699 research outputs found

    The Taxonomy of Telemedicine

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    The purpose of this article is to present a taxonomy for telemedicine. The field has markedly grown, with an increasing number of applications, a variety of technologies, and newly introduced terminology. A taxonomy would serve to bring conceptual clarity to this burgeoning set of alternatives to in-person healthcare delivery. The article starts with a brief discussion of the importance of taxonomy as an information management strategy to improve knowledge sharing, facilitate research and policy initiatives, and provide some guidance for the orderly development of telemedicine. We provide a conceptual context for the proliferation of related concepts, such as telehealth, e-health, and m-health, as well as a classification of the content of these concepts. Our main concern is to develop an explicit taxonomy of telemedicine and to demonstrate how it can be used to provide definitive information about the true effects of telemedicine in terms of cost, quality, and access. Taxonomy development and refinement is an iterative process. If this initial attempt at classification proves useful, subject matter experts could enhance the development and proliferation of telemedicine by testing, revising, and verifying this taxonomy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90498/1/tmj-2E2011-2E0103.pd

    Revisiting Maine’s lobster commons: rescaling political subjects

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    Calls for cross-scalar theoretical and methodological approaches are not new to commons scholarship. Such efforts might be hastened by channelling poststructuralist and critical theory perspectives through the geographic subfield of political ecology, including attention to political scales and subjects. Toward this end, this paper reconsiders Maine’s lobster fishery. This case has provided rich material for watershed commons scholarship, demonstrating the ability of social groups to conserve resources independent of government or markets, and it continues to offer new findings. Recent fieldwork shows that as lobster boat captains advance collective interests through state-supported co-management governance arrangements, concerns of crew and non-fishing community members may be marginalized. Regulatory exclusion prevents broader distribution of resource benefits at a time when employment alternatives are scarce. More pluralistic approaches to commons theory and its policy application have utility well beyond the lobster case

    Machine learning and its applications in reliability analysis systems

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    In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

    The 'Dark Continent' goes north: an exploration of intercultural theatre practice through Handspring and Sogolon Puppet Companies' production of Tall Horse

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    This essay explores the complexities of intercultural interaction, specifically in the context of globalization. These interactions involve not only contact with, but also negotiation of cultural representations. The debates about the processes involved in such encounters are complex and highlight tensions among aesthetics, ideology, the ethics of production, voice, and authorship. The essay begins by outlining some of the key debates and issues specifically for theatre; in particular, it looks at the tension between Brook’s transcultural approach to intercultural theatre and Rustom Bharucha’s insistence on contextualized and historicized interactions. These theoretical positions are explored against the specific example of Tall Horse (2005), an intercultural production by the South African Handspring Puppet Company, the Malian Sogolon Puppet Company, a choreographer from Benin, and a scriptwriter from New York. The essay examines both the ideological issues raised in the text and the practical issues of cross-cultural collaboration and interaction to suggest an approach that may mediate between binaries that seem to dominate cultural interaction

    Patterns of Discrimination: On Photographic Portraits as Documents of Truth in Automated Facial Recognition

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    Denne avhandlingen tar for seg fotografiers rolle i treningen av ansiktsgjenkjenningsalgoritmer, samt i selve den tekniske prosessen hvor ansikter analyseres. Gjennom en lesning av tre ulike kunstprosjekter som på ulike måter anvender eksisterende ansiktsgjenkjenningsteknologi til å problematisere denne praksisen, etablerer jeg hvordan ulike fordommer – særlig hva angår fotografiets status som objektiv representasjon av verden – påvirker systemenes evne til å analysere ansikter. De aktuelle prosjektene er ImageNet Roulette (2019) av Trevor Paglen og AI-forsker Kate Crawford, How do you see me? (2019) av Heather Dewey-Hagborg, og Spirit is a Bone (2013-15) av kunstner-duoen Broomberg & Chanarin. Problemstillingen som oppgaven forsøker å besvare er som følger: hva kan disse kunstprosjektene fortelle publikum om ansiktsgjenkjenningsteknologi som praksis, og hvilken rolle spiller digitalt fotografi som slike systemers bindeledd til den analoge verden «utenfor» dem selv? Som svar på dette tar avhandlingen for seg selve den tekniske arkitekturen og hvordan den legger føringer for ansiktsgjenkjenningssystemers operasjoner alt i designprosessen. I tillegg diskuteres ansiktsgjenkjenning fra et historisk perspektiv, hvor forsøk på å knytte juridisk identitet til kroppen gjennom fotografi spores helt tilbake til mediets oppfinnelse på 1800-tallet.Kunsthistorie mastergradsoppgaveKUN350MAHF-KU

