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检索条件"主题词=deep autoencoder"

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Handling WSN Communication Faults at the Edge with Confidence Attribution for Data Imputation 9

Handling WSN Communication Faults at the Edge with Confidenc...

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9th IEEE World Forum on the Internet of Things (WF-IoT) - The Blue Planet - A Marriage of Sea and Space

作者： Horstmannm, Leonardo Passig Conradi Hoffmann, Jose Luis Scheffel, Roberto Milton Frohlich, Antonio Augusto Univ Fed Santa Catarina Software Hardware Integrat Lab Florianopolis SC Brazil Fed Technol Univ Parana COTSI Toledo PR Brazil

ISBN: (纸本)9798350311617;9798350311624

Due to the nature of Wireless Sensor Networks (WSN), several factors can interfere with sampling and communication. These faults may compromise data quality and disrupt timing and power requirements due to re-sampling and re-transmission. Recent works in the literature propose data imputation and confidence attribution mechanisms as alternatives to overcome missing, or bad-quality, data. Nevertheless, these solutions often lack mechanisms to evaluate the quality of data imputation on-the-fly. This work combines a confidence attribution mechanism previously proposed by the authors with a deep autoencoder (DAE) to promote an effective data imputation mechanism able to handle transient faults in WSNs. We rely on the ability of deep autoencoders to learn data correlation in order to attribute confidence to data based on the loss of information in the encoding-decoding process. The fine-tuning of the confidence attribution parameters considers the discrepancy between the original confidence attribution method and the loss of information calculated for original and predicted data. Finally, through a case-study, we demonstrate that the DAE-based confidence attribution is capable of matching the confidence attribution that relies on the comparison between original and predicted data in more than 86% of the cases, without requiring the original data.

关键词： Data Confidence Attribution Wireless Sensor Networks Connectivity Faults deep autoencoder

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ESDA: An Energy-Saving Data Analytics Fog Service Platform 17th

ESDA: An Energy-Saving Data Analytics Fog Service Platform

引用

17th International Conference on Service-Oriented Computing (ICSOC)

作者： Zhang, Tiehua Shen, Zhishu Jin, Jiong Tagami, Atsushi Zheng, Xi Yang, Yun Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia KDDI Res Inc Fujimino Japan Macquarie Univ Dept Comp Sydney NSW Australia

ISBN: (纸本)9783030337025;9783030337018

The volume of heterogeneous data collected through a variety of sensors is growing exponentially. With the increasing popularity of providing real-time data analytics services at the edge of the network, the process of harvesting and analysing sensor data is thus an inevitable part of enhancing the service experience for users. In this paper, we propose a fog-empowered data analytics service platform to overcome the frequent sensor data loss issue through a novel deep autoencoder model while keeping the minimum energy usage of the managed sensors at the same time. The platform incorporates several algorithms with the purpose of training the individual local fog model, saving the overall energy consumption, as well as operating the service process. Compared with other state-of-the-art techniques for handling missing sensor data, our platform specialises in finding the underlying relationship among temporal sensor data series and hence provides more accurate results on heterogeneous data types. Owing to the superior inference capability, the platform enables the fog nodes to perform real-time data analytics service and respond to such service request promptly. Furthermore, the effectiveness of the proposed platform is verified through the real-world indoor deployment along with extensive experiments.

关键词： Fog computing Service-oriented networking deep autoencoder Energy-saving algorithm

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NetWalk: A Flexible deep Embedding Approach for Anomaly Detection in Dynamic Networks 18

NetWalk: A Flexible Deep Embedding Approach for Anomaly Dete...

引用

24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

作者： Yu, Wenchao Cheng, Wei Aggarwal, Charu C. Zhang, Kai Chen, Haifeng Wang, Wei Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA NEC Labs Amer Inc Princeton NJ USA IBM Res AI San Jose CA USA Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781450355520

Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors" deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NETWALK, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NETWALK has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3). flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NETWALK.

关键词： Anomaly detection dynamic network embedding deep autoencoder clique embedding

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deep Learning Prediction of Chlorophyll Content in Tomato Leaves 9

Deep Learning Prediction of Chlorophyll Content in Tomato Le...

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9th RSI International Conference on Robotics and Mechatronics (ICRoM)

作者： Khoshrou, Mohsen Imanzadeh Zarafshan, Payam Dehghani, Mohammad Chegini, Gholamreza Arabhosseini, Akbar Zakeri, Behzad Univ Tehran Dept Agrotechnol Coll Aburaihan Tehran Iran Univ Tehran Dept Food Technol Coll Aburaihan Tehran Iran

ISBN: (纸本)9781665420945

Precision agriculture has improved crops production around the world. Non-destructive evaluation of chlorophyll contents of plant leaves can be a useful solution, in the field of precision farming. In order to take the required measures, sometimes it is essential to precisely evaluate the chlorophyll content, without cutting the target leaves. In this work, a deep learning methodology is proposed to assess the quantity of chlorophyll in the leaves of the tomato plants, through image processing. This methodology can be extended to any other type of leaves. The proposed method consists of a convolutional denoising autoencoder, to reduce the ambient light noises. Then, using a deep autoencoder network, the valuable features of the plant leaf image are extracted and fed as input to another neural network that evaluates the chlorophyll content of the leaf, taking advantage of support vector regression. To validate the accuracy of the proposed method, measurements were performed using the SPAD chlorophyll meter. The validation results prove the desired accuracy and efficiency of the developed approach.

