核心情态动词在法律英语中的使用及翻译研究

基于《美国宪法》及其27条修正案、《中华人民共和国宪法》英汉对照版本为语料,利用WindowsWord中的查找与定位功能,对比分析两个文本中核心情态动词的使用及分布特点,并对其中最为主要的几个核心情态动词的翻译进行了分析,分析显示,情态动词在《美国宪法》和《中华人民共和国宪法》的使用呈相同规律,但亦存在细微差异。     

Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic features

When cybercriminals communicate with their customers in underground markets, they tend to use secure and customizable instant messaging (IM) software, i.e. Telegram. It is a popular IM software with over 700 million monthly active users (MAU) up to June 2022. In recent years, more and more dark jargons (i.e. an innocent-looking replacement of sensitive terms) appear frequently on Telegram. Therefore, jargons identification is one of the most significant research perspectives to track online underground markets and cybercrimes. This paper proposes a novel Chinese Jargons Identification Framework (CJI-Framework) to identify dark jargons. Firstly, we collect chat history from Telegram groups that are related to the underground market and construct the corpus TUMCC (Telegram Underground Market Chinese Corpus), which is the first Chinese corpus in jargons identification research field. Secondly, we extract seven brand-new features which can be classified into three categories: Vectors-based Features (VF), Lexical analysis-based Features (LF), and Dictionary analysis-based Features (DF), to identify Chinese dark jargons from commonly-used words. Based on these features, we then run a statistical outlier detection to decide whether a word is a jargon. Furthermore, we employ a word vector projection method and a transfer learning method to improve the effect of the framework. Experimental results show that CJI-Framework achieves a remarkable performance with an F1-score of 89.66%. After adaptation for English, it performs better than state-of-the-art English jargons identification method as well. Our built corpus and code have been publicly released to facilitate the reproduction and extension of our work.     

Demand forecasting model using hotel clustering findings for hospitality industry

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.     

Employ Game Teaching Method in the Primary School English Classroom

on the basis of linguistics, psychology and other related theory, we should carry out game teaching method to make students learn easily and happily because of the lack of interest and other problems i...     

Effect of class imbalance in heterogeneous network embedding: An empirical study

Network science has been extensively explored in solving various bibliometrics tasks such as Co-authorship prediction, Author classification, Author clustering, Author ranking, Paper ranking, etc. While majority of the past studies exploit homogeneous bibliographic network (consists of singular type of nodes and edges), in recent past there is a surge in using heterogeneous bibliographic entities and their inter-dependencies using heterogeneous information networks (HIN). Unlike homogeneous bibliographic networks, a bibliographic HIN consists of multi-typed nodes such as Author, Paper, Venue, etc. and corresponding relations. Thus bibliographic HIN is more complex and captures rich semantics of underlying bibliographic data as well as poses more challenges. Since a real-world HIN may have different number of instances for different node types, class imbalance is ubiquitous. Recent studies discuss class imbalance in brief and exploit meta-path-based strategies to address the issue. However, there is no work which quantitatively study the effect of class imbalance in regards to solving real-world bibliometrics tasks. Therefore, this paper first proposes a metric to estimate class imbalance in HIN and study the effects of class imbalance over two bibliometrics tasks, namely (i) Co-authorship prediction and (ii) Author's research area classification, using node features generated by network embedding-based frameworks for DBLP dataset. From various experimental analysis, it is evident that class imbalance in bibliographic HIN is an inherent characteristic and for better performance of the above-mentioned bibliometrics tasks, the bibliographic HINs must consider Author, Paper, and Venue as node types.     

浅谈体育游戏的创编原则与运用

体育游戏的创编应遵循锻炼性、教育启发性、趣味性、安全性、针对性等原则;在体育游戏的运用中,教师应做好场地、器材道具的选择和准备,并做好讲解和组织工作.     

Implementation and Application of CORAL: An Open Source ERM System

With the rapid increase of electronic databases, e-journals, electronic books, and digital collections in libraries, managing electronic resources is also becoming complex and challenging. It is an absolute necessity to have an effective electronic resources management (ERM). This article discusses our experience in choosing and implementing an open source ERM System (CORAL) at The College of New Jersey Library.     

大学生的阅读需求与馆藏建设

通过对上海电机学院图书馆2011年图书流通清单数据的挖掘和分析,提出了大学生阅读需求的主分量分析与提取方法.比较了常用的“使用效率”和新建议的“加权使用效率”提取大学生阅读需求主分量方法的结果,认为该方法是提取大学生阅读需求主分量的更好方法.根据本科生阅读需求,提出一种改善馆藏结构以适应该需求的方法.     

基于差调制的扩频水印算法(英文)

扩展频水印技术是一种鲁棒的水印算法.如何提高扩频水印算法的鲁棒性是当前一大研究热点.充分利用图象的像素与其周围像素之间的差值的统计特性,利用差调制的方法嵌入水印信息,从而降低了原始图象对该水印算法鲁棒性的影响,来提高水印算法的鲁棒性.理论分析表明,该算法可以同时获得较小的误警和误拒概率,从而表明该算法有较好的性能.实验结果表明,该算法可以鲁棒地抵抗常用的攻击,如JPEG压缩、中值滤波、加噪声和图象大小调整.