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2nd Asia Conference on Algorithms, Computing and Machine Learning (CACML)

作者： Gao, Dazheng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9781450399449

In the era of Big Data, more and more IoT devices are generating huge amounts of high-dimensional, real-time and dynamic data streams. As a result, there is a growing interest in how to cluster this data effectively and efficiently. Although a number of popular two-stage data stream clustering algorithms have been proposed, these algorithms still have some problems that are difficult to solve in the face of real-world data streams: poor handling of high-dimensional data streams and difficulty in effective dimensionality reduction;a slow clustering process that makes it difficult to meet real-time requirements;and too many manually defined parameters that make it difficult to cope with evolving data streams. This paper proposes an autoencoder-based fast online clustering algorithm for evolving data stream(AFOCEDS). The algorithm uses a stacked denoising autoencoder to reduce the dimensionality of the data, a multi-threaded approach to improve response speed, and a mechanism to automatically update parameters to cope with evolving data streams. The experiments on several realistic data streams show that AFOCEDS outperforms other algorithms in terms of effectiveness and speed.

关键词： evolving data stream cluster autoencoder

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Graph Regularized autoencoder and its Application in Unsupervised Anomaly Detection

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第8期44卷 4110-4124页

作者： Ahmed, Imtiaz Galoppo, Travis Hu, Xia Ding, Yu Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA BAE Syst Inc Charlotte NC 28277 USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples. Inspired by the success of geodesic distance approximators such as ISOMAP, we propose to use a minimum spanning tree (MST), a graph-based algorithm, to approximate the local neighborhood structure and generate structure-preserving distances among data points. We use this MST-based distance metric to replace the euclidean distance metric in the embedding function of autoencoders and develop a new graph regularized autoencoder, which outperforms a wide range of alternative methods over 20 benchmark anomaly detection datasets. We further incorporate the MST regularizer into two generative adversarial networks and find that using the MST regularizer improves the performance of anomaly detection substantially for both generative adversarial networks. We also test our MST regularized autoencoder on two datasets in a clustering application and witness its superior performance as well.

关键词： Manifolds Anomaly detection Neural networks Laplace equations Dimensionality reduction Decoding Measurement autoencoder clustering minimum spanning tree nonlinear embedding unsupervised learning

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Graph autoencoder-based unsupervised outlier detection

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INFORMATION SCIENCES 2022年 608卷 532-550页

作者： Du, Xusheng Yu, Jiong Chu, Zheng Jin, Lina Chen, Jiaying Xinjiang Univ Sch Informat Sci & Engn Urumqi 830046 Peoples R China

Outlier detection technologies play an important role in various application domains. Most existing outlier detection algorithms have difficulty detecting outliers that are mixed within normal object regions or around dense clusters. To address this problem, we propose a novel graph neural network structure called the graph autoencoder (GAE), which is capable of handling the task of outlier detection in Euclidean structured data. The GAE can perform feature value propagation in the form of a neural network that changes the distribution pattern of the original dataset, which can accurately detect outliers with low deviation. This method first converts the Euclidean structured dataset into a graph using the graph generation module, then inputs the dataset together with its corresponding graph into the GAE for training, and finally determines the top-n objects that are difficult to reconstruct in the output layer of the GAE as outliers. The results of comparing eight state-of-the-art algorithms on eight real-world datasets showed that GAE achieved the highest area under the receiver operating characteristic curve (ROC AUC) on six datasets. By comparing GAE with the autoencoder-based outlier detection algorithm, it was discovered that the proposed method improved the AUC by 16.9% on average for eight datasets. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Outlier detection Anomaly detection autoencoder Graph neural networks Deep learning

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Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network

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NEUROCOMPUTING 2022年第0期488卷 618-633页

作者： Amini, Nima Zhu, Qinqin Univ Waterloo Dept Chem Engn Waterloo ON N2L 3G1 Canada

