The paper posits that kin sociality and eusociality are derived from the handicap-care principles based on the need-based care to the handicappers from the caregivers for the self-interest of the caregivers. In this p...The paper posits that kin sociality and eusociality are derived from the handicap-care principles based on the need-based care to the handicappers from the caregivers for the self-interest of the caregivers. In this paper, handicap is defined as the difficulty to survive and reproduce independently. Kin sociality is derived from the childhood handicap-care principle where the children are the handicapped children who receive the care from the kin caregivers in the inclusive kin group to survive. The caregiver gives care for its self-interest to reproduce its gene. The individual’s gene of kin sociality contains the handicapped childhood and the caregiving adulthood. Eusociality is derived from the adulthood handicap-care principle where responsible adults are the handicapped adults who give care and receive care at the same time in the interdependent eusocial group to survive and reproduce its gene. Queen bees reproduce, but must receive care from worker bees that work but must rely on queen bees to reproduce. A caregiver gives care for its self-interest to survive and reproduce its gene. The individual’s gene of eusociality contains the handicapped childhood-adulthood and the caregiving adulthood. The chronological sequence of the sociality evolution is individual sociality without handicap, kin sociality with handicapped childhood, and eusociality with handicapped adulthood. Eusociality in humans is derived from bipedalism and the mixed habitat. The chronological sequence of the eusocial human evolution is 1) the eusocial early hominins with bipedalism and the mixed habitat, 2) the eusocial early Homo species with bipedalism, the larger brain, and the open habitat, 3) the eusocial late Homo species with bipedalism, the largest brain, and the unstable habitat, and 4) extended eusocial Homo sapiens with bipedalism, the shrinking brain, omnipresent imagination, and the harsh habitat. The omnipresence of imagination in human culture converts eusociality into extended eusociality with both perception and omnipresent imagination.展开更多
The fight against fraud and trafficking is a fundamental mission of customs. The conditions for carrying out this mission depend both on the evolution of economic issues and on the behaviour of the actors in charge of...The fight against fraud and trafficking is a fundamental mission of customs. The conditions for carrying out this mission depend both on the evolution of economic issues and on the behaviour of the actors in charge of its implementation. As part of the customs clearance process, customs are nowadays confronted with an increasing volume of goods in connection with the development of international trade. Automated risk management is therefore required to limit intrusive control. In this article, we propose an unsupervised classification method to extract knowledge rules from a database of customs offences in order to identify abnormal behaviour resulting from customs control. The idea is to apply the Apriori principle on the basis of frequent grounds on a database relating to customs offences in customs procedures to uncover potential rules of association between a customs operation and an offence for the purpose of extracting knowledge governing the occurrence of fraud. This mass of often heterogeneous and complex data thus generates new needs that knowledge extraction methods must be able to meet. The assessment of infringements inevitably requires a proper identification of the risks. It is an original approach based on data mining or data mining to build association rules in two steps: first, search for frequent patterns (support >= minimum support) then from the frequent patterns, produce association rules (Trust >= Minimum Trust). The simulations carried out highlighted three main association rules: forecasting rules, targeting rules and neutral rules with the introduction of a third indicator of rule relevance which is the Lift measure. Confidence in the first two rules has been set at least 50%.展开更多
