{'fraction_non_alphanumeric': 0.011755485893416929, 'fraction_numerical': 0.0, 'mean_word_length': 4.995305164319249, '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}}
Evaluation of user feedback in smart home for situational context identification

In the recent years, smart home projects started to gain great attention from academic as well as industrial communities. However, an essential challenge that all smart home ideas face is the provision of the ground truth i.e. the labeled training data required to train the machine learning algorithms which achieve the smartness of the smart home. Another challenging task is to evaluate the correctness of the collected ground truth so that we can be sure that we train the system with correct data which represents the reality. In order to build a smart home which is interactive and adaptable to the behavior and preferences of its inhabitants, we need to have comprehensive information about the everyday behavior and preferences of the inhabitants of the smart home. This comprehensive information which needs to be collected represents the ground truth in the context of our smart home research. Many technologies have been utilized in order to collect this information. In this paper, we present our approach for collecting the ground truth in smart homes in a nonintrusive way. More importantly, we present our methodology for evaluating the correctness of the collected ground truth.