wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we propose an efficient algorithm to automatically produce specification revisions tackling with attack scenarios caused by the weakened user obligations.
Revised:  In this paper, we propose an efficient algorithm to automatically produce specification revisions tackling with attack scenarios caused by weakened user obligations.
Edit intent: fluency
Original:  By our algorithm, all the revisions  maintain the integrity of the functionalities as the original specification, which are generated using a novel recomposition approach.
Revised:  By our algorithm, all the revisions would be generated to maintain the integrity of the functionalities as the original specification, which are generated using a novel recomposition approach.
Edit intent: clarity
Original:  By our algorithm, all the revisions  maintain the integrity of the functionalities as the original specification, which are generated using a novel recomposition approach.
Revised:  By our algorithm, all the revisions maintain the integrity of the functionalities using a novel recomposition approach.
Edit intent: clarity
Original:  Then, the qualified revisions that can satisfy the security requirements would be efficiently spotted by a hybrid approach combining model checking and machine learning techniques.
Revised:  Then, the eligible revisions that can satisfy the security requirements would be efficiently spotted by a hybrid approach combining model checking and machine learning techniques.
Edit intent: clarity
Original:  To overcome this problem, this work construct a specific dataset, namely TFRD , for TFR-HSS task with commonly used methods, including the interpolation methods and the surrogate model based methods, as baselines to advance the research over temperature field reconstruction.
Revised:  To overcome this problem, this work constructs a specific dataset, namely TFRD , for TFR-HSS task with commonly used methods, including the interpolation methods and the surrogate model based methods, as baselines to advance the research over temperature field reconstruction.
Edit intent: fluency
Original:  To overcome this problem, this work construct a specific dataset, namely TFRD , for TFR-HSS task with commonly used methods, including the interpolation methods and the surrogate model based methods, as baselines to advance the research over temperature field reconstruction.
Revised:  To overcome this problem, this work construct a specific dataset, namely TFRD , for TFR-HSS task with commonly used methods, including the interpolation methods and the machine learning based methods, as baselines to advance the research over temperature field reconstruction.
Edit intent: clarity
Original:  Besides, this work selects four typical reconstruction problem with different heat source information and boundary conditions  and generate the standard samples as training and testing samples for further research.
Revised:  Besides, this work selects four typical reconstruction problem over heat-source systems with different heat-source information and boundary conditions and generate the standard samples as training and testing samples for further research.
Edit intent: clarity
Original:  Besides, this work selects four typical reconstruction problem with different heat source information and boundary conditions  and generate the standard samples as training and testing samples for further research.
Revised:  Besides, this work selects four typical reconstruction problem with different heat source information and boundary conditions , and generate the standard samples as training and testing samples for further research.
Edit intent: fluency
Original:  Besides, this work selects four typical reconstruction problem with different heat source information and boundary conditions  and generate the standard samples as training and testing samples for further research.
Revised:  Besides, this work selects four typical reconstruction problem with different heat source information and boundary conditions and generate the training and testing samples for further research.
Edit intent: clarity
Original:  In addition, there exists no public dataset for widely research of reconstruction methods to further boost the field reconstruction in engineering .
Revised:  In addition, there exists no public dataset for widely research of reconstruction methods to further boost the reconstruction performance and engineering applications .
Edit intent: clarity
Original:  To overcome this problem, this work constructs a specific dataset, namely Temperature Field Reconstruction Dataset (TFRD), for TFR-HSS task with commonly used methods, including the interpolation methods and the machine learning based methods, as baselines to advance the research over temperature field reconstruction.
Revised:  To overcome this problem, this work constructs a novel dataset, namely Temperature Field Reconstruction Dataset (TFRD), for TFR-HSS task with commonly used methods, including the interpolation methods and the machine learning based methods, as baselines to advance the research over temperature field reconstruction.
Edit intent: clarity
Original:  Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and we provide a performance analysis of typical methods  on TFRD, which can be served as the baseline results on this benchmark.
Revised:  Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and a performance analysis of typical methods on TFRD, which can be served as the baseline results on this benchmark.
