Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications

The representation learning of speech, without textual resources, is an area of significant interest for many low resource speech applications. In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers. The learned "time-frequency" representations from the convolutional neural network (CNN) module are further processed with long short term memory (LSTM) layers which generate a contextual vector representation for every windowed segment. The HUC framework, allowing the categorization of the representations into a small number of phoneme-like units, is used to train the model for learning semantically rich speech representations. The targets consist of phoneme-like pseudo labels for each audio segment and these are generated with an iterative k-means algorithm. We explore techniques that improve the speaker invariance of the learned representations and illustrate the effectiveness of the proposed approach on two settings, i) completely unsupervised speech applications on the sub-tasks described as part of the ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition (ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi dataset. In these experiments, we achieve state-of-art results for various ZeroSpeech tasks. Further, on the ASR experiments, the HUC representations are shown to improve significantly over other established benchmarks based on Wav2vec, HuBERT and Best-RQ

Emergency department management of acute exacerbations of chronic obstructive pulmonary disease and factors associated with hospitalization

Background: Currently there is a paucity of information about biomarkers that can predict hospitalization for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) patients presenting to the emergency department (ED). There is limited data on the consistency of ED management of AECOPD with local COPD guidelines. The aim of this study was to identify biomarkers associated with hospitalization in AECOPD patients and to determine if the ED management was concordant with local COPD guidelines. Materials and Methods: We performed a retrospective audit of consecutive AECOPD patients presenting to the Gold Coast Hospital ED over a 6-month period. Results: During the study period, 122 AECOPD patients (51% male, mean age (SE) 71 (±11) years) presented to the ED. Ninety-eight (80%) patients were hospitalized. Univariate analysis identified certain factors associated with hospitalization: Older age, former smokers, home oxygen therapy, weekday presentation, SpO 2 < 92%, and raised inflammatory markers (white cell count (WCC) and C-reactive protein (CRP)). After adjustment for multiple variable, increased age was significantly associated with hospitalization (odds ratio (OR) 1.09; 95% confidence interval (CI): 1.00-1.18; P = 0.05). Radiology assessment and pharmacological management was in accordance with COPD guidelines. However, spirometry was performed in 17% of patients and 28% of patients with hypercapneic respiratory failure received noninvasive ventilation (NIV). Conclusion: We identified several factors on univariate analysis that were associated with hospitalization. Further research is required to determine the utility of these biomarkers in clinical practice. Also, while overall adherence to local COPD guidelines was good, there is scope for improvement in performing spirometry and provision of NIV to eligible patients

FisHook -- An Optimized Approach to Marine Specie Classification using MobileNetV2

Marine ecosystems are vital for the planet's health, but human activities such as climate change, pollution, and overfishing pose a constant threat to marine species. Accurate classification and monitoring of these species can aid in understanding their distribution, population dynamics, and the impact of human activities on them. However, classifying marine species can be challenging due to their vast diversity and the complex underwater environment. With advancements in computer performance and GPU-based computing, deep-learning algorithms can now efficiently classify marine species, making it easier to monitor and manage marine ecosystems. In this paper, we propose an optimization to the MobileNetV2 model to achieve a 99.83% average validation accuracy by highlighting specific guidelines for creating a dataset and augmenting marine species images. This transfer learning algorithm can be deployed successfully on a mobile application for on-site classification at fisheries

Post COVID-19 Guillain Barre syndrome with syndrome of inappropriate secretion of antidiuretic hormone

Guillain Barre syndrome (GBS) is a rare but potentially fatal immune mediated disorder of peripheral nerves and nerve roots usually triggered by infections characterized by ascending paralysis with or without sensory symptoms, hyporeflexia to areflexia. Usually preceded by gastrointestinal or respiratory infection. Post COVID-19 neurological manifestation include GBS, transverse myelitis etc., occur at varying incidence rates at various places. Here we report a 42-year-old lady who had COVID-19 recovered presented with quadriparesis with absent deep tendon reflexes with electro-diagnostically proven AMSAN variety of GBS treated successfully with IVIg. Patient was having hyponatremia which was diagnosed to be due to SIADH and was successfully treated with fluid restriction and tolvaptan. This case is being reported due to combination of COVID-19, COVID vaccination shortly before GBS and hyponatremia due to syndrome of inappropriate secretion of antidiuretic hormone (SIADH) which is quite rare combination

SECURITY AND OTHER VULNERABILITY PREDICTION USING NOVEL DEEP REPRESENTATION OF SOURCE CODE WITH ACTIVE FEEDBACK LOOP

Since the cost of fixing vulnerabilities can be thirty times greater after an application has been deployed, it is recognized that properly-written code can yield potentially large savings. Accordingly, approaches presented herein apply machine learning and Artificial Intelligence (AI) techniques to improve developer experience by enabling developers to avoid introducing potential bugs and/or vulnerabilities while coding. Billions of lines of source code, which have already been written, are utilized as examples of how to write functional and secure code that is easy to read and to debug. By leveraging this wealth of available data, which is complemented with state-of-art machine learning models, enterprise-level software solutions can be developed that have a high standard of coding and are potentially bug-free