2022
Ricardo Llamas; Leobardo Valera, Paula Olaya, Michela Taufer, and Rodrigo Vargas "Downscaling Satellite Soil Moisture using a Modular Spatial Inference Framework" Remote Sensing in Geology, Geomorphology and Hydrology. https://www.mdpi.com/2072-4292/14/13/3137
@Article{rs14133137,
AUTHOR = {Llamas, Ricardo M. and Valera, Leobardo and Olaya, Paula and Taufer, Michela and Vargas, Rodrigo},
TITLE = {Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework},
JOURNAL = {Remote Sensing},
VOLUME = {14},
YEAR = {2022},
NUMBER = {13},
ARTICLE-NUMBER = {3137},
URL = {https://www.mdpi.com/2072-4292/14/13/3137},
ISSN = {2072-4292}, ABSTRACT = {Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor <KKNN> and Random Forest <RF>). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.}, DOI = {10.3390/rs14133137}
}
AUTHOR = {Llamas, Ricardo M. and Valera, Leobardo and Olaya, Paula and Taufer, Michela and Vargas, Rodrigo},
TITLE = {Downscaling Satellite Soil Moisture Using a Modular Spatial Inference Framework},
JOURNAL = {Remote Sensing},
VOLUME = {14},
YEAR = {2022},
NUMBER = {13},
ARTICLE-NUMBER = {3137},
URL = {https://www.mdpi.com/2072-4292/14/13/3137},
ISSN = {2072-4292}, ABSTRACT = {Soil moisture is an important parameter that regulates multiple ecosystem processes and provides important information for environmental management and policy decision-making. Spaceborne sensors provide soil moisture information over large areas, but information is commonly available at coarse resolution with spatial and temporal gaps. Here, we present a modular spatial inference framework to downscale satellite-derived soil moisture using terrain parameters and test the performance of two modeling methods (Kernel-Weighted K-Nearest Neighbor <KKNN> and Random Forest <RF>). We generate monthly and weekly gap-free spatial predictions on soil moisture at 1 km using data from the European Space Agency Climate Change Initiative (ESA-CCI; version 6.1) over two regions in the conterminous United States. RF was the method that performed better in cross-validation when comparing with the reference ESA-CCI data, but KKNN showed a slightly higher agreement with ground-truth information as part of independent validation. We postulate that more heterogeneous landscapes (i.e., high topographic variation) may be more challenging for downscaling and predicting soil moisture; therefore, moisture networks should increase monitoring efforts across these complex landscapes. Future opportunities for development of modular cyberinfrastructure tools for downscaling satellite-derived soil moisture are discussed.}, DOI = {10.3390/rs14133137}
}
Valera, Leobardo, Martine Ceberio, and Vladik Kreinovich. "How to Select a Representative Sample for a Family of Functions?." 15th International Workshop on Constraint Programming and Decision Making CoProD 2022 Halifax, Nova Scotia, Canada, May 30, 2022
@article{valera2022select,
title={How to Select a Representative Sample for a Family of Functions?},
author={Valera, Leobardo and Ceberio, Martine and Kreinovich, Vladik},
year={2022}
}
title={How to Select a Representative Sample for a Family of Functions?},
author={Valera, Leobardo and Ceberio, Martine and Kreinovich, Vladik},
year={2022}
}
2021
Clariandys Rivera-Kempis, Leobardo Valera, and Miguel Angel Sastre-Castillo, Entreprenurial Competence: Using Machine Learning to Classify Entrepreneurs. Sustainability 2021, 13, 8252. https://doi.org/10.3390/su13158252
@Article{su13158252,
AUTHOR = {Rivera-Kempis, Clariandys and Valera, Leobardo and Sastre-Castillo, Miguel A.},
TITLE = {{Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs}},
JOURNAL = {Sustainability},
VOLUME = {13},
YEAR = {2021},
NUMBER = {15},
ARTICLE-NUMBER = {8252},
URL = {https://www.mdpi.com/2071-1050/13/15/8252},
ISSN = {2071-1050},
ABSTRACT = {Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component analysis, ranking and classification of data using the Ward method, linear discriminant analysis, and Gaussian regression among others.},
DOI = {10.3390/su13158252}
}
AUTHOR = {Rivera-Kempis, Clariandys and Valera, Leobardo and Sastre-Castillo, Miguel A.},
TITLE = {{Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs}},
JOURNAL = {Sustainability},
VOLUME = {13},
YEAR = {2021},
NUMBER = {15},
ARTICLE-NUMBER = {8252},
URL = {https://www.mdpi.com/2071-1050/13/15/8252},
ISSN = {2071-1050},
ABSTRACT = {Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component analysis, ranking and classification of data using the Ward method, linear discriminant analysis, and Gaussian regression among others.},
DOI = {10.3390/su13158252}
}
2020
Valera, Leobardo, Martine Ceberio, Olga Kosheleva, and Vladik Kreinovich. "Equations for Which Newton’s Method Never Works: Pedagogical Examples." In Fuzzy Information Processing 2020: Proceedings of the 2020 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2020, p. 413. Springer Nature.
