DICE: Deep intelligent contextual embedding for twitter sentiment analysis

© 2019 IEEE. The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets

Vision-Language Transformer for Interpretable Pathology Visual Question Answering

Pathology visual question answering (PathVQA) attempts to answer a medical question posed by pathology images. Despite its great potential in healthcare, it is not widely adopted because it requires interactions on both the image (vision) and question (language) to generate an answer. Existing methods focused on treating vision and language features independently, which were unable to capture the high and low-level interactions that are required for VQA. Further, these methods failed to offer capabilities to interpret the retrieved answers, which are obscure to humans where the models’ interpretability to justify the retrieved answers has remained largely unexplored. Motivated by these limitations, we introduce a vision-language transformer that embeds vision (images) and language (questions) features for an interpretable PathVQA. We present an interpretable tra nsformer-based P ath- VQA (TraP-VQA), where we embed transformers’ encoder layers with vision and language features extracted using pre-trained CNN and domain-specific language model (LM), respectively. A decoder layer is then embedded to upsample the encoded features for the final prediction for PathVQA. Our experiments showed that our TraP-VQA outperformed the state-of-the-art comparative methods with public PathVQA dataset. Our experiments validated the robustness of our model on another medical VQA dataset, and the ablation study demonstrated the capability of our integrated transformer-based vision-language model for PathVQA. Finally, we present the visualization results of both text and images, which explain the reason for a retrieved answer in PathVQA.ARC (Grant Number: DP200103748)

Bilateral total duplication of clavicle: First reported case

A very rare first case of bilateral duplication of the clavicle is presented here. Duplication of the clavicle has been described in only six reports based on a search of the world literature, with single case of bilateral duplication (incomplete) of clavicle being reported. The detection of anatomic anomalies are increasing with the advancement of technology in medicine field. This case is more of academic interest as it is the first case of total bilateral duplication of clavicle

Classifying vaccine sentiment tweets by modelling domain-specific representation and commonsense knowledge into context-aware attentive GRU

Accepted in International Joint Conference on Neural Networks (IJCNN) 2021. Cite as: arXiv:2106.09589 [cs.CL] https://doi.org/10.48550/arXiv.2106.09589Vaccines are an important public health measure, but vaccine hesitancy and refusal can create clusters of low vaccine coverage and reduce the effectiveness of vaccination programs. Social media provides an opportunity to estimate emerging risks to vaccine acceptance by including geographical location and detailing vaccine-related concerns. Methods for classifying social media posts, such as vaccine-related tweets, use language models (LMs) trained on general domain text. However, challenges to measuring vaccine sentiment at scale arise from the absence of tonal stress and gestural cues and may not always have additional information about the user, e.g., past tweets or social connections. Another challenge in LMs is the lack of commonsense knowledge that are apparent in users metadata, i.e., emoticons, positive and negative words etc. In this study, to classify vaccine sentiment tweets with limited information, we present a novel end-to-end framework consisting of interconnected components that use domain-specific LM trained on vaccine-related tweets and models commonsense knowledge into a bidirectional gated recurrent network (CK-BiGRU) with context-aware attention. We further leverage syntactical, user metadata and sentiment information to capture the sentiment of a tweet. We experimented using two popular vaccine-related Twitter datasets and demonstrate that our proposed approach outperforms state-of-the-art models in identifying pro-vaccine, anti-vaccine and neutral tweets.Australian Government Research Training Program (RTP

K-PathVQA: Knowledge-Aware Multimodal Representation for Pathology Visual Question Answering

ARC (Grant Number: DP200103748

Concepts on Coloring of Cluster Hypergraphs with Application

Coloring of graph theory is widely used in different fields like the map coloring, traffic light problems, etc. Hypergraphs are an extension of graph theory where edges contain single or multiple vertices. This study analyzes cluster hypergraphs where cluster vertices too contain simple vertices. Coloring of cluster networks where composite/cluster vertices exist is done using the concept of coloring of cluster hypergraphs. Proper coloring and strong coloring of cluster hypergraphs have been defined. Along with these, local coloring in cluster hypergraphs is also provided. Such a cluster network, COVID19 affected network, is assumed and colored to visualize the affected regions properly

A comparative analysis of active learning for biomedical text mining

Data Availability Statement: The code and data are available from https://github.com/usmaann (accessed on 14 March 2021).Copyright © 2021 by the authors. An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called active learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL)-based strategies on three unique biomedical datasets. We investigated random sampling (RS), least confidence (LC), informative diversity and density (IDD), margin and maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Furthermore, pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling.This research received no external funding

Sprint interval and sprint continuous training increases circulating CD34+ cells and cardio-respiratory fitness in young healthy women

The improvement of vascular health in the exercising limb can be attained by sprint interval training (SIT). However, the effects on systemic vascular function and on circulating angiogenic cells (CACs) which may contribute to endothelial repair have not been investigated. Additionally, a comparison between SIT and sprint continuous training (SCT) which is less time committing has not been made