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摘要 ：
With the global outbreak of COVID-19, an increasing number of countries have made imported epidemic control a priority, imposing restriction measures to prevent the spread of the virus caused by imported cases. To control the imported epidemic, it is necessary to accurately predict the number of imported cases from different source countries. This paper proposes a novel time series prediction approach called PNICA (Prediction on Number of Imported CAses) that uses deep learning to predict the number of COVID-19 imported cases. On the one hand, the proposed PNICA approach adopts a multi-modal learning strategy to fuse three sources of data: flight data, the epidemic data, and the data of historical imported cases. On the other hand, the proposed PNICA approach extends the traditional transformer model with cross-modal attention to learn the interactions between different data modalities to improve prediction accuracy. We use China as the target country and collect the number of imported cases from four source countries-Japan, USA, Russia, and the UK-as well as the epidemic data and flight data from May to November 2020. Experiments on the collected data demonstrate that the proposed PNICA approach outperforms the baseline methods in predicting the number of imported cases. The ablation study shows that both the multi-modal learning strategy and cross-modal attention can significantly improve prediction performance.
关键词 ：
Transformer model Transformer model Imported case prediction Imported case prediction COVID-19 COVID-19 Cross-modal attention Cross-modal attention Multi-modal learning strategy Multi-modal learning strategy
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| GB/T 7714 | Zhang, Wen , Xie, Rui , Li, Jian et al. Predicting the number of COVID-19 imported cases based on cross-modal transformer: A case study in China [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
| MLA | Zhang, Wen et al. "Predicting the number of COVID-19 imported cases based on cross-modal transformer: A case study in China" . | EXPERT SYSTEMS WITH APPLICATIONS 260 (2024) . |
| APA | Zhang, Wen , Xie, Rui , Li, Jian , Wang, Liang , Li, Xiang , Peng, Peng . Predicting the number of COVID-19 imported cases based on cross-modal transformer: A case study in China . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
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摘要 ：
Deep variational residual auto-encoder (ResNet-VAE) has shown promising outcomes in missing imputation of wastewater quality data. Nevertheless, with its large storage size and computation overhead, it is of great difficulty to deploy wastewater treatment plant (WWTP) sensors for real-time missing imputation. To address this problem, we propose a novel approach called lightweight-ResNet-VAE to compress classical ResNet-VAE by network pruning, weight quantization, and relative indexing in this article. First, we develop a three-step network pruning method to sparsify the weight matrices by removing insignificant weights to reduce the time cost of model inference. Second, we develop weight quantization and use eight shared weights to compress the size of each weight from 32-bit to 3-bit. Finally, the relative indexing is adopted to further compress the size of the classical ResNet-VAE by compressed sparse row (CSR), which greatly accelerates the model computation and saves storage size. Experiments on the Beipai IoT influent quality data set demonstrate that lightweight-ResNet-VAE compresses the size of the classical ResNet-VAE from 301.88 to 26.24 kB with a compression rate of 11.50 times, and outperforms the baseline methods in terms of computation acceleration, storage saving and energy consumption with only a slight decrease on accuracy as 2.74% in MAPE of missing imputation for wastewater quality data due to pruning less significant weights and quantizing the remaining weights.
