A Study of the Effects of Family Precepts,Rules and Ethics on Children’s Education During the Tang Dynasty

From the perspectives of the prosperity of the Sui and Tang dynasties,the rise and fall of the aristocrats and the prevalence of the imperial examination,this paper studies the impact of family precepts,rules,and ethics on children’s education.I believe that an important feature of family precepts in the Tang Dynasty was the development of the cultural tradition of poetry and literature study within the family,which promoted virtues like loyalty,filial piety,diligence,frugality and modesty and had a profound influence on future generations.The rise and fall of Tang Dynasty families was closely related to family precepts,rules,ethics,and learning.Famous and respectable families often regarded these traits as important means to maintain the family’s status,which objectively promoted the development of family scholarship and the importance of children’s education in the Tang Dynasty.The concept of learning as a“carry-on treasure”has become a common belief among scholars and has played a positive role in educating Chinese society,enriching our culture,and keeping society in order.

RULES-IT： incremental transfer learning with RULES family

In today＇s world of excessive development in technologies, sustainability and adaptability of computer applications is a challenge, and future prediction became significant. Therefore, strong artificial intelligence （AI） became important and, thus, statistical machine learning （ML） methods were applied to serve it. These methods are very difficult to understand, and they predict the future without showing how. However, understanding of how machines make their decision is also important, especially in information system domain. Consequently, incremental covering algorithms （CA） can be used to produce simple rules to make difficult decisions. Nevertheless, even though using simple CA as the base of strong AI agent would be a novel idea but doing so with the methods available in CA is not possible. It was found that having to accurately update the discovered rules based on new information in CA is a challenge and needs extra attention. In specific, incomplete data with missing classes is inappropriately considered, whereby the speed and data size was also a concern, and future none existing classes were neglected. Consequently, this paper will introduce a novel algorithm called RULES-IT, in order to solve the problems of incremental CA and introduce it into strong AI. This algorithm is the first incremental algorithm in its family, and CA as a whole, that transfer rules of different domains to improve the performance, generalize the induction, take advantage of past experience in different domain, and make the learner more intelligent. It is also the first to introduce intelligent aspectsinto incremental CA, including consciousness, subjective emotions, awareness, and adjustment. Furthermore, all decisions made can be understood due to the simple representation of repository as rules. Finally, RULES-IT performance will be benchmarked with six different methods and compared with its predecessors to see the effect of transferring rules in the learning process, and to prove how RULES-IT actually solved the shortcoming of current incremental CA in addition to its improvement in the total performance.