wanyu/IteraTeR_v2 huggingface dataset:
Original:  A framework called FENCE is integrated into the CERN GitLab software repository, to automatically configure workspaces where the analysis can be documented by the analysis team and managed by the conveners . Continuous integration is used to guide the writers in applying accurate format and valid statements when preparing papers to be submitted to scientific journals.
Revised:  A framework called FENCE is integrated into the CERN GitLab software repository, to automatically configure workspaces where the analysis can be documented by the analysis team and managed by the relevant coordinators . Continuous integration is used to guide the writers in applying accurate format and valid statements when preparing papers to be submitted to scientific journals.
Edit intent: clarity
Original:  A framework called FENCE is integrated into the CERN GitLab software repository, to automatically configure workspaces where the analysis can be documented by the analysis team and managed by the conveners . Continuous integration is used to guide the writers in applying accurate format and valid statements when preparing papers to be submitted to scientific journals.
Revised:  A framework called FENCE is integrated into the CERN GitLab software repository, to automatically configure workspaces where the analysis can be documented by the analysis team and managed by the conveners . Continuous integration is used to guide the writers in applying consistent and correct formatting when preparing papers to be submitted to scientific journals.
Edit intent: clarity
Original:  Additional software assures the correctness of other aspects such as lists of collaboration authors, funding agencies, and foundations.
Revised:  Additional software assures the correctness of other aspects of each paper, such as the lists of collaboration authors, funding agencies, and foundations.
Edit intent: clarity
Original:  The framework and the workflow therein provides automatic and easy support to the researchers and facilitates each phase of the publication process, allowing authors to focus on the article 's contentsthat describe the results of the ATLAS experiment .
Revised:  The framework and the workflow therein provide automatic and easy support to the researchers and facilitates each phase of the publication process, allowing authors to focus on the article 's contentsthat describe the results of the ATLAS experiment .
Edit intent: fluency
Original:  The framework and the workflow therein provides automatic and easy support to the researchers and facilitates each phase of the publication process, allowing authors to focus on the article 's contentsthat describe the results of the ATLAS experiment .
Revised:  The framework and the workflow therein provides automatic and easy support to the researchers and facilitates each phase of the publication process, allowing authors to focus on the article contents .
Edit intent: clarity
Original:  We found that (a ) the awareness of the disease plays the central role in preventing disease outbreak ;
Revised:  We found that (a ) the awareness of the disease plays the central role in epidemic prevention ;
Edit intent: clarity
Original:  (b) we need a reasonable ratio of "over-reacting" nodes to effectively control the disease outbreak ;
Revised:  (b) a reasonable fraction of "over-reacting" nodes to effectively control the disease outbreak ;
Edit intent: clarity
Original:  (b) we need a reasonable ratio of "over-reacting" nodes to effectively control the disease outbreak ;
Revised:  (b) we need a reasonable ratio of "over-reacting" nodes are needed in epidemic prevention ;
Edit intent: clarity
Original:  The results provide evidence that mass media should not play down the transmissibility and severity of diseases , so that the public can become aware of the disease as soon as possible .
Revised:  The results provide evidence that mass media should not play down the transmissibility and severity of diseases to ensure that people could become aware of the disease as soon as possible .
Edit intent: clarity
Original:  The results provide evidence that mass media should not play down the transmissibility and severity of diseases , so that the public can become aware of the disease as soon as possible .
Revised:  The results provide evidence that mass media should not play down the transmissibility and severity of diseases , so that the public can become aware of the disease and adopt self-protection to protect themselves and the whole population .
Edit intent: clarity
Original: Emotional voice conversion aims to convert the emotion of the speech from one state to another while preserving the linguistic content and speaker identity.
Revised: Emotional voice conversion aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity.
Edit intent: fluency
Original:  We believe that emotions are expressed universally across speakers , therefore, the speaker-independent mapping between emotional states of speech is possible.
Revised:  We consider that there is a common code between speakers for emotional expression in a spoken language , therefore, the speaker-independent mapping between emotional states of speech is possible.
Edit intent: clarity
Original:  We believe that emotions are expressed universally across speakers , therefore, the speaker-independent mapping between emotional states of speech is possible.
Revised:  We believe that emotions are expressed universally across speakers , therefore, a speaker-independent mapping between emotional states of speech is possible.
Edit intent: fluency
Original:  We believe that emotions are expressed universally across speakers , therefore, the speaker-independent mapping between emotional states of speech is possible.
Revised:  We believe that emotions are expressed universally across speakers , therefore, the speaker-independent mapping between emotional states is possible.
Edit intent: clarity
Original:  In this paper, we propose to build a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data.