采用数据挖掘技术从企业已有的产品或服务规划知识中提取顾客需求和设计需求之间的映射规则,是进行产品或服务规划分析的重要方法。针对常用Apriori关联规则挖掘算法运算量大的问题,提出了基于PIETM(Principle of Inclusion-Exclusion a...采用数据挖掘技术从企业已有的产品或服务规划知识中提取顾客需求和设计需求之间的映射规则,是进行产品或服务规划分析的重要方法。针对常用Apriori关联规则挖掘算法运算量大的问题,提出了基于PIETM(Principle of Inclusion-Exclusion and Transaction Mapping)算法的顾客需求映射规则挖掘方法,提取强关联规则。针对规划设计数据量较大时,规则挖掘会产生大量冗余规则的问题,通过采用基于粗糙集的聚类方法对顾客需求以及设计需求可选值进行聚类,实现顾客需求映射规则的聚类分析。最后以某企业叉车方案规划中,顾客需求映射规则的挖掘和聚类分析为例,验证了所提方法的有效性。展开更多
文摘The paper posits that kin sociality and eusociality are derived from the handicap-care principles based on the need-based care to the handicappers from the caregivers for the self-interest of the caregivers. In this paper, handicap is defined as the difficulty to survive and reproduce independently. Kin sociality is derived from the childhood handicap-care principle where the children are the handicapped children who receive the care from the kin caregivers in the inclusive kin group to survive. The caregiver gives care for its self-interest to reproduce its gene. The individual’s gene of kin sociality contains the handicapped childhood and the caregiving adulthood. Eusociality is derived from the adulthood handicap-care principle where responsible adults are the handicapped adults who give care and receive care at the same time in the interdependent eusocial group to survive and reproduce its gene. Queen bees reproduce, but must receive care from worker bees that work but must rely on queen bees to reproduce. A caregiver gives care for its self-interest to survive and reproduce its gene. The individual’s gene of eusociality contains the handicapped childhood-adulthood and the caregiving adulthood. The chronological sequence of the sociality evolution is individual sociality without handicap, kin sociality with handicapped childhood, and eusociality with handicapped adulthood. Eusociality in humans is derived from bipedalism and the mixed habitat. The chronological sequence of the eusocial human evolution is 1) the eusocial early hominins with bipedalism and the mixed habitat, 2) the eusocial early Homo species with bipedalism, the larger brain, and the open habitat, 3) the eusocial late Homo species with bipedalism, the largest brain, and the unstable habitat, and 4) extended eusocial Homo sapiens with bipedalism, the shrinking brain, omnipresent imagination, and the harsh habitat. The omnipresence of imagination in human culture converts eusociality into extended eusociality with both perception and omnipresent imagination.
文摘The fight against fraud and trafficking is a fundamental mission of customs. The conditions for carrying out this mission depend both on the evolution of economic issues and on the behaviour of the actors in charge of its implementation. As part of the customs clearance process, customs are nowadays confronted with an increasing volume of goods in connection with the development of international trade. Automated risk management is therefore required to limit intrusive control. In this article, we propose an unsupervised classification method to extract knowledge rules from a database of customs offences in order to identify abnormal behaviour resulting from customs control. The idea is to apply the Apriori principle on the basis of frequent grounds on a database relating to customs offences in customs procedures to uncover potential rules of association between a customs operation and an offence for the purpose of extracting knowledge governing the occurrence of fraud. This mass of often heterogeneous and complex data thus generates new needs that knowledge extraction methods must be able to meet. The assessment of infringements inevitably requires a proper identification of the risks. It is an original approach based on data mining or data mining to build association rules in two steps: first, search for frequent patterns (support >= minimum support) then from the frequent patterns, produce association rules (Trust >= Minimum Trust). The simulations carried out highlighted three main association rules: forecasting rules, targeting rules and neutral rules with the introduction of a third indicator of rule relevance which is the Lift measure. Confidence in the first two rules has been set at least 50%.
文摘采用数据挖掘技术从企业已有的产品或服务规划知识中提取顾客需求和设计需求之间的映射规则,是进行产品或服务规划分析的重要方法。针对常用Apriori关联规则挖掘算法运算量大的问题,提出了基于PIETM(Principle of Inclusion-Exclusion and Transaction Mapping)算法的顾客需求映射规则挖掘方法,提取强关联规则。针对规划设计数据量较大时,规则挖掘会产生大量冗余规则的问题,通过采用基于粗糙集的聚类方法对顾客需求以及设计需求可选值进行聚类,实现顾客需求映射规则的聚类分析。最后以某企业叉车方案规划中,顾客需求映射规则的挖掘和聚类分析为例,验证了所提方法的有效性。