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arXiv 2022年

作者： Li, Yanjun Peng, Jie Kan, Haibin Zheng, Lijing Institute of Statistics and Applied Mathematics Anhui University of Finance & Economics Anhui Bengbu233030 China Mathematics and Science College of Shanghai Normal University Guilin Road #100 Shanghai200234 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Engineering Research Center of Blockchain Shanghai Institute for Advanced Communication and Data Science Shanghai200433 China School of Mathematics and Physics University of South China Hunan Hengyang421001 China

Recently, much progress has been made to construct minimal linear codes due to their preference in secret sharing schemes and secure two-party computation. In this paper, we put forward a new method to construct minimal linear codes by using vectorial Boolean functions. Firstly, we give a necessary and sufficient condition for a generic class of linear codes from vectorial Boolean functions to be minimal. Based on that, we derive some new three-weight minimal linear codes and determine their weight distributions. Secondly, we obtain a necessary and sufficient condition for another generic class of linear codes from vectorial Boolean functions to be minimal and to be violated the AB condition. As a result, we get three infinite families of minimal linear codes violating the AB condition. To the best of our knowledge, this is the first time that minimal liner codes are constructed from vectorial Boolean functions. Compared with other known ones, in general the minimal liner codes obtained in this paper have higher dimensions. © 2022, CC BY-NC-SA.

关键词： Boolean functions

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METER: An Ensemble DWT-based Method for Identifying Low-rate DDoS Attack in SDN

METER: An Ensemble DWT-based Method for Identifying Low-rate...

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IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC

作者： Cong Wang Yunhe Cui Qing Qian Guowei Shen Hongfeng Gao Saifei Li Guizhou Provincial Key Laboratory of Public Big Data School of Computer Science and Technology Guizhou University Guiyang China School of information GuiZhou University of Finance and Economics Guiyang China School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665400374

As one of the next generation of network architectures, Software-Defined Networking (SDN) decouples the forwarding and control function of the traditional network device. However, it also faces new threats from network attacks, such as the Low-rate Distributed Denial of Service (L-DDoS) attack. To resist L-DDoS attack in SDN, this work proposes METER, an enseMble discrEte wavelet Transform-based method for idEntifying low-Rate DDoS attack in SDN. The rationale for METER is to identify L-DDoS attacks based on a new metric referred to as the Ensemble Wavelet Energy Entropy set (EWEEs), which is calculated by a novel ensemble DWT method. Firstly, the ensemble wavelet coefficients matrix is attained by METER using the ensemble DWT method. After that, METER combines the related entropy values with wavelet energy of the ensemble wavelet coefficients matrix to obtain EWEEs. Furthermore, to increase the precision of the L-DDoS detection method, machine learning methods are used to mine the correlation between EWEEs and L-DDoS attacks for identifying L-DDoS. The experiments were implemented on the RYU controller and Mininet, which demonstrates that METER outperforms the baseline method, in terms of precision, accuracy, F -score, recall, ROC curve, and P-R curve.

关键词： Meters Measurement Support vector machines Resists Denial-of-service attack Ubiquitous computing Entropy

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Distribution regression with censored selection

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Journal of Econometrics 2025年 251卷

作者： Songnian Chen Nianqing Liu Hanghui Zhang Tsingshan Institute for Advanced Business Studies Zhejiang University Hangzhou China School of Economics Zhejiang University Hangzhou China MOE Key Laboratory of Econometrics and Fujian Key Lab of Statistics Xiamen University Xiamen China Paula and Gregory Chow Institute for Studies in Economics Xiamen University Xiamen China Department of Statistics and Data Science School of Economics Xiamen University Xiamen China School of Economics The Shanghai University of Finance and Economics Shanghai China Key Laboratory of Mathematical Economics (SUFE) Ministry of Education Shanghai 200433 China

Chernozhukov, Fernández-Val, and Luo (2023, CFL (2023) hereafter) considered a distribution regression model subject to sample selection with a binary selection mechanism. In this paper, we show how to identify and estimate a semi-parametric distribution regression model subject to a censored selection rule. With censored selection, we do not need to impose the usual outcome exclusion restriction or exclusion of the level of the latent selection variable from the selection sorting function for model identification, unlike CFL (2023). We propose new semiparametric estimators and corresponding inference procedures for model parameters and related functional parameters. We apply our method to investigate wage inequality in the UK for the period 1978–2000 using the Family Expenditure Survey (FES) data. Our findings reveal that (i) the selection sorting exclusion and outcome exclusion restrictions imposed by CFL (2023) are rejected; (ii) there is negative selection into work at most quantile levels for females, but not for males; (iii) in contrast to CFL (2023), our selection sorting effect pattern does not offer clear evidence on assortative matching or glass ceiling in the UK labor market; (iv) the latent gender wage gaps after correcting for selection bias are about 25%–50% of CFL (2023)’s estimates, and are also significantly smaller than the observed wage gaps; (v) similar to CFL (2023), there exists some strong evidence on gender discrimination in the UK labor market.

