@article{bhattarai_design_2023, title = {Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops}, author = {Uddhav Bhattarai and Qin Zhang and Manoj Karkee}, url = {http://arxiv.org/abs/2304.04919}, doi = {10.48550/arXiv.2304.04919}, year = {2024}, date = {2024-03-07}, urldate = {2024-03-07}, journal = {Field Robotics}, publisher = {arXiv}, abstract = {The US apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruit thinning, and harvesting. Blossom thinning is one of the crucial crop load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical, and mechanical thinning are available for large-scale blossom thinning such approaches often yield unpredictable thinning results and may cause damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor intensive and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for blossom thinning in apple orchards using a computer vision system with artificial intelligence, a six degrees of freedom robotic manipulator, and an electrically actuated miniature end-effector for robotic blossom thinning. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system ability to remove varying proportion of flowers from apple flower clusters. During boundary thinning the end effector was actuated around the cluster boundary while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 seconds. The boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 seconds per cluster, whereas center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 seconds per cluster. When commercially adopted, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.}, note = {arXiv:2304.04919 [cs]}, keywords = {AI, Humans, Labor, Thinning}, pubstate = {published}, tppubtype = {article} } @inproceedings{thapa_attention-based_2023, title = {Attention-based Models for Snow-Water Equivalent Prediction}, author = {Krishu K. Thapa and Bhupinderjeet Singh and Supriya Savalkar and Alan Fern and Kirti Rajagopalan and Ananth Kalyanaraman}, url = {http://arxiv.org/abs/2311.03388}, doi = {10.48550/arXiv.2311.03388}, year = {2024}, date = {2024-02-20}, urldate = {2024-02-20}, publisher = {Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-24)}, abstract = {Snow Water-Equivalent (SWE) \textendash the amount of water available if snowpack is melted \textendash is a key decision variable used by water management agencies to make irrigation, flood control, power generation and drought management decisions. SWE values vary spatiotemporally \textendash affected by weather, topography and other environmental factors. While daily SWE can be measured by Snow Telemetry (SNOTEL) stations with requisite instrumentation, such stations are spatially sparse requiring interpolation techniques to create spatiotemporally complete data. While recent efforts have explored machine learning (ML) for SWE prediction, a number of recent ML advances have yet to be considered. The main contribution of this paper is to explore one such ML advance, attention mechanisms, for SWE prediction. Our hypothesis is that attention has a unique ability to capture and exploit correlations that may exist across locations or the temporal spectrum (or both). We present a generic attention-based modeling framework for SWE prediction and adapt it to capture spatial attention and temporal attention. Our experimental results on 323 SNOTEL stations in the Western U.S. demonstrate that our attention-based models outperform other machine learning approaches. We also provide key results highlighting the differences between spatial and temporal attention in this context and a roadmap toward deployment for generating spatially-complete SWE maps.}, note = {arXiv:2311.03388 [physics]}, keywords = {AI, Snow Water Equivalent, Water}, pubstate = {published}, tppubtype = {inproceedings} } @article{fern_agaid_nodate, title = {AgAID Institute\textemdashAI for agricultural labor and decision support}, author = {Alan Fern and Margaret Burnett and Joseph Davidson and Janardhan Rao Doppa and Paola Pesantez-Cabrera and Ananth Kalyanaraman}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aaai.12156}, doi = {10.1002/aaai.12156}, issn = {2371-9621}, year = {2024}, date = {2024-02-16}, urldate = {2024-02-16}, journal = {AI Magazine}, volume = {n/a}, number = {n/a}, 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\textendashAI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.}, note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12156}, keywords = {AI, Farm Ops, Humans, Labor, Water}, pubstate = {published}, tppubtype = {article} } @article{sapkota_immature_2024, title = {Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques}, author = {Ranjan Sapkota and Dawood Ahmed and Martin Churuvija and Manoj Karkee}, url = {https://ieeexplore.ieee.org/document/10474021}, doi = {10.1109/ACCESS.2024.3378261}, issn = {2169-3536}, year = {2024}, date = {2024-01-01}, urldate = {2024-01-01}, journal = {IEEE Access}, pages = {1\textendash1}, abstract = {Detecting and estimating size of apples during the early stages of growth is crucial for predicting yield, pest management, and making informed decisions related to crop-load management, harvest and post-harvest logistics, and marketing. Traditional fruit size measurement methods are laborious and time-consuming. This study employs the state-of-the-art YOLOv8 object detection and instance segmentation algorithm in conjunction with geometric shape fitting techniques on 3D point cloud data to accurately determine the size of immature green apples (or fruitlet) in a commercial orchard environment. The methodology utilized two RGB-D sensors: Intel RealSense D435i and Microsoft Azure Kinect DK. Notably, the YOLOv8 instance segmentation models exhibited proficiency in immature green apple detection, with the YOLOv8m-seg model achieving the highest AP@0.5 and AP@0.75 scores of 0.94 and 0.91, respectively. Using the ellipsoid fitting technique on images from the Azure Kinect, we achieved an RMSE of 2.35 mm, MAE of 1.66 mm, MAPE of 6.15 mm, and an R-squared value of 0.9 in estimating the size of apple fruitlets. Challenges such as partial occlusion caused some error in accurately delineating and sizing green apples using the YOLOv8-based segmentation technique, particularly in fruit clusters. In a comparison with 102 outdoor samples, the size estimation technique performed better on the images acquired with Microsoft Azure Kinect than the same with Intel Realsense D435i. This superiority is evident from the metrics: the RMSE values (2.35 mm for Azure Kinect vs. 9.65 mm for Realsense D435i), MAE values (1.66 mm for Azure Kinect vs. 7.8 mm for Realsense D435i), and the R-squared values (0.9 for Azure Kinect vs. 0.77 for Realsense D435i). This study demonstrated the feasibility of accurately sizing immature green fruit in early growth stages using the combined 3D sensing and shape-fitting technique, which shows promise for improved precision agricultural operations such as optimal crop-load management in orchards.