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In the current situation of worldwide pandemic COVID-19, which has infected 62.5 Million people and caused nearly 1.46 Million deaths worldwide as of Nov 2020. The profoundly powerful and quickly advancing circumstance with COVID-19 has made it hard to get precise, on-request latest data with respect to the virus. Especially, the frontline workers of the battle medical services experts, policymakers, clinical scientists, and so on will require expert specific methods to stay aware of this literature for getting scientific knowledge of the latest research findings. The risks are most certainly not trivial, as decisions made on fallacious, answers may endanger trust or general well being and security of the public. But, with thousands of research papers being dispensed on the topic, making it more difficult to keep track of the latest research. Taking these challenges into account we have proposed COBERT: a retriever-reader dual algorithmic system that answers the complex queries by searching a document of 59K corona virus-related literature made accessible through the Coronavirus Open Research Dataset Challenge (CORD-19). The retriever is composed of a TF-IDF vectorizer capturing the top 500 documents with optimal scores. The reader which is pre-trained Bidirectional Encoder Representations from Transformers (BERT) on SQuAD 1.1 dev dataset built on top of the HuggingFace BERT transformers, refines the sentences from the filtered documents, which are then passed into ranker which compares the logits scores to produce a short answer, title of the paper and source article of extraction. The proposed DistilBERT version has outperformed previous pre-trained models obtaining an Exact Match(EM)/F1 score of 80.6/87.3 respectively.

Human gait analysis is a novel topic in the field of computer vision with many famous applications like prediction of osteoarthritis and patient surveillance. In this application, the abnormal behavior like problems in walking style is detected of suspected patients. The suspected behavior means assessments in terms of knee joints and any other symptoms that directly affected patients’ walking style. Human gait analysis carries substantial importance in the medical domain, but the variability in patients’ clothes, viewing angle, and carrying conditions, may severely affect the performance of a system. Several deep learning techniques, specifically focusing on efficient feature selection, have been recently proposed for this purpose, unfortunately, their accuracy is rather constrained. To address this disparity, we propose an aggregation of robust deep learning features in Kernel Extreme Learning Machine. The proposed framework consists of a series of steps. First, two pre-trained Convolutional Neural Network models are retrained on public gait datasets using transfer learning, and features are extracted from the fully connected layers. Second, the most discriminant features are selected using a novel probabilistic approach named Euclidean Norm and Geometric Mean Maximization along with Conditional Entropy. Third, the aggregation of the robust features is performed using Canonical Correlation Analysis, and the aggregated features are subjected to various classifiers for final recognition. The evaluation of the proposed scheme is performed on a publicly available gait image dataset CASIA B. We demonstrate that the proposed feature aggregation methodology, once used with the Kernel Extreme Learning Machine, achieves accuracy beyond 96%, and outperforms the existing works and several other widely adopted classifiers.

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