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IEEE Conference on Energy Internet and Energy System Integration

作者： Xiying Gao Ning Ye Jinchun Song Haomiao Wang Ye Zhang Yan Guan Xiaowen Song Yingkai Cai Wenshu Zhang Qian Hui Dan Li State Grid Liaoning Electric Power Research Institute Shenyang China The School of Mechanical Engineering and Automation Northeastern University Shenyang China State Grid Liaoning Electric Power Marketing Department Shenyang China State Grid Liaoyang Electric Power B&E Office Liaoyang China

In this paper, we propose a method for performing electricity price execution inspection by using a variational autoencoder technology in deep learning. The variational autoencoder based anomaly detection algorithm (VABAD) can be used both as a discriminant model and as a feature of the generation model, which effectively solves the calculation problem of multiple heterogeneous parameters of current electricity price inspection implementation. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. It is used by autoencoder based anomaly detection methods. Experimental results show that the proposed method has been validated and compared to the existing approaches. The databases used in this paper come from Power Marketing System that occurred in Liaoning, China in 2015.

关键词： Deep learning variational autoencoder Electricity price inspection implementation Data heterogeneity

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MAVGAE: a multimodal framework for predicting asymmetric drug-drug interactions based on variational graph autoencoder

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2024年第7期28卷 1-13页

作者： Deng, Zengqian Xu, Jie Feng, Yinfei Dong, Liangcheng Zhang, Yuanyuan Qingdao Univ Technol Sch Informat & Control Engn Qingdao Peoples R China Qufu Normal Univ Sch Informat Sci & Engn Rizhao Peoples R China

Drug-drug interactions refer to the phenomena wherein the potency, duration, or effectiveness of one or multiple drugs undergo alterations of varying degrees as a result of their concurrent or sequential usage. The accurate identification of potential drug interactions plays a pivotal role in mitigating the risks associated with drug administration in patients, it also helps in minimizing the likelihood of hazardous situations arising during a patient's course of treatment. However, researchers have found that there is a problem of asymmetric drug interactions, where one drug may affect another but not vice versa. This adds to the difficulty of prediction, so in polypharmacy, the order of drug administration is critical to efficacy and safety, and few current studies predict asymmetric DDIs. Aiming at the above problems, we propose a framework based on multimodal data and a variational graph autoencoder named MAVGAE for predicting asymmetric drug interactions. The framework initially encodes multimodal data into low-dimensional representations and then utilizes a variational graph autoencoder for encoding and decoding. During the model training process, supervised learning is employed for the classification task with the incorporation of heterogeneity information, ensuring accurate prediction of drug interactions. Experimental validation on a large-scale drug dataset demonstrates the framework's high accuracy and reliability in predicting non-symmetrical drug interactions, offering effective support and guidance for drug research.

关键词： Artificial intelligence asymmetric drug-drug interaction multimodal variational autoencoder

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Statistical Speech Enhancement Based on Probabilistic Integration of variational autoencoder and Non-Negative Matrix Factorization

Statistical Speech Enhancement Based on Probabilistic Integr...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yoshiaki Bando Masato Mimura Katsutoshi Itoyama Kazuyoshi Yoshii Tatsuya Kawahara Graduate School of Informatics Kyoto University Sakyo-ku Kyoto 606-8501 Japan

ISBN: (纸本)9781538646595

This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervised approach requires a very large amount of pair data for training, it is not robust against unknown environments. Another approach is to use non-negative matrix factorization (NMF) based on basis spectra trained on clean speech in advance and those adapted to noise on the fly. This semi-supervised approach, however, causes considerable signal distortion in enhanced speech due to the unrealistic assumption that speech spectrograms are linear combinations of the basis spectra. Replacing the poor linear generative model of clean speech in NMF with a VAE-a powerful nonlinear deep generative model-trained on clean speech, we formulate a unified probabilistic generative model of noisy speech. Given noisy speech as observed data, we can sample clean speech from its posterior distribution. The proposed method outperformed the conventional DNN-based method in unseen noisy environments.

关键词： Single-channel speech enhancement variational autoencoder Bayesian signal processing Speech Speech enhancement Noise matrix decomposition NMF protocol Unknown environments factorization Signal distortion

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E-VAN : Enhanced variational autoencoder Network for Mitigating Gender Bias in Static Word Embeddings 22

E-VAN : Enhanced Variational AutoEncoder Network for Mitigat...

