Search results for 'Philosophy Data processing' (try it on Scholar)

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  1.  11
    Judith Slein (1984). Philosophy and Data Processing. International Journal of Applied Philosophy 2 (1):75-84.
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  2. Aaron Sloman (1978). The Computer Revolution in Philosophy: Philosophy Science and Models of Mind. Harvester.
    Since 1991 the author has been Professor of Artificial Intelligence and Cognitive Science in the School of Computer Science at the University of Birmingham, UK.
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  3.  41
    James Moor & Terrell Ward Bynum (eds.) (2002). Cyberphilosophy: The Intersection of Philosophy and Computing. Blackwell Pub..
    This cutting edge volume provides an overview of the dynamic new field of cyberphilosophy – the intersection of philosophy and computing.
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  4.  48
    Terrell Ward Bynum & James Moor (eds.) (1998). The Digital Phoenix: How Computers Are Changing Philosophy. Blackwell Publishers.
    This important book, which results from a series of presentations at American Philosophical Association conferences, explores the major ways in which computers ...
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  5. Leslie Burkholder (ed.) (1992). Philosophy and the Computer. Westview Press.
  6. D. Bruce Anderson (ed.) (1974). After Leibniz ...: Discussions on Philosophy and Artificial Intelligence. Available From the National Technical Information Service.
     
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  7.  2
    Edmund F. Byrne (1996). The Two-Tiered Ethics of Electronic Data Processing. Techné: Research in Philosophy and Technology 2 (1):18-27.
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  8.  54
    Bryce Huebner (2015). What is a Philosophical Effect? Models of Data in Experimental Philosophy. Philosophical Studies 172 (12):3273-3292.
    Papers in experimental philosophy rarely offer an account of what it would take to reveal a philosophically significant effect. In part, this is because experimental philosophers tend to pay insufficient attention to the hierarchy of models that would be required to justify interpretations of their data; as a result, some of their most exciting claims fail as explanations. But this does not impugn experimental philosophy. My aim is to show that experimental philosophy could be made more (...)
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  9.  42
    Chris Fox (2005). Foundations of Intensional Semantics. Blackwell Pub..
    This book provides a systematic study of three foundational issues in the semantics of natural language that have been relatively neglected in the past few decades. focuses on the formal characterization of intensions, the nature of an adequate type system for natural language semantics, and the formal power of the semantic representation language proposes a theory that offers a promising framework for developing a computational semantic system sufficiently expressive to capture the properties of natural language meaning while remaining computationally tractable (...)
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  10. Riccardo Pozzo & Marco Sgarbi (eds.) (2011). Begriffs-, Ideen- Und Problemgeschichte Im 21. Jahrhundert. Harrassowitz Verlag, in Kommission.
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  11.  47
    D. Napoletani, M. Panza & D. Struppa (2011). Agnostic Science. Towards a Philosophy of Data Analysis. Foundations of Science 16 (1):1-20.
    In this paper we will offer a few examples to illustrate the orientation of contemporary research in data analysis and we will investigate the corresponding role of mathematics. We argue that the modus operandi of data analysis is implicitly based on the belief that if we have collected enough and sufficiently diverse data, we will be able to answer most relevant questions concerning the phenomenon itself. This is a methodological paradigm strongly related, but not limited to, biology, (...)
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  12.  16
    D. C. Struppa (2011). Agnostic Science. Towards a Philosophy of Data Analysis. Foundations of Science 16 (1):1-20.
    In this paper we will offer a few examples to illustrate the orientation of contemporary research in data analysis and we will investigate the corresponding role of mathematics. We argue that the modus operandi of data analysis is implicitly based on the belief that if we have collected enough and sufficiently diverse data, we will be able to answer most relevant questions concerning the phenomenon itself. This is a methodological paradigm strongly related, but not limited to, biology, (...)
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  13. Hilde Corneliussen (2011). Gender-Technology Relations: Exploring Stability and Change. Palgrave Macmillan.
    Machine generated contents note: -- Acknowledgements -- Disrupting the Impression of Stability in the Gender-Technology Relation -- Changing Images of Computers and its Users since 1980 -- Discursive Developments Within Computer Education -- Variations in Gender-ICT Relations Among Male and Female Computer Students -- Stories About Individual Change and Transformation -- Layered Meanings and Differences Within -- Is there an Elsewhere? -- References -- Endnotes -- Index.
     