Edit intent: clarity
Original:  Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and we provide a performance analysis of typical methods  on TFRD, which can be served as the baseline results on this benchmark.
Revised:  Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and we provide a performance analysis of typical methods is provided on TFRD, which can be served as the baseline results on this benchmark.
Edit intent: coherence
Original:  However, prior methods with common interpolations usually cannot provide accurate reconstruction performance as needed .
Revised:  However, prior methods with common interpolations usually cannot provide accurate reconstruction performance as required .
Edit intent: coherence
Original:  To overcome this problem, this work constructs a novel dataset, namely Temperature Field Reconstruction Dataset (TFRD), for TFR-HSS task with commonly used methods, including the interpolation methods and the machine learning based methods, as baselines to advance the research over temperature field reconstruction .
Revised:  To overcome this problem, this work constructs a novel dataset, namely Temperature Field Reconstruction Dataset (TFRD), for TFR-HSS task .
Edit intent: coherence
Original:  Besides , this work selects three typical reconstruction problem over heat-source systems with different heat-source information and boundary conditions, and generate the training and testing samples for further research.
Revised:  Then , this work selects three typical reconstruction problem over heat-source systems with different heat-source information and boundary conditions, and generate the training and testing samples for further research.
Edit intent: coherence
Original:  Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and a performance analysis of typical methods is provided on TFRD, which can be served as the baseline results on this benchmark.
Revised:  Finally, a comprehensive review of the prior methods for TFR-HSS task . Finally, a performance analysis of typical methods is provided on TFRD, which can be served as the baseline results on this benchmark.
Edit intent: coherence
Original:  Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and a performance analysis of typical methods is provided on TFRD, which can be served as the baseline results on this benchmark.
Revised:  Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and a performance analysis of typical methods is given on TFRD, which can be served as the baseline results on this benchmark.
Edit intent: fluency
Original: Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H \& E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc.
Revised: Cellular composition prediction, i.e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc.
Edit intent: fluency
Original: Automatic License Plate Recognition systems aim to provide an end-to-end solution towards detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames.
Revised: Automatic License Plate Recognition systems aim to provide a solution for detecting, localizing, and recognizing license plate characters from vehicles appearing in video frames.
Edit intent: clarity
Original:  In our paper, we propose a novel two-stage detection pipeline paired with Vision API that aims to provide real-time inference speed along with consistently accurate detection and recognition performance.
Revised:  In our paper, we propose a two-stage detection pipeline paired with Vision API that aims to provide real-time inference speed along with consistently accurate detection and recognition performance.
Edit intent: clarity
Original:  In our paper, we propose a novel two-stage detection pipeline paired with Vision API that aims to provide real-time inference speed along with consistently accurate detection and recognition performance.
Revised:  In our paper, we propose a novel two-stage detection pipeline paired with Vision API that provides real-time inference speed along with consistently accurate detection and recognition performance.
Edit intent: clarity
Original:  We also impose a temporal frame separation strategy to identify multiple vehicle license plates in the same clip.
Revised:  We also impose a temporal frame separation strategy to distinguish between multiple vehicle license plates in the same clip.
Edit intent: clarity
Original:  A: angular momentum), pressure-robustness and Re-semi-robustness (Re: Reynolds number) are three important properties for exactly divergence-free elements in Navier-Stokes simulations.
Revised:  A: angular momentum), pressure-robustness and Re-semi-robustness (Re: Reynolds number) are three important properties of Navier-Stokes simulations with exactly divergence-free elements in Navier-Stokes simulations.
Edit intent: clarity
Original:  Pressure-robustness means that the velocity error estimates are independent of the pressure approximation errors ;
Revised:  Pressure-robustness means that the velocity errors are independent of the pressure approximation errors ;
Edit intent: clarity
Original:  Pressure-robustness means that the velocity error estimates are independent of the pressure approximation errors ;
Revised:  Pressure-robustness means that the velocity error estimates are independent of the continuous pressure ;
Edit intent: clarity
Original:  Re-semi-robustness means that the constants in error estimates do not depend on the inverse of the viscosity explicitly .
