Publications
Peer-reviewed & preprints, newest first
2026
JOURNAL
Budgeted Online Active Learning with Expert Advice and Episodic Priors
Goebel, Kristen, Solow, William, Pesantez-Cabrera, Paola, Keller, Markus, & Fern, Alan. Proceedings of the AAAI Conference on Artificial Intelligence, 40(45), 38496–38504 (2026).
@article{goebel_budgeted_2026,
abstract = {This paper introduces a novel approach to budgeted online active learning from finite-horizon data streams with extremely limited labeling budgets. In agricultural applications, such streams might include daily weather data over a growing season, and labels require costly measurements of weather-dependent plant characteristics. Our method integrates two key sources of prior information: a collection of preexisting expert predictors and episodic behavioral knowledge of the experts based on unlabeled data streams. Unlike previous research on online active learning with experts, our work simultaneously considers query budgets, finite horizons, and episodic knowledge, enabling effective learning in applications with severely limited labeling capacity. We demonstrate the utility of our approach through experiments on various prediction problems derived from both a realistic agricultural crop simulator and real-world data from multiple grape cultivars. The results show that our method significantly outperforms baseline expert predictions, uniform query selection, and existing approaches that consider budgets and limited horizons but neglect episodic knowledge, even under highly constrained labeling budgets.},
author = {Goebel, Kristen and Solow, William and Pesantez-Cabrera, Paola and Keller, Markus and Fern, Alan},
copyright = {Copyright (c) 2026 Association for the Advancement of Artificial Intelligence},
doi = {10.1609/aaai.v40i45.41191},
issn = {2374-3468},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
keywords = {AI, Farm Ops},
language = {en},
month = {March},
number = {45},
pages = {38496--38504},
title = {Budgeted Online Active Learning with Expert Advice and Episodic Priors},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/41191},
urldate = {2026-06-30},
volume = {40},
year = {2026}
}
2026
JOURNAL
Localized Near Surface Temperature Inversion Forecasting Using Long Short-Term Memory
Dinkins, Taylor, Wong, Weng-Keen, Amogi, Basavaraj, Pesantez-Cabrera, Paola, Patel, Jaitun, Khot, Lav, & Fern, Alan. Proceedings of the AAAI Conference on Artificial Intelligence, 40(47), 40249–40257 (2026).
@article{dinkins_localized_2026,
abstract = {Near surface temperature inversions are periods in which a low layer of warm air is trapped between cooler air higher up in the atmosphere and dense cooler air below it near the surface level. By causing cooler air to pool near the surface level, inversions can have detrimental effects for crop growers, including frost, increased moisture, and pesticide drift. As a result, predicting the occurrence and magnitude of these inversions yields substantial benefits for growers. We introduce a Long Short-Term Memory (LSTM) model for temperature inversion forecasting that is able to effectively predict localized, near surface temperature inversions in advance such that growers can take actions to mitigate the detrimental effects. We show a substantial performance gain over a deployed temperature inversion forecasting system, and include a series of ablations that show the benefit of using publicly available terrain-specific feature information when modeling inversions at this scale.},
author = {Dinkins, Taylor and Wong, Weng-Keen and Amogi, Basavaraj and Pesantez-Cabrera, Paola and Patel, Jaitun and Khot, Lav and Fern, Alan},
copyright = {Copyright (c) 2026 Association for the Advancement of Artificial Intelligence},
doi = {10.1609/aaai.v40i47.41462},
issn = {2374-3468},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
keywords = {AI, Farm Ops},
language = {en},
month = {March},
number = {47},
pages = {40249--40257},
title = {Localized Near Surface Temperature Inversion Forecasting Using Long Short-Term Memory},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/41462},
urldate = {2026-06-30},
volume = {40},
year = {2026}
}
2026
CONFERENCE
A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning
Solow, William, Pesantez-Cabrera, Paola, Keller, Markus, Khot, Lav, Saisubramanian, Sandhya, & Fern, Alan. ** (2026).
