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DeepStream: autoencoder-based stream temporal clustering and anomaly detection

COMPUTERS & SECURITY 2021年 106卷 102276-102276页

作者： Harush, Shimon Meidan, Yair Shabtai, Asaf Ben Gurion Univ Negev Dept Software & Informat Syst Engn Beer Sheva Israel

The increasing number of IoT devices in "smart" environments, such as homes, offices, and cities, produce seemingly endless data streams and drive many daily decisions. Conse-quently, there is growing interest in identifying contextual information from sensor data to facilitate the performance of various tasks, e.g., traffic management, cyber attack detection, and healthcare monitoring. The correct identification of contexts in data streams is help-ful for many tasks, for example, it can assist in providing high-quality recommendations to end users and in reporting anomalous behavior based on the detection of unusual con -texts. This paper presents DeepStream, a novel data stream temporal clustering algorithm that dynamically detects sequential and overlapping clusters. DeepStream is tuned to clas-sify contextual information in real time and is capable of coping with a high-dimensional feature space. DeepStream utilizes stacked autoencoders to reduce the dimensionality of unbounded data streams and for cluster representation. This method detects contextual behavior and captures nonlinear relations of the input data, giving it an advantage over existing methods that rely on PCA. We evaluated DeepStream empirically using four sen-sor and IoT datasets and compared it to five state-of-the-art stream clustering algorithms. Our evaluation shows that DeepStream outperforms all of these algorithms. Our evaluation also demonstrates how DeepStream's improved clustering performance results in improved detection of anomalous data. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Stream clustering autoencoder Dimensionality reduction Anomaly detection Activity recognition

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Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2021年 87卷 104225-104225页

作者： Zhuang, Xiaoying Guo, Hongwei Alajlan, Naif Zhu, Hehua Rabczuk, Timon Tongji Univ Dept Geotech Engn Shanghai Peoples R China Bauhaus Univ Weimar Inst Struct Mech Weimar Germany Leibniz Univ Hannover Fac Math & Phys Chair Computat Sci & Simulat Technol Hannover Germany King Saud Univ Coll Comp & Informat Sci ALISR Lab POB 51178 Riyadh 11543 Saudi Arabia

In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher order continuity of the DAEM and integrates a deep autoencoder and the minimum total potential principle in one framework yielding an unsupervised feature learning method. The DAEM is a specific type of feedforward deep neural network (DNN) and can also serve as function approximator. With robust feature extraction capacity, the DAEM can more efficiently identify patterns behind the whole energy system, such as the field variables, natural frequency and critical buckling load factor studied in this paper. The objective function is to minimize the total potential energy. The DAEM performs unsupervised learning based on generated collocation points inside the physical domain so that the total potential energy is minimized at all points. For the vibration and buckling analysis, the loss function is constructed based on Rayleigh's principle and the fundamental frequency and the critical buckling load is extracted. A scaled hyperbolic tangent activation function for the underlying mechanical model is presented which meets the continuity requirement and alleviates the gradient vanishing/explosive problems under bending. The DAEM is implemented using Pytorch and the LBFGS optimizer. To further improve the computational efficiency and enhance the generality of this machine learning method, we employ transfer learning. A comprehensive study of the DAEM configuration is performed for several numerical examples with various geometries, load conditions, and boundary conditions.

关键词： Deep learning autoencoder Activation function Energy method Kirchhoff plate Vibration Buckling Transfer learning

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FEEDBACK TURBO autoencoder

FEEDBACK TURBO AUTOENCODER

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiang, Yihan Kim, Hyeji Asnani, Himanshu Oh, Sewoong Kalman, Sreeram Viswanath, Pramod Univ Washington Seattle WA 98195 USA Samsung AI Res Mountain View CA USA Tata Inst Fundamental Res Mumbai Maharashtra India Univ Illinois Champaign IL USA

ISBN: (纸本)9781509066315

Designing channel codes is one of the core research areas for modern communication systems. Canonical channel codes asymptotically achieve near-capacity performance under large block length regime for additive white gaussian noise channels. However, this achieved success does not generalize to many channels. Channels with output feedback, proposed by Shannon, is one of such channels where practical codes have been unknown for several decades. Recently it has been demonstrated that deep learning based code outperforms the state-of-the-art codes for channels with output feedback. While the success is promising and inspiring, there are a few major challenges that need to be addressed. Firstly, the channel assumes a feedback with a unit step delay, which is not very practical. Second is the lack of generalization to larger block lengths. In this work, we propose Feedback Auto Turbo Encoder (FTAE) which harmoniously combines interleaver and iterative decoding with CNN architectures and demonstrate the blocklength gain and improved performance in the block feedback setting.

