Music therapy is an effective tool to slow down the progress of dementia since interaction with music may evoke emotions that stimulates brain areas responsible for memory. This therapy is most successful when therapists provide adequate and personalized stimuli for each patient. This personalization is often hard. Thus, Artificial Intelligence (AI) methods may help in this task. This paper brings a systematic review of the literature in the field of affective computing in the context of music therapy. We particularly aim to assess AI methods to perform automatic emotion recognition applied to Human-Machine Musical Interfaces (HMMI). To perform the review, we conducted an automatic search in five of the main scientific databases on the fields of intelligent computing, engineering, and medicine. We search all papers released from 2016 and 2020, whose metadata, title or abstract contains the terms defined in the search string. The systematic review protocol resulted in the inclusion of 144 works from the 290 publications returned from the search. Through this review of the state-of-the-art, it was possible to list the current challenges in the automatic recognition of emotions. It was also possible to realize the potential of automatic emotion recognition to build non-invasive assistive solutions based on human-machine musical interfaces, as well as the artificial intelligence techniques in use in emotion recognition from multimodality data. Thus, machine learning for recognition of emotions from different data sources can be an important approach to optimize the clinical goals to be achieved through music therapy.
O aumento significativo de infrações de trânsito tem se tornado algo casual na vida dos brasileiros. A cidade do Recife, estado de Pernambuco, segundo a empresa Holandesa TomTom Traffic, no ano de 2018, ocupava a 10º posição entre as cidades com o pior tráfego de automóveis no mundo. Em 2019 passou a ocupar a 15º posição. Diante disto, esta pesquisa tem como intuito investigar fatores relacionados ao aumento da quantidade de equipamentos de aferição automática e de agentes de trânsito. O objetivo é criar um modelo de predição de delitos no trânsito por turnos, testado com a base real referente ao ano de 2019. Já os dados selecionados para visualização e treinamento das técnicas de Machine Learning foram referentes aos anos de 2017 e 2018, extraídos do portal de dados abertos. Para guiar o processo de mineração e análise de dados, a metodologia CRISP-DM foi aplicada. Além disso, ferramentas como Pentaho PDI, Weka, GretL, Python e Orange Data Mining também foram utilizadas para auxiliar neste processo. Os resultados obtidos apontam que há um aumento significativo de infrações em feriados, principalmente no Corpus Christi. Além disso, as predições mensais apresentam bons resultados quando comparados aos números reais de infrações.
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