{'fraction_non_alphanumeric': 0.013831258644536652, 'fraction_numerical': 0.005532503457814661, 'mean_word_length': 6.098039215686274, '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}}
Application of Image Similarity Detection based on Typical ITAI Servers

In this paper, image detection algorithms based on traditional methods and deep-learning method are migrated from Intel X86 architecture ecosystem to X86 and ARM architectures on the typical localized servers and softwares in Information Technology Application Innovation (ITAI) catalog. By solving the adaptation problems as well as considering the different processor architectures of the localized server, targeted performance optimization methods are proposed, and large-scale performance testing on massive images have been experimented on physical machines, verifying the production capacity of current ITAI servers in image detection scenario.