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AlloyGAN: Domain-Promptable Generative Adversarial Network for Generating Aluminum Alloy Microstructures 22

AlloyGAN: Domain-Promptable Generative Adversarial Network f...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Yin, Biao Fan, Yangyang Josselyn, Nicholas Rundensteiner, Elke Chief Material Scientist DeepAlloy PortlandOR United States Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9798350345346

The global metal market, expected to exceed 18.5 trillion by 2030, faces costly inefficiencies from defects in alloy manufacturing. Although microstructure analysis has improved alloy performance, current numerical models struggle to accurately simulate solidification. In this research, we thus introduce AlloyGAN - the first domain-driven Conditional Generative Adversarial Network (cGAN) involving domain prior for generating alloy microstructures of previously not considered chemical and manufactural compositions. AlloyGAN improves cGAN process by involving prior factors from solidification reaction to generate scientifically valid images of alloy microstructure given basic alloy manufacturing compositions. It achieves a faster and equally accurate alternative to traditional material science methods for assessing alloy microstructures. We contribute (1) a novel Alloy-GAN design for rapid alloy optimization;(2) unique methods that inject prior knowledge of the chemical reaction into cGAN-based models;and (3) metrics from machine learning and chemistry for generation evaluation. Our approach highlights the promise of GAN-based models in the scientific discovery of materials. AlloyGAN has successfully transitioned into an AIGC startup with a core focus on model-generated metallography. We open its interactive demo at: https://***/ © 2023 IEEE.

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How to Open science: A Principle and Reproducibility Review of the Learning Analytics and Knowledge Conference 2023

How to Open Science: A Principle and Reproducibility Review ...

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13th International Conference on Learning Analytics and Knowledge: Towards Trustworthy Learning Analytics, LAK 2023

作者： Haim, Aaron Shaw, Stacy Heffernan, Neil Computer Science Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781450398657

Within the field of education technology, learning analytics has increased in popularity over the past decade. Researchers conduct experiments and develop software, building on each other's work to create more intricate systems. In parallel, open science - which describes a set of practices to make research more open, transparent, and reproducible - has exploded in recent years, resulting in more open data, code, and materials for researchers to use. However, without prior knowledge of open science, many researchers do not make their datasets, code, and materials openly available, and those that are available are often difficult, if not impossible, to reproduce. The purpose of the current study was to take a close look at our field by examining previous papers within the proceedings of the International Conference on Learning Analytics and Knowledge, and document the rate of open science adoption (e.g., preregistration, open data), as well as how well available data and code could be reproduced. Specifically, we examined 133 research papers, allowing ourselves 15 minutes for each paper to identify open science practices and attempt to reproduce the results according to their provided specifications. Our results showed that less than half of the research adopted standard open science principles, with approximately 5% fully meeting some of the defined principles. Further, we were unable to reproduce any of the papers successfully in the given time period. We conclude by providing recommendations on how to improve the reproducibility of our research as a field moving forward. All openly accessible work can be found in an Open science Foundation project1. © 2023 Owner/Author.

关键词： Open data

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Improving Network Intrusion Detection Using Autoencoder Feature Residuals 4

Improving Network Intrusion Detection Using Autoencoder Feat...

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4th International Conference on data Intelligence and Security, ICDIS 2022

作者： Lewandowski, Brian Paffenroth, Randy Campbell, Kayleigh Computer Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester United States Mathematical Sciences Worcester Polytechnic Institute Worcester United States

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

Anomaly detection techniques are often used to identify abnormal network communications by performing comparisons to normal network behavior. Oftentimes, these anomalies prompt additional investigation to determine if the anomaly is an indicator of a network attack. In this work we use the residuals of autoencoders in order to improve the performance of classifiers identifying network attacks. Unlike most existing works, we utilize the residuals of each feature as opposed to a summary residual metric. We explore several strategies for using feature residuals and show their effectiveness at improving general classifier performance across multiple datasets and scenarios. © 2022 IEEE.

关键词： Anomaly detection

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Hidden or Inferred: Fair Learning-to-Rank with Unknown Demographics 24

Hidden or Inferred: Fair Learning-to-Rank with Unknown Demog...