}, note = {Conference Name: IEEE Access}, keywords = {AI, Labor}, pubstate = {published}, tppubtype = {article} } @article{10.1371/journal.pwat.0000184, title = {An invisible water surcharge: Climate warming increases crop water demand in the San Joaquin Valley’s groundwater-dependent irrigated agriculture}, author = {Kelley Moyers and John T. Abatzoglou and Alvar Escriva-Bou and Josu\'{e} Medell\'{i}n-Azuara and Joshua H. Viers}, url = {https://doi.org/10.1371/journal.pwat.0000184}, doi = {10.1371/journal.pwat.0000184}, year = {2024}, date = {2024-01-01}, urldate = {2024-01-01}, journal = {PLOS Water}, volume = {3}, number = {3}, pages = {1-17}, publisher = {Public Library of Science}, abstract = {California’s bountiful San Joaquin Valley (SJV), a critical region for global fruit and nut production, has withstood two severe, multi-year droughts in the past decade, exacerbated by record-breaking high temperature and evaporative demand. We employed climate data and crop coefficients to estimate the crop water demand in the SJV over the past forty years. Our approach, using crop coefficients for Penman-Montieth modeled evapotranspiration, focused on the climate effects on crop water demand, avoiding the confounding factors of changing land use and management practices that are present in actual evapotranspiration. We demonstrate that increases in crop water demand explain half of the cumulative deficits of the agricultural water balance since 1980, exacerbating water reliance on depleting groundwater supplies and fluctuating surface water imports. We call this phenomenon of climate-induced increased crop water demand an invisible water surcharge. We found that in the past decade, this invisible water surcharge on agriculture has increased the crop water demand in the SJV by 4.4% with respect to the 1980\textendash2011 timeframe\textemdashmore than 800 GL per year, a volume as large as a major reservoir in the SJV. Despite potential agronomic adaptation and crop response to climate warming, increased crop water demand adds a stressor to the sustainability of the global fruit and nut supply and calls for changes in management and policies to consider the shifting hydroclimate.}, keywords = {Water}, pubstate = {published}, tppubtype = {article} } @inproceedings{wang_model_2023, title = {Model Evaluation for Geospatial Problems}, author = {Jing Wang and Tyler Hallman and Laurel Hopkins and John Burns Kilbride and W. Douglas Robinson and Rebecca Hutchinson}, url = {https://openreview.net/forum?id=z5dAdYOgbs\&referrer=%5Bthe%20profile%20of%20Jing%20Wang%5D(%2Fprofile%3Fid%3D~Jing_Wang38)}, year = {2023}, date = {2023-12-01}, urldate = {2023-12-01}, abstract = {Geospatial problems often involve spatial autocorrelation and covariate shift, which violate the independent, identically distributed assumption underlying standard cross-validation. In this work, we establish a theoretical criterion for unbiased cross-validation, introduce a preliminary categorization framework to guide practitioners in choosing suitable cross-validation strategies for geospatial problems, reconcile conflicting recommendations on best practices, and develop a novel, straightforward method with both theoretical guarantees and empirical success.}, keywords = {AI, Farm Ops}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{10424249, title = {Apple fruit surface temperature prediction using weather data-driven machine learning models}, author = {Nelson D. Goosman and Basavaraj R. Amogi and Lav R. Khot}, url = {https://ieeexplore.ieee.org/document/10424249}, doi = {10.1109/MetroAgriFor58484.2023.10424249}, year = {2023}, date = {2023-11-08}, urldate = {2023-11-08}, booktitle = {2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)}, pages = {429-433}, keywords = {Fruit Surface Temperature, Heat Stress}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{belkhouja_adversarial_2023, title = {Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features (Extended Abstract)}, author = {Belkhouja, Taha and Doppa, Janardhan Rao}, url = {https://www.ijcai.org/proceedings/2023/767}, doi = {10.24963/ijcai.2023/767}, year = {2023}, date = {2023-08-01}, urldate = {2023-08-01}, volume = {6}, pages = {6845\textendash6850}, abstract = {Electronic proceedings of IJCAI 2023}, note = {ISSN: 1045-0823}, keywords = {AI}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{harrison_identifying_2023, title = {Identifying Complicated Contagion Scenarios from Cascade Data}, author = {Harrison, Galen and Alabsi Aljundi, Amro and Chen, Jiangzhuo and Ravi, S.S. and Vullikanti, Anil Kumar and Marathe, Madhav V. and Adiga, Abhijin}, url = {https://dl.acm.org/doi/10.1145/3580305.3599841}, doi = {10.1145/3580305.3599841}, isbn = {9798400701030}, year = {2023}, date = {2023-08-01}, urldate = {2023-08-01}, booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, pages = {4135\textendash4145}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {KDD '23}, abstract = {We consider the setting of cascades that result from contagion dynamics on large realistic contact networks. We address the question of whether the structural properties of a (partially) observed cascade can characterize the contagion scenario and identify the interventions that might be in effect. Using epidemic spread as a concrete example, we study how social interventions such as compliance in social distancing, extent (and efficacy) of vaccination, and the transmissibility of disease can be inferred. The techniques developed are more generally applicable to other contagions as well. Our approach involves the use of large realistic social contact networks of certain regions of USA and an agent-based model (ABM) to simulate spread under two interventions, namely vaccination and generic social distancing (GSD). Through a machine learning approach, coupled with parameter significance analysis, our experimental results show that subgraph counts of the graph induced by the cascade can be used effectively to characterize the contagion scenario even during the initial stages of the epidemic, when traditional information such as case counts alone are not adequate for this task. Further, we show that our approach performs well even for partially observed cascades. These results demonstrate that cascade data collected from digital tracing applications under poor digital penetration and privacy constraints can provide valuable information about the contagion scenario.}, keywords = {AI}, pubstate = {published}, tppubtype = {inproceedings} } @article{noauthor_regular_nodate, title = {“Regular” CS × Inclusive Design = Smarter Students and Greater Diversity textbar ACM Transactions on Computing Education}, author = {Garcia, Rosalinda; Patricia Morreale; Lara Letaw; Amreeta Chatterjee; Pakati Patel; Sarah Yang; Isaac Tijerina Escobar; Geraldine Jimena Noa; and Margaret Burnett}, url = {https://dl.acm.org/doi/10.1145/3603535}, year = {2023}, date = {2023-07-22}, urldate = {2023-07-22}, journal = {ACM Transactions on Computing Education}, keywords = {Human-Computer Interaction}, pubstate = {published}, tppubtype = {article} } @inproceedings{wang_62_2023, title = {Automatic estimation of trunk cross sectional area using deep learning}, author = {Wang, T. and Sankari, P. and Brown, J. and Paudel, A. and He, L. and Karkee, M. and Thompson, A. and Grimm, C. and Davidson, J.r. and Todorovic, S.