wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that in the bounded-degree graphmodel, Hamiltonicity is not testable with one-sided error probability and query complexity o(n) . This contrasts the known fact that on planar (or minor-free) graph classes, Hamiltonicity is testable with constant query complexity in the bounded-degree graph model with two-sided error . Our proof is an intricate construction that shows how to turn a d-regular graph into a graph that is far from being Hamiltonian, and we use d-regular expander graphs to maintain local Hamiltonicity .
Revised:  We show that in the bounded-degree graphmodel, Hamiltonicity is not testable with one-sided error probability and query complexity o(n) . This contrasts the known fact that on planar (or minor-free) graph classes, Hamiltonicity is testable with constant query complexity in the bounded-degree graph model with two-sided error ) requires a linear number of queries to test Hamiltonicity (Yoshida, Ito, 2010). This is proved via a randomised construction of hard instances. In contrast, our construction is deterministic. So far only very few deterministic constructions of hard instances for property testing are known. We believe that our construction may lead to future insights in graph theory and towards a characterisation of the properties hat are testable in the bounded-degree model .
Edit intent: meaning-changed
Original: I investigate Big Data, the phenomenon, the term, and the discipline, with emphasis on origins of the term , in industry and academics, in computer science and statistics/econometrics.
Revised: I investigate Big Data, the phenomenon, the term, and the discipline, with emphasis on origins of the term , in industry and academics, in computer science and econometrics. The term probably originated in lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s, in which John Mashey figured prominently. The first significant academic references are arguably Weiss and Indurkhya (1998) in computer science and Diebold (2000) in econometrics. An unpublished 2001 research note by Douglas Laney at Gartner enriched the concept significantly.
Edit intent: meaning-changed
Original:  I investigate the origin(s) of the term "Big Data , " in industry and academics, and in computer scienceand econometrics. The term probably originated in lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s , in which John Mashey figured prominently.
Revised:  I investigate the origin(s) of the term "Big Data , " . Its origins are a bit murky and hence intriguing, involving both academics and industry, statistics and computer science, ultimately winding back to lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s , in which John Mashey figured prominently.
Edit intent: meaning-changed
Original:  The term probably originated in lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s , in which John Mashey figured prominently. The first significant (and independent) academic references are arguably Weiss and Indurkhya (1998) in computer science and Diebold (2000) in econometrics. An unpublished 2001 research note by Douglas Laney at Gartner enriched the concept significantly. The Big Data phenomenon continues unabated  .
Revised:  The term probably originated in lunch-table conversations at Silicon Graphics Inc. (SGI) in the mid 1990s . The Big Data phenomenon continues unabated .
Edit intent: coherence
Original:  This paper aims to study the social behaviour of the public in the Greater Region (GR)  and related countries based on Twitter information with machine learning and representation learning methods.
Revised:  This paper aims to study the social behaviour of the public in the Greater Region (GR) through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR and related countries based on Twitter information with machine learning and representation learning methods.
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Original:  Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences in public behaviour between GR and related countries .
Revised:  Moreover, we plot the changing of topics in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences in public behaviour between GR and related countries .
Edit intent: meaning-changed
Original:  Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences in public behaviour between GR and related countries .
Revised:  Moreover, we map out the evolution of public behaviour in each country and region from 2020-01-22 to 2020-06-05 , figuring out the main differences in public behaviour between GR and related countries .
Edit intent: meaning-changed
Original:  Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences in public behaviour between GR and related countries .
Revised:  Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences between GR and related countries .
Edit intent: style
Original:  Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences in public behaviour between GR and related countries . Finally, we conclude that tweets volume of anti-contiguous measures may affect the effeteness of the government policy .
Revised:  Moreover, we map out the evolution of public behaviour in each country from 2020/01/22 to 2020/06/05 , figuring out the main differences in public behaviour between GR and related countries .
Edit intent: coherence
Original: Time series prediction is a challenge for many complex systems, yet in finance predictions are hindered by the very nature of how financial markets work. In efficient markets, the opportunities for stock price predictions leading to profitable trades are supposed to rapidly disappear. In the growing industry of high-frequency trading, the competition over extracting predictions on stock prices from the increasing amount of available information for performing profitable trades is becoming more and more severe. With the development of big data analysis and advanced deep learning methodologies, traders hope to fruitfully analyse market information, e.g. price time series, through machine learning.
Revised: The application of deep learning to time series forecasting is one of the major challenges in present machine learning. We propose a novel methodology that combines machine learning and image processing methods to define and predict market states with intraday financial data. A wavelet transform is applied to the log-return of stock prices for both image extraction and denoising. A convolutional neural network then extracts patterns from denoised wavelet images to classify daily time series, through machine learning.
