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Semi-supervised Cavitation Detection for Centrifugal Pumps

TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS A 2022年第2期46卷 153-162页

作者： Yoo, Donghwi Choi, Minseok Kim, Chungeon Oh, Hyunseok Gwangju Inst Sci & Technol Sch Mech Engn Gwangju South Korea

Cavitation is a dominant failure mode that accelerates the wear and deterioration of pumps. Cavitation can lead to pump malfunction and, eventually, catastrophic failure of the whole system. Therefore, it is important to avoid cavitation in the pump. This paper proposes a semi-supervised learning method that detects cavitation in centrifugal pumps. One-dimensional (1D) vibration signals are converted into two-dimensional (2D) images by the short time Fourier transform. The severity of the cavitation is determined using the variational autoencoder and Mahalanobis distance. The effectiveness of the proposed method is evaluated using the data collected from a 0.75 kW hydraulic pump testbed. It is confirmed that the proposed method can detect cavitation with different severities and help avoid the cavitation phenomenon.

关键词： Anomaly Detection Short Time Fourier Transform variational autoencoder Mahalanobis Distance

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Interpretable fake news detection with topic and deep variational models

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ONLINE SOCIAL NETWORKS AND MEDIA 2023年 36卷

作者： Hosseini, Marjan Sabet, Alireza Javadian He, Suining Aguiar, Derek Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Univ Pittsburgh Dept Informat & Networked Syst Pittsburgh PA 15260 USA

The growing societal dependence on social media and user generated content for news and information has increased the influence of unreliable sources and fake content, which muddles public discourse and lessens trust in the media. Validating the credibility of such information is a difficult task that is susceptible to confirmation bias, leading to the development of algorithmic techniques to distinguish between fake and real news. However, most existing methods are challenging to interpret, making it difficult to establish trust in predictions, and make assumptions that are unrealistic in many real-world scenarios, e.g., the availability of audiovisual features or provenance. In this work, we focus on fake news detection of textual content using interpretable features and methods. In particular, we have developed a deep probabilistic model that integrates a dense representation of textual news using a variational autoencoder and bi-directional Long Short-Term Memory (LSTM) networks with semantic topic-related features inferred from a Bayesian admixture model. Extensive experimental studies with 3 real-world datasets demonstrate that our model achieves comparable performance to state-of-theart competing models while facilitating model interpretability from the learned topics. Finally, we have conducted model ablation studies to justify the effectiveness and accuracy of integrating neural embeddings and topic features both quantitatively by evaluating performance and qualitatively through separability in lower dimensional embeddings.

关键词： Fake news Misinformation Social media Interpretability Topic models variational autoencoder

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Multi-Agent Reinforcement Learning with Clustering and Experience Sharing

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Procedia Computer Science 2024年 246卷 1329-1337页

作者： Kaname Inokuchi Toshiharu Sugawara Department of Computer Science and Communications Engineering Waseda University Japan

We propose a training method for a heterogeneous multi-agent system to improve the learning efficiency in sparse-reward environments. Although extensive research on multi-agent deep reinforcement learning are conducted actively, these studies often assume that all agents are homogeneous to share/utilize learning parameters in their networks. Unfortunately, this is not always the case in real-world applications where heterogeneous autonomous agents, i.e., those with different capabilities and perspectives, must properly cooperate and coordinate with each other. In our learning method, which is an extension of the shared experience actor-critic (SEAC) for a heterogeneous agent environment, agents are classified depending on their features (such as trajectories of the observations, actions and received rewards) using variational autoencoder, and share their experience among agents within each cluster to train their individual agents for improving the learning efficiency in a sparse-reward environment. Our experimental evaluation shows that the proposed method is capable of more efficient cooperative/coordinated behaviors than the baselines while remaining the advantages of SEAC.

关键词： Multi-agent deep reinforcement learning Experience Sharing variational autoencoder heterogeneous multi-agent systems Sparse-reward environment

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Deep generative models of LDLR protein structure to predict variant pathogenicity

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JOURNAL OF LIPID RESEARCH 2023年第12期64卷 100455页

作者： James, Jose K. Norland, Kristjan Johar, Angad S. Kullo, Iftikhar J. Mayo Clin Dept Cardiovasc Med Rochester MN 55905 USA Mayo Clin Gonda Vasc Ctr Rochester MN 55901 USA

The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM's hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly per-formed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental as-says and clinical phenotypes.

