wanyu/IteraTeR_v2 huggingface dataset:
Original: A classical branch of graph algorithms is graph transversals, where one seeks < a minimum-weight subset of nodes in a node-weighted graph G which intersects all copies of subgraphs  F from a fixed family \mathcal F.
Revised: A classical branch of graph algorithms is graph transversals, where one seeks a minimum-weight subset of nodes in a node-weighted graph G which intersects all copies of subgraphs F from a fixed family \mathcal F.
Edit intent: fluency
Original: A classical branch of graph algorithms is graph transversals, where one seeks < a minimum-weight subset of nodes in a node-weighted graph G which intersects all copies of subgraphs  F from a fixed family \mathcal F.
Revised: A classical branch of graph algorithms is graph transversals, where one seeks < a minimum-weight subset of nodes in a node-weighted graph G which intersects all copies of subgraphs ~ F from a fixed family \mathcal F.
Edit intent: fluency
Original:  In the even-cycle transversal ( ECT \ECT ) problem, the goal is to find a minimum-weight hitting set for the set of even cycles in an undirected graph.
Revised:  In the even-cycle transversal ( \ECT ) problem, the goal is to find a minimum-weight hitting set for the set of even cycles in an undirected graph.
Edit intent: clarity
Original:  However, once trained over a finite set of classes, a deep learning model does not have the power to say that a given input does not belong to any of the classes and simply cannot be linked.
Revised:  However, once trained over a finite set of classes, some deep learning models do not have the power to say that a given input does not belong to any of the classes and simply cannot be linked.
Edit intent: clarity
Original:  To cope with partial occlusions, posture deformations, and complex scenes  we employed feature encoding and trajectory associations.
Revised:  To cope with partial occlusions, posture deformations, and complex scenes , we employed feature encoding and trajectory associations.
Edit intent: fluency
Original:  Elements of the anomaly detection framework are optimized to run on CPU-only edge devices with sufficient FPS . The experimental results indicate the proposed method is feasible and achieves satisfactory results in real-life scenarios.
Revised:  Elements of the anomaly detection framework are optimized to run on CPU-only edge devices with sufficient frames per second (FPS) . The experimental results indicate the proposed method is feasible and achieves satisfactory results in real-life scenarios.
Edit intent: clarity
Original:  To achieve decimeter-level accuracy, either fine-grained radio frequency (RF) information (e.g., channel state information ) or coarse-grained RF information (e.g., RSSI) from more than multiple antennas is required.
Revised:  To achieve decimeter-level accuracy, either fine-grained radio frequency (RF) information (e.g., channel state information or coarse-grained RF information (e.g., RSSI) from more than multiple antennas is required.
Edit intent: fluency
Original:  To achieve decimeter-level accuracy, either fine-grained radio frequency (RF) information (e.g., channel state information ) or coarse-grained RF information (e.g., RSSI) from more than multiple antennas is required.
Revised:  To achieve decimeter-level accuracy, either fine-grained radio frequency (RF) information (e.g., channel state information ) or coarse-grained RF information (e.g., access points) is required.
Edit intent: clarity
Original:  Meanwhile, owing to deficiency of single-antenna RSSI which only indicates a distance between a receiver and a transmitter, realizing fine-grained localization accuracy with single coarse-grained RF information is challenging.
Revised:  Meanwhile, owing to deficiency of single-antenna RSSI which only indicates a coarse-grained distance information between a receiver and a transmitter, realizing fine-grained localization accuracy with single coarse-grained RF information is challenging.
Edit intent: fluency
Original:  Our key idea to address this challenge is to leverage computer vision (CV ) and to estimate the most likely Fresnel zone between the receiver and transmitter, where the role of  RSSI is to detect blockage timings.
Revised:  Our key idea to address this challenge is to leverage CV technique and to estimate the most likely Fresnel zone between the receiver and transmitter, where the role of RSSI is to detect blockage timings.
