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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ale, Tolulope Janeja, Vandana P. Schlegel, Nicole-Jeanne Univ Maryland Baltimore Cty Informat Syst 1000 Hilltop Circle Baltimore MD 21250 USA NOAA Geophys Fluid Dynam Lab Princeton NJ USA

ISBN: (纸本)9798350360332;9798350360325

We introduce an anomaly detection method for multivariate time series data with the aim of identifying critical periods and features influencing extreme climate events like snowmelt in the Arctic. This method leverages variational autoencoder (VAE) integrated with dynamic thresholding and correlationbased feature clustering. This framework enhances the VAE's ability to identify localized dependencies and learn the temporal relationships in climate data, thereby improving the detection of anomalies as demonstrated by its higher F1-score on benchmark datasets. The study's main contributions include the development of a robust anomaly detection method, improving feature representation within VAEs through clustering, and creating a dynamic threshold algorithm for localized anomaly detection. This method offers explainability of climate anomalies across different regions.

关键词： Anomaly Detection Dynamic Threshold Climate Extreme variational autoencoder Multivariate Time Series

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Traffic Identification of Mobile Apps Based on variational autoencoder Network 13

Traffic Identification of Mobile Apps Based on Variational A...

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13th International Conference on Computational Intelligence and Security (CIS)

作者： Li, Ding Zhu, Yuefei Lin, Wei Informat Engn Univ Zhengzhou Henan Peoples R China

ISBN: (纸本)9781538648223

Traffic identification is a fundamental issue in network security. Traditional methods, such as depth packet inspection (DPI) and flow-based classifiers, have difficulties in labeling massive samples and extracting features manually. Motivated by the achievements in computer vision, we focus on mobile app traffic, proposing a deep learning model based on variational autoencoder network (VEAN). Our contributions are two-fold. First, we propose a novel method of transforming mobile app traffic flows into vision-meaningful images, and thus enable the machine to identify the traffic in a human way. Then, based on the transformation method, we create an open dataset named IMTD17. Second, an improved network model is proposed, where variational autoencoder (VAE) algorithm is introduced into a two-stage learning. The model realizes the learning from massive unlabeled data, and the feasibility of the replacement for manual feature extraction is illustrated by the visualization analysis of the latent features. The experimental results show that the identification accuracy can reach 99.6%, which satisfies the practical requirement.

关键词： traffic identification mobile app latent feature variational autoencoder

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variational autoencoder for Image-Based Augmentation of Eye-Tracking Data

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JOURNAL OF IMAGING 2021年第5期7卷 83-83页

作者： Elbattah, Mahmoud Loughnane, Colm Guerin, Jean-Luc Carette, Romuald Cilia, Federica Dequen, Gilles Univ Picardie Jules Verne Lab Modelisat Informat Syst MIS F-80080 Amiens France Univ Limerick Fac Sci & Engn Limerick V94 T9PX Ireland Evolucare Technol F-80800 Villers Bretonneux France Univ Picardie Jules Verne Lab CRP CPO F-80000 Amiens France

Over the past decade, deep learning has achieved unprecedented successes in a diversity of application domains, given large-scale datasets. However, particular domains, such as healthcare, inherently suffer from data paucity and imbalance. Moreover, datasets could be largely inaccessible due to privacy concerns, or lack of data-sharing incentives. Such challenges have attached significance to the application of generative modeling and data augmentation in that domain. In this context, this study explores a machine learning-based approach for generating synthetic eye-tracking data. We explore a novel application of variational autoencoders (VAEs) in this regard. More specifically, a VAE model is trained to generate an image-based representation of the eye-tracking output, so-called scanpaths. Overall, our results validate that the VAE model could generate a plausible output from a limited dataset. Finally, it is empirically demonstrated that such approach could be employed as a mechanism for data augmentation to improve the performance in classification tasks.

关键词： deep learning variational autoencoder data augmentation eye-tracking

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SPACNN-LDVAE: SPATIAL ATTENTION CONVOLUTIONAL LATENT DIRICHLET variational autoencoder FOR HYPERSPECTRAL PIXEL UNMIXING

SPACNN-LDVAE: SPATIAL ATTENTION CONVOLUTIONAL LATENT DIRICHL...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chitnis, Soham Mantripragada, Kiran Qureshi, Faisal Z. Birla Inst Tech & Sci Pilani Dept CS & IS Sancoale Goa India Ontario Tech Univ Fac Sci Oshawa ON Canada

ISBN: (纸本)9798350360332;9798350360325

The hyperspectral pixel unmixing aims to find the underlying materials (endmembers) and their proportions (abundances) in pixels of a hyperspectral image. This work extends the Latent Dirichlet variational autoencoder (LDVAE) pixel unmixing scheme by taking into account local spatial context while performing pixel unmixing. The proposed method uses an isotropic convolutional neural network with spatial attention to encode pixels as a dirichlet distribution over endmembers. We have evaluated our model on Samson, Hydice Urban, Cuprite, and OnTech-HSI-Syn-21 datasets. Our model also leverages the transfer learning paradigm for Cuprite Dataset, where we train the model on synthetic data and evaluate it on the real-world data. The results suggest that incorporating spatial context improves both endmember extraction and abundance estimation.

关键词： Hyperspectral image analysis Unmixing Endmember Extraction Abundance Estimation variational autoencoder Deep Learning Spatial Attention Convolution

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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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ContrastVAE: Contrastive variational autoencoder for Sequential Recommendation 22

ContrastVAE: Contrastive Variational AutoEncoder for Sequent...