关键词 ：
Lightweight ResNet-VAE Lightweight ResNet-VAE Mathematical models Mathematical models Quantization (signal) Quantization (signal) Indexing Indexing model compression model compression Computational modeling Computational modeling missing imputation missing imputation wastewater quality wastewater quality Sensors Sensors Imputation Imputation Wastewater Wastewater
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| GB/T 7714 | Zhang, Wen , Li, Rui , Quan, Pei et al. Lightweight Deep Learning for Missing Data Imputation in Wastewater Treatment With Variational Residual Auto-Encoder [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (23) : 38312-38326 . |
| MLA | Zhang, Wen et al. "Lightweight Deep Learning for Missing Data Imputation in Wastewater Treatment With Variational Residual Auto-Encoder" . | IEEE INTERNET OF THINGS JOURNAL 11 . 23 (2024) : 38312-38326 . |
| APA | Zhang, Wen , Li, Rui , Quan, Pei , Chang, Jiang , Bai, Yongsheng , Su, Bojun . Lightweight Deep Learning for Missing Data Imputation in Wastewater Treatment With Variational Residual Auto-Encoder . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (23) , 38312-38326 . |
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摘要 ：
As an essential concept in attention, context defines the overall scope under consideration. In attention- based GNNs, context becomes the set of representation nodes of graph embedding. Current approaches choose immediate neighbors of the target or its subset as the context, which limits the ability of attention to capture long-distance dependency. To address this deficiency, we propose a novel attention-based GNN framework with extended contexts. Concretely, multi-hop nodes are first selected for context expansion according to information transferability and the number of hops. Then, to reduce the computational cost and fit the graph representation learning process, two heuristic context refinement policies are designed by focusing on local graph structure. One is for the graphs with high degrees, multi-hop neighbors with fewer connections to the target are removed to acquire accurate diffused information. The other is for the graphs with low degrees or uniform degree distribution, low-transferability neighbors are dislodged to ensure the graph locality is not obscured by the global information induced by the extended context. Finally, multi-head attention is employed in the refined context. Numerical comparisons with 23 baselines demonstrate the superiority of our method. Extensive model analysis shows that extending context with the informative multi-hop neighbors properly indeed promotes the performance of attention-based GNNs.
关键词 ：
Extended context Extended context PageRank PageRank Graph attention networks Graph attention networks Attention mechanism Attention mechanism Graph neural networks Graph neural networks
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| GB/T 7714 | Quan, Pei , Zheng, Lei , Zhang, Wen et al. ExGAT: Context extended graph attention neural network [J]. | NEURAL NETWORKS , 2024 , 181 . |
| MLA | Quan, Pei et al. "ExGAT: Context extended graph attention neural network" . | NEURAL NETWORKS 181 (2024) . |
| APA | Quan, Pei , Zheng, Lei , Zhang, Wen , Xiao, Yang , Niu, Lingfeng , Shi, Yong . ExGAT: Context extended graph attention neural network . | NEURAL NETWORKS , 2024 , 181 . |
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摘要 ：
With the development of urbanization, the accurate prediction of effluent quality has become increasingly critical for the real-time control of wastewater treatment processes. The conventional method for measuring effluent biochemical oxygen demand (BOD) suffers from significant time delays and high equipment costs, making it less feasible for timely effluent quality assessment. To tackle this problem, we propose a novel approach called En-WBF (ensemble learning based on weighted BoostForest) to predict effluent BOD in a soft-sensing manner. Specifically, we sampled several independent subsets from the original training set by weighted bootstrap aggregation to train a series of gradient BoostTrees as the base models. Then, the predicted effluent BOD was derived by weighting the base models to produce the final prediction. Experiments on real datasets demonstrated that on the UCI dataset, the proposed En-WBF approach achieved a series of improvements, including by 28.4% in the MAE, 40.9% in the MAPE, 29.8% in the MSE, 18.2% in the RMSE, and 2.3% in the R2. On the Fangzhuang dataset, the proposed En-WBF approach achieved a series of improvements, including by 8.8% in the MAE, 9.0% in the MAPE, 12.8% in the MSE, 6.6% in the RMSE, and 1.5% in the R2. This paper contributes a cost-effective and timely solution for wastewater treatment management in real practice with a more accurate effluent BOD prediction, validating the research in the application of ensemble learning methods for environmental monitoring and management.
