wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our new certificates can be orders of magnitude faster to compute, have relatively low memory requirements, and seem to be more reliable in practice .
Revised:  Our new approach has a \mathcal{O memory requirements, and seem to be more reliable in practice .
Edit intent: clarity
Original: The smooth operation of largely deployed Internet of Things IoT applications will depend  , among other things, on effective infrastructure failure detection.
Revised: The smooth operation of largely deployed Internet of Things (IoT) applications will depend , among other things, on effective infrastructure failure detection.
Edit intent: clarity
Original: The smooth operation of largely deployed Internet of Things IoT applications will depend  , among other things, on effective infrastructure failure detection.
Revised: The smooth operation of largely deployed Internet of Things IoT applications will depend on , among other things, on effective infrastructure failure detection.
Edit intent: fluency
Original: The smooth operation of largely deployed Internet of Things IoT applications will depend  , among other things, on effective infrastructure failure detection.
Revised: The smooth operation of largely deployed Internet of Things IoT applications will depend , among other things, effective infrastructure failure detection.
Edit intent: fluency
Original:  Access failures in wireless networks Base Stations (BSs) produce a phenomenon called " Sleeping Cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell 's performance, making it difficult to discover.
Revised:  Access failures in wireless network Base Stations (BSs) produce a phenomenon called " Sleeping Cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell 's performance, making it difficult to discover.
Edit intent: fluency
Original:  Access failures in wireless networks Base Stations (BSs) produce a phenomenon called " Sleeping Cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell 's performance, making it difficult to discover.
Revised:  Access failures in wireless networks Base Stations (BSs) produce a phenomenon called " sleeping cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell 's performance, making it difficult to discover.
Edit intent: fluency
Original:  Access failures in wireless networks Base Stations (BSs) produce a phenomenon called " Sleeping Cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell 's performance, making it difficult to discover.
Revised:  Access failures in wireless networks Base Stations (BSs) produce a phenomenon called " Sleeping Cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on cell performance, making it difficult to discover.
Edit intent: fluency
Original:  Access failures in wireless networks Base Stations (BSs) produce a phenomenon called " Sleeping Cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell 's performance, making it difficult to discover.
Revised:  Access failures in wireless networks Base Stations (BSs) produce a phenomenon called " Sleeping Cells ", which can render a cell catatonic without triggering any alarms or provoking immediate effects on the cell 's performance, making them difficult to discover.
Edit intent: clarity
Original:  To detect this kind of failures , we propose a Machine Learning framework, based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BS with propensity to have their performance affected by the failure.
Revised:  To detect this kind of failure , we propose a Machine Learning framework, based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BS with propensity to have their performance affected by the failure.
Edit intent: fluency
Original:  To detect this kind of failures , we propose a Machine Learning framework, based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BS with propensity to have their performance affected by the failure.
Revised:  To detect this kind of failures , we propose a Machine Learning (ML) framework based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BS with propensity to have their performance affected by the failure.
Edit intent: clarity
Original:  To detect this kind of failures , we propose a Machine Learning framework, based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BS with propensity to have their performance affected by the failure.
Revised:  To detect this kind of failures , we propose a Machine Learning framework, based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BSs with propensity to have their performance affected by the failure.
Edit intent: fluency
Original:  A simple way to classify neighbors is to use  Voronoi diagrams.
Revised:  A simple way to define neighbors is to use Voronoi diagrams.
Edit intent: clarity
Original:  In this paper  we propose a much more realistic approach based on the nature of the radio-propagation and the way the devices choose the BS to which they send access requests.
Revised:  In this paper , we propose a much more realistic approach based on the nature of the radio-propagation and the way the devices choose the BS to which they send access requests.
Edit intent: fluency
Original:  In this paper  we propose a much more realistic approach based on the nature of the radio-propagation and the way the devices choose the BS to which they send access requests.
Revised:  In this paper we propose a much more realistic approach based on the nature of radio-propagation and the way the devices choose the BS to which they send access requests.
Edit intent: fluency
Original:  In this paper  we propose a much more realistic approach based on the nature of the radio-propagation and the way the devices choose the BS to which they send access requests.
Revised:  In this paper we propose a much more realistic approach based on the nature of the radio-propagation and the way devices choose the BS to which they send access requests.
Edit intent: fluency
Original:  We gather data from large-scale simulators that use real location data for BSs and IoT devices , and pose the detection problem as a supervised binary classification problem.
Revised:  We gather data from large-scale simulators that use real location data for BSs and IoT devices and pose the detection problem as a supervised binary classification problem.
Edit intent: fluency
Original:  We measure the effects caused on the detection performance , by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition.
Revised:  We measure the effects on the detection performance , by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition.
Edit intent: clarity
Original:  We measure the effects caused on the detection performance , by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition.
Revised:  We measure the effects caused on the detection performance by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition.
Edit intent: fluency
Original:   Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rates (FPRs ) under 5 \%.
Revised:  The Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rates (FPRs ) under 5 \%.
Edit intent: fluency
Original:   Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rates (FPRs ) under 5 \%.
