@article{sapkota_synthetic_2024,
title = {Synthetic meets authentic: Leveraging LLM generated datasets for YOLO11 and YOLOv10-based apple detection through machine vision sensors},
author = {Ranjan Sapkota and Zhichao Meng and Manoj Karkee},
url = {https://www.sciencedirect.com/science/article/pii/S2772375524002193},
doi = {10.1016/j.atech.2024.100614},
issn = {2772-3755},
year  = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {Smart Agricultural Technology},
volume = {9},
pages = {100614},
abstract = {Training machine learning (ML) models for artificial intelligence (AI) and computer vision-based object detection process typically requires large, labeled datasets, a process often burdened by significant human effort and high costs associated with imaging systems and image acquisition. This research aimed to simplify image data collection for object detection in orchards by avoiding traditional fieldwork with different imaging sensors. Utilizing OpenAI's DALLE, a large language model (LLM) for realistic image generation, we generated and annotated a cost-effective dataset. This dataset, exclusively generated by LLM, was then utilized to train two state-of-the-art deep learning models: YOLOV10 and YOLO11. The YOLO11 model for apple detection was trained with its five configurations (YOLO11n, YOLO11 s, YOLO11 m, YOLO11l and YOLO11x), and YOLOv10 model with its six configurations (YOLOv10n, YOLOv10 s, YOLOv10 m, YOLOv10b, YOLOv10l and YOLOv10x), which was then tested with real-world (outdoor orchard) images captured by a digital (Nikon D5100) camera and a consumer RGB-D camera (Microsoft Azure Kinect). YOLO11 outperformed YOLOv10 as YOLO11x and YOLO11n exhibited superior precision of 0.917 and 0.916, respectively. Furthermore, YOLO11l demonstrated the highest recall among its counterparts, achieving a recall of 0.889. Likewise, the YOLO11n variant excelled in terms of mean average precision (mAP@50), achieving the highest value of 0.958. Validation tests against actual images collected through a digital camera (Nikon D5100) over Scilate apple variety in a commercial orchard environment showed a highest precision of 0.874 for YOLO11 s, recall of 0.877 for YOLO11l and mAP@50 of 0.91 for YOLO11x. Additionally, validation test against actual images collected through a Microsoft Azure camera over the same orchard showed a highest precision, recall and mAP@50 respectively of 0.924, 0.781 and 0.855 with YOLO11x. All variants of YOLO11 surprisingly demonstrated a pre-processing time of just 0.2 milliseconds (ms), which was faster than any variant of YOLOv10. The fastest inference time for the YOLO11n model using the training dataset generated by the language model was 3.2 ms, while YOLOv10n, fastest among YOLOv10 variants, had a longer inference time of 5.5 ms. Likewise, the fastest inference time for the sensor-based images was 7.1 ms (for Nikon D5100 camera images) and 4.7 ms (for Azure images) with YOLO11n. This study presents a pathway for generating large image datasets using LLM in challenging agricultural fields with minimal or no labor-intensive efforts in field data-collection, which could accelerate the development and deployment of computer vision and robotic technologies in orchard environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{singh_sensitivity_2024,
title = {Sensitivity of snow magnitude and duration to hydrology model parameters},
author = {Bhupinderjeet Singh and Tanvir Ferdousi and John T. Abatzoglou and Samarth Swarup and Jennifer C. Adam and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0022169424015890},
doi = {10.1016/j.jhydrol.2024.132193},
issn = {0022-1694},
year  = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
journal = {Journal of Hydrology},
volume = {645},
pages = {132193},
abstract = {Process-based hydrology models are critical for understanding streamflow and water supply under global change. However, these models require parameterization which introduces additional uncertainty into the models. The role that these parameters play in driving uncertainty is under-studied, especially for intermediary processes outside of streamflow. An important example in snowmelt dominant regions are intermediary processes related to snowpack accumulation and ablation. We examine the sensitivity of snow magnitude and duration to eleven parameters relevant to snow processes in the coupled crop-hydrology model VIC-CropSyst using a hybrid global\textendashlocal Distributed Evaluation of Local Sensitivity Analysis approach. With the Pacific Northwest US as a case study, our specific research questions are: (a) What is the sensitivity response of peak snow water equivalent (SWE) and snow duration and how does it vary in the parameter space? (b) What are the key drivers of the sensitivity response? and (c) Which of the most sensitive parameters can we immediately improve by leveraging existing data products? Both target variables were sensitive to less than four of the eleven parameters. We found that peak SWE was most sensitive to either the precipitation partitioning temperature threshold or the albedo of new snow, depending on the geography and associated interplay between hydro-meteorological factors. In contrast, snow duration was primarily sensitive to the albedo of new snow and the albedo decay coefficient during snowmelt. Machine learning explainability workflows applied on the sensitivity response explained the model behavior and determined key geographic and hydro-meteorological drivers of the sensitivity response. Regions where the significant precipitation co-occurred with near-freezing temperature exhibited higher sensitivity of peak SWE to precipitation partitioning. In contrast, much colder high-elevation regions that have a delayed snowmelt-driven runoff when downward shortwave radiation is higher, displayed more sensitivity to albedo parameters. We also noted differences in the list of key parameters and in the level of sensitivity between our work and the limited comparable existing work. This highlights the need for comprehensive sensitivity analysis of snow metrics to become a routine component of hydrology model application studies in addition to streamflow. This is critical for us to better understand model behavior, identify key model parameters that can benefit from more dynamic representation in the models, and strategically improve models to best support decision-makers.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
@misc{sapkota_zero-shot_2024,
title = {Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development},
author = {Ranjan Sapkota and Achyut Paudel and Manoj Karkee},
url = {http://arxiv.org/abs/2411.11285},
doi = {10.48550/arXiv.2411.11285},
year  = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
publisher = {arXiv},
