wanyu/IteraTeR_v2 huggingface dataset:
Original:  In contrast, we propose a proactive approach: Multiple individual changes to the program are collected in an Edit Transaction , which can be made effective if deemed complete.
Revised:  In contrast, we propose a proactive approach: Multiple individual changes to the program are collected in an Edit Transaction , which can be made effective if deemed complete.
Edit intent: fluency
Original:  Edit Transactions are capable of dynamic scoping, allowing a set of changes to be tested in isolation before being extended to the running application.
Revised:  Edit Transactions are capable of dynamic scoping, allowing a set of changes to be tested in isolation before being extended to the running application.
Edit intent: fluency
Original:  We present an implementation of Edit Transactions along withEdit-Transaction -aware tools in Squeak/Smalltalk.
Revised:  We present an implementation of along withEdit-Transaction -aware tools in Squeak/Smalltalk.
Edit intent: fluency
Original:  We present an implementation of Edit Transactions along withEdit-Transaction -aware tools in Squeak/Smalltalk.
Revised:  We present an implementation of Edit Transactions Edit-Transaction -aware tools in Squeak/Smalltalk.
Edit intent: clarity
Original:  We present an implementation of Edit Transactions along withEdit-Transaction -aware tools in Squeak/Smalltalk.
Revised:  We present an implementation of Edit Transactions along with -aware tools in Squeak/Smalltalk.
Edit intent: clarity
Original:  We conclude that workflows using Edit Transactions have the potential to increase confidence in a change, reduce potential for run-time errors, and eventually make live programming more predictable and engaging.
Revised:  We conclude that workflows using Edit Transactions have the potential to increase confidence in a change, reduce potential for run-time errors, and eventually make live programming more predictable and engaging.
Edit intent: fluency
Original:  In the proposed AVDCNN SE model, audio and visual features are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at an output layer.
Revised:  In the proposed AVDCNN SE model, audio and visual data are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at an output layer.
Edit intent: clarity
Original:  In the proposed AVDCNN SE model, audio and visual features are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at an output layer.
Revised:  In the proposed AVDCNN SE model, audio and visual features are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at the output layer.
Edit intent: fluency
Original:  Results show that the AVDCNN yields notably better performance as compared to an audio-only CNN-based SE model  , confirming the effectiveness of integrating visual information into the SE process.
Revised:  Results show that the AVDCNN yields notably better performance , compared with an audio-only CNN-based SE model , confirming the effectiveness of integrating visual information into the SE process.
Edit intent: coherence
Original:  In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model.
Revised:  In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNNs (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model.
Edit intent: fluency
Original:  In the proposed AVDCNN SE model , audio and visual data are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech  at the output layer.
Revised:  In the proposed AVDCNN SE model , audio and visual data are first processed using individual CNNs, and then, fused into a joint net-work to generate enhanced speech at the output layer.
Edit intent: clarity
Original:  The AVDCNN model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation.
Revised:  It is trained in an end-to-end manner, and parameters are jointly learned through back-propagation.
Edit intent: clarity
Original:  Results show that the AVDCNN  yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process  .
Revised:  Results show that the AVDCNN model yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process .
Edit intent: fluency
Original:  Results show that the AVDCNN  yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process  .
Revised:  Results show that the AVDCNN yields notably better performance, compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual in-formation into the SE process .
Edit intent: clarity
Original:  Most SE techniques focus  on addressing audio information only .
Revised:  Most SE techniques focus only on addressing audio information only .
Edit intent: fluency
Original:  Most SE techniques focus  on addressing audio information only .
Revised:  Most SE techniques focus on addressing audio information .
Edit intent: clarity
Original:  Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then , fused into a joint net-work to generate enhanced speech (the primary task) and reconstructed image (the secondary task) at the output layer.
Revised:  Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint net-work to generate enhanced speech (the primary task) and reconstructed image (the secondary task) at the output layer.
Edit intent: fluency
Original:  Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then , fused into a joint net-work to generate enhanced speech (the primary task) and reconstructed image (the secondary task) at the output layer.
Revised:  Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then , fused into a joint network to generate enhanced speech (the primary task) and reconstructed image (the secondary task) at the output layer.
