Tarek Abdelzaher

TL;DR: Evaluation results on Twitter data sets demonstrate that the proposed approach outperforms state-of-the-art baselines in detecting accounts created for information manipulation campaigns and the cost of adversarial attacks on the detection model is high.

TL;DR: In this article, the authors proposed RDeepSense, a deep learning model that provides well-calibrated uncertainty estimations for resource-constrained mobile and embedded devices, which enables the predictive uncertainty by adopting a tunable proper scoring rule as the training criterion and dropout as the implicit Bayesian approximation.

TL;DR: This paper solves the challenge of offline response time analysis of independent periodic tasks with constrained deadlines early in the software development cycle, under generalized rate-monotonic scheduling and outperforms, in terms of schedulability, both global utilization bounds and mechanisms that attain temporal modularity via resource partitioning.

TL;DR: This paper develops data retrieval algorithms for crowd-sensing applications that reduce the underlying network bandwidth consumption or any additive cost metric by exploiting logical dependencies among data items, while maintaining the level of service to the client applications.