@conference {230, title = {Aplicaciones de Internet de las Cosas SIPIA6-Red de Sensores Inal{\'a}mbricos con IPv6}, booktitle = {XV Workshop de Investigadores en Ciencias de la Computaci{\'o}n}, year = {2013}, author = {Mercado, Gustavo and Borgo, Roberto and Gonzalez Antivilo, Francisco and Taffernaberry, Juan Carlos and Diedrichs, Ana and Aguirre, Mat{\'\i}as and Robles, Mar{\'\i}a In{\'e}s and Grunwaldt, Guillermo and Tabacchi, Germ{\'a}n and Tromer, Sebasti{\'a}n and others} } @conference {114, title = {Application of a Bayesian semi-supervised Learning Strategy to Network Intrution Detection}, booktitle = {Proceedings of ASAI 2010, Argentinean Symposioum of Artificial Intelligence}, year = {2010}, publisher = {SADIO}, organization = {SADIO}, address = {Buenos Aires}, author = {Catania, C. A. and Garcia Garino, C. and Bromberg, F.} } @conference {90, title = {Aprendizaje de independencias espec{\'\i}ficas del contexto en Markov random fields}, booktitle = {XVII Congreso Argentino de Ciencias de la Computaci{\'o}n}, year = {2011}, abstract = {

Los modelos no dirigidos o Markov random fields son ampliamente utilizados para problemas que aprenden una distribuci{\'o}n desconocida desde un conjunto de datos. Esto es porque permiten representar una distribuci{\'o}n eficientemente al hacer expl{\'\i}citas las independencias condicionales que pueden existir entre sus variables. Adem{\'a}s de estas independencias es posible representar otras, las Independencias Espec{\'\i}ficas del Contexto (CSIs) que a diferencia de las anteriores s{\'o}lo son v{\'a}lidas bajo ciertos valores que pueden tomar subconjuntos de sus variables. Debido a esto son complicadas de representar y aprenderlas desde datos. En este trabajo presentamos un enfoque para representar CSIs en modelos no dirigidos y un algoritmo que las aprende desde datos utilizando tests estad{\'\i}sticos. Mostramos resultados donde los modelos aprendidos por nuestro algoritmo resultan ser mejores o comparables a modelos aprendidos por otros sin utilizar CSIs.

}, author = {Edera, Alejandro and Bromberg, Facundo} } @conference {168, title = {Arm muscular effort estimation from images using Computer Vision and Machine Learning}, booktitle = {1st International Conference on Ambient Intelligence for Health}, year = {2015}, month = {Dec}, address = {Puerto Varas, Chile}, abstract = {

A problem of great interest in disciplines like occupational\ medicine, ergonomics, and sports, is the measurement of biomechanical\ variables involved in human movement and balance such as internal\ muscle forces and joint torques. This problem \ is solved by a two-step process: data capturing using impractical, intrusive and expensive devices\ that is then used as input in \ complex models for obtaining the biomechanical variables of interest. In this work we present a first step towards\ capturing input \ data through a more automated, non-intrusive and economic process, specifically weight held by an arm subject to isometric\ contraction as a measure of muscular effort. We do so, by processing\ RGB images of the arm with computer vision (Local Binary Patterns\ and Color Histograms) and estimating the effort with machine learning\ algorithms (SVM and Random \ Forests). In the best case we obtained an\ FMeasure= 70.68\%, an Accuracy= 71.66\% and a mean absolute error in\ the predicted weights of 554.16 grs (over 3 possible levels of effort). Considering the difficulty of the problem , it is enlightening to achieve over\ random results indicating that, despite the simplicity of the approach,\ it is possible to extract meaningful information for the \ predictive task.\ Moreover, the simplicity of the approach suggests many lines of further\ improvements: on the image capturing \ side with other kind of images;\ on the feature extraction side with more sophisticated algorithms and\ features; and on the\ knowledge extraction side with more sophisticated\ learning algorithms.\ 

