wanyu/IteraTeR_v2 huggingface dataset:
Original:  We characterise such graph familiesfor ten pairs from the best-known set of graph searches: Breadth FirstSearch (BFS), Depth First Search (DFS), Lexicographic Breadth FirstSearch (LexBFS) and Lexicographic Depth First Search (LexDFS), andMaximal Neighborhood Search (MNS).
Revised:  We characterise such graph familiesfor ten pairs from the best-known set of graph searches: Breadth First Search (BFS), Depth First Search (DFS), Lexicographic Breadth FirstSearch (LexBFS) and Lexicographic Depth First Search (LexDFS), andMaximal Neighborhood Search (MNS).
Edit intent: fluency
Original:  We characterise such graph familiesfor ten pairs from the best-known set of graph searches: Breadth FirstSearch (BFS), Depth First Search (DFS), Lexicographic Breadth FirstSearch (LexBFS) and Lexicographic Depth First Search (LexDFS), andMaximal Neighborhood Search (MNS).
Revised:  We characterise such graph familiesfor ten pairs from the best-known set of graph searches: Breadth FirstSearch (BFS), Depth First Search (DFS), Lexicographic Breadth First Search (LexBFS) and Lexicographic Depth First Search (LexDFS), andMaximal Neighborhood Search (MNS).
Edit intent: fluency
Original:  We characterise such graph familiesfor ten pairs from the best-known set of graph searches: Breadth FirstSearch (BFS), Depth First Search (DFS), Lexicographic Breadth FirstSearch (LexBFS) and Lexicographic Depth First Search (LexDFS), andMaximal Neighborhood Search (MNS).
Revised:  We characterise such graph familiesfor ten pairs from the best-known set of graph searches: Breadth FirstSearch (BFS), Depth First Search (DFS), Lexicographic Breadth FirstSearch (LexBFS) and Lexicographic Depth First Search (LexDFS), and Maximal Neighborhood Search (MNS).
Edit intent: fluency
Original:  Extensive experiments using different datasets and GNN models show that in nearly all cases, our methods are able to reduce the memory requirements by 86 \% to 97\% while achieving  better classification accuracy than other competing approaches, including the full embeddings.
Revised:  Extensive experiments using different datasets and GNN models show that our methods are able to reduce the memory requirements by 86 \% to 97\% while achieving better classification accuracy than other competing approaches, including the full embeddings.
Edit intent: clarity
Original:  Extensive experiments using different datasets and GNN models show that in nearly all cases, our methods are able to reduce the memory requirements by 86 \% to 97\% while achieving  better classification accuracy than other competing approaches, including the full embeddings.
Revised:  Extensive experiments using different datasets and GNN models show that in nearly all cases, our methods are able to reduce the memory requirements by 86 \% to 97\% while achieving , in nearly all cases, better classification accuracy than other competing approaches, including the full embeddings.
Edit intent: clarity
Original:  However, most of the previous works have tried to learn  "patches" applied to an object to fool detectors, which become less effective or even ineffective in squint view angles.
Revised:  However, most of the previous works have tried to learn "patches" applied to an object to fool detectors, which become less effective in squint view angles.
Edit intent: clarity
Original:  The camouflages are robust because they remain adversarial when filmed under arbitrary viewpoint and different illumination conditions, physical because they function well both in the 3D virtual scene and the real world, and targeted because they can cause detectors to misidentify an object as a specific target class.
Revised:  The camouflages are robust because they remain adversarial when filmed under arbitrary viewpoints and different illumination conditions, physical because they function well both in the 3D virtual scene and the real world, and targeted because they can cause detectors to misidentify an object as a specific target class.
Edit intent: fluency
Original:  The camouflages are robust because they remain adversarial when filmed under arbitrary viewpoint and different illumination conditions, physical because they function well both in the 3D virtual scene and the real world, and targeted because they can cause detectors to misidentify an object as a specific target class.
Revised:  The camouflages are robust because they remain adversarial when filmed under arbitrary viewpoint and different illumination conditions, and targeted because they can cause detectors to misidentify an object as a specific target class.
Edit intent: coherence
Original:  In order to make the generated camouflages robust in the physical world, we introduce a combination of viewpoint shifts, lighting and other natural transformations to model the physical phenomena.
Revised:  In order to make the generated camouflages robust in the physical world, we introduce a combination of transformations to model the physical phenomena.