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  14. R. Buckminster Fuller (1979). R. Buckminster Fuller on Education. University of Massachusetts Press.
     
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  15.  1
    Bernhard Pfahringer, Geoffrey Holmes & Achim Hoffmann (eds.) (2010). Discovery Science: 13th International Conference, Ds 2010, Canberra, Australia, October 6-8, 2010: Proceedings. Springer.
    The LNAI series reports state-of-the-art results in artificial intelligence research, development, and education, at a high level and in both printed and electronic form.
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  16.  23
    Douglas S. Robertson (2003). Phase Change: The Computer Revolution in Science and Mathematics. Oxford University Press.
    Robertson's earlier work, The New Renaissance projected the likely future impact of computers in changing our culture. Phase Change builds on and deepens his assessment of the role of the computer as a tool driving profound change by examining the role of computers in changing the face of the sciences and mathematics. He shows that paradigm shifts in understanding in science have generally been triggered by the availability of new tools, allowing the investigator a new way of seeing into questions (...)
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  17. Edgar Selzer (2011). Denn der Mensch Ist Mehr Als Sein Computer: Warum Die Turing-Maschine Das Wittgenstein'sche Sprachspiel Nicht Bewältigen Kann. Trauner.
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  18.  18
    Colin Beardon (1994). Computers, Postmodernism and the Culture of the Artificial. AI and Society 8 (1):1-16.
    The term ‘the artificial’ can only be given a precise meaning in the context of the evolution of computational technology and this in turn can only be fully understood within a cultural setting that includes an epistemological perspective. The argument is illustrated in two case studies from the history of computational machinery: the first calculating machines and the first programmable computers. In the early years of electronic computers, the dominant form of computing was data processing which was a (...)
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  19.  2
    M. Patharkar (2011). From Data Processing to Mental Organs: An Interdisciplinary Path to Cognitive Neuroscience. Mens Sana Monographs 9 (1):218.
    Human brain is a highly evolved coordinating mechanism in the species Homo sapiens. It is only in the last 100 years that extensive knowledge of the intricate structure and complex functioning of the human brain has been acquired, though a lot is yet to be known. However, from the beginning of civilisation, people have been conscious of a 'mind' which has been considered the origin of all scientific and cultural development. Philosophers have discussed at length the various attributes of consciousness. (...)
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  20.  75
    Gary Hatfield (2002). Sense-Data and the Philosophy of Mind: Russell, James, and Mach. Principia 6 (2):203-230.
    The theory of knowledge in early twentieth-century Anglo American philosophy was oriented toward phenomenally described cognition. There was a healthy respect for the mind-body problem, which meant that phenomena in both the mental and physical domains were taken seriously. Bertrand Russell's developing position on sense-data and momentary particulars drew upon, and ultimately became like, the neutral monism of Ernst Mach and William James. Due to a more recent behaviorist and physicalist inspired "fear of the mental", this development has (...)
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  21. Yann Benétreau-Dupin & Guillaume Beaulac (2015). Fair Numbers: What Data Can and Cannot Tell Us About the Underrepresentation of Women in Philosophy. Ergo, an Open Access Journal of Philosophy 2 (3):59-81.
    The low representation (< 30%) of women in philosophy in English-speaking countries has generated much discussion, both in academic circles and the public sphere. It is sometimes suggested (Haslanger 2009) that unconscious biases, acting at every level in the field, may be grounded in gendered schemas of philosophers and in the discipline more widely, and that actions to make philosophy a more welcoming place for women should address such schemas. However, existing data are too limited to fully (...)
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  22.  7
    David B. Resnik (2014). Data Fabrication and Falsification and Empiricist Philosophy of Science. Science and Engineering Ethics 20 (2):423-431.
    Scientists have rules pertaining to data fabrication and falsification that are enforced with significant punishments, such as loss of funding, termination of employment, or imprisonment. These rules pertain to data that describe observable and unobservable entities. In this commentary I argue that scientists would not adopt rules that impose harsh penalties on researchers for data fabrication or falsification unless they believed that an aim of scientific research is to develop true theories and hypotheses about entities that exist, (...)