Edit intent: meaning-changed
Original:  With the development of big data analysis and advanced deep learning methodologies, traders hope to fruitfully analyse market information, e.g. price time series, through machine learning.Spot prices of stocks provide a simple snapshot representation of a financial market . Stock prices fluctuate over time, affected by numerous factors, and the prediction of their changes is at the core of both long-term and short-term financial investing. The collective patterns of price movements are generally referred to as market states . As a paramount example, when stock prices follow an upward trend, it is called a bull market, and when stock prices follow a downward trend is called a bear market 
Revised:  With the development of big data analysis and advanced deep learning methodologies, traders hope to fruitfully analyse market information, e.g. price time series, i.e. a market state is associated with the binary prediction of the daily close price movement based on the wavelet image constructed from the price changes in the first hours of the day. This method overcomes the low signal-to-noise ratio problem in financial time series and gets a competitive prediction accuracy of the market states 'Up' and 'Down' of financial data as tested on the S 
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Original: Currently, the construction of concealed malicious code has become a trend, and attackers try to use this to hide their attack intentions. However, the building of stealthy malware is still unable to counter the reverse analysis of security experts.
Revised: Constructing stealthy malware has gained increasing popularity among cyber attackers to conceal their malicious intent. Nevertheless, the constructed stealthy malware still fails to survive the reverse engineering by security experts.
Edit intent: clarity
Original:  In response to this problem , this paper models a class of malware with  "unbreakable" security attributes--UBM (UnBreakable Malware). We have systematically studied such threats from models, methods, experiments , evaluations, and defenses . Specifically, first, we conducted a formal definition and security attribute research on UBM.
Revised:  In response to this problem , this paper models a class of malware with "unbreakable" security attribute-unbreakable malware (UBM) , evaluations, and defenses . Specifically, first, we conducted a formal definition and security attribute research on UBM.
Edit intent: coherence
Original:  We have systematically studied such threats from models, methods, experiments , evaluations, and defenses . Specifically, first, we conducted a formal definition and security attribute research on UBM.
Revised:  We have systematically studied such threats from models, methods, experiments , and made a systematical probe into this new type of threat through modeling, method analysis, experiments, evaluation and anti-defense capacity tests . Specifically, first, we conducted a formal definition and security attribute research on UBM.
Edit intent: meaning-changed
Original:  We have systematically studied such threats from models, methods, experiments , evaluations, and defenses . Specifically, first, we conducted a formal definition and security attribute research on UBM. We proposed two key characteristics that must be met in order to achieve "unbreakable" security attributes and their corresponding evaluationquaternions.
Revised:  We have systematically studied such threats from models, methods, experiments , evaluations, and defenses . Specifically, we first formalized the definition of UBM and analyzed its security attributes, put forward two core features that are essential for realizing the "unbreakable" security attributes and their corresponding evaluationquaternions.
Edit intent: clarity
Original:  We proposed two key characteristics that must be met in order to achieve "unbreakable" security attributes and their corresponding evaluationquaternions. Second, we summarized and implemented four algorithms that can be used to construct UBM, and verified the " unhackable " security attributes based on the evaluation of the two key features.
Revised:  We proposed two key characteristics that must be met in order to achieve "unbreakable" security attribute, and their relevant tetrad for evaluation. Then, we worked out and implemented four algorithms that can be used to construct UBM, and verified the " unhackable " security attributes based on the evaluation of the two key features.
Edit intent: clarity
Original:  Second, we summarized and implemented four algorithms that can be used to construct UBM, and verified the " unhackable " security attributes based on the evaluation of the two key features. Furthermore, we completed the construction of UBM using the implementation of four verified algorithms and proved the practical applicability of UBM by calculating the volume increment and analyzing the actual counter defense capabilities .
Revised:  Second, we summarized and implemented four algorithms that can be used to construct UBM, and verified the " unhackable " security attributes based on the evaluation of the abovementioned two core features. After that, the four verified algorithms and proved the practical applicability of UBM by calculating the volume increment and analyzing the actual counter defense capabilities .
Edit intent: coherence
Original:  Furthermore, we completed the construction of UBM using the implementation of four verified algorithms and proved the practical applicability of UBM by calculating the volume increment and analyzing the actual counter defense capabilities .
Revised:  Furthermore, we completed the construction of UBM using the implementation of four verified algorithms and proved the practical applicability of UBM by calculating the volume increment and anti-defense capacity, we confirmed real-world applicability of UBM .
Edit intent: clarity
Original:  The approach broadly follows recent work by \mbox{%DIFAUXCMD MM20 user-specified bounds on (i) test accuracy , in particular sensitivity, (ii) the extent to which tests are targeted, formalized as restriction on the effect of true  status on the odds ratio of getting tested and thereby embeddable in logit specifications.
Revised:  The approach utilizes user-specified bounds on (i) test accuracy , in particular sensitivity, (ii) the extent to which tests are targeted, formalized as restriction on the effect of true status on the odds ratio of getting tested and thereby embeddable in logit specifications.
Edit intent: clarity
Original:  The approach broadly follows recent work by \mbox{%DIFAUXCMD MM20 user-specified bounds on (i) test accuracy , in particular sensitivity, (ii) the extent to which tests are targeted, formalized as restriction on the effect of true  status on the odds ratio of getting tested and thereby embeddable in logit specifications.