}, url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_62}, doi = {10.3920/978-90-8686-947-3_62}, isbn = {978-90-8686-393-8}, year = {2023}, date = {2023-07-01}, urldate = {2023-07-01}, booktitle = {Precision agriculture}, pages = {491\textendash498}, publisher = {Wageningen Academic Publishers}, note = {Section: 62}, keywords = {AI, Labor, Pruning}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{parayil_19_2023, title = {Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator}, author = {Parayil, N. and You, A. and Grimm, C. and Davidson, J.r.}, url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_19}, doi = {10.3920/978-90-8686-947-3_19}, isbn = {978-90-8686-393-8}, year = {2023}, date = {2023-07-01}, urldate = {2023-07-01}, booktitle = {Precision agriculture}, pages = {167\textendash174}, publisher = {Wageningen Academic Publishers}, note = {Section: 19}, keywords = {Pruning, Thinning}, pubstate = {published}, tppubtype = {inproceedings} } @article{ghosh_improving_2023, title = {Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis}, author = {Ghosh, Subhankar and Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/25936}, doi = {10.1609/aaai.v37i6.25936}, issn = {2374-3468}, year = {2023}, date = {2023-06-01}, urldate = {2023-06-01}, journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {37}, number = {6}, pages = {7722\textendash7730}, abstract = {Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.}, note = {Number: 6}, keywords = {AI, Water}, pubstate = {published}, tppubtype = {article} } @inproceedings{ghosh_probabilistically_2023, title = {Probabilistically Robust Conformal Prediction}, author = {Ghosh, Subhankar and Shi, Yuanjie and Belkhouja, Taha and Yan, Yan and Doppa, Jana and Jones, Brian}, url = {https://openreview.net/forum?id=4xI4oaqIs2}, year = {2023}, date = {2023-06-01}, urldate = {2023-06-01}, abstract = {Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assumes clean testing data and there is not much known about the robustness of CP algorithms w.r.t natural/adversarial perturbations to testing examples. This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples. PRCP generalizes the standard CP (cannot handle perturbations) and adversarially robust CP (ensures robustness w.r.t worst-case perturbations) to achieve better trade-offs between nominal performance and robustness. We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. The key idea behind aPRCP is to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka "quantile-of-quantile'' design). We provide theoretical analysis to show that aPRCP algorithm achieves robust coverage. Our experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using deep neural networks demonstrate that aPRCP achieves better trade-offs than state-of-the-art CP and adversarially robust CP algorithms.}, keywords = {AI}, pubstate = {published}, tppubtype = {inproceedings} } @article{mishra_reconstructing_2023, title = {Reconstructing an Epidemic Outbreak Using Steiner Connectivity}, author = {Mishra, Ritwick and Heavey, Jack and Kaur, Gursharn and Adiga, Abhijin and Vullikanti, Anil}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/26372}, doi = {10.1609/aaai.v37i10.26372}, issn = {2374-3468}, year = {2023}, date = {2023-06-01}, urldate = {2023-06-01}, journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {37}, number = {10}, pages = {11613\textendash11620}, abstract = {Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.}, note = {Number: 10}, keywords = {AI}, pubstate = {published}, tppubtype = {article} } @article{belkhouja_dynamic_2023, title = {Dynamic Time Warping Based Adversarial Framework for Time-Series Domain}, author = {Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao}, url = {https://ieeexplore.ieee.org/document/9970291}, doi = {10.1109/TPAMI.2022.3224754}, issn = {1939-3539}, year = {2023}, date = {2023-06-01}, urldate = {2023-06-01}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {45}, number = {6}, pages = {7353\textendash7366}, abstract = {Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.}, note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence}, keywords = {AI}, pubstate = {published}, tppubtype = {article} } @inproceedings{noauthor_bidirectional_nodate, title = {Bidirectional alignment for domain adaptive detection with transformers}, author = {He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic}, url = {https://www.amazon.science/publications/bidirectional-alignment-for-domain-adaptive-detection-with-transformers}, year = {2023}, date = {2023-03-08}, urldate = {2023-03-08}, journal = {Amazon Science}, publisher = {Proceedings of International Conference on Computer Vision}, abstract = {We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…}, keywords = {Pruning}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{ferdousi_machine_2023, title = {A Machine Learning Framework to Explain Complex Geospatial Simulations: A Climate Change Case Study}, author = {Tanvir Ferdousi and Mingliang Liu and Kirti Rajagopalan and Jennifer Adam and Abhijin Adiga and Mandy Wilson and SS Ravi and Anil Vullikanti and Madhav V Marathe and Samarth Swarup}, url = {https://tanvir-ferdousi.github.io/assets/pdf/explainability_wsc23.pdf}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, abstract = {The explainability of large and complex simulation models is an open problem. We present a framework to analyze such models by processing multidimensional data through a pipeline of target variable computation, clustering, supervised classification, and feature importance analysis. As a use case, the well-known large-scale hydrology and crop systems simulator VIC-CropSyst is utilized to evaluate how climate change may affect water availability in Washington, United States. We study how snowmelt varies with climate variables (temperature, precipitation) to identify different response characteristics. Based on these characteristics, spatial units are clustered into six distinct classes. A random forest classifier is used with Shapley values to rank static soil and land parameters that help detect each class. The results also include an analysis of risk across different classes to identify areas vulnerable to climate change. This paper demonstrates the usefulness of the proposed framework in providing explainability for large and complex simulations.