Revised:  In this paper, we propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data.
Edit intent: coherence
Original: In the domain of social signal processing, automated audio recognition is a promising avenue for researchers interested in accessing daily behaviors that can contribute to wellbeing and mental health outcomes .
Revised: In the domain of social signal processing, audio recognition is a promising avenue for researchers interested in accessing daily behaviors that can contribute to wellbeing and mental health outcomes .
Edit intent: clarity
Original: In the domain of social signal processing, automated audio recognition is a promising avenue for researchers interested in accessing daily behaviors that can contribute to wellbeing and mental health outcomes .
Revised: In the domain of social signal processing, automated audio recognition is a promising avenue for accessing daily behaviors that can contribute to wellbeing and mental health outcomes .
Edit intent: clarity
Original: In the domain of social signal processing, automated audio recognition is a promising avenue for researchers interested in accessing daily behaviors that can contribute to wellbeing and mental health outcomes .
Revised: In the domain of social signal processing, automated audio recognition is a promising avenue for researchers interested in accessing daily behaviors that contribute to health and well-being .
Edit intent: clarity
Original:  However, despite remarkable advances in mobile computing and machine learning, audio behavior detection models are largely constrained to data collected in controlled settings such as labs or call centers.
Revised:  However, despite advances in mobile computing and machine learning, audio behavior detection models are largely constrained to data collected in controlled settings such as labs or call centers.
Edit intent: clarity
Original:  However, despite remarkable advances in mobile computing and machine learning, audio behavior detection models are largely constrained to data collected in controlled settings such as labs or call centers.
Revised:  However, despite remarkable advances in mobile computing and machine learning, audio behavior detection models are largely constrained to data collected in controlled settings , such as call centers.
Edit intent: clarity
Original:  This is problematic as it means their performance is unlikely to adequately generalize to real-world applications.
Revised:  This is problematic as it means their performance is unlikely to generalize to real-world applications.
Edit intent: clarity
Original:  To develop our model, we collected and annotated a large dataset of over 780 hours of real-world audio data via a wearable audio recorder worn by infants for up to 24 hours in their natural home environments .
Revised:  To develop our model, we collected and annotated a large dataset of over 780 hours of real-world audio data via a wearable audio recorder .
Edit intent: clarity
Original:  Our model has F1 score of 0.597 relative to an F1 score of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by  state-of-the-art , real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Revised:  Our model has F1 score of 0.597 relative to F1 score of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by state-of-the-art , real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Edit intent: fluency
Original:  Our model has F1 score of 0.597 relative to an F1 score of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by  state-of-the-art , real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Revised:  Our model has F1 score of 0.597 relative to an F1 scores of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by state-of-the-art , real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Edit intent: fluency
Original:  Our model has F1 score of 0.597 relative to an F1 score of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by  state-of-the-art , real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Revised:  Our model has F1 score of 0.597 relative to an F1 score of 0.166 and 0.26 achieved by state-of-the-art , real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Edit intent: clarity
Original:  Our model has F1 score of 0.597 relative to an F1 score of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by  state-of-the-art , real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Revised:  Our model has F1 score of 0.597 relative to an F1 score of 0.166 achieved by real-world state-of-practice infant distress classifiers and an F1 score of 0.26 achieved by state-of-the-art real-world infant distress classifiers published last year in Interspeech's paralinguistic challenge.
Edit intent: fluency
Original:  In the current paper, we present a model combining deep spectrum and acoustic features to detect and classify infant distress vocalizations from 24 hour, continuous, raw real-world data collected via a wearable audio recorder.
Revised:  In this paper, we present a model combining deep spectrum and acoustic features to detect and classify infant distress vocalizations from 24 hour, continuous, raw real-world data collected via a wearable audio recorder.
Edit intent: coherence
Original:  In particular, our model has an F1 score of 0.597 relative to F1 scores of 0.166 and 0.26 achieved by state-of-practice and state-of-the-art real-world infant distress classifiers, respectively .
Revised:  In particular, our model has an F1 score of 0.597 vs 0.166 and 0.26 achieved by state-of-practice and state-of-the-art real-world infant distress classifiers, respectively .
Edit intent: clarity
Original:  In particular, our model has an F1 score of 0.597 relative to F1 scores of 0.166 and 0.26 achieved by state-of-practice and state-of-the-art real-world infant distress classifiers, respectively .
Revised:  In particular, our model has an F1 score of 0.597 relative to F1 scores of 0.166 ) .
