wanyu/IteraTeR_v2 huggingface dataset:
Original:  To this end, the resource optimization problem, i.e., resource block (RB ) and power allocation,  are studied for UAV-assisted 5G networks to meet the objective of jointly maximizing the average sum-rate and minimizing the transmit power of UAV while satisfying the URLLC requirements.
Revised:  To this end, the resource optimization problem, i.e., resource block (RB ) and power allocation, are studied for UAV-assisted 5G networks to jointly maximize the average sum-rate and minimizing the transmit power of UAV while satisfying the URLLC requirements.
Edit intent: clarity
Original:  To this end, the resource optimization problem, i.e., resource block (RB ) and power allocation,  are studied for UAV-assisted 5G networks to meet the objective of jointly maximizing the average sum-rate and minimizing the transmit power of UAV while satisfying the URLLC requirements.
Revised:  To this end, the resource optimization problem, i.e., resource block (RB ) and power allocation, are studied for UAV-assisted 5G networks to meet the objective of jointly maximizing the average sum-rate and minimize the transmit power of UAV while satisfying the URLLC requirements.
Edit intent: fluency
Original:  To cope with the sporadic URLLC traffic problem, an efficient online URLLC traffic prediction model based on Gaussian Process Regression (GPR) is proposed to derive optimal URLLC scheduling and transmit power strategy.
Revised:  To cope with the sporadic URLLC traffic problem, an efficient online URLLC traffic prediction model based on Gaussian Process Regression (GPR) is proposed which derives optimal URLLC scheduling and transmit power strategy.
Edit intent: coherence
Original:  Finally, simulation results are provided to show the efficacy of our proposed solution approach  .
Revised:  Finally, simulation results are provided to show our proposed solution approach .
Edit intent: clarity
Original:  Finally, simulation results are provided to show the efficacy of our proposed solution approach  .
Revised:  Finally, simulation results are provided to show the efficacy of our proposed solution approach 's efficiency .
Edit intent: clarity
Original:  We present a code generation technique that lowers an AST to binary code  by stitching together code from a large library of binary AST node implementations .
Revised:  We present a code generation technique that lowers an AST to binary code , by stitching together code from a large library of binary AST node implementations .
Edit intent: fluency
Original:  We present a code generation technique that lowers an AST to binary code  by stitching together code from a large library of binary AST node implementations .
Revised:  We present a code generation technique that lowers an AST to binary code by stitching together code from a large library of binary implementation variants .
Edit intent: clarity
Original:  We show how to construct such a stencil library and describe the copy-and-patch technique that generates optimized binary code.
Revised:  We show how to construct a stencil library and describe the copy-and-patch technique that generates optimized binary code.
Edit intent: clarity
Original:  We show how to construct such a stencil library and describe the copy-and-patch technique that generates optimized binary code.
Revised:  We show how to construct such a stencil library and describe the copy-and-patch algorithm that generates optimized binary code.
Edit intent: clarity
Original:  The result is a code generator with negligible cost: it produces code from an AST in less time than it takes to construct the AST. Compared to LLVM, compilation is two orders of magnitude faster than  -O0 and three orders of magnitude faster than higher optimization levels.
Revised:  The result is a code generator with negligible cost: it produces code from an AST in less time than it takes to construct the AST. Compared to LLVM, compilation is two orders of magnitude faster than LLVM -O0 and three orders of magnitude faster than higher optimization levels.
Edit intent: coherence
Original:  To train this model, we investigate the widely used Permutation Invariant Training (PIT) approach and the recently introduced Heuristic Error Assignment Training (HEAT) approach.
Revised:  To train this model, we investigate the widely used Permutation Invariant Training (PIT) approach and the Heuristic Error Assignment Training (HEAT) approach.
Edit intent: clarity
Original:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and  it achieves a 47 \% improvement over the benchmark \\%DIF > improvement over the benchmark success rate, even in very large-scale instances that are 100x \times100 larger than the training instances.
Revised:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and that it achieves a 47 \% improvement over the benchmark \\%DIF > improvement over the benchmark success rate, even in very large-scale instances that are 100x \times100 larger than the training instances.
Edit intent: fluency
Original:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and  it achieves a 47 \% improvement over the benchmark \\%DIF > improvement over the benchmark success rate, even in very large-scale instances that are 100x \times100 larger than the training instances.
Revised:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and it achieves a 47 \\%DIF > improvement over the benchmark success rate, even in very large-scale instances that are 100x \times100 larger than the training instances.
