wanyu/IteraTeR_v2 huggingface dataset:
Original: waiting times and low resource utilization values at observed estimates of patient demand . Simulation outcomes for the government mandated configuration model based on demand estimated from public health data and larger outpatient consultation times indicated significantly higher resource utilization . Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the government mandated case were performed.
Revised: waiting times and low resource utilization values at observed estimates of patient demand . Simulation outcomes for the government mandated configuration model based on demand estimated from public health data and larger outpatient consultation times indicated significantly higher resource utilization . Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilisation of stressed resources for the government mandated case were performed.
Edit intent: fluency
Original: waiting times and low resource utilization values at observed estimates of patient demand . Simulation outcomes for the government mandated configuration model based on demand estimated from public health data and larger outpatient consultation times indicated significantly higher resource utilization . Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the government mandated case were performed.
Revised: waiting times and low resource utilization values at observed estimates of patient demand . Simulation outcomes for the government mandated configuration model based on demand estimated from public health data and larger outpatient consultation times indicated significantly higher resource utilization . Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the benchmark case were performed.
Edit intent: clarity
Original:  Our analysis also motivated the development of simple analytical approximations of the average utilization of a server in a queueing system with characteristics similar to the PHC doctor/patient system  .
Revised:  Our analysis also motivated the development of simple analytical approximations of the average utilisation of a server in a queueing system with characteristics similar to the PHC doctor/patient system .
Edit intent: fluency
Original: Science has a data management problem, as well as a project management problem.
Revised: Science has a data management as well as a project management problem.
Edit intent: fluency
Original:  While industry data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows, academia-based science is still (mostly) mired in a mindset that's focused on a single final product (a paper), without focusing on incremental improvement and, over all, reproducibility.
Revised:  While industrial grade data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows, academia-based science is still (mostly) mired in a mindset that's focused on a single final product (a paper), without focusing on incremental improvement and, over all, reproducibility.
Edit intent: clarity
Original:  While industry data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows, academia-based science is still (mostly) mired in a mindset that's focused on a single final product (a paper), without focusing on incremental improvement and, over all, reproducibility.
Revised:  While industry data science teams have embraced the *agile* mindset, and adopted or created all kind of tools to create reproducible workflows, academia-based science is still (mostly) mired in a mindset that's focused on a single final product (a paper), without focusing on incremental improvement and, over all, reproducibility.
Edit intent: style
Original:  While industry data science teams have embraced the agile mindset, and adopted or created all kind of tools to create reproducible workflows, academia-based science is still (mostly) mired in a mindset that's focused on a single final product (a paper), without focusing on incremental improvement and, over all, reproducibility.
Revised:  While industry data science teams have embraced the agile mindset, and adopted or created all kind of tools to manage reproducible workflows, academia-based science is still (mostly) mired in a mindset that's focused on a single final product (a paper), without focusing on incremental improvement and, over all, reproducibility.
Edit intent: clarity
Original:  In this report we argue towards the adoption of the agile mindset and agile data science tool in academia, to make a more responsible, and over all, reproducible science.
Revised:  In this report we argue towards the adoption of the agile mindset and agile data science tools in academia, to make a more responsible, and over all, reproducible science.
Edit intent: fluency
Original:  In this report we argue towards the adoption of the agile mindset and agile data science tool in academia, to make a more responsible, and over all, reproducible science.
Revised:  In this report we argue towards the adoption of the agile mindset and agile data science tool in academia, to make a more responsible, sustainable, and above all, reproducible science.
Edit intent: clarity
Original:  This paper makes a comprehensive review of the challenges, tasks, methods,  gaps and opportunities on modeling COVID-19 problems and data .
Revised:  This paper provides a comprehensive review of the challenges, tasks, methods, gaps and opportunities on modeling COVID-19 problems and data .
Edit intent: clarity
Original:  This paper makes a comprehensive review of the challenges, tasks, methods,  gaps and opportunities on modeling COVID-19 problems and data .
Revised:  This paper makes a comprehensive review of the challenges, tasks, methods, gaps and opportunities in relation to modeling COVID-19 problems and data .
Edit intent: clarity
Original:  This paper makes a comprehensive review of the challenges, tasks, methods,  gaps and opportunities on modeling COVID-19 problems and data .
