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An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis

An Integrated Framework for Team Formation and Winner Predic...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Federico Galbiati Ranier X. Gran Brendan D. Jacques Sullivan J. Mulhern Chun-Kit Ngan Department of Computer Science Worcester Polytechnic Institute Worcester USA Data Science Program Worcester Polytechnic Institute Worcester USA

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy.

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Selective Atomic Layer Etching for Silicon Device Fabrication

Selective Atomic Layer Etching for Silicon Device Fabricatio...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Khurana, Mohit Ayalasomayajula, Mukund Chaudhary, Prince Shiva Cornell University Dept. of Mat. Sci. and Engg. Ithaca United States Worcester Polytechnic Institute Dept. of Data Science Worcester United States

ISBN: (纸本)9798350364088

This paper elaborates on the fundamentals of atomic layer etching (ALE) technique and reviews the methods to enable selective ALE for Si materials. Initially, the working principle of the ALE equipment is discussed. Subsequently, important process parameters that modulate the etch properties are elucidated. Some of these include, but are not limited to non-ideality, etch rate per cycle versus time (or dosage) and etch per cycle versus ion energy and anisotropy. In the latter half, challenges associated with ALE of silicon (Si) is elaborated. The selectivity mechanism towards Si, etch thickness per cycle (EPC) and impact if process parameters on the non-ideal nature of etching are elaborated. Finally, future scope and areas for improvement are concluded. © 2024 IEEE.

关键词： Anisotropy

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Stabilizing Adversarial Training for Generative Networks

Stabilizing Adversarial Training for Generative Networks

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Gerych, Walter Hickey, Kevin Hartvigsen, Thomas Buquicchio, Luke Alajaji, Abdulaziz Chandrasekaran, Kavin Mansoor, Hamid Agu, Emmanuel Rundensteiner, Elke University of Virginia School of Data Science CharlottesvilleVA United States King Saud University College of Computer and Informational Sciences Riyadh Saudi Arabia University of Victoria Department of Computer Science VictoriaBC Canada Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States

ISBN: (纸本)9798350324457

Generative modeling is a powerful technique that involves creating machine learning models capable of creating new data similar to the data it was trained on. Generative Adversarial Networks (GANs) are a leading approach for generative modeling. However, GAN training is known to be a notoriously difficult task. GAN convergence issues are largely caused by the supports of the real and generated distributions being disjoint. To tackle this open problem, we propose a novel GAN pre-training process that effectively aligns the supports of the generated and real data prior to applying traditional adversarial GAN training. The key component of our method, called AlignGAN, is learning a mapping between the input data distribution and a latent representation defined over a hypersphere, regularized by a One Class Classifier. This successfully encourages the generator to produce samples throughout the support of the real data, while not generating samples outside the support. We maintain support alignment through low-bandwidth noise convolutions and additional One Class regularization, leading to continued stable GAN training. We validate our approach against leading stabilization methods on three benchmark datasets, showing AlignGAN routinely produces the best results. © 2023 IEEE.

关键词： distribution GAN support alignment

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An Empirical Study of Domain Adaptation: Are We Really Learning Transferable Representations?

An Empirical Study of Domain Adaptation: Are We Really Learn...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Josselyn, Nicholas Yin, Biao Zhang, Ziming Rundensteiner, Elke Department of Data Science Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781665480451

Deep learning often relies on the availability of a large amount of high-quality labeled data, which can be very limited in novel domains. To address such data scarcity, domain adaptation is one promising approach that allows for deep networks to leverage large amounts of available data from a source domain to enhance the model's efficacy on the target domain of interest. However, while there is a plethora of alternate models for domain adaptation proposed over many years in the literature, there is a dearth of studies that objectively compare the relative effectiveness of these models in a rigorous, empirical study. To fill this gap, we provide a thorough, unbiased, empirical study of five state-of-the-art (SOTA) deep domain adaptation models proposed over the past 6 years whose codes are publicly available. Models are evaluated on the complex and diverse domain adaptation tasks featured in the DomainNet benchmark dataset as well as the popular Office-31 dataset. Our results suggest that (1) all 5 models perform similarly, on average, and do not even significantly beat the oldest model, and (2) counter to their intended purpose, the transfer loss functions in the literature do not contribute significantly to learning transferable representations. Our observations suggest that domain adaptation research needs to more thoroughly compare newly proposed models against existing works, along with assessing their loss functions' utility thoroughly. Our code and data splits are made public for reproducibility of results by the community. © 2022 IEEE.

