wanyu/IteraTeR_v2 huggingface dataset:
Original:  Then, we present open problems and their challenges, as well as opportunities for traffic classification.
Revised:  Then, we discuss open problems and their challenges, as well as opportunities for traffic classification.
Edit intent: clarity
Original:  i.e., %DIFDELCMD < {\sc %%% SieveADN ,
Revised:  i.e., %DIFDELCMD < {\sc %%% SieveADN 
Edit intent: fluency
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: fluency
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction %DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: coherence
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: fluency
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox %DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: fluency
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: fluency
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction %DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: fluency
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: fluency
Original: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Revised: %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction , and%DIFDELCMD < {\sc %%% HistApprox .%DIFDELCMD < {\sc %%% SieveADN identifies influential nodes from a special kind of TDNs with efficiency. %DIFDELCMD < {\sc %%% BasicReduction uses%DIFDELCMD < {\sc %%% SieveADN as a basic building block to identify influential nodes from general TDNs. %DIFDELCMD < {\sc %%% HistApprox significantly improves the efficiency of %DIFDELCMD < {\sc %DIFDELCMD < %%% BasicReduction .
Edit intent: fluency
Original:  As an application of the developed interrogation technique the isogeometric design and analysis of lattice-skin structures is investigated . Current additive manufacturing technologies make it possible to produce up to metre size parts with designed geometric features reaching down to submillimetre scale .
Revised:  As an application of the developed interrogation technique the isogeometric design and analysis of lattice-skin structures is investigated .
Edit intent: coherence
Original:  We show that computing the ar  (G,P_3) is hard to approximate to a factor of n^{- 1/2 - \epsilon} even in 3-partite graphs, unless NP{}={}ZPP.
Revised:  We show that computing the ar ar (G,P_3) is hard to approximate to a factor of n^{- 1/2 - \epsilon} even in 3-partite graphs, unless NP{}={}ZPP.
Edit intent: fluency
Original:  We show that computing the ar  (G,P_3) is hard to approximate to a factor of n^{- 1/2 - \epsilon} even in 3-partite graphs, unless NP{}={}ZPP.
Revised:  We show that computing the ar (G,P_3) to a factor of n^{- 1/2 - \epsilon} even in 3-partite graphs, unless NP{}={}ZPP.
Edit intent: clarity
Original:   On the positive side we provide polynomial time algorithm for trees and we show  approximability of the problem on special classes of graphs.
Revised:  On the positive side we provide polynomial time algorithm in trees and we show approximability of the problem on special classes of graphs.
Edit intent: fluency
Original:   On the positive side we provide polynomial time algorithm for trees and we show  approximability of the problem on special classes of graphs.
Revised:  On the positive side we provide polynomial time algorithm for trees and we show the approximability of the problem on special classes of graphs.
Edit intent: fluency
Original: The anti-Ramsey numbers are a fundamental notion in graph theory, introduced in 1978, by Erd\"os , Simonovits and S\'os .
Revised: The anti-Ramsey numbers are a fundamental notion in graph theory, introduced in 1978, by Erd\" os , Simonovits and S\'os .
Edit intent: fluency
Original: The anti-Ramsey numbers are a fundamental notion in graph theory, introduced in 1978, by Erd\"os , Simonovits and S\'os .
Revised: The anti-Ramsey numbers are a fundamental notion in graph theory, introduced in 1978, by Erd\"os , Simonovits and S\' os .
Edit intent: fluency
Original:  Precolored version of the problem is defined in a similar way except that the input graph is given with some fixed colors on some of the edges.
Edit intent: coherence
Original:  Firstly, we show that computing ar(G,P_k) for any odd value of k> 1 is NP-hard.
Revised:  Firstly, we show that computing ar(G,P_k) for every integer k> 1 is NP-hard.
Edit intent: clarity
Original:  Then, we show that even approximating ar(G,P_3) to a factor of n^{ - 1 /2 - \epsilon} is already hard in 3-partite graphs, unless NP{}={}ZPP.
