wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In particular , our result provide a polynomial time O(\sqrt{\log \gamma})-approximation to the sparsest cut problem on planar graph , for the case where all the demand pairs can be covered by \gamma faces.
Revised:  Therefore , our result provide a polynomial time O(\sqrt{\log \gamma})-approximation to the sparsest cut problem on planar graph , for the case where all the demand pairs can be covered by \gamma faces.
Edit intent: coherence
Original:  In particular , our result provide a polynomial time O(\sqrt{\log \gamma})-approximation to the sparsest cut problem on planar graph , for the case where all the demand pairs can be covered by \gamma faces.
Revised:  In particular , our result provides a polynomial time O(\sqrt{\log \gamma})-approximation to the sparsest cut problem on planar graph , for the case where all the demand pairs can be covered by \gamma faces.
Edit intent: fluency
Original:  In particular , our result provide a polynomial time O(\sqrt{\log \gamma})-approximation to the sparsest cut problem on planar graph , for the case where all the demand pairs can be covered by \gamma faces.
Revised:  In particular , our result provide a polynomial time O(\sqrt{\log \gamma})-approximation to the sparsest cut problem on planar graphs , for the case where all the demand pairs can be covered by \gamma faces.
Edit intent: fluency
Original:   As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using  the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.
Revised:  In contrast to more popular analysis of sound and audio recordings, we fully based our approach on airflow signals only. As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.
Edit intent: meaning-changed
Original:   As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using  the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.
Revised:  As the system is intended to be used in a large variety of environments and different patients, we trained and validated the algorithm using the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.
Edit intent: clarity
Original:   As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using  the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.
Revised:  As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using AioCare-collected data and the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.
Edit intent: meaning-changed
Original:   As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using  the large database of spirometry curves which is the NHANES database by the American National Center for Health Statistics.
Revised:  As the system is intended to be used in a large variety of environments and different patients, we train the algorithm using the large database of spirometry curves from the NHANES database by the American National Center for Health Statistics.
Edit intent: clarity
Original:  Using such a massive dataset is a novelty in the field. We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  We trained different classifiers, such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Edit intent: coherence
Original:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression , feed-forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Edit intent: coherence
Original:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network , support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Edit intent: meaning-changed
Original:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine , and random forest (RF) on this data to choose the one of the best performance.
Edit intent: clarity
Original:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest to choose the one of the best performance.
Edit intent: clarity
Original:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  We apply few data preprocessing steps, derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one with the best performance.
Edit intent: clarity
Original:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Revised:  The \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Edit intent: coherence
Original:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Revised:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 %DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Edit intent: fluency
Original:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Revised:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Edit intent: coherence
Original:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Revised:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 %DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Edit intent: fluency
Original:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Revised:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Edit intent: coherence
Original:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Revised:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 %DIFDELCMD < }%%% 95.9\% for the test set, respectively.
Edit intent: fluency
Original:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% 95.9\% for the test set, respectively. The ANN solution was selected as the final classifier.
Revised:  The accuracy, sensitivity and specificity of the classifiers were comparable and equaled within the range 91.1 \div%DIFDELCMD < }%%% 91.2\%, 81.8 \div%DIFDELCMD < }%%% 83.8\% and 95.0 \div%DIFDELCMD < }%%% ANN solution was selected as the final classifier.
Edit intent: coherence
Original:  Classification methodology developed in this study is robust for detecting cough events during spirometry measurements.
Revised:  The classification results on the test set (AioCare data) are: 0.86 (sensitivity), 0.91 (specificity), 0.91 (accuracy) and 0.88 (F1 score). The classification methodology developed in this study is robust for detecting cough events during spirometry measurements.
Edit intent: meaning-changed
Original:  As far as we know, the solution presented in this work is the first  description of the automatic cough detection algorithm based totally on the air flow signals and the first cough detection implemented in the commercial spirometry system that is to be published.
Revised:  As far as we know, the solution presented in this work is the first fully reproducible description of the automatic cough detection algorithm based totally on the air flow signals and the first cough detection implemented in the commercial spirometry system that is to be published.
Edit intent: meaning-changed
Original:  As far as we know, the solution presented in this work is the first  description of the automatic cough detection algorithm based totally on the air flow signals and the first cough detection implemented in the commercial spirometry system that is to be published.
Revised:  As far as we know, the solution presented in this work is the first description of the automatic cough detection algorithm based totally on airflow signals and the first cough detection implemented in the commercial spirometry system that is to be published.
Edit intent: fluency
Original:  As far as we know, the solution presented in this work is the first  description of the automatic cough detection algorithm based totally on the air flow signals and the first cough detection implemented in the commercial spirometry system that is to be published.
Revised:  As far as we know, the solution presented in this work is the first description of the automatic cough detection algorithm based totally on the air flow signals and the first cough detection implemented in a commercial spirometry system that is to be published.
Edit intent: fluency
Original:  The seminal impossibility result of Myerson and Satterthwaite 1983 shows that even for bilateral trade, there is no feasible (IR, truthful and budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit.
Revised:  The 1983 shows that even for bilateral trade, there is no feasible (IR, truthful and budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit.
Edit intent: clarity
Original:  The seminal impossibility result of Myerson and Satterthwaite 1983 shows that even for bilateral trade, there is no feasible (IR, truthful and budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit.
Revised:  The seminal impossibility result of Myerson and Satterthwaite Myerson-Satterthwaite impossibility theorem shows that even for bilateral trade, there is no feasible (IR, truthful and budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit.
