@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 {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} } @conference {183, title = {Low-power wireless sensor network for frost monitoring in agriculture research}, booktitle = {Biennial Congress of Argentina (ARGENCON), 2014 IEEE}, year = {2014}, publisher = {IEEE}, organization = {IEEE}, address = {Bariloche}, abstract = {

This work presents the development of a wireless sensor network (WSN), based on IEEE-802.15.4, in order to be used for frost characterization in precision agriculture by measuring temperature. Our key objective is to reduce the power consumption of the network to the minimum, allowing several measurement points per node and the remote monitoring of the sensors behaviour. For the communication interface between a WSN node and the sensors, we have developed a serial protocol inspired in SDI-12. Preliminary results show a low-cost and low-power WSN. The user can access and use the data for agronomic research.

}, isbn = {978-1-4799-4270-1}, doi = {10.1109/ARGENCON.2014.6868546}, url = {http://dx.doi.org/10.1109/ARGENCON.2014.6868546}, author = {Diedrichs, Ana Laura and Tabacchi, Germ{\'a}n and Grunwaldt, Guillermo and Pecchia, Mat{\'\i}as and Mercado, Gustavo and Gonzalez Antivilo, Francisco} } @proceedings {173, title = {Learning Markov Network Structure using Few Independence Tests.}, journal = {SIAM Data Mining}, year = {2008}, pages = {680--691}, abstract = {
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-of-the-art algorithm for learning the structure of the Markov network of a domain from independence tests on data. DGSIMN, like other independence-based algorithms, works by conducting a series of statistical conditional independence tests toward the goal of restricting the number of possible structures to one, thus inferring that structure as the only possibly correct one. During this process, DGSIMN, like the GSIMN algorithm, uses the axioms that govern the proba bilistic independence relation to avoid unnecessary tests i.e.,tests that can be inferred from the results of known ones. This results in both efficiency and reliability advantages over the simple application of statistical tests. However, one weakness of GSIMN is its rigid and heuristic ordering of the execution of tests, which results in potentially inefficient execution. DGSIMN instead uses a principled strategy, dynamically selecting the locally optimal test that is expected to increase the state of our knowledge about the structure the most. This is done by calculating the expected number of independence facts that will become known (through inference) after executing a particular test (before it is actually evaluated on data), and by selecting the one that is expected to maximize the number of such inferences, thus avoiding their potentially expensive evaluation on data. As we demonstrate in our experiments, this results in an overall decrease in the computational requirements of the algorithm, sometimes dramatically, due to the decreased the number of tests required to be evaluated on data. Experiments show that DGSIMN yields savings of up to 88\% on both sampled and benchmark data while achieving similar\  or better accuracy in most cases.
}, isbn = { 978-1-61197-278-8}, issn = {978-0-89871-654-2}, doi = { 10.1137/1.9781611972788.62}, url = {http://epubs.siam.org/doi/abs/10.1137/1.9781611972788.62}, author = {Gandhi, Parichey and Bromberg, Facundo and Margaritis, Dimitris} } @proceedings {225, title = {Sonda de temperatura para uso agr{\'\i}cola}, journal = {CASE Congreso Argentino de Sistemas Embebidos}, year = {2015}, edition = {2015}, issn = {978-987-45523-3-4}, author = {Guillermo Gr{\"u}nwaldt and Mat{\'\i}as Pecchia and Ana Laura Diedrichs and Germ{\'a}n Tabacchi and Gustavo Mercado} } @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 {221, title = {Towards A Comprehensive Picture Of C-to-U RNA Editing Sites In Angiosperm Mitochondria (under review)}, journal = {Plant Molecular Biology}, year = {2018}, author = {AlejandroA. Edera and Carolina L. Gandini and M. Virginia Sanchez-Puerta} } @article {209, title = {Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions}, year = {2016}, institution = {arXiv.org}, abstract = {

Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequently gives a good estimate of the potential production of the plants. This work presents a low cost, high precision method for the measurement of trunk diameter of grapevines based on Computer Vision techniques. Several methods based on Computer Vision and other techniques are introduced in the literature. These methods present different advantages for crop management: they are amenable to be operated by unknowledgeable personnel, with lower operational costs; they result in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; and they make the measurement process amenable to be embedded in larger autonomous systems, allowing more measurements to be taken with equivalent costs. To date, all existing autonomous methods are either of low precision, or have a prohibitive cost for massive agricultural adoption, leaving the manual Vernier caliper or tape measure as the only choice in most situations. In this work we present a semi-autonomous measurement method that is susceptible to be fully automated, cost effective for mass adoption, and its precision is competitive (with slight improvements) over the caliper manual method.

}, keywords = {computer vision, Gaussian mixture model, Grapevine, Measurements, Trunk Diameter}, issn = {arXiv:1406.4845v2}, url = {http://arxiv.org/abs/1406.4845}, author = {P{\'e}rez, Diego Sebasti{\'a}n and Bromberg, Facundo and Gonzalez-Antivilo, Francisco} } @article {121, title = {CV Resources}, year = {2014}, abstract = {

Computer Vision and Machine Learning resources with a focus on 3D scene understanding, parsing and reconstruction.

}, url = {http://www.cvlibs.net/links.php}, author = {Geiger, Andreas} }