Edit intent: clarity
Original:  Our key idea to address this challenge is to leverage computer vision (CV ) and to estimate the most likely Fresnel zone between the receiver and transmitter, where the role of  RSSI is to detect blockage timings.
Revised:  Our key idea to address this challenge is to leverage computer vision (CV ) and to estimate the most likely Fresnel zone between the receiver and transmitter, where the role of the RSSI is to detect blockage timings.
Edit intent: fluency
Original:  Specifically, historical positions of an obstacle that dynamically blocks the Fresnel zone are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the Fresnel zone boundaries, enabling the estimation of the Fresnel zone and localization of the transmitter.
Revised:  Specifically, historical positions of an obstacle that dynamically blocks the FFZ are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the Fresnel zone boundaries, enabling the estimation of the Fresnel zone and localization of the transmitter.
Edit intent: clarity
Original:  Specifically, historical positions of an obstacle that dynamically blocks the Fresnel zone are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the Fresnel zone boundaries, enabling the estimation of the Fresnel zone and localization of the transmitter.
Revised:  Specifically, historical positions of an obstacle that dynamically blocks the Fresnel zone are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the FFZ boundaries, enabling the estimation of the Fresnel zone and localization of the transmitter.
Edit intent: clarity
Original:  Specifically, historical positions of an obstacle that dynamically blocks the Fresnel zone are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the Fresnel zone boundaries, enabling the estimation of the Fresnel zone and localization of the transmitter.
Revised:  Specifically, historical positions of an obstacle that dynamically blocks the Fresnel zone are detected by the CV technique, and we estimate positions at which a blockage starts and ends via a time series of RSSI. These estimated obstacle positions, in principle, coincide with points on the Fresnel zone boundaries, enabling the estimation of the FFZ and localization of the transmitter.
Edit intent: clarity
Original: Reinforcement learning (RL) algorithms have shown impressive success in exploring high-dimensional environments to learn complex , long-horizon tasks, but can often exhibit unsafe behaviors and require extensive environment interaction when exploration is unconstrained.
Revised: Reinforcement learning (RL) has shown impressive success in exploring high-dimensional environments to learn complex , long-horizon tasks, but can often exhibit unsafe behaviors and require extensive environment interaction when exploration is unconstrained.
Edit intent: clarity
Original: Reinforcement learning (RL) algorithms have shown impressive success in exploring high-dimensional environments to learn complex , long-horizon tasks, but can often exhibit unsafe behaviors and require extensive environment interaction when exploration is unconstrained.
Revised: Reinforcement learning (RL) algorithms have shown impressive success in exploring high-dimensional environments to learn complex tasks, but can often exhibit unsafe behaviors and require extensive environment interaction when exploration is unconstrained.
Edit intent: clarity
Original:  A promising strategy for safe learning in dynamically uncertain environments is requiring that the agent can robustly return to states where task success (and therefore safety) can be guaranteed.
Revised:  A promising strategy for learning in dynamically uncertain environments is requiring that the agent can robustly return to states where task success (and therefore safety) can be guaranteed.
Edit intent: clarity
Original:  While this approach has been successful in low-dimensions, enforcing this constraint in environments with high-dimensional state spaces, such as images, is challenging.
Revised:  While this approach has been successful in low-dimensions, enforcing this constraint in environments with visual observations is exceedingly challenging.
Edit intent: clarity
Original:  We present  Latent Space Safe Sets (LS3), which extends this strategy to iterative, long-horizon tasks with image observations by using suboptimal demonstrations and a learned dynamics model to restrict exploration to the neighborhood of a learned Safe Set where task completion is likely .
Revised:  We present Latent Space Safe Sets (LS3), which uses this representation for long-horizon tasks with image observations by using suboptimal demonstrations and a learned dynamics model to restrict exploration to the neighborhood of a learned Safe Set where task completion is likely .
Edit intent: clarity
Original:  We present  Latent Space Safe Sets (LS3), which extends this strategy to iterative, long-horizon tasks with image observations by using suboptimal demonstrations and a learned dynamics model to restrict exploration to the neighborhood of a learned Safe Set where task completion is likely .
