With the development of the Internet of Things (IoT) technology, intrusion detection has become a key technology that provides solid protection for IoT devices from network intrusion. At present, artificial intelligen...
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With the development of the Internet of Things (IoT) technology, intrusion detection has become a key technology that provides solid protection for IoT devices from network intrusion. At present, artificial intelligence technologies have been widely used in the intrusion detection task in previous methods. However, unknown attacks may also occur with the development of the network and the attack samples are difficult to collect, resulting in unbalanced sample categories. In this case, the previous intrusion detection methods have the problem of high false positive rates and low detection accuracy, which restricts the application of these methods in a real situation. In this article, we propose a novel method based on deep neural networks to tackle the intrusion detection task, which is termed Cognitive Memory-guided autoencoder (CMAE). The CMAE method leverages a memory module to enhance the ability to store normal feature patterns while inheriting the advantages of autoencoder. Therefore, it is robust to the imbalanced samples. Besides, using the reconstruction error as an evaluation criterion to detect attacks effectively detects unknown attacks. To obtain superior intrusion detection performance, we propose feature reconstruction loss and feature sparsity loss to constrain the proposed memory module, promoting the discriminative of memory items and the ability of representation for normal data. Compared to previous state-of-the-art methods, sufficient experimental results reveal that the proposed CMAE method achieves excellent performance and effectiveness for intrusion detection.
Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical imag...
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Effective medical diagnosis is dramatically expensive,especially in third-world *** of the common diseases is pneumonia,and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia,themedical diagnosis of these diseases is a significant ***,transfer learning represents a promising solution in transferring knowledge from generic tasks to specific ***,experimentation and utilization of different models of transfer learning do not achieve satisfactory *** this study,we suggest the implementation of an automatic detectionmodel,namelyCADTra,to efficiently diagnose pneumonia-related *** model is based on classification,denoising autoencoder,and transfer ***,pre-processing is employed to prepare the medical *** depends on an autoencoder denoising(AD)algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features,in order to improve the diagnosis ***,classification is performed using a transfer learning model and a four-layer convolution neural network(FCNN)to detect *** proposed model supports binary classification of chest computed tomography(CT)images and multi-class classification of chest X-ray ***,a comparative study is introduced for the classification performance with and without the denoising *** proposed model achieves precisions of 98%and 99%for binary classification and multi-class classification,respectively,with the different ratios for training and *** demonstrate the efficiency and superiority of the proposed CADTra model,it is compared with some recent state-of-the-art CNN *** achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases and improve the diagnostic efficiency compared to the exis
Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U...
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ISBN:
(纸本)9783031472916;9783031472923
Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U-fibers make SWM-related analysis lag behind relatively matured Deep white matter (DWM) analysis. With the aid of some newly proposed surface-based SWM tractography algorithms, we have developed a specialized SWM filtering method based on a symmetric variational autoencoder (VAE). In this work, we first demonstrate the advantage of the spherical representation and generate these spherical tracts using the triangular mesh and the registered spherical surface. We then introduce the Filtering via symmetric autoencoder for Spherical Superficial White Matter tractography (FASSt) framework with a novel symmetric weights module to perform the filtering task in a latent space. We evaluate and compare our method with the state-of-the-art clustering-based method on diffusion MRI data from Human Connectome Project (HCP). The results show that our proposed method outperform these clustering methods and achieves excellent performance in groupwise consistency and topographic regularity.
Many practical applications require probabilistic prediction of time series to model the distribution on future horizons. With ever-increasing dimensions, much effort has been invested into developing methods that oft...
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Many practical applications require probabilistic prediction of time series to model the distribution on future horizons. With ever-increasing dimensions, much effort has been invested into developing methods that often make an assumption about the independence between time series. Consequently, the probabilistic prediction in high-dimensional environments has become an essential topic with significant challenges. In this paper, we propose a novel probabilistic model called latent adversarial regularized autoencoder, abbreviated as TimeLAR, specifically for high-dimensional multivariate Time Series Prediction (TSP). It integrates the flexibility of Generative Adversarial Networks (GANs) and the capability of autoencoders in extracting higher-level non-linear features. Through flexible autoencoder mapping, TimeLAR learns cross-series relationships and encodes this global information into several latent variables. We design a modified Transformer for these latent variables to capture global temporal patterns and infer latent space prediction distributions, where only one step is required to output multi-step predictions. Furthermore, we employ the GAN to further refine the performance of latent space predictions, by using a discriminator to guide the training of the autoencoder and the Transformer in an adversarial process. Finally, complex distributions of multivariate time series data can be modeled by the non-linear decoder of the autoencoder. The effectiveness of TimeLAR is empirically underpinned by extensive experiments conducted on five real-world high-dimensional time series datasets in the fields of transportation, electricity, and web page views. (c) 2022 Elsevier Ltd. All rights reserved.
A set of autoencoders is trained to perform intra prediction for block-based video coding. Each autoencoder consists of an encoding network and a decoding network. Both encoding network and decoding networks are joint...
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ISBN:
(纸本)9781665475921
A set of autoencoders is trained to perform intra prediction for block-based video coding. Each autoencoder consists of an encoding network and a decoding network. Both encoding network and decoding networks are jointly optimized and integrated into the state-of-the-art VVC reference software VTM-11.0 as an additional intra prediction mode. The simulation is conducted under common test conditions with all intra configurations and the test results show 1.55%, 1.04%, and 0.99% of Y, U, V components Bjontegaard-Delta bit rate saving compared to VTM-11.0 anchor, respectively. The overall relative decoding running time of proposed autoencoder-based intra prediction mode on top of VTM-11.0 are 408% compared to VTM-11.0.
Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular i...
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Cross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal retrieval are twofold: the heterogeneous gap in different modalities and the semantic gap among similar data with various modalities. To address these issues, we adopt self-supervised fashion to bridge the heterogeneous gap by generating the cohesive features of different instances. To mitigate the semantic gap, we use triplet sampling to optimize the semantic loss in inter-modal and intra-modal, which increase the discriminability of our approach. Experimental on two benchmark datasets show the efficiency and robustness of our method, and the extended experiments show the scalability.
Activation functions are essential keys to good performance in a neural network. Many functions can be used, and the choice of which one to use depends on the issues addressed. New adaptable and trainable activation f...
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ISBN:
(数字)9781665488587
ISBN:
(纸本)9781665488587
Activation functions are essential keys to good performance in a neural network. Many functions can be used, and the choice of which one to use depends on the issues addressed. New adaptable and trainable activation functions have been studied lately, which are used to increase network performance. This study intends to evaluate the performance of an artificial neuron that uses adaptive and trainable functions in an autoencoder network for image compression problems. The tested neuron, known as Global-Local Neuron, comprises two complementary components, one with global characteristics and the other with local characteristics. The global component is given by a sine function and the local component by the hyperbolic tangent function. The experiment was carried out in two stages. In the first one, different activation functions, GLN, Tanh, and Sine, were tested in an MLP-type autoencoder neural network model. Different compression ratios were considered when varying the size of the autoencoder bottleneck layer, and 48 samples were obtained for each value of this layer. The metrics used for the evaluation were the loss value obtained in the test set and the number of epochs necessary to reach a stopping criterion. In the second step, the classification accuracy of the images compressed by the encoder block of the previous model was evaluated, using a Wide Residual Networks (WRN) network and the Support Vector Machines (SVM) method. The results obtained indicated that the use of Global-Local Neuron improved the network training speed, obtained better classification accuracy for compression up to 50% in a WRN network, and proved the adaptability in image classification problems.
With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults ...
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ISBN:
(纸本)9781665493130
With excellent feature representation capabilities, deep autoencoder networks have attracted attention in process monitoring. However, it cannot take into account the quality indicators to identify whether the faults are quality-relevant. To address this issue, an orthogonal feature separation autoencoder (OFSAE) method is developed for quality-relevant fault monitoring. The proposed OFSAE mainly consists of the quality-relevant encoder network, quality-irrelevant encoder network, decoder network, and regression network. Through parallel learning and orthogonal projection for process variables, quality-relevant and quality-irrelevant variations can be isolated while maintaining good prediction performance. Finally, in comparison with conventional monitoring methods, the superiority of OFSAE is validated by the Tennessee Eastman process.
The theory of capsule networks and the dynamic routing mechanism for capsules was introduced by Geoffrey Hinton and his research team. In this new approach, they tried to solve typical problems of classical convolutio...
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ISBN:
(纸本)9798350319866
The theory of capsule networks and the dynamic routing mechanism for capsules was introduced by Geoffrey Hinton and his research team. In this new approach, they tried to solve typical problems of classical convolutional neural networks. For example, that the efficiency of neural networks degrades when a geometric transformation is applied on the input image, or when the data is far away from the training dataset. It became clear early on that capsule networks are state-of-the-art solutions for visual data classification tasks. For other tasks their use is less common and in many cases difficult to apply. For example image segmentation or object detection and localization. The efficiency of the capsule networks theory in the field of pointcloud processing is also an open question. In this work we investigated the pointcloud reconstruction capability of capsule networks. In this approach, three different complexity autoencoder networks was selected. We created a decoder network based on capsules theory, which was fitted to the existing autoencoder networks. The efficiency of the networks was tested using four different datasets. As a result of our work, we show the effectiveness of capsule networks in the field of pointcloud reconstruction compared with the selected autoencoder networks.
Lithium-ion batteries (LIBs) are currently the standard for energy storage in portable consumer electronic devices. They are also used in electric vehicles and in some large industrial settings and for grid power stor...
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Lithium-ion batteries (LIBs) are currently the standard for energy storage in portable consumer electronic devices. They are also used in electric vehicles and in some large industrial settings and for grid power storage. The adverse consequences of a dramatic battery failure can be significant compared with the cost of timely replacement or maintenance. Consequently, accurate state-of-health (SOH) prediction is important to inform maintenance or replacement decisions. In this work, we address current challenges related to accuracy and interpretability in data-driven SOH prediction for LIBs by devising a novel physics-informed machine learning prognostic model, termed PIDDA. PIDDA includes three elements: an autoencoder, a physics-informed model training, and a physics-based prediction adjustment. We examine and benchmark our model against alternative data-driven SOH prediction models using the NASA battery prognostic dataset. The computational experiments demonstrate that PIDDA (1) provides significantly higher prediction accuracy;(2) requires less prior data for its predictions;(3) produces more informative and interpretable predictions than alternative models. We conclude with an ablation study of PIDDA to analyze the relative effectiveness of two of its elements, the physics equations in the model training and the physics-based prediction adjustment. The results show that the former (training) provides the heavy lifting in accuracy improvement, roughly two-thirds, and the latter (adjustment) the remaining incremental improvement.
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