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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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Increasing sample efficiency in deep reinforcement learning using generative environment modelling

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EXPERT SYSTEMS 2021年第7期38卷 e12537-e12537页

作者： Andersen, Per-Arne Goodwin, Morten Granmo, Ole-Christoffer Univ Agder Dept ICT Grimstad Norway

Reinforcement learning is a broad scheme of learning algorithms that, in recent times, has shown astonishing performance in controlling agents in environments presented as Markov decision processes. There are several unsolved problems in current state-of-the-art that causes algorithms to learn suboptimal policies, or even diverge and collapse completely. Parts of the solution to address these issues may be related to short- and long-term planning, memory management and exploration for reinforcement learning algorithms. Games are frequently used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible and easy to control environments. Regardless, few games feature the ability to perceive how the algorithm performs exploration, memorization and planning. This article presents The Dreaming variational autoencoder with Stochastic Weight Averaging and Generative Adversarial Networks (DVAE-SWAGAN), a neural network-based generative modelling architecture for exploration in environments with sparse feedback. We present deep maze, a novel and flexible maze game-engine that challenges DVAE-SWAGAN in partial and fully observable state-spaces, long-horizon tasks and deterministic and stochastic problems. We show results between different variants of the algorithm and encourage future study in reinforcement learning driven by generative exploration.

关键词： artificial experience-replay deep reinforcement learning environment Modelling exploration generative adversarial networks generative Modelling Markov decision processes model-based RL neural networks variational autoencoder

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Gated Mixture variational autoencoders for Value Added Tax audit case selection

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KNOWLEDGE-BASED SYSTEMS 2020年 188卷 105048-105048页

作者： Kleanthous, Christos Chatzis, Sotirios Cyprus Univ Technol Dept Elect Engn Comp Engn & Informat CY-3036 Limassol Cyprus Cyprus Tax Dept Nicosia Cyprus

In this work, we address the problem of targeted Value Added Tax (VAT) audit case selection by means of machine learning. This is a challenging problem that has remained rather elusive for EU-based Tax Departments, due to the inadequate quantity of tax audits that can be used for conventional supervised model training. To this end, we devise a novel Gated Mixture variational autoencoder deep network, that can be effectively trained with data from a limited number of audited taxpayers, combined with a large corpus of filed VAT returns. This gives rise to a semi-supervised learning framework that leverages the latest advances in deep learning and robust regularization using variational inference. We developed our approach in collaboration with the Cyprus Tax Department and experimentally deployed it to facilitate its audit selection process;to this end, we used actual VAT data from Cyprus-based taxpayers. This way, we obtained strong empirical evidence that our approach can greatly facilitate the VAT audit case selection process. Specifically, we obtained up to 76% out-of-sample accuracy in detecting whether a significant tax yield will be generated from a specific prospective VAT audit. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Value Added Tax Audit selection variational autoencoder Finite mixture model

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Linked variational autoencoders for Inferring Substitutable and Supplementary Items 19

Linked Variational AutoEncoders for Inferring Substitutable ...

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12th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Rakesh, Vineeth Wang, Suhang Shu, Kai Liu, Huan Technicolor Issy Les Moulineaux France Penn State Univ University Pk PA 16802 USA Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9781450359405

Recommendation in the modern world is not only about capturing the interaction between users and items, but also about understanding the relationship between items. Besides improving the quality of recommendation, it enables the generation of candidate items that can serve as substitutes and supplements of another item. For example, when recommending Xbox, PS4 could be a logical substitute and the supplements could be items such as game controllers, surround system, and travel case. Therefore, given a network of items, our objective is to learn their content features such that they explain the relationship between items in terms of substitutes and supplements. To achieve this, we propose a generative deep learning model that links two variational autoencoders using a connector neural network to create Linked variational autoencoder (LVA). LVA learns the latent features of items by conditioning on the observed relationship between items. Using a rigorous series of experiments, we show that LVA significantly outperforms other representative and state-of-the-art baseline methods in terms of prediction accuracy. We then extend LVA by incorporating collaborative filtering (CF) to create CLVA that captures the implicit relationship between users and items. By comparing CLVA with LVA we show that inducing CF-based features greatly improve the recommendation quality of substitutable and supplementary items on a user level.

关键词： variational autoencoder Deep Learning Link Prediction Recommendation Graphical Model

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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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Generating In-Between Images Through Learned Latent Space Representation Using variational autoencoders

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IEEE ACCESS 2020年 8卷 149456-149467页

作者： Cristovao, Paulino Nakada, Hidemoto Tanimura, Yusuke Asoh, Hideki Univ Tsukuba Dept Compute Sci Tsukuba Ibaraki 3058577 Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058560 Japan

Image interpolation is often implemented using one of two methods: optical flow or convolutional neural networks. These methods are typically pixel-based;they do not work well on objects between images far apart. Because they either rely on a simple frame average or pixel motion, they do not have the required knowledge of the semantic structure of the data. In this paper, we propose a method for image interpolation based on latent representations. We use a simple network structure based on a variational autoencoder and an adjustable hyperparameter that imposes the latent space distribution to generate accurate interpolation. To visualize the effects of the proposed approach, we evaluate a synthetic dataset. We demonstrate that our method outperforms both pixel-based methods and a conventional variational autoencoder, with particular improvements in nonsuccessive images.

关键词： Interpolation Optical imaging Optical computing Semantics Optical noise Aerospace electronics Task analysis Image interpolation latent variables representation learning variational autoencoder

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Deep Learning Anomaly Detection methods to passively detect COVID-19 from Audio

Deep Learning Anomaly Detection methods to passively detect ...