关键词 ：
soft measurement soft measurement biochemical oxygen demand biochemical oxygen demand weighted boosting forest weighted boosting forest ensemble learning ensemble learning wastewater treatment wastewater treatment
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| GB/T 7714 | Su, Bojun , Zhang, Wen , Li, Rui et al. En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest [J]. | WATER , 2024 , 16 (8) . |
| MLA | Su, Bojun et al. "En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest" . | WATER 16 . 8 (2024) . |
| APA | Su, Bojun , Zhang, Wen , Li, Rui , Bai, Yongsheng , Chang, Jiang . En-WBF: A Novel Ensemble Learning Approach to Wastewater Quality Prediction Based on Weighted BoostForest . | WATER , 2024 , 16 (8) . |
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摘要 ：
In recent years, there has been an increase in online review manipulation on the platforms of electronic commerce. Previous studies clarify the benefits and harms of online review manipulation for firms, and they provide mixed conclusions on the influence of online review manipulation. However, the effect of online review manipulation on product sales has not yet been thoroughly studied due to the covert nature of review manipulation. To fill this research gap, this paper examines the different effects of review manipulation in three dimensions: quantity manipulation, quality manipulation, and relation manipulation. Drawing on the Information Manipulation Theory, which reveals the manipulation behaviors of different dimensions, it is proposed that the influence of online review manipulation differs significantly among different information manipulation dimensions. The results of the empirical experiments show that the effect of review quantity manipulation on product sales exhibits an inverted U-shape. In addition, review quality manipulation positively affects product sales, but review relation manipulation exerts a negative effect. Moreover, the magnitude of the effect of review manipulation is contingent upon review manipulation duration. The findings shed light on the heterogeneous effect of review manipulation dimensions on product sales from an information manipulation perspective and suggest a need for improvement in online fraudulent review detection in the early stage of review manipulation.
关键词 ：
Online review manipulation Online review manipulation Quantity manipulation Quantity manipulation Quality manipulation Quality manipulation Review manipulation duration Review manipulation duration Relation manipulation Relation manipulation
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| GB/T 7714 | Wang, Qiang , Zhang, Wen , Li, Jian et al. Benefits or harms? The effect of online review manipulation on sales [J]. | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2023 , 57 . |
| MLA | Wang, Qiang et al. "Benefits or harms? The effect of online review manipulation on sales" . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 57 (2023) . |
| APA | Wang, Qiang , Zhang, Wen , Li, Jian , Ma, Zhenzhong , Chen, Jindong . Benefits or harms? The effect of online review manipulation on sales . | ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS , 2023 , 57 . |
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摘要 ：
Accurate prediction on influent wastewater quality is of great importance to energy saving and chemical dosage reduction of wastewater treatment plants (WWTPs). However, the existing methods ignore the data noise caused by water sensors working in harsh conditions and the intrinsic variable dynamics inherent in the time series of wastewater quality. To tackle this problem, we propose a novel approach called wt-ResLSTM (wavelet transform and Residual Long Short -Term Memory) to predict the influent wastewater quality. Specifically, we adopt wavelet transform and semi-soft thresholding to remove the noise from influent wastewater quality data adaptively. Then, we use autoencoder to learn the latent representation of the recent fluctuation of wastewater quality to capture its transient uncertainty. Further, the residual LSTM is adopted to learn both the long-term and short-term sequential dependencies of influent wastewater quality from the historical wastewater quality and the latent representation of its recent fluctuation. Experiments on the dataset from a large-scale urban WWTP in Beijing demonstrate that the proposed wt-ResLSTM approach outperforms state-of-the-art techniques in predicting influent wastewater quality in terms of level accuracy and directional accuracy.
关键词 ：
Wastewater treatment plant Wastewater treatment plant Residual LSTM Residual LSTM Prediction of influent wastewater quality Prediction of influent wastewater quality Wavelet transform Wavelet transform
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| GB/T 7714 | Zhang, Wen , Zhao, Jiangpeng , Quan, Pei et al. Prediction of influent wastewater quality based on wavelet transform and residual LSTM [J]. | APPLIED SOFT COMPUTING , 2023 , 148 . |
| MLA | Zhang, Wen et al. "Prediction of influent wastewater quality based on wavelet transform and residual LSTM" . | APPLIED SOFT COMPUTING 148 (2023) . |
| APA | Zhang, Wen , Zhao, Jiangpeng , Quan, Pei , Wang, Jiawei , Meng, Xiaoyu , Li, Qun . Prediction of influent wastewater quality based on wavelet transform and residual LSTM . | APPLIED SOFT COMPUTING , 2023 , 148 . |
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摘要 ：
Apart from the well-recognized popularity and importance of online question-and-answer (Q&A) communities, the sustainable growth of these platforms is a serious concern faced by online knowledge-sharing platforms. Personality traits play a significant role in shaping human behavior and decision-making. Numerous studies have attempted to understand the volunteer knowledge contribution and provide solutions to improve low participation. However, the role of personality traits of online knowledge-sharing community users in their volunteer knowledge contribution is still unexplored. Based on the big five personality traits, we have proposed a model to understand the role of personality features of online users. We have collected a cross-sectional dataset from online Q&A community users and applied the SEM-ANN model to conclude our model results that will provide insight into online community users' contribution behavior. Findings show that agreeableness, openness, extrovert, and conscientiousness positively and neuroticism negatively influence online participation to contribute knowledge. We have also observed that the influence of personality traits significantly differs for both genders, and negativity bias is stronger in male users. Study results provide new insight into understanding online users' behavior and provide a distinct angle to understand and add value to previous studies' findings on online Q&A community users.
