Hamed Haddadi

TL;DR: An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.

TL;DR: This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains.

TL;DR: In this article, the authors provide an encyclopedic review of mobile and wireless networking research based on deep learning, which they categorize by different domains and discuss how to tailor deep learning to mobile environments.

TL;DR: This work makes use of a unique, anonymized data set corresponding to one day of traffic for a major European mobile carrier, taking a principled approach to characterize mobile ad traffic along a number of dimensions, such as overall traffic, frequency, as well as possible implications in terms of energy on a mobile device.

TL;DR: This work conducts a multidimensional analysis of information exposure from 81 devices located in labs in the US and UK, characterized in terms of destinations of Internet traffic, whether the contents of communication are protected by encryption, and whether there are unexpected exposures of private and/or sensitive information.