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Tamper-Proofing Video With Hierarchical Attention autoencoder Hashing on Blockchain

IEEE TRANSACTIONS ON MULTIMEDIA 2020年第11期22卷 2858-2872页

作者： Bui, Tu Cooper, Daniel Collomosse, John Bell, Mark Green, Alex Sheridan, John Higgins, Jez Das, Arindra Keller, Jared Robert Thereaux, Olivier Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford GU2 7XH Surrey England Univ Surrey Guildford GU2 7XH Surrey England Adobe Res San Jose CA 95110 USA Natl Arch Richmond TW9 4DU Surrey England Open Data Inst London EC2A 4JE England

We present ARCHANGEL;a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we introduce a novel deep network architecture using a hierarchical attention autoencoder (HAAE) to compute temporal content hashes (TCHs) from minutes or hour-long audio-visual streams. Our TCHs are sensitive to accidental or malicious content modification (tampering). The focus of our self-supervised HAAE is to guard against content modification such as frame truncation or corruption but ensure invariance against format shift (i.e. codec change). This is necessary due to the curatorial requirement for archives to format shift video over time to ensure future accessibility. Second, we describe how the TCHs (and the models used to derive them) are secured via a proof-of-authority blockchain distributed across multiple independent archives. We report on the efficacy of ARCHANGEL within the context of a trial deployment in which the national government archives of the United Kingdom, United States of America, Estonia, Australia and Norway participated.

关键词： Blockchain Feature extraction Visualization Distributed ledger Task analysis Transcoding Streaming media Distributed Ledger Technology content aware hashing autoencoder LSTM attention network content integrity blockchain

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An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2021年第15期122卷 3780-3811页

作者： Phillips, Toby R. F. Heaney, Claire E. Smith, Paul N. Pain, Christopher C. Imperial Coll London Dept Earth Sci & Engn Appl Modelling & Computat Grp London England Jacobs Poundbury England

Using an autoencoder for dimensionality reduction, this article presents a novel projection-based reduced-order model for eigenvalue problems. Reduced-order modeling relies on finding suitable basis functions which define a low-dimensional space in which a high-dimensional system is approximated. Proper orthogonal decomposition (POD) and singular value decomposition (SVD) are often used for this purpose and yield an optimal linear subspace. autoencoders provide a nonlinear alternative to POD/SVD, that may capture, more efficiently, features or patterns in the high-fidelity model results. Reduced-order models based on an autoencoder and a novel hybrid SVD-autoencoder are developed. These methods are compared with the standard POD-Galerkin approach and are applied to two test cases taken from the field of nuclear reactor physics.

关键词： autoencoder machine learning reduced-order modeling model reduction neutron diffusion equation reactor physics

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autoencoder BASED NOVELTY DETECTION FOR GENERALIZED ZERO SHOT LEARNING 26

AUTOENCODER BASED NOVELTY DETECTION FOR GENERALIZED ZERO SHO...

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26th IEEE International Conference on Image Processing (ICIP)

作者： Bhattacharjee, Supritam Mandal, Devraj Biswas, Soma Indian Inst Sci Dept Elect Engn Bengaluru India

ISBN: (纸本)9781538662496

The problem of generalized zero-shot learning deals with the classification of test examples for which training data may or may not be available. Existing baseline algorithms connect the seen and unseen set of categories by learning functions to project the image data into the attribute space or vice versa. However, since the classification framework is trained only on the seen set of categories, the recognition performance is typically biased and algorithms have great difficulty in recognizing novel classes. In this work, we investigate the usefulness of a novelty detector to recognize a given data as coming from the seen or novel set. The proposed novelty detector is based on an autoencoder network structure with reconstruction and triplet cosine embedding losses which can be effectively trained using only the seen data and its categories. Experiments over a variety of benchmark datasets and zero-shot algorithms show the efficacy of the proposed approach.

关键词： Generalized Zero Shot Learning Novelty Detection autoencoder

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autoencoder-K-means Algorithm for Efficient Anomaly Detection to Improve Space Operations

Autoencoder-K-means Algorithm for Efficient Anomaly Detectio...

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International Conference on Smart Applications, Communications and Networking (SmartNets)

作者： Akbarian, Hamid Mahgoub, Imadeldin Williams, Andre Florida Atlantic Univ Dept Comp Engn Boca Raton FL 33431 USA

ISBN: (纸本)9798350385328;9798350385335

As we venture further into space, the need for Artificial Intelligence (AI) based anomaly detection algorithms for space operations significantly escalates. These algorithms are essential for ground-based operators to issue accurate corrective commands that prevent potential disasters and spacecraft failure and reduce operation costs. To address this issue, this paper explores integrating two Deep Unsupervised Learning methods such, as autoencoder (AE) and K-means. This approach integrates the reconstruction and clustering methods to detect anomalies. The AE method learns deep temporal dynamics by reducing the dimensions and denoising the data. K-means then clusters this compressed output from the AE. Performance of AE-K-means measures by calculating precision, recall, and F1-score parameters. Precision is the percentage of detected anomalies that are true positives (TP);recall is the percentage of actual anomalies correctly detected;and the F1-score is used to evaluate the algorithm's performance by applying precision and recall to a single value. The datasets used for this research comprise telemetry data expertly labeled and sourced from the Soil Moisture Active Passive (SMAP) satellite. Our results show that AE-K-means improves anomaly detection in complex multivariate time series datasets, like satellite telemetry, and it can provide an enhanced and smart solution to the challenges of detecting anomalies in space operations and systems.