@inproceedings{valera2020equations,
title={Equations for Which Newton’s Method Never Works: Pedagogical Examples},
author={Valera, Leobardo and Ceberio, Martine and Kosheleva, Olga and Kreinovich, Vladik},
booktitle={Fuzzy Information Processing 2020: Proceedings of the 2020 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2020},
pages={413},
organization={Springer Nature}
}
title={Equations for Which Newton’s Method Never Works: Pedagogical Examples},
author={Valera, Leobardo and Ceberio, Martine and Kosheleva, Olga and Kreinovich, Vladik},
booktitle={Fuzzy Information Processing 2020: Proceedings of the 2020 Annual Conference of the North American Fuzzy Information Processing Society, NAFIPS 2020},
pages={413},
organization={Springer Nature}
}
Valera, Leobardo , Martine Ceberio, and Vladik Kreinovich. "Why Burgers Equation: Symmetry-Based Approach." In Decision Making under Constraints, pp. 211-216. Springer, Cham, 2020.
@incollection{valera2020burgers,
title={Why Burgers Equation: Symmetry-Based Approach},
author={Valera, Leobardo and Ceberio, Martine and Kreinovich, Vladik},
booktitle={Decision Making under Constraints},
pages={211--216},
year={2020},
publisher={Springer}
}
title={Why Burgers Equation: Symmetry-Based Approach},
author={Valera, Leobardo and Ceberio, Martine and Kreinovich, Vladik},
booktitle={Decision Making under Constraints},
pages={211--216},
year={2020},
publisher={Springer}
}
2019
Olumoye, O., Throneberry, G., Garcia, A., Valera, L., Abdelkefi, A., Ceberio, M. (2019). Solving Large Dynamical Systems by Constraint Sampling. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_1
@InProceedings{10.1007/978-3-030-31019-6_1,
author="Olumoye, Omeiza and Throneberry, Glen and Garcia, Angel and Valera, Leobardo and Abdelkefi, Abdessattar and Ceberio, Martine",
editor="Figueroa-Garc{\'i}a, Juan Carlos and Duarte-Gonz{\'a}lez, Mario and Jaramillo-Isaza, Sebasti{\'a}n and Orjuela-Ca{\~{n}}on, Alvaro David and D{\'i}az-Gutierrez, Yesid",
title="Solving Large Dynamical Systems by Constraint Sampling",
booktitle="Applied Computer Sciences in Engineering",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="3--15",
abstract="The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, when the original dynamical system is very large, the resulting reduced-order model, although much ``thinner'', is still as tall as the original system, i.e., it has the same number of equations. In some extreme cases, the number of equations is prohibitive and cannot be loaded in memory. In this work, we combine traditional interval constraint solving techniques with a strategy to reduce the number of equations to consider. We describe our approach and report preliminary promising results.", isbn="978-3-030-31019-6" }
author="Olumoye, Omeiza and Throneberry, Glen and Garcia, Angel and Valera, Leobardo and Abdelkefi, Abdessattar and Ceberio, Martine",
editor="Figueroa-Garc{\'i}a, Juan Carlos and Duarte-Gonz{\'a}lez, Mario and Jaramillo-Isaza, Sebasti{\'a}n and Orjuela-Ca{\~{n}}on, Alvaro David and D{\'i}az-Gutierrez, Yesid",
title="Solving Large Dynamical Systems by Constraint Sampling",
booktitle="Applied Computer Sciences in Engineering",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="3--15",
abstract="The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, when the original dynamical system is very large, the resulting reduced-order model, although much ``thinner'', is still as tall as the original system, i.e., it has the same number of equations. In some extreme cases, the number of equations is prohibitive and cannot be loaded in memory. In this work, we combine traditional interval constraint solving techniques with a strategy to reduce the number of equations to consider. We describe our approach and report preliminary promising results.", isbn="978-3-030-31019-6" }
Valera, Leobardo, Martine Ceberio, and Vladik Kreinovich. ``Derivation of Louisville-Bratu-Gelfand Equation from Shift-or Scale-Invariance." In International Fuzzy Systems Association World Congress, pp. 813-819. Springer, Cham, 2019.
