wanyu/IteraTeR_v2 huggingface dataset:
Original:  More specifically, workers' behavior is analyzed based on worker reliability, worker trustworthiness, and worker successas measures for workers' performance, worker efficiency , and worker elasticityto represent workers' preferences, and worker contest, worker confidence, and worker deceitfulnessto understand workers' strategies.
Revised:  More specifically, workers' behavior is analyzed based on their performances in terms of reliability, trustworthiness, and worker successas measures for workers' performance, worker efficiency , and worker elasticityto represent workers' preferences, and worker contest, worker confidence, and worker deceitfulnessto understand workers' strategies.
Edit intent: clarity
Original:  More specifically, workers' behavior is analyzed based on worker reliability, worker trustworthiness, and worker successas measures for workers' performance, worker efficiency , and worker elasticityto represent workers' preferences, and worker contest, worker confidence, and worker deceitfulnessto understand workers' strategies.
Revised:  More specifically, workers' behavior is analyzed based on worker reliability, worker trustworthiness, and success; their preferences in terms of efficiency and elasticity; and strategies in terms of comfort, confidence, and worker deceitfulnessto understand workers' strategies.
Edit intent: clarity
Original:  Based on statistical analysis, this study can only support that the low ranked group associates with the highest reliable workers with an average reliability of 25\%, while the mixed-ranked group contains the most trustworthy workers with average trustworthiness of 16\%.
Revised:  This study shows that the low-ranked community associates with the highest reliable workers with an average reliability of 25\%, while the mixed-ranked group contains the most trustworthy workers with average trustworthiness of 16\%.
Edit intent: clarity
Original:  Based on statistical analysis, this study can only support that the low ranked group associates with the highest reliable workers with an average reliability of 25\%, while the mixed-ranked group contains the most trustworthy workers with average trustworthiness of 16\%.
Revised:  Based on statistical analysis, this study can only support that the low ranked group associates with the highest reliable workers with an average reliability of 25\%, while the mixed-ranked community contains the most trustworthy workers with average trustworthiness of 16\%.
Edit intent: clarity
Original:  Conclusions: These findings are helpful for task requesters to understand preferences and relations among unknown resources in the platform and plan for task successin a more effective and efficient manner in a software crowdsourcing platform .
Revised:  Such empirical evidence is beneficial to help exploring resourcing options while understanding the relations among unknown resources in the platform and plan for task successin a more effective and efficient manner in a software crowdsourcing platform .
Edit intent: clarity
Original:  Conclusions: These findings are helpful for task requesters to understand preferences and relations among unknown resources in the platform and plan for task successin a more effective and efficient manner in a software crowdsourcing platform .
Revised:  Conclusions: These findings are helpful for task requesters to understand preferences and relations among unknown resources to improve task success .
Edit intent: clarity
Original:  stage-II employs the physical similarity of  labeled and unlabeled data samples to improve the location accuracy.
Revised:  stage-II employs the physical similarity of the labeled and unlabeled data samples to improve the location accuracy.
Edit intent: fluency
Original:  We compare our approach with three baselines in the IEEE 123-node benchmark system , showing that the proposed method outperforms the others by significant margins , especially when label rates are low.
Revised:  We compare our approach with three baselines in the IEEE 123-node benchmark system and show that it outperforms the others by significant margins , especially when label rates are low.
Edit intent: clarity
Original: The core for tackling the fine-grained visual categorization (FGVC) is to learn subtleyet discriminative features.
Revised: The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features.
Edit intent: fluency
Original:  Most previous works achieve this by explicitly selecting thediscriminative parts or integrating the attention mechanism via CNN-based approaches.
Revised:  Most previous works achieve this by explicitly selecting the discriminative parts or integrating the attention mechanism via CNN-based approaches.
Edit intent: fluency
Original:  Theself-attention mechanism aggregates and weights the information from all patches to theclassification token, making it perfectly suitable for FGVC.
Revised:  Theself-attention mechanism aggregates and weights the information from all patches to the classification token, making it perfectly suitable for FGVC.
Edit intent: fluency
Original:  Nonetheless, the classifi-cation token in the deep layer pays more attention to the global information, lackingthe local and low-level features that are essential for FGVC.
Revised:  Nonetheless, the classifi-cation token in the deep layer pays more attention to the global information, lacking the local and low-level features that are essential for FGVC.
Edit intent: fluency
Original:  We design a novel token selection mod-ule called mutual attention weight selection (MAWS) to guide the network effectivelyand efficiently towards selecting discriminative tokens without introducing extra param-eters.
Revised:  We design a novel token selection mod-ule called mutual attention weight selection (MAWS) to guide the network effectively and efficiently towards selecting discriminative tokens without introducing extra param-eters.
