Dictionaries should present business encyclopedic knowledge as well as business linguistic knowledge. This paper, taking Incoterms for international trade as an example （with ＂CIF＂ as the case for analysis）, intro...Dictionaries should present business encyclopedic knowledge as well as business linguistic knowledge. This paper, taking Incoterms for international trade as an example （with ＂CIF＂ as the case for analysis）, introduces 7 types of dictionary structures （overall structure, frame stzucture/megastrncture, macrostructure, microstzucture, distribution structure, cross-reference stzucture/mediostrncture and access structure）, summarizes the knowledge system of international trade terms （consisting of ＂international trade＂, ＂sets of rules of international trade terms＂, ＂Incoterms 2010＂, and ＂CIF＂）, and illustrates the methods of systematically presenting the knowledge system of international trade terms through dictionary structures in an English-English-Chinese bilingualized business English learners＇ dictionary with Oxford Business English Dictionary for Learners of English （New Edition） as a reference. Such presentation is intended for enhancing business English learners as dictionary users to grasp the knowledge of international trade terms as a whole.展开更多
The main purpose of this study is to develop a mathematical model for calculating the probability of money laundering process, by monitoring the behavior of the client using 70 indicators of money laundering. The scie...The main purpose of this study is to develop a mathematical model for calculating the probability of money laundering process, by monitoring the behavior of the client using 70 indicators of money laundering. The scientific method used in this study （received from the Modern Criminology） has great investigative power and it is widely applicable. Hopefully the practical application of this study will increase greatly the probability of detection and punishment of the clients who are implicated in the process of money laundering. In particular, this study will be useful for banks, Financial Intelligence Unit （FIU） of Albania, Department of Economic Crime at the Ministry of Domestic Affairs and Albanian State Intelligence Service （SIS）. Also, the investigation of money laundering will be a useful tool to detect other crimes, such as drug trafficking, human trafficking, illegal arms trade, etc. The prevention of money laundering is simultaneously a powerful strike against terrorism both on national and international levels.展开更多
Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting for...Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.展开更多
文摘Dictionaries should present business encyclopedic knowledge as well as business linguistic knowledge. This paper, taking Incoterms for international trade as an example （with ＂CIF＂ as the case for analysis）, introduces 7 types of dictionary structures （overall structure, frame stzucture/megastrncture, macrostructure, microstzucture, distribution structure, cross-reference stzucture/mediostrncture and access structure）, summarizes the knowledge system of international trade terms （consisting of ＂international trade＂, ＂sets of rules of international trade terms＂, ＂Incoterms 2010＂, and ＂CIF＂）, and illustrates the methods of systematically presenting the knowledge system of international trade terms through dictionary structures in an English-English-Chinese bilingualized business English learners＇ dictionary with Oxford Business English Dictionary for Learners of English （New Edition） as a reference. Such presentation is intended for enhancing business English learners as dictionary users to grasp the knowledge of international trade terms as a whole.
文摘The main purpose of this study is to develop a mathematical model for calculating the probability of money laundering process, by monitoring the behavior of the client using 70 indicators of money laundering. The scientific method used in this study （received from the Modern Criminology） has great investigative power and it is widely applicable. Hopefully the practical application of this study will increase greatly the probability of detection and punishment of the clients who are implicated in the process of money laundering. In particular, this study will be useful for banks, Financial Intelligence Unit （FIU） of Albania, Department of Economic Crime at the Ministry of Domestic Affairs and Albanian State Intelligence Service （SIS）. Also, the investigation of money laundering will be a useful tool to detect other crimes, such as drug trafficking, human trafficking, illegal arms trade, etc. The prevention of money laundering is simultaneously a powerful strike against terrorism both on national and international levels.
基金the National Natural Science Foundation of China under Grant Nos.70601029 and 70221001the Knowledge Innovation Program of the Chinese Academy of Sciences under Grant Nos.3547600,3046540,and 3047540the Strategy Research Grant of City University of Hong Kong under Grant No.7001806
文摘Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.
