wanyu/IteraTeR_v2 huggingface dataset:
Original:  While blocking anonymity seeking editors is perceived to be effective in stemming abuse, collateral damage in the form of unrealized valuable contributions from anonymity seeking editors is typically invisible.
Revised:  While blocking anonymity seeking editors is perceived to be effective in stemming abuse, collateral damage in the form of unrealized valuable contributions from IP-anonymity-seeking editors is nearly invisible.
Edit intent: clarity
Original:  Although Wikipedia has taken steps to block contributions from Tor users since as early as 2005, we demonstrate that these blocks have been imperfect and that tens of thousands of attempts to edit on Wikipedia through Tor have been successful.
Revised:  Although Wikipedia has taken steps to block contributions from Tor users since as early as 2005, we demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful.
Edit intent: clarity
Original:  We draw upon several data sources  to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions of Tor users to  other groups of Wikipedia users.
Revised:  We draw upon several data sources to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions from Tor users to other groups of Wikipedia users.
Edit intent: fluency
Original:  We draw upon several data sources  to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions of Tor users to  other groups of Wikipedia users.
Revised:  We draw upon several data sources to measure and describe the history of Tor editing on Wikipedia over time and to compare contributions of Tor users to those from other groups of Wikipedia users.
Edit intent: clarity
Original:  Our analysis suggests that the Tor users who manage to slip through Wikipedia's ban contribute content that is similar in quality to unregistered Wikipedia contributors and to the initial contributions of registered users.
Revised:  Our analysis suggests that Tor users who manage to slip through Wikipedia's ban contribute content that is similar in quality to unregistered Wikipedia contributors and to the initial contributions of registered users.
Edit intent: clarity
Original: Like many other user-generated content sites , Wikipedia blocks contributions through open proxies like Tor , because of a perception among the Wikipedia community that privacy enhancing tools are a source of vandalism, spam, and abuse.
Revised: User-generated content sites routinely block contributions from users of privacy-enhancing proxies like Tor , because of a perception among the Wikipedia community that privacy enhancing tools are a source of vandalism, spam, and abuse.
Edit intent: clarity
Original: Like many other user-generated content sites , Wikipedia blocks contributions through open proxies like Tor , because of a perception among the Wikipedia community that privacy enhancing tools are a source of vandalism, spam, and abuse.
Revised: Like many other user-generated content sites , Wikipedia blocks contributions through open proxies like Tor because of a perception among the Wikipedia community that privacy enhancing tools are a source of vandalism, spam, and abuse.
Edit intent: fluency
Original: Like many other user-generated content sites , Wikipedia blocks contributions through open proxies like Tor , because of a perception among the Wikipedia community that privacy enhancing tools are a source of vandalism, spam, and abuse.
Revised: Like many other user-generated content sites , Wikipedia blocks contributions through open proxies like Tor , because of a perception that proxies are a source of vandalism, spam, and abuse.
Edit intent: clarity
Original:  While blocking editors that use these tools is done in an attempt to stem abuse , collateral damage in the form of unrealized valuable contributions from IP-anonymity-seeking editors is nearly invisible.
Revised:  Although these blocks might be effective , collateral damage in the form of unrealized valuable contributions from IP-anonymity-seeking editors is nearly invisible.
Edit intent: clarity
Original:  Although Wikipediahas taken steps to block contributions from Tor users since as early as 2005, we demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful.
Revised:  Although Wikipediahas taken steps to block contributions from Tor users since as early as 2005. We demonstrate that these blocks have been imperfect and that thousands of attempts to edit on Wikipedia through Tor have been successful.
Edit intent: fluency
Original:  Our analysis suggests that  Tor users who manage to slip through Wikipedia's ban contribute content that is  similar in quality to unregistered Wikipedia contributors and to the initial contributions of registered users.
Revised:  Our analysis suggests that Tor users who slip through Wikipedia's ban contribute content that is similar in quality to unregistered Wikipedia contributors and to the initial contributions of registered users.
Edit intent: clarity
Original:  Our analysis suggests that  Tor users who manage to slip through Wikipedia's ban contribute content that is  similar in quality to unregistered Wikipedia contributors and to the initial contributions of registered users.
Revised:  Our analysis suggests that Tor users who manage to slip through Wikipedia's ban contribute content that is similar in quality to those from other unregistered participants and to the initial contributions of registered users.
Edit intent: clarity
Original:  Previous studies, which have recapitulated the self-organization of these arrays, ignored MT mechanics and assumed MTs follow geodesics of the cell surface.
Revised:  Computational studies, which have recapitulated the self-organization of these arrays, ignored MT mechanics and assumed MTs follow geodesics of the cell surface.
Edit intent: clarity
Original:  Our analysis further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents.
Revised:  Our neural network approach further shows that a two-parameter modified Zipf-Mandelbrot distribution accurately describes a wide variety of source/destination statistics on moving sample windows ranging from 100,000 to 100,000,000 packets over collections that span years and continents.