Revised:  We present Latent Space Safe Sets (LS3), which extends this strategy to iterative, long-horizon tasks with sparse rewards .
Edit intent: clarity
Original:  In this paper, we arrest this problem from two distinct aspects -- action duration misalignment and motion evolution misalignment.
Revised:  In this paper, we arrest this problem from two distinct aspects -- action duration misalignment and action evolution misalignment.
Edit intent: fluency
Original:  We address them sequentially through a Two-stage Temporal Alignment Network ( TTAN ).
Revised:  We address them sequentially through a Two-stage Action Alignment Network ( TTAN ).
Edit intent: clarity
Original:  In this paper , we present a simple and efficient co-training algorithm , named Multi-Head Co-Training , for semi-supervised image classification .
Revised:  In this paper , we present a simple and efficient algorithm Multi-Head Co-Training , for semi-supervised image classification .
Edit intent: coherence
Original:  In this paper , we present a simple and efficient co-training algorithm , named Multi-Head Co-Training , for semi-supervised image classification .
Revised:  In this paper , we present a simple and efficient co-training algorithm , named Multi-Head Co-Training .
Edit intent: coherence
Original:  We test this idea by selectively retraining different portions of VGG and ResNet architectures on CIFAR-10, Imagenette  and ImageNet using non-adversarial and adversarial data.
Revised:  We test this idea by selectively retraining different portions of VGG and ResNet architectures on CIFAR-10, Imagenette , and ImageNet using non-adversarial and adversarial data.
Edit intent: fluency
Original:  Our experimental results show that susceptibility to adversarial samples is associated with low-level feature extraction layers.
Revised:  Experimental results show that susceptibility to adversarial samples is associated with low-level feature extraction layers.
Edit intent: clarity
Original:  Therefore, retraining  high-level layers is insufficient for achieving robustness.
Revised:  Therefore, retraining of high-level layers is insufficient for achieving robustness.
Edit intent: fluency
Original:  This phenomenon could have two explanations: either , adversarial attacks yield outputs from early layers that are indistinguishable from features found in the attack classes, or adversarial attacks yield outputs from early layers that differ statistically from features for non-adversarial samples and do not permit consistent classification by subsequent layers.
Revised:  Furthermore , adversarial attacks yield outputs from early layers that are indistinguishable from features found in the attack classes, or adversarial attacks yield outputs from early layers that differ statistically from features for non-adversarial samples and do not permit consistent classification by subsequent layers.
Edit intent: coherence
Original:  This phenomenon could have two explanations: either , adversarial attacks yield outputs from early layers that are indistinguishable from features found in the attack classes, or adversarial attacks yield outputs from early layers that differ statistically from features for non-adversarial samples and do not permit consistent classification by subsequent layers.
Revised:  This phenomenon could have two explanations: either , adversarial attacks yield outputs from early layers that differ statistically from features for non-adversarial samples and do not permit consistent classification by subsequent layers.
Edit intent: clarity
Original:  We test this question by large-scale non-linear dimensionality reduction and density modeling on distributions of feature vectors in hidden layers and find that the feature distributions between non-adversarial and adversarial samples differ substantially.
Revised:  We test this question by large-scale non-linear dimensionality reduction and density modeling on distributions of feature vectors in hidden layers and non-adversarial and adversarial samples differ substantially.
Edit intent: coherence
Original: Hundreds of millions of cases and millions of deaths have occurred worldwide due to COVID-19 . The fight against this pandemic is on-going on multiple fronts.
Revised: COVID-19 has led to hundreds of millions of cases and millions of deaths have occurred worldwide due to COVID-19 . The fight against this pandemic is on-going on multiple fronts.
Edit intent: coherence
Original:  In this fight  diagnosis of the disease and isolation of the patients to prevent any spreads play a huge role.
Revised:  In this fight against the virus, diagnosis of the disease and isolation of the patients to prevent any spreads play a huge role.
