The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respiratory sounds, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19.
We present a framework for processing formulas in automatic theorem provers, with generation of detailed proofs. The main components are a generic contextual recursion algorithm and an extensible set of inference rules. Clausification, skolemization, theory-specific simplifications, and expansion of 'let' expressions are instances of this framework. With suitable data structures, proof generation adds only a linear-time overhead, and proofs can be checked in linear time. We implemented the approach in the SMT solver veriT. This allowed us to dramatically simplify the code base while increasing the number of problems for which detailed proofs can be produced, which is important for independent checking and reconstruction in proof assistants.
Manuscript type: Research paper. Research aims: This paper aims to investigate the applicability of the Theory of Planned Behaviour (TPB) in examining individuals' behavioural intention to invest in the capital market. This study extends on the TPB model by considering the role of past behavioural biases (PBB) as a factor in influencing the individuals' behavioural investment intentions. Design/ Methodology/ Approach: This paper employs a hypothesis deductive approach. The research model is tested through structural equation modelling (SEM). Data were collected from 396 individuals in Eastern India through a survey and then analysed.
Research findings:The results of this study demonstrate the applicability of the TPB in predicting the individuals' behavioural intention to invest in the capital market. This study indicates that attitude toward behaviour, subjective norms and perceived behavioural control are significantly associated with behavioural intentions. The findings signify that the inclusion of past PBB can improve the predictive power of the model. behavioural intentions. It also extends the applicability of the TPB in the area of investment decision making. Practitioner/ Policy implications: The findings of this study reveal that behavioural biases are inseparable from normal human beings' decision making. The reason is because behavioural biases can distort the individuals' fundamental valuation of stocks. Therefore, it is imperative that fund managers incorporate this dimension as part of their risk modelling to enhance investment analysis and strategies. The outcome of this study can be used as a guideline for understanding the factors and programmes that need to be instilled to increase online stock trading among current and future investors.
Research limitation:This study is limited to non-financial sectors due to measurement limitations.
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