}, keywords = {color histograms, LBP, muscle arm effort, random forests, SVM}, author = {Abraham, Leandro and Bromberg, Facundo and Forradellas, Raymundo} } @conference {74, title = {Automated 3D segmentation and analysis of cotton plants}, booktitle = {Digital Image Computing Techniques and Applications (DICTA), 2011 International Conference on}, year = {2011}, publisher = {IEEE}, organization = {IEEE}, author = {Paproki, Anthony and Fripp, Jurgen and Salvado, Olivier and Sirault, Xavier and Berry, Scott and Furbank, Robert} } @conference {179, title = {An Autonomous labeling approach to SVM algorithms for network traffic anomaly detection}, booktitle = {Argentine Symposium of Artificial Intelligence (ASAI). Jornadas Argentinas de Inform{\'a}tica. Mar del Plata, Argentina.}, year = {2009}, month = {08/2009}, publisher = {Sociedad Argentina de Inform{\'a}tica}, organization = {Sociedad Argentina de Inform{\'a}tica}, address = {Mar del Plata, Argentina}, abstract = {

In the past years, several support vector machines anomaly detection approaches have been proposed in the network intrusion detetion field. The main advantage of these approaches is that they\  can\  characterize normal traffic\  when trained using a data set\  containing not only normal traffic\  but also possible attacks. Unfortunately, these algorithms seem to be accurate only when the normal traffic\  vastly outnumbers the numbers of attacks or anomalies present in the dataset. This work presents an approach for autonomous labeling of normal traffic\ \  as a way of dealing with situations where\  class distributions do not present the required unbalance. The autonomous labeling process is made by SNORT, a misuse-based intrusion detection system. Experiments\  conducted on the 1998 DARPA dataset show the proposed autonomous labeling approach not only outperforms existing SVM alternatives but also obtains signicant improvement over SNORT itself.

}, author = {Brombereg, Facundo and Catania, Carlos A. and Garcia Garino, Carlos} } @article {113, title = {An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection}, journal = {Expert Systems with Applications}, volume = {39}, year = {2012}, pages = {1822{\textendash}1829}, author = {Catania, Carlos A and Bromberg, Facundo and Garino, Carlos Garc{\'\i}a} } @article {212, title = {An analysis of the impact of Kikuchi approximations}, year = {2016}, abstract = {

Factorizations of probabilistic distributions serve as condensed and efficient representations for modeling problems with uncertainty.
In probabilistic graphical models, it is generally assumed that factorizations are consistent with a graphical structure which simplifies the probabilistic structure by exploiting the conditional independences present in the distribution. In this work we focus con Markov networks, a subtype of graphical models where the graphical structure is an undirected graph, and the factorization is characterized over the maximal cliques of the graph, according with the well-known Hammersley-Clifford Theorem. \ When using that factorization, factors may correspond to the maximal cliques and overlappings of a junction tree. The semantics of a junction tree guarantees that if marginals are consistent, whatever the accuracy of the approximation, the factorization will produce a valid probability distribution (e.g. the sum of the values associated by the factorization to all possible states of the system will add to $1$). However, the number of junction trees, and thus the number of (consistent) factorizations that can be obtained from them is relatively small in comparison with all possible products of marginal distributions, including those not necessarily producing valid factorizations.
\ In this work we investigate, from different perspectives, the quality of marginal factorizations as approximations of probability distributions.\ 
\ In particular, our analysis is focused on the Clique-based Kikuchi approximations (CBKA), a particular type of factorization in probability marginals. In this type of factorization, the marginals are completely determined by the independence graph.\ 
\ Our general goal is to identify whether, and under which conditions, the CBKA can produce accurate or acceptable approximations of an original probability distribution.\ 
\ We show that the class of CBKA includes a much larger set of approximations than those derived from junction trees (or equivalently, chordal graphs).\ 
\ Additionally, we show that the quality of CBKA can be measured by using the Kendall tau rank distance, a measure which considers scenarios where the values produced by the factorizations are mainly relevant to compare or rank the configurations of the system. We analyzed the quality of CBKA in terms of the Kendall distance and also in terms of the Kullback-Liebler divergence, and show that both measures are correlated. Our current results show that such correlation depends on the structure of the underlying distribution, and also on the strength of the dependences, e.g., the hyper-parameters of Dirichlet for synthetic distributions.\ 

}, author = {Federico Schl{\"u}ter and Roberto Santana Hermida and Jos{\'e} Antonio Lozano} } @mastersthesis {207, title = {Aprendizaje de redes de Markov basado en independencias espec{\'\i}ficas del contexto: formalizaci{\'o}n y aplicaci{\'o}n a datos espaciales}, year = {2016}, author = {Edera, Alejandro} }