Through this paper, we seek to reduce the communication barrier between the hearing-impaired community and the larger society who are usually not familiar with sign language in the sub-Saharan region of Africa with the largest occurrences of hearing disability cases, while using Nigeria as a case study. The dataset is a pioneer dataset for the Nigerian Sign Language and was created in collaboration with relevant stakeholders. We pre-processed the data in readiness for two different object detection models and a classification model and employed diverse evaluation metrics to gauge model performance on sign-language to text conversion tasks. Finally, we convert the predicted sign texts to speech and deploy the best performing model in a lightweight application that works in real-time and achieves impressive results converting sign words/phrases to text and subsequently, into speech.
People are capable of learning diverse functional relationships from data; nevertheless, they are most accurate when learning linear relationships, and deviate further from estimating the true relationship when presented with non-linear functions. We investigate whether, when given the opportunity to learn actively, people choose samples in an efficient fashion, and whether better sampling policies improve their ability to learn linear and non-linear functions. We find that, across multiple different function families, people make informative sampling choices consistent with a simple, low-effort policy that minimizes uncertainty at extreme values without requiring adaptation to evidence. While participants were most accurate at learning linear functions, those who more closely adhered to the simple sampling strategy also made better predictions across all non-linear functions. We discuss how the use of this heuristic might reflect rational allocation of limited cognitive resources.
The Social Distancing Index (SDI) measures social distancing behaviour every day across all fifty American states. This study leverages SDI data to model social distancing behaviour with time-series COVID-19 data, as well as an array of political and economic variables. The central aim of this study is to examine three hypotheses: (i) COVID-19 outbreaks within a state will induce social distancing by fear of the virus, (ii) states with more low-income workers will engage in less social distancing due to the nature of essential work, and (iii) political beliefs will influence social distancing behaviour, through the public debate over social distancing policy and a partisan logic defining state stay-at-home orders. We use Vector Autoregressive (VAR) and Beta Regression models to determine the most influential variables in this study. VAR models for time-series relationships between cases and social distancing behaviour in California and Texas, and corresponding Granger-Cause Test results, are investigated through case studies. Significant Beta model variables influencing social distancing behaviour are closely examined through visual data analysis and qualitatively contextualized to describe relationships present in the data. Our results indicate statistically significant relationships between the severity of state outbreaks, age and income distribution, change in governor approval ratings, and social distancing behaviour. There are also clear relationships between the partisan make-up of a state and social distancing behaviour. The results found in this study contribute to growing evidence regarding the impact political polarization has on various aspects of American social life, while giving insight to behavioural dynamics that play a critical role in mitigating the spread of COVID-19.
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