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Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval

作者： Swati Tyagi Jiaheng Xie Rick Andrews University Of Delaware Lerner College of Business and Economics USA

ISBN: (纸本)9781450397629

Recent research has shown that pre-trained context-independent word embeddings display biases such as racial bias, gender bias, etc. Using a novel, tunable algorithm, this study attempts to mitigate the hidden gender bias in static embeddings. In order to train the model, an enhanced variational autoencoder (E-VAN) is used to learn the latent space of the embedding. Then the latent distributions are used while adaptively resampling and re-weighting the rare/under-represented data. While the word embeddings retain semantic information, E-VAN effectively mitigates unwanted biased gendered associations. Our method E-VAN outperforms previous state-of-the-art methods in both quantitative and human evaluation.

关键词： Discriminator Gender Bias Natural Language Processing Semi-Supervised Learning. variational autoencoder Word Embedding

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Image Super-Resolution Based on variational autoencoder and Channel Attention 23

Image Super-Resolution Based on Variational Autoencoder and ...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Jian Xu Yurong Zhao Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Super-resolution (SR) method based on generative adversarial networks (GANs) has achieved excellent performance in both visual perception and image quality. However, there is still room for improvement. Therefore, we propose a variational autoencoder (VAE) network architecture. The VAE encoder can learn the probability distribution of the low-resolution (LR) image and reflect the probability with a latent variable, and the decoder restores the original image through latent variables. The VAE and discriminator work together to effectively distinguish between generated images and real high-resolution (HR) images. In addition, we introduce a channel attention (CA) mechanism into the discriminator to improve the cohesion between channels and extract useful features more effectively. With the help of VAE and CA, the proposed method achieves not only higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values, but also more realistic visual quality. The experimental results verify the feasibility of the proposed method.

关键词： Channel attention Discriminator Image super-resolution variational autoencoder

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Multi-Task with variational autoencoder for Lung Cancer Prognosis on Clinical Data 2020

Multi-Task with Variational Autoencoder for Lung Cancer Prog...

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The 9th International Conference on Smart Media and Applications

作者： Thanh-Hung Vo Guee-Samg Lee Hyung-Jeong Yang Sae-Ryung Kang In-Jae Oh Soo-Hyung Kim Chonnam National University S. Korea Chonnam National University Medical School S. Korea

ISBN: (纸本)9781450389259

Due to the increase of lung cancer in Korea, survival analysis for this kind of cancer gets emerging in recent years. Statistical and traditional machine learning methods usually used by medical doctors for this task. Which the success of deep learning in many tasks of computer vision, natural language processing, some studies starting to use DL for this task. Differ than many fields, data in medicine is difficult to collect and process, then the number of samples usually small and a little bit difficult to apply deep learning approach. In this study, we apply variational autoencoder together with the normal task of survival analysis and analysis the effect of it’s it on the target task. The results show that when combine the VAE with the target task, the network architecture less sensitive with the training size, and then could be trained with small number of sample. The limit of this study is using the internal dataset, then it is difficult to compare to the others.

关键词： variational autoencoder

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Detecting Web Attacks From HTTP Weblogs Using variational LSTM autoencoder Deviation Network

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2210-2222页

作者： Jagat, Rikhi Ram Sisodia, Dilip Singh Singh, Pradeep Natl Inst Technol Raipur Dept Comp Sci & Engn Raipur 492010 India

Web attacks penetrate the web applications' security through unauthorized access to sensitive information, disrupting services, and stealing data. Conventionally, rule-based statistical methods distinguish attackers from legitimate users. However, the training through manually extracted weblog features is time-consuming and requires subject expertise. Additionally, the supervised attack classification method needs massive, labeled weblog data, which is expensive and unfeasible. Also, the unsupervised classification techniques have resolved the labeled data insufficiency problem, but their detection performance is unreliable. Recent studies focus on recognizing web attacks through deep neural network-based anomaly detection. Hence, this study proposes an anomaly detection-based variational LSTM autoencoder Deviation Network (VLADEN) for recognizing web attacks from weblogs. This work resolves the aforementioned issues by extracting the aberrant information encoded in weblog request data to detect web attacks. VLADEN works in three stages: data preprocessing, anomaly and reference score generation, and classification. The variational LSTM self-encoding-based reference score generation ensures that the anomaly score deviates from the normal data. The proposed model is experimentally validated on three publicly available datasets (CSIS2010, FWAF, and HTTPParams) and evaluated using AUC-ROC and AUC-PR-based evaluation metrics. The results demonstrate the models' superior performance in detecting attack requests with minimum domain knowledge and labeled data.

