wanyu/IteraTeR_v2 huggingface dataset:
Original:  Experiments show that the framework is significantly more cost-effective such that using 20\% of the trigger-annotated sentences can result in a comparable performance of conventional supervised approaches using 70\% training data.
Revised:  Experiments show that the framework is significantly more cost-effective such that using 20\% of the trigger-annotated sentences results in a comparable performance of conventional supervised approaches using 70\% training data.
Edit intent: clarity
Original:  Experiments show that the framework is significantly more cost-effective such that using 20\% of the trigger-annotated sentences can result in a comparable performance of conventional supervised approaches using 70\% training data.
Revised:  Experiments show that the framework is significantly more cost-effective such that using 20\% of the trigger-annotated sentences can result in a comparable performance as using 70\% training data.
Edit intent: clarity
Original:  Current approach to enforcing fine-grained policies based on rewriting queries does not scale when number of policies may be in the order of thousands.
Revised:  Current approach to enforcing fine-grained policies based on rewriting queries does not scale when number of policies are in the order of thousands.
Edit intent: clarity
Original:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation  such as Europe's GDPR and California's CCPA  to empower users ways to specify who can have access to their data and for what purposes.
Revised:  This paper identifies such a use case in the context of emerging smart spaces wherein systems may be required by legislation such as Europe's GDPR and California's CCPA to empower users ways to specify who can have access to their data and for what purposes.
Edit intent: clarity
Original:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation  such as Europe's GDPR and California's CCPA  to empower users ways to specify who can have access to their data and for what purposes.
Revised:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation , such as Europe's GDPR and California's CCPA to empower users ways to specify who can have access to their data and for what purposes.
Edit intent: fluency
Original:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation  such as Europe's GDPR and California's CCPA  to empower users ways to specify who can have access to their data and for what purposes.
Revised:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation such as Europe's GDPR and California's CCPA , to empower users ways to specify who can have access to their data and for what purposes.
Edit intent: fluency
Original:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation  such as Europe's GDPR and California's CCPA  to empower users ways to specify who can have access to their data and for what purposes.
Revised:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation such as Europe's GDPR and California's CCPA to empower users to specify who can have access to their data and for what purposes.
Edit intent: clarity
Original:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation  such as Europe's GDPR and California's CCPA  to empower users ways to specify who can have access to their data and for what purposes.
Revised:  Our work identifies one such use case in the context of emerging smart spaces wherein systems may be required by legislation such as Europe's GDPR and California's CCPA to empower users ways to specify who may have access to their data and for what purposes.
Edit intent: fluency
Original:  We present Sieve, a layered approach to implementing fine-grained access control in existing database systems, that exploits a variety of features (index support, UDFs, hints) to scale to large number of policies.
Revised:  We present Sieve, a layered approach of implementing FGAC in existing database systems, that exploits a variety of features (index support, UDFs, hints) to scale to large number of policies.
Edit intent: clarity
Original:  Given a set of policies, Sieve generates guarded expressions that might exploit database indices to filter tuples and given a query exploit context to filter  policies that need to be checked.
Revised:  Given a query, Sieve exploits it's context to filter policies that need to be checked.
Edit intent: clarity
Original:  Given a set of policies, Sieve generates guarded expressions that might exploit database indices to filter tuples and given a query exploit context to filter  policies that need to be checked.
Revised:  Given a set of policies, Sieve generates guarded expressions that might exploit database indices to filter tuples and given a query exploit context to filter the policies that need to be checked.
Edit intent: fluency
Original:   Our experimental results show that SIEVE scales to large data sets and  large policy corpus to support real-time access in applications including emerging smart environments.
Revised:  Our experimental results show that SIEVE scales to large data sets and to large policy corpus to support real-time access in applications including emerging smart environments.
Edit intent: fluency
Original:   Our experimental results show that SIEVE scales to large data sets and  large policy corpus to support real-time access in applications including emerging smart environments.
