Integrated Organizational Machine Learning for Aviation Flight Data

An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has been a well known challenge since database systems were first invented. While integration and automation of data collection efforts within many organizations is quite mature, there are special challenges for flight-based organizations (i.e., the automatic and efficient transmission of aircraft flight data to centralized analytical data processing systems). Furthermore, this creates additional constraints for the operationalization of embedded machine learning methods for classical tasks such as classification and prediction; and magnifying design challenges for the more novel ‘prescriptive-based’ architectures. Our research is focused on the application of a design pattern for a) the integration and automation of data collection and b) an organizationally embedded ensemble machine learning method

Uma abordagem para aumento de empatia das interações textuais em sistemas colaborativos

Empathy plays an essential role in social interactions, for example, in effective teaching-learning processes in teacher-student relationships, and in the company-client or employee-customer relationships, retaining potential partners and providing them with greater satisfaction. In parallel, Computer-Mediated Communication (CMC) support people in their interactions, especially when it is necessary to circumvent space-time limitations. In CMC, there are several approaches to promote empathy in social or human-computer interactions. However, for this type of communication, a little explored mechanism to gain empathy is the use of the theory of Neurolinguistics that presents the possibility of developing a Preferred Representation System (PRS) for cognition in humans. In this context, this work presents the conception and results obtained through the experimental evaluations of the NeuroMessenger library, that uses Neurolinguistics, Psychometry and Text Mining to promote empathy among interlocutors, from the PRS identification and suggestion of textual matching based on this classification. The results showed that the use of the same text pattern (PRS) increases the empathy among the interlocutors of Collaborative Systems, evidencing that the matching feature can significantly improve the communication and construction of rapport in virtual environments.A empatia desempenha um papel essencial em interações sociais, como, por exemplo, em processos de ensino-aprendizagem efetivos nas relações professor-aluno e, nas relações empresa-cliente ou colaborador-consumidor, retendo potenciais parceiros e proporcionando-lhes maior satisfação. Em paralelo, a Comunicação Mediada por Computador (CMC) auxilia as pessoas em suas interações, especialmente quando é necessário contornar as limitações de espaço-tempo. Em CMC, existem diversas abordagens para promover empatia em interações sociais ou humano-computador. Contudo, para esse tipo de comunicação, um mecanismo pouco explorado para ganho de empatia é o uso da teoria da Neurolinguística, a qual apresenta a possibilidade de desenvolvimento de Sistemas Representacionais Preferenciais (SRPs) para cognição em seres humanos. Nesse contexto, o presente trabalho apresenta a concepção e os resultados obtidos por meio de avaliações experimentais da biblioteca NeuroMessenger, a qual utiliza Neurolinguística, Psicometria e Mineração de Textos para promover empatia entre interlocutores, a partir da identificação de SRPs e sugestão de matching (espelhamento) textual baseado nesta classificação. Os resultados mostraram que a utilização do mesmo padrão de texto (SRP) aumenta a empatia entre os interlocutores de Sistemas Colaborativos, evidenciando que o recurso de matching pode melhorar significativamente a comunicação e formação de rapport em ambientes virtuais.São Cristóvão, S