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One-class Classification Using Autoencoder Feature Residuals for Improved IoT Network Intrusion Detection 32

One-class Classification Using Autoencoder Feature Residuals...

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32nd International Conference on Computer Communications and Networks, ICCCN 2023

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

ISBN: (纸本)9798350336184

Networks are rapidly evolving to include more Internet of Things devices as we grow to rely on them for smart home services, health infrastructure, and industrial development. Along with this proliferation, these networks are often the target of cyberattacks that seek to take advantage of their low processing power and the complexity that is involved in protecting heterogeneous networks. We seek to improve our network intrusion detection capabilities for Internet of Things networks by developing methods of detection that utilize the power of deep learning and have the ability to detect zero-day attacks. In this work, we outline a novel feature generation process using autoencoder feature residuals that can be combined with one-class classifiers to effectively detect network attacks on Internet of Things networks using no attack data during the training process. Moreover, we show that our novel feature sets are able to outperform using an original feature set leading to a reduction of typical model hyperparameter tuning activities. © 2023 IEEE.

关键词： Internet of things

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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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An Ensemble-Based Weight-Learning Framework and Algorithm for Imputing Missing Values on High-Dimension Low-Sample Size datasets 23

An Ensemble-Based Weight-Learning Framework and Algorithm fo...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ngan, Chun-Kit Yang, Boyuan Worcester Polytechnic Institute Data Science Program WorcesterMA United States

ISBN: (纸本)9781665494571

High-Dimension, Low-Sample Size (HDLSS) datasets are the datasets that their sample sizes are much smaller than the number of feature dimensions. In spite of that, HDLSS datasets appear in many domains and organizations, including social work, medical fields, international travels, and chemical industry. As many HDLSS datasets are very difficult to be collected, their missing values raise many issues and reduce reliability on the datasets to solve a domain and an organizational problem. To address the issues of the missing values in HDLSS datasets, we propose an Ensemble-Based Weight-Learning (EBWF) framework. Given HDLSS datasets and available Machine Learning (ML) classifiers, the EBWF framework is an automated pipeline that can generate the best imputed dataset. To support the EBWF framework, we develop a novel Record Probability Weight Learning (RPWL) imputation algorithm that combines the data record probability calculation from each ML classifier and weight learning computations among all the ML classifiers to impute the missing values in the dataset. To show the effectiveness of our approach, we conduct an experimental case study and evaluation on a survey dataset, i.e., 212 international travelers in Houston for assessing sexual risk-taking behaviors, as a pilot study, where Houston is a critical transit point frequented by international visitors. Based upon the travelers' survey data, where the survey records' missing rate is in the range of 10% to 70%, and the imputed dataset generated by our approach, we show that the classification performance delivered by our framework and algorithm outperform the other baseline approaches'. © 2021 IEEE.

关键词： Chemical industry Machine learning algorithms Pipelines Organizations Machine learning Probability Classification algorithms

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Recycling of Magnesium Alloy Using the Gravity-Driven Multiple-Effect Thermal System (G-METS)

Recycling of Magnesium Alloy Using the Gravity-Driven Multip...

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Magnesium Technology Symposium held at the TMS Annual Meeting and Exhibition, 2024

作者： Mc Arthur Sehar, Daniel Telgerafchi, Armaghan Ehsani Iurkovskyi, Artem Opoku, Emmanuel 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

ISBN: (纸本)9783031502392

MagnesiumMagnesium metal has been significantly used in commercial applications from automobiles to aluminum alloys to defense weaponry. The surge in structural usage of magnesium over the past years has been due to high strength and excellent stiffness-to-weight ratio. RecyclingRecycling magnesium scrap is a crucial aspect of the sustainability of the economy and environmental welfare. G-METS is an advanced distillationDistillation system used in the conversion of low-grade magnesium scrap into high-purityHigh-purity magnesium metal. This multiple-effect system uses the weight of magnesiumMagnesium as a compressor, to build pressure differences between each effect of the condenser-evaporator, modifying boiling points, and enabling transfer of vaporization enthalpy. This system is exceptionally efficient compared to the energy-intensive conventional distillationDistillation technique. This study compares experiments and models of one- and two-effect distillers. © The Minerals, Metals & Materials Society 2024.

关键词： Magnesium alloys

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DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses 22

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Gui...