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EDGE DIFFERENTIALLY PRIVATE ESTIMATION IN THE β-MODEL VIA JITTERING AND METHOD OF MOMENTS

arXiv

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arXiv 2021年

作者： Chang, Jinyuan Hu, Qiao Kolaczyk, Eric D. Yao, Qiwei Yi, Fengting Joint Laboratory of Data Science and Business Intelligence Southwestern University of Finance and Economics Chengdu611130 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Mathematics and Statistics McGill University MontrealQCH3A 0B8 Canada Department of Statistics London School of Economics and Political Science LondonWC2A 2AE United Kingdom Yunnan Key Laboratory of Statistical Modeling and Data Analysis Yunnan University Kunming650500 China

A standing challenge in data privacy is the trade-off between the level of privacy and the efficiency of statistical inference. Here we conduct an in-depth study of this trade-off for parameter estimation in the β-model (Chatterjee, Diaconis and Sly, 2011) for edge differentially private network data released via jittering (Karwa, Krivitsky and Slavković, 2017). Unlike most previous approaches based on maximum likelihood estimation for this network model, we proceed via method-of-moments. This choice facilitates our exploration of a substantially broader range of privacy levels – corresponding to stricter privacy – than has been to date. Over this new range we discover our proposed estimator for the parameters exhibits an interesting phase transition, with both its convergence rate and asymptotic variance following one of three different regimes of behavior depending on the level of privacy. Because identification of the operable regime is difficult if not impossible in practice, we devise a novel adaptive bootstrap procedure to construct uniform inference across different phases. In fact, leveraging this bootstrap we are able to provide for simultaneous inference of all parameters in the β-model (i.e., equal to the number of nodes), which, to our best knowledge, is the first result of its kind. Numerical experiments confirm the competitive and reliable finite sample performance of the proposed inference methods, next to a comparable maximum likelihood method, as well as significant advantages in terms of computational speed and memory. Copyright © 2021, The Authors. All rights reserved.

关键词： Method of moments

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Chinese News Text Classification and Its Application Based on Combined-Convolutional Neural Network

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Journal of Computers (Taiwan) 2022年第4期33卷 1-14页

作者： Liu, Kai-Feng Zhang, Yu Zhang, Quan-Xin Wang, Yan-Ge Gao, Kai-Long Jiangsu Vocational College of Finance and Economics Huaian223003 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China State Key Laboratory in China for GeoMechanics and Deep Underground Engineering China University of Mining & Technology Beijing100083 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

A method based on combined-convolutional neural network (Combined-CNN) for Chinese news text classification is proposed. First of all, in order to solve the problem of a lack of special term set for Chinese news classification, a vocabulary suitable for Chinese long text classification is made by constructing a data index method. The Word2Vec pre-trained model was used to embed the text features word vectors. Second, by optimizing the structure of the classical convolutional neural network (CNN) model, a new idea of Combined-CNN model is proposed, which solves the problem of incomplete feature extraction of local text blocks and improves the accuracy rate of Chinese news text classification. Effective model regularization and RAdam optimization algorithm are designed in the model to enhance the model training effect. The experimental results show that the precision of the Combined-CNN model for Chinese news text classification reaches 93.69%. Compared with traditional machine learning methods and deep learning algorithms, the accuracy rate is improved by a maximum of 11.82% and 1.9%, respectively, and it is better than the comparison model in Recall and F-Measure. Finally, the Chinese news classification algorithm of the Combined-CNN is applied to realize a personalized recommendation system. © 2022 Computer Society of the Republic of China. All rights reserved.

关键词： Recommender systems

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The Spatiotemporal Traffic Accident Risk Analysis in Urban Traffic Network 9th

The Spatiotemporal Traffic Accident Risk Analysis in Urban T...

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9th International Conference on Mobile Wireless Middleware, Operating Systems and Applications, MOBILWARE 2020

作者： Zhang, Chijun jin, Jing Huang, Qiuyang Du, Zhanwei Yuan, Zhilu Tang, Shengjun Liu, Yang School of Management Science and Information Engineering Jilin University of Finance and Economics Changchun130117 China Jilin Province Key Laboratory of Fintech Jilin University of Finance and Economics Changchun130117 China College of Computer Science and Technology Jilin University Changchun130012 China Research Institute for Smart Cities and Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services School of Architecture and Urban Planning Shenzhen University Shenzhen China Jilin Big Data Research Center for Business Jilin University of Finance and Economics Changchun130117 China

ISBN: (纸本)9783030622046

Traffic accidents seriously threaten people’s lives and property all over the world. Therefore, it is of great significance to human society to have a long-term traffic accidents data with detail temporal and geographic information in a specific space, which can be used for traffic accident hotspots identification to reduce the incidence of traffic accidents. Here, we obtain a one-year dataset of traffic accidents of the city center in Changchun, Northeast China, in 2017. In this paper, we analyze the risk of traffic accident in urban area, and then discover the characteristics of traffic accidents at the temporal and spatial aspect. We construct a traffic network, which takes crossings as nodes and road sections as edges and weighted by the total number of traffic accidents. In addition, we integrate road structure data and meteorological data to explore the characteristics of the traffic network. © 2020, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Risk analysis

