wanyu/IteraTeR_v2 huggingface dataset:
Original:  The K index successfully detects a known case of inflated numbers for papers, citations and h index due to scientific career fraud .
Revised:  The K index successfully detects a known case of a researcher with inflated number of papers, citations and h index due to scientific career fraud .
Edit intent: clarity
Original:  The K index successfully detects a known case of inflated numbers for papers, citations and h index due to scientific career fraud .
Revised:  The K index successfully detects a known case of inflated numbers for papers, citations and h-index due to scientific career fraud .
Edit intent: clarity
Original:  The K index successfully detects a known case of inflated numbers for papers, citations and h index due to scientific career fraud .
Revised:  The K index successfully detects a known case of inflated numbers for papers, citations and h index due to scientific misconduct .
Edit intent: clarity
Original:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Revised:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K-index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Edit intent: clarity
Original:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Revised:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of the prize than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Edit intent: clarity
Original:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Revised:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h-index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Edit intent: clarity
Original:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Revised:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting outliers that suggest that these two indexes are complementary .
Edit intent: clarity
Original:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes are complementary .
Revised:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K index correlates better to the achievement of scientific prizes than the number of papers, citations, citations per paper, citing articles and the h index . Clustering researchers in a K versus h plot reveals interesting patterns and outliers that suggest that these two indexes can present complementary independent information .
Edit intent: clarity
Original: We introduce a new centrality index for a bipartite network of papers and authors that we call K-index.
Revised: We introduce a new centrality index for bipartite network of papers and authors that we call K-index.
Edit intent: fluency
Original:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K-index correlates better to the achievement of the prize than the number of papers, citations, citations per paper, citing articles and the h-index.
Revised:  Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K-index correlates better to the achievement of the prize than the number of papers, citations, citations per paper, citing articles or the h-index.
Edit intent: clarity
Original: Teaching a computer to read a document and answer general questions pertaining to the document is a challenging yet unsolved problem.
Revised: Teaching a computer to read and answer general questions pertaining to the document is a challenging yet unsolved problem.
Edit intent: fluency
Original: Teaching a computer to read a document and answer general questions pertaining to the document is a challenging yet unsolved problem.
Revised: Teaching a computer to read a document and answer general questions pertaining to a document is a challenging yet unsolved problem.
Edit intent: fluency
Original:  In this paper, we describe a novel neural network architecture called  Reasoning Network ( ReasoNet ) for machine comprehension tasks.
Revised:  In this paper, we describe a novel neural network architecture called the Reasoning Network ( ReasoNet ) for machine comprehension tasks.
Edit intent: fluency
Original:  In this paper, we describe a novel neural network architecture called  Reasoning Network ( ReasoNet ) for machine comprehension tasks.
Revised:  In this paper, we describe a novel neural network architecture called Reasoning Network ( ReasoNet ) for machine comprehension tasks.
Edit intent: fluency
Original:  ReasoNet makes use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers.
Revised:  ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers.
Edit intent: fluency
Original:  Different from previous approaches using a fixed number of turns during inference, ReasoNet introduces a termination state to relax this constraint on the reasoning depth.
Revised:  Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth.
Edit intent: fluency
Original:  With the use of reinforcement learning, ReasoNet can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer.
Revised:  With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer.
Edit intent: fluency
Original:  ReasoNet has achieved state-of-the-art performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets  , and a structured Graph Reachability dataset.
Revised:  ReasoNets have achieved exceptional performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets , and a structured Graph Reachability dataset.
Edit intent: clarity
Original: We explored if and how Microsoft Academic (MA) could be used for bibliometric analyses.
Revised: We explore if and how Microsoft Academic (MA) could be used for bibliometric analyses.
Edit intent: fluency
Original:  First, we examined the Academic Knowledge API (AK API), an interface to access MA data  .
Revised:  First, we examine the Academic Knowledge API (AK API), an interface to access MA data .
Edit intent: fluency
Original:  Second, we performed a comparative citation analysis of researchers by normalizing data from MA and Scopus.
Revised:  Second, we perform a comparative citation analysis of researchers by normalizing data from MA and Scopus.
Edit intent: fluency
Original:  We found that MA offers structured and rich metadata, which facilitates data retrieval, handling and processing.
