With the continuous development of education in China,various vocational colleges have responded to the“Double-High Plan”and are actively building teaching innovation teams with“double-qualified teachers.”By impro...With the continuous development of education in China,various vocational colleges have responded to the“Double-High Plan”and are actively building teaching innovation teams with“double-qualified teachers.”By improving the professional level of teachers and increasing practical opportunities,vocational colleges and universities can nurture high-level teachers and improve their teaching level so as to meet the demand for high-level technical skills training in the new era.This paper discusses the significance of constructing a“double-qualified”education model under the background of the“Double-High Plan,”the requirements of the“double-qualified”education model for teachers,the problems in constructing a teaching innovation team with“double-qualified”teachers,and the countermeasures for these problems.展开更多
The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Unif...The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed framework.We apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from text.Further,we apply more state-of-the-art methods to create vectorized features,such as GloVe.Additionally,feature engineering that is specific to URL structure is deployed to detect scams and other threats.For framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error II.For the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more time.By applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in performance.Additionally,eXtreme Gradient Boost(XGB)outperforms other classifiers.With this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.展开更多
文摘With the continuous development of education in China,various vocational colleges have responded to the“Double-High Plan”and are actively building teaching innovation teams with“double-qualified teachers.”By improving the professional level of teachers and increasing practical opportunities,vocational colleges and universities can nurture high-level teachers and improve their teaching level so as to meet the demand for high-level technical skills training in the new era.This paper discusses the significance of constructing a“double-qualified”education model under the background of the“Double-High Plan,”the requirements of the“double-qualified”education model for teachers,the problems in constructing a teaching innovation team with“double-qualified”teachers,and the countermeasures for these problems.
基金supported by a grant of the Ministry of Research,Innovation and Digitization,CNCS-UEFISCDI,Project Number PN-Ⅲ-P4-PCE-2021-0334,within PNCDI Ⅲ.
文摘The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed framework.We apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from text.Further,we apply more state-of-the-art methods to create vectorized features,such as GloVe.Additionally,feature engineering that is specific to URL structure is deployed to detect scams and other threats.For framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error II.For the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more time.By applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in performance.Additionally,eXtreme Gradient Boost(XGB)outperforms other classifiers.With this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.
