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Synthesis-Style Auto-Correlation-Based Transformer: A Learner on Ionospheric TEC Series Forecasting

SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 2023年第10期21卷 e2023SW003472-e2023SW003472页

作者： Yuan, Yuhuan Xia, Guozhen Zhang, Xinmiao Zhou, Chen Wuhan Univ Dept Space Phys Wuhan Peoples R China

Accurate 1-day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long-term dependencies. This study develops a highly accurate model for 1-day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1-day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two-step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto-correlation-based transformer model designed to capture long-range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1-day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto-correlation-based transformer models trained on synthetic data to achieve high-quality 1-day global TEC forecasting.

关键词： ionospheric total electron content prediction auto-correlation-based transformer data augmentation variational autoencoder

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Collaborative Filtering Based on a variational Gaussian Mixture Model

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FUTURE INTERNET 2021年第2期13卷 37-37页

作者： Yang, FengLei Liu, Fei Liu, ShanShan Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China

Collaborative filtering (CF) is a widely used method in recommendation systems. Linear models are still the mainstream of collaborative filtering research methods, but non-linear probabilistic models are beyond the limit of linear model capacity. For example, variational autoencoders (VAEs) have been extensively used in CF, and have achieved excellent results. Aiming at the problem of the prior distribution for the latent codes of VAEs in traditional CF is too simple, which makes the implicit variable representations of users and items too poor. This paper proposes a variational autoencoder that uses a Gaussian mixture model for latent factors distribution for CF, GVAE-CF. On this basis, an optimization function suitable for GVAE-CF is proposed. In our experimental evaluation, we show that the recommendation performance of GVAE-CF outperforms the previously proposed VAE-based models on several popular benchmark datasets in terms of recall and normalized discounted cumulative gain (NDCG), thus proving the effectiveness of the algorithm.

关键词： recommendation system variational autoencoder collaborative filtering Gaussian mixture model

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CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks

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SENSORS 2019年第16期19卷 3445-3445页

作者： Liu, Jianlin Chen, Fenxiong Yan, Jun Wang, Dianhong China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China

Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of variational autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 degrees C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has goo

关键词： wireless sensor networks data compression variational autoencoder downsampling-convolutional restricted boltzmann machine

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EEG Classification of Motor Imagery Using a Novel Deep Learning Framework

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SENSORS 2019年第3期19卷 551-551页

作者： Dai, Mengxi Zheng, Dezhi Na, Rui Wang, Shuai Zhang, Shuailei Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art.

关键词： EEG deep learning short-time Fourier transform convolutional neural network variational autoencoder

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An Improved Convolutional Neural Network for Churn Analysis

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 204-210页

作者： Gopal, Priya Bin MohdNawi, Nazri Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Parit Raja Malaysia

The significance of customer churn analysis has escalated due to the increasing availability of relevant data and intensifying competition. Researchers and practitioners are focused on enhancing prediction accuracy in modeling approaches, with deep neural networks emerging as appealing due to their robust performance across domains. However, the computational demands surge due to the challenges posed by dimensionality and inherent characteristics of the data. To address these issues, this research proposes a novel hybrid model that strategically integrates Convolutional Neural Networks (CNN) and a modified variational autoencoder (VAE). By carefully adjusting the parameters of the VAE to capture the central tendency and range of variation, the study aims to enhance the effectiveness of classifying high-dimensional churn data. The proposed framework's efficacy is evaluated using six benchmark datasets from various domains, with performance metrics encompassing accuracy, f1-score, precision, recall, and response time. Experimental results underscore the prowess of the hybrid technique in effectively handling high-dimensional and imbalanced time series data, thus offering a robust pathway for enhanced churn analysis.

关键词： Customer churn analysis deep learning variational autoencoder convolutional neural networks dimensionality reduction

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Bayesian Multi-Temporal-Difference Learning

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APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING 2022年第1期11卷

作者： Chien, Jen-Tzung Chiu, Yi-Chung Natl Yang Ming Chiao Tung Univ Inst Elect & Comp Engn Hsinchu Taiwan

This paper presents a new sequential learning via a planning strategy where the future samples are predicted by reflecting the past experiences. Such a strategy is appealing to implement an intelligent machine which foresees multiple time steps instead of predicting step by step. In particular, a flexible sequential learning is developed to directly predict future states without visiting all intermediate states. A Bayesian approach to multi-temporal-difference neural network is accordingly proposed to calculate the stochastic belief state for an abstract state machine so as to capture large-span context as well as make high-level prediction. Importantly, the sequence data are represented by multiple jumpy states with varying temporal differences. A Bayesian state machine is trained by maximizing the variational lower bound of log likelihood of sequence data. A generalized sequence model with various number of Markov states is derived with the simplified realization to the previous temporal-difference variational autoencoder. The predictive states are learned to roll forward with jumps. Experiments show that this approach is substantially trained to predict jumpy states in various types of sequence data.

关键词： Bayesian learning variational autoencoder sequential learning temporal-difference learning state machine

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The application of artificial intelligence-assisted technology in cultural and creative product design

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Liang, Jing Jiangxi Inst Fash Technol Sch Fash Media Nanchang 330000 Peoples R China

This study proposes a novel artificial intelligence (AI)-assisted design model that combines variational autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and efficiency in cultural and creative product design. By introducing AI-driven decision support, the model streamlines the design workflow and significantly improves design quality. The study establishes a comprehensive framework and applies the model to four distinct design tasks, with extensive experiments validating its performance. Key factors, including creativity, cultural adaptability, and practical application, are evaluated through structured surveys and expert feedback. The results reveal that the VAE + RL model surpasses alternative approaches across multiple criteria. Highlights include a user satisfaction rate of 95%, a Structural Similarity Index (SSIM) score of 0.92, model accuracy of 93%, and a loss reduction to 0.07. These findings confirm the model's superiority in generating high-quality designs and achieving high user satisfaction. Additionally, the model exhibits strong generalization capabilities and operational efficiency, offering valuable insights and data support for future advancements in cultural product design technology.

