wanyu/IteraTeR_v2 huggingface dataset:
Original:  Numerous theoretical works propose algorithms for the problem and analyze their complexity, but there are no practical algorithms and experimental studies.
Revised:  Numerous theoretical works propose algorithms for the problem and analyzed their complexity, but there are no practical algorithms and experimental studies.
Edit intent: fluency
Original:  Our BDD traverse algorithm controls the number of generated Steiner trees  to reduce the memory and computation costs  .
Revised:  Our BDD traverse algorithm controls the number of generated Steiner trees in order to reduce the memory and computation costs .
Edit intent: coherence
Original:  We show  this algorithm on the one hand can imitate simple hill-climbers like the (1+1) EA, giving the same asymptotic runtime on problems like OneMax, LeadingOnes, or Minimum Spanning Tree.
Revised:  We show that this algorithm on the one hand can imitate simple hill-climbers like the (1+1) EA, giving the same asymptotic runtime on problems like OneMax, LeadingOnes, or Minimum Spanning Tree.
Edit intent: fluency
Original:  In particular, we design a high order HWENO reconstructions for the integrals of source term and fluxes based on the point values of the solution and its spatial derivatives, and the principles of residual distribution schemes are adapted to obtain steady state solutions.
Revised:  In particular, we design a high order HWENO integration for the integrals of source term and fluxes based on the point values of the solution and its spatial derivatives, and the principles of residual distribution schemes are adapted to obtain steady state solutions.
Edit intent: clarity
Original:  In particular, we design a high order HWENO reconstructions for the integrals of source term and fluxes based on the point values of the solution and its spatial derivatives, and the principles of residual distribution schemes are adapted to obtain steady state solutions.
Revised:  In particular, we design a high order HWENO reconstructions for the integrals of source term and fluxes based on the point value of the solution and its spatial derivatives, and the principles of residual distribution schemes are adapted to obtain steady state solutions.
Edit intent: fluency
Original:  The proposed novel HWENO framework enjoys two advantages.
Revised:  Two advantages of the novel HWENO framework enjoys two advantages.
Edit intent: clarity
Original:  First ] , compared with the traditional HWENO framework, the proposed methods do not need to introduce additional auxiliary equations to update the derivatives of the unknown function, and compute them from the current value and the old spatial derivatives .
Revised:  First ] , compared with the traditional HWENO framework, the proposed method does not need to introduce additional auxiliary equations to update the derivatives of the unknown function, and compute them from the current value and the old spatial derivatives .
Edit intent: fluency
Original:  First ] , compared with the traditional HWENO framework, the proposed methods do not need to introduce additional auxiliary equations to update the derivatives of the unknown function, and compute them from the current value and the old spatial derivatives .
Revised:  First ] , compared with the traditional HWENO framework, the proposed methods do not need to introduce additional auxiliary equations to update the derivatives of the unknown variable, and just compute them from the current value and the old spatial derivatives .
Edit intent: clarity
Original:  First ] , compared with the traditional HWENO framework, the proposed methods do not need to introduce additional auxiliary equations to update the derivatives of the unknown function, and compute them from the current value and the old spatial derivatives .
Revised:  First ] , compared with the traditional HWENO framework, the proposed methods do not need to introduce additional auxiliary equations to update the derivatives of the unknown function, and compute them from the current point value of the solution and its old spatial derivatives .
Edit intent: clarity
Original:  This approach saves the computational storage and CPU time, which greatly improves the computational efficiency of the traditional HWENO framework.
Revised:  This approach saves the computational storage and CPU time, and thereby improve the computational efficiency of the traditional HWENO framework.
Edit intent: coherence
Original:  Extensive numerical experiments for one and two-dimensional scalar and systems problems confirm the high order accuracy and good quality of our scheme.
Revised:  Extensive numerical experiments for one- and two-dimensional scalar and systems problems confirm the high order accuracy and good quality of our scheme.
Edit intent: fluency
Original: We introduce a heterogeneous version of a contagious McKean--Vlasov systemwhose heterogeneity has a natural and particularly tractable structure.
Revised: We introduce a heterogeneous formulation of a contagious McKean--Vlasov systemwhose heterogeneity has a natural and particularly tractable structure.
Edit intent: clarity
Original: We introduce a heterogeneous version of a contagious McKean--Vlasov systemwhose heterogeneity has a natural and particularly tractable structure.
Revised: We introduce a heterogeneous version of a contagious McKean--Vlasov systemwhose heterogeneity has a natural and highly tractable structure.