@inproceedings{valera2019derivation,
title={Derivation of Louisville-Bratu-Gelfand Equation from Shift-or Scale-Invariance},
author={Valera, Leobardo and Ceberio, Martine and Kreinovich, Vladik},
booktitle={International Fuzzy Systems Association World Congress},
pages={813--819},
year={2019},
organization={Springer}
}
title={Derivation of Louisville-Bratu-Gelfand Equation from Shift-or Scale-Invariance},
author={Valera, Leobardo and Ceberio, Martine and Kreinovich, Vladik},
booktitle={International Fuzzy Systems Association World Congress},
pages={813--819},
year={2019},
organization={Springer}
}
2018
Cervantes, F., Usevitch, B., Valera, L., Kreinovich, V. (2018). Why Sparse? Fuzzy Techniques Explain Empirical Efficiency of Sparsity-Based Data- and Image-Processing Algorithms. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_32
@Inbook{Cervantes2018,
author="Cervantes, Fernando
and Usevitch, Bryan
and Valera, Leobardo
and Kreinovich, Vladik",
editor="Zadeh, Lotfi A.
and Yager, Ronald R.
and Shahbazova, Shahnaz N.
and Reformat, Marek Z.
and Kreinovich, Vladik",
title="Why Sparse? Fuzzy Techniques Explain Empirical Efficiency of Sparsity-Based Data- and Image-Processing Algorithms",
bookTitle="Recent Developments and the New Direction in Soft-Computing Foundations and Applications: Selected Papers from the 6th World Conference on Soft Computing, May 22-25, 2016, Berkeley, USA",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="419--428", abstract="In many practical applications, it turned out to be efficient to assume that the signal or an image is sparse, i.e., that when we decompose it into appropriate basic functions (e.g., sinusoids or wavelets), most of the coefficients in this decomposition will be zeros. At present, the empirical efficiency of sparsity-based techniques remains somewhat a mystery. In this paper, we show that fuzzy-related techniques can explain this empirical efficiency. A similar explanation can be obtained by using probabilistic techniques; this fact increases our confidence that our explanation is correct.",
isbn="978-3-319-75408-6",
doi="10.1007/978-3-319-75408-6_32",
url="https://doi.org/10.1007/978-3-319-75408-6_32"
}
title="Why Sparse? Fuzzy Techniques Explain Empirical Efficiency of Sparsity-Based Data- and Image-Processing Algorithms",
bookTitle="Recent Developments and the New Direction in Soft-Computing Foundations and Applications: Selected Papers from the 6th World Conference on Soft Computing, May 22-25, 2016, Berkeley, USA",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="419--428", abstract="In many practical applications, it turned out to be efficient to assume that the signal or an image is sparse, i.e., that when we decompose it into appropriate basic functions (e.g., sinusoids or wavelets), most of the coefficients in this decomposition will be zeros. At present, the empirical efficiency of sparsity-based techniques remains somewhat a mystery. In this paper, we show that fuzzy-related techniques can explain this empirical efficiency. A similar explanation can be obtained by using probabilistic techniques; this fact increases our confidence that our explanation is correct.",
isbn="978-3-319-75408-6",
doi="10.1007/978-3-319-75408-6_32",
url="https://doi.org/10.1007/978-3-319-75408-6_32"
}
2017
Valera, L., Contreras, A.G., Ceberio, M. (2018). “On-the-fly” Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_33
@InProceedings{10.1007/978-3-319-67137-6_33,
author="Valera, Leobardo and Contreras, Angel Garcia and Ceberio, Martine", editor="Melin, Patricia and Castillo, Oscar and Kacprzyk, Janusz and Reformat, Marek and Melek, William",
title="``On-the-fly'' Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling",
booktitle="Fuzzy Logic in Intelligent System Design",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="293--299",
abstract="Computer simulations of dynamic systems are really important to better understand some processes or phenomena without having to physically execute them, and/or to make offline decisions, or decisions that do not need immediate, ``on-the-fly'' answers in general. However, given a set of equations describing a dynamic phenomenon, wouldn't it be nice to be able to exploit them more? Instead of simulating a situation, could we gear it or even veer it to a predefined performance? This paper is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. We propose to carry such computations using intervals and constraint solving techniques. However, realistically, aiming to enable such identification and decision on an on-going process or phenomena requires being able to conduct very fast computations on possibly very large systems of equations. We further propose to combine interval and constraint solving techniques with reduced-order modeling techniques to guarantee results in a practical amount of time.",