@inproceedings{solow_hybrid_2026,
abstract = {Accurate prediction of crop states (e.g., phenology stages and cold hardiness) is essential for timely farm management decisions such as irrigation, fertilization, and canopy management to optimize crop yield and quality. While traditional biophysical models can be used for season-long predictions, they lack the precision required for site-specific management. Deep learning methods are a compelling alternative, but can produce biologically unrealistic predictions and require large-scale data. We propose a {\textbackslash}emph\{hybrid modeling\} approach that uses a neural network to parameterize a differentiable biophysical model and leverages multi-task learning for efficient data sharing across crop cultivars in data limited settings. By predicting the {\textbackslash}emph\{parameters\} of the biophysical model, our approach improves the prediction accuracy while preserving biological realism. Empirical evaluation using real-world and synthetic datasets demonstrates that our method improves prediction accuracy by 60{\textbackslash}\% for phenology and 40{\textbackslash}\% for cold hardiness compared to deployed biophysical models.},
author = {Solow, William and Pesantez-Cabrera, Paola and Keller, Markus and Khot, Lav and Saisubramanian, Sandhya and Fern, Alan},
doi = {10.48550/arXiv.2603.15411},
keywords = {AI, Farm Ops},
month = {May},
note = {arXiv:2603.15411 [cs.AI]},
publisher = {arXiv},
title = {A Hybrid Modeling Framework for Crop Prediction Tasks via Dynamic Parameter Calibration and Multi-Task Learning},
url = {http://arxiv.org/abs/2603.15411},
urldate = {2026-07-13},
year = {2026}
}
2025
JOURNAL
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction
Goebel, Kristen, Pesantez-Cabrera, Paola, Keller, Markus, & Fern, Alan. ** (2025).
@article{goebel_transfer_2025,
abstract = {Cold temperatures can cause significant frost damage to fruit crops depending on their resilience, or cold hardiness, which changes throughout the dormancy season. This has led to the development of predictive cold-hardiness models, which help farmers decide when to deploy expensive frost-mitigation measures. Unfortunately, cold-hardiness data for model training is only available for some fruit cultivars due to the need for specialized equipment and expertise. Rather, farmers often do have years of phenological data (e.g. date of budbreak) that they regularly collect for their crops. In this work, we introduce a new transfer-learning framework, Transfer via Auxiliary Labels (TAL), that allows farmers to leverage the phenological data to produce more accurate cold-hardiness predictions, even when no cold-hardiness data is available for their specific crop. The framework assumes a set of source tasks (cultivars) where each has associated primary labels (cold hardiness) and auxiliary labels (phenology). However, the target task (new cultivar) is assumed to only have the auxiliary labels. The goal of TAL is to predict primary labels for the target task via transfer from the source tasks. Surprisingly, despite the vast literature on transfer learning, to our knowledge, the TAL formulation has not been previously addressed. Thus, we propose several new TAL approaches based on model selection and averaging that can leverage recent deep multi-task models for cold-hardiness prediction. Our results on real-world cold-hardiness and phenological data for multiple grape cultivars demonstrate that TAL can leverage the phenological data to improve cold-hardiness predictions in the absence of cold-hardiness data.},
author = {Goebel, Kristen and Pesantez-Cabrera, Paola and Keller, Markus and Fern, Alan},
doi = {10.48550/arXiv.2504.13142},
keywords = {AI, Farm Ops},
month = {April},
note = {arXiv:2504.13142 [cs]},
publisher = {arXiv},
title = {Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction},
url = {http://arxiv.org/abs/2504.13142},
urldate = {2025-04-18},
year = {2025}
}
2025
CONFERENCE
PhenoTracker: A machine learning model to track grape phenology
Balcarcel, Nathan, Pesantez-Cabrera, Paola, Goebel, Kristen, Keller, Markus, Khot, Lav, Fern, Alan, & Kalyanaraman, Ananth. Workshop Proceedings of the 54th International Conference on Parallel Processing (2025).