关键词： Chlorophyll estimation Convolutional neural network (CNN) deep autoencoder Support vector regression (SVR)

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autoencoder-based Safe Reinforcement Learning for Power Augmentation in a Lower-limb Exoskeleton 9

AutoEncoder-based Safe Reinforcement Learning for Power Augm...

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9th RSI International Conference on Robotics and Mechatronics (ICRoM)

作者： Abbasi, Mohammadreza Vossoughi, Gholamreza Karami, Mohammad Koushki, Amirreza Sharif Univ Technol Dept Mech Engn Tehran Iran

ISBN: (纸本)9781665420945

Power augmentation in wearable robots has recently attracted the attention of researchers in industry and academia. Human-robot synchronized control is one of the most substantial goals that needs to be achieved. The change of gait patterns between different users and cycles, and the close interaction of human and robot necessitates the estimation of human motion intention in real-time. Reinforcement learning (RL) is beneficial in such situations mainly due to its online and model-free nature. However, RL algorithms struggle in high-dimensional continuous search spaces in terms of convergence speed and safety. Therefore, this paper proposes an RL-based control strategy for power augmentation in a lower-limb exoskeleton to extract the human motion and minimize the interaction force. A deep autoencoder (AE) neural network is trained and employed for dimensionality reduction to improve the convergence speed and safety. Simulation and two experiments were carried out with a lower-limb exoskeleton on a healthy participant. The results indicate the efficacy of the proposed control strategy and its superior performance compared to model-based approaches while improving speed and safety.

关键词： Reinforcement Learning deep autoencoder Power Augmentation Interaction Force Lower-limb Exoskeleton

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U-FLEX: Unsupervised Feature Learning with Evolutionary eXploration 9th

U-FLEX: Unsupervised Feature Learning with Evolutionary eXpl...

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9th Annual Conference on Machine Learning, Optimization and Data science (LOD)

作者： Bellarmino, Nicolo' Cantoro, Riccardo Squillero, Giovanni Politecn Torino CAD Res Grp Corso Duca Abruzzi 24 I-10129 Turin Italy

ISBN: (纸本)9783031539688;9783031539695

Feature selection is an essential task in machine learning and data mining that involves identifying a subset of relevant features from a larger set. This paper proposes a novel technique for unsupervised feature selection based on a Neural Network in conjunction with an evolutionary algorithm. The proposed method aims to extract subsets of the most discriminative and relevant features from high-dimensional data, which can be eventually used for efficient and accurate machine learning. An evolutionary algorithm is employed to generate the feature subsets, and the goodness of a feature subset is evaluated through the ability of a neural network to reconstruct the whole original input space by mean squared error minimization (in an auto-encoder fashion). Experimental results demonstrate the effectiveness of the proposed approach in finding relevant feature subsets for successive learning tasks, achieving better classification and regression accuracy compared to state-of-the-art feature selection methods.

关键词： Unsupervised Learning Feature Selection Genetic Algorithm deep autoencoder Supervised Learning

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Machine Learning Approximation for Rapid Prediction of High-Dimensional Storm Surge and Wave Responses

Machine Learning Approximation for Rapid Prediction of High-...

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Annual Conference of the Canadian-Society-of-Civil-Engineering (CSCE)

作者： Naeini, Saeed Saviz Snaiki, Reda Univ Quebec Ecole Technol Super Quebec City PQ Canada

ISBN: (纸本)9783031345951;9783031345937;9783031345920

Storm surge and waves are responsible for a substantial portion of the tropical and extratropical cyclones-induced damage in coastal areas of the USA and Canada. High-fidelity, numerical models can provide accurate simulation results of the water elevation, where a hydrodynamic model (e.g., ADCIRC) is coupled with a wave model (e.g., SWAN). However, they are computationally expensive, hence cannot be employed as part of an early warning system for urban flooding hazards or implemented in probabilistic tropical and extratropical cyclones' risk assessment. In this study, an alternative and efficient approach is proposed based on hybrid machine learning approaches. First, a dimensionality reduction technique based on deep autoencoder is developed to encode the spatial information in a reduced state space. Then, a machine learning-based model is developed in the latent space to predict the maximum surge and significant wave height. The latent space is then decompressed back to the original high-dimensional space using the decoder. The high-fidelity data are retrieved from the North Atlantic Comprehensive Coastal Study (NACCS), released by the US Army Corps of Engineers. Due to its high efficiency and accuracy, the proposed methodology can be employed to analyze the impact of input uncertainties on the simulation results. Four machine learning algorithms are used to predict the maximum surge and significant wave height including artificial neural network (ANN), support vector regression (SVR), gradient boosting regression (GBR), and random forest regression (RFR). The coupled autoencoder-ANN model for the prediction of the storm surge (significant wave height) outperformed all other algorithms with a coefficient of determination R-2 of 0.953 (0.921) for the testing set. In addition, the comparison between deep autoencoder and the widely used principal component analysis (PCA) technique indicated the superior performance of the former since it is able to accurately captur

关键词： Storm surge Significant wave height Machine learning deep autoencoder Principal component analysis

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Sound Signal Invariant DAE Neural Network-Based Quantizer Architecture of Audio/Speech Coder Using the Matching Pursuit Algorithm 15th

Sound Signal Invariant DAE Neural Network-Based Quantizer Ar...