Revised:  Extra Trees and Naive Bayes classifiers achieve Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rates (FPRs ) under 5 \%.
Edit intent: clarity
Original:   Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rates (FPRs ) under 5 \%.
Revised:  Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 , respectively, with False Positive Rates (FPRs ) under 5 \%.
Edit intent: coherence
Original:   Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rates (FPRs ) under 5 \%.
Revised:  Extra Trees and Naive Bayes classifiers achieved Receiver Operating Characteristic (ROC) area under the curve scores of 0.996 and 0.993 respectively with False Positive Rate (FPR ) under 5 \%.
Edit intent: clarity
Original:  The proposed framework holds potential for other pattern recognition tasks in smart-cities wireless infrastructures, that would enable the monitoring, prediction and improvement of the Quality of Service (QoS) experienced by IoT applications.
Revised:  The proposed framework holds potential for other pattern recognition tasks in smart-city wireless infrastructures, that would enable the monitoring, prediction and improvement of the Quality of Service (QoS) experienced by IoT applications.
Edit intent: fluency
Original:  Moreover, we develop a symbolic algorithm to detect \lambda -reducibility.
Revised:  Moreover, we develop a symbolic algorithm to detect \lambda -reducibility.
Edit intent: fluency
Original: For many networks, it is useful to think of their nodes as being embedded in a latent space, and such embeddings can affect the probability of whether nodes are adjacent to each other.
Revised: For many networks, it is useful to think of their nodes as being embedded in a latent space, and such embeddings can affect the probabilities for nodes to be adjacent to each other.
Edit intent: clarity
Original:  We define a notion of `` spatial strength centrality '' to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.
Revised:  We define a notion of " spatial strength centrality '' to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.
Edit intent: fluency
Original:  We define a notion of `` spatial strength centrality '' to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.
Revised:  We define a notion of `` spatial strength centrality " to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.
Edit intent: fluency
Original: We consider the problem of outputting succinct encodings of lists of generators for invariant rings  .
Revised: We consider the problem of computing succinct encodings of lists of generators for invariant rings .
Edit intent: clarity
Original:  Mulmuley conjectured that there are always polynomial sized such encodings for all invariant rings\SL\C .
Revised:  Mulmuley conjectured that there are always polynomial sized such encodings for invariant rings of\SL\C .
Edit intent: clarity
Original:  A blockchain is also a distributed system, and as such it shares some similarities with distributed database systems.
Revised:  As blockchain evolves into another data management system, the natural question is how it compares against distributed database systems.
Edit intent: clarity
Original:  Existing works that compare blockchains and distributed database systems focus mainly on high-level properties, such as security and throughput.
Revised:  Existing works on this comparison focus on high-level properties, such as security and throughput.
Edit intent: clarity
Original:  Our paper is to fill this important gap .
Revised:  Our work fills this important gap .
Edit intent: clarity
Original:  Design 34 (2015)] in 2D resp.
Revised:  Design 34 (2015)] in 2D and
Edit intent: clarity
Original: Isometries and their induced symmetries are ubiquitous in the world.
Revised: Isometries are ubiquitous in the world.
Edit intent: clarity
Original:  One computational challenge here is about the }  is key to inheriting the semidirect-product structure from the whole group of isometries, and in turn, the semidirect-product structure is key to our proposed algorithm for efficient orbit computation\emph{ \emph{ \emph{  .
Revised:  One computational challenge here is about the }restricted is key to inheriting the semidirect-product structure from the whole group of isometries, and in turn, the semidirect-product structure is key to our proposed algorithm for efficient orbit computation\emph{ \emph{ \emph{ .
Edit intent: fluency
Original:  One computational challenge here is about the }  is key to inheriting the semidirect-product structure from the whole group of isometries, and in turn, the semidirect-product structure is key to our proposed algorithm for efficient orbit computation\emph{ \emph{ \emph{  .
Revised:  One computational challenge here is about the } is key to preserving the\emph{ \emph{ \emph{ .
Edit intent: clarity
Original:  One computational challenge here is about the }  is key to inheriting the semidirect-product structure from the whole group of isometries, and in turn, the semidirect-product structure is key to our proposed algorithm for efficient orbit computation\emph{ \emph{ \emph{  .
Revised:  One computational challenge here is about the } is key to inheriting the semidirect-product structure from the whole group of isometries, and in turn, the semidirect-product structure is key to our proposed algorithm for efficient orbit computation\emph{semidirect-product structure \emph{ \emph{ .
Edit intent: fluency
Original:  One computational challenge here is about the }  is key to inheriting the semidirect-product structure from the whole group of isometries, and in turn, the semidirect-product structure is key to our proposed algorithm for efficient orbit computation\emph{ \emph{ \emph{  .
Revised:  One computational challenge here is about the } is key to inheriting the semidirect-product structure from the whole group of isometries, and in turn, the semidirect-product structure is key to our proposed algorithm for efficient orbit computation\emph{ \emph{ \emph{any .
Edit intent: fluency
Original:  Much of the prior work on change-point detection (including the only private algorithms for this problem) requires complete knowledge of the pre-change and post-change distributions.
