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Discovering the relationship between the number of film review topics and box office with NLP techniques

Procedia Computer science 2024年 242卷 640-646页

作者： Bo Li Wei Dai Shang Liu Yong Shi School of Finance and Economics Wuhan City Vocational College No. 10 Yezhihu West Road Hongshan District Wuhan 430064 China PKU-WUHAN Institute for Artificial Intelligence No. 770 Gaoxin Avenue Hongshan District Wuhan 430070 China School of Economics Shanghai University 99 Shangda Road BaoShan District Shanghai 200444 China School of Economics and Management University of Chinese Academy of Sciences No. 3 Zhongguancun South Yitiao Beijing 100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 Zhongguancun East Road Beijing 100190 China

Numerous movie reviews on the internet can be used to analyze audience experiences of movies and provide inspiration for the operation and production of movies. In this work, we integrate and match the review data on Douban and the box office data on the website ***, and utilize the topic model in natural language processing to determine the number of each movie review topics, then establish an regression equation to analyze the relationship between the number of topics and movie box office. Our analysis shows a significant positive correlation between film box office and the number of movie review topics, and we have provided an explanation for this. Finally, we also analyzed the practical application value of this work.

关键词： Film Reviews Topic Model Sentiment Analysis Latent Dirichlet Allocation (LDA) Biterm Topic Model (BTM)

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Novel wind speed ensemble forecasting system based on the critic weighing principle of fuzzy information granulation and reverse mixed-frequency modeling

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Energy 2025年 330卷

作者： Lue Li Jun Long Meilan Yuan Guangxi Key Laboratory of Big Data in Finance and Economics Guangxi University of Finance and Economics Nanning 530003 China School of Big Data and Artificial Intelligence Guangxi University of Finance and Economics Nanning 530003 China School of Mathematics and Information Science Guangxi University Nanning 530003 China

Accurate wind speed forecasting is essential for efficient wind power generation. It enhances wind turbine performance, increases power generation efficiency, reduces downtime, and minimizes operational and maintenance costs. However, most current wind speed forecasts are based solely on historic data, ignoring the potential of mixed-frequency and historical wind speed data, which results in poor projections. This work develops an ensemble forecasting system based on fuzzy information granulation and reverse mixed-frequency data modeling to maximize the potential of mixed-frequency and historical data. The system consists of four main modules: data noise reduction, the criteria importance through intercriteria correlation (CRITIC) principle of fuzzy information granulation, model library forecasting, and ensemble modules. Firstly, the noise reduction approach is applied to the original data to improve its quality. Secondly, the fuzzy information granulation methodology is utilized in conjunction with the CRITIC weighting principle to efficiently fuzzy granulate historical wind speed data in order to deal with data uncertainty. The reverse mixed-frequency sampling model, machine learning model, and statistical forecasting model are then employed as submodels for forecasting future wind speed changes. To make the most of a single model, we use the extreme learning machine (ELM) optimized by the ivy algorithm for ensemble forecasting. Finally, an empirical study is conducted using two data sets. The analytical findings reveal that the built forecasting system is accurate and stable. The system’s mean absolute percentage error (MAPE) values are 2.1197% and 2.3710%, respectively. Therefore, the proposed wind speed forecasting system is a very effective and dependable instrument for forecasting, which may offer substantial assistance in decision-making within relevant businesses.

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Efficient learning of quadratic variance function directed acyclic graphs via Topological Layers

arXiv

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arXiv 2021年

作者： Zhou, Wei He, Xin Zhong, Wei Wang, Junhui School of Data Science City University of Hong Kong Wang Yanan Institute for Studies in Economics Department of Statistics School of Economics Fujian Key Lab of Statistics MOE Key Lab of Econometrics Xiamen University School of Statistics and Management Shanghai University of Finance and Economics

Directed acyclic graph (DAG) models are widely used to represent casual relationships among random variables in many application domains. This paper studies a special class of non-Gaussian DAG models, where the conditional variance of each node given its parents is a quadratic function of its conditional mean. Such a class of non-Gaussian DAG models are fairly flexible and admit many popular distributions as special cases, including Poisson, Binomial, Geometric, Exponential, and Gamma. To facilitate learning, we introduce a novel concept of topological layers, and develop an efficient DAG learning algorithm. It first reconstructs the topological layers in a hierarchical fashion and then recoveries the directed edges between nodes in different layers, which requires much less computational cost than most existing algorithms in literature. Its advantage is also demonstrated in a number of simulated examples, as well as its applications to two real-life datasets, including an NBA player statistics data and a cosmetic sales data collected by Alibaba. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Usage: Uncertain Flow Graph and Spatio-Temporal Graph Network-Based Saturation Attack Detection Method