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A Novel Four-Dimensional Prediction Model of Soil Heavy Metal(Loid) Pollution: Geographical Explanations Beyond Artificial Intelligence "Black Box"

SSRN

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SSRN 2023年

作者： Wang, Qi Li, Cangbai Hao, Dongmei Shi, Xuewen Xu, Yafei Liu, Tongxu Sun, Weimin Zheng, Zelong Liu, Jianfeng Li, Wanqi Liu, Wengang Zheng, Jiaxue Li, Fangbai National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management Institute of Eco-environmental and Soil Sciences Guangdong Academy of Science Guangzhou510650 China Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education School of Ecology Environment and Resources Guangdong University of Technology Guangzhou510006 China School of Management Lanzhou University Lanzhou730099 China Guangzhou First People’s Hospital School of Medicine South China University of Technology Guangzhou510180 China Research Institute of Forestry Chinese Academy of Forestry Beijing100091 China School of Data Science and Artificial Intelligence Dongbei University of Finance & Economics Dalian116025 China

The existing artificial intelligence (AI)-based prediction approaches of soil pollutants are insufficient to estimate the geospatial source-sink processes and to juggle the interpretability and accuracy, resulting in poor spatial extrapolation and generalization. In this study, we developed and tested a geographically interpretable four-dimensional AI prediction model of soil heavy metal(loid) (Cd) contents (4DGISHM) in Shaoguan city of China from 2016 to 2030. The 4DGISHM approach characterized spatio-temporal changes in source-sink processes of soil Cd. It was operationalized by estimating spatio-temporal patterns and the effects of drivers and their interactions of soil Cd from local to regional scales using TreeExplainer-based SHAP and parallel ensemble AI algorithms. The results indicate that the MSE and R2 values of the prediction model were 0.012 and 0.938, respectively at 1km spatial resolution. The predicted areas exceeding the risk control values of soil Cd across Shaoguan from 2022 to 2030 increased by 22.920% at baseline scenario. In 2030, enterprise and transportation (SHAP values 0.23 and 0.12, respectively) were the major drivers. Soil Cd was only marginally influenced by the driver interactions. Our approach goes beyond AI "black box" to juggle the spatio-temporal source-sink explanation and accuracy, and advances geographically precise prediction and control of soil pollutants. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Double Varying-coefficient Modeling Approach for Analyzing Longitudinal Observations

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Acta Mathematicae Applicatae Sinica 2019年第3期35卷 671-688页

作者： Qun-fang XU Rui LI School of Business Ningbo UniversityNingbo 315211China School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal UniversityMinistry of EducationShanghai 200062China

The identification of within-subject dependence is important for constructing efficient estimation in longitudinal data *** this paper,we proposed a flexible way to study this dependence by using nonparametric regression ***,we considered the estimation of varying coefficient longitudinal data model with non-stationary varying coefficient autoregressive error process over observational time *** on spline approximation and local polynomial techniques,we proposed a two-stage nonparametric estimation for unknown functional coefficients and didn’t not drop any observations in a hybrid least square loss ***,we showed that the estimated coefficient functions are asymptotically normal and derived the asymptotic biases and variances *** Carlo studies and two real applications were conducted for illustrating the performance of our proposed methods.

关键词： autoregressive process double varying coefficient irregular time quantum locally linear

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Perturbative QCD evidence for spin-2 particles in the di-J/ψ resonances

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science Bulletin 2025年

作者： Zhang, Hong-Fei Ma, Yan-Qing Sang, Wen-Long College of Big Data Statistics Guizhou University of Finance and Economics Guiyang550025 China Research Centre of Big-data Corpus & Language Projects School of Foreign Languages Guizhou University of Finance and Economics Guiyang550025 China School of Physics and State Key Laboratory of Nuclear Physics and Technology Peking University Beijing100871 China Center for High Energy Physics Peking University Beijing100871 China Collaborative Innovation Center of Quantum Matter Beijing100871 China School of Physical Science and Technology Southwest University Chongqing400700 China

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Uniform Pricing Strategy vs. Price Differentiation Strategy in the Presence of Cost Saving and Demand Increasing

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Journal of Systems science & Complexity 2019年第3期32卷 932-946页

作者： CAI Qiang LUO Chunlin TIAN Xin WANG Shouyang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy Sciences Beijing 100190 China School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

Price differentiation or discrimination strategy has been regarded as the best choice for firms with online and offline channels, however, recent years often witnessed the practices of the uniform pricing strategy. This paper aims to address the question whether the uniform pricing strategy may be better for the manufacturer, when the uniform pricing strategy has a positive impact on increasing the ciistomer demand and reducing the operations cost. The research shows that the uniform pricing strategy can be bet ter than the price differentiation strategy when the cost saving and demand increasing are large enough or the consumers' acceptance of online channel lies in a certain interval. Moreover, the manufac tu rers or brand owners need a tradeoff bet ween the benefit from online channel and the negative impact from the offline channel when they implement the price differentiation strategy. Finally, the authors obtain some managerial insights and implications based on the numerical analyses, which are in line with the phenomena in practice.

关键词： Cost saving demand increasing dual-channel price differentiation strategy uniform pricing strategy

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