Revised:  We find that MA offers structured and rich metadata, which facilitates data retrieval, handling and processing.
Edit intent: fluency
Original:  These features have to be considered a major advantage of MA over Google Scholar .
Revised:  These features have to be considered a major advantage of MA over GS .
Edit intent: clarity
Original:  First, MA does not provide the document type of a publication and, second, the 'fields of study' are dynamic, too fine-grained and field-hierarchies are incoherent.
Revised:  First, MA does not provide the document type of a publication . Second, the 'fields of study' are dynamic, too fine-grained and field-hierarchies are incoherent.
Edit intent: coherence
Original:  First, MA does not provide the document type of a publication and, second, the 'fields of study' are dynamic, too fine-grained and field-hierarchies are incoherent.
Revised:  First, MA does not provide the document type of a publication and, second, the 'fields of study' are dynamic, too specific and field hierarchies are incoherent.
Edit intent: clarity
Original:   Nevertheless, we showed that average-based indicators as well as  distribution-based indicators can be calculated with MA data.
Revised:  Nevertheless, we show that an average-based indicators as well as distribution-based indicators can be calculated with MA data.
Edit intent: fluency
Original:   Nevertheless, we showed that average-based indicators as well as  distribution-based indicators can be calculated with MA data.
Revised:  Nevertheless, we showed that average-based indicators as well as a distribution-based indicators can be calculated with MA data.
Edit intent: fluency
Original:  We postulate that MA has the potential to be used for fully-fledged bibliometric analyses.
Revised:  We postulate that MA has the potential to be used for full-fledged bibliometric analyses.
Edit intent: fluency
Original:  Three recent event propagation traces are collected with the user languages being used to identify the Network of Networks (NoN) structure embedded in the Twitter follower networks.
Revised:  Three recent event propagation traces are collected with the Twitter user language field being used to identify the Network of Networks (NoN) structure embedded in the Twitter follower networks.
Edit intent: clarity
Original:  Experimental results find that the between-network follower links, though only account for a small portion of the total follower links, are extremely crucial to event propagation on Twitter.
Revised:  Experimental results find that the between-network follower links, though only account for a small portion of the total follower links, are crucial to event propagation on Twitter.
Edit intent: clarity
Original:  Furthermore, we find that utilizing the NoN model can successfully identify influential follower links such that their removals lead to  remarkable reduction in event propagation on Twitter follower networks.
Revised:  Furthermore, we find that utilizing the NoN model can successfully identify influential follower links such that their removals lead to a remarkable reduction in event propagation on Twitter follower networks.
Edit intent: fluency
Original:  For kernelization, It has been established a nice hardness theory [Bodlaender etc. (ICALP 2008, JCSS2009), Fortnow and Santhanam (STOC 2008, JCSS 2011), Dell and van Melkebeek (STOC 2010, J. ACM 2014), Dell and Marx (SODA 2012), Drucker (FOCS 2012, SIAM J. Comput.
Revised:  In the aspect of kernelization, an impressive hardness theory has been established a nice hardness theory [Bodlaender etc. (ICALP 2008, JCSS2009), Fortnow and Santhanam (STOC 2008, JCSS 2011), Dell and van Melkebeek (STOC 2010, J. ACM 2014), Dell and Marx (SODA 2012), Drucker (FOCS 2012, SIAM J. Comput.
Edit intent: clarity
Original:  For kernelization, It has been established a nice hardness theory [Bodlaender etc. (ICALP 2008, JCSS2009), Fortnow and Santhanam (STOC 2008, JCSS 2011), Dell and van Melkebeek (STOC 2010, J. ACM 2014), Dell and Marx (SODA 2012), Drucker (FOCS 2012, SIAM J. Comput.
Revised:  For kernelization, It has been established a nice hardness theory [Bodlaender etc. (ICALP 2008, JCSS2009), Fortnow and Santhanam (STOC 2008, JCSS 2011), Dell and van Melkebeek (STOC 2010, J. ACM 2014), Drucker (FOCS 2012, SIAM J. Comput.
Edit intent: coherence
Original:  2015)], which can obtain lower bounds for kernel size.
Revised:  2015)], which can obtain lower bounds of kernel size.
