wanyu/IteraTeR_v2 huggingface dataset:
Original:  that is, the number of events d that must be scheduled in a specific order for a bug to be exposed is small . A natural question then is to find a d-hitting family of schedules:  a set of schedules is a d-hitting family if for each set of d events, and for each allowed ordering of these events, there is some schedule in the family that executes these events in this ordering.
Revised:  that is, the number of events d that must be scheduled in a specific order for a bug to be exposed is small . A natural question then is to find a d-hitting family of schedules: a set of schedules a d-hitting family if for each set of d events, and for each allowed ordering of these events, there is some schedule in the family that executes these events in this ordering.
Edit intent: coherence
Original:  A d-hitting family is guaranteed to expose all bugs with d events.
Revised:  The size of a d-hitting family is guaranteed to expose all bugs with d events.
Edit intent: clarity
Original:  When the tree is balanced, our construction is polylogarithmic in the number of events.
Revised:  When the tree is balanced, our constructions are polylogarithmic in the number of events.
Edit intent: clarity
Original:  We find that this framework substantially improves inferences of undirected graphical models under both sparse and group-sparse priors .
Revised:  We find that , together, these methods for variational inference substantially improve learning of sparse undirected graphical models under both sparse and group-sparse priors .
Edit intent: clarity
Original:  We highlight several modeling and computational challenges in the context of the problem, and address them to develop efficient algorithms that infer conjunctive queries from  examples and their explanations.
Revised:  We highlight several modeling and computational challenges in the context of the problem, and address them to develop efficient algorithms that infer conjunctive queries from the examples and their explanations.
Edit intent: fluency
Original:  We show the computational efficiency of the algorithms and favorable properties of inferred queries through a theoretical analysis as well as an experimental study using the TPC-H benchmark .
Revised:  We prove the computational efficiency of the algorithms and favorable properties of inferred queries through a theoretical analysis as well as an experimental study using the TPC-H benchmark .
Edit intent: clarity
Original:  Specifically, in the case of bacteria, distances can be estimated using a range of different evolutionary models, from single nucleotide polymorphisms to large-scale genome rearrangements.
Revised:  In the case of bacteria, distances can be estimated using a range of different evolutionary models, from single nucleotide polymorphisms to large-scale genome rearrangements.
Edit intent: coherence
Original:  Most such methods use the minimal distance as a proxy for true distance, and only occasionally are improvements such as a Jukes-Cantor correction (for SNP models) available to improve this underestimate.
Revised:  In the case of sequence evolution models (such as the Jukes-Cantor correction (for SNP models) available to improve this underestimate.
Edit intent: clarity
Original:  This has an obvious implication for the use of minimal distance in phylogeny reconstruction  .
Revised:  This has obvious implications for the use of minimal distance in phylogeny reconstruction .
Edit intent: fluency
Original:  Thus, it necessitates to revisit the influence maximization problem by taking online activities into consideration.
Edit intent: coherence
Original:  To address the computation challenge, we define an influence centrality via random walks on hypergraph to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs , and use the Hoeffding inequality to bound the approximation error .
Revised:  To address the computation challenge, we define an influence centrality via random walks to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs , and use the Hoeffding inequality to bound the approximation error .
Edit intent: clarity
Original:  To address the computation challenge, we define an influence centrality via random walks on hypergraph to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs , and use the Hoeffding inequality to bound the approximation error .
Revised:  To address the computation challenge, we define an influence centrality via random walks on hypergraph to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs .
Edit intent: coherence
Original:  By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM[ 20 ] , our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle a large amount of online activities .
Revised:  By conducting extensive experiments with real-world datasets, we show our approach is more efficient than the state-of-the-art algorithm IMM[ 20 ] , our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle a large amount of online activities .
Edit intent: clarity
Original:  By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM[ 20 ] , our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle a large amount of online activities .
Revised:  By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM[ 20 ] when we needs to handle a large amount of online activities .
Edit intent: clarity
Original:  By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM[ 20 ] , our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle a large amount of online activities .
Revised:  By conducting extensive experiments with real-world datasets, we show that compared to the state-of-the-art algorithm IMM[ 20 ] , our approach improves the accuracy of selecting the top k most influential users in SANs by up to an order of magnitude, and our approach has a comparable computation time even though it needs to handle large amount of online activities .
Edit intent: fluency
Original: Memory corruption errors in C/C++ programs are still the most common source of security vulnerabilities in today's systems.
Revised: Memory corruption errors in C/C++ programs remain the most common source of security vulnerabilities in today's systems.