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SSRN 2023年

作者： Wang, Kaixi Cui, Yunhe Qian, Qing Chen, Yi Guo, Chun Shen, Guowei Engineering Research Center of Text Computing & Cognitive Intelligence Guizhou University China State Key Laboratory of Public Big Data Guizhou University China Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guizhou University China College of Computer Science and Technology Guizhou University China School of Information Guizhou University of Finance and Economics China

With the development of Software-Defined Networking (SDN), its security problems have become important research content. Among these security problems, saturation attack is one of the most destructive problems. Saturation attacks can destroy SDN networks. Currently, neural graph networks have already been used for detecting saturation attacks in SDN. When constructing the graph based on K-Nearest Neighbors (KNN) algorithm, the number of edges in the graph is fixed. That cannot reflect the different nodes’ relationship well. In addition, when using the Graph Convolutional Network (GCN) to detect saturation attack against SDN, only a partial graph structure and node features will be learned, causing the flow graph nodes information cannot be fully utilized. To overcome these issues, in this work, we propose USAGE, an uncertain flow graph and spatio-temporal graph network-based saturation attack detection method. More specifically, USAGE designs the CRAM algorithm and EGG model. The CRAM algorithm is designed to generate an uncertain flow graph for each traffic flow, which can represent the uncertain relationship among different nodes. EGG is a spatio-temporal graph convolutional network model, which is proposed to detect saturation attacks towards SDN switches. The evaluation results demonstrate that USAGE can generate a better graph than KNN and achieve high detection performance. Meanwhile, USAGE outperforms other methods in terms of TPR, TNR, accuracy, precision, recall, F-score, confusion matrix, ROC curves, and PR curves. © 2023, The Authors. All rights reserved.

关键词： Flow graphs

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Projection-based Consistent Test for Linear Regression Model with Missing Response and Covariates

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Acta Mathematicae Applicatae Sinica 2020年第4期36卷 917-935页

作者： Su-jin ZHENG Si-yu GAO Zhi-hua SUN China Institute for Actuarial Science/School of Insurance Central University of Finance and EconomicsBeijing 100081China School of Mat hematic Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management of Chinese Academy of Sciences Beijing 100049China

In recent years,there has been a large amount of literature on missing *** of them focus on situations where there is only missingness in response or *** this paper,we consider the adequacy check for the linear regression model with the response and covariates missing *** apply model adjustment and inverse probability weighting methods to deal with the missingness of response and covariate,*** order to avoid the curse of dimension,we propose an empirical process test with the linear indicator weighting *** asymptotic properties of the proposed test under the null,local and global alternative hypothe tical models are rigorously investigated.A consisten t wild boot strap method is developed to approximate the critical ***,simulation studies and real data analysis are performed to show that the proposed method performed well.

关键词： consistency linear indicator weighting function empirical process missing response and covariates projection

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Study and prediction of the 2022 global monkeypox epidemic

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Journal of Biosafety and Biosecurity 2022年第2期4卷 158-162页

作者： Fengying Wei Zhihang Peng Zhen Jin Jinjie Wang Ximing Xu Xinyan Zhang Jun Xu Zixiong Ren Yao Bai Xiaoli Wang Bulai Lu Zhaojun Wang Jianguo Xu Senzhong Huang School of Mathematics and Statistics Fuzhou UniversityFuzhou 350116FujianChina Department of Biostatistics School of Public HealthNanjing Medical UniversityNanjing 211166JiangsuChina Complex Systems Research Center Key Discipline of Computer Science and Technology of“Double-First-Class”Project of Shanxi ProvinceShanxi UniversityTaiyuan 030006ShanxiChina Nankai Institute of Economics Binhai Development InstituteNankai UniversityTianjin 300071China Children’s Hospital of Chongqing Medical University Chongqing 400010China Jinzhou Center for Disease Control and Prevention Jinzhou 121000LiaoningChina School of Statistics and Data Science Nankai UniversityTianjin 300071China Xi’an Center for Disease Control and Prevention Xi’an 710061ShaanxiChina Beijing Center for Disease Control and Prevention Beijing 100013BeijingChina Suzhou Center for Disease Control and Prevention Suzhou 215004JiangsuChina State Key Laboratory of Communicable Disease Prevention and Control National Institute for Communicable Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 102206China School of Statistics and Data Science ZhiYing Research Center for Health DataNankai UniversityTianjin 300071China Institute of Public Health Nankai UniversityTianjin 300071China

