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Joint 9th International Conference on Informatics, Electronics and Vision (ICIEV) / 4th International Conference on Imaging, Vision and Pattern Recognition (ICIVPR)

作者： Yokkampon, Umaporn Chumkamon, Sakmongkon Mowshowitz, Abbe Hayashi, Eiji Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Fukuoka Japan CUNY City Coll Dept Comp Sci New York NY USA

ISBN: (纸本)9781728193311

Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on variational autoencoder, identifies spiking raw data by means of spectrum analysis. Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can improve validity and accuracy on all criteria. Therefore, analysis of time series data by combining variational autoencoder and frequency domain spectrum methods can effectively detect anomalies. Contribution- We have proposed an anomaly detection method based on the time series data analysis by combining variational autoencoder and Spectrum analysis, and have benchmarked the method with reference to recent related research.

关键词： Anomaly Detection variational autoencoder Time Series Data

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DEEP variational autoencoder FOR MODELING FUNCTIONAL BRAIN NETWORKS AND ADHD IDENTIFICATION 17

DEEP VARIATIONAL AUTOENCODER FOR MODELING FUNCTIONAL BRAIN N...

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IEEE 17th International Symposium on Biomedical Imaging (ISBI)

作者： Qiang, Ning Dong, Qinglin Sun, Yifei Ge, Bao Liu, Tianming Shaanxi Normal Univ Sch Phys & Informat Technol Xian Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781538693308

In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods and tools to learn functional brain networks (FBNs). Recently, it has already been proven that deep learning can be applied on fMRI data with superb representation power over traditional machine learning methods. Limited by the high-dimension of fMRI volumes, deep learning suffers from the lack of data and overfitting. Generative models are known to have intrinsic ability of modeling small dataset and a Deep variational autoencoder (DVAE) is proposed in this work to tackle the challenge of insufficient data and incomplete supervision. The FBNs learned from fMRI were examined to be interpretable and meaningful and it was proven that DVAE has better performance on neuroimaging dataset over traditional models. With an evaluation on ADHD-200 dataset, DVAE performed excellent on classification accuracies on 4 sites.

关键词： Resting-state fMRI variational autoencoder ADHD Deep Learning Generative Learning

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Transfer Learning Approach for Botnet Detection Based on Recurrent variational autoencoder 3

Transfer Learning Approach for Botnet Detection Based on Rec...

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3rd International Workshop on Systems and Network Telemetry and Analytics (SNTA)

作者： Kim, Jeeyung Sim, Alex Kim, Jinoh Wu, Kesheng Hahm, Jaegyoon Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Texas A&M Univ Commerce TX USA KISTI Daejeon South Korea

ISBN: (纸本)9781450379809

Machine Learning (ML) methods have been widely used in Intrusion Detection Systems (IDS). In particular, many botnet detection methods are based on ML. However, due to the fast-evolving nature of network security threats, it is necessary to frequently retrain the ML tools with up-to-date data, especially because data labeling takes a long time and requires a lot of effort, making it difficult to generate training data. We propose transfer learning as a more effective approach for botnet detection, as it can learn from well curated source data and transfer the knowledge to a target problem domain not seen before. We devise an approach that is effective regardless whether or not the data from the target domain is labeled. More specifically, we train a neural network with the Recurrrent Variation autoencoder (RVAE) structure on the source data, and use RVAE to compute anomaly scores for data records from the target domain. In an evaluation of this transfer learning framework, we use CTU-13 dataset as a source domain and a fresh set of network monitoring data as a target domain. Tests show that the proposed transfer learning method is able to detect botnets better than semi-supervised learning method that was trained on the target domain data. The area under Receiver Operating Characteristic is 0.810 for transfer learning, and 0.779 for directly using RVAE on the target domain data.

关键词： botnet detection transfer learning Recurrent Neural Network variational autoencoder

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A variational autoencoder-Based Secure Transceiver Design Using Deep Learning

A Variational Autoencoder-Based Secure Transceiver Design Us...