}, keywords = {AI, Water}, pubstate = {published}, tppubtype = {inproceedings} } @workshop{saxena_multi-task_2023, title = {Multi-Task Learning for Budbreak Prediction}, author = {Aseem Saxena and Paola Pesantez-Cabrera and Rohan Ballapragada and Markus Keller and Alan Fern}, url = {https://openreview.net/pdf?id=kvGm8DJ-cM}, doi = {10.48550/arXiv.2301.01815}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, booktitle = {2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS)}, publisher = {arXiv}, 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.}, note = {arXiv:2301.01815 [cs]}, keywords = {Cold Hardiness, Computer Science }, pubstate = {published}, tppubtype = {workshop} } @workshop{welankar_grape_2023, title = {Persistent Homology to Study Cold Hardiness of Grape Cultivars}, author = {Welankar Sejal and Paola Pesantez-Cabrera and Bala Krishnamoorthy and Ananth Kalyanaraman}, url = {https://openreview.net/pdf?id=PPoe26Ys-j}, doi = {https://doi.org/10.48550/arXiv.2302.05600}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, booktitle = {2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS)}, publisher = {arXiv}, keywords = {Cold Hardiness, Computer Science , Topological Data Analysis}, pubstate = {published}, tppubtype = {workshop} } @conference{saxena_aaai2023, title = {Grape Cold Hardiness Prediction via Multi-Task Learning}, author = {Aseem Saxena and Paola Pesantez-Cabrera and Rohan Ballapragada and Kin-Ho Lam and Markus Keller and Alan Fern}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/26865}, doi = { https://doi.org/10.1609/aaai.v37i13.26865}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, booktitle = {Association for the Advancement of Artificial Intelligence (AAAI) 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 overlearning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.}, keywords = {Cold Hardiness, Computer and Information Sciences, Machine Learning}, pubstate = {published}, tppubtype = {conference} } @article{abatzoglou_downscaled_2023, title = {Downscaled subseasonal fire danger forecast skill across the contiguous United States}, author = {Abatzoglou, John T. and McEvoy, Daniel J. and Nauslar, Nicholas J. and Hegewisch, Katherine C. and Huntington, Justin L.}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asl.1165}, doi = {10.1002/asl.1165}, issn = {1530-261X}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, journal = {Atmospheric Science Letters}, volume = {24}, number = {8}, pages = {e1165}, abstract = {The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead-times of several weeks can help in wildland fire resource allocation as well as complement short-term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble-based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly-to-monthly fire business decision making for suppression-based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.}, note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1165}, keywords = {Fallow, Water}, pubstate = {published}, tppubtype = {article} } @inproceedings{mishra_community_2023, title = {Community Detection Using Moore-Shannon Network Reliability: Application to Food Networks}, author = {Mishra, Ritwick and Eubank, Stephen and Nath, Madhurima and Amundsen, Manu and Adiga, Abhijin}, editor = {Cherifi, Hocine and Mantegna, Rosario Nunzio and Rocha, Luis M. and Cherifi, Chantal and Micciche, Salvatore}, url = {https://link.springer.com/chapter/10.1007/978-3-031-21131-7_21}, doi = {10.1007/978-3-031-21131-7_21}, isbn = {978-3-031-21131-7}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, booktitle = {Complex Networks and Their Applications XI}, pages = {271\textendash282}, publisher = {Springer International Publishing}, address = {Cham}, series = {Studies in Computational Intelligence}, abstract = {Community detection in networks is extensively studied from a structural perspective, but very few works characterize communities with respect to dynamics on networks. We propose a generic framework based on Moore-Shannon network reliability for defining and discovering communities with respect to a variety of dynamical processes. This approach extracts communities in directed edge-weighted networks which satisfy strong connectivity properties as well as strong mutual influence between pairs of nodes through the dynamical process. We apply this framework to food networks. We compare our results with modularity-based approach, and analyze community structure across commodities, evolution over time, and with regard to dynamical system properties.}, keywords = {AI}, pubstate = {published}, tppubtype = {inproceedings} } @article{bertucci_dendromap_2023, title = {DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps}, author = {Bertucci, Donald and Hamid, Md Montaser and Anand, Yashwanthi and Ruangrotsakun, Anita and Tabatabai, Delyar and Perez, Melissa and Kahng, Minsuk}, url = {https://ieeexplore.ieee.org/document/9904448}, doi = {10.1109/TVCG.2022.3209425}, issn = {1941-0506}, year = {2023}, date = {2023-01-01}, urldate = {2023-01-01}, journal = {IEEE Transactions on Visualization and Computer Graphics}, volume = {29}, number = {1}, pages = {320\textendash330}, abstract = {In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.}, note = {Conference Name: IEEE Transactions on Visualization and Computer Graphics}, keywords = {AI, Humans}, pubstate = {published}, tppubtype = {article} } @inproceedings{you_optical_2022, title = {Optical flow-based branch segmentation for complex orchard environments}, author = {You, Alexander and Grimm, Cindy and Davidson, Joseph R.}, url = {http://arxiv.org/abs/2202.13050}, doi = {10.1109/IROS47612.2022.9982017}, year = {2022}, date = {2022-10-01}, urldate = {2022-10-01}, booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages = {9180\textendash9186}, publisher = {arXiv}, abstract = {Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past work on enabling deep learning algorithms to operate in these environments has typically required large amounts of hand-labeled data to train a deep neural network or physically controlling the conditions under which the environment is perceived. In this paper, we train a neural network system in simulation only using simulated RGB data and optical flow. This resulting neural network is able to perform foreground segmentation of branches in a busy orchard environment without additional real-world training or using any special setup or equipment beyond a standard camera. Our results show that our system is highly accurate and, when compared to a network using manually labeled RGBD data, achieves significantly more consistent and robust performance across environments that differ from the training set.