关键词： genomics proteomics physical biochemistry dyslipidemias lipoproteins/receptors atherosclerosis AF2 AlphaFold2 ASCVD Atherosclerotic Cardiovascular Disease AUC Area Under the Curve EGF Epidermal Growth Factor ESM Evolutionary Scale Modeling EVE Evolutionary model of Variant Effect FH Familial Hypercholesterolemia LA LDLR type A LDL low-density lipoprotein LDLR LDL receptor MI Myocardial Infarction MSA Multiple Sequence Alignment OR Odds Ratio pLDDT predicted Local Distance Difference Test UKB UK Biobank VAE variational autoencoder VUS Variants of Uncertain Significance

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Sentiment Analysis-Based Automatic Stress and Emotion Recognition using Weighted Fused Fusion-Based Cascaded DTCN with Attention Mechanism from EEG Signal

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第5期23卷

作者： Kathole, Atul B. Lonare, Savita Dharmale, Gulbakshee Katti, Jayashree Vhatkar, Kapil Kimbahune, Vinod V. Dr DY Patil Inst Technol Pune Comp Engn Pimpri 411018 Maharashtra India Pimpri Chinchwad Coll Engn Nigdi IT Dept Pimpri 411044 Maharashtra India

When loaded with difficulties in fulfilling daily requirements, a lot of people in today's world experience an emotional pressure known as stress. Stress that lasts for a short duration of time has more advantages as they are good for mental health. But, the persistence of stress for a long duration of time may lead to serious health impacts in individuals, such as high blood pressure, cardiovascular disease, stroke and so on. Long-term stress, if unidentified and not treated, may also result in personality disorder, depression and anxiety. The initial detection of stress has become more important to prevent the health issues that arise due to stress. Detection of stress based on brain signals for analysing the emotion in humans leads to accurate detection outcomes. Using EEG-based detection systems and disease, disability and disorders can be identified from the brain by utilising the brain waves. Sentiment Analysis (SA) is helpful in identifying the emotions and mental stress in the human brain. So, a system to accurately and precisely detect depression in human based on their emotion through the utilisation of SA is of high necessity. The development of a reliable and precise Emotion and Stress Recognition (ESR) system in order to detect depression in real-time using deep learning techniques with the aid of Electroencephalography (EEG) signal-based SA is carried out in this paper. The essentials needed for performing stress and emotion detection are gathered initially from benchmark databases. Next, the pre-processing procedures, like the removal of artifacts from the gathered EEG signal, are carried out on the implemented model. The extraction of the spectral attributes is carried out from the pre-processed. The extracted spectral features are considered the first set of features. Then, with the aid of a Conditional variational autoencoder (CVA), the deep features are extracted from the pre-processed signals forming a second set of features. The weights are opti

关键词： Stress recognition emotion recognition spectral feature extraction variational autoencoder weighted feature fusion adaptive egret swarm optimisation algorithm cascaded deep temporal convolution network with attention mechanism

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Rigorous Experimental Analysis of Tabular Data Generated using TVAE and CTGAN

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第4期15卷 1250-1262页

作者： Yadav, Parul Gaur, Manish Madhukar, Rahul Kumar Verma, Gaurav Kumar, Pankaj Fatima, Nishat Sarwar, Saqib Dwivedi, Yash Raj Inst Engn & Technol Comp Sci & Engn Dept Lucknow 226021 Uttar Pradesh India

Synthetic data generation research has been progressing at a rapid pace and novel methods are being designed every now and then. Earlier, statistical methods were used to learn the distributions of real data and then sample synthetic data from those distributions. Recent advances in generative models have led to more efficient modeling of complex high-dimensional datasets. Also, privacy concerns have led to the development of robust models with lesser risk of privacy breaches. Firstly, the paper presents a comprehensive survey of existing techniques for tabular data generation and evaluation matrices. Secondly, it elaborates on a comparative analysis of state-of- the-art synthetic data generation techniques, specifically CTGAN and TVAE for small, medium, and large-scale datasets with varying data distributions. It further evaluates the synthetic data using quantitative and qualitative metrics/techniques. Finally, this paper presents the outcomes and also highlights the issues and shortcomings which are still need to be addressed.

关键词： Synthetic data generation tabular data generation data privacy conditional generative adversarial networks variational autoencoder

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Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study

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JMIR MEDICAL INFORMATICS 2022年第8期10卷 e38440页

作者： Joe, Byunggill Park, Yonghyeon Hamm, Jihun Shin, Insik Lee, Jiyeon Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Elect & Telecommun Res Inst Daejeon South Korea Tulane Univ Dept Comp Sci New Orleans LA 70118 USA Soongsil Univ Sch AI Convergence Seoul South Korea

