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Modeling Brain Diverse and Complex Hemodynamic Response Patterns via Deep Recurrent autoencoder

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2020年第4期12卷 733-743页

作者： Cui, Yan Zhao, Shijie Chen, Yaowu Han, Junwei Guo, Lei Xie, Li Liu, Tianming Zhejiang Univ Coll Biomed Engn & Instrument Sci Hangzhou 310027 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Univ Georgia Dept Comp Sci Cort Architecture Imaging & Discovery Lab Athens GA 30602 USA Univ Georgia Bioimaging Res Ctr Athens GA 30602 USA

For decades, a variety of task-based functional MRI (tfMRI) data analysis approaches have been developed, including the general linear model (GLM), sparse representations, and independent component analysis (ICA). However, these methods are mainly shallow models and are limited in faithfully modeling the complex, diverse, and concurrent spatial-temporal functional brain activities. Recently, recurrent neural networks (RNNs) have demonstrated great superiority in modeling temporal dependency of signals, while autoencoder models have been proven to be effective in automatically estimating the optimal representations of the original data. These characteristics of RNNs and autoencoders naturally meet the requirement of modeling hemodynamic response patterns in tfMRI data. Thus, we propose a novel unsupervised framework of deep recurrent autoencoder (DRAE) for modeling hemodynamic response patterns in this article. The basic idea of the DRAE model is to combine the deep RNN and the autoencoder to automatically characterize the meaningful functional brain networks and corresponding diverse and complex hemodynamic response patterns simultaneously. The experimental results demonstrate the superiority of the proposed DRAE model in automatically estimating the diverse and complex hemodynamic response patterns.

关键词： Brain modeling Hemodynamics Data models Task analysis Recurrent neural networks Feature extraction autoencoder brain network deep learning hemodynamic response pattern recurrent neural network (RNN) task-based functional magnetic resonance imaging (tfMRI)

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A hierarchical autoencoder learning model for path prediction and abnormality detection

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PATTERN RECOGNITION LETTERS 2020年 130卷 216-224页

作者： Dotti, Dario Popa, Mirela Asteriadis, Stylianos Maastricht Univ Dept Data Sci & Knowledge Engn Maastricht Netherlands

In this paper, we introduce an unsupervised hierarchical framework for modeling trajectories in surveillance scenarios. Inspired by the object recognition field, a novel feature representation optimized for a neural network learning architecture is proposed. Low levels of the hierarchy capture local spatio-temporal motion attributes such as spatial orientation and speed, while higher levels contribute to obtaining richer semantic information. The bottom-up construction of the hierarchical framework exploits the inherent statistical correlations between neighboring elements using an increasing spatio-temporal grid. Cross-entropy based optimization in combination with autoencoders is used to learn weights for subsequent hierarchical layers. Finally, the Bayesian probabilistic framework built on top of the hierarchical model is proposed for applications such as long-term path prediction and abnormality detection. We demonstrate the efficiency of the proposed model on both indoor and outdoor datasets, achieving results comparable with state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Motion features autoencoder Hierarchical learning Behavior understanding Abnormality detection Path prediction

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Deep Residual autoencoder for Blind Universal JPEG Restoration

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IEEE ACCESS 2020年 8卷 63283-63294页

作者： Zini, Simone Bianco, Simone Schettini, Raimondo Univ Milano Bicocca Dept Informat Syst & Commun I-20126 Milan Italy

We propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) leveraging both the learning capacity of deep residual networks and prior knowledge of the JPEG compression pipeline. The proposed method is blind and universal, consisting of a unique model that effectively restores images with any level of compression. It operates in the YCbCr color space and performs JPEG restoration in two phases using two different autoencoders: the first one restores the luma channel exploiting 2D convolutions;the second one, using the restored luma channel as a guide, restores the chroma channels exploiting 3D convolutions. Extensive experimental results on four widely used benchmark datasets (i.e. LIVE1, BDS500, CLASSIC-5, and Kodak) show that our model outperforms state of the art methods, even those using a different set of weights for each compression quality, in terms of all the evaluation metrics considered (i.e. PSNR, PSNR-B, and SSIM). Furthermore, the proposed model shows a greater robustness than state-of-the-art methods when applied to compression qualities not seen during training.

