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1. WFLNNet: Weighted Fusion of Linear and Nonlinear Predictions for Multivariate Time Series | |||
Dan Liu,Yuke Wang,Kun Xie,Ruotian Xie,Wei Liang,Dafang Zhang,Jigang Wen | |||
Computer Science and Technology 19 May 2022 | |||
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Abstract:Multivariate time series forecasting has been widely used in finance, environment, transportation and other fields. However, traditional statistical prediction models usually assume that the time series conforms to a certain distribution or functional form, and cannot capture the complex nonlinear relationships. Although neural network based algorithms have powerful learning abilities, they usually ignore the linear features in time series. By weighted and fused both Linear and Nonlinear Predictions, this paper proposes a novel WFLNNet, where the linear prediction module is designed based on an autoregressive model while the nonlinear prediction module is designed based on the neural network and consists of a feature extraction encoder, an interactive attention network, and a fully connected layer to capture the most effective features in temporal and spatial correlations, as well as a mutual influence among multivariate time series. We have done experiments using 4 real datasets by comparing them with 6 baseline algorithms. The experimental results demonstrate that WFLNNet outperforms the 6 baseline algorithms with more accurate prediction. | |||
TO cite this article:Dan Liu,Yuke Wang,Kun Xie, et al. WFLNNet: Weighted Fusion of Linear and Nonlinear Predictions for Multivariate Time Series[OL].[19 May 2022] http://en.paper.edu.cn/en_releasepaper/content/4757819 |
2. Multidimensional Features Based Model for Social Network User Classification | |||
MO Qin-Chu,DENG Xiao-Long1,SONG Lin-Ming | |||
Computer Science and Technology 12 March 2021 | |||
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Abstract:In recent years, social network users have been the focus of research, but existing research often divides users into normal and malicious users, lacking a more detailed analysis. To address the absence of a precise user classification model, this paper builds a three-dimensional classification model including Anti-bot Analysis, Publicity, and Hashtag Manipulation for measuring user behaviors and classifying users into four categories: harmless users, publicizing users, hashtag hijacking users, and malicious publicizing users. Based on the classification model, this paper has built a set of user classification features, and introduces four new features. We also create two new Weibo user datasets with new features, and re-processes an existing Weibo user dataset. The experimental results show that the model and features proposed in this paper have good classification efficiency for precise Weibo user classification and bot behavior recognition, and have obvious classification improvement for decision trees and BP neural networks. | |||
TO cite this article:MO Qin-Chu,DENG Xiao-Long1,SONG Lin-Ming. Multidimensional Features Based Model for Social Network User Classification[OL].[12 March 2021] http://en.paper.edu.cn/en_releasepaper/content/4753963 |
3. DMOO:Dynamic Mobility Oriented Task Offloading in Vehicular Edge Computing | |||
Cheng Ziqing, LI Zhiyong, Wang Qi, Nouioua Mourad | |||
Computer Science and Technology 29 April 2020 | |||
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Abstract:The rapid development of smart vehicles and their applications constrained by computing resource and time response pose crucial problems in vehicle-to-vehicle (V2V) communications, such as how to provide the required computation capabilities and how to ensure the accomplishment of tasks within a limited time. Mobile edge computing (MEC) has emerged as a new paradigm that can be used to improve vehicular services through computation offloading among smart vehicles. However, new challenges may be encountered in MEC for V2V communication due to the highly dynamic mobility of vehicles. In this paper, we propose a dynamic-mobility-oriented offloading solution(DMOO) for delay-constrained vehicular computation offloading. Specifically, to overcome the problems caused by mobility and limited resources, we design a risk assessment model to estimate the probability for vehicles to be out of range and then develop a discrete artificial bee colony algorithm based on this model to balance the offloading utility and potential risks. Extensive experiment result demonstrate that our solution can significantly enhance the utility and reduce risk compared to other baselines. | |||
TO cite this article:Cheng Ziqing, LI Zhiyong, Wang Qi, et al. DMOO:Dynamic Mobility Oriented Task Offloading in Vehicular Edge Computing[OL].[29 April 2020] http://en.paper.edu.cn/en_releasepaper/content/4751873 |
4. A Process Driven Architecture of Analytical Customer Relationship Management Systems | |||
Liu Jun,Wu Yu | |||
Computer Science and Technology 08 December 2010 | |||
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Abstract:Analytical Customer Relationship Management(CRM) systems which have been proved to be important for enterprises' survive and development, are able to discover valuable knowledge from large scale operational and historical data. In this paper, we propose a process driven architecture of analytical CRM systems based on an existing architecture which has been proved to be feasible in a project. In this architecture, we mainly integrate distributed technology which make this solution more practical and is adapted to the Web environment. | |||
TO cite this article:Liu Jun,Wu Yu. A Process Driven Architecture of Analytical Customer Relationship Management Systems[OL].[ 8 December 2010] http://en.paper.edu.cn/en_releasepaper/content/4394145 |
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