Revised:  Our experimental results show that SIEVE scales to large data sets and large policy corpus thus supporting real-time access in applications including emerging smart environments.
Edit intent: coherence
Original:  Two different approaches are considered in our experimental framework: i)  selecting the entire face as input to the fake detection system, and ii) selecting specific facial regions such as the eyes or nose, among others, as input to the fake detection system.
Revised:  Two different methods are considered in our experimental framework: i) selecting the entire face as input to the fake detection system, and ii) selecting specific facial regions such as the eyes or nose, among others, as input to the fake detection system.
Edit intent: clarity
Original:  Two different approaches are considered in our experimental framework: i)  selecting the entire face as input to the fake detection system, and ii) selecting specific facial regions such as the eyes or nose, among others, as input to the fake detection system.
Revised:  Two different approaches are considered in our experimental framework: i) selecting the entire face as input to the fake detection system, and ii) selecting specific facial regions as input to the fake detection system.
Edit intent: coherence
Original:  These results remark the necessity of further research in order to develop more sophisticated fake detection systems .
Revised:  These results remark the necessity of further research to develop more sophisticated fake detection systems .
Edit intent: coherence
Original:  These results remark the necessity of further research in order to develop more sophisticated fake detection systems .
Revised:  These results remark the necessity of further research in order to develop more sophisticated fake detectors .
Edit intent: clarity
Original:  The exploration swarm is  set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated.
Revised:  The exploration swarm is a set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated.
Edit intent: fluency
Original:  The exploration swarm is  set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated.
Revised:  The exploration swarm is set of equal-sized sub-swarms randomly distributed around the particles space, the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated.
Edit intent: coherence
Original:  The exploration swarm is  set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated.
Revised:  The exploration swarm is set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, and the convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated.
Edit intent: fluency
Original:  The exploration swarm is  set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle of the exploitation swarm as it is stagnated.
Revised:  The exploration swarm is set of equal-sized sub-swarms randomly distributed around the particles space, and the exploitation swarm is artificially generated from a perturbation of the best particle of exploration swarm for a fixed period of iterations, a convergence swarm is artificially generated from a Gaussian perturbation of the best particle in the exploitation swarm as it is stagnated.
Edit intent: clarity
Original:  The exploration and exploitation operations are alternatively conducted until the evolution rate of the exploitation is smaller than a threshold or the maximum number of iterations is reached.
Revised:  The exploration and exploitation operations are alternatively carried out until the evolution rate of the exploitation is smaller than a threshold or the maximum number of iterations is reached.
Edit intent: clarity
Original:  An adaptive inertia weight strategy is applied to different swarms to guarantee their performances of exploration and exploitaiton .
Revised:  An adaptive inertia weight strategy is applied to different swarms to guarantee their performances of exploration and exploitation .
Edit intent: fluency
Original:  Here too the running time is only O(n^{2+o(1))even for polynomially large k}%DIFDELCMD < \le %%% n^{0.168.
Revised:  Here too the running time is )even for polynomially large k}%DIFDELCMD < \le %%% n^{0.168.
Edit intent: clarity
Original: We introduce a novel modeling framework for studying epidemics that is specifically designed to make use of fine-grained spatiotemporal data.
Edit intent: coherence
Original:  Motivated by the current COVID-19 outbreak and the availability of data from contact or location tracing technologies, our model uses marked temporal point processes to represent individual mobility patterns and the course of the disease for each individual in a population.
Revised:  Motivated by the current COVID-19 outbreak , we introduce a novel epidemic model based on marked temporal point processes to represent individual mobility patterns and the course of the disease for each individual in a population.
Edit intent: clarity
Original:  Motivated by the current COVID-19 outbreak and the availability of data from contact or location tracing technologies, our model uses marked temporal point processes to represent individual mobility patterns and the course of the disease for each individual in a population.
Revised:  Motivated by the current COVID-19 outbreak and the availability of data from contact or location tracing technologies, our model uses marked temporal point processes to represent individual mobility patterns and the course of the disease in a population.