isbn="978-3-319-67137-6"
}
author="Valera, Leobardo and Contreras, Angel Garcia and Ceberio, Martine", editor="Melin, Patricia and Castillo, Oscar and Kacprzyk, Janusz and Reformat, Marek and Melek, William",
title="``On-the-fly'' Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling",
booktitle="Fuzzy Logic in Intelligent System Design",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="293--299",
abstract="Computer simulations of dynamic systems are really important to better understand some processes or phenomena without having to physically execute them, and/or to make offline decisions, or decisions that do not need immediate, ``on-the-fly'' answers in general. However, given a set of equations describing a dynamic phenomenon, wouldn't it be nice to be able to exploit them more? Instead of simulating a situation, could we gear it or even veer it to a predefined performance? This paper is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. We propose to carry such computations using intervals and constraint solving techniques. However, realistically, aiming to enable such identification and decision on an on-going process or phenomena requires being able to conduct very fast computations on possibly very large systems of equations. We further propose to combine interval and constraint solving techniques with reduced-order modeling techniques to guarantee results in a practical amount of time.",
isbn="978-3-319-67137-6"
}
Valera, L., Contreras, A.G., Ceberio, M. (2018). “On-the-fly” Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_33
@InProceedings{10.1007/978-3-319-67137-6_33,
author="Valera, Leobardo and Contreras, Angel Garcia and Ceberio, Martine", editor="Melin, Patricia and Castillo, Oscar and Kacprzyk, Janusz and Reformat, Marek and Melek, William",
title="``On-the-fly'' Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling",
booktitle="Fuzzy Logic in Intelligent System Design",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="293--299",
abstract="Computer simulations of dynamic systems are really important to better understand some processes or phenomena without having to physically execute them, and/or to make offline decisions, or decisions that do not need immediate, ``on-the-fly'' answers in general. However, given a set of equations describing a dynamic phenomenon, wouldn't it be nice to be able to exploit them more? Instead of simulating a situation, could we gear it or even veer it to a predefined performance? This paper is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. We propose to carry such computations using intervals and constraint solving techniques. However, realistically, aiming to enable such identification and decision on an on-going process or phenomena requires being able to conduct very fast computations on possibly very large systems of equations. We further propose to combine interval and constraint solving techniques with reduced-order modeling techniques to guarantee results in a practical amount of time.",
isbn="978-3-319-67137-6"
}
author="Valera, Leobardo and Contreras, Angel Garcia and Ceberio, Martine", editor="Melin, Patricia and Castillo, Oscar and Kacprzyk, Janusz and Reformat, Marek and Melek, William",
title="``On-the-fly'' Parameter Identification for Dynamic Systems Control, Using Interval Computations and Reduced-Order Modeling",
booktitle="Fuzzy Logic in Intelligent System Design",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="293--299",
abstract="Computer simulations of dynamic systems are really important to better understand some processes or phenomena without having to physically execute them, and/or to make offline decisions, or decisions that do not need immediate, ``on-the-fly'' answers in general. However, given a set of equations describing a dynamic phenomenon, wouldn't it be nice to be able to exploit them more? Instead of simulating a situation, could we gear it or even veer it to a predefined performance? This paper is concerned with the identification of parameters of dynamic systems that ensure a specific performance or behavior. We propose to carry such computations using intervals and constraint solving techniques. However, realistically, aiming to enable such identification and decision on an on-going process or phenomena requires being able to conduct very fast computations on possibly very large systems of equations. We further propose to combine interval and constraint solving techniques with reduced-order modeling techniques to guarantee results in a practical amount of time.",
isbn="978-3-319-67137-6"
}
L. Valera, A. Garcia, A. Gholamy, M. Ceberio and H. Florez, "Towards predictions of large dynamic systems' behavior using reduced-order modeling and interval computations," 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 345-350, doi: 10.1109/SMC.2017.8122627.