Edit intent: meaning-changed
Original:  The seminal impossibility result of Myerson and Satterthwaite 1983 shows that even for bilateral trade, there is no feasible (IR, truthful and budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit.
Revised:  The seminal impossibility result of Myerson and Satterthwaite 1983 shows that even for bilateral trade, there is no feasible (IR, truthful , budget balanced) mechanism that has welfare as high as the optimal-yet-infeasible VCG mechanism, which attains maximal welfare but runs a deficit.
Edit intent: fluency
Original:  We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, that is feasible and simple (in particular, it is deterministic, prior-independent, and anonymous).
Revised:  We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, which is feasible and simple (in particular, it is deterministic, prior-independent, and anonymous).
Edit intent: coherence
Original:  We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, that is feasible and simple (in particular, it is deterministic, prior-independent, and anonymous).
Revised:  We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, that is feasible and simple (in particular, deterministic, truthful, prior-independent, and anonymous).
Edit intent: clarity
Original:  We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, that is feasible and simple (in particular, it is deterministic, prior-independent, and anonymous).
Revised:  We suggest using the Buyer Trade Reduction (BTR) mechanism, a variant of McAfee's mechanism, that is feasible and simple (in particular, it is deterministic, prior-independent, anonymous).
Edit intent: fluency
Original:  First, in the setting where the buyers' and sellers' values are sampled i.i.d. from the same distribution, we show that for any such market of any size, BTR with one additional buyer whose value is sampled from the same distribution has expected welfare at least as high as the optimal in the original market.
Revised:  First, in the setting where buyers' and sellers' values are sampled i.i.d. from the same distribution, we show that for any such market of any size, BTR with one additional buyer whose value is sampled from the same distribution has expected welfare at least as high as the optimal in the original market.
Edit intent: clarity
Original:  We then move to a more general setting where the buyers' values are sampled from one distribution , and the sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers' distribution .
Revised:  We then move to a more general setting where buyers' values are sampled from one distribution , and the sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers' distribution .
Edit intent: fluency
Original:  We then move to a more general setting where the buyers' values are sampled from one distribution , and the sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers' distribution .
Revised:  We then move to a more general setting where the buyers' values are sampled from one distribution and sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers' distribution .
Edit intent: fluency
Original:  We then move to a more general setting where the buyers' values are sampled from one distribution , and the sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers' distribution .
Revised:  We then move to a more general setting where the buyers' values are sampled from one distribution , and the sellers' from another, focusing on the case where the buyers' distribution first-order stochastically dominates the sellers' .
Edit intent: fluency
Original:  \% We first show that there is no stateless algorithm, which depends only on the input points in one timestep and not on previous states , that both approximates the minimum cost of a shape descriptor and achieves bounded speed .
Revised:  We first show that there is no stateless algorithm, which depends only on the input points in one timestep and not on previous states , that both approximates the minimum cost of a shape descriptor and achieves bounded speed .
Edit intent: coherence
Original:  \% We first show that there is no stateless algorithm, which depends only on the input points in one timestep and not on previous states , that both approximates the minimum cost of a shape descriptor and achieves bounded speed .
Revised:  \% We first show that there is no stateless algorithm, which depends only on the input points in one timestep and not on previous states , that both approximates the minimum cost of a shape descriptor and achieves continuous motion for the shape descriptor .
Edit intent: clarity
Original: Generative models often use human evaluations to determine and justify progress. Unfortunately, existing human evaluation methods are ad-hoc : there is currently no standardized, validated evaluation that: (1) measures perceptual fidelity, (2) is reliable, (3) separates models into clear rank order, and (4) ensures high-quality measurement without intractable cost.
Revised: Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc : there is currently no standardized, validated evaluation that: (1) measures perceptual fidelity, (2) is reliable, (3) separates models into clear rank order, and (4) ensures high-quality measurement without intractable cost.
Edit intent: meaning-changed
Original:  Unfortunately, existing human evaluation methods are ad-hoc : there is currently no standardized, validated evaluation that: (1) measures perceptual fidelity, (2) is reliable, (3) separates models into clear rank order, and (4) ensures high-quality measurement without intractable cost. In response, we construct Human-eYe Perceptual Evaluation (HYPE) , a human metric that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) results in separable model performances, and (4) efficient in cost and time.
Revised:  Unfortunately, existing human evaluation methods are ad-hoc , neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) , a human metric that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) results in separable model performances, and (4) efficient in cost and time.
Edit intent: clarity
Original:  In response, we construct Human-eYe Perceptual Evaluation (HYPE) , a human metric that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) results in separable model performances, and (4) efficient in cost and time.
Revised:  In response, we construct Human-eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) results in separable model performances, and (4) efficient in cost and time.
Edit intent: clarity
Original:  In response, we construct Human-eYe Perceptual Evaluation (HYPE) , a human metric that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) results in separable model performances, and (4) efficient in cost and time.
Revised:  In response, we construct Human-eYe Perceptual Evaluation (HYPE) , a human metric that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time.
Edit intent: clarity
Original:  We introduce two methods. The first, HYPE-Time, measures visual perception under adaptive time constraints to determine the minimum length of time (e.g. , 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake.
Revised:  We introduce two variants: one that measures visual perception under adaptive time constraints to determine the minimum length of time (e.g. , 250ms) that model output such as a generated face needs to be visible for people to distinguish it as real or fake.
Edit intent: clarity