The World Health Organization(WHO)declared monkeypox as a public health emergency of international concern(PHEIC)on July 23,2022,their highest level of *** raised concerns about the management of the global monkeypox outbreak,as well as the scientific analysis and accurate prediction of the future course of the *** study used EpiSIX(an analysis and prediction system for epidemics based on a general SEIR model)to analyze the monkeypox epidemic and to forecast the major tendencies based on data from the USA CDC(https://***)and the WHO(https://***/health-topics/monkeypox).The global outbreak of monkeypox started in the UK on May 2,2022,which marked the beginning of an epidemic *** of October 28,2022,the cumulative number of reported cases worldwide was 77,115,with 36 *** simulations predict that the global monkeypox epidemic will enter a low epidemic status on March 1,2023 with the cumulative number of confirmed cases ranging from 85,000 to 124,000,and the total number of deaths ranging from 60 to *** analysis revealed that the basic reproduction number(R0)of monkeypox virus(MPXV)is near to 3.1 and the percentage of asymptomatic individuals is 13.1%–14.5%,both of which are similar to the data for *** vaccination efficiency against susceptibility(VEs)of individuals who have had monkeypox is~79%,and the vaccination efficiency against infectiousness(VEi)of individuals who have had monkeypox is~76%–82%.The mean incubation period for monkeypox is 8 *** total,94.7%of infected individuals develop symptoms within 20 days and recover within 2 weeks after the confirmation of *** results using EpiSIX showed that ring vaccination was remarkably effective against *** findings confirmed that a 20-day isolation for close contacts is necessary.

关键词： Monkeypox Epidemic Prediction Tendency

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Entity perception of Two-Step-Matching framework for public opinions

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Journal of Safety science and Resilience 2020年第1期1卷 36-43页

作者： Ren-De Li Hao-Tian Ma Zi-Yi Wang Qiang Guo Jian-Guo Liu Library and Research Center of Computer Systems Science University of Shanghai for Science and TechnologyShanghai 200093PR China School of Accountancy and Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and EconomicsShanghai 200433PR China School of Humanities Shanghai University of Finance and EconomicsShanghai 200433PR China Institute of Sina WRD Big Data Shanghai 201204PR China

Entity perception of ambiguous user comments is a critical problem of target identification for huge amount of public *** this paper,a Two-Step-Matching method is proposed to identify the precise target entity from multiple entities ***,potential entities are extracted by BiLSTM-CRF model and characteristic words by TF-IDF model from public ***,the first matching is implemented between potential entities and an official business directory by Jaro-Winkler distance ***,in order to find the pre-cise one,an industry-characteristic dictionary is developed into the second matching *** precise entity is identified according to the count of characteristic words matching to industry-characteristic *** addition,associated rate(global indicator)and accuracy rate(sample indicator)are defined for evaluation of matching *** results for three data sets of public opinions about major public health events show that the highest associated rate and accuracy rate arrive at 0.93 and 0.95,averagely enhanced by 32%and 30%above the case of using the first matching process *** framework provides the method to find the true target entity of really wanted expression from public opinions.

