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People provide information on their thoughts, perceptions, and activities through a wide range of channels, including social media. The wide acceptance of social media results in vast volume of valuable data, in variety of format as well as veracity. Analysis of such 'big data' allows organizations and analysts to make better and faster decisions. However, this data had to be quantified and information has to be extracted, which can be very challenging because of possible data ambiguity and complexity. To address information extraction, many analytic techniques, such as text mining, machine learning, predictive analytics, and diverse natural language processing, have been proposed in the literature. Recent advances in Natural Language Understanding-based techniques more specifically transformer-based architectures can solve sequence-to-sequence modeling tasks while handling long-range dependencies efficiently. In this work, we applied transformer-based sequence modeling on short texts' topic classification and sentiment analysis from user-posted tweets. Applicability of models is investigated on posts from the Great Barrier Reef tweet dataset and obtained findings are encouraging providing insight that can be valuable for researchers working on classification of large datasets as well as large number of target classes. [ABSTRACT FROM AUTHOR]

Gait recognition, a task of identifying people through their walking pattern, has attracted more and more researchers' attention. At present, most skeleton-based gait recognition approaches extract gait features from merely joint coordinates. However, the information, e.g. bone and motion, is equally instructive and discriminative for gait recognition. Thus, this paper proposes a novel multi-stream part-fused graph convolutional network, MS-Gait, to fuse part-level information and capture multi-order features from skeleton data. To be specific, we integrate a channel attention learning mechanism into the graph convolutional networks (GCN) to improve the representational power. In addition, part-level information is merged by capturing features from the skeleton graph and its subgraphs concurrently. Finally, a multi-stream strategy is proposed to model joint, bone, and motion dynamics simultaneously, which is proven to effectively improve the recognition accuracy. On the popular CASIA-B dataset, extensive experiments demonstrate that our method can achieve state-of-the-art performance and is robust to confounding variations. [ABSTRACT FROM AUTHOR]