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In view of the problem of fuzziness of rating and tag in traditional collaborative filtering algorithms,this paper used trapezoidal fuzzy number to describe the mapping relationship between rating and satisfaction.The algorithm considered the impact of sparseness of the rating,constructed a new trapezoidal fuzzy rating model to determine the similarity based on fuzzy rating,analyzed the degree of membership between the tag and the item,and constructed a fuzzy item-tag matrix to measure the similarity based on the degree of tag membership.Finally,it used the improved scoring prediction strategy to estimate the score.The experimental results on the MovieLens dataset show that the proposed algorithm improves the prediction accuracy while suppressing the cold start of the project,alleviating the problems of fuzziness and sparseness,which indicates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Imaging Science & Photochemistry is the property of Imaging Science & Photochemistry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Resources & Environment / Nongye Ziyuan yu Huanjing Xuebao is the property of Journal of Agricultural Resources & Environment Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The stock price is affected by many factors, such as policy, macro-economy and the company's operating conditions, among which there is a certain degree of correlation. So the stock data of high noise and non- stationary feature makes stock prediction difficult. Based on the separability between classes of Mahalanobis distance, this paper proposes structured maximum margin twin suport vector machine (SMM-TWSVM). The method finds two nonparallel hyperplane for positive class samples and negative class samples respectively, so that the Euclidean distance of each class of samples from their own hyperplane is as small as possible, and the Mahalanobis distance of divorced class hyperplane is as large as possible. The experimental results of 8 benchmark datasets show that this method has a stable accuracy in the classification of noisy data, thus improving the prediction performance and anti-noise ability of the model. Meanwhile, it is applied to the prediction of the fluctuation tend of stock price, through the empirical analysis of 14 stocks such as Ping An of China and Shanghai Composite Index, Shanghai A Index, Shanghai 380 Index, the results show that compared with other comparison models, SMM-TWSVM shows better prediction results and has certain practical value. [ABSTRACT FROM AUTHOR]

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Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the problem of low accuracy of construction schedule prediction in location-based management systems (LBMS), this paper adopts Harmony Search Algorithm (HS) to optimize the weights and thresholds of BP neural networks. By integrating practical construction information, an LBMS framework based on HS-BP neural networks is constructed to accurately predict the construction schedule. Finally, the prediction accuracy of HS-BP neural network is verified by taking the construction schedule of roof slab formwork of Guangzhou Guanglianda South China Headquarters Base Project as an example. The flow line of the actual and predicted schedules of the beam slab are also drawn to illustrate the reliability of the LBMS framework based on HS-BP neural network. [ABSTRACT FROM AUTHOR]

Based on the "air-land-water" integrated model system and multi-level collaborative architecture of the basin, this paper proposes a technological system for a non-point source pollution forecasting and warning platform for the Pengxi River basin, and a non-point source pollution forecasting and warning platform for the Pengxi River basin has been established. By automatically collectting and processsing meteorological data, and invoking the meteorological model, land model, hydrodynamic model, and water quality model driven by the control center, the platform can make hourly simulated forecasts of land surface runoff and the flow of pollutants such as TN, TP and NH3-N for the next 3 to 15 days, and warn about the risk of pollutant fluxes, providing technical support for the intelligent basin management and comprehensive control of non-point source pollution. [ABSTRACT FROM AUTHOR]