Edit intent: fluency
Original:  Modern "control-flow hijacking " attacks exploit memory corruption vulnerabilities to divert program execution away from the intended control-flow.
Revised:  Control-flow hijacking attacks exploit memory corruption vulnerabilities to divert program execution away from the intended control-flow.
Edit intent: clarity
Original:  There is intense research interest in defenses based on Control-Flow Integrity (CFI), and this technique is now being integrated into several production compilers.
Revised:  There is intense research interest in defenses based on Control-Flow Integrity (CFI), and this technique is now integrated into several production compilers.
Edit intent: fluency
Original:  However, so far no study has systematically compared the various proposed CFI mechanisms, nor is there even any protocol on how to compare such mechanisms.
Revised:  However, so far no study has systematically compared the various proposed CFI mechanisms, nor is there any protocol on how to compare such mechanisms.
Edit intent: coherence
Original:  We compare a  range of CFI mechanisms using a unified nomenclature based on (i) a qualitative discussion of the conceptual security guarantees, (ii) a quantitative security evaluation, and (iii) an empirical evaluation of their performance on the same test environment.
Revised:  We compare a broad range of CFI mechanisms using a unified nomenclature based on (i) a qualitative discussion of the conceptual security guarantees, (ii) a quantitative security evaluation, and (iii) an empirical evaluation of their performance on the same test environment.
Edit intent: clarity
Original:  We compare a  range of CFI mechanisms using a unified nomenclature based on (i) a qualitative discussion of the conceptual security guarantees, (ii) a quantitative security evaluation, and (iii) an empirical evaluation of their performance on the same test environment.
Revised:  We compare a range of CFI mechanisms using a unified nomenclature based on (i) a qualitative discussion of the conceptual security guarantees, (ii) a quantitative security evaluation, and (iii) an empirical evaluation of their performance in the same test environment.
Edit intent: clarity
Original:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges , and, for open-sourced compiler-based implementations additionally (iii) quantitatively the generated equivalence classes and target sets, and (iv)  runtime performance.
Revised:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges . For open-source compiler-based implementations additionally (iii) quantitatively the generated equivalence classes and target sets, and (iv) runtime performance.
Edit intent: fluency
Original:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges , and, for open-sourced compiler-based implementations additionally (iii) quantitatively the generated equivalence classes and target sets, and (iv)  runtime performance.
Revised:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges , and, for open-sourced compiler-based implementations , we additionally evaluate (iii) quantitatively the generated equivalence classes and target sets, and (iv) runtime performance.
Edit intent: coherence
Original:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges , and, for open-sourced compiler-based implementations additionally (iii) quantitatively the generated equivalence classes and target sets, and (iv)  runtime performance.
Revised:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges , and, for open-sourced compiler-based implementations additionally (iii) the generated equivalence classes and target sets, and (iv) runtime performance.
Edit intent: clarity
Original:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges , and, for open-sourced compiler-based implementations additionally (iii) quantitatively the generated equivalence classes and target sets, and (iv)  runtime performance.
Revised:  For each mechanism, we evaluate (i) protected types of control-flow transfers, (ii) the precision of the protection for forward and backward edges , and, for open-sourced compiler-based implementations additionally (iii) quantitatively the generated equivalence classes and target sets, and (iv) the runtime performance.
Edit intent: fluency
Original: We consider the metric multi-cover problem (MMC) .
Revised: In the metric multi-cover problem (MMC) .
Edit intent: clarity
Original:  Bar-Yehuda 2013%DIFDELCMD < ] %%% gave a 3^{\alpha \cdot k-approximation for }  the MMC problem .
Revised:  Bar-Yehuda 2013%DIFDELCMD < ] %%% \cdot k-approximation for } the MMC problem .
Edit intent: clarity
Original:  Bar-Yehuda 2013%DIFDELCMD < ] %%% gave a 3^{\alpha \cdot k-approximation for }  the MMC problem .
Revised:  Bar-Yehuda 2013%DIFDELCMD < ] %%% gave a 3^{\alpha \cdot k-approximation for } In this article, we consider the MMC problem .