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  23. I. J. Good (1983). The Philosophy of Exploratory Data Analysis. Philosophy of Science 50 (2):283-295.
    This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics. A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not "kinkera". A (...)
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  24.  19
    William F. Brewer & Clark A. Chinn (1994). Scientists' Responses to Anomalous Data: Evidence From Psychology, History, and Philosophy of Science. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:304 - 313.
    This paper presents an analysis of the forms of response that scientists make when confronted with anomalous data. We postulate that there are seven ways in which an individual who currently holds a theory can respond to anomalous data: (1) ignore the data; (2) reject the data; (3) exclude the data from the domain of the current theory; (4) hold the data in abeyance; (5) reinterpret the data; (6) make peripheral changes to the (...)
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  25.  12
    P. Boyd (2003). The Requirements of the Data Protection Act 1998 for the Processing of Medical Data. Journal of Medical Ethics 29 (1):34-35.
    The Data Protection Act 1998 presents a number of significant challenges to data controllers in the health sector. To assist data controllers in understanding their obligations under the act, the Information Commissioner has published guidance, The Use and Disclosure of Health Data, which is reproduced here. The guidance deals, among other things, with the steps that must be taken to obtain patient data fairly, the implied requirements of the act to use anonymised or psuedonymised (...) where possible, an exemption applicable principally to records based research, the right of patients to object to the processing of their data, and the interface of the act and the common law duty of confidence. (shrink)
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  26.  5
    C. L. Haynes, G. A. Cook & M. A. Jones (2007). Legal and Ethical Considerations in Processing Patient-Identifiable Data Without Patient Consent: Lessons Learnt From Developing a Disease Register. Journal of Medical Ethics 33 (5):302-307.
    The legal requirements and justifications for collecting patient-identifiable data without patient consent were examined. The impetus for this arose from legal and ethical issues raised during the development of a population-based disease register. Numerous commentaries and case studies have been discussing the impact of the Data Protection Act 1998 and Caldicott principles of good practice on the uses of personal data. But uncertainty still remains about the legal requirements for processing patient-identifiable data without patient consent (...)
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  27.  18
    Dag Elgesem (1999). The Structure of Rights in Directive 95/46/EC on the Protection of Individuals with Regard to the Processing of Personal Data and the Free Movement of Such Data. [REVIEW] Ethics and Information Technology 1 (4):283-293.
    The paper has three parts. First, a survey and analysis is given ofthe structure of individual rights in the recent EU Directive ondata protection. It is argued that at the core of this structure isan unexplicated notion of what the data subject can `reasonablyexpect' concerning the further processing of information about himor herself. In the second part of the paper it is argued thattheories of privacy popular among philosophers are not able to shed much light on the issues (...)
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  28.  10
    E. F. Bradley & O. T. Denmead (eds.) (1967). The Collection and Processing of Field Data. New York, Interscience Publishers.
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  29.  8
    Harley Shands (1973). Other-Than-Neurological Components Basic to Human Data-Processing Operations. World Futures 14 (1):13-32.
  30.  3
    Tilman Lenssen-erz (1994). Facts or Fantasy? The Rock Paintings of the Brandberg, Namibia, and a Concept of Textualization for Purposes of Data Processing. Semiotica 100 (2-4):169-200.
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  31.  1
    Judy M. Reviewer-Myerson (1991). Book Review: English-Japanese, Japanese-English Dictionary of Computer and Data Processing Terms by George Ferber (MIT Press 1989). [REVIEW] Acm Sigcas Computers and Society 21 (2-4):51.
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  32.  1
    Arthur E. Parry (1983). Data Processing Risk. Acm Sigcas Computers and Society 13 (3):14-18.
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  33. Lyle R. Creamer (1963). Event Uncertainty, Psychological Refractory Period, and Human Data Processing. Journal of Experimental Psychology 66 (2):187.
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  34. David B. Glaeser (1968). Minimizing Turnaround Time for Test Data Processing. In Peter Koestenbaum (ed.), Proceedings. [San Jose? Calif. 7.
     