Based on deep learning, precipitation prediction has been a hot topic in hydrological research in recent years. Monthly precipitation data is a typical small sample data that cannot meet the demands of deep learning for large datasets. Therefore, this paper proposes an improved method for predicting monthly precipitation using a Long Short-term Memory Network (LSTM) by integrating the ideas of signal decomposition and boundary correction. First, to address the " end effect" of sequence decomposition, this paper uses the waveform feature matching extension method to expand the boundaries of the original sequence. Then, the original sequence is decomposed twice by using Extreme-point Symmetric Mode Decomposition (ESMD) and Variational Mode Decomposition (VMD). ESMD is used to extract different sorts of scale information of the monthly precipitation sequence, obtaining several model components with decreasing frequencies and one residual component. After cutting off the most severe extension parts that "pollute" the internal data of each sub-sequence, VMD further smooths the high-frequency components. Finally, LSTM is applied to each sub-sequence for prediction, and the predicted results are reconstructed to obtain the final prediction. The monthly precipitation in Badong County, Hubei Province is selected as an example for verification. Through model comparison analysis, the results show that compared to traditional single SVM and LSTM models, the prediction model combining signal decomposition algorithm has more advantages in monthly precipitation prediction; The method of boundary correction is incorporated into the combined model by using the ESMD algorithm improves the overall prediction accuracy of the model. The prediction effect of high-frequency components is a key factor determining the accuracy of combined model prediction. The method proposed in this paper not only performs the best on the selected evaluation indicators but also significantly improves the fitting effect of extreme data points. In particular, even when facing a small sample dataset, the Nash-Sutcliffe Efficiency coefficient (NSE) of monthly precipitation prediction can reach 0.960 0 by using this model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Reproductive Health/Family Planning is the property of Editorial Board of the Journal of International Reproductive Health/Family Planning and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the era of e-commerce, accurately predicting user purchase intentions has become a crucial factor for enhancing sales efficiency and optimizing the customer experience. Addressing the limitations of traditional ensemble strategies, which often suffer from subjective biases during the model design phase, this paper introduced an adaptive evolutionary ensemble learning model to predict user purchase intentions. This model adaptively selected the optimal base learners and meta-learners, incorporating both the predictive information from the base learners and the differential information between features to expand the feature dimensions, enhancing prediction accuracy. Moreover, to further refine the predictive capabilities of the model, this paper designed a binary adaptive differential evolution algorithm for feature selection, aiming to identify features that significantly influence the prediction outcome. Research results show that the binary adaptive differential evolution algorithm outperforms traditional optimization algorithms in global searches and feature selection. Compared to six common ensemble models and the DeepForest model, the proposed evolutionary ensemble model achieves a 2.76% and 2.72% increase in AUC value, respectively, and effectively mitigates the impacts of data imbalance [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Natural Gas Geoscience is the property of Natural Gas Geoscience and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper addressed the issue of accurate house price forecasting by considering only the trend of price samples or the functional relationship between correlation attributes and prices. To overcome this challenge, this paper proposed a spatiotemporal attention graph convolution long short-term memory model, abbreviated as AG-LSTM, including local feature extraction module, regional feature extraction module, and composite prediction module. The local feature extraction module used isomorphic graph and heterogeneous graph neural network respectively to extract local feature information on the communities and their multiple correlation attributes, as well as the correlation between the communities and their neighboring supporting facilities. The region feature extraction module firstly clustered the adjacent communities, explored the importance of each community to its corresponding region by using graph attention network, and then established a mapping matrix between the communities and their regions. The module extracted the regional features based on the information of these communities and the mapping matrix. The composite prediction module used a long short-term memory model to perform modeling on the composite features time series composed of local and regional features. The paper conducted experiments using the Beijing housing price data from Lianjia website, and the results show that the AG-LSTM model outperformed the existing baseline models. This model can simultaneously explore the influence of location relationship between the communities, location relationship between the communities and their supporting facilities, multiple correlation attributes, and price trend on the time series, to achieve good performance in house price forecasting. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the accuracy of PV power prediction, this paper proposed a new short-term PV power interval prediction model based on TCN. Firstly, the model used the soft threshold and attention mechanism of DRSN to modify the residual module of TCN so as to enhance its capability to extract useful features, and reduce the impact of redundant features. Then, it adopted the SSA to search the optimal hyper-parameters automatically, such as the convolutional kernel size and the number of TCN layers in the convolutional layer of the TCN, to overcome the drawback of the original TCN with insufficient receptive fields. Next, this paper applied the KDE to analyze the error of the point prediction results of the proposed improved TCN short-term PV power forecasting model to obtain its output interval. Finally, comparative simulation experiments show that the RMSE of the proposed SSA-DRSN-TCN model can reach 0.27,which is better than those of LSTM,GRU,CNN-LSTM and TCN, and the KDE method can accurately describe the PV power volatility intervals at 80%,90% and 95% confidence levels, the proposed model verifies the effectiveness in improving the performance of PV power prediction. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Cryptologic Research (2095-7025) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A scheduling method based on equipment load balancing rules is proposed to solve the problem of unbalanced load of each observing device in traditional space target monitoring resource scheduling. In this paper, the visibility of space targets is taken as the mathematical basis, STK software package is used to forecast the visibility of tracking arcs of space targets, the equipment load balancing scheduling rules are integrated into the traditional scheduling process, and the observation requirements are respectively 3 laps, 4 laps and 5 laps as the input for experimental simulation. The generated scheduling scheme is presented in the form of Gantt chart. Finally, the constructed evaluation index is used to evaluate the value. The experimental results show that the scheduling rules based on equipment load balancing can well achieve the balance of the working time of each observation equipment, and can realize the tracking and monitoring of more space targets. [ABSTRACT FROM AUTHOR]