关键词： Deep learning

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DeepScreen: Boosting Depression Screening Performance with an Auxiliary Task

DeepScreen: Boosting Depression Screening Performance with a...

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IEEE International Conference on Big data

作者： Ricardo Flores Avantika Shrestha Elke A. Rundensteiner Data Science Worcester Polytechnic Institute Worcester MA USA Computer Science/Data Science Worcester Polytechnic Institute Worcester MA USA

Depression is a prevalent mental health condition with severe impacts on physical and social health. It is costly and difficult to detect, requiring substantial time from trained mental professionals. To alleviate this burden, recent research explores the diagnostic capabilities of deep learning models trained on modalities extracted from videos of clinical interviews for depression screening. However, training deep learning models is challenging because in the mental health domain datasets contain a small number of patients. To address this challenge, we propose DeepScreen, a recurrent deep-learning architecture for depression screening whose performance is boosted by deploying a self-supervised auxiliary task for selective missing value imputation. DeepScreen leverages a multi-task architecture with a bidirectional recurrent deep learning model and a self-attention mechanism, jointly optimizing the supervised depression and the self-supervised auxiliary task. Our first study assesses the capability of the auxiliary task training of DeepScreen under different correlation levels and masking sub-sequence sizes of multi-variate time series. Found to be effective, our second study evaluates DeepScreen on 15 data sets composed of real-world temporal facial landmark features extracted from responses to different clinical interview questions. The results achieved across all 15 datasets demonstrate that the imputation task boosts the depression prediction metrics significantly. Specifically, DeepScreen improves the F1 score for one of the datasets by 57%. Further, our best-performing model achieves an F1 score of 0.85. This work provides valuable insights into improving deep learning driven mental screening applications by leveraging auxiliary tasks such as imputation for learning a better representation even from small datasets.

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： James Akl Shreedhar Kodate Berk Calli Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at ***/10.5281/zenodo.7734951.

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MXene Property Prediction via Graph Contrastive Learning

MXene Property Prediction via Graph Contrastive Learning

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International Conference on Nanomaterials: Application & Properties (NAP)

作者： Eric Warnemunde Vertina Emily Sutherland N. Aaron Deskins Oren Mangoubi Data Science Worcester Polytechnic Institute Worcester MA USA Physics Worcester Polytechnic Institute Worcester MA USA Chemical Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350380125

ISBN: (纸本)9798350380132

MXenes are an important class of 2-D materials with expected novel properties and myriad applications, including efficient energy conversion in batteries and solar cells, environmental and water treatment, supercapacitors, electromagnetic interference shielding, and catalysts. Unfortunately, MXenes are challenging to synthesize in the laboratory, and numerical models for simulating MXene properties, such as density functional theory (DFT), remain computationally expensive. For this reason, multiple prior works develop machine learning (ML) methods to rapidly predict MXene properties. However, these methods do not take into account the graphical structure of the MXene’s molecular bonds, reducing their accuracy. In this work, we introduce a novel ML method, based on Graph Neural Networks (GNNs), which includes MXene molecular graph information to predict electrical and physical properties. Our method transforms graph datasets to lower-dimensional embedding spaces with graph contrastive learning (GCL), a type of GNN, enabling high-accuracy predictions for downstream models trained with these embeddings. Our method obtains the following root mean square errors (RMSEs) when predicting properties from a DFT-based MXenes dataset (including many MXene properties not previously predicted with deep learning models): 0.418 J for Work Function, $0.518 \mathrm{eV}^{-1}$ for Density of States at Fermi Level, $\mathbf{0. 0 6 7}$ for Poisson’s Ratio, $21.65 \mathrm{~N} / \mathrm{m}$ for Bulk Modulus, 14.56 N/m for Shear Modulus, 35.66 N/m for Young’s Modulus, and 82% accuracy for Magnetic (T/F). Our method obtains lower mean absolute error (MAE) when predicting four target properties from the molecular QM9 dataset compared to a similar-complexity baseline. Predicting MXene properties more accurately, efficiently, and effectively promises to better enable MXene commercialization.

