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Deep reinforcement learning for data-efficient weakly supervised business process anomaly detection

JOURNAL OF BIG DATA 2023年第1期10卷 33页

作者： Elaziz, Eman Abd Fathalla, Radwa Shaheen, Mohamed Arab Acad Sci Technol & Maritime Transport Coll Comp & Informat Technol Alexandria Egypt

The detection of anomalous behavior in business process data is a crucial task for preventing failures that may jeopardize the performance of any organization. Supervised learning techniques are impracticable because of the difficulties of gathering huge amounts of labeled business process anomaly data. For this reason, unsupervised learning techniques and semi-supervised learning approaches trained on entirely labeled normal data have dominated this domain for a long time. However, these methods do not work well because of the absence of prior knowledge of true anomalies. In this study, we propose a deep weakly supervised reinforcement learning-based approach to identify anomalies in business processes by leveraging limited labeled anomaly data. The proposed approach is intended to use a small collection of labeled anomalous data while exploring a huge set of unlabeled data to find new classes of anomalies that are outside the scope of the labeled anomalous data. We created a unique reward function that combined the supervisory signal supplied by a variational autoencoder trained on unlabeled data with the supervisory signal provided by the environment's reward. To further reduce data deficiency, we introduced a sampling method to allow the effective exploration of the unlabeled data and to address the imbalanced data problem, which is a common problem in the anomaly detection field. This approach depends on the proximity between the data samples in the latent space of the variational autoencoder. Furthermore, to efficiently model the sequential nature of business process data and to handle the long-term dependences, we used a long short-term memory network combined with a self-attention mechanism to develop the agent of our reinforcement learning model. Multiple scenarios were used to test the proposed approach on real-world and synthetic datasets. The findings revealed that the proposed approach outperformed five competing approaches by efficiently using the few

关键词： Business process Deep reinforcement learning Weakly supervised learning variational autoencoder Long short-term memory Self-attention Transformers Imbalanced data Process mining Process discovery Conformance checking Balanced accuracy

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Out-of-Distribution Data Generation for Fault Detection and Diagnosis in Industrial Systems

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IEEE ACCESS 2023年 11卷 135061-135073页

作者： Kafunah, Jefkine Verma, Priyanka Ali, Muhammad Intizar Breslin, John G. Univ Galway Sch Engn Galway H91 TK33 Ireland Univ Galway Data Sci Inst Galway H91 TK33 Ireland Dublin City Univ Sch Elect Engn Dublin 9 Ireland

The emergence of Industry 4.0 has transformed modern-day factories into high-tech industrial sites through rapid automation and increased access to real-time data. Deep learning approaches possessing superior capabilities for intelligent, data-driven fault diagnosis have become critical in ensuring process safety and reliability in these industrial sites. However, such applications trained exclusively on in-distribution process data face challenges in the wake of previously unseen out-of-distribution (OOD) data in the real world. This paper addresses the challenge of out-of-distribution data detection for deep learning-based fault diagnosis models by generating synthetic data to simulate real-world anomalies not present in the training set. We propose Manifold Guided Sampling (MGS), a data-driven method for generating synthetic OOD samples from the in-distribution data-supporting manifold estimated through a deep generative model. Synthetic data from MGS enhances the model capacity for prediction uncertainty quantification, resulting in safe and reliable models for real-world industrial process monitoring. Furthermore, the MGS algorithm maintains the in-distribution data feature space as a reference point during data generation to ensure the resulting synthetic OOD data is realistic. We analyze the effectiveness of MGS through experiments conducted on the steel plates faults dataset and demonstrate that augmenting training data with synthetic data from MGS enhances the model performance in OOD detection tasks and provides robustness against dataset distributional shifts. The findings underscore the effectiveness of utilizing synthetic MGS-generated OOD data in scenarios where real-world OOD data is limited, enabling better generalization and more reliable fault detection in practical applications.