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Lin, Chia-Hung Wu, Chao-Chin Chen, Kuan-Fu Lee, Ta-Sung Natl Chiao Tung Univ Inst Commun Engn Hsinchu Taiwan Natl Chiao Tung Univ Ctr mmWave Smart Radar Syst & Technol Hsinchu Taiwan North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781728182988

To achieve new applications for 5G communications, physical layer security has recently drawn significant attention. In a wiretap channel system, our goal is to minimize information leakage to an eavesdropper while maximizing the performance of transmission to the desired or legitimate receiver. Complicated systems or channel models make it difficult to design secrecy systems based on the information theory. In this paper, we propose a deep learning-based transceiver design for secrecy systems as an alternative. Specifically, we modify the loss function design of a variational autoencoder, which is a special type of neural network, making it possible to provide both robust data transmission and security in an unsupervised fashion. We further investigate the impact of an imperfect channel state information and use simulation results to prove that our approach can outperform the existing learning-based methods.

关键词： physical layer security wiretap channel deep learning neural networks variational autoencoder

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High-Dimensional Similarity Search with Quantum-Assisted variational autoencoder 20

High-Dimensional Similarity Search with Quantum-Assisted Var...

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26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Gao, Nicholas Wilson, Max Vandal, Thomas Vinci, Walter Nemani, Ramakrishna Rieffel, Eleanor NASA Ames Res Ctr Quantum Artificial Intelligence Lab Moffett Field CA 94035 USA NASA Ames Res Ctr BAER Inst Moffett Field CA 94035 USA NASA Ames Res Ctr Moffett Field CA 94035 USA KBR Inc Houston TX USA Univ Bristol Quantum Engn CDT Bristol Avon England

ISBN: (纸本)9781450379984

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning [1] is touted as a potential approach to demonstrate quantum advantage within both the gate-model [2, 3] and the adiabatic [4, 5] schemes. For instance, the Quantum-assisted variational autoencoder (QVAE) [6] has been proposed as a quantum enhancement to the discrete VAE [7]. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

关键词： Similarity Search Quantum Machine Learning variational autoencoder Earth Science

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Botnet Detection Using Recurrent variational autoencoder

Botnet Detection Using Recurrent Variational Autoencoder

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IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Kim, Jeeyung Sim, Alex Kim, Jinoh Wu, Kesheng Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Texas A&M Univ Commerce TX USA

ISBN: (纸本)9781728182988

Botnet detection is an active research topic as botnets are a source of many malicious activities, including distributed denial-of-service (DDoS), click-fraud, spamming, and crypto-mining attacks. However, it is getting more complicated to identify botnets due to the continuous evolution of botnet software and families that harness new types of devices and attack vectors. Recent studies employing machine learning (ML) showed improved performance to detect botnets to some extent, but they are still limited and ineffective with the lack of sequential pattern analysis, which is a key to detect various classes of botnets. In this paper, we propose a novel botnet detection method, built upon Recurrent variational autoencoder (RVAE), that effectively captures sequential characteristics of botnet anomalies. We validate the feasibility of the proposed method with the CTU-13 dataset that have been widely employed for botnet detection studies, and show that our method is at least comparable to existing techniques in terms of detection accuracy. In addition, our experimental results show that the proposed method can detect previously unseen botnets by utilizing sequential patterns of network traffic. We will also show how our method can detect botnets in the streaming mode, which is the essential requirement to perform real-time, on-line detection.

关键词： botnet detection anomaly scoring Recurrent Neural Network variational autoencoder network security

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variational autoencoder based Latent Factor Decoding of Multichannel EEG for Emotion Recognition

Variational Autoencoder based Latent Factor Decoding of Mult...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Li, Xiang Zhao, Zhigang Song, Dawei Zhang, Yazhou Niu, Chunyang Zhang, Junwei Huo, Jidong Li, Jing Qilu Univ Technol Shandong Acad Sci Natl Supercomp Ctr Jinan Key Lab Med Artificial IntelligenceShandong Comp Jinan Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Zhengzhou Univ Light Ind Softwarc Engn Coll Zhengzhou Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Jiuquan Satellite Launch Ctr Alxa League Peoples R China

ISBN: (纸本)9781728118673

Robust cross-subject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of the latent variables that related to emotional processing must contribute to building robust recognition models. We propose to utilize variational autoencoder (VAE) to determine the latent factors from the multi-channel EEG. Through sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED), and compared with traditional matrix factorization based (ICA) and autoencoder based (AE) approaches. Experimental results demonstrate that neural network is suitable for unsupervised EEG modeling and our proposed emotion recognition framework achieves the state-of-the-art performance. As far as we know, it is the first work that introduces VAE into multichannel EEG decoding for emotion recognition.

