A Thought on the Integrated Development of Regional Brands

Today, places across China all try to establish regional brands, but most of the brands still focus on specific enterprise brands or local product brands. Many well-known regional brands have been developed with the help of the regional industry promotion policies. But whether the founding of regional brands is a purpose or a mean? This article will make a study based on the “regional values” of regional brands, and explore the necessity of integrated development of regional brands from three aspects: nature of the concept of regional brand, differences between enterprise brand and regional brand, and formation methods of regional brand

Efficient Estimation of Multivariate Semi-nonparametric GARCH Filtered Copula Models

This paper considers estimation of semi-nonparametric GARCH filtered copula models in which the individual time series are modelled by semi-nonparametric GARCH and the joint distributions of the multivariate standardized innovations are characterized by parametric copulas with nonparametric marginal distributions. The models extend those of Chen and Fan (2006) to allow for semi-nonparametric conditional means and volatilities, which are estimated via the method of sieves such as splines. The fitted residuals are then used to estimate the copula parameters and the marginal densities of the standardized innovations jointly via the sieve maximum likelihood (SML). We show that, even using nonparametrically filtered data, both our SML and the two-step copula estimator of Chen and Fan (2006) are still root-n consistent and asymptotically normal, and the asymptotic variances of both estimators do not depend on the nonparametric filtering errors. Even more surprisingly, our SML copula estimator using the filtered data achieves the full semiparametric efficiency bound as if the standardized innovations were directly observed. These nice properties lead to simple and more accurate estimation of Value-at-Risk (VaR) for multivariate financial data with flexible dynamics, contemporaneous tail dependence and asymmetric distributions of innovations. Monte Carlo studies demonstrate that our SML estimators of the copula parameters and the marginal distributions of the standardized innovations have smaller variances and smaller mean squared errors compared to those of the two-step estimators in finite samples. A real data application is presented

A Hybrid Model for Monolingual and Multilingual Toxic Comment Detection

Social media provides a public and convenient platform for people to communicate. However, it is also open to hateful behavior and toxic comments. Social networks, like Facebook, Twitter, and many others, have been working on developing effective toxic comment detection methods to provide better service. Monolingual language model focuses on a single-language and provides high accuracy in detection. Multilingual language model provides better generalization performance. In order to improve the effectiveness of detecting toxic comments in multiple languages, we propose a hybrid model, which fuses monolingual model and multilingual model. We use labeled data to fine-tune the monolingual pre-trained model. We use masked language modeling to semi-supervise the fine-tuning of multilingual pre-trained model on unlabeled data and then use labeled data to fine-tune the model. Through this way, we can fully utilize the large amount of unlabeled data; reduce dependence on labeled comment data; and improve the effectiveness of detection. We also design several comparative experiments. The results demonstrate the effectiveness and advantage of our proposed model, especially compared to the XLM-RoBERTa multilingual fine-tuning model

Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges\u27 expertise. By analyzing judges\u27 trial logic, we find that the order of criminal behaviors affects the final judgement. To solve these problems, we regard intelligent case assignment as a text-matching problem, and propose an intelligent case assignment method based on the chain of criminal behavior elements. This method introduces the chain of criminal behavior elements to enhance the structured semantic analysis of the case. We build a BCTA (Bert-Cnn-Transformer-Attention) model to achieve intelligent case assignment. This model integrates a judge\u27s expertise in the judge\u27s presentation, thus recommending the most compatible judge for the case. Comparing the traditional case assignment methods, our BCTA model obtains 84% absolutely considerable improvement under P@1. In addition, comparing other classic text matching models, our BCTA model achieves an absolute considerable improvement of 4% under P@1 and 9% under Macro F1. Experiments conducted on real-world data set demonstrate the superiority of our method