13 research outputs found

    Who owns educational theory? Big data, algorithms and the expert power of education data science

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    ‘Education data science’ is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University’s laboratory for research and development in learning analytics, and the Center for Digital Data, Analytics and Adaptive Learning, a big data research centre of the commercial education company Pearson. These institutions are becoming methodological gatekeepers with the capacity to conduct new forms of educational research using big data and algorithmic data science methods. The central argument is that as educational data science has migrated from the academic lab to the commercial sector, ownership of the means to produce educational data analyses has become concentrated in the activities of for-profit companies. As a consequence, new theories of learning are being built-in to the tools they provide, in the shape of algorithm-driven technologies of personalization, which can be sold to schools and universities. The paper addresses two themes of this special issue: (1) how education is to be theorized in relation to algorithmic methods and data scientific epistemologies and (2) how the political economy of education is shifting as knowledge production becomes concentrated in data-driven commercial organizations

    HSQC‐NMR‐based profiling approaches for raffinose family oligosaccharides in pulses

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    Background and Objectives Due to insufficient resolution, 1^{1}H nuclear magnetic resonance (NMR) spectroscopy-based methods are limited to quantify carbohydrates. In the past, heteronuclear single quantum coherence (HSQC)-based methods were demonstrated to be superior as the second dimension greatly improves resolution. However, whether these experiments are also suitable to determine structurally similar oligosaccharides such as raffinose family oligosaccharides (RFO) still needs to be demonstrated. Findings By optimizing NMR parameters, well resolved signals for the analysis of glucose, fructose, galactose, sucrose and the RFO raffinose, stachyose, and verbascose were identified. Application of fast HSQC methods in combination with nonuniform sampling enables analyses of sucrose and RFO in pulses (blue lupin seeds, red lentils, kidney beans) within 24 min. If the analytes are present at levels greater than 0.5 g/100 g, HSQC-based methods provide data equivalent to an anion-exchange chromatography-based reference method. Conclusions High resolution fast HSQC-based approaches are suitable tools to analyze complex carbohydrate mixtures as demonstrated for RFO in different pulses. Significance and Novelty Fast HSQC experiments were applied for the first time to analyze structurally similar oligosaccharides. In the future, this approach will be a most valuable tool to analyze complex mixtures of carbohydrates in food products

    Metric Embedding via Shortest Path Decompositions

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    We study the problem of embedding shortest-path metrics of weighted graphs into p\ell_p spaces. We introduce a new embedding technique based on low-depth decompositions of a graph via shortest paths. The notion of Shortest Path Decomposition depth is inductively defined: A (weighed) path graph has shortest path decomposition (SPD) depth 11. General graph has an SPD of depth kk if it contains a shortest path whose deletion leads to a graph, each of whose components has SPD depth at most k1k-1. In this paper we give an O(kmin{1p,12})O(k^{\min\{\frac{1}{p},\frac{1}{2}\}})-distortion embedding for graphs of SPD depth at most kk. This result is asymptotically tight for any fixed p>1p>1, while for p=1p=1 it is tight up to second order terms. As a corollary of this result, we show that graphs having pathwidth kk embed into p\ell_p with distortion O(kmin{1p,12})O(k^{\min\{\frac{1}{p},\frac{1}{2}\}}). For p=1p=1, this improves over the best previous bound of Lee and Sidiropoulos that was exponential in kk; moreover, for other values of pp it gives the first embeddings whose distortion is independent of the graph size nn. Furthermore, we use the fact that planar graphs have SPD depth O(logn)O(\log n) to give a new proof that any planar graph embeds into 1\ell_1 with distortion O(logn)O(\sqrt{\log n}). Our approach also gives new results for graphs with bounded treewidth, and for graphs excluding a fixed minor

    Linear multivariable control : numerical considerations

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    Bibliography: p. 31-32.Grant ERDA-E(49-18)-2087.by Alan J. Laub

    Beyond panoptic surveillance: On the ethical dilemmas of the connected workplace

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    Technological advances such as the Internet-of-Things, big data, and artificial intelligence have enabled new ways of managerial oversight moving away from panoptic surveillance to what we call “connected surveillance”. The COVID-19 pandemic has accelerated the adoption of connected surveillance, which purpose is not only scrutinizing employees’ work performance, but also health, personal beliefs, and other private matters. With the implementation of connected workplaces, therefore, various ethical dilemmas arise. We highlight four emerging dilemmas, namely: (1) the good of the individual versus the good of the community, (2) ownership versus information disclosure, (3) justice versus mercy, and (4) truth versus loyalty. We discuss those ethical dilemmas for the case of corporate wellness programs which is frequently being used as guise to introduce connected surveillance. Following a socio-technical perspective, we discuss ethical responses that focus on people involvement and technology assessment. We highlight practical responses that can aim at mitigating the dilemmas