}, note = {ISSN: 2153-0866}, keywords = {AI, Labor, Pruning, Thinning}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{koul_offline_2022-1, title = {Offline Policy Comparison with Confidence: Benchmarks and Baselines}, author = {Koul, Anurag and Phielipp, Mariano and Fern, Alan}, url = {https://openreview.net/forum?id=hfE9u5d3_dw}, year = {2022}, date = {2022-10-01}, urldate = {2022-10-01}, abstract = {Decision makers often wish to use offline historical data to compare sequential-action policies at various world states. Importantly, computational tools should produce confidence values for such offline policy comparison (OPC) to account for statistical variance and limited data coverage. Nevertheless, there is little work that directly evaluates the quality of confidence values for OPC. In this work, we address this issue by creating benchmarks for OPC with Confidence (OPCC), derived by adding sets of policy comparison queries to datasets from offline reinforcement learning. In addition, we present an empirical evaluation of the textbackslashemphrisk versus coverage trade-off for a class of model-based baselines. In particular, the baselines learn ensembles of dynamics models, which are used in various ways to produce simulations for answering queries with confidence values. While our results suggest advantages for certain baseline variations, there appears to be significant room for improvement in future work.}, keywords = {AI}, pubstate = {published}, tppubtype = {inproceedings} } @workshop{welankar_grape_2022, title = {Extracting patterns in cold hardiness behavior using topological data analysis}, author = {Sejal Welankar and Paola Pesantez-Cabrera and Ananth Kalyanaraman}, url = {https://drive.google.com/file/d/1Mv4rGB1OhnK5Q0W_9To8UkSoqmPkL2WZ/view?usp=share_link}, year = {2022}, date = {2022-09-01}, urldate = {2022-09-01}, journal = {Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022)}, publisher = {arXiv}, abstract = {Prevention of cold injury is essential to maximize throughput for perennial specialty crops such as apples, cherries, wine grapes, etc. To achieve this, it is primordial to study the effects of environmental factors and their variations across different cultivars. To fully analyze and understand the relationship between phenotypes, genotypes, and environmental variables we need high dimensional datasets containing information such as crop height, growth characteristics, photosynthetic activity, and temperature, humidity, soil temperature. However, these datasets usually are incomplete and noisy. Topological data analysis (TDA) provides a general framework to analyze such data, extracting the underlying shape of data. The two main approaches in TDA are the mapper algorithm and persistence homology.}, keywords = {AI, Cold Hardiness, Farm Ops, Topological Data Analysis}, pubstate = {published}, tppubtype = {workshop} } @workshop{saxena_grape_2022, title = {Grape Cold Hardiness Prediction via Multi-Task Learning}, author = {Aseem Saxena and Paola Pesantez-Cabrera and Rohan Ballapragada and Kin-Ho Lam and Markus Keller and Alan Fern}, year = {2022}, date = {2022-09-01}, urldate = {2022-09-01}, journal = {Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022)}, publisher = {Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022)}, 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.}, keywords = {AI, Cold Hardiness, Farm Ops}, pubstate = {published}, tppubtype = {workshop} } @article{bertucci_dendromap_2022, title = {DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps}, author = { Donald Bertucci and Md Montaser Hamid and Yashwanthi Anand and Anita Ruangrotsakun and Delyar Tabatabai and Melissa Perez and Minsuk Kahng}, url = {https://ieeexplore.ieee.org/document/9904448}, doi = {10.1109/TVCG.2022.3209425}, year = {2022}, date = {2022-08-01}, urldate = {2022-08-01}, journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2022 Conference)}, publisher = {arXiv}, abstract = {In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.}, howpublished = {IEEE VIS 2022 Conference and will be published in the IEEE Transactions on Visualization and Computer Graphics}, keywords = {AI, Human-Computer Interaction}, pubstate = {published}, tppubtype = {article} } @article{belkhouja_dynamic_2022, title = {Dynamic Time Warping based Adversarial Framework for Time-Series Domain}, author = {Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao}, url = {https://ieeexplore.ieee.org/document/9970291}, doi = { 10.1109/TPAMI.2022.3224754}, year = {2022}, date = {2022-07-01}, urldate = {2022-07-01}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, publisher = {arXiv}, abstract = {Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as textbackslashem Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard Euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training. The source code of DTW-AR algorithms is available at https://github.com/tahabelkhouja/DTW-AR}, note = {arXiv:2207.04308 [cs]}, keywords = {AI}, pubstate = {published}, tppubtype = {article} } @inproceedings{kokel_hybrid_2022, title = {Hybrid Deep RePReL: Integrating Relational Planning and Reinforcement Learning for Information Fusion}, author = { Harsha Kokel and Nikhilesh Prabhakar and Balaraman Ravindran and Erik Blasch and Prasad Tadepalli and Sriraam Natarajan}, doi = {10.23919/FUSION49751.2022.9841246}, isbn = {978-1-73774-972-1}, year = {2022}, date = {2022-07-01}, urldate = {2022-07-01}, booktitle = {2022 25th International Conference on Information Fusion (FUSION)}, pages = {1--8}, abstract = {Fusion of high-level symbolic reasoning with lower level signal-based reasoning has attracted significant attention. We propose an architecture that integrates the high-level symbolic domain knowledge using a hierarchical planner with a lower level reinforcement learner that uses hybrid data (structured and unstructured). We introduce a novel neuro-symbolic system, Hybrid Deep RePReL that achieves the best of both worlds-the generalization ability of the planner with the effective learning ability of deep RL. Our results in two domains demonstrate the superiority of our approach in terms of sample efficiency as well as generalization to increased set of objects.}, keywords = {AI}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{belkhouja_out--distribution_2022, title = {Out-of-Distribution Detection in Time-Series Domain: A Novel Seasonal Ratio Scoring Approach}, author = { Taha Belkhouja and Yan Yan and Janardhan Rao Doppa}, url = {http://arxiv.org/abs/2207.04306}, doi = {10.48550/arXiv.2207.04306}, year = {2022}, date = {2022-07-01}, urldate = {2022-07-01}, publisher = {arXiv}, abstract = {Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data which is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this paper proposes a novel textbackslashem Seasonal Ratio Scoring (SRS) approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods. Open-source code for SRS method is provided at https://github.com/tahabelkhouja/SRS}, note = {arXiv:2207.04306 [cs]}, keywords = {AI}, pubstate = {published}, tppubtype = {inproceedings} } @article{belkhouja_training_2022, title = {Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis}, author = { Taha Belkhouja and Yan Yan and Janardhan Rao Doppa}, url = {https://ojs.aaai.org/index.php/AAAI/article/view/20552}, doi = {10.1609/aaai.v36i6.20552}, issn = {2374-3468, 2159-5399}, year = {2022}, date = {2022-06-01}, urldate = {2022-06-01}, journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, volume = {36}, number = {6}, pages = {6055--6063}, abstract = {Despite the success of deep neural networks (DNNs) for real-world applications over time-series data such as mobile health, little is known about how to train robust DNNs for time-series domain due to its unique characteristics compared to images and text data In this paper, we fill this gap by proposing a novel algorithmic framework referred as RObust Training for Time-Series (ROTS) to create robust deep models for time-series classification tasks. Specifically, we formulate a min-max optimization problem over the model parameters by explicitly reasoning about the robustness criteria in terms of additive perturbations to time-series inputs measured by the global alignment kernel (GAK) based distance. We also show the generality and advantages of our formulation using the summation structure over time-series alignments by relating both GAK and dynamic time warping (DTW). This problem is an instance of a family of compositional min-max optimization problems, which are challenging and open with unclear theoretical guarantee. We propose a principled stochastic compositional alternating gradient descent ascent (SCAGDA) algorithm for this family of optimization problems. Unlike traditional methods for timeseries that require approximate computation of distance measures, SCAGDA approximates the GAK based distance onthe-fly using a moving average approach. We theoretically analyze the convergence rate of SCAGDA and provide strong theoretical support for the estimation of GAK based distance. Our experiments on real-world benchmarks demonstrate that ROTS creates more robust deep models when compared to adversarial training using prior methods that rely on data augmentation or new definitions of loss functions. We also demonstrate the importance of GAK for time-series data over the Euclidean distance.}, keywords = {AI}, pubstate = {published}, tppubtype = {article} } @article{kalyanaraman_special_2022, title = {Special report: The AgAID AI institute for transforming workforce and decision support in agriculture}, author = {Kalyanaraman, Ananth and Burnett, Margaret and Fern, Alan and Khot, Lav and Viers, Joshua}, url = {https://www.sciencedirect.com/science/article/pii/S0168169922002617}, doi = {10.1016/j.compag.2022.106944}, issn = {0168-1699}, year = {2022}, date = {2022-06-01}, urldate = {2022-08-16}, journal = {Computers and Electronics in Agriculture}, volume = {197}, pages = {106944}, abstract = {Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior \textendash calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.}, keywords = {AI, Education, Farm Ops, Humans, Labor, Water}, pubstate = {published}, tppubtype = {article} } @inproceedings{you_autonomous_2022, title = {An autonomous robot for pruning modern, planar fruit trees}, author = { Alexander You and Nidhi Parayil and Josyula Gopala Krishna and Uddhav Bhattarai and Ranjan Sapkota and Dawood Ahmed and Matthew Whiting and Manoj Karkee and Cindy M. Grimm and Joseph R. Davidson}, url = {http://arxiv.org/abs/2206.07201}, doi = {10.48550/arXiv.2206.07201}, year = {2022}, date = {2022-06-01}, urldate = {2022-06-01}, publisher = {arXiv}, abstract = {Dormant pruning of fruit trees is an important task for maintaining tree health and ensuring high-quality fruit. Due to decreasing labor availability, pruning is a prime candidate for robotic automation. However, pruning also represents a uniquely difficult problem for robots, requiring robust systems for perception, pruning point determination, and manipulation that must operate under variable lighting conditions and in complex, highly unstructured environments. In this paper, we introduce a system for pruning sweet cherry trees (in a planar tree architecture called an upright fruiting offshoot configuration) that integrates various subsystems from our previous work on perception and manipulation. The resulting system is capable of operating completely autonomously and requires minimal control of the environment. We validate the performance of our system through field trials in a sweet cherry orchard, ultimately achieving a cutting success rate of 58%. Though not fully robust and requiring improvements in throughput, our system is the first to operate on fruit trees and represents a useful base platform to be improved in the future.}, note = {arXiv:2206.07201 [cs]}, keywords = {AI, Labor, Pruning}, pubstate = {published}, tppubtype = {inproceedings} } @article{koul_offline_2022, title = {Offline Policy Comparison with Confidence: Benchmarks and Baselines}, author = { Anurag Koul and Mariano Phielipp and Alan Fern}, url = {http://arxiv.org/abs/2205.10739}, doi = {10.48550/arXiv.2205.10739}, year = {2022}, date = {2022-05-01}, urldate = {2022-05-01}, publisher = {arXiv}, abstract = {Decision makers often wish to use offline historical data to compare sequential-action policies at various world states. Importantly, computational tools should produce confidence values for such offline policy comparison (OPC) to account for statistical variance and limited data coverage. Nevertheless, there is little work that directly evaluates the quality of confidence values for OPC. In this work, we address this issue by creating benchmarks for OPC with Confidence (OPCC), derived by adding sets of policy comparison queries to datasets from offline reinforcement learning. In addition, we present an empirical evaluation of the risk versus coverage trade-off for a class of model-based baselines. In particular, the baselines learn ensembles of dynamics models, which are used in various ways to produce simulations for answering queries with confidence values. While our results suggest advantages for certain baseline variations, there appears to be significant room for improvement in future work.