Edit intent: clarity
Original:  The measured model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies  .
Revised:  The inferred model parameters distinguish different network streams and the model leaf parameter strongly correlates with the fraction of the traffic in different underlying network topologies .
Edit intent: clarity
Original:  Instead, we design the first %DIFDELCMD < {\em %%% local algorithm for computing vertex connectivity;
Revised:  Instead, we design the first %DIFDELCMD < {\em %%% local algorithm for computing vertex connectivity;
Edit intent: fluency
Original:  Yet, in most application domains, such as healthcare and security analytics,  privacy risks limit entities to individually learning local models over the sensitive datasets they own .
Revised:  Yet, in most application domains, such as healthcare and security analytics, privacy risks limit entities to individually deduce local models using solely their own datasets .
Edit intent: clarity
Original:  In this work, we present the first formal study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to privately compute joint clusters on their combined sensitive datasets.
Revised:  In this work, we present the first comprehensive study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to privately compute joint clusters on their combined sensitive datasets.
Edit intent: clarity
Original:  In this work, we present the first formal study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to privately compute joint clusters on their combined sensitive datasets.
Revised:  In this work, we present the first formal study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to safely perform cluster analysis over their combined sensitive datasets.
Edit intent: clarity
Original:  First, we provide a formal definition that balances accuracy and privacy , and we present a provably secure protocol along with an optimized version for single linkage clustering .
Revised:  For this problem at hand, we introduce a formal security notion that achieves the required balance between intended accuracy and privacy , and we present a provably secure protocol along with an optimized version for single linkage clustering .
Edit intent: clarity
Original:  Finally, we provide a prototype implementation  and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results.
Revised:  Finally, we provide a prototype implementation of our approach and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results.
Edit intent: fluency
Original:  Finally, we provide a prototype implementation  and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results.
Revised:  Finally, we provide a prototype implementation and experimentally evaluate its feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results.
Edit intent: clarity
Original:  Finally, we provide a prototype implementation  and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results.
Revised:  Finally, we provide a prototype implementation and experimentally evaluate the feasibility and efficiency on synthetic and real datasets, with encouraging results.
Edit intent: clarity
Original:  Finally, we provide a prototype implementation  and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results.
Revised:  Finally, we provide a prototype implementation and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, obtaining encouraging results.
Edit intent: clarity
Original:  For example, for a dataset of one million records and 10 dimensions, our optimized privacy-preserving approximation protocolrequires 35 seconds for end-to-end execution, just 896KB of communication, and achieves 97.09\% accuracy.
Revised:  For example, end-to-end execution, just 896KB of communication, and achieves 97.09\% accuracy.
Edit intent: clarity
Original:  For example, for a dataset of one million records and 10 dimensions, our optimized privacy-preserving approximation protocolrequires 35 seconds for end-to-end execution, just 896KB of communication, and achieves 97.09\% accuracy.
Revised:  For example, for a dataset of one million records and 10 dimensions, our optimized privacy-preserving approximation protocolrequires 35 seconds for end-to-end execution, just 896KB and achieving 97.09\% accuracy.
Edit intent: clarity
Original:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains  , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Revised:  in the context of collaborative learning, multiple parties contribute their datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Edit intent: clarity
Original:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains  , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Revised:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this learning paradigm fails to encompass critical application domains , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Edit intent: clarity
Original:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains  , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Revised:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains that involve highly sensitive data , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Edit intent: fluency
Original:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains  , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Revised:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains , such as healthcare and security analytics, where privacy risks limit entities to individually deduce local models using solely their own datasets.
Edit intent: clarity
Original:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains  , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually deduce local models using solely their own datasets.
Revised:  in the context of collaborative learning, where a plurality of entities contribute their own datasets to jointly deduce global ML models . Despite its efficacy, this new learning paradigm fails to encompass critical application domains , such as healthcare and security analytics, that involve learning over highly sensitive data, wherein privacy risks limit entities to individually train models using only their own datasets.
Edit intent: clarity
Original:  In this work, we present the first comprehensive study for privacy-preserving collaborative hierarchical clustering , overall featuring scalable cryptographic protocols that allow two parties to safely perform cluster analysis over their combined sensitive datasets.
Revised:  In this work, we target privacy-preserving collaborative hierarchical clustering , overall featuring scalable cryptographic protocols that allow two parties to safely perform cluster analysis over their combined sensitive datasets.
Edit intent: clarity
Original:  For this problem at hand, we introduce a formal security notion that achieves the required balance between intended accuracy and privacy and presents a class of two-party hierarchical clustering protocols that guarantee strong privacy protection, provable in our new security model.
Revised:  For this problem at hand, we introduce a formal security definition that aims to achieve the balance between utility and privacy and presents a class of two-party hierarchical clustering protocols that guarantee strong privacy protection, provable in our new security model.
Edit intent: clarity
Original:  For this problem at hand, we introduce a formal security notion that achieves the required balance between intended accuracy and privacy and presents a class of two-party hierarchical clustering protocols that guarantee strong privacy protection, provable in our new security model.
