31,410 research outputs found
Combining link and content-based information in a Bayesian inference model for entity search
An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework. A flexible query model is supported capable to reason with the availability of simple semantics in querie
Efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena
Structural learning of Bayesian Networks (BNs) is a NP-hard problem, which is
further complicated by many theoretical issues, such as the I-equivalence among
different structures. In this work, we focus on a specific subclass of BNs,
named Suppes-Bayes Causal Networks (SBCNs), which include specific structural
constraints based on Suppes' probabilistic causation to efficiently model
cumulative phenomena. Here we compare the performance, via extensive
simulations, of various state-of-the-art search strategies, such as local
search techniques and Genetic Algorithms, as well as of distinct regularization
methods. The assessment is performed on a large number of simulated datasets
from topologies with distinct levels of complexity, various sample size and
different rates of errors in the data. Among the main results, we show that the
introduction of Suppes' constraints dramatically improve the inference
accuracy, by reducing the solution space and providing a temporal ordering on
the variables. We also report on trade-offs among different search techniques
that can be efficiently employed in distinct experimental settings. This
manuscript is an extended version of the paper "Structural Learning of
Probabilistic Graphical Models of Cumulative Phenomena" presented at the 2018
International Conference on Computational Science
Multilinear tensor regression for longitudinal relational data
A fundamental aspect of relational data, such as from a social network, is
the possibility of dependence among the relations. In particular, the relations
between members of one pair of nodes may have an effect on the relations
between members of another pair. This article develops a type of regression
model to estimate such effects in the context of longitudinal and multivariate
relational data, or other data that can be represented in the form of a tensor.
The model is based on a general multilinear tensor regression model, a special
case of which is a tensor autoregression model in which the tensor of relations
at one time point are parsimoniously regressed on relations from previous time
points. This is done via a separable, or Kronecker-structured, regression
parameter along with a separable covariance model. In the context of an
analysis of longitudinal multivariate relational data, it is shown how the
multilinear tensor regression model can represent patterns that often appear in
relational and network data, such as reciprocity and transitivity.Comment: Published at http://dx.doi.org/10.1214/15-AOAS839 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Comprehensive Security Framework for Global Threats Analysis
Cyber criminality activities are changing and becoming more and more professional. With the growth of financial flows through the Internet and the Information System (IS), new kinds of thread arise involving complex scenarios spread within multiple IS components. The IS information modeling and Behavioral Analysis are becoming new solutions to normalize the IS information and counter these new threads. This paper presents a framework which details the principal and necessary steps for monitoring an IS. We present the architecture of the framework, i.e. an ontology of activities carried out within an IS to model security information and User Behavioral analysis. The results of the performed experiments on real data show that the modeling is effective to reduce the amount of events by 91%. The User Behavioral Analysis on uniform modeled data is also effective, detecting more than 80% of legitimate actions of attack scenarios
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