Edit intent: clarity
Original:  It is shown that this system characterises the limit points of a finite particle system with applications to solvency contagion in interbank models and coupled integrate-and-fire models in mathematical neuroscience.
Revised:  It is shown that this formulation characterises the limit points of a finite particle system with applications to solvency contagion in interbank models and coupled integrate-and-fire models in mathematical neuroscience.
Edit intent: clarity
Original:  Furthermore, we provide a result on global uniqueness for the full common noise system under a simple smallness condition on the strength of interactions, and we show that  there is a unique differentiable solution up to an explosion time in the system without common noise .
Revised:  Furthermore, we provide a result on global uniqueness for the full problem with common noise under a smallness condition on the strength of interactions, and we show that there is a unique differentiable solution up to an explosion time in the system without common noise .
Edit intent: clarity
Original:  Experimental results show that our proposed method outperforms the baseline using vanilla BERT features both in LJSpeech and Bilzzard Challenge 2013 datasets, and semantic representations learned from the reverse direction are more effective for enhancing expressiveness.
Revised:  Experimental results show that our proposed method outperforms the baseline using vanilla BERT features both in LJSpeech and Blizzard Challenge 2013 datasets, and semantic representations learned from the reverse direction are more effective for enhancing expressiveness.
Edit intent: fluency
Original:  From our implementation integrated with Fabric v1.4 LTS, we observed up to 17x speedup in block validation when compared to the software-only validator peer, with commit throughput of up to 95,600 tps(~4.5x improvement over the best reported in literature) .
Revised:  From our implementation integrated with Fabric v1.4 LTS, we observed up to 17x speedup in block validation when compared to software-only validator peer, with commit throughput of up to 95,600 tps(~4.5x improvement over the best reported in literature) .
Edit intent: fluency
Original: This paper deals with synchronization of a class of infinite-dimensional nonlinear systems.
Revised: This paper deals with synchronization of a class of infinite-dimensional systems.
Edit intent: clarity
Original: Machine Learning (ML)-based network intrusion detection systems bring many benefits for enhancing the security posture of an organisation.
Revised: Machine Learning (ML)-based network intrusion detection systems bring many benefits for enhancing the cybersecurity posture of an organisation.
Edit intent: clarity
Original:  Many systems have been designed and developed in the research community, often achieving a  perfect detection rate when evaluated using certain datasets.
Revised:  Many systems have been designed and developed in the research community, often achieving a close to perfect detection rate when evaluated using certain datasets.
Edit intent: clarity
Original:  Many systems have been designed and developed in the research community, often achieving a  perfect detection rate when evaluated using certain datasets.
Revised:  Many systems have been designed and developed in the research community, often achieving a perfect detection rate when evaluated using synthetic datasets.
Edit intent: clarity
Original:  However, the high number of academic research has not  translated into practical deployments.
Revised:  However, the high number of academic research has not often translated into practical deployments.
Edit intent: clarity
Original:  This paper tightens the gap by evaluating the generalisability of a common feature set to different network environments and attack types .
Revised:  This paper tightens the gap by evaluating the generalisability of a common feature set to different network environments and attack scenarios .
Edit intent: clarity
Original:  Therefore, two feature sets (NetFlow and CICFlowMeter) have been evaluated across three datasets, i.e.  CSE-CIC-IDS2018, BoT-IoT, and ToN-IoT.
Revised:  Therefore, two feature sets (NetFlow and CICFlowMeter) have been evaluated across three datasets, i.e. , CSE-CIC-IDS2018, BoT-IoT, and ToN-IoT.
Edit intent: fluency
Original:  The results showed that the NetFlow feature set enhances the two ML models ' detection accuracy in detecting intrusions across different datasets .
Revised:  The results show the superiority of the NetFlow feature set enhances the two ML models ' detection accuracy in detecting intrusions across different datasets .
Edit intent: clarity
Original:  In addition, due to the complexity of the learning models, the SHAP , an explainable AI methodology, has been adopted to explain and interpret the classification decisions of two ML models.
Revised:  In addition, due to the complexity of the learning models, the SHAP , an explainable AI methodology, has been adopted to explain and interpret the classification decisions of ML models.
Edit intent: clarity
Original: Neural information retrieval (IR) models are promising mainly because their semantic matching capabilities can ameliorate the well-known synonymy and polysemy problems of word-based symbolic approaches.
Revised: The semantic matching capabilities can ameliorate the well-known synonymy and polysemy problems of word-based symbolic approaches.
Edit intent: coherence
Original: Neural information retrieval (IR) models are promising mainly because their semantic matching capabilities can ameliorate the well-known synonymy and polysemy problems of word-based symbolic approaches.
