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Interpretable fake news detection with topic and deep variational models

ONLINE SOCIAL NETWORKS AND MEDIA 2023年 36卷

作者： Hosseini, Marjan Sabet, Alireza Javadian He, Suining Aguiar, Derek Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Univ Pittsburgh Dept Informat & Networked Syst Pittsburgh PA 15260 USA

The growing societal dependence on social media and user generated content for news and information has increased the influence of unreliable sources and fake content, which muddles public discourse and lessens trust in the media. Validating the credibility of such information is a difficult task that is susceptible to confirmation bias, leading to the development of algorithmic techniques to distinguish between fake and real news. However, most existing methods are challenging to interpret, making it difficult to establish trust in predictions, and make assumptions that are unrealistic in many real-world scenarios, e.g., the availability of audiovisual features or provenance. In this work, we focus on fake news detection of textual content using interpretable features and methods. In particular, we have developed a deep probabilistic model that integrates a dense representation of textual news using a variational autoencoder and bi-directional Long Short-Term Memory (LSTM) networks with semantic topic-related features inferred from a Bayesian admixture model. Extensive experimental studies with 3 real-world datasets demonstrate that our model achieves comparable performance to state-of-theart competing models while facilitating model interpretability from the learned topics. Finally, we have conducted model ablation studies to justify the effectiveness and accuracy of integrating neural embeddings and topic features both quantitatively by evaluating performance and qualitatively through separability in lower dimensional embeddings.

关键词： Fake news Misinformation Social media Interpretability Topic models variational autoencoder

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Data-driven fault diagnosis based on the integrated deep nonlinear dynamic system model

Data-driven fault diagnosis based on the integrated deep non...

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第43届中国控制会议

作者： Xiaochu Tang Na Tao Yi Zhang Yuan Li School of Automation Shenyang Aerospace University College of Information Engineering Shenyang University of Chemical Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To ensure the safety and reliability of complex industrial processes are very ***,extracting multiple features of data effectively is a great significance to improve the accuracy of modeling for fault ***,uncertainty and nonlinearity are main characteristics of industrial process ***,it is challenging for modeling to extract multiple features of process data at the same *** this paper,an integrated nonlinear dynamic system model is proposed for fault diagnosis based on variational autoencoder-linear dynamic system(VAE-LDS).First,the deep learning algorithm variational autoencoder(VAE) is used to extract the nonlinear data feature and learn the potential representation of ***,the VAE model is embedded into the linear dynamic system(LDS) so that the dynamics and uncertainty underlying data can be extracted *** this way,A comprehensive model integrating multiple features can be ***,the proposed method is applied to the TE process for fault diagnosis comparing with other *** results show the proposed method has superior performance.

关键词： Data-driven variational autoencoder Linear Dynamic System Fault Detection Fault Diagnosis

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Generative chemistry: drug discovery with deep learning generative models

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JOURNAL OF MOLECULAR MODELING 2021年第3期27卷 71-71页

作者： Bian, Yuemin Xie, Xiang-Qun Univ Pittsburgh Dept Pharmaceut Sci Sch Pharm Pittsburgh PA 15261 USA Univ Pittsburgh Computat Chem Genom Screening Ctr Sch Pharm Pittsburgh PA 15261 USA Univ Pittsburgh NIH Natl Ctr Excellence Computat Drug Abuse Res Pittsburgh PA 15261 USA Univ Pittsburgh Drug Discovery Inst 335 Sutherland Dr206 Salk Pavilion Pittsburgh PA 15261 USA Univ Pittsburgh Sch Med Dept Computat Biol Pittsburgh PA 15261 USA Univ Pittsburgh Sch Med Dept Struct Biol Pittsburgh PA 15261 USA

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

关键词： Drug discovery Deep learning Generative model Recurrent neural network variational autoencoder Adversarial autoencoder Generative adversarial network

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Supply Chain Management in the Digital Economy: Case Studies of Deep Learning Technology Applications

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JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2024年第1期32卷

作者： Huang, Anzhong Zhuang, Jianming Ren, Yuheng Rao, Yun Tsai, Sangbing Jiangsu Univ Sci & Technol Sch Econ & Management Zhenjiang Peoples R China Univ Hamburg Sch Business Adm Hamburg Germany Univ Cambridge Cambridge England Jianpan Kunlu Internet Things Res Inst Xiamen Co Xiamen Peoples R China Hangzhou Vocat & Tech Coll Coll Entrepreneurship Hangzhou Peoples R China Int Engn & Technol Inst Hong Kong Peoples R China

Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates variational autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

关键词： Supply Chain Management Deep Learning variational autoencoder Graph Neural Network Deep Reinforcement Learning

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Predicting steel column stability with uncertain initial defects using bayesian deep learning

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Zhao, Haoyang Wang, Chen Fan, Jiansheng Tsinghua Univ Dept Civil Engn China Educ Minist Key Lab Civil Engn Safety & Durabil Beijing 100084 Peoples R China