Edit intent: clarity
Original:  In this paper , we present an O(1)-approximation for the MMC problem in any arbitrary metric space, improving the result of Bar-Yehuda 2013%DIFDELCMD < ] %%% and matching the guarantee for fixed-dimensional Euclidean spaces in Bhowmick et al SoCG 13, JoCG 15%DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Revised:  In this paper , we present an 2013%DIFDELCMD < ] %%% and matching the guarantee for fixed-dimensional Euclidean spaces in Bhowmick et al SoCG 13, JoCG 15%DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Edit intent: clarity
Original:  In this paper , we present an O(1)-approximation for the MMC problem in any arbitrary metric space, improving the result of Bar-Yehuda 2013%DIFDELCMD < ] %%% and matching the guarantee for fixed-dimensional Euclidean spaces in Bhowmick et al SoCG 13, JoCG 15%DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Revised:  In this paper , we present an O(1)-approximation for the MMC problem in any arbitrary metric space, improving the result of Bar-Yehuda %DIFDELCMD < ] %%% and matching the guarantee for fixed-dimensional Euclidean spaces in Bhowmick et al SoCG 13, JoCG 15%DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Edit intent: coherence
Original:  In this paper , we present an O(1)-approximation for the MMC problem in any arbitrary metric space, improving the result of Bar-Yehuda 2013%DIFDELCMD < ] %%% and matching the guarantee for fixed-dimensional Euclidean spaces in Bhowmick et al SoCG 13, JoCG 15%DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Revised:  In this paper , we present an O(1)-approximation for the MMC problem in any arbitrary metric space, improving the result of Bar-Yehuda 2013%DIFDELCMD < ] %%% SoCG 13, JoCG 15%DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Edit intent: coherence
Original:  In this paper , we present an O(1)-approximation for the MMC problem in any arbitrary metric space, improving the result of Bar-Yehuda 2013%DIFDELCMD < ] %%% and matching the guarantee for fixed-dimensional Euclidean spaces in Bhowmick et al SoCG 13, JoCG 15%DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Revised:  In this paper , we present an O(1)-approximation for the MMC problem in any arbitrary metric space, improving the result of Bar-Yehuda 2013%DIFDELCMD < ] %%% and matching the guarantee for fixed-dimensional Euclidean spaces in Bhowmick et al %DIFDELCMD < ]%%% . We also obtain an O(1) for the non-uniform version of the MMC , where clients have possibly different coverage requirements .
Edit intent: coherence
Original:  This problem  is often cast as an expensive quadratic assignment problem (QAP).
Revised:  This problem is often cast as a quadratic assignment problem (QAP).
Edit intent: clarity
Original:  In particular, we focus on the Travel Salesman Problem (TSP), i.e. , a NP-hard problem with a discrete search space .
Revised:  In particular, we focus on the Traveling Salesman Problem (TSP), i.e. , a NP-hard problem with a discrete search space .
Edit intent: fluency
Original:  The solutions of the TSP can be codified by arrays of cities, and can be evaluated by a fitness computed according to a cost function (e.g. , the length of a path).
Revised:  The solutions of the TSP can be codified by arrays of cities, and can be evaluated by fitness, computed according to a cost function (e.g. , the length of a path).
Edit intent: clarity
Original:  The solutions of the TSP can be codified by arrays of cities, and can be evaluated by a fitness computed according to a cost function (e.g. , the length of a path).
Revised:  The solutions of the TSP can be codified by arrays of cities, and can be evaluated by a fitness computed according to a cost function (e.g. the length of a path).
Edit intent: fluency
Original:  Our method is based on the evolution of an agent population by means of a `partial imitation' mechanism .
Revised:  Our method is based on the evolution of an agent population by means of a `partial imitation' .
Edit intent: clarity
Original:  In particular, agents receive a random solution and then, interacting among themselves,  imitate the solutions of agents with a higher fitness.
Revised:  In particular, agents receive a random solution and then, interacting among themselves, may imitate the solutions of agents with a higher fitness.
Edit intent: clarity
Original:  Moreover, as stated above, the imitation is only partial, i.e., agents copy only one , randomly chosen, entry of better (array ) solutions .
Revised:  Since the imitation mechanism is only partial, i.e., agents copy only one , randomly chosen, entry of better (array ) solutions .
Edit intent: clarity
Original:  Moreover, as stated above, the imitation is only partial, i.e., agents copy only one , randomly chosen, entry of better (array ) solutions .
Revised:  Moreover, as stated above, the imitation is only partial, agents copy only one , randomly chosen, entry of better (array ) solutions .
Edit intent: coherence
Original:  Moreover, as stated above, the imitation is only partial, i.e., agents copy only one , randomly chosen, entry of better (array ) solutions .
Revised:  Moreover, as stated above, the imitation is only partial, i.e., agents copy only one entry (randomly chosen) of another array (i.e. solution) .
Edit intent: clarity
Original:  In doing so, the population converges towards a shared solution, behaving like a spin system undergoing a cooling process, i.e. , driven towards an ordered phase.
Revised:  In doing so, the population converges towards a shared solution, behaving like a spin system undergoing a cooling process, i.e. driven towards an ordered phase.