@inproceedings{balcarcel_phenotracker_2025,
abstract = {Accurate forecasting of crop phenology supports farm management decisions and mitigation strategies to prevent crop loss. In grapevines, phenological development involves complex, cultivar-specific responses to environmental conditions, making prediction challenging. Traditional process-based models rely primarily on growing degree days (GDD) derived from air temperature, overlooking other influential factors. In this work, we leverage expanded weather data inputs (i.e., air temperature, relative humidity, dew point, precipitation, and wind speed) and machine learning to model grape phenology. Using a 20-year dataset spanning 20 grape cultivars, we train a recurrent neural network to forecast phenological progression. Our model outperforms GDD-based baselines in predicting budbreak, bloom, and veraison growth stages with respective root mean squared error in the ranges of 4.98-8.61 days, 1.22-4.80 days, and 2.24-4.38 days for four major grapevine cultivars. Model also provides confidence intervals for its forecasts.},
address = {New York, NY, USA},
author = {Balcarcel, Nathan and Pesantez-Cabrera, Paola and Goebel, Kristen and Keller, Markus and Khot, Lav and Fern, Alan and Kalyanaraman, Ananth},
booktitle = {Workshop Proceedings of the 54th International Conference on Parallel Processing},
doi = {10.1145/3750720.3758079},
isbn = {979-8-4007-2109-0},
keywords = {AI, Farm Ops},
month = {December},
pages = {104--111},
publisher = {Association for Computing Machinery},
series = {ICPP Workshops '25},
title = {PhenoTracker: A machine learning model to track grape phenology},
url = {https://dl.acm.org/doi/10.1145/3750720.3758079},
urldate = {2026-06-26},
year = {2025}
}
2024
JOURNAL
AgAID Institute—AI for agricultural labor and decision support
Fern, Alan, Burnett, Margaret, Davidson, Joseph, Doppa, Janardhan Rao, Pesantez-Cabrera, Paola, & Kalyanaraman, Ananth. AI Magazine, 45(1) (2024).
@article{fern_agaid_2024,
abstract = {The AgAID Institute is a National AI Research Institute focused on developing AI solutions for specialty crop agriculture. Specialty crops include a variety of fruits and vegetables, nut trees, grapes, berries, and different types of horticultural crops. In the United States, the specialty crop industry accounts for a multibillion dollar industry with over 300 crops grown just along the U.S. west coast. Specialty crop agriculture presents several unique challenges: they are labor-intensive, are easily impacted by weather extremities, and are grown mostly on irrigated lands and hence are dependent on water. The AgAID Institute aims to develop AI solutions to address these challenges, particularly in the face of workforce shortages, water scarcity, and extreme weather events. Addressing this host of challenges requires advancing foundational AI research, including spatio-temporal system modeling, robot sensing and control, multiscale site-specific decision support, and designing effective human–AI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.},
author = {Fern, Alan and Burnett, Margaret and Davidson, Joseph and Doppa, Janardhan Rao and Pesantez-Cabrera, Paola and Kalyanaraman, Ananth},
copyright = {© 2024 The Authors. AI Magazine published by Wiley Periodicals LLC on behalf of the Association for the Advancement of Artificial Intelligence.},
doi = {10.1002/aaai.12156},
issn = {2371-9621},
journal = {AI Magazine},
keywords = {Humans, AI, Farm Ops, Labor, Water},
language = {en},
month = {February},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12156},
number = {1},
title = {AgAID Institute—AI for agricultural labor and decision support},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aaai.12156},
urldate = {2024-02-20},
volume = {45},
year = {2024}
}
2023
JOURNAL
Grape Cold Hardiness Prediction via Multi-Task Learning
Saxena, Aseem, Pesantez-Cabrera, Paola, Ballapragada, Rohan, Lam, Kin-Ho, Keller, Markus, & Fern, Alan. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15717–15723 (2023).
@article{saxena_grape_2023,
abstract = {Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures, such as, sprinklers, heaters, and wind machines, when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.},
author = {Saxena, Aseem and Pesantez-Cabrera, Paola and Ballapragada, Rohan and Lam, Kin-Ho and Keller, Markus and Fern, Alan},
copyright = {Copyright (c) 2023 Association for the Advancement of Artificial Intelligence},
doi = {10.1609/aaai.v37i13.26865},
issn = {2374-3468},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
keywords = {AI, Farm Ops},
language = {en},
month = {June},
note = {Number: 13},
number = {13},
pages = {15717--15723},
title = {Grape Cold Hardiness Prediction via Multi-Task Learning},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26865},
urldate = {2023-08-02},
volume = {37},
year = {2023}
}
2023
CONFERENCE
Multi Task Learning for Budbreak Prediction
Saxena, Aseem, Pesantez, Paola Gabriela, Ballapragada, Rohan, Keller, Markus, & Fern, Alan. ** (2023).