关键词： Deep generative models fault diagnosis process monitoring safety-critical out-of-distribution data variational autoencoder uncertainty estimation

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Deep Generative Networks Coupled With Evidential Reasoning for Dynamic User Preferences Using Short Texts

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第7期35卷 6811-6826页

作者： Vo, Duc-Vinh Tran, Trung-Tin Shirai, Kiyoaki Huynh, Van-Nam FPT Univ Fac Comp Fundamentals Ho Chi Minh City 10000 Vietnam Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea Japan Adv Inst Sci & Technol JAIST Nomi Ishikawa 9231211 Japan

Seeking an efficient solution for the problem of dynamic user preferences on social networks is challenging because the input data are short texts and user preferences usually change over time. This work proposes a novel framework that tackles these challenges based on deep neural networks and the Dempster-Shafer theory of evidence. The framework consists of three primary phases: (1) learning the hidden space of user texts;(2) word generation and mass inference;and (3) mass combination and keyword extraction. In the first phase, user texts are grouped into small batches according to timestamps. Each batch is used for separately training two types of neural networks, the variational autoencoder (VAE) and the Generative Adversarial Network (GAN). In the second phase, the generators in the trained VAE and GAN work independently as two experts to generate bunches of tokens for modeling user preferences. Each bunch is considered as one piece of evidence, and is transformed into the so-called mass function in Dempster-Shafer theory by maximum a posterior estimation. In the final phase, Dempster's rule of combination is utilized for fusing the two independent pieces of evidence into an overall mass. This mass is used for extracting top keywords to form the user preferences within a specific time span. The experiments on short text datasets verified that the proposed method outperforms baseline models on many evaluation metrics. Additionally, the output of the proposed framework could be used for visualization, which is useful in many practical applications.

关键词： Dempster-shafer theory mass function variational autoencoder generative adversarial network user preference user profile user profile visualization

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An uncertainty-aware deep learning architecture with outlier mitigation for prostate gland segmentation in radiotherapy treatment planning

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MEDICAL PHYSICS 2023年第1期50卷 311-322页

作者： Li, Xin Bagher-Ebadian, Hassan Gardner, Stephen Kim, Joshua Elshaikh, Mohamed Movsas, Benjamin Zhu, Dongxiao Chetty, Indrin J. Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Henry Ford Canc Inst Dept Radiat Oncol 2800 W Grand Blvd Detroit MI 48202 USA

Purpose Task automation is essential for efficient and consistent image segmentation in radiation oncology. We report on a deep learning architecture, comprising a U-Net and a variational autoencoder (VAE) for automatic contouring of the prostate gland incorporating interobserver variation for radiotherapy treatment planning. The U-Net/VAE generates an ensemble set of segmentations for each image CT slice. A novel outlier mitigation (OM) technique was implemented to enhance the model segmentation accuracy. Methods The primary source dataset (source_prim) consisted of 19 200 CT slices (from 300 patient planning CT image datasets) with manually contoured prostate glands. A smaller secondary source dataset (source_sec) comprised 640 CT slices (from 10 patient CT datasets), where prostate glands were segmented by 5 independent physicians on each dataset to account for interobserver variability. Data augmentation via random rotation (<5 degrees), cropping, and horizontal flipping was applied to each dataset to increase sample size by a factor of 100. A probabilistic hierarchical U-Net with VAE was implemented and pretrained using the augmented source_prim dataset for 30 epochs. Model parameters of the U-Net/VAE were fine-tuned using the augmented source_sec dataset for 100 epochs. After the first round of training, outlier contours in the training dataset were automatically detected and replaced by the most accurate contours (based on Dice similarity coefficient, DSC) generated by the model. The U-Net/OM-VAE was retrained using the revised training dataset. Metrics for comparison included DSC, Hausdorff distance (HD, mm), normalized cross-correlation (NCC) coefficient, and center-of-mass (COM) distance (mm). Results Results for U-Net/OM-VAE with outliers replaced in the training dataset versus U-Net/VAE without OM were as follows: DSC = 0.82 +/- 0.01 versus 0.80 +/- 0.02 (p = 0.019), HD = 9.18 +/- 1.22 versus 10.18 +/- 1.35 mm (p = 0.043), NCC = 0.59 +/- 0.07 versus 0.62