Revised: Neural information retrieval (IR) models are promising mainly because their semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of word-based symbolic approaches.
Edit intent: clarity
Original: Neural information retrieval (IR) models are promising mainly because their semantic matching capabilities can ameliorate the well-known synonymy and polysemy problems of word-based symbolic approaches.
Revised: Neural information retrieval (IR) models are promising mainly because their semantic matching capabilities can ameliorate the well-known synonymy and polysemy problems of symbolic approaches.
Edit intent: clarity
Original:  However, the power of neural models' dense representations comes at the cost of inefficiency, limiting it to be used as a re-ranker . Sparse representations, on the other hand, can help enhance symbolic or latent-term representations and yet take advantage of an inverted index for efficiency, being amenable to symbolic IR techniques that have been around for decades.
Revised:  However, neural models' dense representations comes at the cost of inefficiency, limiting it to be used as a re-ranker . Sparse representations, on the other hand, can help enhance symbolic or latent-term representations and yet take advantage of an inverted index for efficiency, being amenable to symbolic IR techniques that have been around for decades.
Edit intent: clarity
Original:  However, the power of neural models' dense representations comes at the cost of inefficiency, limiting it to be used as a re-ranker . Sparse representations, on the other hand, can help enhance symbolic or latent-term representations and yet take advantage of an inverted index for efficiency, being amenable to symbolic IR techniques that have been around for decades.
Revised:  However, the power of neural models' dense representations are more suitable for re-ranking, due to their inefficiency . Sparse representations, on the other hand, can help enhance symbolic or latent-term representations and yet take advantage of an inverted index for efficiency, being amenable to symbolic IR techniques that have been around for decades.
Edit intent: clarity
Original:  However, the power of neural models' dense representations comes at the cost of inefficiency, limiting it to be used as a re-ranker . Sparse representations, on the other hand, can help enhance symbolic or latent-term representations and yet take advantage of an inverted index for efficiency, being amenable to symbolic IR techniques that have been around for decades.
Revised:  However, the power of neural models' dense representations comes at the cost of inefficiency, limiting it to be used as a re-ranker . Sparse representations, either in symbolic or latent form, are more efficient with an inverted index for efficiency, being amenable to symbolic IR techniques that have been around for decades.
Edit intent: clarity
Original:  With this method, different segments of an embedding (horizontal buckets) or the embeddings from multiple layers of BERT (vertical buckets) can be selectedand merged so that diverse linguistic aspects can be represented.
Revised:  With this method, different segments of an embedding (horizontal buckets) or the embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects can be represented.
Edit intent: clarity
Original: Level set method is a widely used tool for solving reachability , viability and invariance problems.
Revised: The level set method is a widely used tool for solving reachability , viability and invariance problems.
Edit intent: fluency
Original: Level set method is a widely used tool for solving reachability , viability and invariance problems.
Revised: Level set method is a widely used tool for solving reachability and invariance problems.
Edit intent: clarity
Original:  However, some shortcomings, such as the difficulties of handling dissipation function and constructing terminal condition for solving the Hamilton-Jacobi partial differential equation, limit the application of  level set method in some problems with non-affine nonlinear systems and irregular target sets.
Revised:  However, some shortcomings, such as the difficulties of handling dissipation function and constructing terminal conditions for solving the Hamilton-Jacobi partial differential equation, limit the application of level set method in some problems with non-affine nonlinear systems and irregular target sets.
Edit intent: fluency
Original:  However, some shortcomings, such as the difficulties of handling dissipation function and constructing terminal condition for solving the Hamilton-Jacobi partial differential equation, limit the application of  level set method in some problems with non-affine nonlinear systems and irregular target sets.
Revised:  However, some shortcomings, such as the difficulties of handling dissipation function and constructing terminal condition for solving the Hamilton-Jacobi partial differential equation, limit the application of the level set method in some problems with non-affine nonlinear systems and irregular target sets.
Edit intent: fluency
Original:  In this paper , we propose a method which can effectively avoid the above difficult issues thus has better generality.
Revised:  This paper proposes a method that can effectively avoid the above difficult issues thus has better generality.
Edit intent: coherence
Original:  In this paper , we propose a method which can effectively avoid the above difficult issues thus has better generality.
Revised:  In this paper , we propose a method which can effectively avoid the above tricky issues and thus has better generality.
Edit intent: clarity
Original: Modeling users' preference from his historical sequences is one of the core problem of sequential recommendation.
Revised: Modeling user preference from his historical sequences is one of the core problem of sequential recommendation.