    Beyond Panoptic Surveillance: On the Ethical Dilemmas of the Connected Workplace

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    Technological advances such as the Internet-of-Things, big data, and artificial intelligence have enabled new ways of managerial oversight moving away from panoptic surveillance to what we call “connected surveillance”. The COVID-19 pandemic has accelerated the adoption of connected surveillance which purpose is not only scruitizing employees’ work performance but also health, personal beliefs, and other private matters. With the implementation of connected workplaces, therefore, various ethical dilemmas arise. We highlight four emerging dilemmas, namely: (1) the good of the individual versus the good of the community, (2) ownership versus information disclosure, (3) justice versus mercy, and (4) truth versus loyalty. We discuss those ethical dilemmas for the case of corporate wellness programs which is frequently used as guise to introduce connected surveillance. Following a socio-technical perspective, we discuss ethical responses that focus on people involvement and technology assessment. We also highlight practical responses that can aim at mitigating the dilemmas

    Benchmarking and performance analysis of the CM-2

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    A suite of benchmarking routines testing communication, basic arithmetic operations, and selected kernel algorithms written in LISP and PARIS was developed for the CM-2. Experiment runs are automated via a software framework that sequences individual tests, allowing for unattended overnight operation. Multiple measurements are made and treated statistically to generate well-characterized results from the noisy values given by cm:time. The results obtained provide a comparison with similar, but less extensive, testing done on a CM-1. Tests were chosen to aid the algorithmist in constructing fast, efficient, and correct code on the CM-2, as well as gain insight into what performance criteria are needed when evaluating parallel processing machines

    The air shower simulation framework CORSIKA 8: Development and first applications to muon production

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    Tools to accurately simulate extensive air showers are a key asset for the understanding of ultra-high energy cosmic rays. In this thesis, the Monte Carlo air shower simulation framework CORSIKA 8 is presented. CORSIKA 8 constitutes a next-generation code that aims to combine new functionality with a high level of flexibility and modularity. Notable aspects include the ability to freely combine an arbitrary number of physical processes and to setup simulation environments consisting of several media, including custom atmospheric models. A special feature is the possibility to inspect the complete lineage of particles, which allows linking particles on ground with any of their preceding generations. After describing the foundations of Monte Carlo shower simulations, I explain the architecture of CORSIKA 8 in depth. Focusing on the hadronic and muonic shower components, results obtained with CORSIKA 8 and other simulation codes are compared with each other. Even when using the same hadronic interaction models, a number of differences are observed, in particular regarding low-energy interactions, which have a considerable impact on the lateral distribution of muons at kilometre-scale distances up to a factor of two and more. Making use of the lineage technique, I study the phase space of hadronic interactions in order to quantify the importance for muon production and compare the results with the Heitler–Matthews toy model. At high energies (√s ≳ 500 GeV) particle production in the forward region is confirmed to be especially important, while the central region becomes relevant at low energies (√s ≲ 50 GeV) in particular for muons at large distances. Additionally, I study the impact of modified hadronic interactions on air shower observables. Modified hadron-air cross-sections mainly affect the longitudinal development, causing a larger shift of the maximum muon production depth than of the shower maximum. Artificially increased ρ 0 production, on the other hand, can greatly increase the number of muons with only small impact on other observables. Finally, I also consider the possibility of large multiplicity boson production in the first interaction and study its phenomenology in air showers with a simple toy model. Within the scope of this thesis, I developed the foundations of the CORSIKA 8 framework. Based on the studies that have become possible with CORSIKA 8, I point out some new opportunities towards an improved understanding of muons in air showers

    Privacy, security and data protection in smart cities : a critical EU law perspective

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    "Smart cities" are a buzzword of the moment. Although legal interest is growing, most academic responses at least in the EU, are still from the technological, urban studies, environmental and sociological rather than legal, sectors and have primarily laid emphasis on the social, urban, policing and environmental benefits of smart cities, rather than their challenges, in often a rather uncritical fashion . However a growing backlash from the privacy and surveillance sectors warns of the potential threat to personal privacy posed by smart cities . A key issue is the lack of opportunity in an ambient or smart city environment for the giving of meaningful consent to processing of personal data; other crucial issues include the degree to which smart cities collect private data from inevitable public interactions, the "privatisation" of ownership of both infrastructure and data, the repurposing of “big data” drawn from IoT in smart cities and the storage of that data in the Cloud. This paper, drawing on author engagement with smart city development in Glasgow as well as the results of an international conference in the area curated by the author, argues that smart cities combine the three greatest current threats to personal privacy, with which regulation has so far failed to deal effectively; the Internet of Things(IoT) or "ubiquitous computing"; "Big Data" ; and the Cloud. It seeks solutions both from legal institutions such as data protection law and from "code", proposing in particular from the ethos of Privacy by Design, a new "social impact assessment" and new human:computer interactions to promote user autonomy in ambient environments

    The Democratization of Artificial Intelligence

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    After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms
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