我在Applied Sciences（综合性、交叉性期刊，CiteScore=3.70；IF=2.84）组织了一个Special Issue，大题目是“大数据分析进展”，比较宽泛。该专栏的推出主要是为了回应因为可获取数据和数据分析的平台、工具的快速增长给自然科学和社会科学带来的重大影响。我们特别欢迎（但不限于）下面四类稿件：（1）数据分析中的基础理论分析，例如一个系统的可预测性（比如时间序列的可预测性）、分类问题的最小误差分析、各种数据挖掘结果的稳定性和可信度分析；（2）数据分析的新方法，例如挖掘因果关系的新方法（这和Topic 1也是相关的）、多模态分析的新方法、隐私计算的新方法等等；（3）推出新的、高价值的数据集、数据分析平台、数据分析工具等等；（4）把大数据分析的方法用到自然科学和社会科学的各个分支（并获得洞见），我们特别喜欢用到那些原来定量化程度不高的学科。

投稿链接：https://www.mdpi.com/journal/applsci/special_issues/75Y7F7607U 

投稿截止时期为2023年6月30日，我们处理稿件非常快，欢迎大家投稿支持。

其中第七篇论文已经正式发表：

Balanced Loss Function for Accurate Surface Defect Segmentation

Abstract

The accurate image segmentation of surface defects is challenging for modern convolutional neural networks (CNN)-based segmentation models. This paper identifies that loss imbalance is a critical problem in segmentation accuracy improvement. The loss imbalance problem includes: label imbalance, which impairs the accuracy on less represented classes; easy–hard example imbalance, which misleads the focus of optimization on less valuable examples; and boundary imbalance, which involves an unusually large loss value at the defect boundary caused by label confusion. In this paper, a novel balanced loss function is proposed to address the loss imbalance problem. The balanced loss function includes dynamical class weighting, truncated cross-entropy loss and label confusion suppression to solve the three types of loss imbalance, respectively. Extensive experiments are performed on surface defect benchmarks and various CNN segmentation models in comparison with other commonly used loss functions. The balanced loss function outperforms the counterparts and brings accuracy improvement from 5% to 30%.

论文免费下载链接：

https://www.mdpi.com/2076-3417/13/2/826