Revised:  Re-semi-robustness means that the constants in error estimates do not depend on the inverse of the viscosity .
Edit intent: clarity
Original:  In this paper, based on the pressure-robust reconstruction method in [ Linke and Merdon, {\it Comput.
Revised:  In this paper, based on the pressure-robust reconstruction methods in [ Linke and Merdon, {\it Comput.
Edit intent: fluency
Original:  We shall refer to it as "EMAPR" reconstruction throughout this paper.
Edit intent: coherence
Original:  Some numerical comparisons with the exactly divergence-free methods, pressure-robust reconstruction methods and methods with EMAC formulation on classical elements are also provided .
Revised:  Some numerical comparisons with exactly divergence-free methods, pressure-robust reconstruction methods and methods with EMAC formulation on classical elements are also provided .
Edit intent: fluency
Original:  Some numerical comparisons with the exactly divergence-free methods, pressure-robust reconstruction methods and methods with EMAC formulation on classical elements are also provided .
Revised:  Some numerical comparisons with the exactly divergence-free methods, pressure-robust reconstructions and the EMAC scheme are provided to confirm our theoretical results .
Edit intent: clarity
Original:  Clustering, as a classic method to group similar data points together, has been used for subset selection in some EMO algorithms .
Revised:  Clustering, as a classic method to group similar data points together, has been used for subset selection in some fields .
Edit intent: clarity
Original:  In addition, we discuss a number of empirical insights that can help inform the design of algorithms intended for human-AI collaboration .
Revised:  In addition, we discuss a number of empirical insights that can help inform the design of algorithms intended for human-AI interfaces .
Edit intent: clarity
Original: We study the problem of PAC learning halfspaces on R^d with Massart noise under Gaussian marginals .
Revised: We study the problem of PAC learning halfspaces on R^d with Massart noise under the Gaussian distribution .
Edit intent: clarity
Original:  In the Massart noise model, an adversary is allowed to flip the label of each point x with  probability \eta(x) \leq \eta, for some parameter \eta \in [0,1/2].
Revised:  In the Massart model, an adversary is allowed to flip the label of each point x with probability \eta(x) \leq \eta, for some parameter \eta \in [0,1/2].
Edit intent: clarity
Original:  The goal of the learner is to output a hypothesis with missclassification error opt + \epsilon, where \mathrm{opt is the error of the target halfspace.
Revised:  The goal is to find a hypothesis with missclassification error opt + \epsilon, where \mathrm{opt is the error of the target halfspace.
Edit intent: clarity
Original:  The goal of the learner is to output a hypothesis with missclassification error opt + \epsilon, where \mathrm{opt is the error of the target halfspace.
Revised:  The goal of the learner is to output a hypothesis with misclassification error of OPT + \epsilon, where \mathrm{opt is the error of the target halfspace.
Edit intent: clarity
Original:  The goal of the learner is to output a hypothesis with missclassification error opt + \epsilon, where \mathrm{opt is the error of the target halfspace.
Revised:  The goal of the learner is to output a hypothesis with missclassification error opt + \epsilon, where \mathrm{OPT is the error of the target halfspace.
Edit intent: clarity
Original:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O  (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Revised:  For \eta < 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Edit intent: coherence
Original:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O  (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Revised:  For \eta = 1/2, we give a learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Edit intent: clarity
Original:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O  (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Revised:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for general halfspaces with sample and computational complexity d ^{O (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Edit intent: clarity
Original:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O  (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Revised:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O (\log(1/ \gamma ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Edit intent: fluency
Original:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O  (\log(1/ \epsilon ))} . For \eta < 1/ 2, we establish a lower bound of d^{\Omega(\log(1/\gamma)) where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Revised:  For \eta = 1/2, we prove a lower bound of d^{\Omega (\log(1/\epsilon)) learning algorithm for arbitrary halfspaces in this regime with sample complexity and running time O_\epsilon(1) \, d ^{O (\log(1/ \epsilon ))} . For \eta < 1/ \epsilon), where \gamma =\max\{\epsilon, \min\{\mathbf{Pr}[f(\mathbf{x}) = 1], \mathbf{Pr}[f(\mathbf{x}) = -1]\} \} and f is the target halfspace.
