@conference {245, title = {Toward Self-Determinant Citizen Governance: Trust-Boosting Sociocracy 3.0 with Blockchain}, booktitle = {Symposium on Implementing Collaborative Governance. Models, experiences and challenges to foster policy coordination, and to enhance sustainable community outcomes and public value generation}, year = {2018}, month = {10/2018}, publisher = {Ced4 - System Dynamic Group (Universidad de Palermo)}, organization = {Ced4 - System Dynamic Group (Universidad de Palermo)}, address = {Departamento de Ciencias Pol{\'\i}ticas de la Universidad de Palermo (it{\'a}lia)}, abstract = {

This work introduces a novel technopolitical artifact that opens up the possibility for a system of non-dominant citizen governance that eliminates the representative layer and puts citizens at the top of the decision making process. A socio-political approach that arises by merging two disrupting technologies: Sociocracy 3.0 as an organizational technology for efficiently governing large, self-determinant organizations; and Blockchain as a digital reputation technology for overcoming the limitations preventing standalone Sociocracy 3.0 of being used in organizations governing the public domain.

}, keywords = {Blockchain, citizen governance, reputation, self-determination, Sociocracy 3.0.}, author = {{\`A}lex Ribas and Facundo Bromberg} } @article {219, title = {Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds}, journal = {Computers in Biology and Medicine}, volume = {95}, year = {2018}, month = {04/2018}, pages = {129-139}, chapter = {129}, abstract = {

Background: Muscle activation level is currently being captured using im- practical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non- invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras.

Methods: A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturba- tions in the point of view of the capturing device, greatly simplifying the installation process for end-users.

Results: In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g),\ \ the best variant of the proposed methodology achieved mean absolute errors of about 9.21 \% MVC {\textemdash} an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical.

Conclusions: The results prove that the correlations between the exter- nal geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.\ 

}, keywords = {3d point clouds, biceps activation estimation, biomechanics, ensemble of shape functions, support vector machines, Tele-physiotherapy}, issn = {0010-4825}, doi = {https://doi.org/10.1016/j.compbiomed.2018.02.011}, url = {https://www.sciencedirect.com/science/article/pii/S0010482518300416}, author = {Leandro Abraham and Facundo Bromberg and Raymundo Forradellas} } @article {256, title = {Towards practical 2D grapevine bud detection with fully convolutional networks}, journal = {Computers and Electronics in Agriculture}, volume = {182}, year = {2021}, month = {03/2021}, pages = {105947}, abstract = {

In Viticulture, visual inspection of the plant is a necessary task for measuring relevant variables. In many cases, these visual inspections are susceptible to automation through computer vision methods. Bud detection is one such visual task, central for the measurement of important variables such as: measurement of bud sunlight exposure, autonomous pruning, bud counting, type-of-bud classification, bud geometric characterization, internode length, bud area, and bud development stage, among others. This paper presents a computer method for grapevine bud detection based on a Fully Convolutional Networks MobileNet architecture (FCN-MN). To validate its performance, this architecture was compared in the detection task with a strong method for bud detection, Scanning Windows (SW) based on a patch classifier, showing improvements over three aspects of detection: segmentation, correspondence identification and localization. The best version of FCN-MN showed a detection F1-measure of 88.6\% (for true positives defined as detected components whose intersection-over-union with the true bud is above 0.5), and false positives that are small and near the true bud. Splits {\textendash}false positives overlapping the true bud{\textendash} showed a mean segmentation precision of 89.3\%(21.7), while false alarms {\textendash}false positives not overlapping the true bud{\textendash} showed a mean pixel area of only 8\% the area of a true bud, and a distance (between mass centers) of 1.1 true bud diameters. The paper concludes by discussing how these results for FCN-MN would produce sufficiently accurate measurements of bud variables such as bud number, bud area, and internode length, suggesting a good performance in a practical setup.

}, keywords = {computer vision, Fully convolutional network, Grapevine bud detection, Precision viticulture}, issn = {0168-1699}, doi = {https://doi.org/10.1016/j.compag.2020.105947}, url = {https://www.sciencedirect.com/science/article/pii/S0168169920331525}, author = {Wenceslao Villegas Marset and Diego Sebastian Perez and Carlos Ariel D{\'\i}az and Facundo Bromberg} } @unpublished {229, title = {Prediction of frost events using Bayesian networks and Random Forest}, journal = {IEEE Internet of Things Journal}, year = {2017}, author = {Ana Laura Diedrichs and Facundo Bromberg and Diego Dujovne and Keoma Brun-Laguna and Thomas Watteyne} }