Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urg...Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.展开更多
The legal field heavily relies on audio-visual content such as witness testimonies and trials,making accurate transcription and translation crucial,especially in cross-border cases.This study examines the performance ...The legal field heavily relies on audio-visual content such as witness testimonies and trials,making accurate transcription and translation crucial,especially in cross-border cases.This study examines the performance of neural machine translation(NMT)in handling such material,using the DQF-MQM harmonized error typology to categorize errors by type,including terminology,accuracy,and fluency.Legal translation demands precision,as minor errors can impact legal outcomes.Thus,this analysis focuses on English-to-Arabic translations of Egyptian oral arguments before the International Court of Justice,sourced from DawnNews(Feb 21,2024).It investigates whether errors stem from the ASR-generated transcript or the Google NMT system.The findings aim to guide machine translation post-editors(MTPEs)in identifying lexical and syntactic patterns that typically result in errors,ultimately supporting more accurate and legally sound translations.展开更多
文摘Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.
文摘The legal field heavily relies on audio-visual content such as witness testimonies and trials,making accurate transcription and translation crucial,especially in cross-border cases.This study examines the performance of neural machine translation(NMT)in handling such material,using the DQF-MQM harmonized error typology to categorize errors by type,including terminology,accuracy,and fluency.Legal translation demands precision,as minor errors can impact legal outcomes.Thus,this analysis focuses on English-to-Arabic translations of Egyptian oral arguments before the International Court of Justice,sourced from DawnNews(Feb 21,2024).It investigates whether errors stem from the ASR-generated transcript or the Google NMT system.The findings aim to guide machine translation post-editors(MTPEs)in identifying lexical and syntactic patterns that typically result in errors,ultimately supporting more accurate and legally sound translations.
