Automatic Gender and Age Classification from Offline Handwriting with Bilinear ResNet.

This work focuses on automatic gender and age prediction tasks from handwritten documents. This problem is of interest in a variety of fields, such as historical document analysis and forensic investigations. The challenge for automatic gender and age classification can be demonstrated by the relatively low performances of the existing methods. In addition, despite the success of CNN for gender classification, deep neural networks were never applied for age classification. The published works in this area mostly concentrate on English and Arabic languages. In addition to Arabic and English, this work also considers Hebrew, which was much less studied. Following the success of bilinear Convolutional Neural Network (B-CNN) for fine-grained classification, we propose a novel implementation of a B-CNN with ResNet blocks. To our knowledge, this is the first time the bilinear CNN is applied for writer demographics classification. In particular, this is the first attempt to apply a deep neural network for the age classification. We perform experiments on documents from three benchmark datasets written in three different languages and provide a thorough comparison with the results reported in the literature. B-ResNet was top-ranked in all tasks. In particular, B-ResNet outperformed other models on KHATT and QUWI datasets on gender classification.

Tracking social media during the COVID-19 pandemic: The case study of lockdown in New York State.

Facing the COVID-19 pandemic, governments have implemented a wide range of policies to contain the spread of the virus. During the pandemic, large amounts of COVID-19-related tweets emerge every day. Real-time processing of daily tweets may offer insights for monitoring public opinion about intervention measures implemented. In this work, lockdown policy in New York State has been set as a target of public opinion research. This task includes two stages, stance detection and opinion monitoring. For the stance detection stage, we explored several combinations of different text representations and classification algorithms, finding that the combination of Long Short-Term Memory (LSTM) with Global Vectors for Word Representation (GloVe) outperforms others. Due to the shortage of labeled data, we adopted the data distillation method for the training data augmentation. The augmentation of the training data allows to improve the performance of the model with a very small amount of manually-labeled data. After applying the distillation method, the accuracy of the model has been significantly improved. Utilizing the enhanced model, automatically classified tweets are analyzed over time to monitor the public opinion. By exploring the tweets in New York from January 22nd until September 30th, 2020, we show the correlation of public opinion with COVID-19 cases and mortality data, and the effect of government responses on the opinion shift. These results demonstrate the capability of the presented method to effectively and efficiently monitor public opinion during a pandemic.