Edit intent: clarity
Original:  In addition, to improve the attacks, DPA substantially attacks all the classifications in the fixed region proposals.
Revised:  In addition, to improve the attacks, DPA substantially attacks all the classifications in the fixed proposals.
Edit intent: clarity
Original: In this paper, a new stochastic framework for parameter estimation and uncertainty quantification in colon cancer-induced angiogenesis , using patient data, is presented.
Revised: In this paper, a new framework for obtaining personalized optimal treatment strategies in colon cancer-induced angiogenesis , using patient data, is presented.
Edit intent: clarity
Original: In this paper, a new stochastic framework for parameter estimation and uncertainty quantification in colon cancer-induced angiogenesis , using patient data, is presented.
Revised: In this paper, a new stochastic framework for parameter estimation and uncertainty quantification in colon cancer-induced angiogenesis is presented.
Edit intent: clarity
Original:  The dynamics of colon cancer is given by a stochastic process that captures the inherent randomness in the system.
Revised:  The dynamics of colon cancer is given by a It\'o stochastic process, which helps in modeling the randomness present in the system.
Edit intent: clarity
Original:  The stochastic framework is based on the Fokker-Planck equation that represents the evolution of the  probability density function corresponding to the stochastic process.
Revised:  The stochastic dynamics is then represented by the Fokker-Planck equation that represents the evolution of the probability density function corresponding to the stochastic process.
Edit intent: clarity
Original:  An optimization problem is formulated that takes input individual patient data with randomness present , and is solved to obtain the unknown parameters corresponding to the individual tumor characteristics.
Revised:  An optimization problem is formulated that takes input individual noisy patient data , and is solved to obtain the unknown parameters corresponding to the individual tumor characteristics.
Edit intent: clarity
Original:  Furthermore, sensitivity analysis of the optimal parameter set is performed to determine the parameters that need to be controlled, thus, providing information of the type of drugs that can be used for treatment .
Revised:  Next, a sensitivity analysis of the optimal parameter set is performed to determine the parameters that need to be controlled, thus, providing information of the type of drugs that can be used for treatment .
Edit intent: coherence
Original:  Furthermore, sensitivity analysis of the optimal parameter set is performed to determine the parameters that need to be controlled, thus, providing information of the type of drugs that can be used for treatment .
Revised:  Furthermore, sensitivity analysis of the optimal parameter set is used to determine the parameters that need to be controlled, thus, providing information of the type of drugs that can be used for treatment .
Edit intent: coherence
Original:  Furthermore, sensitivity analysis of the optimal parameter set is performed to determine the parameters that need to be controlled, thus, providing information of the type of drugs that can be used for treatment .
Revised:  Furthermore, sensitivity analysis of the optimal parameter set is performed to determine the parameters to be controlled, thus, providing information of the type of drugs that can be used for treatment .
Edit intent: clarity
Original:  We first decompose an HDR image into a normalized Laplacian pyramid, and use two deep neural networks (DNNs) to estimate the Laplacian pyramid of the desired tone-mapped image from the normalized representation.
Revised:  We first decompose an HDR image into a normalized Laplacian pyramid, and use two deep neural networks (DNNs) to estimate the Laplacian pyramid of the desired tone-mapped image from the normalized representation.
Edit intent: fluency
Original:  We then end-to-end optimize the entire method over a database of HDR images by minimizing the normalized Laplacian pyramid distance (NLPD), a recently proposed perceptual metric.
Revised:  We then end-to-end optimize the entire method over a database of HDR images by minimizing the normalized Laplacian pyramid distance (NLPD), a recently proposed perceptual metric.
Edit intent: fluency
Original:  Two observations motivated the approach: (i) noting the concentration of an article's contribution information in its title;
Revised:  Two observations motivated the approach: (i) noting salient aspects of an article's contribution information in its title;
Edit intent: clarity
Original:  Two observations motivated the approach: (i) noting the concentration of an article's contribution information in its title;
Revised:  Two observations motivated the approach: (i) noting the concentration of an article's contribution in its title;
Edit intent: clarity
Original:  We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently, and that generally indicates the scientific entity type of interest about the scholarly contribution.
Revised:  We identify a set of lexico-syntactic patterns were selected that were easily recognizable, that occur frequently, and that generally indicates the scientific entity type of interest about the scholarly contribution.