Accurate precipitation nowcasting is crucial for daily life, but the current forecasting models need further improvement in terms of accuracy. This paper proposed an innovative forecasting model called BTPN to address this issue. The model introduced bidirectional Transformer to extract features from both the forward and backward directions of spatiotemporal sequences, capturing key information and reducing spatiotemporal feature loss. It combined the convolutional Transformer block with the local encoding of convolution and the global encoding of Transformer to enhance spatiotemporal information extraction and correlation, alleviating the problem of long-term spatiotemporal information loss. The model also incorporated a detail extraction module to reduce the loss of local details and mitigate the issue of dissipation in high-value areas. Evaluation on the HKO-7 dataset shows that the BTPN model surpasses other advanced models in terms of MAE, SSIM, and CSI metrics, demonstrating excellent predictive capability in large-scale precipitation and extreme weather scenarios. The experiments demonstrate that the BTPN model possesses higher forecasting accuracy and promising applications. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: In view of the low accuracy of flow forecast in tidal reach due to the combined influence of runoff and tide, combining the ability of variational mode decomposition (VMD) to process non-stationary signals and the advantages of back propagation (BP) neural network to deal with nonlinear problems, this paper proposes a new method for discharge prediction in tidal reach based on the combination model of tidal current harmonic analysis and VMD-BP neural network. Methods: First, the harmonic analysis method is used to separate the tidal discharge and residual discharge from the original discharge. Second, a BP neural network is constructed based on error backpropagation algorithm. Then, the BP neural network is used to simulate and train the power flow data and residual current data processed by VMD. Finally, the output tidal discharge and residual discharge components are superimposed and reconstructed to obtain the final discharge prediction results. Results: Experiments performed at the Xuliujing section of the Yangtze Estuary show that the discharge prediction accuracy of BP neural network method is better than that of traditional harmonic analysis method, with the root mean square error (RMSE) is decreased by about 3 400 m³ /s, and the relative standard accuracy (RSD) is increased by about 6%. In addition, compared with the traditional method, RMSE of the proposed method in this paper is decreased by about 5 000 m³ /s, and the RSD is increased by about 9%. Conclusions: The combined model based on harmonic analysis and VMD-BP neural network can effectively improve the prediction accuracy of discharge in tidal reach. At the same time, it also provides a new idea for the discharge prediction in the waters with variable flow conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Harbour Engineering is the property of Editorial Office of China Harbour Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Harbour Engineering is the property of Editorial Office of China Harbour Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spatiotemporal crowd flow prediction is one of the key technologies in smart cities. There are mainly two major shortcomings that plague researchers and developers. Firstly, crowd flow is affected by complex factors and previous studies have summarized a variety of spatiotemporal prior knowledge. However, it is difficult for followup work to comprehensively incorporate this prior knowledge as its application scenarios are diverse. Secondly, with the development of deep learning technology, implementing state-of- the-art models is cumbersome work and becomes more and more complicated. To fill in the above gaps, this paper designs time- series sampling interfaces and graph construction interfaces. The time series sampling interfaces can generate different types of time series based on diverse prior knowledge, and the graph construction interfaces can build different types of spatial graphs. Moreover, users can extend the above two interfaces to utilize new spatiotemporal prior knowledge. Based on the TensorFlow framework, this paper implements a variety of advanced spatiotemporal graph models and encapsulates the frequently-used spatiotemporal modeling units. Users can leverage state-of- the- art spatiotemporal models and perform customized development based on these advanced layers. In summary, the spatiotemporal crowd flow prediction tool box UCTB integrates diverse spatiotemporal prior knowledge and a variety of advanced models, which may promote the development of spatiotemporal crowd flow prediction applications. The codes and detailed documents are open-source. The URL of UCTB is https://github.com/uctb/UCTB. [ABSTRACT FROM AUTHOR]

In this paper, the Lightweight Gradient Boosting Tree Model (LGB) is employed to simulate and forecast monthly runoff sequences of the Three Gorges Reservoir, and the forecasting uncertainties are analyzed by using the Hydrological Uncertainty Processor (HUP). The findings of this paper are as follows: ① the comparison between predictions of GBDT and LGB models indicates that the LGB model has higher accuracy than the GBDT model for both calibration and validation periods; ② the preferred forecasts (Q50 values) generated by the HUP model present a higher accuracy than the deterministic prediction generated by the LGB model, especially for the flood season; ③ the 90% uncertainty confidence interval with a narrow bandwidth for both calibration and validation periods can cover most observed points, which suggests that the result of uncertainty analysis is accurate and reasonable. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the problems of node heterogeneity and inapplicability of triangular form balance theory in signed bipartite network modeling, this paper proposed a signed bipartite graph neural network enhanced by potential group assignment and contrast learning, which could extract the display and implicit information fully through complementing each other with homogeneous and heterogeneous spaces. In the homogeneous space, this paper treated nodes as a combination of multiple learnable potential groups, and then mined information among nodes by training automatically. In the heterogeneous space, this paper adopted the attention aggregators with directions to aggregate information of neighbors, and then used the contrast learning for network reconstruction based on mutual information to guide the aggregation process to obtain more expressive node representations. This paper performed comparative experiments with a variety of related models on the signed link prediction task. Experimental results show that it can obtain optimal values for 12 of the 16 evaluation results obtained using four evaluation metrics on four real datasets, which verifies the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Compared to classic natural language processing tasks like Q&A, script event prediction has garnered less attention. In order to deploy subsequent events appropriately, script event prediction intends to accurately forecast future events with a given contextual event. This study is quite important and requires summarization. Based on the script event prediction task, this paper firstly proposed fundamental concept of script event prediction. Then it described argument composition and event representation learning in event characterization. Also, event representation learning was shown from embedded coding and semantic enhancement. Next, this paper summarized script modeling approaches from event pair, event chain, event graph, and integrated modeling in detail. In addition, it examined the impacts of different models, and horizontally evaluated the effects of the existing models. Finally, this paper provided a summary of the issues with the present study, along with an outlook on promising future research areas. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)