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Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society

作者： Oluseun Olulana Kathleen Cachel Fabricio Murai Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester MA Data Science Program Worcester Polytechnic Institute Worcester MA and Computer Science Department Worcester Polytechnic Institute Worcester MA

As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://***/sewen007/***

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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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Multi-Task Learning Using Facial Features for Mental Health Screening

Multi-Task Learning Using Facial Features for Mental Health ...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Flores, Ricardo Shrestha, Avantika Tlachac, Ml Rundensteiner, Elke A. Worcester Polytechnic Institute Data Science Department WorcesterMA United States Bryant University Center for Health and Behavioral Sciences Department of Information Systems and Analytics SmithfieldRI United States Worcester Polytechnic Institute Data Science Department Computer Science Department WorcesterMA United States

ISBN: (纸本)9798350324457

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are prevalent mental health conditions with severe physical and social impacts. They are expensive and detection is difficult, requiring substantial time from trained mental professionals. To alleviate this issue, recent studies explore the diagnostic potential of deep learning models trained on modalities extracted from clinical interview videos, conducted by a virtual agent. However, deep learning models are challenging to train because of the long sequences and small number of participants common in the mental health community. To combat these challenges, we leverage multi-task learning, using temporal facial features as input, to screen for MDD and PTSD. The multi-task architecture is based on a bidirectional GRU model with self-attention. We evaluate our multi-task model on temporal facial features extracted from responses to 15 clinical interview questions conducted by a virtual agent. The results suggest that multi-task learning increases the generalization performance compared to single-task learning. For MDD screening, multi-task learning improved the balanced accuracy over single-task learning for 11 of the 15 datasets. In fact, our multi-learning model increased the MDD screening ability by 25 percent to a balanced accuracy of 0.87 in some scenarios. This work provides valuable findings for the future of mental screening applications leveraging temporal facial features. © 2023 IEEE.

关键词： deep learning digital health multivariate time series self-attention sequential modeling

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Heterogeneous Hyper-Graph Neural Networks for Context-Aware Human Activity Recognition

Heterogeneous Hyper-Graph Neural Networks for Context-Aware ...

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Ge, Wen Mou, Guanyi Agu, Emmanuel O. Lee, Kyumin Worcester Polytechnic Institute Computer Science Department WorcesterMA United States

ISBN: (纸本)9781665453813

Context-aware Human Activity Recognition (CHAR) is challenging due to the need to recognize the user's current activity from signals that vary significantly with contextual factors such as phone placements and the varied styles with which different users perform the same activity. In this paper, we argue that context-aware activity visit patterns in realistic in-the-wild data can equivocally be considered as a general graph representation learning task. We posit that exploiting underlying graphical patterns in CHAR data can improve CHAR task performance and representation learning. Building on the intuition that certain activities are frequently performed with the phone placed in certain positions, we focus on the context-aware human activity problem of recognizing the tuple. We demonstrate that CHAR data has an underlying graph structure that can be viewed as a heterogenous hypergraph that has multiple types of nodes and hyperedges (an edge connecting more than two nodes). Subsequently, learning representations becomes a graph node representation learning problem. After task transformation, we further propose a novel Heterogeneous HyperGraph Neural Network architecture for Context-aware Human Activity Recognition (HHGNN-CHAR), with three types of heterogeneous nodes (user, phone placement, and activity). Connections between all types of nodes are represented by hyperedges. Rigorous evaluation demonstrated that on an unscripted, in-the-wild CHAR dataset, our proposed framework significantly outperforms state-of-the-art (SOTA) baselines including CHAR models that do not exploit graphs, and GNN variants that do not incorporate heterogeneous nodes or hyperedges with overall improvements 14.04% on Matthews Correlation Coefficient (MCC) and 7.01% on Macro F1 scores. © 2023 IEEE.

关键词： Graph neural networks

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Swarm Intelligence Path-Planning Pipeline and Algorithms for UAVs: Simulation, Analysis and Recommendation 26

Swarm Intelligence Path-Planning Pipeline and Algorithms for...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Harris, Wyatt Tseng, Sean Viso, Tabatha Weissman, Max Ngan, Chun-Kit Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States Data Science Program Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA United States