关键词： JPEG restoration deep learning residual network autoencoder

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A Sequential autoencoder for Teleconnection Analysis

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REMOTE SENSING 2020年第5期12卷 851-851页

作者： He, Jiena Eastman, J. Ronald Clark Univ Grad Sch Geog Worcester MA 01610 USA Clark Univ Clark Labs Worcester MA 01610 USA

Many aspects of the earth system are known to have preferred patterns of variability, variously known in the atmospheric sciences as modes or teleconnections. Approaches to discovering these patterns have included principal components analysis and empirical orthogonal teleconnection (EOT) analysis. The latter is very effective but is computationally intensive. Here, we present a sequential autoencoder for teleconnection analysis (SATA). Like EOT, it discovers teleconnections sequentially, with subsequent analyses being based on residual series. However, unlike EOT, SATA uses a basic linear autoencoder as the primary tool for analysis. An autoencoder is an unsupervised neural network that learns an efficient neural representation of input data. With SATA, the input is an image time series and the neural representation is a unidimensional time series. SATA then locates the 0.5% of locations with the strongest correlation with the neural representation and averages their temporal vectors to characterize the teleconnection. Evaluation of the procedure showed that it is several orders of magnitude faster than other approaches to EOT, produces teleconnection patterns that are more strongly correlated to well-known teleconnections, and is particularly effective in finding teleconnections with multiple centers of action (such as dipoles).

关键词： image times series teleconnection climate Empirical Orthogonal Teleconnection Principal Components Analysis Empirical Orthogonal Functions autoencoder Deep Learning

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Adaptable feature-selecting and threshold-moving complete autoencoder for DDoS flood attack mitigation

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2020年 55卷

作者： Ko, Ili Chambers, Desmond Barrett, Enda Natl Univ Ireland Galway Univ Rd Galway Ireland

DDoS attacks remain one of the top cyber threats targeting the financial, health care, retail, gaming, and political sectors, which affects Internet service disruption, data or monetary loss. Security experts have predicted that the development of 5G technology will increase the frequency and the vector of DDoS attacks. Moreover, enhanced DDoS attack technology utilises artificial intelligence [1], which will escalate the level of difficulty to identify malicious traffic correctly to mitigate the attack effectively. The Internet service provider (ISP) is the connector between the users and the Internet. Deploying DDoS mitigation systems within the ISP domain can offer an efficient solution. Therefore, we propose a dynamic learning system (DLS) for the ISP. The DLS is an unsupervised ensemble model using the Complete autoencoder (CA) as base learners to classify network traffic. The utmost difference between the CA and the regular autoencoder is that the CA exploits the imbalanced characteristic of the attack data to generate a binary classification via a class switch. When the predicted number of normal IP addresses is over 50% of the total IP addresses, the CA swaps the class of the IP addresses. The CA is directed by a reference object (RO), which is either a reference limit or the mean of a reference error function ((RL1) over bar), to furnish the automation to the DLS. The DLS was trained with a TCP-ICMP flood attack and tested with a UDP-TCP and a UDP-TCP-ICMP flood attack data set. The average Recall, Precision and F1 Score are all above 0.97. Additionally, the DLS outperformed the K-means and the Self-Organising Map models on a UDP flood attack data set.

关键词： Cyber security Deep learning autoencoder Complete autoencoder DDoS mitigation Machine learning AI Network security Unsupervised learning Ensemble machine learning

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ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks

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NEURAL NETWORKS 2020年 131卷 64-77页

作者： Cohen, Assaf Cohen, Aviad Nissim, Nir Ben Gurion Univ Negev Cyber Secur Res Ctr Malware Lab Beer Sheva Israel Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel Ben Gurion Univ Negev Dept Ind Engn & Management Beer Sheva Israel

Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection. Steganalysis is the counter-steganography domain which aims at detecting the existence of steganography within a host file. The presence of steganography in files raises suspicion regarding the file itself, as well as its origin and receiver, and might be an indication of a sophisticated attack. The JPEG file format is one of the most popular image file formats and thus is an attractive and commonly used carrier for steganography embedding. State-of-the-art JPEG steganalysis methods, which are mainly based on neural networks, are limited in their ability to detect sophisticated steganography use cases. In this paper, we propose ASSAF, a novel deep neural network architecture composed of a convolutional denoising autoencoder and a Siamese neural network, specially designed to detect steganography in JPEG images. We focus on detecting the J-UNIWARD method, which is one of the most sophisticated adaptive steganography methods used today. We evaluated our novel architecture using the BOSSBase dataset, which contains 10,000 JPEG images, in eight different use cases which combine different JPEG's quality factors and embedding rates (bpnzAC). Our results show that ASSAF can detect stenography with high accuracy rates, outperforming, in all eight use cases, the state-of-the-art steganalysis methods by 6% to 40%. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Steganography Steganalysis Deep learning autoencoder Siamese neural network Convolution neural network

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Human Action Recognition Based on a Spatio-Temporal Video autoencoder

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第11期34卷

作者： Sousa e Santos, Anderson Carlos Pedrini, Helio Univ Estadual Campinas Inst Comp BR-13083852 Campinas SP Brazil

Due to rapid advances in the development of surveillance cameras with high sampling rates, low cost, small size and high resolution, video-based action recognition systems have become more commonly used in various computer vision applications. Human operators can be supported with the aid of such systems to detect events of interest in video sequences, improving recognition results and reducing failure cases. In this work, we propose and evaluate a method to learn two-dimensional (2D) representations from video sequences based on an autoencoder framework. Spatial and temporal information is explored through a multi-stream convolutional neural network in the context of human action recognition. Experimental results on the challenging UCF101 and HMDB51 datasets demonstrate that our representation is capable of achieving competitive accuracy rates when compared to other approaches available in the literature.

