wanyu/IteraTeR_v2 huggingface dataset:
Original: In a world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Revised: In the world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Edit intent: fluency
Original: In a world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Revised: In a world, in which acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Edit intent: fluency
Original: In a world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Revised: In a world, in which the acceptance and the identification with social communities are highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Edit intent: clarity
Original: In a world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Revised: In a world, in which the acceptance and the social community membership is highly desired, the ability to predict evolution of groups over time appears to be a fascinating research task, yet very complex .
Edit intent: clarity
Original: In a world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex .
Revised: In a world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a vital but very complex research problem .
Edit intent: clarity
Original:  Therefore, the problem decomposition has been performed, and a new, adaptable and generic method for group evolution prediction in social networks (called GEP) is proposed in this paper.
Revised:  Therefore, we propose a new, adaptable and generic method for group evolution prediction in social networks (called GEP) is proposed in this paper.
Edit intent: coherence
Original:  analysis of numerous parameters ( time window type and size, community detection method, evolution chain length, classifier used , etc. ), (2) comparative analysis against other existing methods, (3) adaptation of the transfer learning concept to group evolution prediction, (4) enhancing the classification model with a more appropriate training set, and (5) prediction of more distant and multiple following future events.
Revised:  analysis of numerous parameters ( time window type and size, group detection method, evolution chain length, classifier used , etc. ), (2) comparative analysis against other existing methods, (3) adaptation of the transfer learning concept to group evolution prediction, (4) enhancing the classification model with a more appropriate training set, and (5) prediction of more distant and multiple following future events.
Edit intent: fluency
Original:  analysis of numerous parameters ( time window type and size, community detection method, evolution chain length, classifier used , etc. ), (2) comparative analysis against other existing methods, (3) adaptation of the transfer learning concept to group evolution prediction, (4) enhancing the classification model with a more appropriate training set, and (5) prediction of more distant and multiple following future events.
Revised:  analysis of numerous parameters ( time window type and size, community detection method, evolution chain length, prediction models , etc. ), (2) comparative analysis against other existing methods, (3) adaptation of the transfer learning concept to group evolution prediction, (4) enhancing the classification model with a more appropriate training set, and (5) prediction of more distant and multiple following future events.
Edit intent: clarity
Original:  analysis of numerous parameters ( time window type and size, community detection method, evolution chain length, classifier used , etc. ), (2) comparative analysis against other existing methods, (3) adaptation of the transfer learning concept to group evolution prediction, (4) enhancing the classification model with a more appropriate training set, and (5) prediction of more distant and multiple following future events.
Revised:  analysis of numerous parameters ( time window type and size, community detection method, evolution chain length, classifier used , etc. 
Edit intent: coherence
Original:  Additionally, many new predictive features reflecting the community state at a given time are proposed as well as rankings of the features most valuable for the classification process are provided.
Revised:  Additionally, many new predictive features reflecting the group state at a given time are proposed as well as rankings of the features most valuable for the classification process are provided.
Edit intent: clarity
Original:  It has been conjectured that many more gaps would exist, and that the overall time hierarchy would be relatively simple -- indeed, this is known to be the case in restricted graph families such as cycles and grids.
Revised:  It has been conjectured that many more gaps exist, and that the overall time hierarchy would be relatively simple -- indeed, this is known to be the case in restricted graph families such as cycles and grids.
Edit intent: fluency
Original:  It has been conjectured that many more gaps would exist, and that the overall time hierarchy would be relatively simple -- indeed, this is known to be the case in restricted graph families such as cycles and grids.
Revised:  It has been conjectured that many more gaps would exist, and that the overall time hierarchy is relatively simple -- indeed, this is known to be the case in restricted graph families such as cycles and grids.
Edit intent: fluency
Original:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le  , and \Theta(n^{ pq/(p+q)^2 })  in the high end  and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le  , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le  in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Revised:  \ge1 , 2^{\Theta(\log ^{\alpha n)} \le , and \Theta(n^{ pq/(p+q)^2 }) in the high end and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Edit intent: fluency
Original:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le  , and \Theta(n^{ pq/(p+q)^2 })  in the high end  and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le  , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le  in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Revised:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le , and \Theta(n^{ pq/(p+q)^2 }) in the high end of the complexity spectrum, and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Edit intent: fluency
Original:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le  , and \Theta(n^{ pq/(p+q)^2 })  in the high end  and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le  , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le  in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Revised:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le , and \Theta(n^{ pq/(p+q)^2 }) in the high end and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% and \Theta((\log^* n)^{ q/p }) \le in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Edit intent: fluency
Original:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le  , and \Theta(n^{ pq/(p+q)^2 })  in the high end  and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le  , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le  in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Revised:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le , and \Theta(n^{ pq/(p+q)^2 }) in the high end and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ \alpha }) \le in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Edit intent: fluency
Original:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le  , and \Theta(n^{ pq/(p+q)^2 })  in the high end  and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le  , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le  in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Revised:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le , and \Theta(n^{ pq/(p+q)^2 }) in the high end and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le 1 in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Edit intent: coherence
Original:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le  , and \Theta(n^{ pq/(p+q)^2 })  in the high end  and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le  , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le  in the low end of the complexity spectrum (p%DIFDELCMD < \ge %%% q) .
