This paper discusses the challenges of technological entrepreneurship education in the current education system and the questions that need to be answered to improve the efficacy and efficiency of technological entrepreneurship education. The nature of technological entrepreneurship requires a diversified set of skills for success; however, the traditional education system focuses on single discipline. Consequently, it is difficult for either engineers and scientists who are lacking managerial skills or management students who are lacking of engineer or science oriented knowledge to be successful. A further concern is that different communities have entirely different perceptions of how entrepreneurship is defined often causing both confusion and disagreement in communications between researchers and educators with each other. The paper considers the existing literature and develops a series of comprehensive questions that still need to be addressed. By answering these questions, the traditional education methods can be transformed to be more appropriate and useful for technological entrepreneurship education.
In order to improve the reality, useful and whole performance of network Intrusion Detection System (IDS), the solution to get independent features used to (or evidence) IDS is presented in the paper. The approach of auto-recognition of inter-relativity of the features is developed, which is classification method for features. The features picked up by the method are used as the input of back propagate neural network (BPNN)
. The features are inter-independent, or weak relative. On the base of the chosen features, an IDS is built. Tests show that the approach and IDS developed in the article is useful and available. It is conclusion that a set of inter-independent features should beprovided for IDS. The inter-relative degree of features can be required with the help of the method developed in the article.
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