Edit intent: clarity
Original:  We end by discussing what may have facilitated this massive gain in accuracy, including using supervised deep spectrum features and the fact that we collected and annotated a massive dataset of 780 hours of real-world audio datawith over 25 hours of labelled distress .
Revised:  We end by discussing how dataset size can facilitate such gains in accuracy, including using supervised deep spectrum features and the fact that we collected and annotated a massive dataset of 780 hours of real-world audio datawith over 25 hours of labelled distress .
Edit intent: clarity
Original:  In this paper, we present a novel dataset of infant distress vocalizations compiled from over 780 hours of  real-world audio data, collected via recorders worn by infants .
Revised:  In this paper, we present a novel dataset of infant crying compiled from over 780 hours of real-world audio data, collected via recorders worn by infants .
Edit intent: clarity
Original:  In this paper, we present a novel dataset of infant distress vocalizations compiled from over 780 hours of  real-world audio data, collected via recorders worn by infants .
Revised:  In this paper, we present a novel dataset of infant distress vocalizations compiled from over 780 hours of real-world audio data, captured via recorders worn by infants .
Edit intent: fluency
Original:  We also pay close attention to engineering issues , in particular so that the library integrates well with Agda's own standard library, as well as supporting as many of the modes of Agda as possible.
Revised:  We also pay close attention to engineering issues so that the library integrates well with Agda's own standard library, as well as supporting as many of the modes of Agda as possible.
Edit intent: coherence
Original:  We also pay close attention to engineering issues , in particular so that the library integrates well with Agda's own standard library, as well as supporting as many of the modes of Agda as possible.
Revised:  We also pay close attention to engineering issues , in particular so that the library integrates well with Agda's own standard library, as well as being compatible with as many of the modes of Agda as possible.
Edit intent: clarity
Original: In this paper we introduce a new symbolic-numeric strategy to obtain semidiscretizations of a  PDE that preserve  local conservation laws.
Revised: This paper introduces a new symbolic-numeric strategy to obtain semidiscretizations of a PDE that preserve local conservation laws.
Edit intent: coherence
Original: In this paper we introduce a new symbolic-numeric strategy to obtain semidiscretizations of a  PDE that preserve  local conservation laws.
Revised: In this paper we introduce a new symbolic-numeric strategy for finding semidiscretizations of a PDE that preserve local conservation laws.
Edit intent: clarity
Original: In this paper we introduce a new symbolic-numeric strategy to obtain semidiscretizations of a  PDE that preserve  local conservation laws.
Revised: In this paper we introduce a new symbolic-numeric strategy to obtain semidiscretizations of a given PDE that preserve local conservation laws.
Edit intent: clarity
Original: This paper describes a statistically-principled semi-supervised method of automatic chord estimation (ACE) that can make effective use of any music signals regardless of the availability of chord annotations.
Revised: This paper describes a statistically-principled semi-supervised method of automatic chord estimation (ACE) that can make effective use of music signals regardless of the availability of chord annotations.
Edit intent: clarity
Original:  The typical approach to ACE is to train a deep classification model (neural chord estimator) in a supervised manner by using only a limited amount of annotated music signals.
Revised:  The typical approach to ACE is to train a deep classification model (neural chord estimator) in a supervised manner by using only annotated music signals.
Edit intent: clarity
Original:  In this discriminative approach, prior knowledge about chord label sequences ( characteristics of model output) has scarcely been taken into account.
Revised:  In this discriminative approach, prior knowledge about chord label sequences ( model output) has scarcely been taken into account.
Edit intent: clarity
Original:  In contract , we propose a unified generative and discriminative approach in the framework of amortized variational inference.
Revised:  In contrast , we propose a unified generative and discriminative approach in the framework of amortized variational inference.
Edit intent: coherence
Original:  More specifically, we formulate a deep generative model that represents the complex generative process of chroma vectors (observed variables) from the discrete labels and continuous textures of chords (latent variables) .
Revised:  More specifically, we formulate a deep generative model that represents the generative process of chroma vectors (observed variables) from the discrete labels and continuous textures of chords (latent variables) .
Edit intent: clarity
Original:  More specifically, we formulate a deep generative model that represents the complex generative process of chroma vectors (observed variables) from the discrete labels and continuous textures of chords (latent variables) .
Revised:  More specifically, we formulate a deep generative model that represents the complex generative process of chroma vectors (observed variables) from discrete labels and continuous textures of chords (latent variables) .
Edit intent: fluency
Original:  More specifically, we formulate a deep generative model that represents the complex generative process of chroma vectors (observed variables) from the discrete labels and continuous textures of chords (latent variables) .
Revised:  More specifically, we formulate a deep generative model that represents the complex generative process of chroma vectors (observed variables) from the discrete labels and continuous features (latent variables) .