关键词： Information Theory Channel Coding Deep Learning Feedback Channel autoencoder

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An autoencoder Based Model for Detecting Fraudulent Credit Card Transaction

An Autoencoder Based Model for Detecting Fraudulent Credit C...

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International Conference on Computational Intelligence and Data Science (ICCIDS)

作者： Misra, Sumit Thakur, Soumyadeep Ghosh, Manosij Saha, Sanjoy Kumar Jadavpur Univ Dept Comp Sci & Engn Kolkata India

With the rapid growth in credit card based financial transactions, it has become important to identify the fraudulent ones. In this work, a two stage model is proposed to identify such fraudulent transactions. To make a fraud detection system trustworthy, both miss in fraud detection and false alarms are to minimized Understanding and learning the complex associations among the transaction attributes is a major problem. To address this issue, at the first stage of the proposed model an autoencoder is used to transform the transaction attributes to a feature vector of lower dimension. The feature vector thus obtained is used as the input to a classifier at the second stage. Experiment is done on a benchmarked dataset. It is observed that in terms of F1-measure, proposed two stage model performs better than the systems relying on only classifier and other autoencoder based systems. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Fraud Detection Financial Transaction autoencoder

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Laughter synthesis: A comparison between Variational autoencoder and autoencoder 5

Laughter synthesis: A comparison between Variational autoenc...

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5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Mansouri, Nadia Lachiri, Zied Tunis El Manar Univ Signal Image & Informat Technol Res Lab LR SITI Natl Engn Sch Tunis ENIT Tunis Tunisia

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175133

Laughter is one of the most famous non verbal sounds that human produce since birth, it conveys messages about our emotional state. These characteristics make it an important sound that should be studied in order to improve the human-machine interactions. In this paper we investigate the audio laughter generation process from its acoustic features. This suggested process is considered as an analysis-transformation-synthesis benchmark based on unsupervised dimensionality reduction techniques: The standard autoencoder (AE) and the variational autoencoder (VAE). Therefore, the laughter synthesis methodology consists of transforming the extracted high-dimensional log magnitude spectrogram into a low-dimensional latent vector. This latent vector contains the most valuable information used to reconstruct a synthetic magnitude spectrogram that will be passed through a specific vocoder to generate the laughter waveform. We systematically, exploit the VAE to create new sound (speech-laugh) based on the interpolation process. To evaluate the performance of these models two evaluation metrics were conducted: objective and subjective evaluations.

关键词： Laughter audio generation variational autoencoder autoencoder interpolation objective and subjective evaluation

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An autoencoder-Based Dimensionality Reduction Algorithm for Intelligent Clustering of Mineral Deposit Data

An Autoencoder-Based Dimensionality Reduction Algorithm for ...

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Chinese Intelligent Automation Conference

作者： Li, Yan Luo, Xiong Chen, Maojian Zhu, Yueqin Gao, Yang Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China China Geol Survey Dev & Res Ctr Beijing 100037 Peoples R China China Informat Technol Secur Evaluat Ctr Beijing 100085 Peoples R China

ISBN: (纸本)9789813290501;9789813290495

Currently, there has been a dramatic growth of data size and data dimension in geophysics, while achieving the rapid advancements of geological big data technologies with the support of various detection methods. Then, high-dimensional and massive geological data impose very challenging obstacles to traditional data analysis approaches. Given the success of deep learning methods and techniques in big data analysis applications, it is expected that they are also able to achieve the satisfactory performance in dealing with high-dimensional complex geological data. Hence, through the combination of one of the effective implementations of deep learning, i.e., autoencoder, and a clustering algorithm, i.e., K-means, in this paper we achieve the dimensionality reduction for complex data, so as to extract useful data features from mineral deposit data, with the purpose of improving computational efficiency. The experimental results demonstrate the effectiveness our developed method.

关键词： High-dimensional data Dimensionality reduction autoencoder K-means algorithm

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A SYNDROME-BASED autoencoder FOR POINT CLOUD GEOMETRY COMPRESSION

A SYNDROME-BASED AUTOENCODER FOR POINT CLOUD GEOMETRY COMPRE...

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IEEE International Conference on Image Processing (ICIP)

作者： Milani, Simone Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781728163956

Point cloud compression has been extensively-investigated in the past twenty years to find effective solutions that reduce the coded bit stream and permits adapting the coded bit rate to different scenarios. Despite these efforts, predictive strategies have so far performed poorly because of the low correlation level of the input data and the flexibility requirements, which imply minimizing the decoding dependences. The current paper proposes a convolutional autoencoder that applies the principles of Distributed Source Coding (DSC) to the deep representations of voxelized point cloud geometry data. The hidden variables, called syndromes, enable reconstructing the coded point cloud geometry from different reference data. The proposed strategy overcomes the state-of-the-art solutions in terms of flexibility and rate-distortion performance.