@inproceedings{saxena_multi_2023,
abstract = {Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.},
author = {Saxena, Aseem and Pesantez, Paola Gabriela and Ballapragada, Rohan and Keller, Markus and Fern, Alan},
keywords = {AI, Farm Ops},
language = {en},
month = {January},
title = {Multi Task Learning for Budbreak Prediction},
url = {https://openreview.net/forum?id=kvGm8DJ-cM},
urldate = {2025-08-18},
year = {2023}
}
2023
JOURNAL
Persistent Homology to Study Cold Hardiness of Grape Cultivars
Welankar, Sejal, Pesantez-Cabrera, Paola, Krishnamoorthy, Bala, Mills, Lynn, Keller, Markus, & Kalyanaraman, Ananth. ** (2023).
@article{welankar_persistent_2023,
abstract = {Persistent homology is a branch of computational algebraic topology that studies shapes and extracts features over multiple scales. In this paper, we present an unsupervised approach that uses persistent homology to study divergent behavior in agricultural point cloud data. More specifically, we build persistence diagrams from multidimensional point clouds, and use those diagrams as the basis to compare and contrast different subgroups of the population. We apply the framework to study the cold hardiness behavior of 5 leading grape cultivars, with real data from over 20 growing seasons. Our results demonstrate that persistent homology is able to effectively elucidate divergent behavior among the different cultivars; identify cultivars that exhibit variable behavior across seasons; and identify seasonal correlations.},
annote = {Comment: 5 pages, 12 figures, AAAI 2023 Workshop AIAFS},
author = {Welankar, Sejal and Pesantez-Cabrera, Paola and Krishnamoorthy, Bala and Mills, Lynn and Keller, Markus and Kalyanaraman, Ananth},
doi = {10.48550/arXiv.2302.05600},
keywords = {AI, Farm Ops},
month = {February},
note = {arXiv:2302.05600 [math]},
publisher = {AAAI 2023 Workshop AIAFS},
title = {Persistent Homology to Study Cold Hardiness of Grape Cultivars},
url = {http://arxiv.org/abs/2302.05600},
urldate = {2025-08-18},
year = {2023}
}
2021
JOURNAL
A scoping review on the use, processing and fusion of geographic data in virtual assistants
Granell, Carlos, Pesantez-Cabrera, Paola G., Vilches-Blázquez, Luis M., Achig, Rosario, Luaces, Miguel R., Cortiñas-Álvarez, Alejandro, Chayle, Carolina, & Morocho, Villie. Transactions in GIS, 25(4), 1784–1808 (2021).