Edit intent: clarity
Original:  We design an efficient sampling algorithm for our model that can be used to predict the spread of infectious diseases such as COVID-19 under different testing and tracing strategies,
Revised:  Our model can make use and benefit from data gathered by a variety of contact tracing technologies and it can quantify the effects that different testing and tracing strategies,
Edit intent: coherence
Original:  social distancing measures, and business restrictions , given location or contact histories of individuals . Building on this algorithm , we use Bayesian optimization to estimate the risk of exposure of each individual at the sites they visit, the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Revised:  social distancing measures, and business restrictions may have on the course of the disease . Building on this algorithm , we use Bayesian optimization to estimate the risk of exposure of each individual at the sites they visit, the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Edit intent: clarity
Original:  social distancing measures, and business restrictions , given location or contact histories of individuals . Building on this algorithm , we use Bayesian optimization to estimate the risk of exposure of each individual at the sites they visit, the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Revised:  social distancing measures, and business restrictions , given location or contact histories of individuals . Building on our model , we use Bayesian optimization to estimate the risk of exposure of each individual at the sites they visit, the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Edit intent: clarity
Original:  Experiments using measured COVID-19 data and mobility patterns from T\"{u}bingen, a town in the southwest of Germany, demonstrate that our model can be used to quantify the effects of tracing, testing, and containment strategies at an unprecedented spatiotemporal resolution.
Revised:  Experiments using real COVID-19 data and mobility patterns from T\"{u}bingen, a town in the southwest of Germany, demonstrate that our model can be used to quantify the effects of tracing, testing, and containment strategies at an unprecedented spatiotemporal resolution.
Edit intent: clarity
Original:  Building on our model , we use Bayesian optimization to estimate the risk of exposure of each individual at the sites they visit , the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Revised:  Building on this algorithm , we use Bayesian optimization to estimate the risk of exposure of each individual at the sites they visit , the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Edit intent: clarity
Original:  Building on our model , we use Bayesian optimization to estimate the risk of exposure of each individual at the sites they visit , the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Revised:  Building on our model , we use Bayesian optimization to estimate the transmission rate due to infectious individuals at the sites they visit , the percentage of symptomatic individuals, and the difference in transmission rate between asymptomatic and symptomatic individuals from historical longitudinal testing data.
Edit intent: clarity
Original: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Revised: optimization to estimate the transmission rate of infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Edit intent: fluency
Original: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Revised: optimization to estimate the transmission rate due to infectious individuals at sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Edit intent: fluency
Original: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Revised: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and in their households, as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Edit intent: fluency
Original: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Revised: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Edit intent: clarity
Original: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Revised: optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data . Simulations using fine-grained demographic data and site locations from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions .
Edit intent: clarity
Original: Multiple lines of evidence at the individual and population level strongly suggest that infection hotspots, or superspreading events, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Revised: Multiple lines of evidence strongly suggest that infection hotspots, or superspreading events, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Edit intent: clarity
Original: Multiple lines of evidence at the individual and population level strongly suggest that infection hotspots, or superspreading events, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Revised: Multiple lines of evidence at the individual and population level strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Edit intent: clarity
Original:  In this work, we aim to fill this gap by introducing a temporal point process framework that explicitly represents sites where infection hotspots may occur .
Revised:  In this work, we introduce a temporal point process framework that explicitly represents sites where infection hotspots may occur .
Edit intent: clarity
Original:  In this work, we aim to fill this gap by introducing a temporal point process framework that explicitly represents sites where infection hotspots may occur .
Revised:  In this work, we aim to fill this gap by introducing a temporal point process modeling framework that specifically represents visits to the sites where individuals get in contact and infect each other .
Edit intent: clarity
Original:  Under our model, overdispersion on the number of secondary infections emerges naturally .
Revised:  Under our model, the number of secondary infections emerges naturally .
Edit intent: clarity