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Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling

INFORMATION SCIENCES 2021年 568卷 403-426页

作者： Lee, Taek-Ho Lee, Junghye Jun, Chi-Hyuck Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn 77 Cheongam Ro Pohang 37673 South Korea Ulsan Natl Inst Sci & Technol UNIST Dept Ind Engn 50 UNIST Gil Ulsan 44919 South Korea

Sharing electronic health record data is essential for advanced analysis, but may put sensitive information at risk. Several studies have attempted to address this risk using contextual embedding, but with many hospitals involved, they are often inefficient and inflexible. Thus, we propose a bilingual autoencoder-based model to harmonize local embeddings in different spaces. Cross-hospital reconstruction of embeddings makes encoders map embeddings from hospitals to a shared space and align them spontaneously. We also suggest two-phase training to prevent distortion of embeddings during harmonization with hospitals that have biased information. In experiments, we used medical event sequences from the Medical Information Mart for Intensive Care-III dataset and simulated the situation of multiple hospitals. For evaluation, we measured the alignment of events from different hospitals and the prediction accuracy of a patient's diagnosis in the next admission in three scenarios in which local embeddings do not work. The proposed method efficiently harmonizes embeddings in different spaces, increases prediction accuracy, and gives flexibility to include new hospitals, so is superior to previous methods in most cases. It will be useful in predictive tasks to utilize distributed data while preserving private information. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BYNC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Distributed EHR Contextual embedding Space alignment autoencoder Predictive tasks

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Anomaly detection for hydropower turbine unit based on variational modal decomposition and deep autoencoder

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ENERGY REPORTS 2021年 7卷 938-946页

作者： Wang, Hongteng Liu, Xuewei Ma, Liyong Zhang, Yong Huadian Elect Power Res Inst Co LTD Hangzhou 310030 Peoples R China Harbin Inst Technol Sch Informat Sci & Engn Weihai 264209 Peoples R China Harbin Inst Technol Sch Ocean Engn Weihai 264209 Peoples R China

Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed. VMD is employed to the data collected by multiple sensors to obtain the sub signal of each data. These sub signals in each time-period constitute two-dimensional data. The autoencoder based on convolutional neural network is used to complete unsupervised learning, and the reconstruction residual of autoencoder is used for anomaly detection. The experimental results show that the deep autoencoder can increase the interval between abnormal and normal data distribution, and VMD can effectively reduce the number of samples in the overlapping area. Compared with traditional autoencoder method, the proposed method improves the recall, precision and F1 scores by 0.140, 0.205 and 0.175, respectively. The proposed method achieves better anomaly detection performance than other methods. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Hydropower turbine Anomaly detection autoencoder Variational mode decomposition

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Single-board device individual authentication based on hardware performance and autoencoder transformer models

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COMPUTERS & SECURITY 2024年 137卷

作者： Sanchez, Pedro Miguel Celdran, Alberto Huertas Bovet, Gerome Perez, Gregorio Martinez Univ Murcia Dept Informat & Commun Engn Murcia 30100 Spain Univ Zurich UZH Dept Informat IfI Commun Syst Grp CSG CH-8050 Zurich Switzerland Armasuisse Sci & Technol Cyber Def Campus CH-3602 Thun Switzerland

The proliferation of the Internet of Things (IoT) has led to the emergence of crowdsensing applications, where a multitude of interconnected devices collaboratively collect and analyze data. Ensuring the authenticity and integrity of the data collected by these devices is crucial for reliable decision-making and maintaining trust in the system. Traditional authentication methods are often vulnerable to attacks or can be easily duplicated, posing challenges to securing crowdsensing applications. Besides, current solutions leveraging device behavior are mostly focused on device identification, which is a simpler task than authentication. To address these issues, an individual IoT device authentication framework based on hardware behavior fingerprinting and Transformer autoencoders is proposed in this work. To support the design, a threat model details the security problems faced when performing hardware-based authentication in IoT. This solution leverages the inherent imperfections and variations in IoT device hardware to differentiate between devices with identical specifications. By monitoring and analyzing the behavior of key hardware components, such as the CPU, GPU, RAM, and Storage on devices, unique fingerprints for each device are created. The performance samples are considered as time series data and used to train outlier detection transformer models, one per device and aiming to model its normal data distribution. Then, the framework is validated within a spectrum crowdsensing system leveraging Raspberry Pi devices. After a pool of experiments, the model from each device is able to individually authenticate it between the 45 devices employed for validation. An average True Positive Rate (TPR) of 0.74 +/- 0.13 and an average maximum False Positive Rate (FPR) of 0.06 +/- 0.09 demonstrate the effectiveness of this approach in enhancing authentication, security, and trust in crowdsensing applications.

