Over forty years ago, Vannevar Bush articulated his vision of a “Memex” machine: “associative indexing, … whereby any item may be caused at will to select immediately and automatically another” [Bush 45]. In the ...
ISBN:
(纸本)0201142376
Over forty years ago, Vannevar Bush articulated his vision of a “Memex” machine: “associative indexing, … whereby any item may be caused at will to select immediately and automatically another” [Bush 45]. In the sixties, Engelbart [Engelbart, English 68] built collaborative systems to provide idea structuring and sharing. Nelson [Nelson 81] coined “hypertext” and proposed world-wide networks for publishing, linking, annotating and indexing multiple versions of documents. With increasing numbers of research projects, papers, panels and conferences, and commercially available systems (e.g. Notecards by Xerox, Guide by Owl and HyperCard by Apple) in recent years, hypertext may be an idea whose time has finally come — or at least a phenomenon not to be *** goal of this panel is not to define hypertext or hypermedia (at its simplest: non-linearly arranged and accessed information), debate its uniqueness, explain implementation issues, or survey the many applications and contributions in the field (see [Conklin 87] for an excellent survey of Hypertext, and the Proceedings of Hypertext '87 Workshop at University of North Carolina, Chapel Hill). Rather, we intend to approach it from the perspective of the information user: reader, searcher, author. The panel will address the following issues:Are the processes of authoring and understanding helped or hindered by the non-linear structure of hypertext, for which kinds of tasks and users? What is the difference between a hypertext writer and a knowledge engineer? In searching for information, what is the difference between browsing and querying?What experiments need to be done? What tools, environment or interfaces can improve the process of information creation and access? Can the overhead of creating or interpreting structure be reduced?When will hypertext replace paper, or should it? How do functions of author and reader co-evolve? Could this revolutionize society like the printing press? Why didn't the panelists cre
A method to segment and describe visible surfaces of three-dimensional (3-D) objects is presented by first segmenting the surfaces into simple surface patches and then using these patches and their boundaries to descr...
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In Fourier optics, the Fourier transformation is performed on the amplitude of the electromagnetic wave, whereas in the computational method the transformation is usually applied to the grayness of its image which is ...
In Fourier optics, the Fourier transformation is performed on the amplitude of the electromagnetic wave, whereas in the computational method the transformation is usually applied to the grayness of its image which is proportional to the intensity of the electromagnetic signal.
The paper outlines the role of Information Retrieval techniques in the construction of Knowledge-Based systems. A Functional Communication Structure selects and communicates the relevant information by means of fuzzy ...
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A study of some languages which may be used for expert system building is conducted. The characteristics necessary in an expert system building language are detailed. Languages that are unable to provide for many diff...
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Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep lear...
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Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.
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