The capability of deep learning (DL) techniques for dealing with non-linear, dynamic and correlated data has paved the way for DL-based fault detection and diagnosis (FDD). Among them, autoencoders (AEs) have shown their potential to serve as the fault detection network. However, misclassifying faulty samples that share similar patterns to normal samples is a common drawback of AEs. In this work, a sourceaware autoencoder (SAAE) is proposed as an extension of AEs to incorporate faulty samples in the training stage. In SAAE, flexibility in tuning recall and precision trade-off, ability to detect unseen faults and applicability in imbalanced data sets are achieved. Bidirectional long short-term memory (BiLSTM) with skip connections SAAE is designed as the structure of the fault detection network. Further, a deep network with BiLSTM and residual neural network (ResNet) is proposed for the subsequent fault diagnosis step to avoid randomness imposed by the order of the input features. A framework for combining fault detection and fault diagnosis networks is also presented without the assumption of having a perfect fault detection network. A comprehensive comparison among relevant existing techniques in the literature and SAAE-ResNet is also conducted on the Tennessee-Eastman process, which shows the superiority of the proposed FDD method. (C) 2021 Elsevier B.V. All rights reserved.

关键词： autoencoder Deep learning Fault detection and diagnosis Anomaly detection Tennessee eastman process

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Ball bearing multiple failure diagnosis using feature-selected autoencoder model

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第7-8期120卷 4803-4819页

作者： Cheng, Ren-Chi Chen, Kuo-Shen Natl Cheng Kung Univ Dept Mech Engn Tainan Taiwan

Recently, with the advance in information technology, pure data-driven approaches such as machine learnings have been widely applied in status diagnosis. However, the accuracy of those predictions strongly relies on the original data, which largely depends on the selected sensors and signal features. Furthermore, for unsupervised machine learning schemes, although it could avoid the concern of labeling in training, it lacks a quantified evaluation of the prediction results. These concerns significantly limit the effectiveness of modern machine learning and thus should be investigated. Meanwhile, ball bearings are fundamental key machine elements in rotating machinery and their condition monitoring should be critical for both quality control and longevity assessment. In this paper, by utilizing ball bearing failure diagnosis as the main theme, the flow of feature selection and evaluation, as well as the evaluation flow for multiple failure diagnosis, is developed for accessing the status of bearings in their imbalance, lubrication, and grease contamination levels based on unsupervised machine learning. The experimental results indicated that with proper feature selection, the failure identification could be more definite. Finally, a novel model based on the second norm to quantify the classification level of each cluster in hyperspace is proposed as the measure for unsupervised machine learning as the basis for performance evaluation and optimization of unsupervised machine learning schemes and should benefit related machine reliability evaluation studies and applications.

关键词： Ball bearing Unsupervised machine learning Feature extraction autoencoder Status diagnosis

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Monocular depth estimation with multi-view attention autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 33759-33770页

作者： Jung, Geunho Yoon, Sang Min Kookmin Univ Coll Comp Sci HCI Lab 77 Jeongneung Ro Seoul 02707 South Korea

Depth map estimation from a single RGB image is a fundamental computer vision and image processing task for various applications. Deep learning based depth map estimation has improved prediction accuracy compared with traditional approaches by learning huge numbers of RGB-D images, but challenging issues remain for distorted and blurry reconstruction in object boundaries because the features are not enforced during training. This paper presents a multi-view attention autoencoder embedded in a deep neural network to emphasize self-representative features, which provide robust depth maps by simultaneously accentuating useful features and reducing redundant features to improve depth map estimation performance. Qualitative and quantitative experiments were conducted to verify the proposed network effectiveness, which can be utilized for three-dimensional scene reconstruction and understanding.

关键词： Depth estimation autoencoder Attention module

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Denoised Internal Models:A Brain-inspired autoencoder Against Adversarial Attacks

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Machine Intelligence Research 2022年第5期19卷 456-471页

作者： Kai-Yuan Liu Xing-Yu Li Yu-Rui Lai Hang Su Jia-Chen Wang Chun-Xu Guo Hong Xie Ji-Song Guan Yi Zhou School of Life Sciences and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201602China School of Life Sciences Tsinghua UniversityBeijing 100084China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-intelligence Nanophotonics School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this *** the pipeline in the human brain for visual signal processing,DIM adopts a two-stage *** the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the *** by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each *** evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

关键词： Brain-inspired learning autoencoder robustness adversarial attack generative model

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Deep Attributed Network Embedding via Weisfeiler-Lehman and autoencoder

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IEEE ACCESS 2022年 10卷 61342-61353页

作者： Al-Furas, Amr Thabit Alrahmawy, Mohammed F. Al-Adrousy, Waleed Mohamed Elmougy, Samir Ibb Univ Comp Sci Dept Ibb Yemen Mansoura Univ Fac Comp & Informat Comp Sci Dept Mansoura 35516 Egypt