关键词： interobserver variation outlier mitigation prostate cancer U-Net variational autoencoder

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Joint Image and Feature Levels Disentanglement for Generalizable Vehicle Re-identification

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第12期24卷 15259-15273页

作者： Kuang, Zhenyu He, Chuchu Huang, Yue Ding, Xinghao Li, Huafeng Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart City Xiamen 361005 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Peoples R China

Domain generalization (DG), which doesn't require any data from target domains during training, is more challenging but practical than unsupervised domain adaptation (UDA). Since different vehicles of the same type have a similar appearance, neural networks always rely on a small amount of useful information to distinguish them, meaning that is more significant to remove ID-unrelated information for vehicle re-Identification (re-ID). Therefore, it is the key to eliminating the interference of a large amount of redundant information for the generalizable vehicle re-ID method. To address this unique challenge, we propose a novel disentanglement learning method that encourages variational autoencoder (VAE) network to reduce ID-unrelated features of vehicles by minimizing image reconstruction errors and providing sufficient representation to vehicle labels. To capture the intrinsic characteristics associated with the DG task, our core idea is to build the identity information streaming framework to separate ID-related and ID-unrelated information at the image and feature levels. In contrast with the general decoupling methods, our method leverages the decoupling of joint image and feature levels to extract more generalizable features. Furthermore, we present a brand-new vehicle dataset of truck types named "Optimus Prime (Opri)", which includes multiple images of each truck captured by cameras at different high-speed toll gates. Experimental results on public datasets demonstrate that our method can achieve promising results and outperform several state-of-the-art approaches. Our codes and models are available at JIFD.

关键词： Task analysis Feature extraction Image reconstruction Cameras Training Neural networks Interference Vehicle re-identification variational autoencoder domain generalization representation disentanglement

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Multi-objective latent space optimization of generative molecular design models

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PATTERNS 2024年第10期5卷 101042页

作者： Abeer, A. N. M. Nafiz Urban, Nathan M. Weil, M. Ryan Alexander, Francis J. Yoon, Byung-Jun Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA Frederick Natl Lab Strateg & Data Sci Initiat Frederick MD 21702 USA Argonne Natl Lab Comp Environm & Life Sci Lemont IL 60439 USA

Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties. While the efficacy of the initial model strongly depends on the training data, the sampling efficiency of the model for suggesting novel molecules with enhanced properties can be further enhanced via latent space optimization (LSO). In this paper, we propose a multi-objective LSO method that can significantly enhance the performance of generative molecular design (GMD). The proposed method adopts an iterative weighted retraining approach, where the respective weights of the molecules in the training data are determined by their Pareto efficiency. We demonstrate that our multi-objective GMD LSO method can significantly improve the performance of GMD for jointly optimizing multiple molecular properties.

关键词： generative molecular design GMD variational autoencoder VAE drug discovery latent space optimization LSO multi-objective optimization

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DISENTANGLEMENT LEARNING FOR variational autoencoderS APPLIED TO AUDIO-VISUAL SPEECH ENHANCEMENT

DISENTANGLEMENT LEARNING FOR VARIATIONAL AUTOENCODERS APPLIE...

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Carbajal, Guillaume Richter, Julius Gerkmann, Timo Univ Hamburg Signal Proc SP Hamburg Germany

ISBN: (纸本)9781665448703

Recently, the standard variational autoencoder has been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. variational autoencoders have then been conditioned on a label describing a high-level speech attribute (e.g. speech activity) that allows for a more explicit control of speech generation. However, the label is not guaranteed to be disentangled from the other latent variables, which results in limited performance improvements compared to the standard variational autoencoder. In this work, we propose to use an adversarial training scheme for variational autoencoders to disentangle the label from the other latent variables. At training, we use a discriminator that competes with the encoder of the variational autoencoder. Simultaneously, we also use an additional encoder that estimates the label for the decoder of the variational autoencoder, which proves to be crucial to learn disentanglement. We show the benefit of the proposed disentanglement learning when a voice activity label, estimated from visual data, is used for speech enhancement.