Edit intent: fluency
Original:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and  it achieves a 47 \% improvement over the benchmark \\%DIF > improvement over the benchmark success rate, even in very large-scale instances that are 100x \times100 larger than the training instances.
Revised:  Experiments demonstrate that our model is able to generalize well in previously unseen problem instances, and it achieves a 47 \% improvement over the benchmark \\%DIF > improvement over the benchmark success rate, even in very large-scale instances that are \times100 larger than the training instances.
Edit intent: clarity
Original:  The autonomous features of the fleet elements of the delivery network are enabled by  increased throughput, improved coverage, and near-user computation capabilities of vertical heterogeneous networks (VHetNets).
Revised:  The autonomous features of fleet elements of the delivery network are enabled by increased throughput, improved coverage, and near-user computation capabilities of vertical heterogeneous networks (VHetNets).
Edit intent: fluency
Original:  The autonomous features of the fleet elements of the delivery network are enabled by  increased throughput, improved coverage, and near-user computation capabilities of vertical heterogeneous networks (VHetNets).
Revised:  The autonomous features of the fleet elements of the delivery network are enabled by the increased throughput, improved coverage, and near-user computation capabilities of vertical heterogeneous networks (VHetNets).
Edit intent: fluency
Original:  A high altitude platform station (HAPS), located around 20~km above  ground level in a quasi-stationary manner, serves as the main enabler of the vision we present.
Revised:  A high altitude platform station (HAPS), located around 20~km above the ground level in a quasi-stationary manner, serves as the main enabler of the vision we present.
Edit intent: fluency
Original: Rapid and affordable methods of testing for COVID-19 infection are essential to manage infection rates and prevent medical facilities from becoming overwhelmed.
Revised: Rapid and affordable methods of testing for COVID-19 infections are essential to manage infection rates and prevent medical facilities from becoming overwhelmed.
Edit intent: fluency
Original: Rapid and affordable methods of testing for COVID-19 infection are essential to manage infection rates and prevent medical facilities from becoming overwhelmed.
Revised: Rapid and affordable methods of testing for COVID-19 infection are essential to reduce infection rates and prevent medical facilities from becoming overwhelmed.
Edit intent: clarity
Original:   This study demonstrates that crowdsourced cough audio samples acquired on smartphones across the world and paired with COVID-19 status labels can be used to develop an AI algorithm that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Revised:  This study demonstrates that crowdsourced cough audio samples acquired on smartphones from around the world can be used to develop an AI algorithm that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Edit intent: clarity
Original:   This study demonstrates that crowdsourced cough audio samples acquired on smartphones across the world and paired with COVID-19 status labels can be used to develop an AI algorithm that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Revised:  This study demonstrates that crowdsourced cough audio samples acquired on smartphones across the world and paired with COVID-19 status labels can be used to develop a AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Edit intent: clarity
Original:  Furthermore, this AI algorithm is able to generalize to crowdsourced samples from Latin America and clinical samples from South Asia, without further training using the specific samples  .
Revised:  Furthermore, we show that our method is able to generalize to crowdsourced samples from Latin America and clinical samples from South Asia, without further training using the specific samples .
Edit intent: clarity
Original:  Furthermore, this AI algorithm is able to generalize to crowdsourced samples from Latin America and clinical samples from South Asia, without further training using the specific samples  .
Revised:  Furthermore, this AI algorithm is able to generalize to crowdsourced samples from Latin America and clinical samples from South Asia, without further training using the specific samples from those regions .
Edit intent: clarity
Original:  This study demonstrates that crowdsourced cough audio samples  acquired on smartphones from around the world can be used to develop a AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Revised:  This study demonstrates that crowdsourced cough audio samples recorded and acquired on smartphones from around the world can be used to develop a AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Edit intent: clarity
Original:  This study demonstrates that crowdsourced cough audio samples  acquired on smartphones from around the world can be used to develop a AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Revised:  This study demonstrates that crowdsourced cough audio samples acquired on smartphones from around the world can be used to develop an AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1\% (75.2\%-78.3\%).
Edit intent: fluency
Original: NeurIPS 2020 requested that research paper submissions include impact statements on ' potential nefarious uses and the consequences of failure.
Revised: NeurIPS 2020 requested that research paper submissions include impact statements on " potential nefarious uses and the consequences of failure.
Edit intent: fluency
Original:  ' When researching, designing, and implementing systems , a key challenge to anticipating risks , however, is to overcome what Clarke (1962) called 'failures of imagination.'