}, note = {arXiv:2205.10739 [cs]}, keywords = {AI}, pubstate = {published}, tppubtype = {article} } @inproceedings{guizani_how_2022, title = {How to Debug Inclusivity Bugs? A Debugging Process with Information Architecture}, author = { Mariam Guizani and Igor Steinmacher and Jillian Emard and Abrar Fallatah and Margaret Burnett and Anita Sarma}, url = {https://www.computer.org/csdl/proceedings-article/icse-seis/2022/959400a090/1EmrhU2EkcE}, doi = {10.1109/ICSE-SEIS55304.2022.9794009}, isbn = {978-1-66549-594-3}, year = {2022}, date = {2022-05-01}, urldate = {2022-05-01}, booktitle = {2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)}, pages = {90--101}, publisher = {IEEE Computer Society}, abstract = {Although some previous research has found ways to find inclusivity bugs (biases in software that introduce inequities), little attention has been paid to how to go about fixing such bugs. Without a process to move from finding to fixing, acting upon such findings is an ad-hoc activity, at the mercy of the skills of each individual developer. To address this gap, we created Why/Where/Fix, a systematic inclusivity debugging process whose inclusivity fault localization harnesses Information Architecture(IA)-the way user-facing information is organized, structured and labeled. We then conducted a multi-stage qualitative empirical evaluation of the effectiveness of Why/Where/Fix, using an Open Source Software (OSS) project\'s infrastructure as our setting. In our study, the OSS project team used the Why/Where/Fix process to find inclusivity bugs, localize the IA faults behind them, and then fix the IA to remove the inclusivity bugs they had found. Our results showed that using Why/Where/Fix reduced the number of inclusivity bugs that OSS newcomer participants experienced by 90\%. Diverse teams have been shown to be more productive as well as more innovative. One form of diversity, cognitive diversity - differences in cognitive styles - helps generate diversity of thoughts. However, cognitive diversity is often not supported in software tools. This means that these tools are not inclusive of individuals with different cognitive styles (e.g., those who like to learn through process vs. those who learn by tinkering), which burdens these individuals with a cognitive \“tax\” each time they use the tool. In this work, we present an approach that enables software developers to: (1) evaluate their tools, especially those that are information-heavy, to find \“inclusivity bugs\”- cases where diverse cognitive styles are unsupported, (2) find where in the tool these bugs lurk, and (3) fix these bugs. Our evaluation in an open source project shows that by following this approach developers were able to reduce inclusivity bugs in their projects by 90\%.}, keywords = {Human-Computer Interaction}, pubstate = {published}, tppubtype = {inproceedings} } @article{belkhouja_adversarial_2022, title = {Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features}, author = {Belkhouja, Taha and Doppa, Janardhan Rao}, url = {https://www.jair.org/index.php/jair/article/view/13543}, doi = {10.1613/jair.1.13543}, issn = {1076-9757}, year = {2022}, date = {2022-04-01}, urldate = {2022-09-01}, journal = {Journal of Artificial Intelligence Research}, volume = {73}, pages = {1435\textendash1471}, abstract = {Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT). To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness.}, keywords = {AI}, pubstate = {published}, tppubtype = {article} } @article{homayouni_estimation_2022, title = {Estimation of proper shaking parameters for pistachio trees based on their trunk size}, author = {Homayouni, Taymaz and Gholami, Akram and Toudeshki, Arash and Afsah-Hejri, Leili and Ehsani, Reza}, url = {https://www.sciencedirect.com/science/article/pii/S1537511022000411}, doi = {10.1016/j.biosystemseng.2022.02.008}, issn = {1537-5110}, year = {2022}, date = {2022-04-01}, urldate = {2022-07-07}, journal = {Biosystems Engineering}, volume = {216}, pages = {121\textendash131}, abstract = {Trunk shaking is the most common mechanical harvesting system for harvesting pistachio. Harvesting machine operators often subjectively decide how to set the shaking parameters such as frequency and duration and this requires experience. The main objectives of this study were to evaluate the effect of tree morphology and shaking parameters such as trunk size and shaking pattern on the energy distribution through the branches and to optimise the shaking intensity of individual pistachio trees based on a tree-specific feedback loop. Wireless 3D accelerometer sensors were built and used to measure vibration transmission through the tree canopy at different locations and to monitor the energy transmission between the machine shaker head and the tree trunk. Thirty trees were selected for this study and were divided into three groups based on the trunk circumference size. To study the effect of a shaking pattern on the vibration transmission through the tree, four shaking patterns were selected and tested. Shaking duration was measured and it showed an average of 30% longer time compared to the shaking pattern duration. The effect of all four shaking patterns was analysed using continuous wavelet transform. The responses of trees were analysed and the optimum shaking intensity for each tree was determined. A model was developed to estimate the optimum shaking intensity for pistachio trees based on their trunk size. The model showed that 37, 57, and 65% are the optimum shaking intensity percentages for small, medium, and large trees, respectively.}, keywords = {Labor}, pubstate = {published}, tppubtype = {article} } @article{khanna_finding_2022, title = {Finding AI’s Faults with AAR/AI: An Empirical Study}, author = { Roli Khanna and Jonathan Dodge and Andrew Anderson and Rupika Dikkala and Jed Irvine and Zeyad Shureih and Kin-Ho Lam and Caleb R. Matthews and Zhengxian Lin and Minsuk Kahng and Alan Fern and Margaret Burnett}, url = {https://doi.org/10.1145/3487065}, doi = {10.1145/3487065}, issn = {2160-6455}, year = {2022}, date = {2022-03-01}, urldate = {2022-03-01}, journal = {ACM Transactions on Interactive Intelligent Systems}, volume = {12}, number = {1}, pages = {1:1--1:33}, abstract = {Would you allow an AI agent to make decisions on your behalf? If the answer is “not always,” the next question becomes “in what circumstances”? Answering this question requires human users to be able to assess an AI agent\textemdashand not just with overall pass/fail assessments or statistics. Here users need to be able to localize an agent’s bugs so that they can determine when they are willing to rely on the agent and when they are not. After-Action Review for AI (AAR/AI), a new AI assessment process for integration with Explainable AI systems, aims to support human users in this endeavor, and in this article we empirically investigate AAR/AI’s effectiveness with domain-knowledgeable users. Our results show that AAR/AI participants not only located significantly more bugs than non-AAR/AI participants did (i.e., showed greater recall) but also located them more precisely (i.e., with greater precision). In fact, AAR/AI participants outperformed non-AAR/AI participants on every bug and were, on average, almost six times as likely as non-AAR/AI participants to find any particular bug. Finally, evidence suggests that incorporating labeling into the AAR/AI process may encourage domain-knowledgeable users to abstract above individual instances of bugs; we hypothesize that doing so may have contributed further to AAR/AI participants’ effectiveness.}, keywords = {AI, Human-Computer Interaction}, pubstate = {published}, tppubtype = {article} } @inproceedings{dodge_how_2022, title = {How Do People Rank Multiple Mutant Agents?