This research proposes an AI platform for data sharing across multiple domains. Since the data in the smart city concept are domain-specific processed, the existing smart city architecture is suffered from cross-domain data interpretation. To go beyond the digital transformation efforts in smart city development, the AI city is created on the architecture of cross-domain data connectivity and transform learning in the machine learning paradigm. In this research, the health and human behavioral data are targeted on human traceability and contactless technologies. To measure the inhabitants quality of life (QoL), the primary emotion expression study is conducted to interpret the emotional states and the mental health of people in the urbanized city. The results of information augmentation draw attention to the immersive visualization of the Thammasat model.
In this paper, we proposed a two-phase project on emotion corpus creation based on multi-knowledge of cognitive semantics, discourse analysis, paralinguistics, and computer science. Data were gathered from Thai lexicon of five main Thai dictionaries and thesaurus, in addition to written and spoken texts of people with depression in Thai and facial expression with speech situation. We found that semantic primes and features of each emotion were needed to serve as a guideline of emotion categorization in Thai context. We introduced the step-by-step methods of the first phase to create Thai emotion corpus entailing both verbal and nonverbal corpora. The way to classify emotion corpus by focusing on the specific text of depression as well as to find the guidelines of labelling facial expression in the situation of specific emotions was explored. Lastly, the step of creating emotion corpus in the second phase was introduced with some suggestions and discussion.
This article aims to present two essential points. Firstly, syntactic complexity value is an indicator of language development of children, especially in relation to narratives due to maturation and increased cognitive development. Secondly, complexity is measurable and assessable. This article argues that syntactic complexity as numerically measured has benefits for studying the close development of children in different age groups in which differences of language pattern and innovation may not be readily discerned. In order for a more accurate comparison between different age groups, the information employed was narratives of Thai children in the CHILDES database, Thai Frog Story series. The age groups were divided into 4 tiers: 4, 6, 9 and 11 years old respectively. Each group contained ten children which were compared to ten adults. The study found that syntactic complexity of children’s narratives develops increasingly until it resembles to that of adults.
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