1,015 research outputs found

    In Homage of Change

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    MediaScape: towards a video, music, and sound metacreation

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    We present a new media work, MediaScape, which is an initial foray into a fully interdisciplinary metacreativity. This paper defines metacreation, and we present examples of metacreative art within the fields of music, sound art, the history of generative narrative, and discuss the potential of the “open-documentary” as an immediate goal of metacreative video. Lastly, we describe MediaScape in detail, and present some future directions

    Detecting Internet visual plagiarism in higher education photography with Google™ Search by Image : proposed upload methods and system evaluation

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    Thesis (M. Tech. (Design and Studio Art)) - Central University of Technology, Free State, 2014The Information Age has presented those in the discipline of photography with very many advantages. Digital photographers enjoy all the perquisites of convenience while still producing high-quality images. Lecturers find themselves the authorities of increasingly archaic knowledge in a perpetual race to keep up with technology. When inspiration becomes imitation and visual plagiarism occurs, lecturers may find themselves at a loss for taking action as content-based image retrieval systems, like Google™ Search by Image (SBI), have not yet been systematically tested for the detection of visual plagiarism. Currently there exists no efficacious method available to photography lecturers in higher education for detecting visual plagiarism. As such, the aim of this study is to ascertain the most effective uploading methods and precision of the Google™ SBI system which lecturers can use to establish a systematic workflow that will combat visual plagiarism in photography programmes. Images were selected from the Google™ Images database by means of random sampling and uploaded to Google™ SBI to determine if the system can match the images to their Internet source. Each of the images received a black and white conversion, a contrast adjustment and a hue shift to ascertain whether the system can also match altered images. Composite images were compiled to establish whether the system can detect images from the salient feature. Results were recorded and the precision values calculated to determine the system’s success rate and accuracy. The results were favourable and 93.25% of the adjusted images retrieved results with a precision value of 0.96. The composite images had a success rate of 80% when uploaded intact with no dissections and a perfect precision value of 1.00. Google™ SBI can successfully be used by the photography lecturer as a functional visual plagiarism detection system to match images unethically appropriated by students from the Internet

    Digital Image Users and Reuse: Enhancing practitioner discoverability of digital library reuse based on user file naming behavior

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    Diese Dissertation untersucht Geräte, die Praktiker verwenden, um die Wiederverwendung von digitalen Bibliotheksmaterialien zu entdecken. Der Autor führt zwei Verifikationsstudien durch, in denen zwei zuvor angewandte Strategien untersucht werden, die Praktiker verwenden, um die Wiederverwendung digitaler Objekte zu identifizieren, insbesondere Google Images Reverse Image Lookup (RIL) und eingebettete Metadaten. Es beschreibt diese Strategiebeschränkungen und bietet einen neuen, einzigartigen Ansatz zur Verfolgung der Wiederverwendung, indem der Suchansatz des Autors basierend auf dem Benennungsverhalten von Benutzerdateien verwendet wird. Bei der Untersuchung des Nutzens und der Einschränkungen von Google Images und eingebetteten Metadaten beobachtet und dokumentiert der Autor ein Muster des Benennungsverhaltens von Benutzerdateien, das vielversprechend ist, die Wiederverwendung durch den Praktiker zu verbessern. Der Autor führt eine Untersuchung zur Bewertung der Dateibenennung durch, um dieses Muster des Verhaltens der Benutzerdateibenennung und die Auswirkungen der Dateibenennung auf die Suchmaschinenoptimierung zu untersuchen. Der Autor leitet mehrere signifikante Ergebnisse ab, während er diese Studie fertigstellt. Der Autor stellt fest, dass Google Bilder aufgrund der Änderung des Algorithmus kein brauchbares Werkzeug mehr ist, um die Wiederverwendung durch die breite Öffentlichkeit oder andere Benutzer zu entdecken, mit Ausnahme von Benutzern aus der Industrie. Eingebettete Metadaten sind aufgrund der nicht persistenten Natur eingebetteter Metadaten kein zuverlässiges Bewertungsinstrument. Der Autor stellt fest, dass viele Benutzer ihre eigenen Dateinamen generieren, die beim Speichern und Teilen von digitalen Bildern fast ausschließlich für Menschen lesbar sind. Der Autor argumentiert, dass, wenn Praktiker Suchbegriffe nach den "aggregierten Dateinamen" modellieren, sie ihre Entdeckung wiederverwendeter digitaler Objekte erhöhen.This dissertation explores devices practitioners utilize to discover the reuse of digital library materials. The author performs two verification studies investigating two previously employed strategies that practitioners use to identify digital object reuse, specifically Google Images reverse image lookup (RIL) and embedded metadata. It describes these strategy limitations and offers a new, unique approach for tracking reuse by employing the author's search approach based on user file naming behavior. While exploring the utility and limitations of Google Images and embedded metadata, the author observes and documents a pattern of user file naming behavior that exhibits promise for improving practitioner's discoverability of reuse. The author conducts a file naming assessment investigation to examine this pattern of user file naming behavior and the impact of file naming on search engine optimization. The author derives several significant findings while completing this study. The author establishes that Google Images is no longer a viable tool to discover reuse by the general public or other users except for industry users because of its algorithm change. Embedded metadata is not a reliable assessment tool because of the non-persistent nature of embedded metadata. The author finds that many users generate their own file names, almost exclusively human-readable when saving and sharing digital images. The author argues that when practitioners model search terms after the "aggregated file names" they increase their discovery of reused digital objects
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