The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …

The interest in social media sentiment analysis and opinion mining for public security events
has increased over the years. The availability of social media platforms for communication …

Deep supervised learning has achieved great success in the last decade. However, its
defects of heavy dependence on manual labels and vulnerability to attacks have driven …

Deep neural networks excel at learning from labeled data and achieve state-of-the-art
resultson a wide array of Natural Language Processing tasks. In contrast, learning from …

Sentiment analysis as a field has come a long way since it was first introduced as a task
nearly 20 years ago. It has widespread commercial applications in various domains like …

Contextualized word embeddings such as ELMo and BERT provide a foundation for strong
performance across a wide range of natural language processing tasks by pretraining on …

Soft sensors have been extensively developed and applied in the process industry. One of
the main challenges of the data-driven soft sensors is the lack of labeled data and the need …

Forming a reliable judgement of a machine learning (ML) model's appropriateness for an
application ecosystem is critical for its responsible use, and requires considering a broad …

In recent years, many applications are using various forms of deep learning models. Such
methods are usually based on traditional learning paradigms requiring the consistency of …

People increasingly use microblogging platforms such as Twitter during natural disasters
and emergencies. Research studies have revealed the usefulness of the data available on …