abstract = {Currently, deep learning-based instance segmentation for various applications (e.g., Agriculture) is predominantly performed using a labor-intensive process involving extensive field data collection using sophisticated sensors, followed by careful manual annotation of images, presenting significant logistical and financial challenges to researchers and organizations. The process also slows down the model development and training process. In this study, we presented a novel method for deep learning-based instance segmentation of apples in commercial orchards that eliminates the need for labor-intensive field data collection and manual annotation. Utilizing a Large Language Model (LLM), we synthetically generated orchard images and automatically annotated them using the Segment Anything Model (SAM) integrated with a YOLO11 base model. This method significantly reduces reliance on physical sensors and manual data processing, presenting a major advancement in "Agricultural AI". The synthetic, auto-annotated dataset was used to train the YOLO11 model for Apple instance segmentation, which was then validated on real orchard images. The results showed that the automatically generated annotations achieved a Dice Coefficient of 0.9513 and an IoU of 0.9303, validating the accuracy and overlap of the mask annotations. All YOLO11 configurations, trained solely on these synthetic datasets with automated annotations, accurately recognized and delineated apples, highlighting the method's efficacy. Specifically, the YOLO11m-seg configuration achieved a mask precision of 0.902 and a mask mAP@50 of 0.833 on test images collected from a commercial orchard. Additionally, the YOLO11l-seg configuration outperformed other models in validation on 40 LLM-generated images, achieving the highest mask precision and mAP@50 metrics. Keywords: YOLO, SAM, SAMv2, YOLO11, YOLOv11, Segment Anything, YOLO-SAM},
note = {arXiv:2411.11285},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
pubstate = {published},
tppubtype = {misc}
}
@article{savalkar_earlier_2024,
title = {Earlier planting in a future climate fails to replicate historical production conditions for US spring wheat.},
author = {Supriya Savalkar and Michael Pumphrey and Kimberly Campbell and Fabio Scarpare and Tanvir Ferdousi and Samarth Swarup and Claudio St\"{o}ckle and Kirti Rajagopalan},
url = {https://www.researchsquare.com/article/rs-4940971/v1},
doi = {10.21203/rs.3.rs-4940971/v1},
year  = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
publisher = {Research Square},
abstract = {Global warming can increase crop heat stress exposure, adversely affecting crop yields and quality. Earlier planting is widely considered in climate change literature as a potential adaptation strategy by shifting the growing season to cooler periods. However, the effectiveness of earlier planting in achieving overall temperature exposures during the crop growth season that are at least as favorable as historical conditions remains unclear. Our objective is to comprehensively assess the potential effectiveness of earlier planting as an adaptation strategy by addressing two key questions: How effective is earlier planting in reducing exposure to excessively high temperatures across different growth stages? What are the associated tradeoffs in temperature exposure, and can historical conditions be matched with this adaptation strategy? We focus on major US spring wheat growing states as a case study, analyzing lethal, critical, suboptimal, and optimal temperature thresholds by growth stage to quantify the impact of earlier planting. Our findings indicate that while earlier planting does reduce exposure to critical and lethal high-temperature categories during some reproductive stages, it generally fails to replicate historical production conditions for the majority of the US spring wheat production region (contributing 85% of the current production). This trend persists across all future time frames and emission scenarios. The Pacific Northwest US presents an exception; however, even in this region, certain early and late growth stages may experience worse-than-historical conditions requiring management. The planting window with historically equivalent temperature exposures also narrows from 11 weeks to 1-7 weeks, presenting additional logistical challenges. Given that the Pacific Northwest US accounts for less than 15% of the national spring wheat production, at a national scale, current production levels are unlikely to be sustained by primarily relying on earlier planting as an adaptation strategy. This is a critical consideration, especially given that many climate change assessments frequently list earlier planting as an effective adaptation strategy without the capacity to fully explore its tradeoffs. Exploring additional adaptation strategies to maintain current national production levels is important.},
note = {ISSN: 2693-5015},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{sapkota_comparing_2024,
title = {Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments},
author = {Ranjan Sapkota and Dawood Ahmed and Manoj Karkee},
url = {https://www.sciencedirect.com/science/article/pii/S258972172400028X},
doi = {10.1016/j.aiia.2024.07.001},
issn = {2589-7217},
year  = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
journal = {Artificial Intelligence in Agriculture},
volume = {13},
pages = {84\textendash99},
abstract = {Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information for various automated or robotic tasks such as selective harvesting and precision pruning. This study compares the one-stage YOLOv8 and the two-stage Mask R-CNN machine learning models for instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected in dormant season, includes images of dormant apple trees, which were used to train multi-object segmentation models delineating tree branches and trunks. Dataset 2, collected in the early growing season, includes images of apple tree canopies with green foliage and immature (green) apples (also called fruitlet), which were used to train single-object segmentation models delineating only immature green apples. The results showed that YOLOv8 performed better than Mask R-CNN, achieving good precision and near-perfect recall across both datasets at a confidence threshold of 0.5. Specifically, for Dataset 1, YOLOv8 achieved a precision of 0.90 and a recall of 0.95 for all classes. In comparison, Mask R-CNN demonstrated a precision of 0.81 and a recall of 0.81 for the same dataset. With Dataset 2, YOLOv8 achieved a precision of 0.93 and a recall of 0.97. Mask R-CNN, in this single-class scenario, achieved a precision of 0.85 and a recall of 0.88. Additionally, the inference times for YOLOv8 were 10.9 ms for multi-class segmentation (Dataset 1) and 7.8 ms for single-class segmentation (Dataset 2), compared to 15.6 ms and 12.8 ms achieved by Mask R-CNN's, respectively. These findings show YOLOv8's superior accuracy and efficiency in machine learning applications compared to two-stage models, specifically Mask-R-CNN, which suggests its suitability in developing smart and automated orchard operations, particularly when real-time applications are necessary in such cases as robotic harvesting and robotic immature green fruit thinning.},
keywords = {Artificial intelligence, Automation, Deep learning, Machine Learning, Machine vision, Mask R-CNN, Robotics, YOLOv8},
pubstate = {published},
tppubtype = {article}
}
@article{amogi_edge_2024,
title = {Edge compute algorithm enabled localized crop physiology sensing system for apple (textitMalus domestica Borkh.) crop water stress monitoring},
author = {Basavaraj R. Amogi and Nisit Pukrongta and Lav R. Khot and Bernardita V. Sallato},
url = {https://www.sciencedirect.com/science/article/pii/S0168169924005283},
doi = {10.1016/j.compag.2024.109137},
issn = {0168-1699},
year  = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
journal = {Computers and Electronics in Agriculture},
volume = {224},
pages = {109137},
