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Implementation of Rapid Code Transformation Process Using Deep Learning Approaches
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作者 Bao Rong Chang Hsiu-Fen Tsai Han-Lin Chou 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第7期107-134,共28页
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 signi􀀀cantly.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 veri􀀀cation 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 quali􀀀ed 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 signi􀀀cantly speed up the entire code transformation process by 1.38 times on average compared with our previous work. 展开更多
关键词 Code transformation model variational simhash piecewise longest common subsequence explainable AI LIME
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Code Transform Model Producing High-Performance Program 被引量:1
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作者 Bao Rong Chang Hsiu-Fen Tsai Po-Wen Su 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第10期253-277,共25页
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
关键词 Newly generated programs GPT-2 predetermined generative programs variational Simhash algorithm piecewise longest common subsequence
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A Multisource Contour Matching Method Considering the Similarity of Geometric Features 被引量:6
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作者 Wenyue GUO Anzhu YU +4 位作者 Qun SUN Shaomei LI Qing XU Bowei WEN Yuanfu LI 《Journal of Geodesy and Geoinformation Science》 2020年第3期76-87,共12页
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
关键词 multisource contour matching geometric feature similarity measurement longest common subsequence feature descriptor
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北京市出租车载客热点路段挖掘及分析 被引量:1
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作者 孟妮娜 李金秋 +1 位作者 高晨博 王正阳 《测绘地理信息》 CSCD 2023年第1期68-72,共5页
从出租车轨迹数据中挖掘载客热点路段,能够为城市交通道路的规划和出租车的运营管理提供重要依据。以北京市出租车原始轨迹数据为基础,设计并建立了出租车载客行驶的载客数据模型,利用该模型对相似的载客轨迹信息进行了挖掘,结合时空信... 从出租车轨迹数据中挖掘载客热点路段,能够为城市交通道路的规划和出租车的运营管理提供重要依据。以北京市出租车原始轨迹数据为基础,设计并建立了出租车载客行驶的载客数据模型,利用该模型对相似的载客轨迹信息进行了挖掘,结合时空信息提取并分析了不同时段的热点路段。通过北京市交通发展年度报告验证了该模型和挖掘方法的正确性和有效性。 展开更多
关键词 出租车轨迹 载客数据模型 路网匹配 最长公共子序列(longest common subsequence LCSS)方法 热点路段
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