390 research outputs found
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
Secure and Reconfigurable Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT)
The Internet of things (IoT) is revolutionizing the management and control of
automated systems leading to a paradigm shift in areas such as smart homes,
smart cities, health care, transportation, etc. The IoT technology is also
envisioned to play an important role in improving the effectiveness of military
operations in battlefields. The interconnection of combat equipment and other
battlefield resources for coordinated automated decisions is referred to as the
Internet of battlefield things (IoBT). IoBT networks are significantly
different from traditional IoT networks due to the battlefield specific
challenges such as the absence of communication infrastructure, and the
susceptibility of devices to cyber and physical attacks. The combat efficiency
and coordinated decision-making in war scenarios depends highly on real-time
data collection, which in turn relies on the connectivity of the network and
the information dissemination in the presence of adversaries. This work aims to
build the theoretical foundations of designing secure and reconfigurable IoBT
networks. Leveraging the theories of stochastic geometry and mathematical
epidemiology, we develop an integrated framework to study the communication of
mission-critical data among different types of network devices and consequently
design the network in a cost effective manner.Comment: 8 pages, 9 figure
Navigable networks as Nash equilibria of navigation games
Common sense suggests that networks are not random mazes of purposeless connections,
but that these connections are organized so that networks can perform their functions well.
One function common to many networks is targeted transport or navigation. Here, using
game theory, we show that minimalistic networks designed to maximize the navigation
efficiency at minimal cost share basic structural properties with real networks. These
idealistic networks are Nash equilibria of a network construction game whose purpose is to
find an optimal trade-off between the network cost and navigability. We show that these
skeletons are present in the Internet, metabolic, English word, US airport, Hungarian road
networks, and in a structural network of the human brain. The knowledge of these skeletons
allows one to identify the minimal number of edges, by altering which one can efficiently
improve or paralyse navigation in the network
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
We propose Factual News Graph (FANG), a novel graphical social context
representation and learning framework for fake news detection. Unlike previous
contextual models that have targeted performance, our focus is on
representation learning. Compared to transductive models, FANG is scalable in
training as it does not have to maintain all nodes, and it is efficient at
inference time, without the need to re-process the entire graph. Our
experimental results show that FANG is better at capturing the social context
into a high fidelity representation, compared to recent graphical and
non-graphical models. In particular, FANG yields significant improvements for
the task of fake news detection, and it is robust in the case of limited
training data. We further demonstrate that the representations learned by FANG
generalize to related tasks, such as predicting the factuality of reporting of
a news medium.Comment: To appear in CIKM 202
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