Revised:  Then, we show that even approximating ar(G,P_3) to a factor of n^{ -1 /2 - \epsilon} is already hard in 3-partite graphs, unless NP{}={}ZPP.
Edit intent: fluency
Original:  Then, we show that even approximating ar(G,P_3) to a factor of n^{ - 1 /2 - \epsilon} is already hard in 3-partite graphs, unless NP{}={}ZPP.
Revised:  Then, we show that even approximating ar(G,P_3) to a factor of n^{ - 1 /2 - \epsilon} is hard already in 3-partite graphs, unless NP{}={}ZPP.
Edit intent: fluency
Original:  On the positive side we introduce the notion of color connected coloring, which leads to polynomial time algorithm in trees and we show the approximability of the problem on special classes\textrm{  of graphs.
Revised:  On the positive side we introduce the notion of color connected coloring, and, employing this structural property, we obtain a linear time algorithm to compute\textrm{ of graphs.
Edit intent: clarity
Original:  On the positive side we introduce the notion of color connected coloring, which leads to polynomial time algorithm in trees and we show the approximability of the problem on special classes\textrm{  of graphs.
Revised:  On the positive side we introduce the notion of color connected coloring, which leads to polynomial time algorithm in trees and we show the approximability of the problem on special classes\textrm{ar of graphs.
Edit intent: fluency
Original:  For given graphs G and H the anti-Ramsey number ar(G,H) is defined to be the maximum number k such that there exists an assignment of k colors to the edges of G in which every copy of H in G has at least two edges with the same color.
Revised:  For given graphs G and H the anti-Ramsey number ar(G,H) is defined to be the maximum number k such that there exists an assignment of k colors to the edges of G in which every copy of H in G has at least two edges with the same color.
Edit intent: fluency
Original:  Usually, combinatorists study extremal values of anti-Ramsey numbers for various classes of graphs.
Edit intent: coherence
Original:  There are works on  computational complexity of the problem when H is a star.
Revised:  There are works on the computational complexity of the problem when H is a star.
Edit intent: fluency
Original:  First  we observe that when k is close to n the problem is hard;
Revised:  First , we observe that when k is close to n the problem is hard;
Edit intent: fluency
Original:  We provide a deep characterization of the problem for paths of constant length.
Revised:  We provide a characterization of the problem for paths of constant length.
Edit intent: clarity
Original:  We investigate further and show that even approximating ar(G,P_3) to a factor of n^{-1/2 - \epsilon} is hard already in 3-partite graphs, unless NP = ZPP.
Revised:  We investigate further and show that approximating ar(G,P_3) to a factor of n^{-1/2 - \epsilon} is hard already in 3-partite graphs, unless NP = ZPP.
Edit intent: clarity
Original:  We investigate further and show that even approximating ar(G,P_3) to a factor of n^{-1/2 - \epsilon} is hard already in 3-partite graphs, unless NP = ZPP.
Revised:  We investigate further and show that even approximating ar(G,P_3) to a factor of n^{-1/2 - \epsilon} is hard already in 3-partite graphs, unless P = ZPP.
Edit intent: clarity
Original:  Along this line, we first introduce the notion of color connected coloring , and , employing this structural property , we obtain a linear time algorithm to compute \textrm{ar}(G,P_k), for every integer k, when the host graph, G, is a tree .
Revised:  We introduce the notion of color connected coloring , and , employing this structural property , we obtain a linear time algorithm to compute \textrm{ar}(G,P_k), for every integer k, when the host graph, G, is a tree .
Edit intent: coherence
Original:  Along this line, we first introduce the notion of color connected coloring , and , employing this structural property , we obtain a linear time algorithm to compute \textrm{ar}(G,P_k), for every integer k, when the host graph, G, is a tree .
Revised:  Along this line, we first introduce the notion of color connected coloring and employing this structural property , we obtain a linear time algorithm to compute \textrm{ar}(G,P_k), for every integer k, when the host graph, G, is a tree .
