{'fraction_non_alphanumeric': 0.03231939163498099, 'fraction_numerical': 0.0076045627376425855, 'mean_word_length': 4.791208791208791, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Experiments in Boundary Recognition at the University of Iowa

We opted to work solely on localized (frame pair-wise) measures, seeking to establish just what they could contributed in a pure environment to the task. This clearly has significant impact on the ability to detect gradual transitions, as will be discussed later, but it was interesting to discover that our composite metrics could perform as well as they did (e.g., 30% recall & 33% precision for our product metric) for an inherently frame sequence-driven task.