ISBN: (纸本)9789897586927

This research work aims to support domain experts in the selection of proper path planning algorithms for UAVs to solve a domain business problem (i.e., the last mile delivery of goods). In-depth analysis, insight, and recommendations of three promising approaches, including reinforcement learning-based, bio-inspired-based, and physics-based are used to address the multi-agent UAV path planning problem. Specifically, the contributions of this work are fourfold: First, we develop a unified pipeline to implement each approach to conduct this analysis. Second, we build a 2D UAV path planning environment to simulate each approach. Third, using this 2D environment, we run the 450 simulations in three different group sizes of swarm UAV agents (i.e., 3, 5, and 10) within three environments of varying complexity (i.e., Easy, Intermediate, and Hard). We aggregate the simulation data and compare their performance in terms of success rate, run-time, and path length while using the classical A* Search as a baseline. Finally, based upon the performance of each approach and our analytical investigations, we provide informed recommendations for the optimal use case of each UAV path planning approach. The recommendations are presented using parameters for environmental complexity and urgency of goods delivery. Copyright © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Motion planning

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HAR-CTGAN: A Mobile Sensor data Generation Tool for Human Activity Recognition

HAR-CTGAN: A Mobile Sensor Data Generation Tool for Human Ac...

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2022 IEEE International Conference on Big data, Big data 2022

作者： Deoliveira, Joshua Gerych, Walter Koshkarova, Aruzhan Rundensteiner, Elke Agu, Emmanuel Data Science Program Worcester Polytechnic Institute WorcesterMA United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9781665480451

Human activity recognition (HAR) is the process of using mobile sensor data to determine the physical activities performed by individuals. HAR is the backbone of many mobile healthcare applications, such as passive health monitoring systems, early diagnosing systems, and fall detection systems. Effective HAR models rely on deep learning architectures and big data in order to accurately classify activities. Unfortunately, HAR datasets are expensive to collect, are often mislabeled, and have large class imbalances. State-of-the-art approaches to address these challenges utilize Generative Adversarial Networks (GANs) for generating additional synthetic data along with their labels. Problematically, these HAR GANs only synthesize continuous features - features that are represented by real numbers - recorded from gyroscopes, accelerometers, and other sensors that produce continuous data. This is limiting since mobile sensor data commonly has discrete features that provide additional context such as device location and the time-of-day, which have been shown to substantially improve HAR classification. Hence, we studied Conditional Tabular Generative Adversarial Networks (CTGANs) for data generation to synthesize mobile sensor data containing both continuous and discrete features, a task never been done by state-of-the-art approaches. We show HAR-CTGANs generate data with greater realism resulting in allowing better downstream performance in HAR models, and when state-of-the-art models were modified with HAR-CTGAN characteristics, downstream performance also improves. © 2022 IEEE.

关键词： Large dataset

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FSL-LFMG: Few-Shot Learning with Augmented Latent Features and Multitasking Generation for Enhancing Multiclass Classification on Tabular data 16th

FSL-LFMG: Few-Shot Learning with Augmented Latent Features a...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Nur, Aviv A. Ngan, Chun-Kit Bardeli, Rolf Data Science Program Worcester Polytechnic Institute 100 Institute Rd WorcesterMA United States thyssenkrupp Materials Services GmbH Essen Germany

ISBN: (纸本)9789897587214

In this work, we propose advancing ProtoNet that employs augmented latent features (LF) by an autoencoder and multitasking generation (MG) by STUNT in the few-shot learning (FSL) mechanism. Specifically, the achieved contributions to this work are threefold. First, we propose an FSL-LFMG framework to develop an end-to-end few-shot multiclass classification workflow on tabular data. This framework is composed of three main stages that include (i) data augmentation at the sample level utilizing autoencoders to generate augmented LF, (ii) data augmentation at the task level involving self-generating multitasks using the STUNT approach, and (iii) the learning process taking place on ProtoNet, followed by various model evaluations in our FSL mechanism. Second, due to the outlier and noise sensitivity of K-means clustering and the curse of dimensionality of Euclidean distance, we enhance and customize the STUNT approach by using K-medoids clustering that is less sensitive to noisy outliers and Manhattan distance that is the most preferable for highdimensional data. Finally, we conduct an extensive experimental study on four diverse domain datasets—Net Promoter Score segmentation, Dry Bean type, Wine type, and Forest Cover type—to prove that our FSLLFMG approach on the multiclass classification outperforms the Tree Ensemble models and the One-vs-therest classifiers by 7.8% in 1-shot and 2.5% in 5-shot learning. © 2024 by Paper published under CC license (CC BY-NC-ND 4.0)

关键词： Decision trees

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