Edit intent: fluency
Original:  We highlight that the adopted `partial imitation' mechanism allows the population to generate new solutions over time, before reaching the final equilibrium.
Revised:  We highlight that the adopted `partial imitation' mechanism allows the population to generate solutions over time, before reaching the final equilibrium.
Edit intent: clarity
Original:  Remarkably, results of numerical simulations show that our method is able to find the optimal solution in all considered search spaces .
Revised:  Results of numerical simulations show that our method is able to find the optimal solution in all considered search spaces .
Edit intent: coherence
Original: In every sphere of existence, relational datasets are being generated at an alarmingly rapid rate  . Compressing these datasets could significantly reduce storage and archival costs.
Revised: Relational datasets are being generated at an alarmingly rapid rate . Compressing these datasets could significantly reduce storage and archival costs.
Edit intent: clarity
Original:  In this paper, we study compression algorithms that leverage the relational structure to compress datasets to a much greater extent.
Revised:  We study compression algorithms that leverage the relational structure to compress datasets to a much greater extent.
Edit intent: coherence
Original:  We prove the asymptotic optimality of our compression algorithm and conduct experiments to show the effective of our system: Squish achieves a reduction of over 50\\%DIF < in compression relative to systems developed %DIF > in storage size relative to systems  in prior work on a variety of real datasets.
Revised:  We prove the asymptotic optimality of our compression algorithm and conduct experiments to show the effectiveness of our system: Squish achieves a reduction of over 50\\%DIF < in compression relative to systems developed %DIF > in storage size relative to systems in prior work on a variety of real datasets.
Edit intent: fluency
Original:  We prove the asymptotic optimality of our compression algorithm and conduct experiments to show the effective of our system: Squish achieves a reduction of over 50\\%DIF < in compression relative to systems developed %DIF > in storage size relative to systems  in prior work on a variety of real datasets.
Revised:  We prove the asymptotic optimality of our compression algorithm and conduct experiments to show the effective of our system: Squish achieves a reduction of over 50\\%DIF < in compression relative to systems developed %DIF > in storage size relative to systems developed in prior work on a variety of real datasets.
Edit intent: fluency
Original: Given an ideal , a in A[x_1, \ldots, x_n], where A is a Noetherian , commutative ring, we give a Gr\"obner basis algorithm to determine the Krull dimension of A[x_1,\ldots,x_n]/a  . When A is a field, Krull dimension is equal to combinatorial dimension, but this may not be true in the case of arbitrary Noetherian rings.
Revised: Given an ideal a in A[x_1, \ldots, x_n], where A is a Noetherian , commutative ring, we give a Gr\"obner basis algorithm to determine the Krull dimension of A[x_1,\ldots,x_n]/a . When A is a field, Krull dimension is equal to combinatorial dimension, but this may not be true in the case of arbitrary Noetherian rings.
Edit intent: fluency
Original: Given an ideal , a in A[x_1, \ldots, x_n], where A is a Noetherian , commutative ring, we give a Gr\"obner basis algorithm to determine the Krull dimension of A[x_1,\ldots,x_n]/a  . When A is a field, Krull dimension is equal to combinatorial dimension, but this may not be true in the case of arbitrary Noetherian rings.
Revised: Given an ideal , a in A[x_1, \ldots, x_n], where A is a Noetherian integral domain, we propose an approach to compute the Krull dimension of A[x_1,\ldots,x_n]/a . When A is a field, Krull dimension is equal to combinatorial dimension, but this may not be true in the case of arbitrary Noetherian rings.
Edit intent: clarity
Original:  We also introduce the notion of combinatorial dimension of A[x_1, \ldots,x_n]/a and give a Gr\"obner basis algorithm to compute it for A-algebras that have a free A-module representation w.r.t. a lexicographic monomial ordering.
Revised:  We also introduce the notion of combinatorial dimension of A[x_1, \ldots,x_n]/a and give a Gr\"obner basis method to compute it for A-algebras that have a free A-module representation w.r.t. a lexicographic monomial ordering.
Edit intent: clarity
Original:  We also introduce the notion of combinatorial dimension of A[x_1, \ldots,x_n]/a and give a Gr\"obner basis algorithm to compute it for A-algebras that have a free A-module representation w.r.t. a lexicographic monomial ordering.
Revised:  We also introduce the notion of combinatorial dimension of A[x_1, \ldots,x_n]/a and give a Gr\"obner basis algorithm to compute it for A-algebras that have a free A-module representation w.r.t. a lexicographic ordering.
Edit intent: fluency