Studying the viral growth of a connective action network using information event signatures

The Arab spring and Occupy Wall Street movements demonstrated that networks of individuals who share interests or grievances could quickly form on social media. There is a reciprocal relationship between the growth of these networks and the information that flows through them. This study examines this relationship by using viral information event signatures, which show the changing rate of sharing of a specific message over a period of time. The Occupy movement and the digital interactions of its participants provides a context and rich corpus of data from which to study the relationship between the signatures of information flows and the growth the Occupy network. Using exploratory data analysis and multivariate regression to analyze Occupy related tweets drawn from a corpus of over 64 million tweets, this study first provides a parameterized signature model and then uses regression to show that a relationship exists between the shape of the signature and the rate at which key actors gain followers. This work also finds a quadratic decline, over the life cycle of the movement, in the rate at which the actors gain followers. The contributions of this work include the parameterized signature model, a demonstration of its usefulness, and a new perspective on the growth of the Occupy movement.

ONLINE PEER-TO-PEER TRAFFIC IDENTIFICATION BASED ON COMPLEX EVENTS PROCESSING OF TRAFFIC EVENT SIGNATURES

Peer-to-Peer (P2P) applications are bandwidth-heavy and lead to network congestion. The masquerading nature of P2P traffic makes conventional methods of its identification futile. In order to manage and control P2P traffic efficiently preferably in the network, it is necessary to identify such traffic online and accurately. This paper proposes a technique for online P2P identification based on traffic events signatures. The experimental results show that it is able to identify P2P traffic on the fly with an accuracy of 97.7%, precision of 98% and recall of 99.2%.

AMORPHOUS EVENT PREVENTION IN WIRELESS SENSOR NETWORK

In many applications wireless sensor can be used to detect the events in those applications. With the advances in sensing, communication, and computation, there is an increasing need to track mobile events such as air pollutant diffusion, toxic gas leakage, or wildfire spreading using mobile sensors such as robots. Lots of existing work use control theory to plan the path of mobile sensors by assuming that the event evolution is known in advance. This assumption has severely limited the applicability of existing approaches. In this paper we aim to design a detecting, tracking and preventing approach that is capable of identifying multiple events with dynamic event signatures and providing event evolution history that may include event merge, split, create and destroy. We also focused on the power consumption.

A Signature-Based Power Model for MPSoC on FPGA

This paper presents a framework for high-level power estimation of multiprocessor systems-on-chip (MPSoC) architectures on FPGA. The technique is based on abstract execution profiles, called event signatures, and it operates at a higher level of abstraction than, for example, commonly used instruction-set simulator (ISS)-based power estimation methods and should thus be capable of achieving good evaluation performance. As a consequence, the technique can be very useful in the context of early system-level design space exploration. We integrated the power estimation technique in a system-level MPSoC synthesis framework. Subsequently, using this framework, we designed a range of different candidate architectures which contain different numbers of MicroBlaze processors and compared our power estimation results to those from real measurements on a Virtex-6 FPGA board.