@article{granell_scoping_2021,
abstract = {Virtual assistants are a growing area of research in academia and industry, with an impact on people’s daily lives. Many disciplines in science are moving towards the incorporation of intelligent virtual assistants in multiple scenarios and application domains, and GIScience is not external to this trend since they may be connected to intelligent spatial decision support systems. This article presents a scoping review to indicate relevant literature pertinent to intelligent virtual assistants and their usage of geospatial information and technologies. In particular, the study was designed to find critical aspects of GIScience and how to contribute to the development of virtual assistants. Moreover, this work explores the most prominent research lines as well as relevant technologies/platforms to determine the main challenges and current limitations regarding the use and implementation of virtual assistants in geospatial-related fields. As a result, this review shows the current state of geospatial applications regarding the use of intelligent virtual assistants, as well as revealing gaps and limitations in the use of spatial methods, standards, and resources available in spatial data infrastructures to develop intelligent decision systems based on virtual assistants for a wide array of application domains.},
author = {Granell, Carlos and Pesantez-Cabrera, Paola G. and Vilches-Blázquez, Luis M. and Achig, Rosario and Luaces, Miguel R. and Cortiñas-Álvarez, Alejandro and Chayle, Carolina and Morocho, Villie},
copyright = {© 2020 John Wiley \& Sons Ltd},
doi = {10.1111/tgis.12720},
file = {Full Text PDF:/Users/agaidinstitute/Zotero/storage/D65JSEM5/Granell et al. - 2021 - A scoping review on the use, processing and fusion of geographic data in virtual assistants.pdf:application/pdf},
issn = {1467-9671},
journal = {Transactions in GIS},
language = {en},
number = {4},
pages = {1784--1808},
title = {A scoping review on the use, processing and fusion of geographic data in virtual assistants},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12720},
urldate = {2026-09-02},
volume = {25},
year = {2021}
}
2020
JOURNAL
Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach
Hilliger, Isabel, Ortiz-Rojas, Margarita, Pesantez-Cabrera, Paola, Scheihing, Eliana, Tsai, Yi-Shan, Muñoz-Merino, Pedro J., Broos, Tom, Whitelock-Wainwright, Alexander, & Pérez-Sanagustín, Mar. The Internet and Higher Education, 45, 100726 (2020).
@article{hilliger_identifying_2020,
abstract = {Learning Analytics (LA) is perceived to be a promising strategy to tackle persisting educational challenges in Latin America, such as quality disparities and high dropout rates. However, Latin American universities have fallen behind in LA adoption compared to institutions in other regions. To understand stakeholders' needs for LA services, this study used mixed methods to collect data in four Latin American Universities. Qualitative data was obtained from 37 interviews with managers and 16 focus groups with 51 teaching staff and 45 students, whereas quantitative data was obtained from surveys answered by 1884 students and 368 teaching staff. According to the triangulation of both types of evidence, we found that (1) students need quality feedback and timely support, (2) teaching staff need timely alerts and meaningful performance evaluations, and (3) managers need quality information to implement support interventions. Thus, LA offers an opportunity to integrate data-driven decision-making in existing tasks.},
author = {Hilliger, Isabel and Ortiz-Rojas, Margarita and Pesantez-Cabrera, Paola and Scheihing, Eliana and Tsai, Yi-Shan and Muñoz-Merino, Pedro J. and Broos, Tom and Whitelock-Wainwright, Alexander and Pérez-Sanagustín, Mar},
doi = {10.1016/j.iheduc.2020.100726},
issn = {1096-7516},
journal = {The Internet and Higher Education},
keywords = {Higher education, Institutional adoption, Latin America, Learning analytics, Mixed methods, Stakeholder perspectives},
month = {April},
pages = {100726},
title = {Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach},
url = {https://www.sciencedirect.com/science/article/pii/S1096751620300026},
urldate = {2026-09-02},
volume = {45},
year = {2020}
}
2020
JOURNAL
Coordinating learning analytics policymaking and implementation at scale
Broos, Tom, Hilliger, Isabel, Pérez-Sanagustín, Mar, Htun, Nyi-Nyi, Millecamp, Martijn, Pesantez-Cabrera, Paola, Solano-Quinde, Lizandro, Siguenza-Guzman, Lorena, Zuñiga-Prieto, Miguel, Verbert, Katrien, & De Laet, Tinne. British Journal of Educational Technology, 51(4), 938-954 (2020).
