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Modeling physical activity propagation, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. However, there has been lacking of scientific and quantitative study to elucidate how social communication may deliver physical activity interventions. In this(More)
Modeling and predicting human behaviors, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. The user diversity, dynamic behaviors, and hidden social influences make the problem more challenging. In this work, we propose a deep learning model named Social(More)
In recent years, deep learning has spread beyond both academia and industry with many exciting real-world applications. The development of deep learning has presented obvious privacy issues. However, there has been lack of scientific study about privacy preservation in deep learning. In this paper, we concentrate on the auto-encoder, a fundamental component(More)
Human behavior modeling is a key component in application domains such as healthcare and social behavior research. In addition to accurate prediction, having the capacity to understand the roles of human behavior determinants and to provide explanations for the predicted behaviors is also important. Having this capacity increases trust in the systems and(More)
New horizons are emerging within healthcare delivery, education, intervention provision, and tracking. We study a health social network that has tracked physical activities, biomarkers, and posts the participants have shared, throughout a one-year program. The program was aimed at helping people to adopt healthy behaviors and to lose weight. In this paper,(More)
Modeling physical activity propagation, such as activity level and intensity, is a key to preventing obesity from cascading through communities, and to helping spread wellness and healthy behavior in a social network. However, there have not been enough scientific and quantitative studies to elucidate how social communication may deliver physical activity(More)
Human behavior prediction is a key component to studying the spread of wellness and healthy behavior in a social network. In this paper, we introduce an <i>ontology-based Restricted Boltzmann Machine</i> (ORBM) model for human behavior prediction in health social networks. We first propose a bottom-up algorithm to learn the user representation from(More)
Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named socialized Gaussian process (SGP), for socialized human behavior modeling. Instead of(More)