abstract = {Elevated air temperature (\>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p \< 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{noauthor_slicing-aided_nodateb,
title = {Slicing-Aided Hyper Inference for Enhanced Fruit Bud Detection and Counting in Apple Orchards during Dormant Season},
author = {Dawood Ahmed, Ranjan Sapkota, Martin Churuvija, Matthew Whiting, Manoj Karkee},
url = {https://doi.org/10.13031/aim.202401055},
year  = {2024},
date = {2024-08-02},
publisher = {ASABE},
abstract = {To meet the global food demand amidst the growing population, there‘s a critical need to augment productivity in the specialty crop industry. However, workforce shortage in agriculture presents a significant challenge to increasing agricultural productivity, emphasizing the need for automating various farm operations such as harvesting, canopy management, and crop-load management practices. A critical aspect of crop-load management is determining existing crop-load along with target yield, which can be used for optimizing crop-load in orchards. Buds, the initial phase of the flowering and fruiting process, are critical for early crop-load estimation (number of fruiting sites per branch or tree) and can facilitate informed pruning decisions to achieve the desired crop-load. Yet, the detection of these buds poses a huge challenge due to their miniature size and variable environmental conditions (including lighting) of commercial orchards. This research explores the performances of a Convolutional Neural Network (CNN) model and a Detection Transformer (DETR) model in detecting fruiting buds in apple orchards, using standard inference and a hyper inferencing technique. Additionally, the system quantifies buds and employs an association algorithm to correlate each bud to its respective branch, facilitating the estimation of the number of buds per branch. Despite the challenges due to the size of buds and environmental complexity, the proposed system demonstrated precision of 80.2 %, recall of 80.1 %, and f1-score of 80.15 % in detecting fruit buds, using a YOLOv8 model. The use of the Slicing Aided Hyper Inferencing Technique improved the overall recall score of the model. Additionally, the system quantified buds in a frame for the most salient branch using the branch-bud association with an accuracy of 73%. This study demonstrated a promising workflow in detecting and counting fruiting buds in a dynamic orchard environment to facilitate current manual pruning operation as well as future studies on automated pruning.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{noauthor_data-driven_nodateb,
title = {Data-Driven Model to Improve Mechanical Harvesters for Nut Trees},
author = {Mohsen Farajijalal, Samira Malek, Arash Toudeshki, Joshua H. Viers, Reza Ehsani},
url = {https://doi.org/10.13031/aim.202400858},
year  = {2024},
date = {2024-08-02},
publisher = {ASABE},
abstract = {The economic impact of California‘s agricultural sector is the most important in the United States. California produces more than 80% of the world‘s almonds. Currently, the trunk shaker harvesting machine is the most widely used for harvesting almond trees in California. During the harvest, the operator, based on their experience, set the shaking parameters such as duration and shaking frequency. Manual adjustments by operators lead to variability in fruit removal, and in some cases, it could cause tree damage. This study aimed to develop a data-driven mathematical model required to build an intelligent tree shaker machine capable of optimizing shaking parameters autonomously. A sensor system to monitor force distribution throughout the tree canopy was designed and implemented. Data was collected from an almond orchard in California during the summer of 2023. A quadratic mathematical model was developed using machine learning regression methods to estimate relationships between trunk diameter, acceleration, and shaking duration. The findings show that trunk diameter positively correlates with acceleration and shaking time duration. Our research demonstrates the potential for intelligent harvesting machines that can adjust parameters based on real-time sensor data, ultimately improving fruit removal efficiency, and potentially reducing tree damage.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{singh_dynamic_2024,
title = {Dynamic precipitation phase partitioning improves modeled simulations of snow across the Northwest US},
author = {Bhupinderjeet Singh and Mingliang Liu and John Abatzoglou and Jennifer Adam and Kirti Rajagopalan},
url = {https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2284/},
doi = {10.5194/egusphere-2024-2284},
year  = {2024},
date = {2024-08-01},
urldate = {2024-08-01},
journal = {EGUsphere},
pages = {1\textendash24},
abstract = {\<p\>\<strong class="journal-contentHeaderColor"\>Abstract.\</strong\> While the importance of dynamic precipitation phase partitioning to get accurate estimates of rain versus snow amounts has been established, hydrology models rely on simplistic static temperature-based partitioning. We evaluate model skill changes for a suite of snow metrics between static and dynamic partitioning. We used the VIC-CropSyst coupled crop hydrology model across the Pacific Northwest US as a case study. We found that transition to the dynamic method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (textasciitilde50 % mean error reduction), (b) daily SWE in winter months (reduction of relative bias from -30 % to -4 %), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for a majority of the observational snow telemetry stations considered (depending on the metric, 75 % to 88 % of stations showed improvements). However, there was a degradation in model-observation agreement for snow-off dates, likely because errors in modeled snowmelt dynamics\—which cannot be resolved by changing the precipitation partitioning\—become important at the end of the cold season. Additionally, the transition from static to dynamic partitioning resulted in an 8 % mean increase in the snowmelt contribution to runoff. These results emphasize that the hydrological modeling community should transition to incorporating dynamic precipitation partitioning so we can better understand model behavior, improve model accuracies, and better support management decision support for water resources.\</p\>},
note = {Publisher: Copernicus GmbH},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{belakaria_active_2024,
title = {Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes},
author = {Syrine Belakaria and Benjamin Letham and Janardhan Rao Doppa and Barbara Engelhardt and Stefano Ermon and Eytan Bakshy},
url = {http://arxiv.org/abs/2407.09739},
doi = {10.48550/arXiv.2407.09739},
year  = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
publisher = {arXiv},
abstract = {We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Our aim is to efficiently learn the importance of different input variables, e.g., in vehicle safety experimentation, we study the impact of the thickness of various components on safety objectives. Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value. We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures (DGSMs) under Gaussian process surrogate models. We showcase the first application of active learning directly to DGSMs, and develop tractable uncertainty reduction and information gain acquisition functions for these measures. Through comprehensive evaluation on synthetic and real-world problems, our study demonstrates how these active learning acquisition strategies substantially enhance the sample efficiency of DGSM estimation, particularly with limited evaluation budgets. Our work paves the way for more efficient and accurate sensitivity analysis in various scientific and engineering applications.},