Revised:  This paper makes a comprehensive review of the challenges, tasks, methods, gaps and opportunities on modeling COVID-19 problems , data and objectives .
Edit intent: clarity
Original:  It constructs a research landscape of COVID-19 modeling  , and further categorizes,  compares and discusses the related work on modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and trends, medical treatments, non-pharmaceutical intervention effect , drug and vaccine development, psychological, economic and social  impact, and misinformation, etc.
Revised:  It constructs a research landscape of COVID-19 modeling , and further categorizes, compares and discusses the related work on modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical intervention effect , drug and vaccine development, psychological, economic and social impact, and misinformation, etc.
Edit intent: clarity
Original:  It constructs a research landscape of COVID-19 modeling  , and further categorizes,  compares and discusses the related work on modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and trends, medical treatments, non-pharmaceutical intervention effect , drug and vaccine development, psychological, economic and social  impact, and misinformation, etc.
Revised:  It constructs a research landscape of COVID-19 modeling , and further categorizes, compares and discusses the related work on modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and trends, medical treatments, non-pharmaceutical interventions and their effects , drug and vaccine development, psychological, economic and social impact, and misinformation, etc.
Edit intent: clarity
Original:  It constructs a research landscape of COVID-19 modeling  , and further categorizes,  compares and discusses the related work on modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and trends, medical treatments, non-pharmaceutical intervention effect , drug and vaccine development, psychological, economic and social  impact, and misinformation, etc.
Revised:  It constructs a research landscape of COVID-19 modeling , and further categorizes, compares and discusses the related work on modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and trends, medical treatments, non-pharmaceutical intervention effect , drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc.
Edit intent: fluency
Original:  The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, data-driven learning by shallow and deep machine learning, simulation systems , social science methods, and hybrid methods .
Revised:  The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, data-driven learning by shallow and deep machine learning, simulation modeling , social science methods, and hybrid methods .
Edit intent: clarity
Original:  The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, data-driven learning by shallow and deep machine learning, simulation systems , social science methods, and hybrid methods .
Revised:  The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, data-driven learning by shallow and deep machine learning, simulation systems , social science methods, and hybrid modeling .
Edit intent: fluency
Original:  This is surprising since data analysis in HEP matches the SQL model well: it is fully structured data queried using combinations of selections, projections, joins, and reductions .
Revised:  This is surprising since HEP data analysis matches the SQL model well: it is fully structured data queried using combinations of selections, projections, joins, and reductions .
Edit intent: clarity
Original:  This is surprising since data analysis in HEP matches the SQL model well: it is fully structured data queried using combinations of selections, projections, joins, and reductions .
Revised:  This is surprising since data analysis in HEP matches the SQL model well: the data is fully structured data queried using combinations of selections, projections, joins, and reductions .
Edit intent: coherence
Original: The performance of applications, such as personal assistants , search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs).
Revised: The performance of applications, such as personal assistants and search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs).
Edit intent: clarity
Original: The performance of applications, such as personal assistants , search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs).
Revised: The performance of applications, such as personal assistants , search engines, relies on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs).
Edit intent: clarity
Original:  To ensure their quality one important task is Knowledge Validation , which measures the degree to which statements or triples of a Knowledge Graph (KG) are correct.
Revised:  To ensure their quality one important task is knowledge validation , which measures the degree to which statements or triples of a Knowledge Graph (KG) are correct.
Edit intent: fluency
Original:  To ensure their quality one important task is Knowledge Validation , which measures the degree to which statements or triples of a Knowledge Graph (KG) are correct.
Revised:  To ensure their quality one important task is Knowledge Validation , which measures the degree to which statements or triples of KGs are semantically correct.
Edit intent: clarity
Original:  KGs inevitably contains incorrect and incomplete statements, which may hinder the adoption of such KGs in business applications as they are not trustworthy.
Revised:  KGs inevitably contain incorrect and incomplete statements, which may hinder the adoption of such KGs in business applications as they are not trustworthy.
Edit intent: fluency
Original:  KGs inevitably contains incorrect and incomplete statements, which may hinder the adoption of such KGs in business applications as they are not trustworthy.
