@conference {247, title = {Exploring the Influence of Self-determination in the Collective Intelligence of Collaborative Organizations}, booktitle = {IFKAD, International Forum on Knowledge Asset Dynamics}, year = {2019}, month = {09/19}, address = {Matera, Italy}, abstract = {

In recent years, positive correlations between some factors of collaborative group task processes and the increasing of collective intelligence (CI) have been presented. This work introduces an hypothesis that argues the existence of a new factor of positive influence for the increasing of collective intelligence in collaborative group tasks operating in cooperative environments: self-determination. Therefore, we present an argumentation based on Cooperative Multiagent Systems that spotlights the significance of self-determination in these particular environments. Furthermore, we also introduce a preliminary design of an experimental setup and a methodological framework for validating the hypothesis empirically in human organizations. Our propose consists on measuring, on the one hand, the level of self-determination from the individuals that participate on the decision-making processes, and on the other hand, on measuring the level of collective intelligence achieved by performing collaborative group task. Finally, we propose to use statistical analysis to explore if there are positive correlations between self-determination and collective intelligence in cooperative environments, such as collaborative organizations.

}, author = {Ribas, Alexandre}, editor = {Bromberg, Facundo} } @conference {246, title = {Exploring the Influence of Self-determination in the Collective Intelligence of Collaborative Organizations}, booktitle = {Symposium on implementing collaborative governance}, year = {2018}, month = {10/2018}, address = {Palermo, Italy.}, 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 = {Ribas, Alexandre and Brombereg, Facundo} } @proceedings {174, title = {Efficient and Robust Independence-Based Markov Network Structure Discovery.}, journal = {20th International Joint Conference of Artificial Inteliigence (IJCAI)}, year = {2007}, pages = {2431-2436 }, publisher = {Morgan Kaufmann Publishers Inc.}, address = {San Francisco, CA}, abstract = {

In this paper we introduce a novel algorithm for the induction of the Markov network structure of a domain from the outcome of conditional independence tests on data. Such algorithms work by successively restricting the set of possible structures until there is only a single structure consistent with the conditional independence tests executed. Existing independence-based algorithms have wellknown shortcomings, such as rigidly ordering the sequence of tests they perform, resulting in potential inefficiencies in the number of tests required, and committing fully to the test outcomes, resulting in lack of robustness in case of unreliable tests. We address both problems through a Bayesian particle filtering approach, which uses a population of Markov network structures to maintain the posterior probability distribution over them, given the outcomes of the tests performed. Instead of a fixed ordering, our approach greedily selects, at each step, the optimally informative from a pool of candidate tests according to information gain. In addition, it maintains multiple candidate structures weighed by posterior probability, which makes it more robust to errors in the test outcomes. The result is an approximate algorithm (due to the use of particle filtering) that is useful in domains where independence tests are uncertain (such as applications where little data is available) or expensive (such as cases of very large data sets and/or distributed data).

}, url = {http://www.aaai.org/Library/IJCAI/2007/ijcai07-391.php}, author = {Bromberg, Facundo and Margaritis, Dimitris} } @proceedings {176, title = {Efficient Markov network structure discovery using independence tests}, journal = {Proceedings of the SIAM Conference in Data Mining}, year = {2006}, pages = {141--152}, address = {Bethesda, Maryland, USA}, abstract = {

We present two algorithms for learning the structure of a Markov network from discrete data: GSMN and GSIMN. Both algorithms use statistical conditional independence tests on data to infer the structure by successively constraining the set of structures consistent with the results of these tests. GSMN is a natural adaptation of the Grow-Shrink algorithm of Margaritis and Thrun for learning the structure of Bayesian networks. GSIMN extends GSMN by additionally exploiting Pearl{\textquoteright}s well-known properties of conditional independence relations to infer novel independencies from known independencies, thus avoiding the need to perform these tests. Experiments on artificial and real data sets show GSIMN can yield savings of up to 70\% with respect to GSMN, while generating a Markov network with comparable or in several cases considerably improved quality. In addition to GSMN, we also compare GSIMN to a forward-chaining implementation, called GSIMN-FCH, that produces all possible conditional independence results by repeatedly applying Pearl{\textquoteright}s theorems on the known conditional independence tests. The results of this comparison show that GSIMN is nearly optimal in terms of the number of tests it can infer, under a fixed ordering of the tests performed.