Edit intent: fluency
Original:  We verify the effectiveness of FFVT on three benchmarks where FFVT achievesthe state-of-the-art performance.
Revised:  We verify the effectiveness of FFVT on three benchmarks where FFVT achieves the state-of-the-art performance.
Edit intent: fluency
Original:  In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs  in ultra-dense  D2D mmWave networks.
Revised:  In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs in ultra-dense device-to-device ( D2D mmWave networks.
Edit intent: clarity
Original:  In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs  in ultra-dense  D2D mmWave networks.
Revised:  In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs in ultra-dense D2D ) mmWave communication networks.
Edit intent: clarity
Original:  To overcome this challenge, we resort to design a deep learning architecture based on graphic neural network to finish the joint beam selection and link activation, called as GBLinks model, with taking into account the network topology information .
Revised:  To overcome this challenge, this paper resorts to design a deep learning architecture based on graphic neural network to finish the joint beam selection and link activation, called as GBLinks model, with taking into account the network topology information .
Edit intent: clarity
Original:  To overcome this challenge, we resort to design a deep learning architecture based on graphic neural network to finish the joint beam selection and link activation, called as GBLinks model, with taking into account the network topology information .
Revised:  To overcome this challenge, we resort to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, called as GBLinks model, with taking into account the network topology information .
Edit intent: fluency
Original:  To overcome this challenge, we resort to design a deep learning architecture based on graphic neural network to finish the joint beam selection and link activation, called as GBLinks model, with taking into account the network topology information .
Revised:  To overcome this challenge, we resort to design a deep learning architecture based on graphic neural network to finish the joint beam selection and link activation, with taking the network topology information .
Edit intent: clarity
Original:  We further present an unsupervised Lagrangian dual learning framework to train the parameters of GBLinks  .
Revised:  We further present an unsupervised Lagrangian dual learning framework to train the parameters of GBLinks model .
Edit intent: clarity
Original:  Numerical results show that the proposed GBLinks model can converges to a stable point with the number of iterations increases, in terms of the average sum rate.
Revised:  Numerical results show that the proposed GBLinks model can converges to a stable point with the number of iterations increases, in terms of the sum rate.
Edit intent: clarity
Original:  It also shows that GBLinks can reach near-optimal solution through comparing with the exhaustively search in small-scale  D2D mmWave  networks and outperforms selfish beam selection strategy with activating all links .
Revised:  Furthermore, the GBLinks model can reach near-optimal solution through comparing with the exhaustively search in small-scale D2D mmWave networks and outperforms selfish beam selection strategy with activating all links .
Edit intent: clarity
Original:  It also shows that GBLinks can reach near-optimal solution through comparing with the exhaustively search in small-scale  D2D mmWave  networks and outperforms selfish beam selection strategy with activating all links .
Revised:  It also shows that GBLinks can reach near-optimal solution through comparing with the exhaustive search scheme in small-scale D2D mmWave networks and outperforms selfish beam selection strategy with activating all links .
Edit intent: clarity
Original:  It also shows that GBLinks can reach near-optimal solution through comparing with the exhaustively search in small-scale  D2D mmWave  networks and outperforms selfish beam selection strategy with activating all links .
Revised:  It also shows that GBLinks can reach near-optimal solution through comparing with the exhaustively search in small-scale D2D mmWave communication networks and outperforms selfish beam selection strategy with activating all links .
Edit intent: clarity
Original:  Without human movement, the global stability of the endemic equilibrium point is also established using Lyapunov functions.
Edit intent: coherence
Original: In recent years, benefiting from the expressivepower of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in faceclustering .
Revised: In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in faceclustering .
Edit intent: fluency
Original: In recent years, benefiting from the expressivepower of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in faceclustering .
Revised: In recent years, benefiting from the expressivepower of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering .
Edit intent: fluency
Original:  However, rare attention has been paidto GCN-based clustering on imbalanced data.
Revised:  However, rare attention has been paid to GCN-based clustering on imbalanced data.
Edit intent: fluency
Original:  The problem of imbalanced linkage labelsis similar to that in image classification task, but thelatter is a particular problem in GCN-based clus-tering via linkage prediction.
Revised:  The problem of imbalanced linkage labels is similar to that in image classification task, but thelatter is a particular problem in GCN-based clus-tering via linkage prediction.
Edit intent: fluency
Original:  The problem of imbalanced linkage labelsis similar to that in image classification task, but thelatter is a particular problem in GCN-based clus-tering via linkage prediction.
Revised:  The problem of imbalanced linkage labelsis similar to that in image classification task, but the latter is a particular problem in GCN-based clus-tering via linkage prediction.