@article{https://doi.org/10.1111/bjet.12934,
abstract = {Abstract Many Latin-American institutions recognise the potential of learning analytics (LA). However, the number of actual LA implementations at scale remains limited, notwithstanding considerable effort made to formulate guidelines and frameworks to support the LA policy development. Guidance on how to coordinate the interaction between the LA policymaking and implementation is mostly missing, leaving a difficult challenge up to practitioners. In this study we propose a coordination model to support future LA initiatives at scale. We explore the problem by comparing two cases in Belgium and Ecuador. Following up we use the LA implementation timeline as a driver for planning the interaction between the policymaking and implementation. We continue by testing an application of the model with LA experts predominantly from Latin-American institutions, asking them to map low-level items of the SHEILA policy framework to four implementation phases. The results of this mapping support that LA policy building can be spread over time, that it can coincide with LA implementation at scale, and that both efforts can be coordinated. It is hoped that this study will provide additional guidance for future Latin-American and other LA initiatives.},
author = {Broos, Tom and Hilliger, Isabel and Pérez-Sanagustín, Mar and Htun, Nyi-Nyi and Millecamp, Martijn and Pesantez-Cabrera, Paola and Solano-Quinde, Lizandro and Siguenza-Guzman, Lorena and Zuñiga-Prieto, Miguel and Verbert, Katrien and De Laet, Tinne},
doi = {https://doi.org/10.1111/bjet.12934},
eprint = {https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12934},
journal = {British Journal of Educational Technology},
keywords = {Global South, Higher Education, Learning Analytics, Organisational change, Policy},
number = {4},
pages = {938-954},
title = {Coordinating learning analytics policymaking and implementation at scale},
url = {https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.12934},
volume = {51},
year = {2020}
}
2020
JOURNAL
2020
CONFERENCE
Towards a Methodology for creating Internet of Things (IoT) Applications based on Microservices
Cabrera, Edwin, Cárdenas, Paola, Cedillo, Priscila, & Pesantez-Cabrera, Paola. 2020 IEEE International Conference on Services Computing (SCC) (2020).
@inproceedings{cabrera_towards_2020,
abstract = {The Internet of Things (IoT) represents the new industrial revolution, in which physical and virtual objects are interconnected. On the other hand, microservices architectures have broken the monolithic and centralized way to build software, and provide systems with high-quality characteristics (e.g., resilience, availability, modularity, and portability). Therefore, the idea of merging those technologies can constitute a powerful strategy to be applied in environments that demand the distribution and management of many IoT devices using high-quality software. In this context, several studies that integrate IoT with microservices solutions have been analyzed. However, most of these studies aim to satisfy the functional requirements related to software and hardware, without taking into account software engineering methodologies and good practices that allow the creation of software for IoT devices considering their distributed nature. Thus, this paper presents the first approach to an agile methodology that i) contemplates the main characteristics of the IoT and ii) guides the development of appropriate software solutions based on microservices architectures to manage IoT environments acknowledging the serious difficulties that microservices imply.},
author = {Cabrera, Edwin and Cárdenas, Paola and Cedillo, Priscila and Pesantez-Cabrera, Paola},
booktitle = {2020 IEEE International Conference on Services Computing (SCC)},
doi = {10.1109/SCC49832.2020.00072},
issn = {2474-2473},
keywords = {Agile, Computer architecture, Domain-Driven Design, Internet of Things, Merging, Methodology, MicroIoT, Microservices, Resilience, Service computing, Software, Software engineering},
month = {November},
note = {ISSN: 2474-2473},
pages = {472--474},
title = {Towards a Methodology for creating Internet of Things (IoT) Applications based on Microservices},
url = {https://ieeexplore.ieee.org/abstract/document/9284589},
urldate = {2026-09-02},
year = {2020}
}
2020
CONFERENCE
Proposal for the Design and Evaluation of a Dashboard for the Analysis of Learner Behavior and Dropout Prediction in Moodle
Sigua, Edisson, Aguilar, Bryan, Pesantez-Cabrera, Paola, & Maldonado-Mahauad, Jorge. 2020 XV Conferencia Latinoamericana de Tecnologias de Aprendizaje (LACLO) (2020).