    Revisiting maine's lobster commons: Rescaling political subjects

    Get PDF
    Calls for cross-scalar theoretical and methodological approaches are not new to commons scholarship. Such efforts might be hastened by channelling poststructuralist and critical theory perspectives through the geographic subfield of political ecology, including attention to political scales and subjects. Toward this end, this paper reconsiders Maine’s lobster fishery. This case has provided rich material for watershed commons scholarship, demonstrating the ability of social groups to conserve resources independent of government or markets, and it continues to offer new findings. Recent fieldwork shows that as lobster boat captains advance collective interests through state-supported co-management governance arrangements, concerns of crew and non-fishing community members may be marginalized. Regulatory exclusion prevents broader distribution of resource benefits at a time when employment alternatives are scarce. More pluralistic approaches to commons theory and its policy application have utility well beyond the lobster case

    Bone Age Assessment with less human intervention

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    Biomedical imaging allows doctors to examine the condition of a patient’s organs or tissues without a surgical procedure. Various modalities of imaging techniques have been developed, such as X-radiation (X-ray), Magnetic Resonance Imaging (MRI), and Computed Tomography (CT). For example, the Bone Age Assessment (BAA) evaluates the maturity in infants, children, and adolescents using their hand radiographs. It plays an essential role in diagnosing a patient with growth disorders or endocrine disorders, such that needed treatments could be provided. Computer-aided diagnosis (CAD) systems have been introduced to extract features from regions of interest in this field automatically. Recently, several deep learning methods are proposed to perform automated bone age assessment by learning visual features. This study proposes a BAA model, including image preprocessing procedures and transfer learning with a limited number of annotated samples. The goal is to examine the efficiency of data augmentations by using a publicly available X-ray data set. The model achieves a comparable MAE of 5.8 months, RMSE of 7.3 months, and accuracy (within 1 year) of more than 90% on the data set. We also study whether generating samples by a Generative Adversarial Network could be a valuable technique for training the model and prevent it from overfitting when the samples are insufficient

    The Impact of Cross-References on the Readability of the U.S. Internal Revenue Code

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    Scholars and practitioners have long argued that U.S. income tax law (“the Tax Code”) is excessively complex and difficult to understand, and hence imposes non-trivial adjudication, administration, planning, and compliance costs across the spectrum of income tax stakeholders: the courts, the Internal Revenue Service, tax practitioners, business managers, and individual taxpayers. Hence, there is considerable interest in reducing the effort needed to accurately understand and apply the provisions of income tax law. Prior scholarly work has strongly argued that exceptions to Tax Code provisions as expressed by cross-references embedded in the Tax Code text constitute a major source of reading complexity. The goal of the study was to gain a first empirical understanding about the readability impacts on users who encounter cross-references while reading Tax Code provisions. The study included a human subjects task performance experiment with 75 undergraduate and graduate accounting student participants who were completing or had completed an introductory level course in federal income taxation. Participants were presented with integrated tax scenarios and accompanying sets of scenario questions. Copies of several Tax Code sections were the only reference materials available to the study participants. The study was based on a within-subjects experimental design. To investigate the prior work argument, cross-references embedded in the Tax Code reference materials provided to study participants that expressed exceptions were all assigned to one cross-reference category, and all other cross-references that served different purposes were assigned to a second category. As responses to scenario questions were binary (correct/incorrect), logistic regression was used to test study hypotheses. The study’s major finding was that reading cross-references assigned to the exceptions category had a very strong negative effect on task performance, while reading cross-references assigned to the second category had a modest positive effect on task performance. The finding thus supports decades of analysis and argument that cross-references related to expressing exceptions are a major source of Tax Code reading complexity. This outcome warrants further research into statutory exception language, that subset of statutory language used to express exceptions. Such a subset will include cross-references as one of many language elements that are available for the purpose of expressing exceptions

    Colorism Experiences of Non-White Women Leaders in Higher Education

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    As the population of the United States becomes more diverse the ethnic makeup of postsecondary institutions expands. Women of color (WOC) represent a growing number within the academic community earning more postsecondary degrees then men and serve as leaders in higher education throughout the county. The increased presence of WOC inacademic positions of power, such as deans, directors, supervisors, tenured faculty, presidents, etc., indicate America’s progression towards inclusivity. However, colorism, a subset of racism favoring and advantaging lighter skin complexions and disadvantaging darker skin tones, exist as a predictor of socioeconomic status, educational attainment, martial capital, occupational, and interpersonal success for WOC. This quantitative study examines colorism experiences of non-White female leaders in the academy. Survey items focused on skin tone discrimination within colleges and universities and sought to answer the following research questions: 1) To what degree has colorism been a factor in the careers of WOC who are in positions of power, 2) To what degree are experiences with colorism associated with social justice perceptions of higher education, 3) What demographics of WOC are most associated with experiences of colorism (age, skin tone, SES) and 4) How have WOC coped with and/or responded to colorism in their workplace experiences? Findings show that colorism negatively influences the career outcomes of WOC, contributes to lowered perceptions of social justice in higher education, and affects their coping mechanisms. Redressing skin tone bias from a human resource and conflict resolution perspective can help build more inclusive organizational teams across diverse workplaces
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