关键词： autoencoder K-means NearestNeighbor F1-score Tanh ReLu

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autoencoder-Based PPG Compression for Resource-Constrained Device 22

Autoencoder-Based PPG Compression for Resource-Constrained D...

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22nd IEEE Statistical Signal Processing Workshop (SSP)

作者： Santiwongkarn, Suvichak Kornworakarn, Natakorn Suksompong, Prapun Choksatchawathi, Tanut Kaewlee, Thitikorn Wilaiprasitporn, Theerawit Thammasat Univ Sirindhorn Int Inst Technol Sch Informat Comp & Commun Technol Pathum Thani Thailand Vidyasirimedhi Inst Sci & Technol Sch Informat Sci & Technol Rayong Thailand Sense AI Co Ltd Rayong Thailand

ISBN: (纸本)9781665452458

PPG-enabled wearables are increasingly prevalent and have the potential to improve remote healthcare systems significantly. However, their implementation can be challenging under resource constraints. To alleviate these constraints, this article proposed a lightweight autoencoder-based lossy PPG compression system for data streaming applications. The system reduces the amount of transmitted data by a factor of 8 while maintaining the information with the signal RMSE at 0.026 and peak MAE at 0.020. This is possible through the loss-conditional training approach on specific task learning. Its practical functionality and advantages are verified on a resource-constrained Docker container and an Arduino Nano 33 BLE microcontroller board. This system cut down transmission energy consumption by 84%.

关键词： Signal Compression Photoplethysmograph Feature Extraction autoencoder Loss-conditional Training

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CRE-TSCAE: A Novel Classification Model Based on Stacked Convolutional autoencoder for Dual-Target RSVP-BCI Tasks

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2024年第7期71卷 2080-2094页

作者： Chen, Hongying Wang, Dan Xu, Meng Chen, Yuanfang Beijing Univ Technol Beijing Peoples R China Beijing Univ Technol Beijing 100022 Peoples R China Beijing Machine & Equipment Inst Beijing 100854 Peoples R China

Objective: The Rapid Serial Visual Presentation (RSVP) paradigm facilitates target identification in a rapid picture stream, which is applied extensively in military target surveillance and police monitoring. Most researchers concentrate on the single target RSVP-BCI whereas the study of dual-target is scarcely conducted, limiting RSVP application considerably. Methods: This paper proposed a novel classification model named Common Representation Extraction-Targeted Stacked Convolutional autoencoder (CRE-TSCAE) to detect two targets with one nontarget in RSVP tasks. CRE generated a common representation for each target class to reduce variability from different trials of the same class and distinguish the difference between two targets better. TSCAE aimed to control uncertainty in the training process while requiring less target training data. The model learned a compact and discriminative feature through the training from several learning tasks so as to distinguish each class effectively. Results: It was validated on the World Robot Contest 2021 and 2022 ERP datasets. Experimental results showed that CRE-TSCAE outperformed the state-of-the-art RSVP decoding algorithms and the Average ACC was 71.25%, improving 6.5% at least over the rest. Conclusion: It demonstrated that CRE-TSCAE showed a strong ability to extract discriminative latent features in detecting the differences among two targets with nontarget, which guaranteed increased classification accuracy. Significance: CRE-TSCAE provided an innovative and effective classification model for dual-target RSVP-BCI tasks and some insights into the neurophysiological distinction between different targets.

关键词： Feature extraction Task analysis Brain modeling Classification algorithms Electroencephalography Convolution Training autoencoder classification Rapid Serial Visual Presentation (RSVP) feature extraction

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Rapid quantitative analysis of Rongalite adulteration in rice flour using autoencoder and residual-based model associated with portable Raman spectroscopy

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 304卷 123382页

作者： Li, Shiwen Li, Tian Cai, Yaoyi Yao, Zekai He, Miaolei Hunan Normal Univ Coll Engn & Design Changsha 410081 Hunan Peoples R China