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15th International Symposium on Neural Networks (ISNN)

作者： Avramov, Vladislav Herasimovich, Vadzim Petrovsky, Alexander Belarusian State Univ Informat & Radioelect Minsk BELARUS

ISBN: (纸本)9783319925370;9783319925363

The paper is devoted to the quantization algorithm development based on the neural networks framework. This research is considered in the context of the scalable real-time audio/speech coder based on the perceptually adaptive matching pursuit algorithm. The encoder parameterizes the input sound signal frame with some amount of real numbers that are need to be compactly represented in binary form, i.e. quantized. The neural network quantization approach gives great opportunity for such a goal because the data quantized in whole vector but not in separate form and it can effectively use correlations between each element of the input coded vector. deep autoencoder (DAE) neural network-based architecture for the quantization part of the encoding algorithm is shown. Its structure and learning features are described. Conducted experiments points out the big compression ratio with high reconstructed signal quality of the developed audio/speech coder quantization scheme.

关键词： Neural network deep autoencoder Audio/speech coding Quantization Stepwise activation function

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deep Learning for Secure Transmission in Wireless Communication Networks

Deep Learning for Secure Transmission in Wireless Communicat...

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IEEE International Conference on Communications (ICC)

作者： Olowononi, Felix O. Rawat, Danda B. Kamhoua, Charles A. Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA US Army Res Lab Adelphi MD USA

ISBN: (纸本)9781665426718

Advancements in information and communication technologies have contributed significantly to the optimization of next generation wireless communications networks. Moreover, wireless communications play a huge role in electronic warfare and the emergence of new technologies continue to provoke a corresponding revolution in battlefield operations. Nevertheless, the widened attack surface that result from the increased wireless networking of battlefield devices lure attackers like jammers and eavesdroppers who seek to explore the vulnerability of the network through the physical layer. Consequently, it is imperative to direct research efforts not only towards advancing spectral and energy efficiency but also secure wireless communications. In order to address the challenges, we leverage on the recent developments in the use of machine learning (ML) for wireless communications to propose a novel approach for secure waveform transmission in battlefield operations. Specifically, we use the learning-based end-to-end representation of communication systems where the transmitter and receiver are represented by two deep neural networks (DNN). The goal is to prevent the eavesdropper from accessing the communications between the transmitter and the receiver without necessarily a feedback mechanism as in the conventional communication system. In comparison to traditional optimization methods that are usually iterative in nature, the advantages of employing deep learning (DL) includes its ability to address the challenges of statistically inclined methods. Using simulations, we compare the proposed method with other methods to ascertain its credibility.

关键词： Battlefield communications deep autoencoder Wireless security

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Outlier Resistant Unsupervised deep Architectures for Attributed Network Embedding 20

Outlier Resistant Unsupervised Deep Architectures for Attrib...

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13th Annual ACM International Conference on Web Search and Data Mining (WSDM)

作者： Bandyopadhyay, Sambaran Lokesh, N. Vivek, Saley Vishal Murty, M. N. IBM Res Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781450368223

Attributed network embedding is the task to learn a lower dimensional vector representation of the nodes of an attributed network, which can be used further for downstream network mining tasks. Nodes in a network exhibit community structure and most of the network embedding algorithms work well when the nodes, along with their attributes, adhere to the community structure of the network. But real life networks come with community outlier nodes, which deviate significantly in terms of their link structure or attribute similarities from the other nodes of the community they belong to. These outlier nodes, if not processed carefully, can even affect the embeddings of the other nodes in the network. Thus, a node embedding framework for dealing with both the link structure and attributes in the presence of outliers in an unsupervised setting is practically important. In this work, we propose a deep unsupervised autoencoders based solution which minimizes the effect of outlier nodes while generating the network embedding. We use both stochastic gradient descent and closed form updates for faster optimization of the network parameters. We further explore the role of adversarial learning for this task, and propose a second unsupervised deep model which learns by discriminating the structure and the attribute based embeddings of the network and minimizes the effect of outliers in a coupled way. Our experiments show the merit of these deep models to detect outliers and also the superiority of the generated network embeddings for different downstream mining tasks. To the best of our knowledge, these are the first unsupervised non linear approaches that reduce the effect of the outlier nodes while generating Network Embedding.

关键词： network representation learning community outliers adversarial learning deep autoencoder graph mining social networks

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