@INPROCEEDINGS{8122627,
author={Valera, Leobardo and Garcia, Angel and Gholamy, Afshin and Ceberio, Martine and Florez, Horacio},
booktitle={2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
title={Towards predictions of large dynamic systems' behavior using reduced-order modeling and interval computations},
year={2017},
volume={},
number={},
pages={345-350},
doi={10.1109/SMC.2017.8122627}}
author={Valera, Leobardo and Garcia, Angel and Gholamy, Afshin and Ceberio, Martine and Florez, Horacio},
booktitle={2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
title={Towards predictions of large dynamic systems' behavior using reduced-order modeling and interval computations},
year={2017},
volume={},
number={},
pages={345-350},
doi={10.1109/SMC.2017.8122627}}
2016
F. Cervantes, B. Usevitch, L. Valera, V. Kreinovich and O. Kosheleva, "Fuzzy techniques provide a theoretical explanation for the heuristic ℓp-regularization of signals and images," 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016, pp. 1323-1327, doi: 10.1109/FUZZ-IEEE.2016.7737842.
@INPROCEEDINGS{7737842,
author={Cervantes, Fernando and Usevitch, Brian and Valera, Leobardo and Kreinovich, Vladik and Kosheleva, Olga},
booktitle={2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
title={Fuzzy techniques provide a theoretical explanation for the heuristic ℓp -regularization of signals and images},
year={2016},
volume={},
number={},
pages={1323-1327},
doi={10.1109/FUZZ-IEEE.2016.7737842}}
author={Cervantes, Fernando and Usevitch, Brian and Valera, Leobardo and Kreinovich, Vladik and Kosheleva, Olga},
booktitle={2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
title={Fuzzy techniques provide a theoretical explanation for the heuristic ℓ
year={2016},
volume={},
number={},
pages={1323-1327},
doi={10.1109/FUZZ-IEEE.2016.7737842}}
Valera, Leobardo, and Martine Ceberio. "Using Interval Constraint Solving Techniques to better understand and predict future behaviors of dynamic problems." In 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 1-6. IEEE, 2016.
@inproceedings{valera2016using,
title={Using Interval Constraint Solving Techniques to better understand and predict future behaviors of dynamic problems},
author={Valera, Leobardo and Ceberio, Martine},
booktitle={2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)},
pages={1--6},
organization={IEEE}
}
title={Using Interval Constraint Solving Techniques to better understand and predict future behaviors of dynamic problems},
author={Valera, Leobardo and Ceberio, Martine},
booktitle={2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)},
pages={1--6},
organization={IEEE}
}
2015
Ceberio, Martine, Leobardo Valera, Olga Kosheleva, and Rodrigo Romero. ``Model reduction: Why it is possible and how it can potentially help to control swarms of unmanned arial vehicles (uavs)." In 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), pp. 1-6. IEEE, 2015.
@inproceedings{ceberio2015model,
title={Model reduction: Why it is possible and how it can potentially help to control swarms of unmanned arial vehicles (uavs)},
author={Ceberio, Martine and Valera, Leobardo and Kosheleva, Olga and Romero, Rodrigo},
booktitle={2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC)},
pages={1--6},
year={2015},
organization={IEEE}
}
author={Ceberio, Martine and Valera, Leobardo and Kosheleva, Olga and Romero, Rodrigo},
booktitle={2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC)},
pages={1--6},
year={2015},
organization={IEEE}
}
Zapata, Francisco, Octavio Lerma, Leobardo Valera, and Vladik Kreinovich. ``How to speed up software migration and modernization: Successful strategies developed by precisiating expert knowledge." In 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), pp. 1-6. IEEE, 2015.
@inproceedings{zapata2015speed,
title={How to speed up software migration and modernization: Successful strategies developed by precisiating expert knowledge},
author={Zapata, Francisco and Lerma, Octavio and Valera, Leobardo and Kreinovich, Vladik},
booktitle={2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC)},
pages={1--6},
year={2015}, organization={IEEE}
}
title={How to speed up software migration and modernization: Successful strategies developed by precisiating expert knowledge},
author={Zapata, Francisco and Lerma, Octavio and Valera, Leobardo and Kreinovich, Vladik},
booktitle={2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC)},
pages={1--6},
year={2015}, organization={IEEE}
}
Lerma, Octavio, Leobardo Valera, Deana Pennington, and Vladik Kreinovich. "Testing a power law model of knowledge propagation: case study of the Out of Eden Walk Project." (2015).
@article{lerma2015testing,
title={Testing a power law model of knowledge propagation: case study of the Out of Eden Walk Project},
author={Lerma, Octavio and Valera, Leobardo and Pennington, Deana and Kreinovich, Vladik},
year={2015}
}
}
title={Testing a power law model of knowledge propagation: case study of the Out of Eden Walk Project},
author={Lerma, Octavio and Valera, Leobardo and Pennington, Deana and Kreinovich, Vladik},
year={2015}
}
}