Edit intent: clarity
Original:  Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then , fused into a joint net-work to generate enhanced speech (the primary task) and reconstructed image (the secondary task) at the output layer.
Revised:  Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then , fused into a joint net-work to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer.
Edit intent: fluency
Original:  It is trained in an end-to-end manner, and parameters are jointly learned through back-propagation.
Revised:  The model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation.
Edit intent: clarity
Original:  Results show that the AVDCNN model yields notably better performance , compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual in-formation into the SE process.
Revised:  Results show that the AVDCNN model yields a notably superior performance compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual in-formation into the SE process.
Edit intent: clarity
Original:  Results show that the AVDCNN model yields notably better performance , compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual in-formation into the SE process.
Revised:  Results show that the AVDCNN model yields notably better performance , compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process.
Edit intent: clarity
Original: Recent experiments and simulations have demonstrated that proteins can fold on the ribosome , but the importance of co-translational folding for the fitness of an organism remains an open question .
Revised: Recent experiments and simulations have demonstrated that proteins can fold on the ribosome . However, the extent and generality of fitness effects resulting from co-translational folding for the fitness of an organism remains an open question .
Edit intent: clarity
Original: Recent experiments and simulations have demonstrated that proteins can fold on the ribosome , but the importance of co-translational folding for the fitness of an organism remains an open question .
Revised: Recent experiments and simulations have demonstrated that proteins can fold on the ribosome , but the importance of co-translational folding remain open questions .
Edit intent: clarity
Original:  Using agent-based simulations we find that social social interaction leads to a larger increase in the average consumer welfare when there is at least some diversity of preferences in the consumer population .
Revised:  Using agent-based simulations we find that social social interaction leads to a consistent increase in the average consumer welfare when there is at least some diversity of preferences in the consumer population .
Edit intent: clarity
Original: Consider the MAXVAR risk measure on L^2 space.
Revised: This paper is concerned with the MAXVAR risk measure on L^2 space.
Edit intent: clarity
Original:  In this paper, we investigate the problem of mining opinions by extracting aspects of entities on the collection of informal short texts.
Revised:  In this paper, we investigate the problem of mining opinions on the collection of informal short texts.
Edit intent: clarity
Original: This paper presents simpler high-level specifications of more complex variants of the Paxos algorithm for distributed consensus  .
Revised: This paper presents simpler specifications of more complex variants of the Paxos algorithm for distributed consensus .
Edit intent: clarity
Original:  The development of the specifications is by using a method and language for expressing complex control flows and synchronization conditions precisely at a high level.
Revised:  The development of the specifications uses a method and language for expressing complex control flows and synchronization conditions precisely at a high level.
Edit intent: coherence
Original: This paper presents simpler specifications of more complex variants of the Paxos algorithm for distributed consensus, as a case study of high-level  specification of distributed algorithms.
Revised: This paper presents simpler specifications of more complex variants of the Paxos algorithm for distributed consensus, as case studies of high-level specification of distributed algorithms.
Edit intent: fluency
Original:  The  development of the specifications uses a method and language for expressing complex control flows and synchronization conditions precisely at a high level  .
Revised:  The development of the specifications is the use of a method and language for expressing complex control flows and synchronization conditions precisely at a high level .
Edit intent: clarity
Original:  we created higher-level control flows and synchronization conditions than all previous specifications, and obtained specifications that are significantly simpler and smaller, even matching or smaller than abstract specifications that omit many algorithm details , (3)
Revised:  we created higher-level control flows and synchronization conditions than all previous specifications, and obtained specifications that are much simpler and smaller, even matching or smaller than abstract specifications that omit many algorithm details , (3)
Edit intent: clarity
Original:   We systematically translated the resulting specifications into TLA+ and developed machine-checked safety proofs, which also allowed us to detect and fix a subtle safety violation in an earlier specification.
Revised:  5. We systematically translated the resulting specifications into TLA+ and developed machine-checked safety proofs, which also allowed us to detect and fix a subtle safety violation in an earlier specification.