Edit intent: clarity
Original:  In this fight  diagnosis of the disease and isolation of the patients to prevent any spreads play a huge role.
Revised:  In this fight diagnosis of the disease and isolation of the patients to prevent any spread play a huge role.
Edit intent: fluency
Original:  Machine Learning approaches have assisted  the diagnosis of COVID-19 cases by analyzing chest X-ray and CT-scan images of patients.
Revised:  Machine Learning approaches have assisted in the diagnosis of COVID-19 cases by analyzing chest X-ray and CT-scan images of patients.
Edit intent: fluency
Original:  Machine Learning approaches have assisted  the diagnosis of COVID-19 cases by analyzing chest X-ray and CT-scan images of patients.
Revised:  Machine Learning approaches have assisted the diagnosis of COVID-19 cases by analyzing chest X-rays and CT-scan images of patients.
Edit intent: fluency
Original:   In this research we present a simple and shallow Convolutional Neural Network based approach, TeliNet, to classify  CT-scan images of COVID-19 patients  .
Revised:  In this research we present a simple and shallow Convolutional Neural Network based approach, TeliNet, to classify these CT-scan images of COVID-19 patients .
Edit intent: coherence
Original:  Constructing minimal linear codes violating the Ashikhmin-Barg condition and  determining their weight distributions have been an interesting research topic in coding theory and cryptography.
Revised:  Constructing minimal linear codes violating the Ashikhmin-Barg condition and then determining their weight distributions have been an interesting research topic in coding theory and cryptography.
Edit intent: coherence
Original:  Constructing minimal linear codes violating the Ashikhmin-Barg condition and  determining their weight distributions have been an interesting research topic in coding theory and cryptography.
Revised:  Constructing minimal linear codes violating the Ashikhmin-Barg condition and determining their weight distributions have been interesting in coding theory and cryptography.
Edit intent: clarity
Original:  In this paper, basing on exponential sums and Krawtchouk polynomials, we first prove that g_{(m,k) in \mbox{ Heng-Ding-Zhou}0pt , which is the characteristic function of some subset }  in F_3^m, can be generalized to be f (m,k)%DIFDELCMD < } %%% for obtaining a minimal linear code violating the Ashikhmin-Barg condition;
Revised:  In this paper, basing on exponential sums , Krawtchouk polynomials, we first prove that g_{(m,k) in \mbox{ Heng-Ding-Zhou}0pt , which is the characteristic function of some subset } in F_3^m, can be generalized to be f (m,k)%DIFDELCMD < } %%% for obtaining a minimal linear code violating the Ashikhmin-Barg condition;
Edit intent: fluency
Original:  In this paper, basing on exponential sums and Krawtchouk polynomials, we first prove that g_{(m,k) in \mbox{ Heng-Ding-Zhou}0pt , which is the characteristic function of some subset }  in F_3^m, can be generalized to be f (m,k)%DIFDELCMD < } %%% for obtaining a minimal linear code violating the Ashikhmin-Barg condition;
Revised:  In this paper, basing on exponential sums and Krawtchouk polynomials, in \mbox{ Heng-Ding-Zhou}0pt , which is the characteristic function of some subset } in F_3^m, can be generalized to be f (m,k)%DIFDELCMD < } %%% for obtaining a minimal linear code violating the Ashikhmin-Barg condition;
Edit intent: coherence
Original:  In this paper, basing on exponential sums and Krawtchouk polynomials, we first prove that g_{(m,k) in \mbox{ Heng-Ding-Zhou}0pt , which is the characteristic function of some subset }  in F_3^m, can be generalized to be f (m,k)%DIFDELCMD < } %%% for obtaining a minimal linear code violating the Ashikhmin-Barg condition;
Revised:  In this paper, basing on exponential sums and Krawtchouk polynomials, we first prove that g_{(m,k) in \mbox{ Heng-Ding-Zhou}0pt , which is the characteristic function of some subset } in F_3^m, can be generalized to be f %DIFDELCMD < } %%% for obtaining a minimal linear code violating the Ashikhmin-Barg condition;
Edit intent: coherence
Original:  secondly, we employ _{(m,k) linear codes violating the Ashikhmin-Barg condition, whose minimal distance is better than that of codes  in \cite{Heng-Ding-Zhou}.