}, isbn = {978-0-89871-611-5}, doi = {10.1137-1.9781611972764.13}, url = {http://epubs.siam.org/doi/abs/10.1137/1.9781611972764.13}, author = {Bromberg, Facundo and Margaritis, Dimitris and Honavar, Vasant} } @proceedings {177, title = {Estimaci{\'o}n de carga muscular mediante im{\'a}genes}, journal = {Argentinean Symposium of Artificial Intelligence (ASAI) - Jornadas Argentinas de Inform{\'a}tica}, year = {2014}, pages = {91--98}, publisher = {Sociedad Argentina de Inform{\'a}tica}, address = {Buenos Aires, Argentina}, abstract = {

Un problema de gran inter{\'e}s en disciplinas como la ocupacional, ergon{\'o}mica y deportiva, es la medici{\'o}n de variables biomec{\'a}nicas involucradas en\ el movimiento humano (como las fuerzas musculares internas y torque de articulaciones).\ Actualmente este problema se resuelve en un proceso de dos pasos.\ Primero capturando datos con dispositivos\ poco pr{\'a}cticos, intrusivos y costosos.\ Luego estos datos son usados como entrada en modelos complejos para obtener\ las variables biomec{\'a}nicas como salida. El presente trabajo representa una alternativa automatizada, no intrusiva y econ{\'o}mica al primer paso, proponiendo la\ captura de estos datos a trav{\'e}s de im{\'a}genes. En trabajos futuros la idea es automatizar todo el proceso de c{\'a}lculo de esas variables. En este trabajo elegimos un\ caso particular de medici{\'o}n de variables biomec{\'a}nicas: el problema de estimar\ el nivel discreto de carga muscular que est{\'a}n ejerciendo los m{\'u}sculos de un brazo. Para \ estimar a partir de im{\'a}genes est{\'a}ticas del brazo ejerciendo la fuerza de\ sostener la carga, el nivel de la misma, realizamos un\ proceso de clasificaci{\'o}n.\ Nuestro enfoque utiliza Support Vector Machines para clasificaci{\'o}n, combinada\ con una etapa de pre-procesamiento que extrae caracter{\'\i}sticas visuales utilizando variadas t{\'e}cnicas (Bag of Keypoints, Local Binary Patterns, Histogramas de\ Color, Momentos de Contornos) En los mejores casos (Local Binary Patterns y\ Momentos de Contornos) obtenemos medidas de performance en la clasificaci{\'o}n\ (Precision, Recall, F-Measure y Accuracy) superiores al 90 \%.

}, url = {http://43jaiio.sadio.org.ar/proceedings/ASAI/search.html}, author = {Abraham, Leandro and Bromberg, Facundo and Forradellas, Raymundo} } @article {170, title = {Efficient Markov network discovery using particle filters}, journal = {Computational Intelligence}, volume = {25}, year = {2009}, month = {11/2009}, pages = {367{\textendash}394}, abstract = {

In this paper, we introduce an efficient independence-based algorithm for the induction of the Markov network (MN) structure of a domain from the outcomes of independence test conducted on data. Our algorithm utilizes a particle filter (sequential Monte Carlo) method to maintain a population of MN structures that represent the posterior probability distribution over structures, given the outcomes of the tests performed. This enables us to select, at each step, the maximally informative test to conduct next from a pool of candidates according to information gain, which minimizes the cost of the statistical tests conducted on data. This makes our approach useful in domains where independence tests are expensive, such as cases of very large data sets and/or distributed data. In addition, our method maintains multiple candidate structures weighed by posterior probability, which allows flexibility in the presence of potential errors in the test outcomes.