Revised:  \ge1 , 2^{\Theta(\log ^{q/p n)} \le , and \Theta(n^{ pq/(p+q)^2 }) in the high end and \Theta(\log^{ p/q }\log^* n) \ge 2^{\Theta(\log^{\alpha\log^* n)}} \le , %DIFDELCMD < \smash{2^{\Theta( \log^{q/p} \log^* n )}}%%% , and \Theta((\log^* n)^{ q/p }) \le in the low end %DIFDELCMD < \ge %%% q) .
Edit intent: clarity
Original:  However, the enormous characters can be decomposed into a few fundamental structural radicals, only about 500.
Revised:  However, the enormous characters can be decomposed into a compact set of about 500 fundamental and structural radicals.
Edit intent: clarity
Original:  This paper introduces the Radical Analysis Networks (RAN) that recognize Chinese characters by identifying radicals and analyzing 2D spatial structures between them.
Revised:  This paper introduces a novel radical analysis network (RAN) that recognize Chinese characters by identifying radicals and analyzing 2D spatial structures between them.
Edit intent: clarity
Original:  This paper introduces the Radical Analysis Networks (RAN) that recognize Chinese characters by identifying radicals and analyzing 2D spatial structures between them.
Revised:  This paper introduces the Radical Analysis Networks (RAN) that recognize Chinese characters by identifying radicals and analyzing two-dimensional spatial structures among them.
Edit intent: fluency
Original:  Then a decoder based on recurrent neural networks is employed, who aims to generate a caption of Chinese character by detecting radicals and 2D structures through a spatial attention mechanism.
Revised:  Then a decoder based on recurrent neural networks is employed, aiming at generating captions of Chinese characters by detecting radicals and 2D structures through a spatial attention mechanism.
Edit intent: coherence
Original:  Then a decoder based on recurrent neural networks is employed, who aims to generate a caption of Chinese character by detecting radicals and 2D structures through a spatial attention mechanism.
Revised:  Then a decoder based on recurrent neural networks is employed, who aims to generate a caption of Chinese character by detecting radicals and two-dimensional structures through a spatial attention mechanism.
Edit intent: fluency
Original:  The manner of treating Chinese character input as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning .
Revised:  The manner of treating a Chinese character as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning .
Edit intent: clarity
Original:  The manner of treating Chinese character input as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning .
Revised:  The manner of treating Chinese character input as a composition of radicals rather than a single character class largely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning .
Edit intent: clarity
Original:  The manner of treating Chinese character input as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning .
Revised:  The manner of treating Chinese character input as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes , namely zero-shot learning .
Edit intent: clarity
Original:  In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ( A4NT ), to combat such text-based adversaries.
Revised:  In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation ( A^4NT ), to combat such text-based adversaries.
Edit intent: clarity
Original:  Importantly, we propose and evaluate techniques to impose constraints on our A4NT to preserve the semantics of the input text.
Revised:  Importantly, we propose and evaluate techniques to impose constraints on our A^4NT to preserve the semantics of the input text.
Edit intent: clarity
Original:  A4NT learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input.
Revised:  A^4NT learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input.
Edit intent: clarity
Original: Currently data explosion poses great challenges to approximate aggregation on efficiency and accuracy.
Revised: The current data explosion poses great challenges to approximate aggregation on efficiency and accuracy.
Edit intent: fluency
Original: Currently data explosion poses great challenges to approximate aggregation on efficiency and accuracy.
Revised: Currently data explosion poses great challenges to the approximate aggregation with an efficiency and accuracy.
Edit intent: clarity
Original:  To address this problem, we propose a novel approach to calculate aggregation answers in high accuracy using only a small share of data.
Revised:  To address this problem, we propose a novel approach to calculate the aggregation answers with a high accuracy using only a small share of data.
Edit intent: clarity
Original:  To address this problem, we propose a novel approach to calculate aggregation answers in high accuracy using only a small share of data.
Revised:  To address this problem, we propose a novel approach to calculate aggregation answers in high accuracy using only a small portion of the data.
Edit intent: clarity
Original:  We introduce leverages to reflect individual differences of samples from the statistical perspective.
Revised:  We introduce leverages to reflect individual differences in the samples from a statistical perspective.
Edit intent: clarity
Original:  Two kinds of estimators, the leverage-based estimator  and the sketch estimator (a "rough picture" of the aggregation answer), are in constraint relations and iteratively improved according to the actual conditions until their difference is below a threshold.
Revised:  Two kinds of estimators, the leverage-based estimator , and the sketch estimator (a "rough picture" of the aggregation answer), are in constraint relations and iteratively improved according to the actual conditions until their difference is below a threshold.
Edit intent: fluency
Original:  Due to the iteration mechanism and the leverages, our approach achieves  high accuracy.
Revised:  Due to the iteration mechanism and the leverages, our approach achieves a high accuracy.
Edit intent: fluency
Original:  Moreover, some features, including not requiring recording  sampled data and easy to extend to various execution modes ( such as , the online mode), make our approach well suited to deal with big data.