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Wang, Yu Zhang, Hengrui Liu, Zhiwei Yang, Liangwei Yu, Philip S. Univ Illinois Chicago IL 60607 USA Salesforce San Francisco CA USA

ISBN: (纸本)9781450392365

Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods suffer from the following issues: 1) sparsity of user-item interactions, 2) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of variational autoencoders to address these challenges simultaneously. Firstly, we introduce ContrastELBO, a novel training objective that extends the conventional single-view ELBO to two-view case and theoretically builds a connection between VAE and contrastive learning from a two-view perspective. Then we propose Contrastive variational autoencoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation. We further introduce two simple yet effective augmentation strategies named model augmentation and variational augmentation to create a second view of a sequence and thus making contrastive learning possible. Experiments on four benchmark datasets demonstrate the effectiveness of ContrastVAE and the proposed augmentation methods. Codes are available at https://***/YuWang1024/ContrastVAE

关键词： Sequential Recommendation variational autoencoder Contrastive Learning

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Noise-robust Early Detection of Cooling Fan Deterioration with a variational autoencoder-based Method 9

Noise-robust Early Detection of Cooling Fan Deterioration wi...

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9th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Yanagihashi, Hiroyuki Sudo, Takashi Toshiba Co Ltd Corp Res & Dev Ctr Kawasaki Kanagawa Japan

ISBN: (纸本)9784886864314

Electrical equipment usually requires air cooling by fans, and it is important to detect early signs of fan deterioration. However, when using sensors to detect deterioration of fans in the real world, the sensor signal is often mixed with external noise. In this paper, we analyze the deterioration trends in the operating sounds of cooling fans actually used for 3 and 7 years in uninterruptible power supplies. Furthermore, we propose a deep learning-based method for noise-robust early detection of deterioration based on fan operating sounds called variational autoencoder-based deterioration estimation (VAE-DE). Existing anomaly detection methods based on autoencoders often react to external noise mixed into deterioration trend, which causes false positives. While another conventional method using variational autoencoders has the property of being robust against noise, it is not reactive to slight sound changes that accompany fan deterioration. If a deterioration trend is known, noise-robust early detection of deterioration can be achieved through learning using a loss function that includes a term for separation from normal sounds. Experiments investigating deterioration sounds in a fan showed changes in latent variables for early deterioration sounds and corresponding increases in the anomaly score. We also show that anomaly scores can be robustly estimated even in the presence of noise. The results reveal that the proposed method performs better than conventional methods.

关键词： deterioration estimation cooling fan anomaly detection neural network variational autoencoder

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Text Generation with Syntax-Enhanced variational autoencoder

Text Generation with Syntax-Enhanced Variational Autoencoder

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuan, Weijie Ding, Linyi Meng, Kui Liu, Gongshen Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9780738133669

Text generation is one of the essential yet challenging tasks in natural language processing. However, the input text alone is usually hard to provide enough information to generate the desired output. Previous work attempts to incorporate syntactic information into the generative models based on variational autoencoder(VAE). But these methods have difficulty in adequately modeling the tree structure of syntactic data. In this paper, we formulate the syntactic structure as a graph and introduce a syntax encoder based on graph neural network(GNN) to model the syntactic information of sentences. Based on the syntax encoder, we propose a novel syntax-enhanced variational autoencoder(SEVAE) with two variants. The variant SEVAEm merges sentence information and syntactic information into one latent space to enrich the fine-grained syntactic information of latent representations. And the variant SEVAE-s with two separate latent spaces allows the sentence decoder to dynamically attend to semantic and syntactic information from two latent variables. Experiments on two benchmark datasets show that our methods achieve significant and consistent improvements compared with previous work.

关键词： text generation variational autoencoder syntactic modeling attention mechanism

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Whisper Speech Enhancement Using Joint variational autoencoder for Improved Speech Recognition 22

Whisper Speech Enhancement Using Joint Variational Autoencod...

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Interspeech Conference

作者： Agrawal, Vikas Kumar, Shashi Rath, Shakti P. Samsung R&D Inst India Bangalore Karnataka India Reverie Language Technol Bangalore Karnataka India

ISBN: (纸本)9781713836902

Whispering is the natural choice of communication when one wants to interact quietly and privately. Due to vast differences in acoustic characteristics of whisper and natural speech, there is drastic degradation in the performance of whisper speech when decoded by the Automatic Speech Recognition (ASR) system trained on neutral speech. Recently, to handle this mismatched train and test scenario Denoising autoencoders (DA) are used which gives some improvement. To improve over DA performance we propose another method to map speech from whisper domain to neutral speech domain via Joint variational Auto-Encoder (JVAE). The proposed method requires time-aligned parallel data which is not available, so we developed an algorithm to convert parallel data to time-aligned parallel data. JVAE jointly learns the characteristics of whisper and neutral speech in a common latent space which significantly improves whisper recognition accuracy and outperforms traditional autoencoder based techniques. We benchmarked our method against two baselines, first being ASR trained on neutral speech and tested on whisper dataset and second being whisper test set mapped using DA and tested on same neutral ASR. We achieved an absolute improvement of 22.31% in Word Error Rate (WER) over the first baseline and an absolute 5.52% improvement over DA.

关键词： whisper speech recognition autoencoder wTIMIT variational autoencoder jointVAE

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