Edit intent: fluency
Original: In this work, we propose a method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene.
Revised: We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene.
Edit intent: clarity
Original:  We introduce an end-to-end solution to this problem and propose a deep neural network that takes the limited field-of-view photo as input and produces an environment map as a panorama and a light mask prediction over the panorama as the output.
Revised:  We introduce an end-to-end deep neural network that takes the limited field-of-view photo as input and produces an environment map as a panorama and a light mask prediction over the panorama as the output.
Edit intent: clarity
Original:  Our method does not rely on any assumptions on scene appearance, geometry, material properties, or lighting.
Revised:  Our method does not rely on any assumptions on scene geometry, material properties, or lighting.
Edit intent: clarity
Original:  Consequently, using our illumination estimates for applications like 3D object insertion lead to results that are photo-realistic, which we demonstrate over a large set of examples and via a user study.
Revised:  Consequently, using our illumination estimates for applications like 3D object insertion , we can achieve results that are photo-realistic, which we demonstrate over a large set of examples and via a user study.
Edit intent: clarity
Original:  Consequently, using our illumination estimates for applications like 3D object insertion lead to results that are photo-realistic, which we demonstrate over a large set of examples and via a user study.
Revised:  Consequently, using our illumination estimates for applications like 3D object insertion lead to results that are photo-realistic, which is validated via a perceptual user study.
Edit intent: clarity
Original:  However, only in this century they were stated, proved and applied to obtain interesting results , most notably by Mabillard and Wagner . Here we prove and apply a version of the r-fold Whitney trick when general position r-tuple intersections have positive dimension.
Revised:  However, only in this century they were stated, proved and applied to obtain interesting results . Here we prove and apply a version of the r-fold Whitney trick when general position r-tuple intersections have positive dimension.
Edit intent: coherence
Original:  Given the input keywords, in this paper, we study how to assemble a query graph that is to represent users' query intention accurately and efficiently.
Revised:  Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently.
Edit intent: fluency
Original:  Then, we formally define the query graph assembly (QGA) problem.
Revised:  Then, we formally define the query graph assembly (QGA) problem.
Edit intent: fluency
Original:  The algorithm  time complexity is O(k^{2l} \cdot l^{3l}), where l is the number of the input keywords and k is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword.
Revised:  The algorithm 's time complexity is O(k^{2l} \cdot l^{3l}), where l is the number of the input keywords and k is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword.
Edit intent: fluency
Original:  The algorithm  time complexity is O(k^{2l} \cdot l^{3l}), where l is the number of the input keywords and k is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword.
Revised:  The algorithm time complexity is O(k^{2l} \cdot l^{3l}), where l is the number of the keywords and k is a tunable parameter, i.e., the maximum number of candidate entity/class vertices and predicate edges allowed to match each keyword.
Edit intent: clarity
Original:  Although the QGA problem is intractable, both l and k are small in practice.
Revised:  Although QGA is intractable, both l and k are small in practice.
Edit intent: clarity
Original:  Furthermore, this algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs.
Revised:  Furthermore, the algorithm's time complexity does not depend on the RDF graph size, which guarantees the good scalability of our system in large RDF graphs.
Edit intent: coherence
Original:  Experiments on DBpedia and Freebase datasets confirm the superiority of our system on both effectiveness and efficiency.
Revised:  Experiments on DBpedia and Freebase confirm the superiority of our system on both effectiveness and efficiency.
Edit intent: clarity
Original: {\em   We construct a dense point set of n points in the plane with ne^{\Omega\left({\log n}\right)} halving lines.
Revised: {\em We construct a dense set of n points in the plane with ne^{\Omega\left({\log n}\right)} halving lines.
Edit intent: fluency
Original: We present an algorithm for crime prediction based on the near-repeat victimization model solved by a  Green's function scheme.
Revised: Applying machine learning (ML) toward social issues such as crime has a hope to protect people by well-controlled security manners. On the other hand, the ML has a potential drawback to reinforce an irrational bias in places and people deemed at risk, if the ML is based on a Green's function scheme.
Edit intent: coherence
Original:  We find that the cascade of the crimes has a long-time, logarithmic tail , which is consistent with an earlier study on other data of burglaries.