关键词： Affective Computing Latent Factor Decoding Emotion Recognition EEG variational autoencoder

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CosmoVAE: variational autoencoder for CMB Image Inpainting

CosmoVAE: Variational Autoencoder for CMB Image Inpainting

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Yi, Kai Guo, Yi Fan, Yanan Hamann, Jan Wang, Yu Guang Univ New South Wales Sch Math & Stat Sydney NSW Australia Univ New South Wales Sch Phys Sydney NSW Australia

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

Cosmic microwave background radiation (CMB) is critical to the understanding of the early universe and precise estimation of cosmological constants. Due to the contamination of thermal dust noise in the galaxy, the CMB map that is an image on the two-dimensional sphere has missing observations, mainly concentrated on the equatorial region. The noise of the CMB map has a significant impact on the estimation precision for cosmological parameters. Inpainting the CMB map can effectively reduce the uncertainty of parametric estimation. In this paper, we propose a deep learning-based variational autoencoder CosmoVAE, to restoring the missing observations of the CMB map. The input and output of CosmoVAE are square images. To generate training, validation, and test data sets, we segment the full-sky CMB map into many small images by Cartesian projection. CosmoVAE assigns physical quantities to the parameters of the VAE network by using Fourier coefficients, which are sampled by the angular power spectrum of the Gaussian random field as latent variables. CosmoVAE adopts a new loss function to improve the learning performance of the model, which consists of Li reconstruction loss, Kullback-Leibler divergence between the posterior distribution of encoder network and the prior distribution of latent variables, perceptual loss, and total-variation regularizer. The proposed model achieves state of the art performance for Planck Commander 2018 CMB map inpainting.

关键词： variational autoencoder cosmic microwave background inpainting deep learning convolutional neural networks uncertainty quantification KL-divergence regularization perceptual loss total variation angular power spectrum VGG-16 ImageNet

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Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder

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NEUROIMAGE 2021年 244卷 118588-118588页

作者： Zhang, Xiaodi Maltbie, Eric A. Keilholz, Shella D. Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Emory Univ Hlth Sci Res Bldg1760 Haygood DrSuiteW200 Atlanta GA 30322 USA

Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.

关键词： Resting state fMRI Spatiotemporal dynamics Deep learning variational autoencoder Resting state networks

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Crash data augmentation using variational autoencoder

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ACCIDENT ANALYSIS AND PREVENTION 2021年 151卷 105950-105950页

作者： Islam, Zubayer Abdel-Aty, Mohamed Cai, Qing Yuan, Jinghui Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA

In this paper, we present a data augmentation technique to reproduce crash data. The dataset comprising crash and non-crash events are extremely imbalanced. For instance, the dataset used in this paper consists of only 625 crash events for over 6.5 million non-crash events. Thus, learning algorithms tend to perform poorly on these datasets. We have used variational autoencoder to encode all the events into a latent space. After training, the model could successfully separate crash and non-crash events. To generate data, we sampled from the latent space containing crash data. The generated data was compared with the real data from different statistical aspects. t-Test, Levene-test and Kolmogrove Smirnov test showed that the generated data was statistically similar to the real data. It was also compared to some of the minority oversampling techniques like SMOTE and ADASYN as well as the GAN framework for generating data. Crash prediction models based on Logistic Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were used to compare the generated data from the different oversampling techniques. Overall, variational autoencoder (VAE) showed excellent results compared to the other data augmentation methods. Specificity is improved by 8% and 4% for VAE-LR and VAE-SVM respectively when compared to SMOTE while the sensitivity is improved by 6% and 5% when compared to ADASYN. Moreover, VAE generated data also helps to overcome the overfitting problem in SMOTE and ADASYN since there is flexibility in choosing the decision boundary.

关键词： variational autoencoder Data augmentation Crash prediction

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