Edit intent: fluency
Original:  The problem of imbalanced linkage labelsis similar to that in image classification task, but thelatter is a particular problem in GCN-based clus-tering via linkage prediction.
Revised:  The problem of imbalanced linkage labelsis similar to that in image classification task, but thelatter is a particular problem in GCN-based clustering via linkage prediction.
Edit intent: fluency
Original:  Significantly biasedgraph representations in training can cause catas-trophic overfitting of a GCN model.
Revised:  Significantly biased graph representations in training can cause catas-trophic overfitting of a GCN model.
Edit intent: fluency
Original:  Significantly biasedgraph representations in training can cause catas-trophic overfitting of a GCN model.
Revised:  Significantly biasedgraph representations in training can cause catastrophic overfitting of a GCN model.
Edit intent: fluency
Original: Homography estimation is an important task in computer vision  , such as image stitching, video stabilization, and camera calibration.
Revised: Homography estimation is an important task in computer vision applications , such as image stitching, video stabilization, and camera calibration.
Edit intent: clarity
Original:  Traditional homography estimation methods heavily depend on the quantity and distribution of feature points , leading to poor robustness in textureless scenes.
Revised:  Traditional homography estimation methods heavily depend on the quantity and distribution of feature correspondences , leading to poor robustness in textureless scenes.
Edit intent: clarity
Original:  Traditional homography estimation methods heavily depend on the quantity and distribution of feature points , leading to poor robustness in textureless scenes.
Revised:  Traditional homography estimation methods heavily depend on the quantity and distribution of feature points , leading to poor robustness in low-texture scenes.
Edit intent: clarity
Original:  The learning solutions, on the contrary, try to learn robust deep features but demonstrate unsatisfying performance in the scenes of low overlap rates.
Revised:  The learning solutions, on the contrary, try to learn robust deep features but demonstrate unsatisfying performance in the scenes with low overlap rates.
Edit intent: clarity
Original:  In this paper, we address the two problems simultaneously , by designing a contextual correlation layer , which can capture the long-range correlation on feature maps and flexibly be bridged in a learning framework.
Revised:  In this paper, we address these two problems simultaneously , by designing a contextual correlation layer , which can capture the long-range correlation on feature maps and flexibly be bridged in a learning framework.
Edit intent: coherence
Original:  In this paper, we address the two problems simultaneously , by designing a contextual correlation layer , which can capture the long-range correlation on feature maps and flexibly be bridged in a learning framework.
Revised:  In this paper, we address the two problems simultaneously by designing a contextual correlation layer , which can capture the long-range correlation on feature maps and flexibly be bridged in a learning framework.
Edit intent: fluency
Original:  In this paper, we address the two problems simultaneously , by designing a contextual correlation layer , which can capture the long-range correlation on feature maps and flexibly be bridged in a learning framework.
Revised:  In this paper, we address the two problems simultaneously , by designing a contextual correlation layer , which can capture the long-range correlation within feature maps and flexibly be bridged in a learning framework.
Edit intent: clarity
Original:  In this paper, we address the two problems simultaneously , by designing a contextual correlation layer , which can capture the long-range correlation on feature maps and flexibly be bridged in a learning framework.
Revised:  In this paper, we address the two problems simultaneously , by designing a contextual correlation layer , which can capture the long-range correlation on feature maps and can be flexibly used in a learning framework.
Edit intent: clarity
Original:  Moreover, we equip our network with  depth perception capability, by introducing a novel depth-aware shape-preserved loss.
Revised:  Moreover, we equip our network with a depth perception capability, by introducing a novel depth-aware shape-preserved loss.
Edit intent: fluency
Original:  Extensive experiments demonstrate the superiority of our method over other state-of-the-art solutions in the synthetic benchmark dataset and real-world dataset.
Revised:  Extensive experiments demonstrate the superiority of our method over state-of-the-art solutions in the synthetic benchmark dataset and real-world dataset.
Edit intent: clarity
Original:  Inputs to hand gesture recognition systems can be in several forms, such as depth images, monocular RGB, or skeleton joint points .
Revised:  Inputs to hand gesture recognition and suggest several improvements .
Edit intent: clarity
Original:  We observe that raw depth images possess low contrasts in the hand regions of interest (ROI).
Revised:  We observe that raw depth images possess low contrast in the hand regions of interest (ROI).
Edit intent: fluency
Original:  They do not highlight important details to learn , such as finger bending information (whether a finger is overlapping the palm, or another finger).
Revised:  They do not highlight important fine details , such as finger bending information (whether a finger is overlapping the palm, or another finger).
Edit intent: clarity
Original:  They do not highlight important details to learn , such as finger bending information (whether a finger is overlapping the palm, or another finger).