Edit intent: clarity
Original:  We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently, and that generally indicates the scientific entity type of interest about the scholarly contribution.
Revised:  We identify a set of lexico-syntactic patterns that are easily recognizable, occurred frequently, and that generally indicates the scientific entity type of interest about the scholarly contribution.
Edit intent: fluency
Original:  We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently, and that generally indicates the scientific entity type of interest about the scholarly contribution.
Revised:  We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently, and positionally indicated a scientific entity type of interest about the scholarly contribution.
Edit intent: clarity
Original:  It has been empirically evaluated on a collection of 50,237 titles that cover nearly all articles in the ACL Anthology.
Revised:  It has been empirically evaluated on a collection of 50,237 CL titles covering all articles in the ACL Anthology.
Edit intent: clarity
Original:  It has extracted 19,799 research problems ;
Revised:  In total, 19,799 research problems ;
Edit intent: clarity
Original:  It has extracted 19,799 research problems ;
Revised:  It has extracted 19,799 research problems ,
Edit intent: fluency
Original:  18,111 solutions ;
Revised:  18,111 solutions ,
Edit intent: fluency
Original:  20,033 resources ;
Revised:  20,033 resources ,
Edit intent: fluency
Original:  1,059 languages ;
Revised:  1,059 languages ,
Edit intent: fluency
Original:  6,878 tools ;
Revised:  6,878 tools ,
Edit intent: fluency
Original:  and 21,687 methods  at an average extraction precision of 75\% .
Revised:  and 21,687 methods were extracted at an average extraction precision of 75\% .
Edit intent: clarity
Original:  and 21,687 methods  at an average extraction precision of 75\% .
Revised:  and 21,687 methods at an average precision of 75\% .
Edit intent: clarity
Original: Real-world datasets often present different degrees of imbalanced (i.e., long-tailed or skewed) distributions.
Revised: In predictive tasks, real-world datasets often present different degrees of imbalanced (i.e., long-tailed or skewed) distributions.
Edit intent: clarity
Original:  While the majority ( a.k.a., heador frequent ) classes have sufficient samples, the minority ( a.k.a., tailor rare ) classes can be under-represented by a rather limited number of samples.
Revised:  While the majority ( the head ) classes have sufficient samples, the minority ( a.k.a., tailor rare ) classes can be under-represented by a rather limited number of samples.
Edit intent: clarity
Original:  While the majority ( a.k.a., heador frequent ) classes have sufficient samples, the minority ( a.k.a., tailor rare ) classes can be under-represented by a rather limited number of samples.
Revised:  While the majority ( a.k.a., heador frequent ) classes have sufficient samples, the minority ( the tail ) classes can be under-represented by a rather limited number of samples.
Edit intent: clarity
Original:   On one hand, data resampling is a common approach to tackling class imbalance.
Revised:  On one hand, data re-sampling is a common approach to tackling class imbalance.
Edit intent: fluency
Original:  On the other hand, dimension reduction, which reduces the feature space, is a conventional machine learning technique for building stronger classification models on a dataset.
Revised:  On the other hand, dimension reduction, which reduces the feature space, is a conventional technique for reducing noise and inconsistencies in a dataset.
Edit intent: clarity
Original:  However, the possible synergy between feature selection and data resampling for high-performance imbalance classification has rarely been investigated before.
Revised:  However, the possible synergy between feature selection and data re-sampling for high-performance imbalance classification has rarely been investigated before.
Edit intent: fluency
Original:  To address this issue, this paper carries out a comprehensive empirical study on the joint influence of feature selection and resampling on two-class imbalance classification.
Revised:  To address this issue, we carry out a comprehensive empirical study on the joint influence of feature selection and resampling on two-class imbalance classification.
Edit intent: clarity
Original:  To address this issue, this paper carries out a comprehensive empirical study on the joint influence of feature selection and resampling on two-class imbalance classification.
Revised:  To address this issue, this paper carries out a comprehensive empirical study on the joint influence of feature selection and re-sampling on two-class imbalance classification.
Edit intent: fluency
Original:  Specifically, we study the performance of two opposite pipelines for imbalance classification , i.e., applying feature selection before or after data resampling .
Revised:  Specifically, we study the performance of two opposite pipelines for imbalance classification by applying feature selection before or after data resampling .