关键词： Entity perception BiLSTM-CRF model Jaro-Winkler distance algorithm User comments Public opinions

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Quantum algorithm for Gaussian process regression

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Physical Review A 2022年第1期106卷 012406-012406页

作者： Meng-Han Chen Chao-Hua Yu Jian-Liang Gao Kai Yu Song Lin Gong-De Guo Jing Li College of Mathematics and Informatics Fujian Normal University Fuzhou 350117 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330032 China Research Computing & Data Science Graduate School & Business School Imperial College London United Kingdom State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

The Gaussian process is a widely used model for regression problems in supervised machine learning. However, predicting new inputs via a Gaussian process model becomes computationally inefficient when training a large data set. This paper proposes a fast quantum algorithm for prediction based on the Gaussian process regression. The proposed quantum algorithm consists of two subalgorithms: the first one aims to efficiently prepare the squared exponential covariance matrices and covariance functions vector with annihilation and creation operators; the other is to obtain predictive mean values and covariance values for new inputs. Evidence is also shown that the proposed quantum Gaussian process regression algorithm can achieve quadratic speedup over the classical counterpart.

关键词： Quantum algorithms

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Optimizing Carbon Emissions in Green Logistics for Time-Dependent Routing

SSRN

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SSRN 2024年

作者： Liu, Yiming Yu, Yang Roberto, Baldacci Tang, Jiafu Sun, Wei State Key Laboratory of Synthetic Automation for Process Industries Institute of Intelligent Data and Systems Engineering Northeastern University Shenyang110819 China Dalian University of Technology School of Economics and Management Liaoning Dalian116024 China Division of Engineering Management and Decision Sciences College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation P.O. Box 34110 Doha Qatar College of Management Science and Engineering Dongbei University of Finance and Economics Shahekou Dalian116026 China Business School Liaoning University Shenyang110316 China

This paper considers a green vehicle routing problem termed the time-dependent green vehicle routing problem with time windows (TDGVRPTW). The TDGVRPTW is an extension of the green vehicle routing problem with time windows in green logistics. It considers time-dependent vehicle speed and aims to minimize carbon emissions. Since the travel times and carbon emissions between locations depend on the departure time from the starting location, optimizing carbon emissions requires determining the optimal departure times from both the depot and customers. This paper presents a branch-price-and-cut (BPC) algorithm to solve the TDGVRPTW. The problem is formulated based on a set-partitioning model. To solve the pricing problem associated with the set-partitioning model, we first define backward non-dominated time-dependent arcs and optimal timed routes. We also introduce three types of routes, forward, backward, and patchwork optimal timed routes, and analyze their characteristics. Then, we introduce a method to adjust the times in backward and patchwork optimal timed routes from the latest to the earliest. We prove that this method guarantees correctness and effectively reduces complexity. We propose a bidirectional labeling algorithm to generate routes. The pricing problem employs state-of-the-art techniques, including limited memory subset row cuts (lm-SRCs), ng-route relaxation, and bucket graphs to enhance the algorithm’s efficiency. The BPC algorithm is tested on a set of instances derived from benchmark instances in the literature. The results demonstrate the effectiveness of the proposed algorithm in solving the TDGVRPTW. © 2024, The Authors. All rights reserved.

关键词： Travel time

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Dependency, Reciprocity, and Mentorship in Long-Term Research Collaboration: A Co-Authorship Matrix-Based Multivariate Time Series Analysis

SSRN

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SSRN 2023年

作者： Zhu, Yongjun Kim, Donghun Jiang, Ting Zhao, Yi He, Jiangen Chen, Xinyi Lou, Wen Department of Library and Information Science Yonsei University Seoul Korea Republic of Department of Library and Information Science Sungkyunkwan University Seoul Korea Republic of School of Economics and Management Nanjing University of Science and Technology Nanjing China School of Information Sciences The University of Tennessee Knoxville United States Department of Cultural Media Yonsei University Seoul Korea Republic of Faculty of Economics and Management East China Normal University Shanghai China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Ministry of Education of China Shanghai China

In this study, we examine the roles of dependency, reciprocity, and mentorship in the prediction of long-term research collaboration in five disciplines. We use co-authorship matrix-based multivariate time series features and interpretable machine learning to train long-term collaboration prediction models and interpret the feature importance of trained models. Overall, long-term research collaboration that is defined using various standards was rare across the examined disciplines, and the prediction results were moderate to good. We found dependency, reciprocity, and mentorship to have different roles in different disciplines. Among the three, mentorship was important in predicting long-term research collaboration in Agriculture, Geology, and Library and Information science. Reciprocity, which measures the interdependence between two researchers was important to prediction in the fields of Agriculture and Geology. Finally, dependency was important in all the disciplines with varying degrees of importance. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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