Revised:  KGs inevitably contains incorrect and incomplete statements, which may hinder their adoption in business applications as they are not trustworthy.
Edit intent: clarity
Original:  In this paper, we propose and implement a validation approach that computes a confidence score for every triple and instance in a KG . The computed score is based on finding the same instances across different weighted knowledge sources and comparing their features.
Revised:  In this paper, we propose and implement a Validator that computes a confidence score for every triple and instance in a KG . The computed score is based on finding the same instances across different weighted knowledge sources and comparing their features.
Edit intent: clarity
Original:  In this paper, we propose and implement a validation approach that computes a confidence score for every triple and instance in a KG . The computed score is based on finding the same instances across different weighted knowledge sources and comparing their features.
Revised:  In this paper, we propose and implement a validation approach that computes a confidence score for every triple and instance in KGs . The computed score is based on finding the same instances across different weighted knowledge sources and comparing their features.
Edit intent: fluency
Original:  By assigning to each observation a probability of being an outlying data point, the degree of outlyingness can be determined .
Revised:  The procedure allows to return a probability for each observation being outlying data as well as a specific cause for an outlier assignment in terms of the provided feature space .
Edit intent: clarity
Original:  Moreover, by providing the auto_data_cleaning function the data preprocessing can be carried out in one cast, without manual tuning and providing suitable results.
Revised:  Moreover, by providing the auto_data_cleaning function the data preprocessing can be carried out in one cast, without comprehensive tuning and providing suitable results.
Edit intent: clarity
Original:  The primary motivation of the package is the preprocessing of energy system data , however, the package is also suited for other moderate and large sized time series data set .
Revised:  The primary motivation of the package is the preprocessing of energy system data .
Edit intent: coherence
Original: In this paper, we present our system design for conducting recurring daily studies on Amazon Mechanical Turk.
Revised: In this paper, we present our system design for conducting longitudinal daily-task studies with the same workers throughout on Amazon Mechanical Turk.
Edit intent: clarity
Original:  Taking place over a time period of 31 days, our study utilized a self-contained app which workers used to complete daily tasks without requiring extra HITs .
Revised:  The completion of the daily tasks did not require extra interaction with the Mechanical Turk platform, yet paid workers through it .
Edit intent: clarity
Original:  This differs somewhat from the typical rapid completion of one-off tasks  on Amazon Mechanical Turk.
Revised:  This differs somewhat from the typical rapid completion of one-off tasks that workers are used to on Amazon Mechanical Turk.
Edit intent: clarity
Original:  This atypical use of the platform prompted us to study aspects related to long-term user retention over the study period : payment schedule (amount and structure over time) , regular notifications, payment satisfaction and overall satisfaction .
Revised:  This atypical use of the platform prompted us to evaluate aspects related to long-term user retention over the study period : payment schedule (amount and structure over time) , regular notifications, payment satisfaction and overall satisfaction .
Edit intent: clarity
Original:  This atypical use of the platform prompted us to study aspects related to long-term user retention over the study period : payment schedule (amount and structure over time) , regular notifications, payment satisfaction and overall satisfaction .
Revised:  This atypical use of the platform prompted us to study aspects related to long-term worker retention and engagement over the study period : payment schedule (amount and structure over time) , regular notifications, payment satisfaction and overall satisfaction .
Edit intent: clarity
Original:  This atypical use of the platform prompted us to study aspects related to long-term user retention over the study period : payment schedule (amount and structure over time) , regular notifications, payment satisfaction and overall satisfaction .
Revised:  This atypical use of the platform prompted us to study aspects related to long-term user retention over the study period , in particular the impacts of payment schedule (amount and structure over time) , regular notifications, payment satisfaction and overall satisfaction .
Edit intent: clarity
Original:  This atypical use of the platform prompted us to study aspects related to long-term user retention over the study period : payment schedule (amount and structure over time) , regular notifications, payment satisfaction and overall satisfaction .
Revised:  This atypical use of the platform prompted us to study aspects related to long-term user retention over the study period : payment schedule (amount and structure over time) and reminder notifications .
Edit intent: clarity
Original:  We find that using the Mechanical Turk platform in this way leads to data of comparable quality to that of lab based studies, and that our study design choices show a statistically significant effect in keeping workers engaged .