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  35. N. Hinske (1981). Electronic-Data-Processing and Lexicography-the Newest Brainstorm for the Application of Edp is Anticipated for Historical-Philosophical Work and Texts. Philosophisches Jahrbuch 88 (1):153-159.
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  36. Richard J. Kones (1973). Capillary Endothelium and Data Processing: A Hypothesis. Perspectives in Biology and Medicine 16 (2):288-291.
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  37. Michael Mahoney (1985). An Annotated Bibliography on the History of Data Processing by James W. Cortada. [REVIEW] Isis: A Journal of the History of Science 76:594-595.
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  38. Michael Mahoney (1990). Historical Dictionary of Data Processing by James W. Cortada. [REVIEW] Isis: A Journal of the History of Science 81:400-401.
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  39. Grigori Mints (1994). Logic and Computer Science, Edited by Odifreddi Piergiorgio, APIC Studies in Data Processing, Vol. 31, Academic Press, London, San Diego, Etc., 1990, Xii+ 430 Pp. [REVIEW] Journal of Symbolic Logic 59 (3):1111-1114.
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  40. Judy M. Myerson (1991). Book Review: English-Japanese, Japanese-English Dictionary of Computer and Data Processing Terms by George Ferber. [REVIEW] Acm Sigcas Computers and Society 21 (2-4):51.
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  41. H. Schepers (1981). Electronic-Data-Processing Experiences in Editing. Philosophisches Jahrbuch 88 (1):159-164.
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  42. Anders Winroth (2006). Linda Fowler-Magerl, Clavis Canonum: Selected Canon Law Collections Before 1140. Access with Data Processing. (Monumenta Germaniae Historica, Hilfsmittel, 21.) Hannover: Hahnsche Buchhandlung, 2005. Pp. 282 Plus CD-ROM; Black-and-White Figures. €25. [REVIEW] Speculum 81 (4):1189-1190.
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  43. Shanshan Gu Wu & Yanfei Yu Lv (2006). Workshop on Web-Based Massive Data Processing-Session 1-Streaming Data-Modelling and Guaranteeing Quality of Service Over Data Streams. In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag 13-24.
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  44. Kohsuke Yanai, Ryoichi Ueda & Sagawa Nobutoshi (2011). Tree-Structured Data Processing Platform for Large-Scale Data Mining. Transactions of the Japanese Society for Artificial Intelligence 26 (5):594-606.
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  45. Min Li Yu & Longbo Zhang (2006). Workshop on Web-Based Massive Data Processing-Session 3-Massive Data Systems-Supporting Complex Query with Structured Overlays in Schema-Based P2P System. [REVIEW] In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer-Verlag 115-121.
     
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  46.  14
    M. Wynn (2002). Valuing the World: The Emotions as Data for the Philosophy of Religion. [REVIEW] International Journal for Philosophy of Religion 52 (2):97-113.
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  47. Thomas Case (1888). Physical Realism Being an Analytical Philosophy From the Physical Objects of Science to the Physical Data of Sense. Longmans, Green, and Co.
     
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  48. T. N. Ganapathy (1984). Bertrand Russell's Philosophy of Sense-Data. Dept. Of Philosophy, Ramakrishna Mission Vivekananda College.
     
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  49. B. Sambasiva Prasad (1984). A Critique of the Philosophy of Sense-Data. Sri Venkateswara University.
     
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  50. Richard Schacht & American Philosophical Association (1997). Philosophy in America 1994 Summary and Data : A Survey / C. American Philosophical Association.
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