关键词 ：
knowledge contribution knowledge contribution Q&A community Q&A community SEM-ANN SEM-ANN big five personality traits big five personality traits
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| GB/T 7714 | Mustafa, Sohaib , Zhang, Wen . Why Do I Share? Participants' Personality Traits and Online Participation [J]. | INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION , 2023 , 40 (14) : 3763-3781 . |
| MLA | Mustafa, Sohaib et al. "Why Do I Share? Participants' Personality Traits and Online Participation" . | INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 40 . 14 (2023) : 3763-3781 . |
| APA | Mustafa, Sohaib , Zhang, Wen . Why Do I Share? Participants' Personality Traits and Online Participation . | INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION , 2023 , 40 (14) , 3763-3781 . |
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摘要 ：
The task of detecting fraudulent reviewers is of great importance to E-commerce platforms. Existing research has invested much effort into developing comprehensive features and advanced techniques to detect fraudulent reviewers. However, most of these studies have ignored the data imbalance problem inherent in fraudulent reviewer detection: non-fraudulent reviewers are the majority, while fraudulent reviewers are the minority in real practice. To fill this gap, we propose a novel approach called ImDetector to detect fraudulent reviewers while handling data imbalance based on weighted latent Dirichlet allocation (LDA) and Kullback-Leibler (KL) divergence. Specifically, we develop a weighted LDA model to extract the latent topics of reviewers distributed on the review features. Asymmetric KL divergence is adopted to make the similarity measure between reviewers biased toward the fraudulent minority when using the K-nearest-neighbor for classification. By mapping the reviewers to the latent topics of features derived from the weighted LDA model and measuring the similarities between reviewers using asymmetric KL divergence, the data imbalance problem in fraudulent reviewer detection is alleviated. Extensive experiments on the Yelp.com dataset demonstrate that the proposed ImDetector approach is superior to the state-of-the-art techniques used for fraudulent reviewer detection. We also explain the experimental results and present the managerial implications of this paper.
关键词 ：
Kullback-Leibler divergence Kullback-Leibler divergence Imbalanced data Imbalanced data Weighted LDA Weighted LDA Fraudulent reviewer detection Fraudulent reviewer detection E-commerce E-commerce
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| GB/T 7714 | Zhang, Wen , Xie, Rui , Wang, Qiang et al. A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback-Leibler divergence [J]. | DECISION SUPPORT SYSTEMS , 2022 , 157 . |
| MLA | Zhang, Wen et al. "A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback-Leibler divergence" . | DECISION SUPPORT SYSTEMS 157 (2022) . |
| APA | Zhang, Wen , Xie, Rui , Wang, Qiang , Yang, Ye , Li, Jian . A novel approach for fraudulent reviewer detection based on weighted topic modelling and nearest neighbors with asymmetric Kullback-Leibler divergence . | DECISION SUPPORT SYSTEMS , 2022 , 157 . |
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摘要 ：
In this paper, we propose a coronavirus disease (COVID-19) epidemiological model called SEIR-FMi (Susceptible -Exposed-Infectious-Recovery with Flow and Medical investments) to study the effects of intra-city population movement, inter-city population movement, and medical resource investment on the spread of the COVID-19 epidemic. We theoretically derived the reproduction number of the SEIR-FMi model by using the next -generation matrix method and empirically simulate the individual impacts of population movement and medi-cal resource investment on epidemic control. We found that intra-and inter-city population movements will increase the risk of epidemic spread, and the effect of inter-city population movement on low-risk areas is higher than that on high-risk areas. Increasing medical resource investment can not only speed up the recover rate of patients but also reduce the growth rate of infected cases and shorten the spread duration of the epidemic. We collected data on intra-city population movement, inter-city population movement, medical resource investment, and confirmed cases in the cities of Wuhan, Jingzhou, and Xiangyang, Hubei Province, China, from January 15 to March 15, 2020. Using the collected data, we validated that the proposed SEIR-FMi model performs well in simulating the spread of COVID-19 in the three cities. Meanwhile, this study confirms that three non -pharmaceutical interventions, namely community isolation, population mobility control, and medical resource aid, applied during the epidemic period are indispensable in controlling the spread of COVID-19 in the three cities.