关键词： Device behavior fingerprinting Device authentication Transformer Behavioral data Hardware fingerprinting autoencoder

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Improved autoencoder With LSTM Module and KL Divergence for Anomaly Detection

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Huang, Wei Zhang, Bingyang Zhang, Kaituo Gao, Hua Wan, Rongchun Zhejiang Univ Technol Coll Comp Sci Hangzhou 310023 Peoples R China Zhejiang HOUDAR Intelligent Technol Co Ltd Hangzhou 310023 Peoples R China

The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional autoencoder (CAE) and deep support vector data description (SVDD) have been universally used and have demonstrated significant success in detecting anomalies. However, the over-reconstruction ability of CAE network for anomalous data can easily lead to high false-negative rate in detecting anomalous data. On the other hand, the deep support vector data description (Deep SVDD) model has the drawback of feature collapse, which leads to a decrease in detection accuracy for anomalies. To address these problems, we propose the Improved autoencoder with LSTM module and Kullback-Leibler divergence (IAE-LSTM-KL) model in this article. An LSTM network is added after the encoder to memorize feature representations of normal data. Meanwhile, the phenomenon of feature collapse can also be mitigated by penalizing the featured input to SVDD module via KL divergence. The efficacy of the IAE-LSTM-KL model is validated through experiments on both synthetic and real-world datasets. Experimental results show that IAE-LSTM-KL model yields higher detection accuracy for anomalies. In addition, it is also found that the IAE-LSTM-KL model demonstrates enhanced robustness to contaminated outliers in the dataset.

关键词： Data models Long short term memory Vectors Anomaly detection Feature extraction Training Mathematical models autoencoder deep support vector data description (Deep SVDD) hypersphere collapse LSTM

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Missing data recovery using autoencoder for multi-channel acoustic scene classification 30

Missing data recovery using autoencoder for multi-channel ac...

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30th European Signal Processing Conference (EUSIPCO)

作者： Shiroma, Yuki Kinoshita, Yuma Imoto, Keisuke Shiota, Sayaka Ono, Nobutaka Kiya, Hitoshi Tokyo Metropolitan Univ Tokyo Japan Doshisha Univ Kyoto Japan

ISBN: (纸本)9789082797091

In this paper, we propose a method of missing data recovery using an autoencoder for multi-channel signals. Recently, many deep neural network-based classification methods using multi-channel signals have been proposed. The advantage of using multi-channel signals is that both frequency and spatial information can be used. However, systems using such signals are vulnerable to missing data because of a mismatch between the training and testing data. To minimize the mismatch, some techniques that include simulated missing data to the training data have been proposed. However, it is difficult to prepare missing data covering all possible mismatch situations. Therefore, we focus on using an autoencoder to recover the missing data without any assumptions of missing situations. In the case of multi-channel data inputted into an autoencoder, channel relationships are compressed into a low-dimensional hidden layer. Then, the autoencoder outputs data to reconstruct the input data from the layer. Therefore, when multi-channel input into the autoencoder has some missing channels, the output of the autoencoder is expected to recover some missing channel information by using the hidden layer. Since the autoencoder is regarded as a preprocessing of acoustic tasks, we evaluated the proposed method using an acoustic classification task. From the experimental results, we confirmed that the proposed method can recover missing data and improve the classification performance.

关键词： Missing data recovery autoencoder Multi-channel signals

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Internet Routing Anomaly Detection Using LSTM Based autoencoder 2

Internet Routing Anomaly Detection Using LSTM Based Autoenco...

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2nd International Conference on Computer Science and Software Engineering (CSASE)

作者： Muosa, Ali Hassan Ali, A. H. Univ Thi Qar Fac Comp Sci & Math Dept Comp Sci Thi Qar Iraq Univ Kufa Dept Elect & Commun Najaf Iraq

ISBN: (纸本)9781665426329

The internet is formed by thousands of interconnected Autonomous Systems (ASes). The Border Gateway Protocol (BGP) exchanges routing information between autonomous domains. Anomalies in BGP are exceptional (misconfiguration, outage, and attacks). When they happen, the consequences can be widely hurtful. Anomaly detection in the internet routing section does not follow the normal behavior as an element. It is more important than anything else to detect internet routing anomalies using the BGP update messages quickly and accurately. This paper will propose a Short-Term Long Memory-based autoencoders network (LSTM-AE) method for internet routing anomaly detection and trains itself to repeat clean datasets effectively. These memory units are a type of artificial Recurrent Neural Networks (RNN) architecture used in deep learning, are convenient for historical study time series modeling and anomaly detection using LSTM memory units instead of ordinary neurons to build the coder. Using LSTM-AE detects anomalies in 11 events to four kinds of anomalies through the data collected as time series.

关键词： BGP LSTM autoencoder Anomaly Detection Time Series Deep Learning

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Deep Convolutional autoencoder Architecture for Predictive Maintenance Applications 30

Deep Convolutional Autoencoder Architecture for Predictive M...