note = {arXiv:2407.09739},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
pubstate = {published},
tppubtype = {inproceedings}
}
@proceedings{shi_conformal_2024,
title = {Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration},
author = {Yuanjie Shi and Subhankar Ghosh and Taha Belkhouja and Janardhan Rao Doppa and Yan Yan},
url = {http://arxiv.org/abs/2406.06818},
doi = {10.48550/arXiv.2406.06818},
year  = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
publisher = {arXiv},
abstract = {Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or conditional probability. Although the valid coverage guarantee has been extensively studied for classification problems, CP often produces large prediction sets which may not be practically useful. This issue is exacerbated for the setting of class-conditional coverage on imbalanced classification tasks. This paper proposes the Rank Calibrated Class-conditional CP (RC3P) algorithm to reduce the prediction set sizes to achieve class-conditional coverage, where the valid coverage holds for each class. In contrast to the standard class-conditional CP (CCP) method that uniformly thresholds the class-wise conformity score for each class, the augmented label rank calibration step allows RC3P to selectively iterate this class-wise thresholding subroutine only for a subset of classes whose class-wise top-k error is small. We prove that agnostic to the classifier and data distribution, RC3P achieves class-wise coverage. We also show that RC3P reduces the size of prediction sets compared to the CCP method. Comprehensive experiments on multiple real-world datasets demonstrate that RC3P achieves class-wise coverage and 26.25% reduction in prediction set sizes on average.},
note = {arXiv:2406.06818},
keywords = {Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {proceedings}
}
@proceedings{pandit_learning_2024,
title = {Learning Decentralized Multi-Biped Control for Payload Transport},
author = {Bikram Pandit and Ashutosh Gupta and Mohitvishnu S. Gadde and Addison Johnson and Aayam Kumar Shrestha and Helei Duan and Jeremy Dao and Alan Fern},
url = {http://arxiv.org/abs/2406.17279},
doi = {10.48550/arXiv.2406.17279},
year  = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
publisher = {arXiv},
abstract = {Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectiveness and configurability for transport over rough terrain that is more suitable for legs rather than wheels. For this purpose, we consider multi-biped robot carriers, where wheels are replaced by multiple bipedal robots attached to the carrier. Our main contribution is to design a decentralized controller for such systems that can be effectively applied to varying numbers and configurations of rigidly attached bipedal robots without retraining. We present a reinforcement learning approach for training the controller in simulation that supports transfer to the real world. Our experiments in simulation provide quantitative metrics showing the effectiveness of the approach over a wide variety of simulated transport scenarios. In addition, we demonstrate the controller in the real-world for systems composed of two and three Cassie robots. To our knowledge, this is the first example of a scalable multi-biped payload transport system.},
note = {arXiv:2406.17279},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Robotics},
pubstate = {published},
tppubtype = {proceedings}
}
@article{kirkpatrick_who_2024,
title = {Who shares about AI? Media exposure, psychological proximity, performance expectancy, and information sharing about artificial intelligence online},
author = {Alex W. Kirkpatrick and Amanda D. Boyd and Jay D. Hmielowski},
url = {https://doi.org/10.1007/s00146-024-01997-x},
doi = {10.1007/s00146-024-01997-x},
issn = {1435-5655},
year  = {2024},
date = {2024-06-01},
urldate = {2024-06-01},
journal = {AI \& SOCIETY},
abstract = {Media exposure can shape audience perceptions surrounding novel innovations, such as artificial intelligence (AI), and could influence whether they share information about AI with others online. This study examines the indirect association between exposure to AI in the media and information sharing about AI online. We surveyed 567 US citizens aged 18 and older in November 2020, several months after the release of Open AI’s transformative GPT-3 model. Results suggest that AI media exposure was related to online information sharing through psychological proximity to the impacts of AI and positive AI performance expectancy in serial mediation. This positive indirect association became stronger the more an individual perceived society to be changing due to new technology. Results imply that public exposure to AI in the media could significantly impact public understanding of AI, and prompt further information sharing online.},
keywords = {Artificial intelligence, Information sharing, Media exposure, Psychological distance, Public engagement with science and technology},
pubstate = {published},
tppubtype = {article}
}
@article{marshall_californias_2024,
title = {California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change},
author = {Adrienne M. Marshall and John T. Abatzoglou and Stefan Rahimi and Dennis P. Lettenmaier and Alex Hall},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.2320600121},
doi = {10.1073/pnas.2320600121},
year  = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {121},
number = {20},
pages = {e2320600121},
abstract = {The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term “snow deluge” and use anomalously high snowpack in California’s Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California’s 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges\textemdashwhich we define as the 20 y April 1 SWE event\textemdashare projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.},
note = {Publisher: Proceedings of the National Academy of Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{garcia_matchmaker_2024,
title = {The Matchmaker Inclusive Design Curriculum: A Faculty-Enabling Curriculum to Teach Inclusive Design Throughout Undergraduate CS},
author = {Rosalinda Garcia and Patricia Morreale and Gail Verdi and Heather Garcia and Geraldine Jimena Noa and Spencer P. Madsen and Maria Jesus Alzugaray-Orellana and Elizabeth Li and Margaret Burnett},
url = {https://dl.acm.org/doi/10.1145/3613904.3642475},
doi = {10.1145/3613904.3642475},
isbn = {9798400703300},
year  = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems},
pages = {1\textendash22},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI '24},
abstract = {Despite efforts to raise awareness of societal and ethical issues in CS education, research shows students often do not act upon their new awareness (Problem 1). One such issue, well-established by HCI research, is that much of technology contains barriers impacting numerous populations\textemdashsuch as minoritized genders, races, ethnicities, and more. HCI has inclusive design methods that help\textemdashbut these skills are rarely taught, even in HCI classes (Problem 2). To address Problems 1 and 2, we created the Matchmaker Curriculum to pair CS faculty\textemdashincluding non-HCI faculty\textemdashwith inclusive design elements to allow for inclusive design skill-building throughout their CS program. We present the curriculum and a field study, in which we followed 18 faculty along their journey. The results show how the Matchmaker Curriculum equipped 88% of these faculty with enough inclusive design teaching knowledge to successfully embed actionable inclusive design skill-building into 13 CS courses.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@misc{flynn_uncovering_2024,
