传统的文本生成对抗方法主要采用位置置换、字符替换等方式,耗费时间较长且效果较差。针对以上问题,该文提出一种基于改进蚁群算法的对抗样本生成模型IGAS(Improved ant colony algorithm to Generate Adversarial Sample),利用蚁群算...传统的文本生成对抗方法主要采用位置置换、字符替换等方式,耗费时间较长且效果较差。针对以上问题,该文提出一种基于改进蚁群算法的对抗样本生成模型IGAS(Improved ant colony algorithm to Generate Adversarial Sample),利用蚁群算法的特点生成对抗样本,并利用类形字进行优化。首先,构建城市节点群,利用样本中的词构建城市节点群;然后对原始输入样本,利用改进的蚁群算法生成对抗样本;再针对生成结果,通过构建的中日类形字典进行字符替换,生成最终的对抗样本;最后在黑盒模式下进行对抗样本攻击实验。实验在情感分类、对话摘要生成、因果关系抽取等多种领域验证了该方法的有效性。展开更多
Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accur...Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks(ANNs) were developed to map soil units using digital elevation model(DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base(WRB)classification criteria than the Soil Taxonomy(ST) system, but more soil classes could be predicted when using ST(7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error(interpolation error) and validation error(extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.展开更多
文摘传统的文本生成对抗方法主要采用位置置换、字符替换等方式,耗费时间较长且效果较差。针对以上问题,该文提出一种基于改进蚁群算法的对抗样本生成模型IGAS(Improved ant colony algorithm to Generate Adversarial Sample),利用蚁群算法的特点生成对抗样本,并利用类形字进行优化。首先,构建城市节点群,利用样本中的词构建城市节点群;然后对原始输入样本,利用改进的蚁群算法生成对抗样本;再针对生成结果,通过构建的中日类形字典进行字符替换,生成最终的对抗样本;最后在黑盒模式下进行对抗样本攻击实验。实验在情感分类、对话摘要生成、因果关系抽取等多种领域验证了该方法的有效性。
文摘Detailed soil surveys involve costly and time-consuming work and require expert knowledge. Since soil surveys provide information to meet a wide range of needs, new methods are necessary to map soils quickly and accurately. In this study, multilayer perceptron artificial neural networks(ANNs) were developed to map soil units using digital elevation model(DEM) attributes. Several optimal ANNs were produced based on a number of input data and hidden units. The approach used test and validation areas to calculate the accuracy of interpolated and extrapolated data. The results showed that the system and level of soil classification employed had a direct effect on the accuracy of the results. At the lowest level, smaller errors were observed with the World Reference Base(WRB)classification criteria than the Soil Taxonomy(ST) system, but more soil classes could be predicted when using ST(7 soils in the case of ST vs. 5 with WRB). Training errors were below 11% for all the ANN models applied, while the test error(interpolation error) and validation error(extrapolation error) were as high as 50% and 70%, respectively. As expected, soil prediction using a higher level of classification presented a better overall level of accuracy. To obtain better predictions, in addition to DEM attributes, data related to landforms and/or lithology as soil-forming factors, should be used as ANN input data.