Revised:  secondly, we employ _{(m,k) linear codes violating the Ashikhmin-Barg condition, whose minimal distance is better than that of codes constructed in \cite{Heng-Ding-Zhou}.
Edit intent: clarity
Original:  In this work , an overview of the components, benefits, challenges, methods, and open problems of anomaly detection in smart manufacturing and robotic finishing systems are discussed.
Revised:  In this paper , an overview of the components, benefits, challenges, methods, and open problems of anomaly detection in smart manufacturing and robotic finishing systems are discussed.
Edit intent: clarity
Original: The task of table structure recognition is  to recognize the internal structure of a table , which is a key step to make machines understand tables .
Revised: Table structure recognition is to recognize the internal structure of a table , which is a key step to make machines understand tables .
Edit intent: clarity
Original: The task of table structure recognition is  to recognize the internal structure of a table , which is a key step to make machines understand tables .
Revised: The task of table structure recognition is to recognize the internal structure of a table .
Edit intent: coherence
Original:  However, tabular data in unstructured digital documents, e.g. Portable Document Format (PDF) and images, are difficult to parse into structured machine-readable format, due to complexity and diversity in their structure and style,  especially for complex tables.
Revised:  However, due to the complexity and diversity in their structure and style, especially for complex tables.
Edit intent: coherence
Original:   In the first stage, we use the FCN to predict the potential regions of the table row (column) separators, so as to obtain the bounding boxes of the basic grids in the table.
Revised:  In the first stage, we use the FCN to predict the potential regions of the table row (column) separators, and obtain the fine grid structure of the table.
Edit intent: clarity
Original:   Extensive experiments on other publicly available table structure recognition datasets show that our model achieves state-of-the-art.
Revised:  Extensive experiments on other publicly available datasets demonstrate that our model achieves state-of-the-art.
Edit intent: clarity
Original:  We then adapt and extend a prior intra-procedural symbolic execution algorithm to build a bottom-up, inter-procedural and incremental analysis for detecting vulnerabilities.
Revised:  We then adapt and extend a prior intra-procedural symbolic execution algorithm to build a bottom-up, inter-procedural analysis for detecting vulnerabilities.
Edit intent: clarity
Original:  Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train a ML model requested by some model owners, and get compensated for data contribution.
Revised:  Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and get compensated for data contribution.
Edit intent: fluency
Original:  Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train a ML model requested by some model owners, and get compensated for data contribution.
Revised:  Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train a ML model requested by some model owners, and receive compensation for data contribution.
Edit intent: clarity
Original:  2) data security against  curious model and data owners;
Revised:  2) data security against the curious model and data owners;
Edit intent: fluency
Original:  and 4) resilience to malicious model owner who intents to evade the payment.
Revised:  and 4) resilience to malicious model owners who intend to evade payment.
Edit intent: fluency
Original:  OmniLytics is implemented as a smart contract on the Ethereum blockchain to guarantee the atomicity of payment.
Revised:  OmniLytics is implemented as a blockchain smart contract to guarantee the atomicity of payment.
Edit intent: clarity
Original:  We implement a working prototype of OmniLytics on Ethereum , and perform extensive experiments to measure its gas cost and execution time under various parameter combinations , demonstrating its high computation and cost efficiency and strong practicality .
Revised:  We implement a working prototype of OmniLytics on Ethereum blockchain and perform extensive experiments to measure its gas cost and execution time under various parameter combinations , demonstrating its high computation and cost efficiency and strong practicality .
Edit intent: clarity
Original: In this paper , we propose a deep unfitted Nitsche method for computing elliptic interface problems with high contrasts in high dimensions.
Revised: This paper proposes a deep unfitted Nitsche method for computing elliptic interface problems with high contrasts in high dimensions.
Edit intent: coherence