title = {Uncovering implementable dormant pruning decisions from three different stakeholder perspectives},
author = {Deanna Flynn and Abhinav Jain and Heather Knight and Cristina G. Wilson and Cindy Grimm},
url = {http://arxiv.org/abs/2405.04030},
doi = {10.48550/arXiv.2405.04030},
year  = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
publisher = {arXiv},
abstract = {Dormant pruning, or the removal of unproductive portions of a tree while a tree is not actively growing, is an important orchard task to help maintain yield, requiring years to build expertise. Because of long training periods and an increasing labor shortage in agricultural jobs, pruning could benefit from robotic automation. However, to program robots to prune branches, we first need to understand how pruning decisions are made, and what variables in the environment (e.g., branch size and thickness) we need to capture. Working directly with three pruning stakeholders \textendash horticulturists, growers, and pruners \textendash we find that each group of human experts approaches pruning decision-making differently. To capture this knowledge, we present three studies and two extracted pruning protocols from field work conducted in Prosser, Washington in January 2022 and 2023. We interviewed six stakeholders (two in each group) and observed pruning across three cultivars \textendash Bing Cherries, Envy Apples, and Jazz Apples \textendash and two tree architectures \textendash Upright Fruiting Offshoot and V-Trellis. Leveraging participant interviews and video data, this analysis uses grounded coding to extract pruning terminology, discover horticultural contexts that influence pruning decisions, and find implementable pruning heuristics for autonomous systems. The results include a validated terminology set, which we offer for use by both pruning stakeholders and roboticists, to communicate general pruning concepts and heuristics. The results also highlight seven pruning heuristics utilizing this terminology set that would be relevant for use by future autonomous robot pruning systems, and characterize three discovered horticultural contexts (i.e., environmental management, crop-load management, and replacement wood) across all three cultivars.},
note = {arXiv:2405.04030 [cs]},
keywords = {Computer Science - Robotics},
pubstate = {published},
tppubtype = {misc}
}
@article{blanco_relating_2024,
title = {Relating microtensiometer-based trunk water potential with sap flow, canopy temperature, and trunk and fruit diameter variations for irrigated ‘Honeycrisp’ apple},
author = {Victor Blanco and Lee Kalcsits},
url = {https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1393028/full},
doi = {10.3389/fpls.2024.1393028},
issn = {1664-462X},
year  = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
journal = {Frontiers in Plant Science},
volume = {15},
abstract = {\<p\>Instrumentation plays a key role in modern horticulture. Thus, the microtensiomenter, a new plant-based sensor that continuously monitors trunk water potential (Ψ$_textrmtrunk$) can help in irrigation management decisions. To compare the response of the Ψ$_textrmtrunk$ with other continuous tree water status indicators such as the sap flow rate, the difference between canopy and air temperatures, or the variations of the trunk and fruit diameter, all the sensors were installed in 2022 in a commercial orchard of ‘Honeycrisp’ apple trees with M.9 rootstocks in Washinton State (USA). From the daily evolution of the Ψ$_textrmtrunk$, five indicators were considered: predawn, midday, minimum, daily mean, and daily range (the difference between the daily maximum and minimum values). The daily range of Ψ$_textrmtrunk$ was the most linked to the maximum daily shrinkage (MDS; R$^textrm2$ = 0.42), the canopy-to-air temperature (Tc-Ta; R$^textrm2$ = 0.32), and the sap flow rate (SF; R$^textrm2$ = 0.30). On the other hand, the relative fruit growth rate (FRGR) was more related to the minimum Ψ$_textrmtrunk$ (R$^textrm2$ = 0.33) and the daily mean Ψ$_textrmtrunk$ (R$^textrm2$ = 0.32) than to the daily range of Ψ$_textrmtrunk$. All indicators derived from Ψ$_textrmtrunk$ identified changes in tree water status after each irrigation event and had low coefficients of variation and high sensitivity. These results encourage Ψ$_textrmtrunk$ as a promising candidate for continuous monitoring of tree water status, however, more research is needed to better relate these measures with other widely studied plant-based indicators and identify good combinations of sensors and threshold values.\</p\>},
note = {Publisher: Frontiers},
keywords = {Continuous measurements, fruit growth, Plant-based sensors, Precision irrigation, Tree water status indicators, water potential},
pubstate = {published},
tppubtype = {article}
}
@misc{gharsallaoui_streamflow_2024,
title = {Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach},
author = {Mohammed Amine Gharsallaoui and Bhupinderjeet Singh and Supriya Savalkar and Aryan Deshwal and Yan Yan and Ananth Kalyanaraman and Kirti Rajagopalan and Janardhan Rao Doppa},
url = {http://arxiv.org/abs/2406.00133},
doi = {10.48550/arXiv.2406.00133},
year  = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
publisher = {arXiv},
abstract = {Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but using simplifying assumptions which can lead to poor accuracy. Data-driven approaches offer a powerful alternative, but they require large amount of training data and tend to produce predictions that are inconsistent with physical laws. This paper studies a constrained reasoning and learning (CRL) approach where physical laws represented as logical constraints are integrated as a layer in the deep neural network. To address small data setting, we develop a theoretically-grounded training approach to improve the generalization accuracy of deep models. For uncertainty quantification, we combine the synergistic strengths of Gaussian processes (GPs) and deep temporal models (i.e., deep models for time-series forecasting) by passing the learned latent representation as input to a standard distance-based kernel. Experiments on multiple real-world datasets demonstrate the effectiveness of both CRL and GP with deep kernel approaches over strong baseline methods.},
note = {arXiv:2406.00133 [cs]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {misc}
}
@article{das_effectiveness_2024,
title = {Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning},
author = {Shubhomoy Das and Md Rakibul Islam and Nitthilan Kannappan Jayakodi and Janardhan Rao Doppa},
url = {https://www.jair.org/index.php/jair/article/view/14741},
doi = {10.1613/jair.1.14741},
issn = {1076-9757},
year  = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
journal = {Journal of Artificial Intelligence Research},
volume = {80},
pages = {127\textendash170},
abstract = {Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies. 