The stability of steel columns is difficult to predict accurately due to uncertain initial defects such as geometric imperfections and residual stress. To address this issue, we propose a probabilistic model that uses variational autoencoder (VAE) and transfer learning to estimate the loading capacities of steel columns. Our model can predict the confidence intervals of buckling loads without knowing the exact distribution of initial defects, providing more comprehensive information for engineering applications than traditional deterministic strength index. We establish a dataset of 1500 load-displacement curves of steel columns using four data augmentation approaches, and analyze the data distribution to validate the model's assumptions. Various criterions, including the mean squared error (MSE), the prediction interval coverage probability (PICP), and the prediction interval normalized average width (PINAW), are adopted to comprehensively measure the performance of confidence interval prediction. The numerical experiment validates that the trained model accurately predicts the confidence intervals for load-displacement responses, which perfectly cover the true curves with reasonable PINAW. Finally, we conduct a case study with a practical experiment to illustrate the model's potential application in failure probability calculation and reliability design. Our proposed model provides a promising probabilistic solution for quantifying the impact of uncertain parameters on structural analysis and significantly simplifies probability-based reliability design and optimal design processes.

关键词： Stability prediction variational autoencoder Initial defects Probabilistic computing Uncertainty quantification

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Scene text detection using structured information and an end-to-end trainable generative adversarial networks

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PATTERN ANALYSIS AND APPLICATIONS 2024年第2期27卷 33-33页

作者： Naveen, Palanichamy Hassaballah, Mahmoud KPR Inst Engn & Technol Dept Elect & Elect Engn Coimbatore India Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci AlKharj 16278 Saudi Arabia South Valley Univ Fac Comp & Informat Dept Comp Sci Qena Egypt

Scene text detection poses a considerable challenge due to the diverse nature of text appearance, backgrounds, and orientations. Enhancing robustness, accuracy, and efficiency in this context is vital for several applications, such as optical character recognition, image understanding, and autonomous vehicles. This paper explores the integration of generative adversarial network (GAN) and network variational autoencoder (VAE) to create a robust and potent text detection network. The proposed architecture comprises three interconnected modules: the VAE module, the GAN module, and the text detection module. In this framework, the VAE module plays a pivotal role in generating diverse and variable text regions. Subsequently, the GAN module refines and enhances these regions, ensuring heightened realism and accuracy. Then, the text detection module takes charge of identifying text regions in the input image via assigning confidence scores to each region. The comprehensive training of the entire network involves minimizing a joint loss function that encompasses the VAE loss, the GAN loss, and the text detection loss. The VAE loss ensures diversity in generated text regions and the GAN loss guarantees realism and accuracy, while the text detection loss ensures high-precision identification of text regions. The proposed method employs an encoder-decoder structure within the VAE module and a generator-discriminator structure in the GAN module. Rigorous testing on diverse datasets including Total-Text, CTW1500, ICDAR 2015, ICDAR 2017, ReCTS, TD500, COCO-Text, SynthText, Street View Text, and KIAST Scene Text demonstrates the superior performance of the proposed method compared to existing approaches.

关键词： Text detection Generative adversarial network variational autoencoder GAN loss VAE loss

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Inferring global-scale temporal latent topics from news reports to predict public health interventions for COVID-19

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PATTERNS 2022年第3期3卷 100435页

作者： Wen, Zhi Powell, Guido Chafi, Imane Buckeridge, David L. Li, Yue McGill Univ Sch Comp Sci Montreal PQ Canada McGill Univ Sch Populat & Global Hlth Montreal PQ Canada

The COVID-19 pandemic has highlighted the importance of non-pharmacological interventions (NPIs) for controlling epidemics of emerging infectious diseases. Despite their importance, NPIs have been monitored mainly through the manual efforts of volunteers. This approach hinders measurement of the NPI effectiveness and development of evidence to guide their use to control the global pandemic. We present EpiTopics, a machine learning approach to support automation of NPI prediction and monitoring at both the document level and country level by mining the vast amount of unlabeled news reports on COVID-19. EpiTopics uses a 3-stage, transfer-learning algorithm to classify documents according to NPI categories, relying on topic modeling to support result interpretation. We identified 25 interpretable topics under 4 distinct and coherent COVID-related themes. Importantly, the use of these topics resulted in significant improvements over alternative automated methods in predicting the NPIs in labeled documents and in predicting country-level NPIs for 42 countries.

