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Image Clustering: Utilizing Teacher-Student Model and autoencoder

IEEE ACCESS 2023年 11卷 104846-104857页

作者： Fei, Zhengshun Gong, Haibo Guo, Junhao Wang, Jinglong Jin, Wuyin Xiang, Xinjian Ding, Xiasheng Zhang, Ni Lanzhou Univ Technol Sch Mech & Elect Engn Lanzhou 730050 Peoples R China Zhejiang Univ Sci & Technol Prov Key Inst Robot Sch Automat & Elect Engn Hangzhou 310023 Peoples R China Zhejiang Chenlong Sawing Machine Co Ltd Lishui 321404 Peoples R China

Complete and high-quality labeled dataset is indispensable for image classification. Considering the often arduous task of data labeling, clustering algorithms are commonly utilized in the preliminary stages of data classification to perform preliminary categorization. Traditional clustering algorithms, such as K-means and Gaussian Mixture Model, often struggle to effectively cluster images. In this paper, we propose a novel image clustering method utilizing teacher-student model and autoencoder. Specifically, the teacher model is a fully connected autoencoder and the student model consists of multiple convolutional autoencoders. We firstly obtain sub-datasets by applying the K-means algorithm to cluster the output of the teacher model, then utilize the student model to achieve clustering through an iterative data exchange method where the same data can converge. In addition, the proposed method can filter low-quality data to a certain extent by recording the frequency of exchanges because clustering is achieved during multiple exchanges of data. Experiments on a synthetic dataset and three benchmark image datasets (MNIST, FashionMNIST and USPS) show that our method can achieve satisfactory clustering results.

关键词： Image clustering Teacher-student model autoencoder

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Physical Layer Key Generation Scheme for MIMO System Based on Feature Fusion autoencoder

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IEEE INTERNET OF THINGS JOURNAL 2023年第16期10卷 14886-14895页

作者： Chen, Yanru Chen, Zhengyu Zhang, Yuanyuan Luo, Zhiyuan Li, Yang Xing, Bin Guo, Bing Chen, Liangyin Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Inst Ind Internet Res Chengdu 610065 Peoples R China Inst Southwestern Commun Sci & Technol Secur Commun Lab Chengdu 610041 Peoples R China Ind Big Data Applicat Technol Natl Engn Lab Beijing 100040 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

Recently, the use of wireless channel state information (CSI) to generate encryption keys in the physical layer has gained significant attention from researchers. Unlike classical cryptography, this approach relies on the variability of the wireless channel, channel reciprocity, and spatial decorrelation to ensure security, making it more lightweight and providing strong randomness. This article proposes a physical layer key generation scheme for wireless LAN MIMO systems based on feature fusion autoencoder (FFAEncoder) to address the issue of a high key disagreement rate (KDR). Our approach involves extracting amplitude and phase features separately, fusing them through multiplication operator in a neural network, and using an autoencoder to extract common features. The proposed scheme was evaluated on multiple data sets in different real-world scenarios, and it was found that the transmitter and receiver codeword's mean squared error (MSE) and mean absolute error (MAE) were smaller than those of the current models, indicating better key generation performance. Additionally, the proposed scheme's KDR was smaller, with a decay rate faster than that of the other two models in the same environment, and the primary key bits were 1/2 and 1/3 that of the other models, respectively, as the signal-to-noise ratio (SNR) increased.

关键词： autoencoder channel reciprocity feature fusion MIMO physical layer key generation

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Cross-Domain Translation Learning Method Utilizing autoencoder Pre-Training for Super-Resolution of Radar Sparse Sensor Arrays

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IEEE ACCESS 2023年 11卷 61773-61785页

作者： Kim, Jong-Hoon Hong, Ic-Pyo Kongju Natl Univ Smart Nat Space Res Ctr Cheonan 31080 South Korea Kongju Natl Univ Dept Smart Informat Technol Engn Cheonan 31080 South Korea

This study proposes a convolutional neural network based image translation framework to faithfully realize the super-resolution of radar sensor sparse measurement. We exploited the autoencoder model able to learn a latent representation of input domain data via the iteration of encoding and decoding processes. Based on the belief that the low resolution data retains an essential measurement to fully represent the sparse information of the high resolution counterpart, we assumed that input (source domain of low-resolution denoted by X) and output (target domain of high-resolution denoted by Y) share a common latent code. On the other hand, from the observation that the recovery quality of Y-to-Y translation is far more favorable than the outcome from X-to-Y translation, we performed the pre-training for finding the latent code (z(y)) of the Y domain data, and subsequently the acquired z(y) serving as a label for predicting the latent code (z(x)) of X domain data. As a result, in our proposed network, encoder and decoder parts were trained with separate minimization strategies relative to respective loss functions. For the sake of verifying the effect of the pre-training scheme, we introduced hyper-parameters, alpha and beta, to regulate the training rate for the encoder and decoder parts respectively. As metrics for quantitative assessment, detection rate (DR), false alarm rate (FAR) and ROC curve were utilized. Our network set with 0.9 of alpha and 0.1 of beta showed a higher ROC curve plot across the whole range of DR and FAR than a basic autoencoder. DRs of the proposed network and comparative model were respectively 92.23% and 91.00% at FAR of 2%.