关键词： Speech enhancement conditional generative model variational autoencoder disentanglement learning adversarial training semi-supervised learning audio-visual.

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Ultrasound Anomaly Detection Based on variational autoencoders 12

Ultrasound Anomaly Detection Based on Variational Autoencode...

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12th International Symposium on Image and Signal Processing and Analysis (ISPA)

作者： Milkovic, Fran Filipovic, Branimir Subasic, Marko Petkovic, Tomislav Loncaric, Sven Budimir, Marko Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Inst Nucl Technol Zagreb Croatia

ISBN: (纸本)9781665426398

Analysis of ultrasonic testing (UT) data is a time-consuming assignment. In order to make it less demanding we propose an approach based on a variational autoencoder (VAE) to filter out the scans without anomalies/defects and in doing so, partially automate the procedure. The implemented approach uses an additional encoder network allowing to encode the reconstructed images. The differences in encodings of input and reconstructed images have shown to be good indicators of anomalous data. Anomaly detection results surpass the results of other VAE based anomaly criteria.

关键词： ultrasonic testing anomaly detection variational autoencoder deep learning computer vision

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Representing particles' motion patterns in microfluidic imaging platform using deep variational embeddings

JOURNAL OF PHYSICS-PHOTONICS

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JOURNAL OF PHYSICS-PHOTONICS 2025年第3期7卷 035005-035005页

作者： Hong, Tianqi Peng, Meimei Smieja, Marek Fang, Qiyin McMaster Univ Sch Biomed Engn Hamilton ON Canada McMaster Univ Dept Pathol & Mol Med Hamilton ON Canada McMaster Univ Dept Engn Phys Hamilton ON Canada

Understanding the motion properties of cells or particles is important in microfluidic imaging applications. Motion-related analysis has proven to be a valuable tool for phenotyping particulates in biological samples. However, relying solely on trajectory features from individual cells may not always be sufficient to describe their overall motion patterns. This highlights the need for a more effective solution focusing on rotational components in movement. In this study, we developed a generalized motion pattern representation framework using deep variational embeddings to characterize biological samples with different morphology. First, we build a simplified optical setup with sufficient throughput to record sequential frames of cells containing orientational changes. Then, a self-supervised learning pipeline was developed to embed its motion pattern into a latent space. The latent variables are visualized as the generalized motion pattern to represent a sequence of consecutive frames. Finally, segment key frames of individual cells' motion to divide a motion trajectory into consecutive sub-trajectories. Each sub-trajectory has a predefined specific meaning to be collected for downstream motion-related analysis. Our framework has been verified with two cell types with common shapes: plate-like erythrocytes and rod-like yeasts. The results demonstrate that the motion pattern representation is distinct and interpretable for these two samples. Utilized in a motion segmentation application, the represented motion achieved over 90% accuracy with unsupervised clustering, which has significantly enhanced relevant motion analysis. These promising findings underscore the practical value of our developed framework in extracting informative motion patterns for phenotyping.

关键词： motion pattern representation microfluidic imaging variational autoencoder gated recurrent units

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PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2022年第191期19卷 20220094-20220094页

作者： Semenova, Elizaveta Xu, Yidan Howes, Adam Rashid, Theo Bhatt, Samir Mishra, Swapnil Flaxman, Seth Univ Oxford Oxford Oxon England Univ Michigan Ann Arbor MI USA Imperial Coll London London England Univ Copenhagen Copenhagen Denmark

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.

关键词： spatial modelling Gaussian process prior small-area estimation Bayesian inference variational autoencoder

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