Edit intent: clarity
Original:  We complement this lower bound with a new learning algorithm for this regime with sample complexity and runtime d ^{O _{\eta (\log(1/ \gamma ))} \mathrm{poly 1/ \epsilon).
Revised:  We complement this lower bound with a new learning algorithm for this regime with sample complexity and runtime d ^{O _{\eta (\log(1/ \epsilon ))} \mathrm{poly 1/ \epsilon).
Edit intent: clarity
Original:  While noise inputs to StyleGAN2 are essential for good synthesis, we find that , for small datasets, coarse-scale noise interferes with latent variables  because both control long-scale image effects.
Revised:  While noise inputs to StyleGAN2 are essential for good synthesis, we find that coarse-scale noise interferes with latent variables because both control long-scale image effects.
Edit intent: clarity
Original:  This workis focused on the parameterization problem of high-level geometric primitives that are most frequently used , including points, lines, planes, ellipsoids, cylinders, and cones.
Revised:  In this work, we focus on the parameterization problem of high-level geometric primitives that are most frequently used , including points, lines, planes, ellipsoids, cylinders, and cones.
Edit intent: fluency
Original:  This workis focused on the parameterization problem of high-level geometric primitives that are most frequently used , including points, lines, planes, ellipsoids, cylinders, and cones.
Revised:  This workis focused on the parameterization of frequently used geometric primitives including points, lines, planes, ellipsoids, cylinders, and cones.
Edit intent: clarity
Original:  We first present a unified representation of those geometric primitives usingquadrics which yields a consistent and concise formulation.
Revised:  We first present a unified representation quadrics which yields a consistent and concise formulation.
Edit intent: clarity
Original:  We first present a unified representation of those geometric primitives usingquadrics which yields a consistent and concise formulation.
Revised:  We first present a unified representation of those geometric primitives using which yields a consistent and concise formulation.
Edit intent: clarity
Original:  Then we further study a decomposed model of quadrics that discloses the symmetric and degenerated nature of quadrics . Based on the decomposition, we develop physically meaningful quadrics factors in the settings of the graph-SLAM problem.
Revised:  Then we further study a decomposed model of quadrics that discloses the symmetric and degenerated nature of quadrics . Based on the decomposition, we develop physically meaningful quadrics factors in the settings of a graph-SLAM problem.
Edit intent: fluency
Original:  Finally, in simulation experiments, it is shown that the decomposed formulation has better efficiency and robustness to observation noises than baseline parameterizations.
Revised:  Then in simulation experiments, it is shown that the decomposed formulation has better efficiency and robustness to observation noises than baseline parameterizations.
Edit intent: fluency
Original:  And in real-world experiments, the proposed back-end framework is demonstrated to be capable of building compact and regularized maps.
Revised:  Finally, in real-world experiments, the proposed back-end framework is demonstrated to be capable of building compact and regularized maps.
Edit intent: fluency
Original:  In particular, we achieve new state-of-the-art robustness on CIFAR-10 (from 57.20\% to 62.04\%) and CIFAR-100 (from 30.03\% to 30.18\%) without extra training data.
Edit intent: coherence
Original:  Unlike most other works, the GPP layer, instead of complex and heavy maps, uses road and intersections graphs obtained directly from internet applications like OpenStreetMaps (OSM).
Revised:  For environment representation, instead of complex and heavy maps, uses road and intersections graphs obtained directly from internet applications like OpenStreetMaps (OSM).
Edit intent: clarity
Original:  Unlike most other works, the GPP layer, instead of complex and heavy maps, uses road and intersections graphs obtained directly from internet applications like OpenStreetMaps (OSM).
Revised:  Unlike most other works, the GPP layer, instead of complex and heavy grid maps, the GPP layer uses road network information obtained directly from internet applications like OpenStreetMaps (OSM).
Edit intent: clarity