Revised:  They do not highlight important details to learn , such as finger orientation, overlap between the finger and palm, or another finger).
Edit intent: clarity
Original:  We refer the reader to literature in the field of linear algebra for a much detailed introduction to the related fields.
Revised:  We refer the reader to literature in the field of linear algebra for a more detailed introduction to the related fields.
Edit intent: clarity
Original:  Keywords: Matrix decomposition, Computing process, Complexity, Floating point operations (flops), Low-rank approximation, Pivot, LU decomposition for nonzero leading principle minors, CRdecomposition, CUR/Skeleton decomposition, Biconjugate decomposition, Coordinate transformation, Hessenberg decomposition .
Revised:  Keywords: Existence and computing of matrix decompositions, Floating point operations (flops), Low-rank approximation, Pivot, LU decomposition for nonzero leading principle minors, CRdecomposition, CUR/Skeleton decomposition, Biconjugate decomposition, Coordinate transformation, Hessenberg decomposition .
Edit intent: clarity
Original:  Keywords: Matrix decomposition, Computing process, Complexity, Floating point operations (flops), Low-rank approximation, Pivot, LU decomposition for nonzero leading principle minors, CRdecomposition, CUR/Skeleton decomposition, Biconjugate decomposition, Coordinate transformation, Hessenberg decomposition .
Revised:  Keywords: Matrix decomposition, Computing process, Complexity, Floating point operations (flops), Low-rank approximation, Pivot, LU decomposition for nonzero leading principle minors, CRdecomposition, CUR/Skeleton decomposition, Coordinate transformation, Hessenberg decomposition .
Edit intent: fluency
Original: In recent years , the deep learning community has largely focused on the accuracy of deep generative models, resulting in impressive improvements in several research fields .
Revised: In recent years , the deep learning community has largely focused on the quality of deep generative models, resulting in impressive improvements in several research fields .
Edit intent: clarity
Original: In recent years , the deep learning community has largely focused on the accuracy of deep generative models, resulting in impressive improvements in several research fields .
Revised: In recent years , the deep learning community has largely focused on the accuracy of deep generative models, resulting in highly accurate and successful solutions .
Edit intent: clarity
Original:  However, this scientific race for quality comes at a tremendous computational cost, which incurs vast energy consumption and greenhouse gas emissions.
Revised:  However, this race for quality comes at a tremendous computational cost, which incurs vast energy consumption and greenhouse gas emissions.
Edit intent: clarity
Original:  If the current exponential growth of computational consumption persists, Artificial Intelligence (AI) will sadly become a considerable contributor to global warming.
Edit intent: coherence
Original:  Currently, researchers in the field of AI judge scientific works mostly based on the improvement in accuracy, log-likelihood, reconstruction or opinion scores, all of which entirely obliterates the actual computational cost of generative models.
Edit intent: coherence
Original:  In this paper, we introduce the idea of relying on a multi-objective measure based on Pareto optimality, which simultaneously integrates the models accuracy, as well as the environmental impact of their training .
Revised:  In this paper, we suggest relying on a multi-objective measure based on Pareto optimality, which simultaneously integrates the models accuracy, as well as the environmental impact of their training .
Edit intent: clarity
Original:  In this paper, we introduce the idea of relying on a multi-objective measure based on Pareto optimality, which simultaneously integrates the models accuracy, as well as the environmental impact of their training .
Revised:  In this paper, we introduce the idea of relying on a multi-objective measure based on Pareto optimality, which takes into account both the quality of the model and its energy consumption .
Edit intent: clarity
Original:  By applying this measure on the current state-of-the-art in generative audio models, we show that this measure drastically changes the perceived significance of the results in the field, encouraging optimal training techniques and resource allocation.
Revised:  By applying our measure on the current state-of-the-art in generative audio models, we show that this measure drastically changes the perceived significance of the results in the field, encouraging optimal training techniques and resource allocation.
Edit intent: fluency
Original:  By applying this measure on the current state-of-the-art in generative audio models, we show that this measure drastically changes the perceived significance of the results in the field, encouraging optimal training techniques and resource allocation.
Revised:  By applying this measure on the current state-of-the-art in generative audio models, we show that it can drastically change the significance of the results in the field, encouraging optimal training techniques and resource allocation.
Edit intent: clarity
Original:  We hope that this type of measure will be widely adopted, in order to help the community to better evaluate the significance of their work, while bringing computational cost -- and in fine carbon emissions -- in the spotlight of AI research.
Revised:  We hope that this type of metric can be widely used by the community to better evaluate the significance of their work, while bringing computational cost -- and in fine carbon emissions -- in the spotlight of AI research.
Edit intent: clarity