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arXiv 2022年

作者： Ahanor, Izuwa Medal, Hugh Trapp, Andrew C. Department of Industrial and Systems Engineering University of Tennessee-Knoxville United States Business School Data Science Program Worcester Polytechnic Institute United States

While most methods for solving mixed-integer optimization problems compute a single optimal solution, a diverse set of near-optimal solutions can often lead to improved outcomes. We present a new method for finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions. Specifically, within a branch-and-bound framework, we investigated parameterized node selection rules that explicitly consider diversity. Our results indicate that our approach significantly increases the diversity of the final solution set. When compared with two existing methods, our method runs with similar runtime as regular node selection methods and gives a diversity improvement between 12% and 190%. In contrast, popular node selection rules, such as best-first search, in some instances performed worse than state-of-the-art methods by more than 35% and gave an improvement of no more than 130%. Further, we find that our method is most effective when diversity in node selection is continuously emphasized after reaching a minimal depth in the tree and when the solution set has grown sufficiently large. Our method can be easily incorporated into integer programming solvers and has the potential to significantly increase the diversity of solution sets. Copyright © 2022, The Authors. All rights reserved.

关键词： Integer programming

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GAN for Generating User-Specific Human Activity data From An Incomplete Training Corpus

GAN for Generating User-Specific Human Activity Data From An...

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IEEE International Conference on Big data

作者： Walter Gerych Harrison Kim Joshua DeOliveira MaryClare Martin Luke Buquicchio Kavin Chandrasekaran Abdulaziz Alajaji Hamid Mansoor Elke Rundensteiner Emmanuel Agu Data Science Program Worcester Polytechnic Institute Worcester MA Northeastern University Boston MA Worcester Polytechnic Institute Worcester MA College of the Holy Cross Worcester MA

ISBN: (纸本)9781665445993

Human activity recognition (HAR), the task of predicting the activities performed by an individual using mobile sensor data, is an active and important area of research. Unfortunately, it is very costly to collect the data required to train robust HAR classifiers. To tackle this issue, there has been an increasing focus on generating synthetic HAR data for augmentation purposes. The state-of-the-art generative HAR approaches utilize Generative Adversarial Networks (GANs) to produce realistic synthetic HAR data. However, these solutions can not generate personalized data that matches the behavior of particular users, limiting their potential use cases. This is particularly problematic in the mobile health domain, where the target users are often elderly or disabled and are thus likely to have activity signals that are unique from the general population. To overcome this drawback, we propose a novel controllable GAN solution Control-HAR-GAN. Our approach learns user and activity signals independently, and when generating synthetic instances, practitioners can specify both the activity to be generated as well as the user the data should match. This has the added benefit in that our model supports novel user-activity pairs by generating examples that match the data that would have been recorded by a particular user if they had performed the target activity, even if the user never performed that activity during the data collection process. We show that our model outperforms the existing HAR GAN approach in generating observed user-activity pairs by up to 10%. Additionally, our approach can also perform the task of novel user-activity pair generation, which is impossible for existing approaches.

关键词： Training Sociology Big data Activity recognition Generative adversarial networks data models Task analysis

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Are Fair Learning To Rank Models Really Fair? An Analysis Using Inferred Gender

Are Fair Learning To Rank Models Really Fair? An Analysis Us...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Alexander Pietrick Alyssa Romportl Shailen Smith Oluseun Olulana Kathleen Cachel Elke Rundensteiner Departments of Computer Science and Data Science Worcester Polytechnic Institute Worcester MA United States Departments of Mathematics and Psychology St. Olaf College Northfield MN United States Department of Mathematics Stony Brook University Stony Brook NY United States Department of Data Science Worcester Polytechnic Institute Worcester MA United States

Fair Learning To Rank (LTR) frameworks require demographic information; however, that information is often unavailable. Inference algorithms may infer the missing demographic information to supply to the fair LTR model. In this study, we analyze the effect of using a trained fair LTR model with uncertain demographic inferences. We show that inferred data results in varying levels of fairness and utility depending on inference accuracy. Specifically, less accurate inferred data adversely affects the rankings’ fairness, while more accurate inferred data creates fairer rankings. Therefore, we recommend that a careful evaluation of demographic inference algorithms before use is critical.

