在这项研究中，我们提出了使用深度学习方法进行多模式模因分类的特征提取。模因通常是一张照片或视频，其中年轻一代在社交媒体平台上共享文本，表达了与文化相关的想法。由于它们是表达情感和感受的有效方法，因此可以对模因背后的情绪进行分类的好分类器很重要。为了使学习过程更有效，请减少过度拟合的可能性，并提高模型的普遍性，需要一种良好的方法来从所有模式中提取共同特征。在这项工作中，我们建议使用不同的多模式神经网络方法进行多模式特征提取，并使用提取的功能来训练分类器以识别模因中的情感。

在这项工作中，我们提出了用于商业产品分类的多模式模型，该模型结合了使用简单的融合技术从Textual（Camembert和Flaubert）和视觉数据（SE-Resnext-50）中提取的功能。所提出的方法显着优于单峰模型的性能以及在我们的特定任务上报告的类似模型的报告。我们进行了多种融合技术的实验，并发现，结合单峰网络的单个嵌入的最佳性能技术是基于结合串联和平均特征向量的方法。每种模式都补充了其他方式的缺点，表明增加模态的数量可能是改善多标签和多模式分类问题的有效方法。

由于在线学习和评估平台（例如Coursera，Udemy，Khan Academy等）的兴起，对论文（AES）和自动论文评分的自动评估（AES）已成为一个严重的问题。研究人员最近提出了许多用于自动评估的技术。但是，其中许多技术都使用手工制作的功能，因此从特征表示的角度受到限制。深度学习已成为机器学习中的新范式，可以利用大量数据并确定对论文评估有用的功能。为此，我们提出了一种基于复发网络（RNN）和卷积神经网络（CNN）的新型体系结构。在拟议的体系结构中，多通道卷积层从嵌入矢量和基本语义概念中学习并捕获单词n-gram的上下文特征，并使用max-pooling操作在论文级别形成特征向量。 RNN的变体称为双门复发单元（BGRU），用于访问以前和后续的上下文表示。该实验是对Kaggle上的八个数据集进行的，以实现AES的任务。实验结果表明，我们提出的系统比其他基于深度学习的AES系统以及其他最新AES系统的评分精度明显更高。

Acquiring a better understanding of drought impacts becomes increasingly vital under a warming climate. Traditional drought indices describe mainly biophysical variables and not impacts on social, economic, and environmental systems. We utilized natural language processing and bidirectional encoder representation from Transformers (BERT) based transfer learning to fine-tune the model on the data from the news-based Drought Impact Report (DIR) and then apply it to recognize seven types of drought impacts based on the filtered Twitter data from the United States. Our model achieved a satisfying macro-F1 score of 0.89 on the DIR test set. The model was then applied to California tweets and validated with keyword-based labels. The macro-F1 score was 0.58. However, due to the limitation of keywords, we also spot-checked tweets with controversial labels. 83.5% of BERT labels were correct compared to the keyword labels. Overall, the fine-tuned BERT-based recognizer provided proper predictions and valuable information on drought impacts. The interpretation and analysis of the model were consistent with experiential domain expertise.

Under climate change, the increasing frequency, intensity, and spatial extent of drought events lead to higher socio-economic costs. However, the relationships between the hydro-meteorological indicators and drought impacts are not identified well yet because of the complexity and data scarcity. In this paper, we proposed a framework based on the extreme gradient model (XGBoost) for Texas to predict multi-category drought impacts and connected a typical drought indicator, Standardized Precipitation Index (SPI), to the text-based impacts from the Drought Impact Reporter (DIR). The preliminary results of this study showed an outstanding performance of the well-trained models to assess drought impacts on agriculture, fire, society & public health, plants & wildlife, as well as relief, response & restrictions in Texas. It also provided a possibility to appraise drought impacts using hydro-meteorological indicators with the proposed framework in the United States, which could help drought risk management by giving additional information and improving the updating frequency of drought impacts. Our interpretation results using the Shapley additive explanation (SHAP) interpretability technique revealed that the rules guiding the predictions of XGBoost comply with domain expertise knowledge around the role that SPI indicators play around drought impacts.