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International Conference on Machine Learning and Applications (ICMLA)

作者： Wen Ge Guanyi Mou Emmanuel O. Agu Kyumin Lee Computer Science Department Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Human Activity Recognition (HAR) is an important task in ubiquitous computing, with impactful real-world applications. While recent state-of-the-art HAR research has demonstrated impressive performance, some key aspects remain under-explored. First, we believe that for optimal performance, HAR models should be both Context-Aware (CA) and personalized. However, prior work has predominantly focused on being Context-Aware (CA), largely ignoring being User-Aware (UA). We argue that learning user-specific differences in performing various activities is as critical as considering user context while performing HAR tasks. Secondly, we believe that the predictions of HAR models should be unified, reliably recognizing the same activity even when performed by different users. As such, the representations utilized by CA and UA models should explicitly place different users performing the same activity closer together. Moreover, identifying the user performing an activity is useful in applications such as thwarting cheating by having another person perform medically-prescribed activities. To bridge this gap, we introduce Contrastive Learning with Auxiliary User Detection for Identifying Activities (CLAUDIA), a novel framework designed to address these issues. Specifically, we expand the contextual scope of the CAHAR task by integrating User Identification (UI) within the CAHAR framework, jointly predicting both CAHAR and UI in a new task called User and Context-Aware HAR (UCA-HAR). This approach enriches personalized and contextual understanding by jointly learning user-invariant and user-specific patterns. Inspired by state-of-the-art designs in the visual domain, we introduce a supervised contrastive loss objective on instance-instance pairs to enhance model efficacy and improve learned feature quality. Through theoretical exposition, empirical analysis of real-world datasets, and rigorous experimentation, we demonstrate the significance of each component of CLAUDIA and discus

关键词： Correlation coefficient Visualization Contrastive learning Machine learning Predictive models Ubiquitous computing Distance measurement Human activity recognition Reliability Context modeling

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Virtual-Coordinate Based Sampling and Embedding for Machine Learning with Graph data

Virtual-Coordinate Based Sampling and Embedding for Machine ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Zheyi Qin Anura P. Jayasumana Randy Paffenroth Electrical and Computer Engineering Colorado State University Fort Collins USA Mathematical Sciences Computer Science and Data Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Graph embedding techniques remains the only alternative for Graph Neural Networks (GNNs) in machine learning for applications for which data in the form of graphs. GNNs, however, require substantial computational resources and are not able to provide certain advantages of conventional neural networks, such as transfer learning. The existing embedding based techniques, however, rely on random walk based strategies, which are not efficient in capturing the topology information, or use more structured strategies followed by feature engineering to achieve the required embedding. We propose VCNN (VirtualCoordinate based Neural Networks) technique that directly uses distances to an extremely small set (< 1 %) of random nodes (anchors) for network sampling and embedding, thus eliminating the feature engineering step. Experiments with two benchmark datasets show that a simple shallow neural network configuration using VC can achieve comparable performance to existing embedding-based techniques requiring deeper neural networks, thus also eliminating the need to experiment with multiple neural network configurations. This finding is particularly relevant for applications where computational efficiency is crucial. This research extends the machine learning toolbox for networks and data in the form of networks, highlighting the importance of strategic parameter use and model simplicity.

关键词： Network topology Social networking (online) Neural networks Transfer learning Machine learning Benchmark testing Predictive models data models Topology Tuning

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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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Multi-task Learning with Pre-trained Language Models for Mental Illness Screening

Multi-task Learning with Pre-trained Language Models for Men...

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IEEE International Conference on Big data

作者： Avantika Shrestha ML Tlachac Ricardo Flores Kevin Hickey Elke A. Rundensteiner Data Science Department Worcester Polytechnic Institute Worcester MA USA Department of Information Systems and Analytics Center for Health and Behavioral Sciences Bryant University Smithfield RI USA Department of Computer Science University of Concepción Concepción Chile Data Science Department Computer Science Department Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illness screening potential of language models on modalities such as transcripts. Such datasets suffer from a limited number of participants. To overcome these challenges, we take a two-pronged approach: (1) we leverage multi-task learning to model multiple illnesses concurrently namely MDD and PTSD screening, and (2) we plug in pre-trained language models as a backbone, namely, Bidirectional Encoder Representations from Transformers (BERT) variants – which are capable of learning the linguistic content of clinical interview transcripts. In particular, we experiment with three multi-task weighting strategies and five BERT variants, applying them to 15 transcript sets extracted from the Distress Analysis Interview Corpus. Our results indicate that leveraging multi-task learning, especially with meta-weighting, increases the screening performance compared to single-task learning. Our multi-task learning model strategies improved the F1 scores for all 15 datasets for both MDD and PTSD screening. Notably, multitask learning improved MDD screening ability by 20 percent with transcripts regarding ‘regret’ and achieved F1 of 0.89 and 0.82 respectively for MDD and PTSD screening with transcripts regarding ‘medical history’. Our findings may help researchers develop more effective mental illness screening models.

