{'fraction_non_alphanumeric': 0.015060240963855422, 'fraction_numerical': 0.0, 'mean_word_length': 4.884955752212389, '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}}
Automatic Generation of Smart Human-Machine Interfaces

These last years have seen a rapid growth of applications able to interact with humans in a sophisticated way: mood interpretation and adaptation is the next research frontier and it gets closer day by day. This paper introduces a model-driven process able to automate the creation of modern Human-Machine Interfaces. In particular, by proposing a probabilistic extension of Dynamic State Machines, this paper explores the usage of such a formalism in the automatic generation of applications able to understand the mood of the user and to react at the best. The approach is applied to an example case study.