Revised:  Moreover, some features, such as not requiring recording sampled data and easy to extend to various execution modes ( such as , the online mode), make our approach well suited to deal with big data.
Edit intent: fluency
Original:  Moreover, some features, including not requiring recording  sampled data and easy to extend to various execution modes ( such as , the online mode), make our approach well suited to deal with big data.
Revised:  Moreover, some features, including not requiring recording the sampled data and easy to extend to various execution modes ( such as , the online mode), make our approach well suited to deal with big data.
Edit intent: fluency
Original:  Moreover, some features, including not requiring recording  sampled data and easy to extend to various execution modes ( such as , the online mode), make our approach well suited to deal with big data.
Revised:  Moreover, some features, including not requiring recording sampled data and easy to extend to various execution modes ( e.g. , the online mode), make our approach well suited to deal with big data.
Edit intent: clarity
Original:  Experiments show that our approach has  extraordinary performance, and when compared with the uniform sampling, our approach can achieve high-quality answers with only 1/3 of the same sample size.
Revised:  Experiments show that our approach has an extraordinary performance, and when compared with the uniform sampling, our approach can achieve high-quality answers with only 1/3 of the same sample size.
Edit intent: fluency
Original:  The focus of this article is mainly on broken ultraweak variational formulations of steady boundary value problems, however, many of the ideas presented within are general enough that they be extended to any such well-posed variational formulation.
Revised:  The focus of this article is mainly on broken ultraweak variational formulations of stationary boundary value problems, however, many of the ideas presented within are general enough that they be extended to any such well-posed variational formulation.
Edit intent: clarity
Original:  having the same coefficient matrix as the  DPG method but  a different load.
Revised:  having the same coefficient matrix as the DPG method but has a different load.
Edit intent: fluency
Original:  These results clearly demonstrate the utility of  goal-oriented adaptive mesh refinement with DPG methods for quantities of interest with either interior or boundary terms.
Revised:  These results clearly demonstrate the utility of the goal-oriented adaptive mesh refinement with DPG methods for quantities of interest with either interior or boundary terms.
Edit intent: fluency
Original:  These results clearly demonstrate the utility of  goal-oriented adaptive mesh refinement with DPG methods for quantities of interest with either interior or boundary terms.
Revised:  These results clearly demonstrate the utility of goal-oriented adaptive mesh refinement strategies for quantities of interest with either interior or boundary terms.
Edit intent: clarity
Original:  We consider a single cell uplink in which many devices randomly transmit a data payload to the base station.
Revised: Future 5G cellular networks supporting ultra-reliable, low-latency communications (URLLC) could employ random access communication to reduce the overhead compared to scheduled access techniques used in 4G networks. We consider a single cell uplink in which many devices randomly transmit a data payload to the base station.
Edit intent: clarity
Original:  The proposed random access model gives an upper bound for the total uplink traffic that can be successfully decoded for a single receive antenna given the latency constraint, and provides insights for 5G cellular system design .
Revised:  The proposed analytical framework can provide insights for resource allocation strategies in general random access systems and in specific 5G cellular system design .
Edit intent: clarity
Original: In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions , based on PWL activation units such as rectifier or maxout.
Revised: We investigate the complexity of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions , based on PWL activation units such as rectifier or maxout.
Edit intent: clarity
Original: In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions , based on PWL activation units such as rectifier or maxout.
Revised: In this paper, we study the representational power of deep neural networks (DNN) that represent piecewise linear (PWL) functions , based on PWL activation units such as rectifier or maxout.
Edit intent: clarity
Original: Typically, a PWL function from a DNN can be seen as a large family of linear functions acting on millions of such regions.
Revised: Typically, a PWL function represented by a DNN can be seen as a large family of linear functions acting on millions of such regions.
Edit intent: clarity
Original:  We directly build upon the work of Montufar et al. (2014), Montufar (2017) and Raghu et al. (2017) by refining the upper and lower bounds on the number of linear regions for rectified and maxout networks.
Revised:  We directly build upon the work of Montufar et al. (2014), Montufar (2017) and Raghu et al. (2017) by refining the upper and lower bounds for the maximum number of linear regions for rectified and maxout networks.
Edit intent: fluency
Original:  This tree and its edge labeling can be computed in polynomial time .
Revised:  We provide a linear-time algorithm for the recognition of xenology relations and for the construction of its least-resolved edge-labeled phylogenetic tree .
Edit intent: clarity
Original:  Tensor-train decomposition is adopted because of its powerful representation performance and tensor order linear scalability .
Revised:  Tensor-train decomposition is adopted because of its powerful representation ability and linear scalability to tensor order .
Edit intent: clarity
Original:  We propose an algorithm named STTO ( Sparse Tensor-train Optimization  ) which considers incomplete data as sparse tensor and uses first-order optimization method to find the factors of tensor-train decomposition.
Revised:  We propose an algorithm named Sparse Tensor-train Optimization ) which considers incomplete data as sparse tensor and uses first-order optimization method to find the factors of tensor-train decomposition.
Edit intent: coherence