@inproceedings{sigua_proposal_2020,
abstract = {The rapid development of technology has meant that over the past two decades Information and Communications Technologies (ICT) become increasingly involved in the teaching process and seek to change traditional learning models. With the support of modern technology, virtual platforms that encourage the adoption of a new learning paradigm in which geographical/temporal limitations no longer pose a difficulty have been developed and refined. These virtual learning platforms, also known as Learning Management Systems (LMS), store student and teacher interactions with course resources, and these interactions are stored in database engines. However, all the information generated by LMS has not been processed in a way that is helpful for the use of teachers and students, mainly because in most cases, students' interactions with these systems focus on downloading class material, delivering assignments, and reading announcements, leaving aside indicators that can be presented in the form of visualizations that allow actions to be taken during the development of the learning process. Thus, this study proposes the design, implementation, and evaluation of a dashboard for the analysis of learner behavior and prediction of dropout on the Moodle platform. The proposed tool will help students to manage their learning process, easily and effectively monitor their progress in an online course, and teachers to know what students do before, during and after a virtual class. The latter for the purpose of being able to detect early students at risk of dropping out.},
author = {Sigua, Edisson and Aguilar, Bryan and Pesantez-Cabrera, Paola and Maldonado-Mahauad, Jorge},
booktitle = {2020 XV Conferencia Latinoamericana de Tecnologias de Aprendizaje (LACLO)},
doi = {10.1109/LACLO50806.2020.9381148},
keywords = {Dashboard, Dropout, Engines, Information and communication technology, Learning Analytics, Learning management systems, Monitoring, Moodle, Prediction, Proposals, Tools, Visualization},
month = {October},
pages = {1--6},
title = {Proposal for the Design and Evaluation of a Dashboard for the Analysis of Learner Behavior and Dropout Prediction in Moodle},
url = {https://ieeexplore.ieee.org/document/9381148},
urldate = {2026-09-02},
year = {2020}
}
2020
CONFERENCE
Towards an evaluation method of how accessible serious games are to older adults
Pesantez-Cabrera, Paola, Acosta, María Inés, Jimbo, Verónica, Sinchi, Pablo, & Cedillo, Priscila. 2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH) (2020).
@inproceedings{pesantez-cabrera_towards_2020,
abstract = {The loss of cognitive and motor functions in humans increases with age, and the aging population is expected to continue growing significantly in the following years. In this context, serious games have become a tool that supports health professionals in mitigating age-related cognitive problems. Additionally, the accessibility provided by those tools is a determinant factor when users need to adapt themselves to a particular technology. Therefore, this paper presents an accessibility model and an evaluation method useful for assessing how accessible serious games are to older adults, based on the Games Accessibility Guidelines (GAG) proposed by the International Game Developers Association and the ISO/IEC 25040. In order to validate and ensure the feasibility of this study, each activity of the proposed method has been applied to a real game that was created for improving certain cognitive functions (i.e., A Clockwork Brain suite of serious games).},
author = {Pesantez-Cabrera, Paola and Acosta, María Inés and Jimbo, Verónica and Sinchi, Pablo and Cedillo, Priscila},
booktitle = {2020 IEEE 8th International Conference on Serious Games and Applications for Health (SeGAH)},
doi = {10.1109/SeGAH49190.2020.9201655},
issn = {2573-3060},
keywords = {Accessibility, Aging, Evaluation, Games, Guidelines, Measurement, Older Adults, Serious Games, Standards, Tools, Usability},
month = {August},
note = {ISSN: 2573-3060},
pages = {1--8},
title = {Towards an evaluation method of how accessible serious games are to older adults},
url = {https://ieeexplore.ieee.org/abstract/document/9201655},
urldate = {2026-09-02},
year = {2020}
}
2020
CONFERENCE
A Software Architecture Proposal for a Data Platform on Active Mobility and Urban Environment
Quinde, Christian, Guillermo, David, Siguenza-Guzman, Lorena, Orellana, Daniel, & Pesantez-Cabrera, Paola. Information and Communication Technologies (2020).