Revised:  " However, as researchers, practitioners and system designers , a key challenge to anticipating risks , however, is to overcome what Clarke (1962) called 'failures of imagination.'
Edit intent: clarity
Original:  ' When researching, designing, and implementing systems , a key challenge to anticipating risks , however, is to overcome what Clarke (1962) called 'failures of imagination.'
Revised:  ' When researching, designing, and implementing systems , a key challenge to anticipating risks is overcoming what Clarke (1962) called 'failures of imagination.'
Edit intent: coherence
Original:  The prevalent notion of computational harms -- narrowly construed as either allocational or representational harms -- does not fully capture the context dependent and unobservable nature of harms across the wide range of AI infused systems.
Revised:  The prevalent notion of computational harms -- narrowly construed as either allocational or representational harms -- does not fully capture the open, context dependent, and unobservable nature of harms across the wide range of AI infused systems.
Edit intent: clarity
Original: The current literature primarily addresses only a small range of examples of harms to motivate algorithmic fixes, overlooking the wider scope of probable harms and the way these harms may affect different stakeholders.
Revised: The current literature focuses on a small range of examples of harms to motivate algorithmic fixes, overlooking the wider scope of probable harms and the way these harms may affect different stakeholders.
Edit intent: clarity
Original: The current literature primarily addresses only a small range of examples of harms to motivate algorithmic fixes, overlooking the wider scope of probable harms and the way these harms may affect different stakeholders.
Revised: The current literature primarily addresses only a small range of examples of harms to motivate algorithmic fixes, overlooking the wider scope of probable harms and the way these harms might affect different stakeholders.
Edit intent: fluency
Original:  The system affordances and possible usage scenarios may also exacerbate harms in unpredictable ways, as they determine stakeholders' control(including  non-users) over how they  interact with a system output.
Revised:  The system affordances may also exacerbate harms in unpredictable ways, as they determine stakeholders' control(including non-users) over how they interact with a system output.
Edit intent: clarity
Original:  The system affordances and possible usage scenarios may also exacerbate harms in unpredictable ways, as they determine stakeholders' control(including  non-users) over how they  interact with a system output.
Revised:  The system affordances and possible usage scenarios may also exacerbate harms in unpredictable ways, as they determine stakeholders' control(including of non-users) over how they interact with a system output.
Edit intent: clarity
Original:  The system affordances and possible usage scenarios may also exacerbate harms in unpredictable ways, as they determine stakeholders' control(including  non-users) over how they  interact with a system output.
Revised:  The system affordances and possible usage scenarios may also exacerbate harms in unpredictable ways, as they determine stakeholders' control(including non-users) over how they use and interact with a system output.
Edit intent: clarity
Original:  To effectively assist in anticipating and identifying harmful uses, we argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances , uses, and outputs , as well as viable proxies for assessing harms in the widest sense.
Revised:  To effectively assist in anticipating harmful uses, we argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances , uses, and outputs , as well as viable proxies for assessing harms in the widest sense.
Edit intent: clarity
Original:  To effectively assist in anticipating and identifying harmful uses, we argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances , uses, and outputs , as well as viable proxies for assessing harms in the widest sense.
Revised:  To effectively assist in anticipating and identifying harmful uses, we argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances , as well as viable proxies for assessing harms in the widest sense.
Edit intent: clarity
Original:  Since their breakthrough result in 2012, no improvement upon the O(k^3) bound has been found .
Revised:  Since their breakthrough result in 2012, no improvement upon the O(k^3) have been shown .
Edit intent: clarity
Original:  In this paper, we interpret Kratsch and Wahlstr\"om's result through the lens of Bollob\'as's Two-Families Theorem from extremal combinatorics .
Revised:  In this paper, we draw inspiration from the renowned Bollob\'as's Two-Families Theorem from extremal combinatorics .
Edit intent: clarity
Original:  This is specifically true in ticketing systems where prediction of the type and subtype of tickets given text to optimal routing is a multi billion dollar industry.
Revised:  This is specifically true in ticketing systems where prediction of the type and subtype of tickets given new incoming ticket text to find out optimal routing is a multi billion dollar industry.
Edit intent: clarity
Original:  By novel use of any text search engine the authors stumbled upon an industrial algorithm class which can accurately ( 86 \% and above ) predict classification of any text \\%DIF > and above ) predict classification of any text given prior labelled text data .