Background: A backdoor attack controls the output of a machine learning model in 2 stages. First, the attacker poisons the training data set, introducing a back door into the victim's trained model. Second, during test time, the attacker adds an imperceptible pattern called a trigger to the input values, which forces the victim's model to output the attacker's intended values instead of true predictions or decisions. While backdoor attacks pose a serious threat to the reliability of machine learning-based medical diagnostics, existing backdoor attacks that directly change the input values are detectable relatively easily. Objective: The goal of this study was to propose and study a robust backdoor attack on mortality-prediction machine learning models that use electronic health records. We showed that our backdoor attack grants attackers full control over classification outcomes for safety-critical tasks such as mortality prediction, highlighting the importance of undertaking safe artificial intelligence research in the medical field. Methods: We present a trigger generation method based on missing patterns in electronic health record data. Compared to existing approaches, which introduce noise into the medical record, the proposed backdoor attack makes it simple to construct backdoor triggers without prior knowledge. To effectively avoid detection by manual inspectors, we employ variational autoencoders to learn the missing patterns in normal electronic health record data and produce trigger data that appears similar to this data. Results: We experimented with the proposed backdoor attack on 4 machine learning models (linear regression, multilayer perceptron, long short-term memory, and gated recurrent units) that predict in-hospital mortality using a public electronic health record data set. The results showed that the proposed technique achieved a significant drop in the victim's discrimination performance (reducing the area under the precision-recall curve by at

关键词： medical machine learning neural network mortality prediction backdoor attack electronic health record data Medical Information Mart for Intensive Care-III missing value mask meta-information variational autoencoder

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Synthesis-Style Auto-Correlation-Based Transformer: A Learner on Ionospheric TEC Series Forecasting

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SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 2023年第10期21卷

作者： Yuan, Yuhuan Xia, Guozhen Zhang, Xinmiao Zhou, Chen Wuhan Univ Dept Space Phys Wuhan Peoples R China

Accurate 1-day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long-term dependencies. This study develops a highly accurate model for 1-day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1-day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two-step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto-correlation-based transformer model designed to capture long-range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1-day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto-correlation-based transformer models trained on synthetic data to achieve high-quality 1-day global TEC forecasting.

关键词： ionospheric total electron content prediction auto-correlation-based transformer data augmentation variational autoencoder

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FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network

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BRIEFINGS IN BIOINFORMATICS 2024年第2期25卷 bbae097页

作者： Yang, Chengyuan Kumar, Himansu Kim, Pora Univ Texas Hlth Sci Ctr Houston McWilliams Sch Biomed Informat Houston TX 77030 USA

Kinase fusion genes are the most active fusion gene group in human cancer fusion genes. To help choose the clinically significant kinase so that the cancer patients that have fusion genes can be better diagnosed, we need a metric to infer the assessment of kinases in pan-cancer fusion genes rather than relying on the sample frequency expressed fusion genes. Most of all, multiple studies assessed human kinases as the drug targets using multiple types of genomic and clinical information, but none used the kinase fusion genes in their study. The assessment studies of kinase without kinase fusion gene events can miss the effect of one of the mechanisms that enhance the kinase function in cancer. To fill this gap, in this study, we suggest a novel way of assessing genes using a network propagation approach to infer how likely individual kinases influence the kinase fusion gene network composed of similar to 5K kinase fusion gene pairs. To select a better seed of propagation, we chose the top genes via dimensionality reduction like a principal component or latent layer information of six features of individual genes in pan-cancer fusion genes. Our approach may provide a novel way to assess of human kinases in cancer.

关键词： kinase fusion gene gene assessment network propagation variational autoencoder feature reduction

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Developing Deep Learning Models to Simulate Human Declarative Episodic Memory Storage

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第3期10卷 6-15页

作者： Kamruzzaman, Abu Tappert, Charles C. Pace Univ Seidenberg Sch Comp Sci & Informat Syst Pleasantville NY 10570 USA

Human like visual and auditory sensory devices became very popular in recent years through the work of deep learning models that incorporate aspects of brain processing such as edge and line detectors found in the visual cortex. However, very little work has been done on the human memory, and thus our aim is to model human long-term declarative episodic memory storage using deep learning methods. An innovative way of deep neural network was created on supervised feature learning dataset such as MNIST to achieve high accuracy as well as storing the models hidden layers for future extraction. Convolutional Neural Network (CNN) learning models with transfer learning models were trained to imitate the long-term declarative episodic memory storage of human. A Recurrent Neural Network (RNN) in the form of Long Short Term Memory (LSTM) model was assembled in layers and then trained and evaluated. A variational autoencoder was also used for training and evaluation to mimic the human memory model. Frameworks were constructed using TensorFlow for training and testing the deep learning models.

关键词： Convolutional neural network long short term memory variational autoencoder deep learning memory model machine learning

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