Rice flour is a raw material for various foods and is used as a substitute for wheat flour. However, some merchants adulterate rice flour with the illegal additive Rongalite to extend the shelf life and earn illegal profits. Rongalite is highly carcinogenic, and ingestion of more than 10 g can even cause death. high-performance liquid chromatography (HPLC) and mass spectrometry (MS) are currently the main methods for detecting food adulteration, however, the existing methods have many limitations, complex operation, expensive instrumentation, etc. Raman spectroscopy has the advantages of convenience and non-destructive samples, but Raman spectroscopy can be affected by interference such as fluorescence background that affects detection, in addition to the problem of difficult quantitative analysis due to nonlinear bias. In this article, we used the preprocessing method of Savitzky-Golay smoothing filtering and VTPspline to improve the quality of the spectra and proposed the SARNet, which combines autoencoder and residual network to achieve the quantitative analysis of Rongalite content in rice flour. The new model combines a linear model with a nonlinear model, which can solve the nonlinear problem effectively. Experiments showed that the new SARNet model achieved state-of-the-art results, achieving the best R2 of 0.9703 and RMSEP of 0.0075. The lowest Rongalite concentration detected by the portable Raman spectrometer was 0.49%. In summary, the proposed method using portable Raman spectroscopy combined with machine learning has low detection bias and high accuracy, which can realize quantitative analyses of adulterated Rongalite in rice flour quickly. The method provides an accurate and nondestructive analytical tool in the field of food detection.

关键词： Machine learning Food adulteration Portable Raman spectroscopy autoencoder Rice flour

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FACT: autoencoder and Attention Conv-LSTM-Based Collaborative Framework for Cloud Cover Prediction

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IEEE ACCESS 2024年 12卷 131488-131504页

作者： Patel, Manan Tanwar, Sudeep Kumar Jadav, Nilesh Gupta, Rajesh Pau, Giovanni Sharma, Gulshan Alqahtani, Fayez Tolba, Amr Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India Kore Univ Enna Fac Engn & Architecture I-94100 Enna Italy Univ Johannesburg Dept Elect Engn Technol ZA-2006 Johannesburg South Africa King Saud Univ Coll Comp & Informat Sci Software Engn Dept Riyadh 12372 Saudi Arabia King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia

Cloud cover forecasting, as an essential paradigm of research, facilitates several applications for various fields, such as weather forecasting, agriculture, aviation, and climate modeling. Nonetheless, despite the accurate predictions, cloud dynamics and its complexity worsen the performance of existing works. Therefore, we propose FACT, a novel framework that utilizes an attention-based convolutional long short-term memory (ConvLSTM) architecture to perform the prediction for the next frame of clouds using a time series dataset of satellite images of cloud cover for a country. Furthermore, an autoencoder model is considered to improve the prediction performance by encoding the frames. The encoding approach helps to reduce the computational complexity of the prediction model, further maintaining enhanced accuracy. Next, we apply post-processing techniques to the acquired prediction result by thresholding the pixel intensity values to produce sharper and clearer cloud images. The proposed model is evaluated and analyzed using various performance assessment metrics, such as Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) metrics. A minimum MSE loss of 0.30 (30%) is achieved. Thus, it shows that the proposed model outperforms existing literature by improving the prediction in the domain of cloud cover forecasting.

关键词： Clouds Predictive models Forecasting Accuracy Meteorology Satellite images Weather forecasting autoencoders Long short term memory Cloud cover forecasting deep learning artificial intelligence: attention-based ConvLSTM autoencoder satellite image

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Mixing autoencoder With Classifier: Conceptual Data Visualization

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IEEE ACCESS 2020年 8卷 105301-105310页

作者： Hartono, Pitoyo Chukyo Univ Sch Engn Nagoya Aichi 4668666 Japan

In this paper, a neural network that is able to form a low-dimensional topological hidden representation is explained. The neural network can be trained as an autoencoder, as a classifier or as a mixture of both and produces a different low-dimensional topological map for each. When it is trained as an autoencoder, the inherent topological structure of the data can be visualized, while when it is trained as a classifier, a topological structure that is further constrained by a given concept, for example, the labels of the data, can be formed. Here, the resulting visualization is not only structural but also conceptual. The proposed neural network significantly differs from many dimensional reduction models, primarily in its ability to execute both supervised and unsupervised dimensional reduction and its ability to visualize not only the structure of high-dimensional data but also the concept assigned to them at various levels of abstraction.

关键词： autoencoder concept visualization dimensional reduction learning representations neural network self-organizing maps topological representations

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Deep Learning-Based autoencoder for m-User Wireless Interference Channel Physical Layer Design

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IEEE ACCESS 2020年 8卷 174679-174691页

作者： Wu, Dehao Nekovee, Maziar Wang, Yue Univ Sussex Ctr Adv Commun Mobile Technol & IoT Brighton BN1 9QJ E Sussex England Quantrom Technol Ltd London SE21 8DU England Samsung Res & Dev Inst Network Stand & Res Commun Res Grp London TW18 4QE England

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this article, we address the dynamic interference in a multi-user Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using -n-psk or n-QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged.

关键词： Deep learning autoencoder 5G physical layer interference channel

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