Revised:  The approach broadly follows recent work by \mbox{%DIFAUXCMD MM20 user-specified bounds on (i) test accuracy and (ii) the extent to which tests are targeted, formalized as restriction on the effect of true status on the odds ratio of getting tested and thereby embeddable in logit specifications.
Edit intent: coherence
Original:  The approach broadly follows recent work by \mbox{%DIFAUXCMD MM20 user-specified bounds on (i) test accuracy , in particular sensitivity, (ii) the extent to which tests are targeted, formalized as restriction on the effect of true  status on the odds ratio of getting tested and thereby embeddable in logit specifications.
Revised:  The approach broadly follows recent work by \mbox{%DIFAUXCMD MM20 user-specified bounds on (i) test accuracy , in particular sensitivity, (ii) the extent to which tests are targeted, formalized as restriction on the effect of true infection status on the odds ratio of getting tested and thereby embeddable in logit specifications.
Edit intent: meaning-changed
Original: Over the past few years, there has been  substantial effort towards automated detection of fake news  . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users  . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Revised: Over the past few years, there has been a substantial effort towards automated detection of fake news . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Edit intent: fluency
Original: Over the past few years, there has been  substantial effort towards automated detection of fake news  . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users  . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Revised: Over the past few years, there has been substantial effort towards automated detection of fake news on social media platforms . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Edit intent: meaning-changed
Original: Over the past few years, there has been  substantial effort towards automated detection of fake news  . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users  . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Revised: Over the past few years, there has been substantial effort towards automated detection of fake news . Existing research has modeled the structure, style , content, and patterns in dissemination of online posts , as well as the demographic traits of users . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Edit intent: meaning-changed
Original: Over the past few years, there has been  substantial effort towards automated detection of fake news  . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users  . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Revised: Over the past few years, there has been substantial effort towards automated detection of fake news . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users who interact with them . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Edit intent: clarity
Original: Over the past few years, there has been  substantial effort towards automated detection of fake news  . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users  . However, no attention has been directed towards modeling the properties of online communities that interact with fake news .
Revised: Over the past few years, there has been substantial effort towards automated detection of fake news . Existing research has modeled the structure, style and contentof news articles , as well as the demographic traits of users . However, no attention has been directed towards modeling the properties of online communities that interact with the posts .
Edit intent: clarity
Original:  In this work, we propose a novel approach via graph-based modeling of online communities. Our method aggregates information with respect to: 1) the nature of the content disseminated, 2) content-sharing behavior of users, and 3) the social network of those users.
Revised:  In this work, we propose a novel social context-aware fake news detection framework, SAFER, based on graph neural networks (GNNs). The proposed framework aggregates information with respect to: 1) the nature of the content disseminated, 2) content-sharing behavior of users, and 3) the social network of those users.
Edit intent: meaning-changed
Original:  We  empirically demonstrate that this yields significant improvements over existing text and user-based techniques for fake news detection .
Revised:  We furthermore perform a systematic comparison of several GNN models for this task and introduce novel methods based on relational and hyperbolic GNNs, which have not been previously used for user or community modeling within NLP. We empirically demonstrate that this yields significant improvements over existing text and user-based techniques for fake news detection .
Edit intent: meaning-changed
Original:  Indeed, we prove an error bound for the approximation error of the probability of failure, which behaves like the discretization accuracy of the PDE multiplied by an approximation of the probability of failure .
Revised:  Indeed, we prove an error bound for the approximation error of the probability of failure, which behaves like the discretization accuracy of the PDE multiplied by an approximation of the probability of failure , the first order reliability method (FORM) estimate. This bound requires convexity of the failure domain. For non-convex failure domains, we prove an error bound for the relative error of the FORM estimate.
Edit intent: meaning-changed
Original:  Extensive evaluations on perfect ground truth exhibit superior performance and  up to 20 \% accuracy gain to the originally proposed Least Squares solution. The renormalization performs similarly to the optimal Maximum Likelihood estimate, despite arriving to the solution by different means. By this, we extend the set of common Computer Vision problems which can be cast into the renormalization scheme.
Revised:  Extensive evaluations on perfect ground truth exhibit superior performance and up to 20 
Edit intent: coherence
Original:  Here, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model  BERT and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter.
Revised:  Here, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter.
Edit intent: clarity
Original:  Here, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model  BERT and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter.
Revised:  Here, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model BERT and evaluate the proposed model on two publicly available datasets annotated for racism, sexism, hate or offensive content on Twitter.
Edit intent: clarity
Original:  Next, we introduce a bias alleviation mechanism  to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection .
Revised:  Next, we introduce a bias alleviation mechanism in hate speech detection task to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection .
Edit intent: clarity
Original:  Toward that end, we use a regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples.
Revised:  Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples.
Edit intent: clarity
Original:  To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE) , respectively.
Revised:  To evaluate our bias alleviation mechanism, we employ a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE) , respectively.
Edit intent: fluency