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    <value>The dataset consists of 1,252 video files, with a total length of 6 days, 20 hours, 27 minutes and 30 seconds.</value>
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  <data name="BText2" xml:space="preserve">
    <value>Metadata (Tags) for content analysis of AV works</value>
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  <data name="BText3" xml:space="preserve">
    <value>a list of categories for searching for visual and sound objects was gradually iterated.</value>
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    <value>External database usage</value>
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    <value>Number of images</value>
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    <value>We received the Vašulka archive on two discs (Woody and Madlenka), on which there are digitized objects of various characters. In addition to audiovisual works, these are exhibition catalogs, grant applications and other archival materials, not only from the Vašulka work, but also from their close colleagues, in a total size of 536 GB. At first, we excluded only digital objects from the content of the entire archive, the extensions of which indicated that they were audiovisual works. Specifically, these are the selected suffixes: ifo, vob, mpg, mpeg, rec, dir, mob, san, dav, mov, avi, ppj, swf, dad, wp3, mp4, prproj, flc and fli.</value>
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    <value>Out of the total number of 1,800 identified audiovisual works, we successfully converted 880 video files of various formats to the mp4 format using the h.264 codec, while 920 files could not be converted (for example, objects with the mob extension). The set of identified videos consists of a data corpus with a size of 137 GB, which we use in our project solution.</value>
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    <value>In addition to cataloging data, which will provide a basic orientation in the content of the archive, we worked on the definition of tags for content analysis of the videos. The work had the character of a gradual iteration with regard to the frequency of visual occurrence and sound objects occurence in videos.</value>
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    <value>Tags: Visual objects</value>
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  <data name="Text2.2" xml:space="preserve">
    <value>With regard to the progress of work and the results of the work of artificial neural networks and the need to enable web users to get acquainted with the main thematic motives of Vašulková's work,</value>
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    <value>The criteria for selecting categories were a) frequency of given categories occurrence in the dataset, b) importance of relevant categories in terms of Vašulková creation poetics (eg elements, machine vision, violin), c) ability of artificial neural networks to find relevant categories with sufficient outputs accuracy, d ) items quantity in terms of the Vasulka Live Archive website interface user friendliness.</value>
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    <value>Tab. 1 shows the occurrence of individual categories in the dataset in terms of frequency.</value>
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    <value>Tab. 1: Occurrence of individual image categories in the dataset in terms of frequency</value>
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    <value>Tags: Audio objects</value>
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    <value>Tab. 2 contains list of all the audio tags that have been used during work within the tagging interface. Audio tags are sorted from longest to shortest duration (frequency of occurrence). The biggest difficulty of the obtained dataset is the too small representation of most of the selected tags, so it was necessary to expand most of the tags with audio material from external databases (FSD50k). Without artificial expansion of the dataset, it would not be possible to achieve too high quality trained models of neural networks.</value>
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    <value>Tab. 2: Occurrence of individual sound categories in the dataset in terms of frequency</value>
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  <data name="Text8" xml:space="preserve">
    <value>The total number of occurrences of all tags in all images is 100,087.</value>
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    <value>Time</value>
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  <data name="Title" xml:space="preserve">
    <value>Dataset</value>
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