关键词： Super-resolution radar sensor arrays sparse measurement autoencoder pre-training latent representation image-to-image translation

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GAAE: a novel genetic algorithm based on autoencoder with ensemble classifiers for imbalanced healthcare data

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JOURNAL OF SUPERCOMPUTING 2023年第1期79卷 541-572页

作者： Ram, Pintu Kumar Kuila, Pratyay Natl Inst Technol Dept Comp Sci & Engn Sikkim 737139 India

With the advancement of artificial intelligence (AI) and machine learning techniques, the diseases can be early diagnosed and detected with the help of known healthcare data of similar diseases. However, the analysis of healthcare data becomes challenging due to the imbalance behavior of such data. Such behavior has a skewed nature and the samples of imbalanced dataset contain the majority and minority instances. The quantity of majority instances is much higher than that of the minority instances. Thus, the classifier always gets biased toward the majority instances while classifying an unknown instance. This is not a perfect deal by any of the classifiers especially for disease diagnosis. Therefore, it is important to balance the dataset by considering both the majority and minority class samples. In this context, Genetic Algorithm based on autoencoder (GAAE) model is proposed to process the imbalance data. Initially, an autoencoder is trained by the help of genetic operators and both the majority and minority samples. The chromosome is efficiently designed to represent an autoencoder. The error function is designed as the fitness function with the help of ensemble of classifiers. The optimized autoencoder generates the synthetic data of minority class to balance the data. Once the imbalanced data is balanced using the proposed GAAE, then the feature selection is done based on the correlation coefficient. Then various classifiers, multi-layer perceptron (MLP), k-nearest neighbor (k-NN), C4.5 decision tree (DT) and random forest (RF) are employed for the data classification. An extensive simulation is performed on the proposed approach and comparison is done with the existing approaches. It is observed that consideration of both the majority and minority samples to generate the synthetic data helps the classifiers to perform better. In addition, statistical analysis has also been performed.

关键词： Imbalanced data GAAE autoencoder Ensemble classifier Feature selection

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection autoencoder Reconstruction error Machine learning

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Variational autoencoder for synthetic insurance data

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INTELLIGENT SYSTEMS WITH APPLICATIONS 2024年 24卷

作者： Jamotton, Charlotte Hainaut, Donatien Univ Catholique Louvain UCLouvain Inst Stat Biostat & Actuarial Sci ISBA Voie Roman Pays 20-L1 04-01 B-1348 Louvain La Neuve Belgium

This article explores the application of Variational autoencoders (VAEs) to insurance data. Previous research has demonstrated the successful implementation of generative models, especially VAEs, across various domains, such as image recognition, text classification, and recommender systems. However, their application to insurance data, particularly to heterogeneous insurance portfolios with mixed continuous and discrete attributes, remains unexplored. This study introduces novel insights into utilising VAEs for unsupervised learning tasks in actuarial science, including dimension reduction and synthetic data generation. We propose a VAE model with a quantile transformation for continuous (latent) variables, a reconstruction loss that combines categorical cross-entropy and mean squared error, and a KL divergence-based regularisation term. Our VAE model's architecture circumvents the need to pre-train and fine-tune a neural network to encode categorical variables into n-dimensional representative vectors within a continuous vector space of dimension R n . We assess our VAE's ability to reconstruct complex insurance data and generate synthetic insurance policies using a motor portfolio. Our experimental results and analysis highlight the potential of VAEs in addressing challenges related to data availability in the insurance industry.

关键词： autoencoder Variational inference Synthetic data generation Heterogeneous insurance data Dimension reduction

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Evolving Domain Generalization via Latent Structure-Aware Sequential autoencoder

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 14514-14527页

作者： Qin, Tiexin Wang, Shiqi Li, Haoliang City Univ Hong Kong Dept Elect & Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Comp Sci Kowloon Hong Kong Peoples R China

Domain generalization (DG) refers to the problem of generalizing machine learning systems to out-of-distribution (OOD) data with knowledge learned from several provided source domains. Most prior works confine themselves to stationary and discrete environments to tackle such generalization issue arising fromOODdata. However, in practice, manytasks in non-stationary environments (e.g., autonomous-driving car system, sensor measurement) involve more complex and continuously evolving domain drift, emerging new challenges for model deployment. In this paper, we first formulate this setting as the problem of evolving domain generalization. To deal with the continuously changing domains, we propose MMD-LSAE, a novel framework that learns to capture the evolving patterns among domains for better generalization. Specifically, MMD-LSAE characterizes OOD data in non-stationary environments with two types of distribution shifts: covariate shift and concept shift, and employs deep autoencoder modules to infer their dynamics in latent space separately. In these modules, the inferred posterior distributions of latent codes are optimized to align with their corresponding prior distributions via minimizing maximum mean discrepancy (MMD). We theoretically verify that MMD-LSAE has the inherent capability to implicitly facilitate mutual information maximization, which can promote superior representation learning and improved generalization of themodel. Furthermore, the experimental results on both synthetic and real-world datasets show that our proposed approach can consistently achieve favorable performance based on the evolving domain generalization setting.