Edit intent: fluency
Original: Modeling users' preference from his historical sequences is one of the core problem of sequential recommendation.
Revised: Modeling users' preference from his historical sequences is one of the core problems of sequential recommendation.
Edit intent: fluency
Original:  Existing methods in such fields are widely distributed from conventional methods to deep learning methods.
Revised:  Existing methods in this field are widely distributed from conventional methods to deep learning methods.
Edit intent: clarity
Original:  However, most of them only model users' interests within their own sequences and ignore the fine-grained utilization of dynamic collaborative signals among different user sequences, making them insufficient to explore users' preferences.
Revised:  However, most of them only model users' interests within their own sequences and ignore the dynamic collaborative signals among different user sequences, making them insufficient to explore users' preferences.
Edit intent: clarity
Original:  However, most of them only model users' interests within their own sequences and ignore the fine-grained utilization of dynamic collaborative signals among different user sequences, making them insufficient to explore users' preferences.
Revised:  However, most of them only model users' interests within their own sequences and ignore the fine-grained utilization of dynamic collaborative signals among different user sequences, making it insufficient to explore users' preferences.
Edit intent: coherence
Original:  We take inspiration from dynamic graph neural networks to cope with this challenge, unifying the user sequence modeling and dynamic interaction information among users into one framework.
Revised:  We take inspiration from dynamic graph neural networks to cope with this challenge, modeling the user sequence modeling and dynamic interaction information among users into one framework.
Edit intent: clarity
Original:  We take inspiration from dynamic graph neural networks to cope with this challenge, unifying the user sequence modeling and dynamic interaction information among users into one framework.
Revised:  We take inspiration from dynamic graph neural networks to cope with this challenge, unifying the user sequence and dynamic collaborative signals into one framework.
Edit intent: clarity
Original:  We propose a new method named \emph{Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects the sequence of different users through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
Revised:  We propose a new method named \emph{Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects the sequence of different users through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
Edit intent: fluency
Original:  We propose a new method named \emph{Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects the sequence of different users through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
Revised:  We propose a new method named \emph{Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information.
Edit intent: clarity
Original:  Furthermore, we design a Dynamic Graph Attention Neural Network to achieve the information propagation and aggregation among different users and their sequences in the dynamic graph.
Revised:  Furthermore, we design a Dynamic Graph Recommendation Network to extract user's preferences from the dynamic graph.
Edit intent: clarity
Original:  Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction task for the user node to the item node in a dynamic graph.
Revised:  Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction between the user node to the item node in a dynamic graph.
Edit intent: clarity
Original:  Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction task for the user node to the item node in a dynamic graph.
Revised:  Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction task for the user node and the item node in a dynamic graph.
Edit intent: clarity
Original: This paper presents a systematic review of Python packages focused on time series analysis.
Revised: This paper presents a systematic review of Python packages with a focus on time series analysis.
Edit intent: fluency
Original:  The objective is first to provide an overview of the different time series analysis tasks and preprocessing methods implemented, but also to give an overview of the development characteristics of the packages (e.g., dependencies, community size, etc. ).
Revised:  The objective is to provide (1) an overview of the different time series analysis tasks and preprocessing methods implemented, but also to give an overview of the development characteristics of the packages (e.g., dependencies, community size, etc. ).
Edit intent: coherence
Original:  The objective is first to provide an overview of the different time series analysis tasks and preprocessing methods implemented, but also to give an overview of the development characteristics of the packages (e.g., dependencies, community size, etc. ).
Revised:  The objective is first to provide an overview of the different time series analysis tasks and preprocessing methods implemented, and (2) an overview of the development characteristics of the packages (e.g., dependencies, community size, etc. ).
Edit intent: coherence
Original:  The objective is first to provide an overview of the different time series analysis tasks and preprocessing methods implemented, but also to give an overview of the development characteristics of the packages (e.g., dependencies, community size, etc. ).
Revised:  The objective is first to provide an overview of the different time series analysis tasks and preprocessing methods implemented, but also to give an overview of the development characteristics of the packages (e.g., documentation, dependencies, and community size ).
Edit intent: clarity
Original:  After the filtering process, 40 packages were analyzed.
Revised:  Following the filtering process, 40 packages were analyzed.
Edit intent: fluency
Original:  We classified the packages according to the analysis tasks implemented, the methods related to data preparation, and the means to evaluate the results produced (methods and access to evaluation data).
Revised:  We classified the packages according to the analysis tasks implemented, the methods related to data preparation, and the means for evaluating the results produced (methods and access to evaluation data).
Edit intent: fluency