关键词 ：
Effective reproduction number Effective reproduction number Population movement Population movement Basic reproduction number Basic reproduction number Medical resources Medical resources COVID-19 COVID-19 SEIR-FMi model SEIR-FMi model
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| GB/T 7714 | Zhang, Wen , Xie, Rui , Dong, Xuefan et al. SEIR-FMi: A coronavirus disease epidemiological model based on intra-city movement, inter-city movement and medical resource investment [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2022 , 149 . |
| MLA | Zhang, Wen et al. "SEIR-FMi: A coronavirus disease epidemiological model based on intra-city movement, inter-city movement and medical resource investment" . | COMPUTERS IN BIOLOGY AND MEDICINE 149 (2022) . |
| APA | Zhang, Wen , Xie, Rui , Dong, Xuefan , Li, Jian , Peng, Peng , Gonzalez, Ernesto D. R. Santibanez . SEIR-FMi: A coronavirus disease epidemiological model based on intra-city movement, inter-city movement and medical resource investment . | COMPUTERS IN BIOLOGY AND MEDICINE , 2022 , 149 . |
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摘要 ：
Accurate traffic congestion prediction is crucial for efficient urban intelligent transportation systems (ITS). Though most existing methods attempt to characterize spatial correlation and temporal correlation in traffic congestion, few of them consider spatial heterogeneity and temporal heterogeneity: spatial correlation depends on temporality, and temporal correlation depends on spatiality in traffic congestion. To address this problem, this paper proposes a novel approach called TCP-BAST with bilateral alternation to simultaneously capture both the correlation and the heterogeneity between spatiality and temporality to improve traffic congestion prediction. First, to capture spatial correlation and spatial heterogeneity, we propose a spatial-temporal alternation (STA) module with multi-head graph attention networks and temporal embedding. Second, to capture temporal correlation and temporal heterogeneity, we propose a temporal-spatial alternation (TSA) module with multi-head masked attention networks and spatial embedding. Third, to predict the traffic congestion of multiple road sections in a traffic network, we propose a spatial-temporal fusion (STF) module to fuse the multi-grained spatialtemporal features derived from the STA and TSA modules. The experimental results on a real-world traffic dataset demonstrate that the proposed TCP-BAST approach outperforms the baseline methods in terms of both the mean absolute error (MAE) and the root mean squared error (RMSE). Both spatial-temporal alternation and temporal-spatial alternation are important for improving traffic congestion prediction, with the former being more critical than the latter. (C) 2022 Elsevier Inc. All rights reserved.
关键词 ：
Spatial heterogeneity Spatial heterogeneity Bilateral alternation Bilateral alternation Spatial-temporal fusion Spatial-temporal fusion Traffic congestion prediction Traffic congestion prediction Temporal heterogeneity Temporal heterogeneity
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| GB/T 7714 | Zhang, Wen , Yan, Shaoshan , Li, Jian . TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality [J]. | INFORMATION SCIENCES , 2022 , 608 : 718-733 . |
| MLA | Zhang, Wen et al. "TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality" . | INFORMATION SCIENCES 608 (2022) : 718-733 . |
| APA | Zhang, Wen , Yan, Shaoshan , Li, Jian . TCP-BAST: A novel approach to traffic congestion prediction with bilateral alternation on spatiality and temporality . | INFORMATION SCIENCES , 2022 , 608 , 718-733 . |
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