Revised:  We find that using the Mechanical Turk platform for conducting longitudinal daily task studies is a viable method to augment or replace traditional lab studies .
Edit intent: clarity
Original: In Psychology, actions are paramount for humans to perceive and separate sound events.
Revised: In Psychology, actions are paramount for humans to identify sound events.
Edit intent: clarity
Original:  however, it has not been asked if identifying actions can benefit Sound Event Classification (SEC), as opposed to mapping the audio directly to a sound event.
Revised:  however, it has not been asked whether identifying actions can benefit Sound Event Classification (SEC), as opposed to mapping the audio directly to a sound event.
Edit intent: fluency
Original:  The resulting annotations for each audio recording relate actions to a database of sound events for the first time ~Annotations will be released after revision. . The annotations were used to create semantic representations called Action Vectors (AVs).
Revised:  The resulting annotations for each audio recording relate actions to a database of sound events for the first time Annotations will be released after revision. . The annotations were used to create semantic representations called Action Vectors (AVs).
Edit intent: fluency
Original:  The resulting annotations for each audio recording relate actions to a database of sound events for the first time ~Annotations will be released after revision. . The annotations were used to create semantic representations called Action Vectors (AVs).
Revised:  The resulting annotations for each audio recording relate actions to a database of sound events for the first time ~ . The annotations were used to create semantic representations called Action Vectors (AVs).
Edit intent: coherence
Original:  We evaluated SEC by comparing the AVs with two types of audio features -- log-mel spectrograms and state of the art audio embeddings.
Revised:  We evaluated SEC by comparing the AVs with two types of audio features -- log-mel spectrograms and state-of-the-art audio embeddings.
Edit intent: fluency
Original: This paper proposes Capstan: a scalable, parallel-patterns-based, reconfigurable-dataflow accelerator (RDA) for sparse and dense tensor applications.
Revised: This paper proposes Capstan: a scalable, parallel-patterns-based, reconfigurable dataflow accelerator (RDA) for sparse and dense tensor applications.
Edit intent: fluency
Original: In this paper  we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences.
Revised: In this paper , we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences.
Edit intent: fluency
Original: In this paper  we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences.
Revised: In this paper we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences.
Edit intent: fluency
Original:  We expand the variational autoencoder collaborative filtering from a single-domain to a multi domain setting.
Revised:  We expand the variational autoencoder collaborative filtering from a single-domain to a multi-domain setting.
Edit intent: fluency
Original:  The intuition can be taken to its extreme, where, in a cross-domain setup, the user history in a source domain is enough to generate high quality recommendations in a target one.
Revised:  The intuition can be taken to its extreme, where, in a cross-domain setup, the user history in a source domain is enough to generate high-quality recommendations in a target one.
Edit intent: fluency
Original:  Surprisingly, we find that in select cases, a POE recommender that does not access the target domain user representation can surpass a strong VAE recommender baseline trained on the target domain .
Revised:  Surprisingly, we find that in some cases, a POE recommender that does not access the target domain user representation can surpass a strong VAE recommender baseline trained on the target domain .
Edit intent: clarity
Original: We propose new preconditioned iterative solvers for linear systems arising in primal-dual interior-point methods for convex quadratic programming problems.
Revised: We propose new preconditioned iterative solvers for linear systems arising in primal-dual interior point methods for convex quadratic programming problems.
Edit intent: fluency
Original:  Additionally , we release two new audio datasets: FM synth timbre URL and subtractive synth pitch URL Using these datasets, we demonstrate new rank-based synthesizer-motivated evaluation criteria for existing audio representations.
Revised:  Additionally , we release two new audio datasets: FM synth timbre URL and subtractive synth pitch URL Using these datasets, we demonstrate new rank-based evaluation criteria for existing audio representations.
Edit intent: clarity
Original:  Finally, we propose novel approaches to synthesizer hyperparameter optimization , and demonstrate how perceptually-correlated auditory distances could enable new applications in synthesizer design .
Revised:  Finally, we propose a novel approach to synthesizer hyperparameter optimization , and demonstrate how perceptually-correlated auditory distances could enable new applications in synthesizer design .
Edit intent: fluency