Our previous work has introduced the newly generated program using the code transformation model GPT-2,verifying the generated programming codes through simhash(SH)and longest common subsequence(LCS)algo-rithms.Howeve...Our previous work has introduced the newly generated program using the code transformation model GPT-2,verifying the generated programming codes through simhash(SH)and longest common subsequence(LCS)algo-rithms.However,the entire code transformation process has encountered a time-consuming problem.Therefore,the objective of this study is to speed up the code transformation process signicantly.This paper has proposed deep learning approaches for modifying SH using a variational simhash(VSH)algorithm and replacing LCS with a piecewise longest common subsequence(PLCS)algorithm to faster the verication process in the test phase.Besides the code transformation model GPT-2,this study has also introduced MicrosoMASS and Facebook BART for a comparative analysis of their performance.Meanwhile,the explainable AI technique using local interpretable model-agnostic explanations(LIME)can also interpret the decision-making ofAImodels.The experimental results show that VSH can reduce the number of qualied programs by 22.11%,and PLCS can reduce the execution time of selected pocket programs by 32.39%.As a result,the proposed approaches can signicantly speed up the entire code transformation process by 1.38 times on average compared with our previous work.展开更多
This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer(GPT-2)reasonably,improving the pro...This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer(GPT-2)reasonably,improving the program execution performance significantly.Besides,a theoretical estimation in statistics has given the minimum number of generated programs as required,which guarantees to find the best one within them.The proposed approach can help the voice assistant machine resolve the problem of inefficient execution of application code.In addition to GPT-2,this study develops the variational Simhash algorithm to check the code similarity between sample program and newly generated program,and conceives the piecewise longest common subsequence algorithm to examine the execution’s conformity from the two programs mentioned above.The code similarity check deducts the redundant generated programs,and the output conformity check finds the best-performing generative program.In addition to texts,the proposed approach can also prove the other media,including images,sounds,and movies.As a result,the newly generated program outperforms the sample program significantly because the number of code lines reduces 27.21%,and the program execution time shortens 24.62%.展开更多
The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of ta...The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.展开更多
基金supported by the Ministry of Science and Technology,Taiwan,under Grant Nos.MOST 111-2221-E-390-012 and MOST 111-2622-E-390-001.
文摘Our previous work has introduced the newly generated program using the code transformation model GPT-2,verifying the generated programming codes through simhash(SH)and longest common subsequence(LCS)algo-rithms.However,the entire code transformation process has encountered a time-consuming problem.Therefore,the objective of this study is to speed up the code transformation process signicantly.This paper has proposed deep learning approaches for modifying SH using a variational simhash(VSH)algorithm and replacing LCS with a piecewise longest common subsequence(PLCS)algorithm to faster the verication process in the test phase.Besides the code transformation model GPT-2,this study has also introduced MicrosoMASS and Facebook BART for a comparative analysis of their performance.Meanwhile,the explainable AI technique using local interpretable model-agnostic explanations(LIME)can also interpret the decision-making ofAImodels.The experimental results show that VSH can reduce the number of qualied programs by 22.11%,and PLCS can reduce the execution time of selected pocket programs by 32.39%.As a result,the proposed approaches can signicantly speed up the entire code transformation process by 1.38 times on average compared with our previous work.
基金This work is fully supported by the Ministry of Science and Technology,Taiwan,Republic of China,under Grant Nos.MOST 110-2622-E-390-001 and MOST 109-2622-E-390-002-CC3.
文摘This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer(GPT-2)reasonably,improving the program execution performance significantly.Besides,a theoretical estimation in statistics has given the minimum number of generated programs as required,which guarantees to find the best one within them.The proposed approach can help the voice assistant machine resolve the problem of inefficient execution of application code.In addition to GPT-2,this study develops the variational Simhash algorithm to check the code similarity between sample program and newly generated program,and conceives the piecewise longest common subsequence algorithm to examine the execution’s conformity from the two programs mentioned above.The code similarity check deducts the redundant generated programs,and the output conformity check finds the best-performing generative program.In addition to texts,the proposed approach can also prove the other media,including images,sounds,and movies.As a result,the newly generated program outperforms the sample program significantly because the number of code lines reduces 27.21%,and the program execution time shortens 24.62%.
基金National Science Foundation of China(Nos.41801388,41901397)。
文摘The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.