关键词： latent topic models transfer learning variational autoencoder non-pharmacological interventions COVID-19 public health surveillance

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Disentangling Forced Trends in the North Atlantic Jet From Natural Variability Using Deep Learning

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JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2024年第14期129卷

作者： Hermoso, Alejandro Schemm, Sebastian Swiss Fed Inst Technol Inst Atmospher & Climate Sci Zurich Switzerland

Regional weather variability and extremes over Europe are strongly linked to variations in the North Atlantic jet stream, especially during the winter season. Projections of the evolution of the North Atlantic jet are essential for estimating the regional impacts of climate change. Therefore, separating forced trends in the North Atlantic jet from its natural variability is an extremely relevant task. Here, a deep learning based method, the Latent Linear Adjustment autoencoder (LLAE), is used for this purpose on an ensemble of fully-coupled climate simulations. The LLAE is based on a variational autoencoder and an additional linear component. The model uses detrended temperature and geopotential to predict the component of the zonal wind associated with natural variability. The residual between this prediction and the original wind field is interpreted as the forced component of the jet. The method is first tested for the geostrophic wind for which the forced trend can be obtained analytically from the difference between geostrophic wind computed from detrended and full geopotential. Despite the large variability of the total trends, the LLAE is shown to be effective in extracting the forced component of the trend for each individual ensemble member in both geostrophic and full wind fields. The LLAE-derived forced trend shows an increase in the upper-level zonal wind speed along a southwest-northeast oriented band over the ocean and a jet extension toward Europe. These are common characteristics over different periods and show some similarities to the upper-level zonal wind speed trend obtained from the ERA5 reanalysis. The North Atlantic jet stream, which is a narrow and strong air current flowing from west to east, has a substantial influence on daily weather variability and temperature and precipitation extremes in Europe. It is therefore essential to investigate how the North Atlantic jet is being modified under climate change. Trends in the North Atlantic jet a

关键词： jet streams North Atlantic forced trends dynamical adjustment variational autoencoder

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Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Kwon, Han Jo Heo, Jun Park, Su Hwan Park, Sung Who Byon, Iksoo Pusan Natl Univ Sch Med Biomed Res Inst Pusan Natl Univ HospDept Ophthalmol Gudeok Ro 179 Busan 49241 South Korea Pusan Natl Univ Yangsan Hosp Res Inst Convergence Biomed Sci & Technol Dept Ophthalmol Geumo Ro 20 Yangsan Si 50612 Gyeongsangnam D South Korea

This study aims to propose a generative deep learning model (GDLM) based on a variational autoencoder that predicts macular optical coherence tomography (OCT) images following full-thickness macular hole (FTMH) surgery and evaluate its clinical accuracy. Preoperative and 6-month postoperative swept-source OCT data were collected from 150 patients with successfully closed FTMH using 6 x 6 mm2 macular volume scan datasets. Randomly selected and augmented 120,000 training and 5000 validation pairs of OCT images were used to train the GDLM. We assessed the accuracy and F1 score of concordance for neurosensory retinal areas, performed Bland-Altman analysis of foveolar height (FH) and mean foveal thickness (MFT), and predicted postoperative external limiting membrane (ELM) and ellipsoid zone (EZ) restoration accuracy between artificial intelligence (AI)-OCT and ground truth (GT)-OCT images. Accuracy and F1 scores were 94.7% and 0.891, respectively. Average FH (228.2 vs. 233.4 mu m, P = 0.587) and MFT (271.4 vs. 273.3 mu m, P = 0.819) were similar between AI- and GT-OCT images, within 30.0% differences of 95% limits of agreement. ELM and EZ recovery prediction accuracy was 88.0% and 92.0%, respectively. The proposed GDLM accurately predicted macular OCT images following FTMH surgery, aiding patient and surgeon understanding of postoperative macular features.

关键词： Deep learning Generative deep learning model Macular hole Optical coherence tomography variational autoencoder

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VAE-based anomaly detection for embedded computer electronic components 22

VAE-based anomaly detection for embedded computer electronic...

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Proceedings of the 2022 6th International Conference on Computer Science and Artificial Intelligence

作者： Shuda Gao Zhong Ma Zhanzhuang He Yuanhong Mao Xi'an Microelectronic Technology Institute China

ISBN: (纸本)9781450397773

The current maintenance of aerospace equipment generally uses regular maintenance, scheduled maintenance, seasonal maintenance, after-the-fact maintenance, and replacement maintenance. These methods are ill-timed, time-consuming, and wasteful of materials. Monitoring the reliability and healthy operating status of each embedded computer electronic component is essential, and maintenance staff will benefit greatly from a data-driven approach to anomaly detection. It can be altered from "repair afterward" to "repair as necessary" and from " repair regularly" to "repair at any time" to solve the practical problems arising from maintenance. The variational autoencoder (VAE), which is based on the component storage aging acceleration data, is used in this paper to model the component's normal operating status and perform anomaly detection. The precision and recall of this anomaly detection method are 0.950 and 0.977. This method evaluates the operating status and reliability of each component, improves the reliability and service life of the computer, and establishes the technological framework for the next generation of computer Prognostics and Health Management (PHM) systems.

关键词： Anomaly detection variational autoencoder Embedded computer

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