Edit intent: fluency
Original:  Along this line, we first introduce the notion of color connected coloring , and , employing this structural property , we obtain a linear time algorithm to compute \textrm{ar}(G,P_k), for every integer k, when the host graph, G, is a tree .
Revised:  Along this line, we first introduce the notion of color connected coloring , and , employing this structural property . We obtain a linear time algorithm to compute \textrm{ar}(G,P_k), for every integer k, when the host graph, G, is a tree .
Edit intent: fluency
Original:  However, the control-flow abstractions they adopt are ad-hoc , and so are the data models they use.
Revised:  However, such approaches typically come with ad-hoc , and so are the data models they use.
Edit intent: clarity
Original:  Specifically, we propose a declarative language based on SQL that supports the conceptual modelling of control-flow conditions and of persistent data updates with external inputs .
Revised:  Specifically, we propose a declarative language based on SQL that supports the agile modelling of relational data-aware processes directly on top of relational databases .
Edit intent: clarity
Original:  We show how this language can be automatically translated into a concrete procedural SQL dialect, consequently providing in-database process execution support.
Revised:  We show how this approach can be automatically translated into a concrete procedural SQL dialect, consequently providing in-database process execution support.
Edit intent: clarity
Original:  Semiflexible biopolymers stiffen nonlinearly in response to mechanical loads, whereas the crosslinker dynamics allow for stress relaxation over time.
Revised:  Semiflexible biopolymers nonlinearly stiffen in response to mechanical loads, whereas the crosslinker dynamics allow for stress relaxation over time.
Edit intent: fluency
Original:  The convergence of our iterative algorithm depends on the number of discrete level sets around the source points from which distance information propagates.
Revised:  The convergence of our algorithm depends on the number of discrete level sets around the source points from which distance information propagates.
Edit intent: clarity
Original:  In the experiments, we show how our method scales with the size of the problem by using the proposed graph representation .
Revised:  In the experiments, we show how our method scales with the size of the problem .
Edit intent: clarity
Original:  In this work, we present a minimalist parallel algorithm based on front propagation to compute approximate geodesic distances on meshes that is practical and simple to implement.
Revised:  In this work, we present a minimalistic parallel algorithm based on front propagation to compute approximate geodesic distances on meshes that is practical and simple to implement.
Edit intent: fluency
Original:  For this reason, different approaches have been proposed to compute geodesic distances including exact and approximate methods.
Edit intent: coherence
Original:  In this work, we present a minimalistic parallel algorithm based on front propagation to compute approximate geodesic distances on meshes that is practical and simple to implement .
Revised:  In this work, we present a minimalistic parallel algorithm based on front propagation to compute approximate geodesic distances on meshes . Our method is practical and simple to implement .
Edit intent: clarity
Original:  In the experiments , we show how our method scales with the size of the problem.
Revised:  We report experiments that show how our method scales with the size of the problem.
Edit intent: clarity
Original:  We compare its mean error and processing times with such measures computed using other methods.
Revised:  We compare the mean error and processing times with such measures computed using other methods.
Edit intent: clarity
Original:  We compare its mean error and processing times with such measures computed using other methods.
Revised:  We compare its mean error and processing time obtained by our method with such measures computed using other methods.
Edit intent: clarity
Original:  In this work, we address the problem of inpainting on surfaces through a new method based on dictionary learning and sparse coding.
Revised:  In this paper, we investigate the problem of inpainting on surfaces through a new method based on dictionary learning and sparse coding.
Edit intent: clarity
Original:   , using crown decomposition, we show that such problems admit kernels having linear number of vertices.
Revised:  Moreover , using crown decomposition, we show that such problems admit kernels having linear number of vertices.
Edit intent: coherence
Original:  A weighted instance G=(V,E,w)of Independent Set is \gamma-stable if it has a unique optimal solution that remains the unique optimal solution under multiplicative perturbations of the weights by a factor of at most \gamma\geq 1.