}, keywords = {graphical model structure learning, Markov networks, particle filters, sequential Monte Carlo}, doi = {10.1111/j.1467-8640.2009.00347.x}, url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8640.2009.00347.x/abstract;jsessionid=C95A98B67CD44AF9ABF59B3B0CCAA979.f01t02?userIsAuthenticated=false\&deniedAccessCustomisedMessage=}, author = {Margaritis, Dimitris and Bromberg, Facundo} } @article {171, title = {Efficient Markov network structure discovery using independence tests}, journal = {Journal of Artificial Intelligence Research}, volume = {35}, year = {2009}, pages = {449{\textendash}484}, abstract = {

We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN. Both algorithms use statistical independence tests to infer the structure by successively constraining the set of structures consistent with the results of these tests. Until very recently, algorithms for structure learning were based on maximum likelihood estimation, which has been proved to be NP-hard for Markov networks due to the difficulty of estimating the parameters of the network, needed for the computation of the data likelihood. The independence-based approach does not require the computation of the likelihood, and thus both GSMN* and GSIMN can compute the structure efficiently (as shown in our experiments). GSMN* is an adaptation of the Grow-Shrink algorithm of Margaritis and Thrun for learning the structure of Bayesian networks. GSIMN extends GSMN* by additionally exploiting Pearls well-known properties of the conditional independence relation to infer novel independences from known ones, thus avoiding the performance of statistical tests to estimate them. To accomplish this efficiently GSIMN uses the Triangle theorem, also introduced in this work, which is a simplified version of the set of Markov axioms. Experimental comparisons on artificial and real-world data sets show GSIMN can yield significant savings with respect to GSMN*, while generating a Markov network with comparable or in some cases improved quality. We also compare GSIMN to a forward-chaining implementation, called GSIMN-FCH, that produces all possible conditional independences resulting from repeatedly applying Pearls theorems on the known conditional independence tests. The results of this comparison show that GSIMN, by the sole use of the Triangle theorem, is nearly optimal in terms of the set of independences tests that it infers.

}, doi = { 10.1613/jair.2773}, url = {http://www.jair.org/papers/paper2773.html}, author = {Bromberg, Facundo and Margaritis, Dimitris and Honavar, Vasant} } @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} } @mastersthesis {161, title = {El enfoque IBMAP para aprendizaje de estructuras de redes de Markov}, volume = {Doctorado en Ciencias de la Computaci{\'o}n (PhD in Computer Science)}, year = {2014}, month = {11/2014}, pages = {138}, type = {Doctorado}, abstract = {

Las redes de Markov\ son modelos probabil{\'\i}sticos gr{\'a}ficos,\ una herramienta computacional para el modelado eficiente de distribuciones de probabilidad\ que utiliza grafos para facilitar la representaci{\'o}n de problemas complejos.\ Estos modelos han sido dise{\~n}ados para ser manipulados por sistemas expertos,\ utilizando la teor{\'\i}a de la probabilidad para razonar eficientemente bajo condiciones de incertidumbre.\ Sin embargo, una limitaci{\'o}n importante para el uso de estos modelos es que en la pr{\'a}ctica\ resulta complejo dise{\~n}arlos manualmente, ya que el conocimiento de expertos no siempre es suficiente,\ sumado al hecho de que muchos dominios reales poseen una gran dimensionalidad.\ Por esto, el aprendizaje\ de estos modelos a partir de datos\ es un t{\'o}pico que ha tomado gran relevancia, ya que la disponibilidad de datos digitales es cada vez mayor,\ resultando adem{\'a}s en un mecanismo interesante para descubrir nuevo conocimiento a partir de datos digitales.