关键词： Analytical models Inference algorithms

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DOGMH: A Diffusion and Optimization Approach for Generative Molecular Health Informatics in Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Yang, Bolin Deng, Jie Liang, Xiaoxuan Chen, Zhenghan Wu, Ruoxue University of Chinese Academy of Sciences College of Life Sciences Beijing100081 China Nanyang Technological University College of Computing and Data Science Singapore117417 Singapore University of Massachusetts Amherst Department of ECE Amherst01002 United States Microsoft Bing Copilot Beijing100000 China Worcester Polytechnic Institute Department of Computer Science Worcester01609 United States

This is a task of computational chemistry and pharmaceutical development, with rapidly growing relevance for consumer healthcare electronics: deducing three-dimensional molecular structures from two-dimensional diagrams. In this paper, we introduce a new generative model, DOGMH, specifically tailored for applications on consumer electronic devices, which considerably increases the accuracy in this process. DOGMH borrows from the diffusion mechanisms of classical nonequilibrium thermodynamics and treats atoms as individual particles, excelling in the inversion of the diffusion process-turning noise into organized molecular structures. Our approach leverages a specially tailored bilevel optimization technique that ensures roto-translational invariance and allows for efficient processing on resource-constrained consumer devices. We show that DOGMH outperforms existing methods in terms of both conformational synthesis and distance distribution accuracy. DOGMH also provides the ability to create AI-enabled interactive 3D visualizations of molecular structures for consumer health education. Our work paves the way for advanced molecular informatics capabilities in consumer-grade devices and will potentially revolutionize personal healthcare management through AI-enhanced insights. By bridging the gap between complex molecular modeling and consumer electronics, DOGMH will open up a new frontier in intelligent, molecularly aware health devices accessible to the general public. © 2025 IEEE.

关键词： Diffusion

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Topological data Analysis to Engineer Features from Audio Signals for Depression Detection 19

Topological Data Analysis to Engineer Features from Audio Si...

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19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020

作者： Tlachac, M.L. Sargent, Adam Toto, Ermal Paffenroth, Randy Rundensteiner, Elke Worcester Polytechnic Institute Data Science Department WorcesterMA United States Worcester Polytechnic Institute Computer Science and Mathematics Departments WorcesterMA United States

ISBN: (纸本)9781728184708

Topological data Analysis (TDA) can be used to extract features from raw signal data. These features, in the form of Betti curves, can be leveraged by machine learning models. Betti curves have been shown to mitigate bias from individual differences when classifying signals. One of the major challenges in detecting depression from audio clips is the variance of audio expression between participants. Thus, we hypothesize that Betti curves could help mitigate this audio expression variance. In this research, we are the first to construct Betti curves from audio signals. We then leverage these Betti curves to detect depression with audio clips from open-ended clinical interviews and scripted crowd-sourced recordings. For both datasets, machine learning models built on Betti curves achieve statistically significantly higher F1, AUC, and Accuracy scores than the same models built on only state-of-the-art audio engineered features. The AUC metric improvement was 0.054 for the clinical interviews and 0.066 for the crowd-sourced audio. Thus, we demonstrate TDA can be useful in screening for depression from audio signals. © 2020 IEEE.

关键词： Topology

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Recurrent Variational Open-Set Recognition

Recurrent Variational Open-Set Recognition

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2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021

作者： Diamond, N'Yoma Jordan, Sean Norman, Amelia Buquicchio, Luke Rundensteiner, Elke Worcester Polytechnic Institute Department of Data Science WorcesterMA United States University of Maryland Baltimore County Department of Mathematics and Statistics BaltimoreMD United States University of Virginia Department of Computer Science CharlottesvilleVA United States

ISBN: (纸本)9781665405959

The Variational Open-Set Recognition (VOSR) model was developed to mitigate shortcomings of previous open-set classification algorithms. However, one of the problems with VOSR is that it was not developed to operate recurrently, meaning it potentially underperforms when operating on time-series data. In this paper we explore alterations to VOSR toward developing a recurrent rVOSR model which is more adept at the task of open-set recognition on time-series data. Based on our experimentation, we found that the implementation of rVOSR over VOSR results in a slight performance improvement on open-set recognition tasks when operating over time sequence data. © 2021 IEEE.