Edit intent: clarity
Original:  Given chroma vectors as observed data, the posterior distributions of latent chord labels and textures are computed approximately by using deep classification and recognition models, respectively.
Revised:  Given chroma vectors as observed data, the posterior distributions of the latent labels and features are computed approximately by using deep classification and recognition models, respectively.
Edit intent: clarity
Original:  These three models are combined to form a variational autoencoder and  trained jointly in a semi-supervised manner.
Revised:  These three models form a variational autoencoder and trained jointly in a semi-supervised manner.
Edit intent: clarity
Original:  These three models are combined to form a variational autoencoder and  trained jointly in a semi-supervised manner.
Revised:  These three models are combined to form a variational autoencoder and can be trained jointly in a semi-supervised manner.
Edit intent: clarity
Original:  The experimental results show that the performance of the classification model can be improved by additionally using non-annotated music signals and/or by regularizing the classification model with the Markov model of chord labels and the generative model of chroma vectors even in the fully-supervised condition .
Revised:  The experimental results show that the regularization of the classification model can be improved by additionally using non-annotated music signals and/or by regularizing the classification model with the Markov model of chord labels and the generative model of chroma vectors even in the fully-supervised condition .
Edit intent: clarity
Original:  The experimental results show that the performance of the classification model can be improved by additionally using non-annotated music signals and/or by regularizing the classification model with the Markov model of chord labels and the generative model of chroma vectors even in the fully-supervised condition .
Revised:  The experimental results show that the performance of the classification model based on the Markov prior of chord labels and the generative model of chroma vectors even in the fully-supervised condition .
Edit intent: clarity
Original:  This paper presents ChewBaccaNN, a 0.7 mm^2 sized binary CNN accelerator designed in globalfoundries 22 nm technology.
Revised:  This paper presents ChewBaccaNN, a 0.7 mm^2 sized binary CNN accelerator designed in GlobalFoundries 22 nm technology.
Edit intent: fluency
Original:  By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 233 GOPS is achieved while consuming just 1.2 mW at 0.4V/154MHz for the inference of binary CNNs with  7x7 kernels, leading to a core energy efficiency of 223 TOPS/W. This is up to } 4.4 x better than other specialized binary acceleratorswhile supporting full flexibility in kernel configurations.
Revised:  By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 233 GOPS is achieved while consuming just 1.2 mW at 0.4V/154MHz during inference of binary CNNs with 7x7 kernels, leading to a core energy efficiency of 223 TOPS/W. This is up to } 4.4 x better than other specialized binary acceleratorswhile supporting full flexibility in kernel configurations.
Edit intent: coherence
Original:  By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 233 GOPS is achieved while consuming just 1.2 mW at 0.4V/154MHz for the inference of binary CNNs with  7x7 kernels, leading to a core energy efficiency of 223 TOPS/W. This is up to } 4.4 x better than other specialized binary acceleratorswhile supporting full flexibility in kernel configurations.
Revised:  By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 233 GOPS is achieved while consuming just 1.2 mW at 0.4V/154MHz for the inference of binary CNNs with up to 7x7 kernels, leading to a core energy efficiency of 223 TOPS/W. This is up to } 4.4 x better than other specialized binary acceleratorswhile supporting full flexibility in kernel configurations.
Edit intent: fluency
Original:  By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 233 GOPS is achieved while consuming just 1.2 mW at 0.4V/154MHz for the inference of binary CNNs with  7x7 kernels, leading to a core energy efficiency of 223 TOPS/W. This is up to } 4.4 x better than other specialized binary acceleratorswhile supporting full flexibility in kernel configurations.
Revised:  By exploiting efficient data re-use, data buffering, latch-based memories, and voltage scaling, a throughput of 233 GOPS is achieved while consuming just 1.2 mW at 0.4V/154MHz for the inference of binary CNNs with 7x7 kernels, leading to a core energy efficiency of 223 TOPS/W. This is up to \mu} 4.4 x better than other specialized binary acceleratorswhile supporting full flexibility in kernel configurations.
Edit intent: fluency
Original:  With as little as 3.9 mJ, using an 8-fold ResNet-18 , a Top-1 accuracy on ImageNet of 67.5\% can be achieved, which is just 1.8 \% less than using the \\%DIF > from the full-precision ResNet-18.
Revised:  With as little as 3.9 mJ, using an 8-fold ResNet-18 , a Top-1 accuracy on ImageNet of 67.5\% can be achieved, which is just 1.8 \\%DIF > from the full-precision ResNet-18.
Edit intent: clarity