Revised:  In this paper authors describe a class of industrial standard algorithms which can accurately ( 86 \% and above ) predict classification of any text \\%DIF > and above ) predict classification of any text given prior labelled text data .
Edit intent: clarity
Original:  By novel use of any text search engine the authors stumbled upon an industrial algorithm class which can accurately ( 86 \% and above ) predict classification of any text \\%DIF > and above ) predict classification of any text given prior labelled text data .
Revised:  By novel use of any text search engine the authors stumbled upon an industrial algorithm class which can accurately ( 86 \\%DIF > and above ) predict classification of any text given prior labelled text data .
Edit intent: fluency
Original:  The proposed methods were  compared with established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments.
Revised:  The proposed methods were thoroughly compared with established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments.
Edit intent: clarity
Original:  It was found that the proposed robust ellipse detection was superior to  the popular least median of squares method in both simulated and real-world datasets.
Revised:  It was found that the proposed robust ellipse detection was superior to the popular least median of squares , in both simulated and real-world datasets.
Edit intent: clarity
Original:  The results  show promise for the application of the proposed methods for automatic extraction of circular targets from images and mechanical pipes from point clouds.
Revised:  The results show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.
Edit intent: clarity
Original:  The new methods include a robust Monte Carlo-based approach for detecting points following elliptical patterns from outliers;
Revised:  The methods introduce new developments in: (i) robust Monte Carlo-based approach for detecting points following elliptical patterns from outliers;
Edit intent: clarity
Original:  and procedure to detect cylinders from three-dimensional point clouds.
Revised:  and (iii) extraction of cylinder from 3D point clouds.
Edit intent: clarity
Original:  The proposed process for detecting non-overlapping ellipses outperformed three established methods, proposed by Fornaciari, Patraucean, and Pangiotakis, in images .
Revised:  The proposed process for detecting non-overlapping ellipses outperformed three established methods, proposed by Fornaciari, Patraucean, and Panagiotakis, respectively .
Edit intent: clarity
Original:  Our second contribution, therefore, is to clarify weighted ensemble's properties by providing a lower bound on the asymptotic variance of the method 's estimates.
Revised:  Our second contribution, therefore, is to clarify weighted ensemble's properties by providing a lower bound on the asymptotic variance of weighted ensemble 's estimates.
Edit intent: clarity
Original:  We give a constructive proof and numerical examples to show  weighted ensemble can approach this optimal variance bound, in many cases reducing the variance of MCMC estimates by multiple orders of magnitude.
Revised:  Third, we give a constructive proof and numerical examples to show weighted ensemble can approach this optimal variance bound, in many cases reducing the variance of MCMC estimates by multiple orders of magnitude.
Edit intent: coherence
Original:  We give a constructive proof and numerical examples to show  weighted ensemble can approach this optimal variance bound, in many cases reducing the variance of MCMC estimates by multiple orders of magnitude.
Revised:  We give a constructive proof and numerical examples to show that weighted ensemble can approach this optimal variance bound, in many cases reducing the variance of MCMC estimates by multiple orders of magnitude.
Edit intent: fluency
Original:  However, in reality, agents may have some preference intensities or cardinal utilities that, e.g., indicate that they like an an object much more than another object, and not taking these into account can result in a loss in welfare.
Revised:  However, in reality, agents may have some preference intensities or cardinal utilities that, e.g., indicate that they like an object much more than another object, and not taking these into account can result in a loss in welfare.
Edit intent: fluency
Original: Graph neural networks  are able to achieve promising performance on multiple graph downstream tasks such as node classification and link prediction.
Revised: Graph neural networks (GNNs) are able to achieve promising performance on multiple graph downstream tasks such as node classification and link prediction.
Edit intent: clarity
Original:  Comparatively lesser work has been done to design graph neural networks (GNNs ) which can operate directly for community detection in a graph . Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities.
Revised:  Comparatively lesser work has been done to design GNNs which can operate directly for community detection in a graph . Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities.
Edit intent: clarity
Original:  Comparatively lesser work has been done to design graph neural networks (GNNs ) which can operate directly for community detection in a graph . Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities.
Revised:  Comparatively lesser work has been done to design graph neural networks (GNNs ) which can operate directly for community detection on graphs . Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities.
Edit intent: clarity
Original:  Design of an unsupervised community detection loss function to train a GNN  is a fundamental challenge to propose an integrated solution .
Revised:  Designing an unsupervised loss function to train a GNN is a fundamental challenge to propose an integrated solution .
Edit intent: clarity