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

作者： Yin, Biao Josselyn, Nicholas Considine, Thomas Kelley, John Rinderspacher, Berend Jensen, Robert Snyder, James Zhang, Ziming Rundensteiner, Elke Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350345346

Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing 3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation - promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://***/ © 2023 IEEE.

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A Hybrid Video-to-Text Summarization Framework and Algorithm on Cascading Advanced Extractiveand Abstractive-based Approaches for Supporting Viewers' Video Navigation and Understanding 5

A Hybrid Video-to-Text Summarization Framework and Algorithm...

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5th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2022

作者： Ramakrishnan, Aishwarya Ngan, Chun-Kit Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

In this work, we propose the development of a hybrid video-to-text summarization (VTS) framework on cascading the advanced and code-accessible extractive and abstractive (EA) approaches for supporting viewers' video navigation and understanding. More precisely, the contributions of this paper are three-fold. First, we devise an automated and unified hybrid VTS framework that takes an arbitrary video as an input, generates the text transcripts from its human dialogues, and then summarizes the text transcripts into one short video synopsis. Second, we advance the binary merge-sort approach and expand its use to develop an intuitive and heuristic abstractive-based algorithm, with the time complexity O(TLlogTL) and the space complexity O(TL), where TL is the total number of word tokens on a text, to dynamically and successively split and merge a long piece of text transcripts, which exceeds the input text size limitation of an abstractive model, to generate one final semantic video synopsis. At the end, we test the feasibility of applying this proposed framework and algorithm in conducting the preliminarily experimental evaluations on three different videos, as a pilot study, in genres, contents, and lengths. We show that our approach outperforms and/or levels most of the individual EA methods stated above by 75% in terms of the ROUGE F1-Score measurement. © 2022 IEEE.

关键词： Semantics

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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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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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2023 IEEE International Conference on Big data, Bigdata 2023

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

ISBN: (纸本)9798350324457

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts. © 2023 IEEE.

关键词： foodborne illness learning with noisy labels semi-supervised learning social media text classification

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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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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Shrestha, Avantika Tlachac, M.L. Flores, Ricardo Hickey, Kevin 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 University of Concepcion Department of Computer Science Biobio Concepcion Chile Worcester Polytechnic Institute Data Science Department Computer Science Department WorcesterMA United States

ISBN: (纸本)9798350362480

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. © 2024 IEEE.

关键词： Multi-task learning

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Efficient Recycling of Magnesium Alloys via Partial Distillation Using Gravity-Multiple Effect Thermal System (G-METS)

Efficient Recycling of Magnesium Alloys via Partial Distilla...

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Magnesium Technology Symposium, 2025, held as part of the TMS Annual Meeting and Exhibition, TMS 2025

作者： McArthur Sehar, Daniel Saito, Kenichi Telgerafchi, Armaghan Gallego, Maya Sridhar, Meera Iurkovskyi, Artem Powell, Adam Department of Materials Science and Engineering Program Worcester Polytechnic Institute 100 Institute Rd. WorcesterMA01609 United States Department of Mechanical Engineering Shibaura Institute of Technology Tokyo Japan Department of Mechanical Engineering Worcester Polytechnic Institute WorcesterMA United States

ISBN: (纸本)9783031810602

MagnesiumMagnesium’s remarkable stiffnessStiffness-to-weight and high strengthStrength-to-weight ratios could enable substantial vehicle lightweighting versus aluminum and steel. However, primary production is very energy- and emissions-intensive, and recyclingRecycling cannot cost-effectively produce magnesiumMagnesium of sufficient purity for die casting and other structural applications. Distillation can produce high-purity magnesiumMagnesium but is capital- and energy-intensive. Gravity-Driven Multiple Effect Thermal System (G-METS) is a continuous low-pressure magnesiumMagnesium distillation process used in primary production or recyclingRecycling in which heat re-use leads to just 0.5–1 kWh energy consumption per kgMg Mg product—which is 80–90% less energy than current Mg distillation. This paper presents new partial distillation experiments showing 99.96% product purity. An industrial distiller design can help to estimate capital costs. New modeling work describes temperature and pressure change in the vapor conduit, energy use and losses in the industrial distiller, and effects of evaporator flow vanes on the separation of volatile elements such as zinc. © The Minerals, Metals & Materials Society 2025.

关键词： Magnesium alloys

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