Datafication; from this follows a commitment to collective institutional forms of governing Data. Conceiving of Data as a collective or public resource subject to democratic ordering accounts for the importance of population-based relationality in the digital economy. This recognizes a greater number of relevant interests in Data production and recasts the subject of legal concern from interpersonal violation to the condition of population-level Data relations under which Data is produced and used. DDM therefore responds not only to salient forms of injustice identified by other Data governance reforms, but also to significant forms of injustice missed by individualist accounts. In doing so, DDM also provides a Theory of Data governance from which to defend forms of socially beneficial Data production that individualist accounts may foreclose. Part Four concludes by outlining some examples of what regimes that conceive of Data as democratic could look like in practice.
Syeda Tayyba Tehrim - One of the best experts on this subject based on the ideXlab platform.
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A robust extension of VIKOR method for bipolar fuzzy sets using connection numbers of SPA Theory based metric spaces
Artificial Intelligence Review, 2020Co-Authors: Muhammad Riaz, Syeda Tayyba TehrimAbstract:The purpose of this study is to introduce an innovative multi-attribute group decision making (MAGDM) based on bipolar fuzzy set (BFS) by unifying“ VIseKriterijumska Optimizacija I Kompromisno Rasenje (VIKOR)” method. The VIKOR method is considered to be a useful MAGDM method, specifically in conditions where an expert is unable to determine his choice correctly at the initiation of designing a system. The method of VIKOR is suitable for problems containing conflicting attributes, with an assumption that compromising is admissible for conflict decision, the expert wishes a solution very near to the best, and the different alternatives or choices are processed according to all developed attributes. The Theory of set pair analysis is a state-of-the-art uncertainty Theory which consists of three factors, including “identity degree”, “discrepancy degree”, and “contrary degree” of connection numbers (CNs) and coincidence with many existing theories dealing with vagueness in the given information. Consequently, inspired by this, in the present study, we make an effort to improve the Theory of Data measurement by introducing some metric spaces using CNs of BFSs. In this research paper, we extend VIKOR method in the context of CNs based metrics, which are obtained form bipolar fuzzy numbers (BFNs). Firstly, we develop CNs of BFNs as well as metric spaces based on CNs. We also discuss some interesting properties of proposed metric spaces. Secondly, we develop VIKOR method using CNs based metrics to handle an MAGDM problem under bipolar fuzzy type information. The predominance of proposed metric spaces is also studied by the means of examples. Furthermore, we demonstrate the efficiency of the extended VIKOR method by solving a numerical example, sensitivity analysis and a detailed comparison with some existing approaches.
Magnus O Myreen - One of the best experts on this subject based on the ideXlab platform.
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x86 tso a rigorous and usable programmer s model for x86 multiprocessors
Communications of The ACM, 2010Co-Authors: Peter Sewell, Susmit Sarkar, Scott Owens, Francesco Zappa Nardelli, Magnus O MyreenAbstract:Exploiting the multiprocessors that have recently become ubiquitous requires high-performance and reliable concurrent systems code, for concurrent Data structures, operating system kernels, synchronization libraries, compilers, and so on. However, concurrent programming, which is always challenging, is made much more so by two problems. First, real multiprocessors typically do not provide the sequentially consistent memory that is assumed by most work on semantics and verification. Instead, they have relaxed memory models, varying in subtle ways between processor families, in which different hardware threads may have only loosely consistent views of a shared memory. Second, the public vendor architectures, supposedly specifying what programmers can rely on, are often in ambiguous informal prose (a particularly poor medium for loose specifications), leading to widespread confusion. In this paper we focus on x86 processors. We review several recent Intel and AMD specifications, showing that all contain serious ambiguities, some are arguably too weak to program above, and some are simply unsound with respect to actual hardware. We present a new x86-TSO programmer's model that, to the best of our knowledge, suffers from none of these problems. It is mathematically precise (rigorously defined in HOL4) but can be presented as an intuitive abstract machine which should be widely accessible to working programmers. We illustrate how this can be used to reason about the correctness of a Linux spinlock implementation and describe a general Theory of Data-race freedom for x86-TSO. This should put x86 multiprocessor system building on a more solid foundation; it should also provide a basis for future work on verification of such systems.
Andrew Mcgregor - One of the best experts on this subject based on the ideXlab platform.
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space efficient estimation of robust statistics and distribution testing
International Conference on Supercomputing, 2010Co-Authors: Steve Chien, Katrina Ligett, Andrew McgregorAbstract:The generic problem of estimation and inference given a sequence of i.i.d. samples has been extensively studied in the statistics, property testing, and learning communities. A natural quantity of interest is the sample complexity of the particular learning or estimation problem being considered. While sample complexity is an important component of the computational efficiency of the task, it is also natural to consider the space complexity: do we need to store all the samples as they are drawn, or is it sufficient to use memory that is significantly sublinear in the sample complexity? Surprisingly, this aspect of the complexity of estimation has received significantly less attention in all but a few specific cases. While space-bounded, sequential computation is the purview of the field of Data-stream computation, almost all of the literature on the algorithmic Theory of Data-streams considers only "empirical problems", where the goal is to compute a function of the Data present in the stream rather than to infer something about the source of the stream. Our contributions are two-fold. First, we provide results connecting space efficiency to the estimation of robust statistics from a sequence of i.i.d. samples. Robust statistics are a particularly interesting class of statistics in our setting because, by definition, they are resilient to noise or errors in the sampled Data. We show that this property is enough to ensure that very space-efficient stream algorithms exist for their estimation. In contrast, the numerical value of a "non-robust" statistic can change dramatically with additional samples, and this limits the utility of any finite length sequence of samples. Second, we present a general result that captures a trade-off between sample and space complexity in the context of distributional property testing.
Goult, Benjamin T - One of the best experts on this subject based on the ideXlab platform.
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The Mechanical Basis of Memory – the MeshCODE Theory
'Frontiers Media SA', 2021Co-Authors: Goult, Benjamin TAbstract:One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix (ECM) adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying Theory of Data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for Data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer
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The Mechanical Basis of Memory - the MeshCODE Theory
'MDPI AG', 2020Co-Authors: Goult, Benjamin TAbstract:The MeshCODE framework outlined here represents a unifying Theory of Data storage in animals, providing read/write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins, that contain force-dependent switches, can store information persistently which can be written/updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each and every synapse creating a meshwork of switches that forms a code, a MeshCODE. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, propagating this coding throughout the brain and to the entire organism. Based on established biophysical principles, a mechanical basis for memory provides a physical location for Data storage in the brain. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format identifies an addressable read/write memory system supporting the view of the mind as an organic supercomputer
