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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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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Qin, Zheyi Paffenroth, Randy Jayasumana, Anura P. Colorado State University Electrical and Computer Engineering Fort Collins United States Worcester Polytechnic Institute Department of Data Science Program WorcesterMA United States

ISBN: (纸本)9798350324457

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph's topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

关键词： graph analytics graph coordinate graph embedding green AI network embedding

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

arXiv

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arXiv 2024年

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

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. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive analytics

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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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Leveraging LLMs for Integrated Sentiment and Topic Analysis on African Social Media

Leveraging LLMs for Integrated Sentiment and Topic Analysis ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Harriet Sibitenda Ruofan Hu Elke Rundensteiner Awa Diattara Assitan Traore Cheikh Ba Computer Science Department University of Gaston Berger Saint Louis Senegal Data Science Program Worcester Polytechnic Institute Worcester USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA Data Science Department Actroll France

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for sentiment, topic, and theme extraction, comparing the accuracy of these methods with human annotations, and (2) extracting key insights from the data to provide policymakers with actionable recommendations for sustainable development. For this study, we utilized a data corpus of 22,036 posts from Twitter and YouTube, all focused on development issues in Africa. We applied topic modeling to extract relevant topics from the corpus and used similarity analysis, powered by Large Language Models, to link these topics to prevalent development themes. Additionally, we leveraged unsupervised models such as VADER and Large Language Models to extract sentiment related to the identified topics. To validate these model-generated sentiments, we conducted a small crowdsourced study to gather human-annotated labels as ground truth. Our sentiment analysis findings show improvements with models like TextBlob, VADER, and Llama. Fine-tuning, partic-ularly with BERT, achieved an impressive Fl score of 0.988. Meanwhile, Llama demonstrated strong precision (0.72) and balanced accuracy (0.55) in capturing contextual sentiment. We identified 304 topics using BERTopic and Llama, with robust coherence (0.81 C-v) and divergence (0.58 IRBO). In theme analysis, the One-vs-Rest classification with ensemble voting performed exceptionally well, with ‘Poverty’ achieving the highest F1 score of 0.89. Our results suggest that African policymakers prioritize addressing corruption, unemployment, drought, and instability, while closely monitoring the positive impacts of policy interventions.

关键词： Sentiment analysis Analytical models Accuracy Video on demand Social networking (online) Large language models Machine learning Web sites data mining Unemployment

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Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics

arXiv

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arXiv 2024年

作者： Olulana, Oluseun Cachel, Kathleen Murai, Fabricio Rundensteiner, Elke Data Science Program Worcester Polytechnic Institute WorcesterMA United States Computer Science Department Worcester Polytechnic Institute WorcesterMA United States

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/*** © 2024, CC BY-NC-ND.

关键词： Decision making

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META: Deep Learning Pipeline for Detecting Anomalies on Multimodal Vibration Sewage Treatment Plant data 16th

META: Deep Learning Pipeline for Detecting Anomalies on Mult...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Krastev, Simeon Ammartayakun, Aukkawut Jayshankar Mishra, Kewal Koduri, Harika Schuman, Eric Morris, Drew Feng, Yuan Reddy Bandi, Sai Supreeth Ngan, Chun-Kit Yeung, Andrew Li, Jason Ko, Nigel Emdad, Fatemeh Rundensteiner, Elke Ho, Heiton M. H. Wong, T.K. Chan, Jolly P. C. Data Science Program Worcester Polytechnic Institute 100 Institute Rd WorcesterMA United States XTRA Sensing Limited WanChai Hong Kong Drainage Services Department The Government of the Hong Kong Special Administrative Region Wanchai Hong Kong

ISBN: (纸本)9789897587214

In this paper, we propose a hybrid anomaly detection pipeline, META, which integrates Multimodal-feature Extraction (ME) and a Transformer-based Autoencoder (TA) for predictive maintenance of sewage treatment plants. META uses a three-step approach: First, it employs a signal averaging method to remove noise and improve the quality of signals related to pump health. Second, it extracts key signal properties from three vibration directions (Axial, Radial X, Radial Y), fuses them, and performs dimensionality reduction to create a refined PCA feature set. Third, a Transformer-based Autoencoder (TA) learns pump behavior from the PCA features to detect anomalies with high precision. We validate META with an experimental case study at the Stonecutters Island Sewage Treatment Works in Hong Kong, showing it outperforms state-of-the-art methods in metrics like MCC and F1-score. Lastly, we develop a web-based Sewage Pump Monitoring System hosting the META pipeline with an interactive interface for future use. © 2024 by Paper published under CC license (CC BY-NC-ND 4.0)

关键词： Predictive maintenance

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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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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity 24

An Enhanced BERTopic Framework and Algorithm for Improving T...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Sawant, Sahil Yu, Jinhong Pandya, Kirtikumar Ngan, Chun-Kit Bardeli, Rolf Data Science Program Worcester Polytechnic Institute WorcesterMA United States Big Data and Consumer Analytics Vodafone GmbH NRW Duesseldorf Germany

ISBN: (纸本)9798350319934

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets. © 2022 IEEE.

关键词： Embeddings

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Design of the Continuous Gravity-Driven Multiple-Effect Thermal System (G-METS) for Efficient Low-Cost Magnesium Recycling

Design of the Continuous Gravity-Driven Multiple-Effect Ther...

引用

Magnesium Technology Symposium held during the 152nd TMS Annual Meeting and Exhibition, TMS 2023

作者： Sehar, Daniel Mcarthur Espinosa, Gabriel Telgerafchi, Armaghan Ehsani Chinwego, Chinenye Lynch, Keira Perrin, Benjamin Powell, Adam Materials Science and Engineering Program Department of Mechanical Engineering Worcester Polytechnic Institute WorcesterMA United States Department of Mechanical Engineering Worcester Polytechnic Institute WorcesterMA United States 100 Institute Rd WorcesterMA01609 United States

ISBN: (纸本)9783031226458

Recent global supply shortages, combined with need for light-weight structural components for efficient transportation, have led to very high prices for magnesium due to its low density and high part stiffness/weight. This study continues prior work on magnesium recycling using multiple-effect distillation with gravity as the parameter to create the pressure difference between effects, called G-METS. The long-term vision is for continuous distillation, but experiments to date have only used batch distillers with maximum rate of 1 kg/h. and minimum energy consumption of 2 kWh/kg. This study presents the design of laboratory-scale continuous distiller including a custom-made furnace to sustain a high-power density and temperature gradient for rapid boiling and efficient recovery of magnesium. © The Minerals, Metals & Materials Society 2023.

关键词： Distillation

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