254,790 research outputs found

    The visualization of a graph semantics of imperative languages

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    This work aims to present the software support for teaching in the field of formal semantics of imperative programming languages. The main part focuses on a software tool that provides a visual representation of the individual steps of the calculation in categorical semantics, which can also be referred to as graph semantics. The use of software tools in teaching to visually represent computational steps considerably facilitates understanding by students and can also serve as a good basis for supporting distance learning. Our program works in the standard form: after reading the correct user input, a visual representation of the meaning of the program is generated in the form of a category of states, which is displayed as an oriented graph. For better extensibility, the program is implemented as a web application

    The What-And-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding

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    The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where fllter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simultaneously represent the position, orientation, ancl size of all tbe figures in a scene (where they are). This spatial map may he used to direct spatially localized attention to these image features. A multiscale array of oriented detectors, followed by competitve and interpolative interactions between position, orientation, and size scales, is used to define the Where filter. This analysis discloses several issues that need to be dealt with by a spatial mapping system that is based upon oriented filters, such as the role of cliff filters with and without normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties across orientation than across size scale. Several computationally efficient Where filters are proposed. The Where filter rnay be used for parallel transformation of multiple image figures into invariant representations that are insensitive to the figures' original position, orientation, and size. These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can he used to rapidly search in parallel for a desired target in a scene. Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding. The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentive spatial localization and orientation, whereas a What processing stream is used for attentive object learning and recognition.Advanced Research Projects Agency (ONR-N00014-92-J-4015, AFOSR 90-0083); British Petroleum (89-A-1204); National Science Foundation (IRI-90-00530, Graduate Fellowship); Office of Naval Research (N00014-91-J-4100, N00014-95-1-0409, N00014-95-1-0657); Air Force Office of Scientific Research (F49620-92-J-0499, F49620-92-J-0334

    Computer-mediated knowledge communication

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    New communication technologies enable an array of new working and learning scenarios in which knowledge is being communicated. This article deals with the question to what extent these technologies can impede or facilitate knowledge communication. First, the various computer-based communication technologies will be classified. Second, effects of the medium on knowledge communication will be discussed based on results of studies of the current special priority program "Net-based Knowledge Communication in Groups". Third and last, computer-based possibilities to facilitate computer-mediated knowledge communication will be reviewNeue Kommunikationstechnologien ermöglichen eine Reihe neuer Arbeits- und Lernszenarien in denen Wissen kommuniziert wird. Dieser Beitrag beschäftigt sich damit, inwiefern diese Technologien Wissenskommunikation einschränken oder fördern können. Dazu werden in einem ersten Schritt die verschiedenen computerbasierten Kommunikationstechnologien untergliedert. In einem zweiten Schritt werden Wirkungen des Mediums auf die Wissenskommunikation diskutiert. Dazu werden u. a. die Ergebnisse von Studien des aktuellen Forschungsschwerpunkts "Netzbasierte Wissenskommunikation in Gruppen" berichtet. In einem dritten und letzten Schritt werden computerbasierte Möglichkeiten zusammengefasst, computervermittelte Wissenskommunikation zu förd

    User-centered visual analysis using a hybrid reasoning architecture for intensive care units

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    One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

    Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

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    This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly
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