Network embedding plays a critical role in many applications. Node classification, link prediction, and network visualization are examples of such applications. Attributed network embedding aims to learn the low-dimensional representation of network nodes by integrating network architecture and attribute information. The network architectures of many real-world applications are complex, and the relations between network architectures and their attributed nodes are opaque. Thus, shallow models fail to capture deep nonlinear information when an attributed network is embedded, leading to unreliable embedding. In the present paper, a Deep Attributed Network Embedding via Weisfeiler-Lehman and autoencoder (DANE-WLA) is proposed in order to capture high nonlinearity and preserve the many proximities in the network attribute information of nodes and structures. Weisfeiler-Lehman proximity schema was used to capture the node dependency between both node edges and node attributes based on information sequences. Then, a deep autoencoder was applied to invest complex nonlinear information. Extensive experiments were conducted on benchmark datasets to verify that DANE-WLA is computationally efficient for various tasks requiring network embedding. The experimental results show that our model outperforms the state-of-the-art network embedding models.

关键词： Task analysis Kernel Deep learning Network architecture Time complexity Social networking (online) Neural networks Attributed network embedding autoencoder link prediction node classification visualization Weisfeiler-Lehman proximity schema

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Wafer Bin Map Recognition With autoencoder-Based Data Augmentation in Semiconductor Assembly Process

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第2期35卷 198-209页

作者： Shen, Po-Cheng Lee, Chia-Yen Natl Cheng Kung Univ Inst Mfg Informat & Syst Tainan 70101 Taiwan Natl Taiwan Univ Dept Informat Management Taipei 10617 Taiwan

Semiconductor manufacturers use the wafer bin map recognition (WBMR) system to identify failure modes in processing. This study proposes an WBMR system embedded with three modules: data preprocessing, region classification, and systematic pattern recognition. After using a revised Jaccard index to separate random patterns from systematic patterns, we compare three data augmentation techniques, particularly autoencoder-based, to find the best augmented method that addresses any data imbalance problems between the defect classes. We propose an adaptive algorithm to determine the amount of generated data. We describe the two tools, t-distributed stochastic neighbor embedding (t-SNE) and earth mover's distances (EMD) we use to quantify and visualize the information content of the augmented dataset. Finally, we use an inception architecture of convolutional neural network (CNN) to improve the WBMR system's recognition accuracy. An empirical study of the semiconductor assembly manufacturer and a public dataset validate that our proposed WBMR system effectively recognizes different types of defective patterns.

关键词： Systematics Costs Cost function Pattern recognition Convolutional neural networks Semiconductor device modeling Production Semiconductor manufacturing wafer bin map recognition autoencoder data imbalance deep learning

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Automated Grading of Breast Cancer Histopathology Images Using Multilayered autoencoder

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Computers, Materials & Continua 2022年第5期71卷 3407-3423页

作者： Shakra Mehak M.Usman Ashraf Rabia Zafar Ahmed M.Alghamdi Ahmed S.Alfakeeh Fawaz Alassery Habib Hamam Muhammad Shafiq Knowledge Unit of System and Technology University of Management&TechnologySialkot Campus51310Pakistan Department of Computer Science University of Management and TechnologySialkot Campus51310Pakistan Faculty of Computing and Technology University of Engineering&TechnologyNarowal51610Pakistan College of Computer Science and Engineering University of Jeddah21493Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Breast cancer(BC)is the most widely recognized cancer in women *** 2018,627,000 women had died of breast cancer(World Health Organization Report 2018).To diagnose BC,the evaluation of tumours is achieved by analysis of histological *** present,the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC *** contemplate three elements,*** count,*** formation,and *** atypia,which is a laborious process that witness’s variations in expert’s ***,some algorithms have been proposed for the detection of mitotic cells,but nuclear atypia in breast cancer histopathology has not received much *** atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast,non-invasive breast(usual ductal hyperplasia,atypical ductal hyperplasia)and pre-invasive breast(ductal carcinoma in situ)and invasive breast *** proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification *** classification results show the value of the multilayer autoencoder model in the evaluation of nuclear *** proposed method has indicated promising results,making them more fit in breast cancer grading.

关键词： Breast cancer nuclear atypia autoencoder

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