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Creators/Authors contains: "Sanchez, Estefania"

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  1. ; ; ; ; ; ; (, CSCW’22 Companion)
    Current youth online safety and risk detection solutions are mostly geared toward parental control. As HCI researchers, we acknowledge the importance of leveraging a youth-centered approach when building Artificial Intelligence (AI) tools for adolescents online safety. Therefore, we built the MOSafely, Is that ‘Sus’ (youth slang for suspicious)? a web-based risk detection assessment dashboard for youth (ages 13-21) to assess the AI risks identified within their online interactions (Instagram and Twitter Private conversations). This demonstration will showcase our novel system that embedded risk detection algorithms for youth evaluations and adopted the human–in–the loop approach for using youth evaluations to enhance the quality of machine learning models. 
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  2. ; ; ; ; ; (, IEEE International Conference on Computer Communications (ICC))
    A smart home with a controller that can understandand predict the interaction between the external environment and the user’s behavior and preferences can provide significant energy efficiency and savings. Unfortunately, experimentation of real world homes for the development of such a controller is prohibitively expensive. In this paper we describe techniques through which such experiments can be performed on scaled testbed with an accelerated time. We illustrate how the modeling of different geographical areas can be performed by the mapping of the model’s temperature and time to their real-world equivalents. We train three different machine learning models for predicting different sensor readings in the testbed, and find that the achieved predictive accuracy supports the feasibility of the development of future smart climate controllers. 
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