关键词： Feature extraction Anomaly detection Long short term memory Security SQL injection HTTP Deep learning deep learning deviation network long short-term memory variational autoencoder web attack weblog

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mmFall: Fall Detection Using 4-D mmWave Radar and a Hybrid variational RNN autoencoder

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第2期19卷 1245-1257页

作者： Jin, Feng Sengupta, Arindam Cao, Siyang Univ Arizona Dept Elect & Comp Engn Tucson AZ 85719 USA

Elderly fall prevention and detection becomes extremely crucial with the fast aging population globally. In this article, we propose mmFall, a novel fall detection system, which comprises 1) the emerging millimeter-wave (mmWave) radar sensor to collect the human body's point cloud along with the body centroid and 2) a hybrid variational recurrent neural network (RNN) autoencoder (HVRAE) to compute the anomaly level of the body motion based on the acquired point cloud. A fall is detected when the spike in anomaly level and the drop in centroid height occur simultaneously. The mmWave radar sensor offers privacy-compliance and high sensitivity to motion, over the traditional sensing modalities. However, 1) randomness in radar point cloud and 2) difficulties in fall collection/labeling in the traditional supervised fall detection approaches are the two major challenges. To overcome the randomness in radar data, the proposed HVRAE uses variational inference, a generative approach rather than a discriminative approach, to infer the posterior probability of the body's latent motion state every frame, followed by a RNN to summarize the temporal features over multiple frames. Moreover, to circumvent the difficulties in fall data collection/labeling, the HVRAE is built upon an autoencoder architecture in a semisupervised approach, which is only trained on the normal activities of daily living (ADL). In the inference stage, the HVRAE will generate a spike in the anomaly level once an abnormal motion, such as fall, occurs. During the experiment,(1) we implemented the HVRAE along with two other baselines, and tested on the data set collected in an apartment. The receiver operating characteristic (ROC) curve indicates that our proposed model outperforms baselines and achieves 98% detection out of 50 falls at the expense of just 2 false alarms. Note to Practitioners-Traditional nonwearable fall detection approaches typically make use of a vision-based sensor, such as camera, to mo

关键词： Radar Three-dimensional displays Radar detection Senior citizens Doppler radar Radar antennas Radar measurements Anomaly detection fall detection millimeter wave (mmWave) radar recurrent autoencoder (RAE) semisupervised learning variational autoencoder

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Comparison of Improved variational autoencoder Models for Human Face Generation

Comparison of Improved Variational Autoencoder Models for Hu...

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作者： Lijie He School of mathematics and statistics Shandong University

The variational autoencoder(VAE) model has evolved a number of VAE improved models in the past 10 years,including CVAE,WAE,NVAE,*** models have greatly improved the calculation speed of VAE and the resolution of generated *** main goal of this paper is to compare the principles of these different models and the effect of generating *** principle analysis method is mainly used to study the improvement direction of the VAE model in different *** main ideas for improving the VAE model include optimizing the loss function,optimizing the objective function,introducing other parameters,or improving code *** models even add many algorithms in computer vision in for improving image *** the experiments in this paper,results show the image results processed by different *** the current optimal model NVAE,this algorithm has solved most of the VAE image blur problems,and has achieved perfection in image *** improvement of the VAE model in the future may require innovative *** the current principle and model structure,the room for improvement of the VAE model is relatively *** final experiment shows that to improve the quality of VAE generated images,it is required to optimize the objective function,optimize the algorithm and add image visual processing,and then the generated images will be significantly improved.

关键词： variational autoencoder deep learning image generation principle analysis

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Facial Image Inpainting with variational autoencoder

Facial Image Inpainting with Variational Autoencoder

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Ching-Ting Tu Yi-Fu Chen Department of Applied Mathematics National Chung Hsing University Taichung City Taiwan Dept. of Computer Science and Information Engineering Tamkang University New Taipei City Taiwan

ISBN: (数字)9781728141923

ISBN: (纸本)9781728141930

This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing area; for example, a male with a scarf, it is difficult to predict he has a beard in the covered area or not? In this paper, we propose a novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance. Given a trained standard variational autoencoder (VAE) for un-occluded face generation. To be specified, we search for the possible set of VAE coding vector for the current occluded input image, and the predicted coding should be robust to the missing area. The possible facial appearance set is then recovered through the decoder of VAE model. Experiments show that our method successfully predicts recovered results in large missing regions; these results are diverse, and all are reasonable to be consistent with the observable facial area, i.e., both the facial geometry and the personal characteristics are preserved.

关键词： image inpainting variational autoencoder sampling Face Sampling Imagery (Psychotherapy) Image images Facies Beard Scarves decoders occluding

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