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IEEE International Conference on Digital Health (ICDH) / IEEE World Congress on Services (SERVICES)

作者： Murthy, Shreesha Narasimha Agu, Emmanuel Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781665416856

The world has been severely affected by COVID-19, an infectious disease caused by the SARS-Cov-2 coronavirus. COVID-19 incubates in a patient for 7 days before symptoms manifest. The identification of the presence of COVID-19 is challenging as its symptoms are similar to influenza symptoms such as cough, cold, runny nose, and chills. COVID-19 affects human speech sub-systems involved in respiration, phonation, and articulation. We propose a deep anomaly detection framework for passive, speech-based detection of COVID-related anomalies in voice samples of COVID-19 affected individuals. The low percentage of positive cases and extreme imbalance in available COVID audio datasets present a challenge to machine learning classifiers but create an opportunity to utilize anomaly detection techniques. We investigate COVID detection from audio using various types of deep anomaly detectors and convolutional autoencoders. Contrastive loss methods are also explored to force our models to learn discrepancies between COVID and non-COVID cough data representations. In contrast with prior work that controlled data collection, our work focuses on crowdsourced datasets that are true representatives of the general population. In rigorous evaluation, the variational autoencoder with the elliptic envelope as the anomaly detector analyzing Mel Filterbanks audio representations performed best with an AUC of 0.65, outperforming the state of the art.

关键词： Convolutional autoencoder variational autoencoder Contrastive Learning COVID-19 Anomaly detection Cough

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LEARNING OF LINEAR VIDEO PREDICTION MODELS IN A MULTI-MODAL FRAMEWORK FOR ANOMALY DETECTION

LEARNING OF LINEAR VIDEO PREDICTION MODELS IN A MULTI-MODAL ...

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IEEE International Conference on Image Processing (ICIP)

作者： Slavic, Giulia Alemaw, Abrham Shiferaw Marcenaro, Lucio Regazzoni, Carlo Univ Genoa DITEN Genoa Italy

ISBN: (纸本)9781665441155

This paper proposes a method for performing future-frame prediction and anomaly detection on video data in a multi-modal framework based on Dynamic Bayesian Networks (DBNs). In particular, odometry data and video data from a moving vehicle are fused. A Markov Jump Particle Filter (MJPF) is learned on odometry data, and its features are used to aid the learning of a Kalman variational autoencoder (KVAE) on video data. Consequently, anomaly detection can be performed on video data using the learned model. We evaluate the proposed method using multi-modal data from a vehicle performing different tasks in a closed environment.

关键词： variational autoencoder anomaly detection Kalman Filter data fusion Dynamic Bayesian Networks

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A Secure Federated Learning Mechanism for Data Privacy Protection 20

A Secure Federated Learning Mechanism for Data Privacy Prote...

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20th Int Conf on Ubiquitous Comp and Communicat (IUCC) / 20th Int Conf on Comp and Information Technology (CIT) / 4th Int Conf on Data Science and Computational Intelligence (DSCI) / 11th Int Conf on Smart Computing, Networking, and Serv (SmartCNS)

作者： Lin, Hui Liu, Wenxin Wang, Xiaoding Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou Fujian Peoples R China Fujian Prov Univ Engn Res Ctr Cyber Secur & Educ Informatizat Fuzhou Fujian Peoples R China

ISBN: (纸本)9781665466677

The combination of big data and machine learning brings more convenience to people, but also brings security risks of data privacy leakage. The services provided by traditional machine learning can no longer meet the needs of privacy protection. The emergence of federated learning technology has alleviated privacy disclosure threats, however adversaries can still infer from the data model or even reconstruct the raw training data, causing the data privacy of the raw training data to be leaked. To solve this problem, we propose a secure federated learning mechanism based on variational autoencoder (VAE) to resist inference attacks. Participants use raw data to generate forged data through a VAE and train a local model with forged data, thereby protecting the data privacy and guaranteeing the quality of the global model. The experimental results show that the proposed secure federated learning mechanism can guarantee the high accuracy of the global model while reducing the probability of the raw data of the participants being reconstructed.

关键词： Federated Learning variational autoencoder Data Privacy

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DyDiff-VAE: A Dynamic variational Framework for Information Diffusion Prediction 21

DyDiff-VAE: A Dynamic Variational Framework for Information ...

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Wang, Ruijie Huang, Zijie Liu, Shengzhong Shao, Huajie Liu, Dongxin Li, Jinyang Wang, Tianshi Sun, Dachun Yao, Shuochao Abdelzaher, Tarek Univ Illinois Dept Comp Sci Champaign IL 61820 USA Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781450380379

This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for other potential users and predict the corresponding user rankings. Inferring user interests from diffusion data lies the foundation of diffusion prediction, because users often forward the information in which they are interested or the information from those who share similar interests. Their interests also evolve over time as the result of the dynamic social influence from neighbors and the time-sensitive information gained inside/outside the social media. Existing works fail to model users' intrinsic interests from the diffusion data and assume user interests remain static along the time. DyDiff-VAE advances the state of the art in two directions: (i) We propose a dynamic encoder to infer the evolution of user interests from observed diffusion data. (ii) We propose a dual attentive decoder to estimate the propagation likelihood by integrating information from both the initial cascade content and the forwarding user sequence. Extensive experiments on four real-world datasets from Twitter and Youtube demonstrate the advantages of the proposed model;we show that it achieves 43.3% relative gains over the best baseline on average. Moreover, it has the lowest run-time compared with recurrent neural network based models.

关键词： Social Networks Diffusion Prediction Deep Learning variational autoencoder Attention

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