Mai ElSherief

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In this paper we explore the notion of mobile users' similarity as a key enabler of innovative applications hinging on opportunistic mobile encounters. In particular, we analyze the performance of known similarity metrics, applicable to our problem domain, as well as propose a novel temporal-based metric, in an attempt to quantify the inherently qualitative(More)
In this paper we establish fundamental limits on the performance of knowledge sharing in opportunistic social networks. In particular, we introduce a novel information-theoretic model to characterize the performance limits of knowledge sharing policies. Towards this objective, we first introduce the notions of knowledge gain and its upper bound, knowledge(More)
Gender-based violence (GBV) is a global epidemic that is powered, in part, by a culture of silence and denial of the seriousness of its repercussions. In this paper, we present one of the first investigations of GBV in social media. Considering Twitter as an open pervasive platform that provides means for open discourse and community engagement, we study(More)
Motivation and Objective: Earlier social studies, e.g., Homophily [Lazarsfeld and Merton (1954)], have shown that people tend to have similarities with others in close proximity. In our demo, coined O’BTW, we exploit the ubiquity of mobile phones and resource-rich users’ social structure to develop opportunistic similarity-based mobile social networking(More)
Street harassment is a global problem. In this paper, we seek to gain insights into the characteristics of neighborhoods in which street harassment has occurred. We analyze over 7,800 worldwide street harassment incidents, gathered by the Hollaback project [7], to study the association of street harassment with walkability scores and the number of transit(More)
The ubiquity of sensors has introduced a variety of new opportunities for data collection. In this paper, we attempt to answer the question: Given M workers in a spatial environment and N probing resources, where N < M , which N workers should be queried to answer a specific question? To solve this research question, we propose two querying algorithms: one(More)
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