关键词： Human action recognition autoencoder convolutional neural networks spatio-temporal information multi-stream networks

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Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification

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OPTIK 2020年 206卷 163712-000页

作者： Ahmad, Muhammad Shabbir, Sidrah Oliva, Diego Mazzara, Manuel Distefano, Salvatore Univ Messina Dipartimento Matemat & Informat MIFT I-98121 Messina Italy Khwaja Fareed Univ Engn & Informat Technol Rahim Yar Khan 64200 Pakistan Univ Guadalajara Dept Comp Sci Guadalajara Jalisco Mexico Innopolis Univ Inst Software Dev & Engn Innopolis 420500 Russia

Hyperspectral imaging has been extensively utilized in several fields, and it benefits from detailed spectral information contained in each pixel, generating a thematic map for classification to assign a unique label to each sample. However, the acquisition of labeled data for classification is expensive in terms of time and cost. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. In this paper, a spatial prior generalized fuzziness extreme learning machine autoencoder (GFELM-AE) based active learning is proposed, which contextualizes the manifold regularization to the objective of ELM-AE. Experiments on a benchmark dataset confirmed that the GFELM-AE presents competitive results compared to the state-of-the-art, leading to the improved statistical significance in terms of F1-score, precision, and recall.

关键词： Extreme learning machine autoencoder Active learning Spatial spectral information Fuzziness

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Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional autoencoder

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REMOTE SENSING 2020年第11期12卷 1816-1816页

作者： Kalinicheva, Ekaterina Sublime, Jeremie Trocan, Maria ISEP DaSSIP Team LISITE F-92130 Issy Les Moulineaux France Univ Paris 13 UMR 7030 CNRS LIPN F-93430 Villetaneuse France

Nowadays, satellite image time series (SITS) analysis has become an indispensable part of many research projects as the quantity of freely available remote sensed data increases every day. However, with the growing image resolution, pixel-level SITS analysis approaches have been replaced by more efficient ones leveraging object-based data representations. Unfortunately, the segmentation of a full time series may be a complicated task as some objects undergo important variations from one image to another and can also appear and disappear. In this paper, we propose an algorithm that performs both segmentation and clustering of SITS. It is achieved by using a compressed SITS representation obtained with a multi-view 3D convolutional autoencoder. First, a unique segmentation map is computed for the whole SITS. Then, the extracted spatio-temporal objects are clustered using their encoded descriptors. The proposed approach was evaluated on two real-life datasets and outperformed the state-of-the-art methods.

关键词： satellite image time series unsupervised learning clustering segmentation 3D convolutional network autoencoder

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AGE REGRESSION WITH SPECIFIC FACIAL LANDMARKS BY DUAL DISCRIMINATOR ADVERSARIAL autoencoder

AGE REGRESSION WITH SPECIFIC FACIAL LANDMARKS BY DUAL DISCRI...

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IEEE International Conference on Image Processing (ICIP)

作者： Lan, Li-Chi Liu, Tsung-Jung Liu, Kuan-Hsien Natl Chung Hsing Univ Grad Inst Commun Engn Taichung Taiwan Natl Chung Hsing Univ Dept Elect Engn Taichung Taiwan Natl Taichung Univ Sci & Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781665441155

Facial age conversion is to generate faces of different age groups from the input face and retain the characteristics of the original face. Most of the existing methods are exploring the aging of the face, while ignoring the rejuvenation. In addition to improving aging, we will also explore the regression of human faces. Due to the lack of images of the same person in a longer age range, it becomes a challenging task. Since the generated faces are relatively unreal, we developed a novel model based on Conditional Adversarial autoencoder (CAAE). This model uses two discriminators to generate a more realistic image. Furthermore, by considering specific facial landmarks where the face shape has changed greatly in different age groups, the face shape belonging to the corresponding age group can be obtained. Moreover, the collected database is divided into different races for training to improve the age development of different races.

关键词： Face aging face synthesis generative adversarial network (GAN) discriminator autoencoder

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