12,463 research outputs found

    Summer learning experience for girls in grades 7–9 boosts confidence and interest in computing careers

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    Academic exposure to computer science, encouragement to study computer science, and connecting personal interests to computing areas influence women to pursue degrees in computer science. Guided by these recommendations, we designed and offered a summer learning experience for girls in grades 7--9 in summer 2016. The goal of the program was to improve girls\u27 perceptions of learning computer science through academic exposure in the informal setting of a girls-only summer camp. In this paper we present a study of the girls\u27 perceptions of CS learning. Four constructs were used to develop pre- and post-survey items: computing confidence, intent to persist, social supports, and computing outcomes expectations. The camp appeared to have positively influenced the girls on two of the four constructs, by improving computing confidence and positive perceptions of computing careers

    Security Code Smells in Android ICC

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    Android Inter-Component Communication (ICC) is complex, largely unconstrained, and hard for developers to understand. As a consequence, ICC is a common source of security vulnerability in Android apps. To promote secure programming practices, we have reviewed related research, and identified avoidable ICC vulnerabilities in Android-run devices and the security code smells that indicate their presence. We explain the vulnerabilities and their corresponding smells, and we discuss how they can be eliminated or mitigated during development. We present a lightweight static analysis tool on top of Android Lint that analyzes the code under development and provides just-in-time feedback within the IDE about the presence of such smells in the code. Moreover, with the help of this tool we study the prevalence of security code smells in more than 700 open-source apps, and manually inspect around 15% of the apps to assess the extent to which identifying such smells uncovers ICC security vulnerabilities.Comment: Accepted on 28 Nov 2018, Empirical Software Engineering Journal (EMSE), 201

    Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

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    Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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