关键词： Artificial intelligence Cultural and creative product design variational autoencoder Reinforcement learning Design optimization

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Multi-Dimensional Information Alignment in Different Modalities for Generalized Zero-Shot and Few-Shot Learning

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INFORMATION 2023年第3期14卷 148页

作者： Cai, Jiyan Wu, Libing Wu, Dan Li, Jianxin Wu, Xianfeng Wuhan Univ Sch Cyber Sci & Engn Wuhan 430072 Peoples R China Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Deakin Univ Sch Informat Technol Geelong 3217 Australia Jianghan Univ Inst Interdisciplinary Res Wuhan 430056 Peoples R China

Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are always existing new categories and large amounts of unlabeled data in realistic scenarios. Previous work for GZSL usually maps the visual information of the visible classes and the semantic description of the invisible classes into the identical embedding space to bridge the gap between the disjointed visible and invisible classes, while ignoring the intrinsic features of visual images, which are sufficiently discriminative to classify themselves. To better use discriminative information from visual classes for GZSL, we propose the n-CADA-VAE. In our approach, we map the visual feature of seen classes to a high-dimensional distribution while mapping the semantic description of unseen classes to a low-dimensional distribution under the same latent embedding space, thus projecting information of different modalities to corresponding space positions more accurately. We conducted extensive experiments on four benchmark datasets (CUB, SUN, AWA1, and AWA2). The results show our model's superior performance in generalized zero-shot as well as few-shot learning.

关键词： multi-dimensional alignment intrinsic discriminative features generalized zero-shot learning variational autoencoder

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Disentangling cobionts and contamination in long-read genomic data using sequence composition

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G3-GENES GENOMES GENETICS 2024年第11期14卷 jkae187页

作者： Weber, Claudia C. Wellcome Genome Campus Tree Life Wellcome Sanger Inst Hinxton CB10 1SA England

The recent acceleration in genome sequencing targeting previously unexplored parts of the tree of life presents computational challenges. Samples collected from the wild often contain sequences from several organisms, including the target, its cobionts, and contaminants. Effective methods are therefore needed to separate sequences. Though advances in sequencing technology make this task easier, it remains difficult to taxonomically assign sequences from eukaryotic taxa that are not well represented in databases. Therefore, reference-based methods alone are insufficient. Here, I examine how we can take advantage of differences in sequence composition between organisms to identify symbionts, parasites, and contaminants in samples, with minimal reliance on reference data. To this end, I explore data from the Darwin Tree of Life project, including hundreds of high-quality HiFi read sets from insects. Visualizing two-dimensional representations of read tetranucleotide composition learned by a variational autoencoder can reveal distinct components of a sample. Annotating the embeddings with additional information, such as coding density, estimated coverage, or taxonomic labels allows rapid assessment of the contents of a dataset. The approach scales to millions of sequences, making it possible to explore unassembled read sets, even for large genomes. Combined with interactive visualization tools, it allows a large fraction of cobionts reported by reference-based screening to be identified. Crucially, it also facilitates retrieving genomes for which suitable reference data are absent.

关键词： variational autoencoder data visualization unsupervised learning long-read sequencing

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Exploiting Missing Value Patterns for a Backdoor Attack on Machine Learning Models of Electronic Health Records: Development and Validation Study

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JMIR MEDICAL INFORMATICS 2022年第8期10卷 e38440页

作者： Joe, Byunggill Park, Yonghyeon Hamm, Jihun Shin, Insik Lee, Jiyeon Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Elect & Telecommun Res Inst Daejeon South Korea Tulane Univ Dept Comp Sci New Orleans LA 70118 USA Soongsil Univ Sch AI Convergence Seoul South Korea

Background: A backdoor attack controls the output of a machine learning model in 2 stages. First, the attacker poisons the training data set, introducing a back door into the victim's trained model. Second, during test time, the attacker adds an imperceptible pattern called a trigger to the input values, which forces the victim's model to output the attacker's intended values instead of true predictions or decisions. While backdoor attacks pose a serious threat to the reliability of machine learning-based medical diagnostics, existing backdoor attacks that directly change the input values are detectable relatively easily. Objective: The goal of this study was to propose and study a robust backdoor attack on mortality-prediction machine learning models that use electronic health records. We showed that our backdoor attack grants attackers full control over classification outcomes for safety-critical tasks such as mortality prediction, highlighting the importance of undertaking safe artificial intelligence research in the medical field. Methods: We present a trigger generation method based on missing patterns in electronic health record data. Compared to existing approaches, which introduce noise into the medical record, the proposed backdoor attack makes it simple to construct backdoor triggers without prior knowledge. To effectively avoid detection by manual inspectors, we employ variational autoencoders to learn the missing patterns in normal electronic health record data and produce trigger data that appears similar to this data. Results: We experimented with the proposed backdoor attack on 4 machine learning models (linear regression, multilayer perceptron, long short-term memory, and gated recurrent units) that predict in-hospital mortality using a public electronic health record data set. The results showed that the proposed technique achieved a significant drop in the victim's discrimination performance (reducing the area under the precision-recall curve by at

关键词： medical machine learning neural network mortality prediction backdoor attack electronic health record data Medical Information Mart for Intensive Care-III missing value mask meta-information variational autoencoder

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