关键词： Domain generalization non-stationary environments autoencoder kernel methods

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Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder

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INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023年第3期58卷 1284-1298页

作者： Munyendo, Leah Njoroge, Daniel Zhang, Yanyan Hitzmann, Bernd Univ Hohenheim Dept Proc Analyt & Cereal Sci Garbenstr 23 D-70599 Stuttgart Germany Dedan Kimathi Univ Technol Inst Food Bioresources Technol Dedan Kimathi 10143 Nyeri Kenya Univ Hohenheim Dept Flavor Chem Fruwirthstr 12 D-70599 Stuttgart Germany

Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta-adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.

关键词： adulteration autoencoder chicory coffee geographical origin NIR spectroscopy

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Efficient Blind Source Separation Method for fMRI Using autoencoder and Spatiotemporal Sparsity Constraints

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IEEE ACCESS 2023年 11卷 50364-50381页

作者： Khalid, Muhammad Usman Khawaja, Bilal A. Nauman, Malik Muhammad Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh 11564 Saudi Arabia Islamic Univ Madinah Fac Engn Dept Elect Engn Madinah 42351 Saudi Arabia Univ Brunei Darussalam Fac Integrated Technol Bandar Seri Begawan BE1410 Brunei

Diversity measures exploited by blind source separation (BSS) methods are usually based on either statistical attributes/geometrical structures or sparse/overcomplete (underdetermined) representations of the signals. This leads to some inefficient BSS methods that are derived from either a mixing matrix (mm), sparse weight vectors (sw), or sparse code (sc). In contrast, the proposed efficient method, sparse spatiotemporal BSS (ssBSS), avoids computational complications associated with lag sets, deflation strategy, and repeated error matrix computation using the whole dataset. It solves the spatiotemporal data reconstruction model (STEM) with l(1)-norm penalization and l(0)-norm constraints using Neumann's alternating projection lemma and block coordinate descent approach to yield the desired bases. Its specific solution allows incorporating a three-step autoencoder and univariate soft thresholding for a block update of the source/mixing matrices. Due to the utilization of both spatial and temporal information, it can better distinguish between the sources and yield interpretable results. These steps also make ssBSS unique because, to the best of my knowledge, no mixing matrix based BSS method incorporates sparsity of both features and a multilayer network structure. The proposed method is validated using synthetic and various functional magnetic resonance imaging (fMRI) datasets. Results reveal the superior performance of the proposed ssBSS method compared to the existing methods based on mmBSS and swBSS. Specifically, overall, a 14% increase in the mean correlation value and 91% reduction in computation time over the ssICA algorithm was discovered.

关键词： Sparse representation autoencoder BSS fMRI activation maps PCA l(0)-norm l(1)-norm

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ARCADE: Adversarially Regularized Convolutional autoencoder for Network Anomaly Detection

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第2期20卷 1305-1318页

作者： Lunardi, Willian Tessaro Lopez, Martin Andreoni Giacalone, Jean-Pierre Technol & Innovat Inst Secure Syst Res Ctr Abu Dhabi U Arab Emirates

As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy risks. Anomaly-based Intrusion Detection Systems (IDSs) play an essential role in network security. In this paper, we present a practical unsupervised anomaly-based deep learning detection system called ARCADE (Adversarially Regularized Convolutional autoencoder for unsupervised network anomaly DEtection). With a convolutional autoencoder (AE), ARCADE automatically builds a profile of the normal traffic using a subset of raw bytes of a few initial packets of network flows so that potential network anomalies and intrusions can be efficiently detected before they cause more damage to the network. ARCADE is trained exclusively on normal traffic. An adversarial training strategy is proposed to regularize and decrease the AE's capabilities to reconstruct network flows that are out-of-the-normal distribution, thereby improving its anomaly detection capabilities. The proposed approach is more effective than state-of-the-art deep learning approaches for network anomaly detection. Even when examining only two initial packets of a network flow, ARCADE can effectively detect malware infection and network attacks. ARCADE presents 20 times fewer parameters than baselines, achieving significantly faster detection speed and reaction time.

关键词： Unsupervised anomaly detection autoencoder generative adversarial networks automatic feature extraction deep learning cybersecurity

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