@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 = {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} }