32,118 research outputs found
Challenging Social Cognition Models of Adherence:Cycles of Discourse, Historical Bodies, and Interactional Order
Attempts to model individual beliefs as a means of predicting how people follow clinical advice have dominated adherence research, but with limited success. In this article, we challenge assumptions underlying this individualistic philosophy and propose an alternative formulation of context and its relationship with individual actions related to illness. Borrowing from Scollon and Scollon’s three elements of social action – “historical body,” “interaction order,” and “discourses in place” – we construct an alternative set of research methods and demonstrate their application with an example of a person talking about asthma management. We argue that talk- or illness-related behavior, both viewed as forms of social action, manifest themselves as an intersection of cycles of discourse, shifting as individuals move through these cycles across time and space. We finish by discussing how these dynamics of social action can be studied and how clinicians might use this understanding when negotiating treatment with patients
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
The Role of Urban Morphology Design on Enhancing Physical Activities and Public Health
Along with environmental pollutions, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural- social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, the consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and consequently, the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture
Future Person Localization in First-Person Videos
We present a new task that predicts future locations of people observed in
first-person videos. Consider a first-person video stream continuously recorded
by a wearable camera. Given a short clip of a person that is extracted from the
complete stream, we aim to predict that person's location in future frames. To
facilitate this future person localization ability, we make the following three
key observations: a) First-person videos typically involve significant
ego-motion which greatly affects the location of the target person in future
frames; b) Scales of the target person act as a salient cue to estimate a
perspective effect in first-person videos; c) First-person videos often capture
people up-close, making it easier to leverage target poses (e.g., where they
look) for predicting their future locations. We incorporate these three
observations into a prediction framework with a multi-stream
convolution-deconvolution architecture. Experimental results reveal our method
to be effective on our new dataset as well as on a public social interaction
dataset.Comment: Accepted to CVPR 201
- …