}, author = { Jonathan Dodge and Andrew A. Anderson and Matthew Olson and Rupika Dikkala and Margaret Burnett}, url = {https://doi.org/10.1145/3490099.3511115}, doi = {10.1145/3490099.3511115}, isbn = {978-1-4503-9144-3}, year = {2022}, date = {2022-03-01}, urldate = {2022-03-01}, booktitle = {27th International Conference on Intelligent User Interfaces}, pages = {191--211}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {IUI '22}, abstract = {Faced with several AI-powered sequential decision-making systems, how might someone choose on which to rely? For example, imagine car buyer Blair shopping for a self-driving car, or developer Dillon trying to choose an appropriate ML model to use in their application. Their first choice might be infeasible (i.e., too expensive in money or execution time), so they may need to select their second or third choice. To address this question, this paper presents: 1) Explanation Resolution, a quantifiable direct measurement concept; 2) a new XAI empirical task to measure explanations: “the Ranking Task”; and 3) a new strategy for inducing controllable agent variations\textemdashMutant Agent Generation. In support of those main contributions, it also presents 4) novel explanations for sequential decision-making agents; 5) an adaptation to the AAR/AI assessment process; and 6) a qualitative study around these devices with 10 participants to investigate how they performed the Ranking Task on our mutant agents, using our explanations, and structured by AAR/AI. From an XAI researcher perspective, just as mutation testing can be applied to any code, mutant agent generation can be applied to essentially any neural network for which one wants to evaluate an assessment process or explanation type. As to an XAI user’s perspective, the participants ranked the agents well overall, but showed the importance of high explanation resolution for close differences between agents. The participants also revealed the importance of supporting a wide diversity of explanation diets and agent “test selection” strategies.}, keywords = {AI, Human-Computer Interaction}, pubstate = {published}, tppubtype = {inproceedings} } @inproceedings{chatterjee_inclusivity_2022, title = {Inclusivity Bugs in Online Courseware: A Field Study}, author = { Amreeta Chatterjee and Lara Letaw and Rosalinda Garcia and Doshna Umma Reddy and Rudrajit Choudhuri and Sabyatha Sathish Kumar and Patricia Morreale and Anita Sarma and Margaret Burnett}, url = {https://doi.org/10.1145/3501385.3543973}, doi = {10.1145/3501385.3543973}, isbn = {978-1-4503-9194-8}, year = {2022}, date = {2022-01-01}, urldate = {2022-01-01}, booktitle = {Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1}, pages = {356--372}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, series = {ICER '22}, abstract = {Motivation: Although asynchronous online CS courses have enabled more diverse populations to access CS higher education, research shows that online CS-ed is far from inclusive, with women and other underrepresented groups continuing to face inclusion gaps. Worse, diversity/inclusion research in CS-ed has largely overlooked the online courseware\textemdashthe web pages and course materials that populate the online learning platforms\textemdashthat constitute asynchronous online CS-ed’s only mechanism of course delivery. Objective: To investigate this aspect of CS-ed’s inclusivity, we conducted a three-phase field study with online CS faculty, with three research questions: (1) whether, how, and where online CS-ed’s courseware has inclusivity bugs; (2) whether an automated tool can detect them; and (3) how online CS faculty would make use of such a tool. Method: In the study’s first phase, we facilitated online CS faculty members’ use of GenderMag (an inclusive design method) on two online CS courses to find their own courseware’s inclusivity bugs. In the second phase, we used a variant of the GenderMag Automated Inclusivity Detector (AID) tool to automatically locate a “vertical slice” of such courseware inclusivity bugs, and evaluated the tool’s accuracy. In the third phase, we investigated how online CS faculty used the tool to find inclusivity bugs in their own courseware. Results: The results revealed 29 inclusivity bugs spanning 6 categories in the online courseware of 9 online CS courses; showed that the tool achieved an accuracy of 75% at finding such bugs; and revealed new insights into how a tool could help online CS faculty uncover assumptions about their own courseware to make it more inclusive. Implications: As the first study to investigate the presence and types of cognitive- and gender-inclusivity bugs in online CS courseware and whether an automated tool can find them, our results reveal new possibilities for how to make online CS education a more inclusive virtual environment for gender-diverse students.}, keywords = {Education, Human-Computer Interaction}, pubstate = {published}, tppubtype = {inproceedings} } @article{dodge_after-action_2021, title = {After-Action Review for AI (AAR/AI)}, author = { Jonathan Dodge and Roli Khanna and Jed Irvine and Kin-ho Lam and Theresa Mai and Zhengxian Lin and Nicholas Kiddle and Evan Newman and Andrew Anderson and Sai Raja and Caleb Matthews and Christopher Perdriau and Margaret Burnett and Alan Fern}, url = {https://doi.org/10.1145/3453173}, doi = {10.1145/3453173}, issn = {2160-6455}, year = {2021}, date = {2021-01-01}, urldate = {2021-01-01}, journal = {ACM Transactions on Interactive Intelligent Systems}, volume = {11}, number = {3-4}, pages = {29:1--29:35}, abstract = {Explainable AI is growing in importance as AI pervades modern society, but few have studied how explainable AI can directly support people trying to assess an AI agent. Without a rigorous process, people may approach assessment in ad hoc ways\textemdashleading to the possibility of wide variations in assessment of the same agent due only to variations in their processes. AAR, or After-Action Review, is a method some military organizations use to assess human agents, and it has been validated in many domains. Drawing upon this strategy, we derived an After-Action Review for AI (AAR/AI), to organize ways people assess reinforcement learning agents in a sequential decision-making environment. We then investigated what AAR/AI brought to human assessors in two qualitative studies. The first investigated AAR/AI to gather formative information, and the second built upon the results, and also varied the type of explanation (model-free vs. model-based) used in the AAR/AI process. Among the results were the following: (1) participants reporting that AAR/AI helped to organize their thoughts and think logically about the agent, (2) AAR/AI encouraged participants to reason about the agent from a wide range of perspectives, and (3) participants were able to leverage AAR/AI with the model-based explanations to falsify the agent’s predictions.}, keywords = {AI, Human-Computer Interaction}, pubstate = {published}, tppubtype = {article} }