Revised:  For this problem at hand, we introduce a formal security notion that achieves the required balance between intended accuracy and privacy and present a two-party hierarchical clustering protocols that guarantee strong privacy protection, provable in our new security model.
Edit intent: clarity
Original:  Finally, we provide a prototype implementation of our approach and experimentally evaluate its feasibility and efficiency on synthetic and real datasets, obtaining  encouraging results.
Revised:  Finally, we provide a prototype implementation of our approach and experimentally evaluate their performance and accuracy on synthetic and real datasets, obtaining encouraging results.
Edit intent: clarity
Original:  For example, end-to-end execution of our secure approximate protocol , over 1M 10-dimensional records, completes in 35 sec, transferring only 896KB and achieving 97.09\% accuracy.
Revised:  For example, end-to-end execution of our secure approximate protocol for over 1M 10-dimensional records, completes in 35 sec, transferring only 896KB and achieving 97.09\% accuracy.
Edit intent: clarity
Original: In this paper, first we survey the state-of-the-art SDN based Vehicular ad-hoc Network (SDVN) architectures for their networking infrastructure design, functionalities, benefits, and challenges .
Revised: In this paper, first we survey the state-of-the-art SDN basedVehicular ad-hoc Network (SDVN) architectures for their networking infrastructure design, functionalities, benefits, and challenges .
Edit intent: fluency
Original: In this paper, first we survey the state-of-the-art SDN based Vehicular ad-hoc Network (SDVN) architectures for their networking infrastructure design, functionalities, benefits, and challenges .
Revised: In this paper, first we survey the state-of-the-art SDN based Vehicular ad-hoc Network (SDVN) architectures for their net-working infrastructure design, functionalities, benefits, and challenges .
Edit intent: clarity
Original: In this paper, first we survey the state-of-the-art SDN based Vehicular ad-hoc Network (SDVN) architectures for their networking infrastructure design, functionalities, benefits, and challenges .
Revised: In this paper, first we survey the state-of-the-art SDN based Vehicular ad-hoc Network (SDVN) architectures for their networking infrastructure design, functionalities, benefits, and chal-lenges .
Edit intent: fluency
Original:  Then we discuss these SDVN architectures against major security threats that violate the key security services such as availability , confidentiality, authentication, and data integrity.
Revised:  Then we discuss these SDVN architectures against majorsecurity threats that violate the key security services such as availability , confidentiality, authentication, and data integrity.
Edit intent: fluency
Original:  Then we discuss these SDVN architectures against major security threats that violate the key security services such as availability , confidentiality, authentication, and data integrity.
Revised:  Then we discuss these SDVN architectures against major security threats that violate the key security services such asavailability , confidentiality, authentication, and data integrity.
Edit intent: fluency
Original: Finally, we discuss the lessons learned with the directions of future research work towards provisioning stringent security and privacy solutions in future SDVN architectures.
Revised: Finally, we discuss the lessons learned with the directions offuture research work towards provisioning stringent security and privacy solutions in future SDVN architectures.
Edit intent: fluency
Original: Finally, we discuss the lessons learned with the directions of future research work towards provisioning stringent security and privacy solutions in future SDVN architectures.
Revised: Finally, we discuss the lessons learned with the directions of future research work towards provisioning stringent security andprivacy solutions in future SDVN architectures.
Edit intent: fluency
Original:  To the best of our knowledge , this is the first comprehensive work that presents such a survey and analysis on SDVNs in the era of future generation networks (e.g., 5G, and Information centric networking) and applications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Revised:  To the best of ourknowledge , this is the first comprehensive work that presents such a survey and analysis on SDVNs in the era of future generation networks (e.g., 5G, and Information centric networking) and applications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Edit intent: fluency
Original:  To the best of our knowledge , this is the first comprehensive work that presents such a survey and analysis on SDVNs in the era of future generation networks (e.g., 5G, and Information centric networking) and applications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Revised:  To the best of our knowledge , this is the first comprehensive work that presents sucha survey and analysis on SDVNs in the era of future generation networks (e.g., 5G, and Information centric networking) and applications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Edit intent: fluency
Original:  To the best of our knowledge , this is the first comprehensive work that presents such a survey and analysis on SDVNs in the era of future generation networks (e.g., 5G, and Information centric networking) and applications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Revised:  To the best of our knowledge , this is the first comprehensive work that presents such a survey and analysis on SDVNs in the era of future generationnetworks (e.g., 5G, and Information centric networking) and applications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Edit intent: fluency
Original:  To the best of our knowledge , this is the first comprehensive work that presents such a survey and analysis on SDVNs in the era of future generation networks (e.g., 5G, and Information centric networking) and applications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Revised:  To the best of our knowledge , this is the first comprehensive work that presents such a survey and analysis on SDVNs in the era of future generation networks (e.g., 5G, and Information centric networking) andapplications (e.g., intelligent transportation system, and IoT-enabled advertising in VANETs).
Edit intent: fluency