@inproceedings{quinde_software_2020,
abstract = {Over time Geographic Information Systems (GIS) have evolved from monolithic software to dynamic platforms interacting with other systems. Consequently, characteristics such as availability, scalability, interoperability, and failure handling have become essential. Due to the vast diversity of applications and user levels, and the growing complexity of data types and models handling geospatial data, information management has developed into a complex, often overlooked task, leading to delayed results and/or disorganization of information. The goal of this paper is to propose a software architecture design to support mobility data collection, analysis, and visualization. The proposal is based on the process for software architectures stated by Bredemeyer Consulting, comprising five stages: commit, requirements, design, validation, and deployment. Likewise, the Attribute Driven Design (ADD) method has been used for the design stage where the selected architectural pattern was Service Oriented Architecture (SOA) since it provides the scalability and interoperability attributes required for this study. The Architecture Tradeoff Analysis Method (ATAM) has been chosen to identify the risks of the proposal and to evaluate the architecture to ensure that all requirements have been satisfactorily met. The model was validated using the data and projects of the LlactaLAB research group.},
address = {Cham},
author = {Quinde, Christian and Guillermo, David and Siguenza-Guzman, Lorena and Orellana, Daniel and Pesantez-Cabrera, Paola},
booktitle = {Information and Communication Technologies},
doi = {10.1007/978-3-030-62833-8_37},
editor = {Rodriguez Morales, Germania and Fonseca C., Efraín R. and Salgado, Juan Pablo and Pérez-Gosende, Pablo and Orellana Cordero, Marcos and Berrezueta, Santiago},
isbn = {978-3-030-62833-8},
keywords = {Architecture balance analysis method, Attribute Driven Design, Geographic Information System, Geospatial information, Software architecture},
language = {en},
pages = {501--515},
publisher = {Springer International Publishing},
title = {A Software Architecture Proposal for a Data Platform on Active Mobility and Urban Environment},
year = {2020}
}
2016
CONFERENCE
Detecting Communities in Biological Bipartite Networks
Pesantez-Cabrera, Paola, & Kalyanaraman, Ananth. Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2016).
@inproceedings{pesantez-cabrera_detecting_2016,
abstract = {Methods to uncover and extract community structures are required in a number of biological applications where networked data and their interactions can be modeled as graphs, and observing tightly-knit groups of vertices ("communities") can offer insights into the structural and functional building blocks of the underlying network. While classical applications of community detection have focused largely on detecting molecular complexes from protein-protein networks and other similar graphs, there is an increasing need for extending the community detection operation to work for heterogeneous data sets --- i.e., networks built out of multiple types of data. In this paper, we address the problem of identifying communities from biological bipartite networks --- networks where interactions are observed between two different types of vertices (e.g., genes and diseases, drugs and protein complexes, plants and pollinators). Toward detecting communities in such bipartite networks, we make the following contributions: i) we define a variant of the bipartite modularity function defined by Murata to overcome one of its limitations; ii) we present an algorithm (biLouvain), building on an efficient heuristic that was originally developed for unipartite networks; and iii) we present a thorough experimental evaluation of our algorithm compared to other state-of-the-art methods to identify communities on bipartite networks. Experimental results show that our biLouvain algorithm identifies communities that have a comparable or better quality (bipartite modularity) than existing methods, while significantly reducing the time-to-solution between one and three orders of magnitude.},
address = {New York, NY, USA},
author = {Pesantez-Cabrera, Paola and Kalyanaraman, Ananth},
booktitle = {Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics},
doi = {10.1145/2975167.2975177},
isbn = {978-1-4503-4225-4},
month = {October},
pages = {98--107},
publisher = {Association for Computing Machinery},
series = {BCB '16},
title = {Detecting Communities in Biological Bipartite Networks},
url = {https://dl.acm.org/doi/10.1145/2975167.2975177},
urldate = {2026-09-01},
year = {2016}
}
2016
PUBLICATION
Kinetic Model of Development and Aging of Artificial Skin Based on Analysis of Microscopy Data
Pesantez-Cabrera, Paola, Neubeck, Cläre von, Sowa, Marianne B., & Miller, John H..
@incollection{pesantez-cabrera_kinetic_2016,
abstract = {Artificial human skin is available commercially or can be grown in the laboratory from established cell lines. Standard microscopy techniques show that artificial human skin has a fully developed...},
author = {Pesantez-Cabrera, Paola and Neubeck, Cläre von and Sowa, Marianne B. and Miller, John H.},
booktitle = {Microscopy and Analysis},
doi = {10.5772/63402},
isbn = {978-953-51-2579-2 978-953-51-2578-5 978-953-51-5076-3},
language = {en},
month = {September},
publisher = {IntechOpen},
title = {Kinetic Model of Development and Aging of Artificial Skin Based on Analysis of Microscopy Data},
url = {https://www.intechopen.com/chapters/50820},
urldate = {2026-09-02},
year = {2016}
}