Edit intent: coherence
Original:  Specifically, we study the performance of two opposite pipelines for imbalance classification , i.e., applying feature selection before or after data resampling .
Revised:  Specifically, we study the performance of two opposite pipelines for imbalance classification , i.e., applying feature selection before or after data re-sampling .
Edit intent: fluency
Original:  We conduct a large amount of experiments( a total of 9225 experiments) on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms.
Revised:  We conduct a large number of experiments, with a total of 9225 experiments) on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms.
Edit intent: clarity
Original:  We conduct a large amount of experiments( a total of 9225 experiments) on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms.
Revised:  We conduct a large amount of experiments( a total of 9225 tests, on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms.
Edit intent: clarity
Original:  We conduct a large amount of experiments( a total of 9225 experiments) on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms.
Revised:  We conduct a large amount of experiments( a total of 9225 experiments) on 52 publicly available datasets, using 9 feature selection methods, 6 re-sampling approaches for class imbalance learning, and 3 well-known classification algorithms.
Edit intent: fluency
Original:  Experimental results show that there is no constant winner between the two pipelines , thus both of them should be considered to derive the best performing model for imbalance classification.
Revised:  Experimental results show that there is no constant winner between the two pipelines ; thus both of them should be considered to derive the best performing model for imbalance classification.
Edit intent: fluency
Original:  We also find that the performance of an imbalance classification model  depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Revised:  We find that the performance of an imbalance classification model depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Edit intent: coherence
Original:  We also find that the performance of an imbalance classification model  depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Revised:  We also find that the performance of an imbalance classification model not only depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Edit intent: clarity
Original:  We also find that the performance of an imbalance classification model  depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Revised:  We also find that the performance of an imbalance classification model depends on the classifier adopted and the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Edit intent: coherence
Original:  We also find that the performance of an imbalance classification model  depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Revised:  We also find that the performance of an imbalance classification model depends on the classifier adopted , the ratio between the number of majority and minority samples , but also depends on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Edit intent: coherence
Original:  We also find that the performance of an imbalance classification model  depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features (SFR) . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Revised:  We also find that the performance of an imbalance classification model depends on the classifier adopted , the ratio between the number of majority and minority samples (IR), as well as on the ratio between the number of samples and features . Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.
Edit intent: coherence
Original: The prosperity of the cryptocurrency ecosystem drives the needs for digital asset trading platforms.
Revised: The prosperity of the cryptocurrency ecosystem drives the need for digital asset trading platforms.
Edit intent: fluency
Original:  Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of  CEX.
Revised:  Beyond centralized exchanges (CEXs), decentralized exchanges (DEXs) are introduced to allow users to trade cryptocurrency without transferring the custody of their digital assets to the middlemen, thus eliminating the security and privacy issues of traditional CEX.
Edit intent: clarity
Original:  The scammers have gained a profit of at least 16 million from 40,165 \16  potential victims.
Revised:  The scammers have gained a profit of at least \16 potential victims.
Edit intent: clarity
Original:  This paper proposes a simple and improved vanilla adversarial training process for NLP  , which we name Attacking to Training ( A2T ).
Revised:  This paper proposes a simple and improved vanilla adversarial training process for NLP models , which we name Attacking to Training ( A2T ).
Edit intent: clarity
Original:  This paper proposes a simple and improved vanilla adversarial training process for NLP  , which we name Attacking to Training ( A2T ).
Revised:  This paper proposes a simple and improved vanilla adversarial training process for NLP , which we name Attacking to Training ( A2T ).
Edit intent: fluency
Original:  The core part of A2T is a new and cheaper word substitution attack optimized for vanilla adversarial training.
Revised:  The core part of A2T is a new and cheaper word substitution attack optimized for vanilla adversarial training.
Edit intent: fluency
Original:  We use A2T to train BERT and RoBERTa models on IMDB, Rotten Tomatoes, Yelp, and SNLI datasets.
Revised:  We use A2T to train BERT and RoBERTa models on IMDB, Rotten Tomatoes, Yelp, and SNLI datasets.
Edit intent: fluency
Original:  Our results  show that it is possible to train empirically robust NLP models using a much cheaper adversary.
Revised:  Our results empirically show that it is possible to train empirically robust NLP models using a much cheaper adversary.
Edit intent: clarity