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30th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Catak, Yigit Sahin, Kerem Gunye, Osman Berke Ozkan, Huseyin Sabanci Univ Muhendislik & Doga Bilimleri Fak Istanbul Turkey Cozum Makina AR GE Dept Istanbul Turkey

ISBN: (纸本)9781665450928

Maintenance of the machinery is a crucial task in industrial production sectors working with machinery. The most important aspect of maintenance is timing. Executing maintenances more frequently or sparsely than the necessary amount causes separate problems resulting with unnecessary expenses or halts in the production. To prevent these problems, a smart system to decide the timing of the maintenance must be established. In this study, we develop an auto-encoder extension of previously proposed deep convolutional network that is trained successfully on the modelling of electroencephalogram (EEG) signals with high performance. The auto-encoder extracts features from the vibration signals collected from the machinery. This method allows us to synthesize multi-channel vibration data which we use to classify the type of the failure that the machinery bearing is going to face, without expert field knowledge and with a high accuracy. The performance of the proposed network is tested on the publicly available Case Western Reserve University (CWRU) bearing dataset with the classification accuracy. Proposed network showed a better classification performance, allowed smaller bottleneck feature sizes and faster training times compared to the Normalized Sparse Auto-Encoder - Locally Connected Network (NSAE-LCN), which is one of the best performing networks on the same dataset.

关键词： autoencoder Predictive Maintenance Time Series Processing Deep Learning

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AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2024年 110卷 108085页

作者： Lu, Pengli Jiang, Jicheng Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Gansu Peoples R China

Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-genedisease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model.

关键词： MiRNA-disease association autoencoder Random walk MiRNA-gene-disease heterogeneous network

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MMD-encouraging convolutional autoencoder: a novel classification algorithm for imbalanced data

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APPLIED INTELLIGENCE 2021年第10期51卷 7384-7401页

作者： Li, Bin Gong, Xiaofeng Wang, Chen Wu, Ruijuan Bian, Tong Li, Yanming Wang, Zhiyuan Luo, Ruisen Sichuan Univ Coll Elect Engn 24 South Sect 1One Ring Rd Chengdu Peoples R China Rutgers Univ New Brunswick Dept Comp Sci Piscataway NJ 08854 USA Chengdu Dagongbochuang Informat Technol Co Ltd Chengdu 610059 Peoples R China

Imbalanced data classification problem is widely existed in commercial activities and social production. It refers to the scenarios with considerable gap of sample amount among classes, thus significantly deteriorating the performance of the traditional classification algorithms. The previous dealing methods often focus on resampling and algorithm adjustment, but ignore enhancing the ability of feature learning. In this study, we have proposed a novel algorithm for imbalanced data classification: Maximum Mean Discrepancy-Encouraging Convolutional autoencoder (MMD-CAE), from the perspective of feature learning. The algorithm adopts a two-phase target training process. The cross entropy loss is employed to calculate reconstruction loss of data, and the Maximum Mean Discrepancy (MMD) with intra-variance constraint is used to stimulate the feature discrepancy in bottleneck layer. By encouraging maximization of MMD between two-class samples, and mapping the original space to a higher dimension space via kernel skills, the features can be learned to form a more effective feature space. The proposed algorithm is tested on ten groups of samples with different imbalance ratios. The performance metrics of recall rate, F1 score, G-means and AUC verify that the proposed algorithm surpasses the existing state-of-the-art methods in this field, also with stronger generalization ability. This study could shed new lights on the related studies in terms of constituting more effective feature space via the proposed MMD with intra-variance constraint method, and the holistic MMD-CAE algorithm for imbalanced data classification.

关键词： Imbalanced data autoencoder Two-phase target training Maximum mean discrepancy Inter-class distance

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Improved autoencoder for unsupervised anomaly detection

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第12期36卷 7103-7125页

作者： Cheng, Zhen Wang, Siwei Zhang, Pei Wang, Siqi Liu, Xinwang Zhu, En Natl Univ Def Technol Sch Comp 109 Deya Rd Changsha 410073 Hunan Peoples R China

Deep autoencoder-based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish anomalies from normal data. However, this assumption does not always hold in practice, especially in unsupervised anomaly detection, where the training data is anomaly contaminated. We observe that the autoencoder generalizes so well on the training data that it can reconstruct both the normal data and the anomalous data well, leading to poor anomaly detection performance. Besides, we find that anomaly detection performance is not stable when using reconstruction error as anomaly score, which is unacceptable in the unsupervised scenario. Because there are no labels to guide on selecting a proper model. To mitigate these drawbacks for autoencoder-based anomaly detection methods, we propose an Improved autoencoder for unsupervised Anomaly Detection (IAEAD). Specifically, we manipulate feature space to make normal data points closer using anomaly detection-based loss as guidance. Different from previous methods, by integrating the anomaly detection-based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion. Experiments on five image data sets empirically validate the effectiveness and stability of our method.

关键词： autoencoder deep learning unsupervised anomaly detection

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