Revised:  We find a cascade influence of crimes that has a long-time, logarithmic tail , which is consistent with an earlier study on other data of burglaries.
Edit intent: clarity
Original:  We find that the cascade of the crimes has a long-time, logarithmic tail , which is consistent with an earlier study on other data of burglaries.
Revised:  We find that the cascade of the crimes has a long-time, logarithmic tail ; this result is consistent with an earlier study on other data of burglaries.
Edit intent: clarity
Original:  We find that the cascade of the crimes has a long-time, logarithmic tail , which is consistent with an earlier study on other data of burglaries.
Revised:  We find that the cascade of the crimes has a long-time, logarithmic tail , which is consistent with an earlier study on burglaries.
Edit intent: clarity
Original:  The presented method is a powerful tool not only to predict crimes but also to analyze their correlations, because the Green's function can describe how a past crime influences the future events .
Revised:  The present method is a powerful tool not only to predict crimes but also to analyze their correlations, because the Green's function can describe how a past crime influences the future events .
Edit intent: fluency
Original:  The presented method is a powerful tool not only to predict crimes but also to analyze their correlations, because the Green's function can describe how a past crime influences the future events .
Revised:  The presented method is a powerful tool not only to predict events but also to analyze their correlations, because the Green's function can describe how a past crime influences the future events .
Edit intent: clarity
Original:  The presented method is a powerful tool not only to predict crimes but also to analyze their correlations, because the Green's function can describe how a past crime influences the future events .
Revised:  The presented method is a powerful tool not only to predict crimes but also to reveal their hidden causality .
Edit intent: clarity
Original:  Here we develop an algorithm that finds a causality of eventsonly by learning a spatio-temporal dataset, employing a Green's function scheme  .
Revised:  Here we develop an algorithm that forecasts cascading events, by employing a Green's function scheme .
Edit intent: clarity
Original:  We find a cascade influence of  crimes that has a long-time, logarithmic tail;
Revised:  We find a cascade influence of the crimes that has a long-time, logarithmic tail;
Edit intent: fluency
Original:  The present method is a powerful tool not only to predict events but also to reveal their hidden causality .
Revised:  This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green's function method is the low computational cost in the case of high density of events and/or large amount of the training data .
Edit intent: coherence
Original: A grand goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks.
Revised: An important goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks.
Edit intent: clarity
Original: The process of desertification in the semi-arid climatic zone is considered by many as a catastrophic regime shift, since the positive feedback of vegetation density on growth rates yields a system that supports alternative steady states.
Revised: The process of desertification in the semi-arid climatic zone is considered by many as a catastrophic regime shift, since the positive feedback of vegetation density on growth rates yields a system that admits alternative steady states.
Edit intent: clarity
Original:   Here we present a large-scale analysis of vegetationdynamics for ~2.5 \ 10^6 \ km^2 of the African Sahel region, with spatial resolution of 30 \times 30 meters, using three consecutive snapshots.
Revised:  Here we present a large-scale dynamics for ~2.5 \ 10^6 \ km^2 of the African Sahel region, with spatial resolution of 30 \times 30 meters, using three consecutive snapshots.
Edit intent: clarity
Original:   Here we present a large-scale analysis of vegetationdynamics for ~2.5 \ 10^6 \ km^2 of the African Sahel region, with spatial resolution of 30 \times 30 meters, using three consecutive snapshots.
Revised:  Here we present a large-scale analysis of vegetation for ~2.5 \ 10^6 \ km^2 of the African Sahel region, with spatial resolution of 30 \times 30 meters, using three consecutive snapshots.
Edit intent: clarity
Original:   Here we present a large-scale analysis of vegetationdynamics for ~2.5 \ 10^6 \ km^2 of the African Sahel region, with spatial resolution of 30 \times 30 meters, using three consecutive snapshots.
Revised:  Here we present a large-scale analysis of vegetationdynamics for ~2.5 \times 10^6 \ km^2 of the African Sahel region, with spatial resolution of 30 \times 30 meters, using three consecutive snapshots.
Edit intent: fluency