15,611 research outputs found
Detection and localization of change points in temporal networks with the aid of stochastic block models
A framework based on generalized hierarchical random graphs (GHRGs) for the
detection of change points in the structure of temporal networks has recently
been developed by Peel and Clauset [1]. We build on this methodology and extend
it to also include the versatile stochastic block models (SBMs) as a parametric
family for reconstructing the empirical networks. We use five different
techniques for change point detection on prototypical temporal networks,
including empirical and synthetic ones. We find that none of the considered
methods can consistently outperform the others when it comes to detecting and
locating the expected change points in empirical temporal networks. With
respect to the precision and the recall of the results of the change points, we
find that the method based on a degree-corrected SBM has better recall
properties than other dedicated methods, especially for sparse networks and
smaller sliding time window widths.Comment: This is an author-created, un-copyedited version of an article
accepted for publication/published in Journal of Statistical Mechanics:
Theory and Experiment. IOP Publishing Ltd is not responsible for any errors
or omissions in this version of the manuscript or any version derived from
it. The Version of Record is available online at
http://dx.doi.org/10.1088/1742-5468/2016/11/11330
A linguistically-driven methodology for detecting impending and unfolding emergencies from social media messages
Natural disasters have demonstrated the crucial role of social media before, during and after emergencies
(Haddow & Haddow 2013). Within our EU project Sland \ub4 ail, we aim to ethically improve \ub4
the use of social media in enhancing the response of disaster-related agen-cies. To this end, we
have collected corpora of social and formal media to study newsroom communication of emergency
management organisations in English and Italian. Currently, emergency management agencies
in English-speaking countries use social media in different measure and different degrees,
whereas Italian National Protezione Civile only uses Twitter at the moment. Our method is developed
with a view to identifying communicative strategies and detecting sentiment in order to
distinguish warnings from actual disasters and major from minor disasters. Our linguistic analysis
uses humans to classify alert/warning messages or emer-gency response and mitigation ones based
on the terminology used and the sentiment expressed. Results of linguistic analysis are then used
to train an application by tagging messages and detecting disaster- and/or emergency-related terminology
and emotive language to simulate human rating and forward information to an emergency
management system
Machine Understanding of Human Behavior
A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior
Hybrid solutions to the feature interaction problem
In this paper we assume a competitive marketplace where the features are developed by different enterprises, which cannot or will not exchange information. We present a classification of feature interaction in this setting and introduce an on-line technique which serves as a basis for the two novel <i>hybrid</i> approaches presented. The approaches are hybrid as they are neither strictly off-line nor on-line, but combine aspects of both. The two approaches address different kinds of feature interactions, and thus are complimentary. Together they provide a complete solution by addressing interaction detection and resolution. We illustrate the techniques within the communication networks domain
Monitoring data in R with the lumberjack package
Monitoring data while it is processed and transformed can yield detailed
insight into the dynamics of a (running) production system. The lumberjack
package is a lightweight package allowing users to follow how an R object is
transformed as it is manipulated by R code. The package abstracts all logging
code from the user, who only needs to specify which objects are logged and what
information should be logged. A few default loggers are included with the
package but the package is extensible through user-defined logger objects.Comment: Accepted for publication in the Journal of Statistical Softwar
Preventing Distributed Denial-of-Service Attacks on the IMS Emergency Services Support through Adaptive Firewall Pinholing
Emergency services are vital services that Next Generation Networks (NGNs)
have to provide. As the IP Multimedia Subsystem (IMS) is in the heart of NGNs,
3GPP has carried the burden of specifying a standardized IMS-based emergency
services framework. Unfortunately, like any other IP-based standards, the
IMS-based emergency service framework is prone to Distributed Denial of Service
(DDoS) attacks. We propose in this work, a simple but efficient solution that
can prevent certain types of such attacks by creating firewall pinholes that
regular clients will surely be able to pass in contrast to the attackers
clients. Our solution was implemented, tested in an appropriate testbed, and
its efficiency was proven.Comment: 17 Pages, IJNGN Journa
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