关键词： Recurrent neural networks

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Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation

Transferring Indoor Corrosion Image Assessment Models to Out...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Nicholas Josselyn Biao Yin Thomas Considine John Kelley Berend Rinderspacher Robert Jensen James Snyder Ziming Zhang Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA ARD DEVCOM Army Research Laboratory Aberdeen Proving Ground MD USA ARD DEVCOM Army Research Laboratory Northeast Regional Extended Site Burlington MA USA ARD DEVCOM Army Research Laboratory Adelphi MD USA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA

Corrosion of materials impacts critical economic sectors from infrastructure, transportation, defense, health, to the environment. The development of safe anti-corrosive materials is thus an important area of study in materials science. Corrosion science of preparing materials and then monitoring their corrosion under adverse conditions is labor intensive, time consuming, and extremely costly. While deep learning has become popular in automating various engineering tasks, the development of deep models for corrosion assessment is lacking. We are the first to study deep domain adaptation (DA) models for the automated assessment of the corrosion status of anti-corrosive materials. Corrosion data, i.e., photographic images of treated corroding materials, is abundant when produced in artificially controlled laboratory settings, while corrosion image data sets from rich natural outdoor environments are more challenging to produce and thus much smaller. We leverage the more readily available indoor corrosion data to train a classifier and then transfer it via deep domain adaptation to also perform well on the small yet more realistic outdoor corrosion image data set – without requiring target labels. We empirically compare 5 popular domain adaptation models on real-world corrosion image data sets. Our study finds that DA achieves 27% improvement in test accuracy compared to the performance of the no-DA baseline for classifying real-world outdoor corrosion data.

关键词： Economics Deep learning Adaptation models Materials science and technology Corrosion Biological system modeling Laboratories

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Gaussian mixture graphical lasso with application to edge detection in brain networks

arXiv

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arXiv 2021年

作者： Yin, Hang Liu, Xinyue Kong, Xiangnan Data Science Worcester Polytechnic Institute Worcester United States Alexa AI Amazon Boston United States Computer Science Worcester Polytechnic Institute Worcester United States

Sparse inverse covariance estimation (i.e., edge detection) is an important research problem in recent years, where the goal is to discover the direct connections between a set of nodes in a networked system based upon the observed node activities. Existing works mainly focus on unimodal distributions, where it is usually assumed that the observed activities are generated from a single Gaussian distribution (i.e., one graph). However, this assumption is too strong for many real-world applications. In many real-world applications (e.g., brain networks), the node activities usually exhibit much more complex patterns that are difficult to be captured by one single Gaussian distribution. In this work, we are inspired by Latent Dirichlet Allocation (LDA) [4] and consider modeling the edge detection problem as estimating a mixture of multiple Gaussian distributions, where each corresponds to a separate sub-network. To address this problem, we propose a novel model called Gaussian Mixture Graphical Lasso (MGL). It learns the proportions of signals generated by each mixture component and their parameters iteratively via an EM framework. To obtain more interpretable networks, MGL imposes a special regularization, called Mutual Exclusivity Regularization (MER), to minimize the overlap between different sub-networks. MER also addresses the common issues in read-world data sets, i.e., noisy observations and small sample size. Through the extensive experiments on synthetic and real brain data sets, the results demonstrate that MGL can effectively discover multiple connectivity structures from the observed node activities. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution

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Improving access to housing and supportive services for runaway and homeless youth: Reducing vulnerability to human trafficking in New York City

A I I E Transactions

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A I I E Transactions 2024年第3期56卷

作者： Yaren Bilge Kaya Kayse Lee Maass Geri L. Dimas Renata Konrad Andrew C. Trapp Meredith Dank a Mechanical and Industrial Engineering Northeastern University Boston MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USA c WPI Business School Worcester Polytechnic Institute Worcester MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USAc WPI Business School Worcester Polytechnic Institute Worcester MA USA d New York University New York City NY USA

Recent estimates indicate that there are over a million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to the vulnerability of RHY towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth.

关键词： Human trafficking homelessness capacity expansion mixed-integer linear programming homeless shelters

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Improving Access to Housing and Supportive Services for Runaway and Homeless Youth: Reducing Vulnerability to Human Trafficking in New York City

arXiv

引用

arXiv 2022年

作者： Kaya, Yaren Bilge Maass, Kayse Lee Dimas, Geri L. Konrad, Renata Trapp, Andrew C. Dank, Meredith Mechanical and Industrial Engineering Northeastern University BostonMA United States Data Science Worcester Polytechnic Institute WorcesterMA United States Wpi Business School Worcester Polytechnic Institute WorcesterMA United States New York University New York CityNY United States

Recent estimates indicate that there are over 1 million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to youth's vulnerability towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth. © 2022, CC BY-NC-SA.

关键词： Housing

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