Revised:  Much of the prior work on change-point detection---including the only private algorithms for this problem) requires complete knowledge of the pre-change and post-change distributions.
Edit intent: fluency
Original:  Much of the prior work on change-point detection (including the only private algorithms for this problem) requires complete knowledge of the pre-change and post-change distributions.
Revised:  Much of the prior work on change-point detection (including the only private algorithms for this problem---requires complete knowledge of the pre-change and post-change distributions.
Edit intent: fluency
Original:  In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing  and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss.
Revised:  In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing , and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss.
Edit intent: fluency
Original:  Also, we present several use-cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.
Revised:  Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.
Edit intent: fluency
Original: Inheritances , divorces or liquidations of companies require that a common asset is divided among the entitled parties.
Revised: Inheritance , divorces or liquidations of companies require that a common asset is divided among the entitled parties.
Edit intent: fluency
Original: Inheritances , divorces or liquidations of companies require that a common asset is divided among the entitled parties.
Revised: Inheritances , divorces or liquidations of companies require common assets to be divided among the entitled parties.
Edit intent: fluency
Original:  Legal methods usually consider the market value of goods, while fair division procedures take into account the parties' preferences expressed as cardinal utilities.
Revised:  Legal methods usually consider the market value of goods, while fair division theory takes into account the parties' preferences expressed as cardinal utilities.
Edit intent: clarity
Original: To understand how rich dynamics emerge in neural populations, we require models which exhibit a wide range of dynamics while remaining interpretable in terms of connectivity and single-neuron dynamics.
Revised: To understand how rich dynamics emerge in neural populations, we require models exhibiting a wide range of dynamics while remaining interpretable in terms of connectivity and single-neuron dynamics.
Edit intent: fluency
Original: To understand how rich dynamics emerge in neural populations, we require models which exhibit a wide range of dynamics while remaining interpretable in terms of connectivity and single-neuron dynamics.
Revised: To understand how rich dynamics emerge in neural populations, we require models which exhibit a wide range of activity patterns while remaining interpretable in terms of connectivity and single-neuron dynamics.
Edit intent: clarity
Original:  The proof formalized is close to that of Hughes and Cresswell 9 , except that it is given for a system based on a different choice of axioms .
Revised:  The proof formalized is close to that of Hughes and Cresswell 9 , but the system, based on a different choice of axioms .
Edit intent: coherence
Original:  Here the proof is based on a Hilbert-style presentation better described as a Mendelson system augmented with axiom schemes for K, T, S4, and B, and the necessitation rule as  rule of inference.
Revised:  Here the proof is based on a Hilbert-style presentation better described as a Mendelson system augmented with axiom schemes for K, T, S4, and B, and the necessitation rule as a rule of inference.
Edit intent: fluency
Original: Graph Convolutional Networks (GCNs) have been widely studied for graph data representation and learning .
Revised: Graph Convolutional Networks (GCNs) have shown very powerful for graph data representation and learning .
Edit intent: clarity
Original:  Existing GCNs generally conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors ( biased by its own representation ) . However, this fixed aggregation strategy is not guaranteed to be optimal for GCN based graph learning and also can be affected by some graph structure noises, such as incorrect or undesired edge connections.
Revised:  Existing GCNs generally conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors which is biased by its own representation ) . However, this fixed aggregation strategy is not guaranteed to be optimal for GCN based graph learning and also can be affected by some graph structure noises, such as incorrect or undesired edge connections.
Edit intent: coherence
Original:  Existing GCNs generally conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors ( biased by its own representation ) . However, this fixed aggregation strategy is not guaranteed to be optimal for GCN based graph learning and also can be affected by some graph structure noises, such as incorrect or undesired edge connections.
Revised:  Existing GCNs generally conduct feature aggregation on a fixed neighborhood graph in which each node computes its representation by aggregating the feature representations of all its neighbors ( biased by its own representation . However, this fixed aggregation strategy is not guaranteed to be optimal for GCN based graph learning and also can be affected by some graph structure noises, such as incorrect or undesired edge connections.
Edit intent: fluency
Original:  More importantly, GmCN can be theoretically interpreted by a unified regularization framework, based on which we derive a simple update algorithm to determine the optimal mask adaptively in GmCN training process.
Revised:  GmCN can be theoretically interpreted by a unified regularization framework, based on which we derive a simple update algorithm to determine the optimal mask adaptively in GmCN training process.
Edit intent: coherence
Original:  More importantly, GmCN can be theoretically interpreted by a unified regularization framework, based on which we derive a simple update algorithm to determine the optimal mask adaptively in GmCN training process.
Revised:  More importantly, GmCN can be theoretically interpreted by a regularization framework, based on which we derive a simple update algorithm to determine the optimal mask adaptively in GmCN training process.
Edit intent: clarity
Original:  Experiments on several datasets demonstrate the effectiveness of GmCN.
Revised:  Experiments on several datasets validate the effectiveness of GmCN.
Edit intent: clarity
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %DIFDELCMD < %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %DIFDELCMD < %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Edit intent: fluency