Revised:  A weighted instance G=(V,E,w)of Independent Set is \gamma-stable if it has a unique optimal solution that remains the unique optimum under multiplicative perturbations of the weights by a factor of at most \gamma\geq 1.
Edit intent: clarity
Original:   In this work, we use the standard LP as well as the Sherali-Adams hierarchy to design algorithms for (\Delta-1  )-stable instances on graphs of maximum degree \Delta, for (k-1 )-stable instances on k-colorable graphs and for (1 + \varepsilon)-stable instances on planar graphs.
Revised:  In this work, we use the standard LP as well as the Sherali-Adams hierarchy to design algorithms for (\Delta-1 )-stable instances on graphs of maximum degree \Delta, (k - 1 )-stable instances on k-colorable graphs and for (1 + \varepsilon)-stable instances on planar graphs.
Edit intent: fluency
Original:   In this work, we use the standard LP as well as the Sherali-Adams hierarchy to design algorithms for (\Delta-1  )-stable instances on graphs of maximum degree \Delta, for (k-1 )-stable instances on k-colorable graphs and for (1 + \varepsilon)-stable instances on planar graphs.
Revised:  In this work, we use the standard LP as well as the Sherali-Adams hierarchy to design algorithms for (\Delta-1 )-stable instances on graphs of maximum degree \Delta, for (k-1 )-stable instances on k-colorable graphs and (1 + \varepsilon)-stable instances on planar graphs.
Edit intent: fluency
Original:  We also show that the integrality gap of relaxations of several maximization problems reduces dramatically on stable instances.
Edit intent: coherence
Original:  Using results of Makarychev and Makarychev (2018) as well as combinatorial techniques, we obtain \Delta-certified algorithms for Independent Set on graphs of maximum degree \Delta  and (1+\varepsilon)-certified algorithms on planar graphs.
Revised:  We obtain \Delta-certified algorithms for Independent Set on graphs of maximum degree \Delta and (1+\varepsilon)-certified algorithms on planar graphs.
Edit intent: coherence
Original:  Using results of Makarychev and Makarychev (2018) as well as combinatorial techniques, we obtain \Delta-certified algorithms for Independent Set on graphs of maximum degree \Delta  and (1+\varepsilon)-certified algorithms on planar graphs.
Revised:  Using results of Makarychev and Makarychev (2018) as well as combinatorial techniques, we obtain \Delta-certified algorithms for Independent Set on graphs of maximum degree \Delta , and (1+\varepsilon)-certified algorithms on planar graphs.
Edit intent: fluency
Original:  Finally, we prove that an algorithm of Berman and F\"{u (1994)  is a \left( \frac{\Delta+1 + \varepsilon\right)-certified algorithm  on graphs of maximum degree \Delta where all weights are equal to 1.
Revised:  Finally, we analyze the algorithm of Berman and F\"{u (1994) is a \left( \frac{\Delta+1 + \varepsilon\right)-certified algorithm on graphs of maximum degree \Delta where all weights are equal to 1.
Edit intent: clarity
Original:  Finally, we prove that an algorithm of Berman and F\"{u (1994)  is a \left( \frac{\Delta+1 + \varepsilon\right)-certified algorithm  on graphs of maximum degree \Delta where all weights are equal to 1.
Revised:  Finally, we prove that an algorithm of Berman and Furer (1994) is a \left( \frac{\Delta+1 + \varepsilon\right)-certified algorithm on graphs of maximum degree \Delta where all weights are equal to 1.
Edit intent: fluency
Original:  Finally, we prove that an algorithm of Berman and F\"{u (1994)  is a \left( \frac{\Delta+1 + \varepsilon\right)-certified algorithm  on graphs of maximum degree \Delta where all weights are equal to 1.
Revised:  Finally, we prove that an algorithm of Berman and F\"{u (1994) and prove that it is a \left( \frac{\Delta+1 + \varepsilon\right)-certified algorithm on graphs of maximum degree \Delta where all weights are equal to 1.
Edit intent: coherence