En este trabajo, la investigaci{\'o}n se centra en un enfoque espec{\'\i}fico de\ aprendizaje de redes de Markov:\ los algoritmos basados en independencia.\ Estos algoritmos est{\'a}n dise{\~n}ados para aprender la estructura de independencias del modelo,\ que es la componente que codifica de un modo compacto el conocimiento sobre la distribuci{\'o}n.\ Los algoritmos basados en independencia han sido dise{\~n}ados bajo lineamientos te{\'o}ricos\ que permiten aprender la estructura de un modo eficiente y robusto, a partir de\ la ejecuci{\'o}n de un conjunto de tests estad{\'\i}sticos de independencia\ sobre los datos.\ Com{\'u}nmente, los resultados de dichos tests son utilizados como restricciones\ que gu{\'\i}an una b{\'u}squeda en el espacio de las estructuras de independencia posibles,\ convergiendo a una estructura que satisface los resultados de todos los tests.\ Estos algoritmos garantizan que la estructura aprendida es correcta\ bajo la suposici{\'o}n de que los tests estad{\'\i}sticos son confiables.\ No obstante, un hecho muy com{\'u}n en la pr{\'a}ctica suele ser que los datos disponibles no son suficientes para obtener resultados correctos desde los tests estad{\'\i}sticos.\ Cuando esto sucede, los algoritmos basados en independencia\ acumulan y propagan suposiciones de\ independencia incorrectas,\ resultando en un aprendizaje con gran cantidad de errores estructurales.\ Este hecho, que resulta en una limitaci{\'o}n de real importancia\ a la hora de aplicar esta tecnolog{\'\i}a,\ es la motivaci{\'o}n de la presente tesis.

En este trabajo se presenta el enfoque de m{\'a}ximo a posteriori basado en independencias\ para aprendizaje de estructuras de redes de Markov (IBMAP, del ingl{\'e}s independence-based maximum-a-posteriori).\ Dado que los algoritmos tradicionales descartan la estructura correcta cada vez que ejecutan\ un test err{\'o}neo, se propone un enfoque que asigna probabilidades a las distintas estructuras,\ sin descartar ninguna. Para esto, se propone una funci{\'o}n de puntaje de estructuras basada en tests estad{\'\i}sticos denominada IB-score\ (puntaje basado en independencias).\ Esta funci{\'o}n permite computar de un modo aproximado la probabilidad a posteriori\ de una estructura dados los datos Pr(G|D), combinando los resultados de un conjunto de tests estad{\'\i}sticos.\ De este modo, las diferentes estructuras poseen un puntaje m{\'a}s alto o m{\'a}s bajo seg{\'u}n\ las probabilidades de las independencias que codifican.\ En resumen, el enfoque propuesto consiste en la maximizaci{\'o}n de la funci{\'o}n IB-score sobre el espacio de todas las estructuras posibles.\ A modo de instanciaci{\'o}n de este enfoque se presenta una srie de algoritmos que maximizan dicha funci{\'o}n\ con diversos mecanismos de optimizaci{\'o}n.\ 

Para validar el enfoque se evalu{\'o} el desempe{\~n}o de las distintas instanciaciones de la b{\'u}squeda,\ evaluando la calidad de las estructuras aprendidas con respecto\ a algoritmos basados en independencia que pertenecen al estado del arte.\ Se presentan resultados sistem{\'a}ticos sobre una gran variedad de dimensiones del problema,\ demostrando que este enfoque permite mejorar significativamente la calidad de las estructuras aprendidas.\ Se demuestra tambi{\'e}n que dichas mejoras pueden obtenerse a un costo computacional\ competitivo respecto de los algoritmos del estado del arte.\ Dicha experimentaci{\'o}n fue llevada a cabo sobre datos sint{\'e}ticos y datos del mundo real, y en una aplicaci{\'o}n de aprendizaje de estructuras para algoritmos evolutivos.

}, author = {Schl{\"u}ter, Federico and Bromberg, Facundo} }