This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.},
keywords = {knowledge discovery, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
@booklet{schrader_smart_nodate,
title = {Smart Vineyard Concepts to Reality in Washington State},
author = {Mark Jacob Schrader and Lav R Khot},
url = {https://s3-us-west-2.amazonaws.com/sites.cahnrs.wsu.edu/wp-content/uploads/sites/66/2024/04/19133949/2024-SpringVEEN-FINAL.pdf},
year  = {2024},
date = {2024-04-25},
address = {Viticulture and Enology Extension News},
month = {04},
keywords = {},
pubstate = {published},
tppubtype = {booklet}
}
@article{savalkar_errors_2024b,
title = {Errors in temporal disaggregation of temperature can lead to non-negligible biases in agroecosystem risk assessment 1},
author = {Supriya Savalkar and Md. Redwan Ahmad Khan and Bhupinderjeet Singh and Matt Pruett and R. Troy Peters and Claudio O St\"{o}ckle and Sean E. Hill and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0168192324000674},
doi = {10.1016/j.agrformet.2024.109952},
issn = {0168-1923},
year  = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Agricultural and Forest Meteorology},
volume = {349},
pages = {109952},
abstract = {Models are crucial for simulating complex systems and decision-making, but they have uncertainties that must be characterized and understood. One uncertainty that has been overlooked in agroecosystem assessments is that arising from the temporal disaggregation of temperature and solar radiation. Our study used data from an agricultural weather station network to investigate (a) the errors associated with hourly temporal disaggregation of daily temperatures and solar radiation, (b) how these input errors impact two agroecosystem models, (c) the sensitivity of change assessments to disaggregation errors, and (d) how high-temporal-resolution weather station networks can be leveraged to correct disaggregation errors in daily gridded meteorological data products. Our findings demonstrate that temporal temperature disaggregation errors can have a significant impact on agroecosystem model output, with large errors in sunburn risk estimation (\>100% median deviation percentage) but minimal effects on chill accumulation (\<5% median deviation percentage). However, we were able to achieve significant reductions in error (\>75% error reduction in sunburn risk assessment in majority of cases) by integrating simple monthly statistics from station observations into the disaggregation process. Our study highlights the importance of understanding uncertainties in agroecosystem models stemming from temporal disaggregation of temperature, and the potential benefits of utilizing simple adjustments leveraging weather station networks to improve model accuracy and applicability for decision-making.},
keywords = {Agroecosystems modeling, Input error propagation into models, Radiation disaggregation, Temperature disaggregation, Temperature disaggregation error adjustment},
pubstate = {published},
tppubtype = {article}
}
@article{savalkar_errors_2024,
title = {Errors in temporal disaggregation of temperature can lead to non-negligible biases in agroecosystem risk assessment},
author = {Supriya Savalkar and Md. Redwan Ahmad Khan and Bhupinderjeet Singh and Matt Pruett and R. Troy Peters and Claudio O St\"{o}ckle and Sean E. Hill and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0168192324000674},
doi = {10.1016/j.agrformet.2024.109952},
issn = {0168-1923},
year  = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Agricultural and Forest Meteorology},
volume = {349},
pages = {109952},
abstract = {Models are crucial for simulating complex systems and decision-making, but they have uncertainties that must be characterized and understood. One uncertainty that has been overlooked in agroecosystem assessments is that arising from the temporal disaggregation of temperature and solar radiation. Our study used data from an agricultural weather station network to investigate (a) the errors associated with hourly temporal disaggregation of daily temperatures and solar radiation, (b) how these input errors impact two agroecosystem models, (c) the sensitivity of change assessments to disaggregation errors, and (d) how high-temporal-resolution weather station networks can be leveraged to correct disaggregation errors in daily gridded meteorological data products. Our findings demonstrate that temporal temperature disaggregation errors can have a significant impact on agroecosystem model output, with large errors in sunburn risk estimation (\>100% median deviation percentage) but minimal effects on chill accumulation (\<5% median deviation percentage). However, we were able to achieve significant reductions in error (\>75% error reduction in sunburn risk assessment in majority of cases) by integrating simple monthly statistics from station observations into the disaggregation process. Our study highlights the importance of understanding uncertainties in agroecosystem models stemming from temporal disaggregation of temperature, and the potential benefits of utilizing simple adjustments leveraging weather station networks to improve model accuracy and applicability for decision-making.},
keywords = {Agroecosystems modeling, Input error propagation into models, Radiation disaggregation, Temperature disaggregation, Temperature disaggregation error adjustment},
pubstate = {published},
tppubtype = {article}
}
@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}
}
@article{zhao_evaluation_2024,
title = {Evaluation of Neural Network Effectiveness on Sliding Mode Control of Delta Robot for Trajectory Tracking},
author = {Anni Zhao and Arash Toudeshki and Reza Ehsani and Joshua H. Viers and Jian-Qiao Sun},
url = {https://www.mdpi.com/1999-4893/17/3/113},
doi = {10.3390/a17030113},
issn = {1999-4893},
year  = {2024},
date = {2024-03-01},
urldate = {2024-03-01},
journal = {Algorithms},
volume = {17},
number = {3},
pages = {113},
abstract = {The Delta robot is an over-actuated parallel robot with highly nonlinear kinematics and dynamics. Designing the control for a Delta robot to carry out various operations is a challenging task. Various advanced control algorithms, such as adaptive control, sliding mode control, and model predictive control, have been investigated for trajectory tracking of the Delta robot. However, these control algorithms require a reliable input\textendashoutput model of the Delta robot. To address this issue, we have created a control-affine neural network model of the Delta robot with stepper motors. This is a completely data-driven model intended for control design consideration and is not derivable from Newton’s law or Lagrange’s equation. The neural networks are trained with randomly sampled data in a sufficiently large workspace. The sliding mode control for trajectory tracking is then designed with the help of the neural network model. Extensive numerical results are obtained to show that the neural network model together with the sliding mode control exhibits outstanding performance, achieving a trajectory tracking error below 5 cm on average for the Delta robot. Future work will include experimental validation of the proposed neural network input\textendashoutput model for control design for the Delta robot. Furthermore, transfer learnings can be conducted to further refine the neural network input\textendashoutput model and the sliding mode control when new experimental data become available.},
note = {Number: 3 
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {delta robot, neural networks, sliding mode control},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{thapa_attention-based_2024,