关键词： Water Accuracy Photovoltaic cells Contrastive learning Transforms Predictive models Supercapacitors Graph neural networks Root mean square Magnetic properties

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Screening Students for Stress Using Fitbit data

Screening Students for Stress Using Fitbit Data

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IEEE International Conference on Big data

作者： Rebecca Lopez Avantika Shrestha Kevin Hickey Xingtong Guo ML Tlachac Shichao Liu Elke A. Rundensteiner Data Science Worcester Polytechnic Institute Worcester MA USA Architectural Engineering Worcester Polytechnic Institute Worcester MA USA Information Systems and Analytics Bryant University Smithfield RI USA Computer Science and Data Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The pressures faced by college students frequently lead to heightened levels of stress. Wearable devices, which collect sensor data in a non-intrusive manner, present an opportunity for early detection of stress. Nonetheless, there is a lack of diversity in current research concerning psychological assessments, physiological metrics, and time series features. In this work, we utilize a Fitbit dataset and evaluate its use in predicting stress through machine learning. Our results demonstrate that physiological data such as calories burned and sleep hold promise for stress screening, with F1 scores reaching up to 0.81. These findings illustrate the potential of wearable technology for continuous stress monitoring and emphasize the need for selecting appropriate data aggregation levels and physiological modalities for effective screening.

关键词： Performance evaluation Wearable Health Monitoring Systems Time series analysis Human factors Self-supervised learning Mental health Robustness Biomedical monitoring Monitoring Standards

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Soil Subsurface Channel Statistical Characterization for Drone-Borne Intelligent GPR Advancement

Soil Subsurface Channel Statistical Characterization for Dro...

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IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

作者： Noushin Khosravi Largani Seyed Reza Zekavat Vincent Filardi Data Science Department Worcester Polytechnic Institute (WPI) Worcester MA USA

ISBN: (数字)9798350351118

ISBN: (纸本)9798350351125

The advancement of drone-borne intelligent Ground Penetration Radar (GPR) is hinged upon accurate received signal feature extraction, which relies on high-performance soil subsurface channel estimation. This requires the optimal design of the transmitted waveform, which needs soil subsurface channel parameter statistics. In this paper, we investigate the statistics of soil subsurface channel parameters for different soil textural classes (defined by sand and clay percentage, and adopted based on the USDA textural triangle). Channel parameters statistics of interest are the gain correlation matrices, the statistical distributions of gain and Time-of-Arrival (ToA), and the mean Power-Delay Profile (PDP). Channel gain correlation matrix and its distribution are critical to waveform design. Mean PDP offers insight into the number of soil layers. The results of this paper contribute to the intelligent optimal waveform design for GPR, the implementation of feature extraction in ML applications, and accurate estimation of soil layers, moisture, and permittivity.

关键词： Wireless communication Maximum likelihood estimation Correlation Accuracy Moisture Channel estimation Soil Feature extraction Soil texture Permittivity

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LLM-based Hierarchical Label Annotation for Foodborne Illness Detection on Social Media

LLM-based Hierarchical Label Annotation for Foodborne Illnes...

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IEEE International Conference on Big data

作者： Dongyu Zhang Ruofan Hu Dandan Tao Hao Feng Elke Rundensteiner AML ByteDance San Jose USA Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Foodborne illnesses pose a threat to public health, leading to morbidity, mortality, and economic burden annually. Social media, while providing a rich timely source for training AI models for surveillance, requires effective tools for annotation. While Large Language Models (LLMs) have shown promise for generating simple labels, here hierarchical labels composed of entity types like food type and symptom (at individual word level) and the foodborne illness event (at complete post level) are required. For this, we introduce ICL2FID, the first LLM-based hierarchical labeling framework designed to annotate social media posts for foodborne illness detection at two levels using only a few demonstration examples. To utilize the interconnection between post and word levels, ICL2FID instructs the LLM to leverage information from one level when predicting the other level. To combat model hallucination and cyclic dependencies, a verification step improves evidence propagation between interconnected word and post-level labeling tasks. Strategies for custom selection of demonstration examples are designed reducing biases and increasing representation. We compare ICL2FID against traditional supervised learning and other LLM methods, demonstrating that it not only achieves superior accuracy but does so at a fraction of the cost and time. These findings highlight ICL2FID’s potential as a viable alternative for hierarchical label generation in scenarios with limited resources and huge data sets. Code is available at https://***/zdy93/ICL2FID.

关键词： Training Social networking (online) Annotations Biological system modeling Surveillance Large language models Supervised learning Mortality Labeling Public healthcare

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