关键词： Mental disorders Bidirectional control Human factors Linguistics Multitasking Transformers Depression Encoding Interviews Plugs

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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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Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral

arXiv

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arXiv 2025年

作者： Fard, Reza Saadati Agu, Emmanuel Busaranuvong, Palawat Kumar, Deepak Gautam, Shefalika Tulu, Bengisu Strong, Diane The Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

Chronic wounds affect 8.5 million Americans, especially the elderly and patients with diabetes. Since such wounds can take up to nine months to heal, regular care is crucial to ensure proper healing, and prevent severe outcomes such as limb amputations. However, many patients receive care in their homes from visiting nurses and caregivers with variable wound expertise, resulting in inconsistent care. Problematic, non-healing wounds should be referred to experts in wound clinics to avoid adverse outcomes such as limb amputations. Unfortunately, due to the lack of wound expertise, referral decisions made in nonclinical settings can be erroneous, delayed or unnecessary. This paper proposes the Deep Multimodal Wound Assessment Tool (DM-WAT), a novel machine learning framework to support visiting nurses in deciding whether to refer chronic wound patients to see a wound specialist. DM-WAT analyzes smartphonecaptured wound images and clinical notes from Electronic Health Records (EHRs) to recommend whether a patient should be referred to a wound specialist. DM-WAT extracts visual features from wound images using DeiT-Base-Distilled, a Vision Transformer (ViT) architecture. Distillation-based training facilitates representation learning and knowledge transfer from a larger teacher model to DeiT-Base so that DeiT-Base-Distilled performs well on our small wound image dataset of 205 wound images. DM-WAT extracts text features from clinical notes using DeBERTa-base, which improves context comprehension by disentangling content and position information from clinical notes. DeBERTa-base's disentangled attention mechanism ensures robust extraction of complex syntactic and semantic dependencies from clinical text. Visual and text features are combined using an intermediate fusion approach. To overcome the challenges posed by a small and imbalanced dataset, DM-WAT integrates image and text augmentation alongside transfer learning via pre-trained feature extractors to achieve high pe

关键词： Nursing

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The Karp dataset

arXiv

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arXiv 2025年

作者： DiCicco, Mason Worden, Eamon Olsen, Conner Gangaram, Nikhil Reichman, Daniel Heffernan, Neil Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

Understanding the mathematical reasoning capabilities of Large Language Models (LLMs) is a central topic in the study of artificial intelligence. This new domain necessitates the creation of datasets of reasoning tasks for both training and benchmarking the performance of LLMs. To this end, we introduce the Karp dataset: The first dataset composed of detailed proofs of NP-completeness reductions. The reductions vary in difficulty, ranging from simple exercises of undergraduate courses to more challenging reductions from academic papers. We compare the performance of state-of-the-art models on this task and demonstrate the effect of fine-tuning with the Karp dataset on reasoning capacity. © 2025, CC BY.

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Simple Quantum Coins Enable Pretty Good State Transfer on Every Hypercube

arXiv

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arXiv 2024年

作者： Zhan, Hanmeng Computer Science Department Worcester Polytechnic Institute WorcesterMA United States

We consider pretty good state transfer in coined quantum walks between antipodal vertices on the hypercube Qd. When d is a prime, this was proven to occur in the arc-reversal walk with Grover coins. We extend this result by constructing weighted Grover coins that enable pretty good state transfer on every Qd. Our coins are real, and require modification of the weight on only one arc per vertex. We also generalize our approach and establish a sufficient condition for pretty good state transfer to occur on other graphs. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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

arXiv

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arXiv 2024年

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

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. © 2024, CC BY.

关键词： Graph neural networks

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Discrete Quantum Walks with Marked Vertices and Their Average Vertex Mixing Matrices

arXiv

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arXiv 2024年

作者： Mohan, Amulya Zhan, Hanmeng Computer Science Department Worcester Polytechnic Institute WorcesterMA United States

We study the discrete quantum walk on a regular graph X that assigns negative identity coins to marked vertices S and Grover coins to the unmarked ones. We find combinatorial bases for the eigenspaces of the transtion matrix, and derive a formula for the average vertex mixing matrix Mc. We then find bounds for entries in Mc, and study when these bounds are tight. In particular, the average probabilities between marked vertices are lower bounded by a matrix determined by the induced subgraph X[S], the vertex-deleted subgraph X\S, and the edge deleted subgraph X − E(S). We show this bound is achieved if and only if the marked vertices have walk-equitable neighborhoods in the vertex-deleted subgraph. Finally, for quantum walks attaining this bound, we determine when Mc[S,S] is symmetric, positive semidefinite or uniform. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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