关键词： point cloud compression geometry distributed source coding autoencoder UNet

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Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional autoencoder

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SENSORS 2021年第15期21卷 4968-4968页

作者： Kim, Jungsuk Ko, Jungbeom Choi, Hojong Kim, Hyunchul Gachon Univ Dept Biomed Engn 191 Hambakmoe Ro Incheon 2199 South Korea Kumoh Natl Inst Technol Dept Medial IT Convergence Engn 350-27 Gum Daero Gumi 39253 South Korea Univ Calif Berkeley Sch Informat 102 South Hall 4600 Berkeley CA 94704 USA

As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing to the limitations of manual inspection, significant efforts have been made to automate the inspection by utilizing high resolution CCD or CMOS sensors. Despite the advanced sensor technology, setting the pass/fail criteria based on small failure samples has always been challenging in traditional machine vision approaches. To overcome these problems, we propose an advanced PCB inspection system based on a skip-connected convolutional autoencoder. The deep autoencoder model was trained to decode the original non-defect images from the defect images. The decoded images were then compared with the input image to identify the defect location. To overcome the small and imbalanced dataset in the early manufacturing stage, we applied appropriate image augmentation to improve the model training performance. The experimental results reveal that a simple unsupervised autoencoder model delivers promising performance, with a detection rate of up to 98% and a false pass rate below 1.7% for the test data, containing 3900 defect and non-defect images.

关键词： deep learning autoencoder detect detection PCB defeat detection printed circuit board manufacturing

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Biased autoencoder for collaborative filtering with temporal signals

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115775-115775页

作者： Dou, Runliang Arslan, Oguzhan Zhang, Ce Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

Recommendation systems are used in various types of online platforms and in e-commerce. Collaborative filtering (CF) is one of the most popular approaches for recommendation systems and has been widely studied in academia. In recent years, several models based on neural networks that can discover nonlinear relationships have been proposed and compared to traditional CF models. The results showed that they performed better in terms of their prediction accuracy. However, these models do not consider user bias and item bias together, and they do not include temporal signals. This paper proposes a biased autoencoder model (Biased AutoRec) for CF, which is built on the well-known AutoRec CF approach. Several approaches are also proposed to integrate temporal signals into the Biased AutoRec model to merge the power of nonlinearity and temporal signals. Experiments on several public datasets showed that the new models outperformed the AutoRec model, which outperformed the prediction accuracy of previous state-of-the-art CF models (i.e., biased matrix factorization, RBM-CF, LLORMA).

关键词： Collaborative filtering AutoRec Temporal dynamics Bias autoencoder

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Anomaly detection during milk processing by autoencoder neural network based on near-infrared spectroscopy

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JOURNAL OF FOOD ENGINEERING 2021年 299卷 110510-110510页

作者： Vasafi, Pegah Sadeghi Paquet-Durand, Olivier Brettschneider, Kim Hinrichs, Joerg Hitzmann, Bernd Univ Hohenheim Dept Proc Analyt & Cereal Sci 150i Garbenstr 23 D-70599 Stuttgart Germany

Anomaly detection during milk processing (such as changes in fat or temperature, added water or cleaning solution) can assure a satisfactory final product quality, including compositional and hygienic characteristics, as well as adulteration with water. The use of near-infrared (NIR) spectroscopy for change detection in complex dairy matrix is discussed. The autoencoder neural network plays fundamental role in anomaly detection. To evaluate this capability, the raw spectra obtained from NIR as well as first derivative and combination of both were analysed. An autoencoder was trained by 1.5% fat UHT-milk (measured at 5 degrees C) and applied to detect possible changes happening during the milk processing. The trained autoencoder using first derivative spectra was capable to detect 5% added water and 9% cleaning solution in the milk. Also, with the combination spectra, it was able to recognize a difference of 0.1% in fat concentration. In addition, both procedures were able to detect different production methods (specific procedure of suppliers such as homogenization level or pressure) and difference of 10 degrees C in the temperature. It can be concluded, that using an autoencoder neural network in combination with near-infrared spectroscopy is a reliable method to monitor the milk processing. By doing so, abnormal changes can be detected early, controlling the process becomes easier and the quality and safety of the product is guaranteed.

关键词： Anomaly detection autoencoder Near-infrared spectroscopy

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