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 = {https://ojs.aaai.org/index.php/AAAI/article/view/30337},
doi = {10.1609/aaai.v38i21.30337},
issn = {2374-3468},
year  = {2024},
date = {2024-03-01},
urldate = {2024-03-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {38},
number = {21},
pages = {22969\textendash22975},
abstract = {Snow Water-Equivalent (SWE)\textemdashthe amount of water available if snowpack is melted\textemdashis a key decision variable used by water management agencies to make irrigation, flood control, power generation, and drought management decisions. SWE values vary spatiotemporally\textemdashaffected 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 spatiotemporal 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 = {Number: 21},
keywords = {Track: Emerging Applications},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{chemingui_offline_2024,
title = {Offline Model-Based Optimization via Policy-Guided Gradient Search},
author = {Yassine Chemingui and Aryan Deshwal and Trong Nghia Hoang and Janardhan Rao Doppa},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/29001},
doi = {10.1609/aaai.v38i10.29001},
issn = {2374-3468},
year  = {2024},
date = {2024-03-01},
urldate = {2024-03-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {38},
number = {10},
pages = {11230\textendash11239},
abstract = {Offline optimization is an emerging problem in many experimental engineering domains including protein, drug or aircraft design, where online experimentation to collect evaluation data is too expensive or dangerous. To avoid that, one has to optimize an unknown function given only its offline evaluation at a fixed set of inputs. A naive solution to this problem is to learn a surrogate model of the unknown function and optimize this surrogate instead. However, such a naive optimizer is prone to erroneous overestimation of the surrogate (possibly due to over-fitting on a biased sample of function evaluation) on inputs outside the offline dataset. Prior approaches addressing this challenge have primarily focused on learning robust surrogate models. However, their search strategies are derived from the surrogate model rather than the actual offline data. To fill this important gap, we introduce a new learning-to-search perspective for offline optimization by reformulating it as an offline reinforcement learning problem. Our proposed policy-guided gradient search approach explicitly learns the best policy for a given surrogate model created from the offline data. Our empirical results on multiple benchmarks demonstrate that the learned optimization policy can be combined with existing offline surrogates to significantly improve the optimization performance.},
note = {Number: 10},
keywords = {SO: Learning to Search},
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}
}
@misc{adiga_value-based_2024,
title = {Value-based Resource Matching with Fairness Criteria: Application to Agricultural Water Trading},
author = {Abhijin Adiga and Yohai Trabelsi and Tanvir Ferdousi and Madhav Marathe and S. S. Ravi and Samarth Swarup and Anil Kumar Vullikanti and Mandy L. Wilson and Sarit Kraus and Reetwika Basu and Supriya Savalkar and Matthew Yourek and Michael Brady and Kirti Rajagopalan and Jonathan Yoder},
url = {http://arxiv.org/abs/2402.06576},
doi = {10.48550/arXiv.2402.06576},
year  = {2024},
date = {2024-02-01},
urldate = {2024-02-01},
publisher = {arXiv},
abstract = {Optimal allocation of agricultural water in the event of droughts is an important global problem. In addressing this problem, many aspects, including the welfare of farmers, the economy, and the environment, must be considered. Under this backdrop, our work focuses on several resource-matching problems accounting for agents with multi-crop portfolios, geographic constraints, and fairness. First, we address a matching problem where the goal is to maximize a welfare function in two-sided markets where buyers' requirements and sellers' supplies are represented by value functions that assign prices (or costs) to specified volumes of water. For the setting where the value functions satisfy certain monotonicity properties, we present an efficient algorithm that maximizes a social welfare function. When there are minimum water requirement constraints, we present a randomized algorithm which ensures that the constraints are satisfied in expectation. For a single seller\textendashmultiple buyers setting with fairness constraints, we design an efficient algorithm that maximizes the minimum level of satisfaction of any buyer. We also present computational complexity results that highlight the limits on the generalizability of our results. We evaluate the algorithms developed in our work with experiments on both real-world and synthetic data sets with respect to drought severity, value functions, and seniority of agents.},
note = {arXiv:2402.06576 [cs]},
keywords = {Computer Science - Data Structures and Algorithms, Computer Science - Multiagent Systems},
pubstate = {published},
tppubtype = {misc}
}
@article{bhattarai_design_2024,
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 = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22330},
doi = {10.1002/rob.22330},
issn = {1556-4967},
year  = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Journal of Field Robotics},
volume = {41},
number = {5},
pages = {1366\textendash1385},
abstract = {The United States (US) apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruitlet 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 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 precision blossom thinning in apple orchards using a deep learning-based computer vision system, a six-degrees-of-freedom UR5e robotic manipulator, and an electrically actuated miniature end-effector. 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's ability to remove varying proportions 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 s. Field evaluation results showed that the boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 s per cluster, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 s per cluster. Upon further improvement for commercial adoption, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.22330},
keywords = {agricultural automation, agricultural robotics, artificial intelligence in agriculture, blossom thinning, robotic thinning},
pubstate = {published},
tppubtype = {article}
}
@article{kiraga_reference_2024,
title = {Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman\textendashMonteith Equation in a Highly Advective Environment},
author = {Shafik Kiraga and R. Troy Peters and Behnaz Molaei and Steven R. Evett and Gary Marek},
url = {https://www.mdpi.com/2073-4441/16/1/12},
doi = {10.3390/w16010012},
issn = {2073-4441},
year  = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Water},
volume = {16},
number = {1},
pages = {12},
abstract = {Accurate estimation of reference evapotranspiration (ETr) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman\textendashMonteith equation (ASCE-PM) estimates ETr across various timescales using ground weather station data. However, discrepancies persist between estimated ETr and measured ETr obtained from weighing lysimeters (ETr-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ETr estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996\textendash1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ETr estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ETr across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning.},
note = {Number: 1 
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {advective environments, aerodynamic components, genetic algorithm, Machine Learning, radiation components, reference evapotranspiration},
