Deletion is a core facet of Online Social Networks (OSNs). For users, deletion is a tool to remove what they have shared and control their data. For OSNs, robust deletion is both an obligation to their users and a risk when developer mistakes inevitably oc ...
Datacenters are the heart of our digital lives. Online applications, such as social-networking and e-commerce, run inside datacenters under strict Service Level Objectives for their tail latency. Tight latency SLOs are necessary for such services to remain ...
We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not ...
In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an an ...
Background: Wet markets are markets selling fresh meat and produce. Wet markets are critical for food security and sustainable development in their respective regions. Due to their cultural significance, they attract numerous visitors and consequently gene ...
This thesis is devoted to information-theoretic aspects of community detection. The importance of community detection is due to the massive amount of scientific data today that describes relationships between items from a network, e.g., a social network. I ...
Information about world events is disseminated through a wide variety of news channels, each with specific considerations in the choice of their reporting. Although the multiplicity of these outlets should ensure a variety of viewpoints, recent reports sug ...
We consider a distributed social learning problem where a network of agents is interested in selecting one among a finite number of hypotheses. The data collected by the agents might be heterogeneous, meaning that different sub-networks might observe data ...
