传统的遥感制图需要专业人员借助专业软件对大量分散的局部遥感影像进行复杂的处理,具有很大的局限性。基于Google全球高分辨率遥感影像、Google Maps API V3与KML语言提出一套简易的交互式网上地理数据采集与制图的方案。该方案基于B/...传统的遥感制图需要专业人员借助专业软件对大量分散的局部遥感影像进行复杂的处理,具有很大的局限性。基于Google全球高分辨率遥感影像、Google Maps API V3与KML语言提出一套简易的交互式网上地理数据采集与制图的方案。该方案基于B/S架构,利用Google Maps API侦听器获取地物轮廓特征点的经纬度坐标并按照自定义地理模型渐进地构造地物图形,利用Ajax技术实现前后台数据通信,利用Microsoft XMLDOM方法解决后台数据存储与操作,利用KML图层鼠标事件返回的属性及临时KML图层解决数据选择与操作的可视化。该方案用户界面友好,可用于快速制作大比例尺街区图。展开更多
Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms,which often suffer from premature convergence and poor recall in sparse,complex API mapping spaces.To address th...Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms,which often suffer from premature convergence and poor recall in sparse,complex API mapping spaces.To address this,we propose QIMIG,a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering.QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima.Simultaneously,its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings.Evaluated on 9 real-world migration rules derived from 57,447 open-source projects,QIMIG statistically significantly outperforms state-of-the-art baselines such as UNSGA-III.The framework achieves a global mean F1-score of 0.92,exceeding the best-performing baseline by an absolute margin of 0.05,and demonstrates strong stability in resolving complex mapping structures.展开更多
基金partially supported by the Shanghai Yangfan Special Project,24YF2719900Shanghai Soft Science Research Youth Program(25692112700)+1 种基金China Postdoctoral Science Foundation General Program(2024M761927)Shanghai Key Technology R&D Program“Technical Standards”Project(25DZ2201200).
摘要Automated library migration reduces refactoring costs but challenges traditional evolutionary algorithms,which often suffer from premature convergence and poor recall in sparse,complex API mapping spaces.To address this,we propose QIMIG,a multi-objective optimization framework integrating quantum-inspired encoding with quality-aware and greedy heuristic filtering.QIMIG utilizes a probabilistic Q-bit representation to maintain population diversity and avoid local optima.Simultaneously,its heuristic components leverage historical usage context to filter semantic noise and guide the search toward valid mappings.Evaluated on 9 real-world migration rules derived from 57,447 open-source projects,QIMIG statistically significantly outperforms state-of-the-art baselines such as UNSGA-III.The framework achieves a global mean F1-score of 0.92,exceeding the best-performing baseline by an absolute margin of 0.05,and demonstrates strong stability in resolving complex mapping structures.