pubstate = {published},
tppubtype = {article}
}
@inproceedings{hoque_irrnet_2024,
title = {IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery},
author = {Oishee Bintey Hoque and Samarth Swarup and Abhijin Adiga and Sayjro Kossi Nouwakpo and Madhav Marathe},
url = {https://openaccess.thecvf.com/content/CVPR2024W/Vision4Ag/html/Hoque_IrrNet_Advancing_Irrigation_Mapping_with_Incremental_Patch_Size_Training_on_CVPRW_2024_paper.html},
year  = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
pages = {5460\textendash5469},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@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}
}
@incollection{hamid_how_2024,
title = {How to Measure Diversity Actionably in Technology},
author = {Md Montaser Hamid and Amreeta Chatterjee and Mariam Guizani and Andrew Anderson and Fatima Moussaoui and Sarah Yang and Isaac Escobar and Anita Sarma and Margaret Burnett},
editor = {Daniela Damian and Kelly Blincoe and Denae Ford and Alexander Serebrenik and Zainab Masood},
url = {https://doi.org/10.1007/978-1-4842-9651-6_27},
doi = {10.1007/978-1-4842-9651-6_27},
isbn = {978-1-4842-9651-6},
year  = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {Equity, Diversity, and Inclusion in Software Engineering: Best Practices and Insights},
pages = {469\textendash485},
publisher = {Apress},
address = {Berkeley, CA},
abstract = {Md Montaser Hamid, Amreeta Chatterjee, Mariam Guizani, Andrew Anderson, Fatima Moussaoui, Sarah Yang, Isaac Tijerina Escobar, Anita Sarma, and Margaret Burnett},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
@article{belkhouja_out\textendashdistribution_2023,
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 = {https://dl.acm.org/doi/10.1145/3630633},
doi = {10.1145/3630633},
issn = {2157-6904},
year  = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
journal = {ACM Trans. Intell. Syst. Technol.},
volume = {15},
number = {1},
pages = {8:1\textendash8:24},
abstract = {Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data that 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 article proposes a novel 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.},
keywords = {},
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}
}
@incollection{kirkpatrick_chapter_2023,
title = {Chapter 11: Fearing the future: examining the conditional indirect correlation of attention to artificial intelligence news on artificial intelligence attitudes},
author = {Alex W. Kirkpatrick and Jay D. Hmielowski and Amanda Boyd},
url = {https://www.elgaronline.com/edcollchap/book/9781803920306/book-part-9781803920306-20.xml},
isbn = {978-1-80392-030-6},
year  = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
abstract = {Artificial intelligence (AI) is changing industries globally. It is also having both positive and negative effects on society. People often learn about technology through media, and so news about AI could have significant impacts on public perceptions of AI. Employing agenda-setting theory, we explore associations between attention to AI news and attitudes about AI. Results of a survey suggest attention to AI news content is associated with perceived economic risks associated with AI. Higher levels of perceived economic risk were associated with greater fear tied to AI. Higher levels of fear were associated with holding more negative perceptions of AI but only among people with lower incomes. We found a negative indirect association between attention to AI news content and negative perceptions of AI through perceived AI risk and fear in serial mediation. This negative indirect association was stronger among people with lower incomes. We discuss the potential effects of media on the public understanding of AI.},
note = {Section: Research Handbook on Artificial Intelligence and Communication},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
@article{zhao_data-driven_2023,
title = {Data-Driven Inverse Kinematics Approximation of a Delta Robot with Stepper Motors},
author = {Anni Zhao and Arash Toudeshki and Reza Ehsani and Jian-Qiao Sun},
url = {https://www.mdpi.com/2218-6581/12/5/135},
doi = {10.3390/robotics12050135},
issn = {2218-6581},
year  = {2023},
date = {2023-10-01},
urldate = {2023-10-01},
journal = {Robotics},
volume = {12},
number = {5},
pages = {135},
abstract = {The Delta robot is a parallel robot that is over-actuated and has a highly nonlinear dynamic model, which poses a significant challenge to its control design. The inverse kinematics that maps the motor angles to the position of the end effector is highly nonlinear and extremely important for the control design of the Delta robot. It has been experimentally shown that geometry-based inverse kinematics is not accurate enough to capture the dynamics of the Delta robot due to manufacturing component errors, measurement errors, joint flexibility, backlash, friction, etc. To address this issue, we propose a neural network model to approximate the inverse kinematics of the Delta robot with stepper motors. The neural network model is trained with randomly sampled experimental data and implemented on the hardware in an open-loop control for trajectory tracking. Extensive experimental results show that the neural network model achieves excellent performance in terms of the trajectory tracking of the Delta robot under different operation conditions, and outperforms the geometry-based inverse kinematics model. A critical numerical observation indicates that neural networks trained with the specific trajectory data fall short of anticipated performance due to a lack of data. Conversely, neural networks trained on random experimental data capture the rich dynamics of the Delta robot and are quite robust to model uncertainties compared to geometry-based inverse kinematics.},
note = {Number: 5 
Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {delta robot, inverse kinematics, neural networks, stepper motor},
pubstate = {published},
tppubtype = {article}
}
@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}
}
@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}
}
@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{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{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{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{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{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}
}
@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}
}
@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}
}
@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_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}
}
@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}
}
@misc{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 = {2022-08-16},
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 = {Labor},
pubstate = {published},
tppubtype = {misc}
}
@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}
}
@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}
}
@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}
}
@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{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}
}
@inproceedings{noauthor_slicing-aided_nodate,
title = {Slicing-Aided Hyper Inference for Enhanced Fruit Bud Detection and Counting in Apple Orchards during Dormant Season},
url = {https://doi.org/10.13031/aim.202401055},
urldate = {2024-08-09},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{noauthor_data-driven_nodate,
title = {Data-Driven Model to Improve Mechanical Harvesters for Nut Trees},
url = {https://doi.org/10.13031/aim.202400858},
urldate = {2024-08-09},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}