Edit intent: fluency
Original:  Thus, it was proposed as an open problem in different occasions [Fernau etc. (STACS 2009), Misra etc. (Discrete Optimization 2011), Kratsch (Bulletin of the EATCS 2014), Cygan etc. (Dagstuhl Seminars on kernel 2014)].
Revised:  Thus, it was proposed as an open problem in different occasions [Fernau etc. (STACS 2009), Misra etc. (Discrete Optimization 2011), Kratsch (Bulletin of the EATCS 2014), Cygan etc. (Dagstuhl Seminars on kernels 2014)].
Edit intent: fluency
Original:  Problems that are in NP but are neither in the class P nor NP-complete are called NP-intermediate.
Edit intent: coherence
Original:  Thus, finding natural candidate NP-intermediate problems is an important open question\not in computation complexity  .
Revised:  Thus, finding natural NP-intermediate problems is an important open question\not in computation complexity .
Edit intent: clarity
Original:  It has worst case running time O( n^{1/2}m^{1/2} sR^{10 /delta ^{10 ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of an optimal solution.
Revised:  It has worst case running time n^{1/2}m^{1/2} sR^{10 /delta ^{10 ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of an optimal solution.
Edit intent: clarity
Original:  It has worst case running time O( n^{1/2}m^{1/2} sR^{10 /delta ^{10 ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of an optimal solution.
Revised:  It has worst case running time O( n^{1/2}m^{1/2} sR^{10 /delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of an optimal solution.
Edit intent: clarity
Original:  It has worst case running time O( n^{1/2}m^{1/2} sR^{10 /delta ^{10 ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of an optimal solution.
Revised:  It has worst case running time O( n^{1/2}m^{1/2} sR^{10 /delta ^{10 ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of the optimal solution.
Edit intent: fluency
Original:  It has worst case running time n^{1/2}m^{1/2}s poly(log(n), log(m), R,  1/delta), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of the optimal solution .
Revised:  It has worst case running time n^{1/2}m^{1/2}s poly(log(n), log(m), R, r, 1/delta), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of the optimal solution .
Edit intent: clarity
Original:  It has worst case running time n^{1/2}m^{1/2}s poly(log(n), log(m), R,  1/delta), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R an upper bound on the trace of the optimal solution .
Revised:  It has worst case running time n^{1/2}m^{1/2}s poly(log(n), log(m), R, 1/delta), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R , r upper bounds on the size of the optimal solution .
Edit intent: clarity
Original:  An example are SDPs in which the input matrices have low-rank: For SDPs with the maximum rank of any input matrix bounded by r , we show the quantum algorithm runs in time poly(log(n), log(m)  , r, R, delta)m^{1/2}.
Revised:  An example are SDPs in which the input matrices have low-rank: For SDPs with the maximum rank of any input matrix bounded by rank , we show the quantum algorithm runs in time poly(log(n), log(m) , r, R, delta)m^{1/2}.
Edit intent: clarity
Original:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%%   ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log  (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r  upper bounds on the size of the optimal primal and dual solutions.
Revised:  It has worst-case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%% ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual solutions.
Edit intent: fluency
Original:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%%   ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log  (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r  upper bounds on the size of the optimal primal and dual solutions.
Revised:  It has worst case running time n^{ %DIFDELCMD < \MBLOCKRIGHTBRACEm%%% ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual solutions.
Edit intent: fluency
Original:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%%   ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log  (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r  upper bounds on the size of the optimal primal and dual solutions.
Revised:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%% m ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual solutions.
Edit intent: fluency
Original:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%%   ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log  (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r  upper bounds on the size of the optimal primal and dual solutions.
Revised:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%% ^{ %DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual solutions.
Edit intent: fluency
Original:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%%   ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log  (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r  upper bounds on the size of the optimal primal and dual solutions.
Revised:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%% ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log (n), \log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual solutions.
Edit intent: fluency
Original:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%%   ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log  (n), log (m), R, r, 1/ delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r  upper bounds on the size of the optimal primal and dual solutions.
Revised:  It has worst case running time n^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEm%%% ^{ 1/2%DIFDELCMD < \MBLOCKRIGHTBRACEspoly%%% (log (n), log (m), R, r, 1/ \delta ), with n and s the dimension and sparsity of the input matrices, respectively, m the number of constraints, delta the accuracy of the solution, and R, r upper bounds on the size of the optimal primal and dual solutions.
Edit intent: fluency