wanyu/IteraTeR_v2 huggingface dataset:
Original:  Extensive-form (that is, tree-form) games generalize normal-form games by modeling both sequential and simultaneous moves, as well as private information.
Revised:  Extensive-form games generalize normal-form games by modeling both sequential and simultaneous moves, as well as private information.
Edit intent: coherence
Original:  Extensive-form (that is, tree-form) games generalize normal-form games by modeling both sequential and simultaneous moves, as well as private information.
Revised:  Extensive-form (that is, tree-form) games generalize normal-form games by modeling both sequential and simultaneous moves, as well as imperfect information.
Edit intent: clarity
Original:  Because of the sequential nature and presence of partial information in the game, extensive-form correlation possesses significantly different properties than the normal-form counterpart , many of which are still open research directions.
Revised:  Because of the sequential nature and presence of private information in the game, extensive-form correlation possesses significantly different properties than the normal-form counterpart , many of which are still open research directions.
Edit intent: clarity
Original:  Because of the sequential nature and presence of partial information in the game, extensive-form correlation possesses significantly different properties than the normal-form counterpart , many of which are still open research directions.
Revised:  Because of the sequential nature and presence of partial information in the game, correlation in extensive-form games possesses significantly different properties than the normal-form counterpart , many of which are still open research directions.
Edit intent: clarity
Original:  Because of the sequential nature and presence of partial information in the game, extensive-form correlation possesses significantly different properties than the normal-form counterpart , many of which are still open research directions.
Revised:  Because of the sequential nature and presence of partial information in the game, extensive-form correlation possesses significantly different properties than its counterpart in normal-form games , many of which are still open research directions.
Edit intent: clarity
Original:  In this article, we give the first uncoupled no-regret dynamics that converge with high probability to the set of EFCEs in n-player general-sum extensive-form games with perfect recall.
Revised:  In this article, we give the first uncoupled no-regret dynamics that converge to the set of EFCEs in n-player general-sum extensive-form games with perfect recall.
Edit intent: clarity
Original: A back-end model is a key element of modern speaker verification systems.
Edit intent: clarity
Original:  In this paper , we propose a novel attention-based back-end model, which can be used for both text-independent (TI) and text-dependent (TD) speaker verification with multiple enrollment utterances , and employ scaled-dot self-attention and feed-forward self-attention networks as architectures that learn the intra-relationships of the enrollment utterances.
Revised:  In this paper , we propose a novel attention back-end model, which can be used for both text-independent (TI) and text-dependent (TD) speaker verification with multiple enrollment utterances , and employ scaled-dot self-attention and feed-forward self-attention networks as architectures that learn the intra-relationships of the enrollment utterances.
Edit intent: clarity
Original:  In this paper , we propose a novel attention-based back-end model, which can be used for both text-independent (TI) and text-dependent (TD) speaker verification with multiple enrollment utterances , and employ scaled-dot self-attention and feed-forward self-attention networks as architectures that learn the intra-relationships of the enrollment utterances.
Revised:  In this paper , we propose a novel attention-based back-end model, which can be used for both text-independent (TI) and text-dependent (TD) speaker verification , and employ scaled-dot self-attention and feed-forward self-attention networks as architectures that learn the intra-relationships of the enrollment utterances.
Edit intent: clarity
Original:  which are time delay neural network (TDNN ) and ResNettrained using the additive-margin-based softmax loss and the uniform loss, and compare them with the conventional PLDA or cosine scoring approaches . Experimental results on a multi-genre dataset called CN-Celeb show that the performance of our proposed approach outperforms PLDA scoring with TDNN and cosine scoring with ResNet by around 14.1\% and 7.8\% in relative EER, respectively.
Revised:  which are time delay neural network (TDNN ) and ResNettrained using the additive-margin-based softmax loss and the uniform loss, and compare them with the conventional PLDA or cosine scoring approaches . Experimental results using multiple enrollment utterances on CNCeleb show that the performance of our proposed approach outperforms PLDA scoring with TDNN and cosine scoring with ResNet by around 14.1\% and 7.8\% in relative EER, respectively.
Edit intent: clarity
Original: Dietary supplements (DS) have been widely used by consumers, but the information around the effectiveness and safety of DS is disparate or incomplete, making barriers to consumers to find information effectively.
Revised: Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, making barriers to consumers to find information effectively.
Edit intent: fluency
Original: Dietary supplements (DS) have been widely used by consumers, but the information around the effectiveness and safety of DS is disparate or incomplete, making barriers to consumers to find information effectively.
Revised: Dietary supplements (DS) have been widely used by consumers, but the information around the effectiveness and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively.
Edit intent: coherence
Original:  Conversational agent  systems have been applied to the healthcare domain  but there is no such a system to answer consumers regarding DS use, although widespread use of the dietary supplement .
Revised:  Conversational agent systems have been applied to the healthcare domain , but there is no such a system to answer consumers regarding DS use, although widespread use of the dietary supplement .
Edit intent: fluency
Original:  Conversational agent  systems have been applied to the healthcare domain  but there is no such a system to answer consumers regarding DS use, although widespread use of the dietary supplement .
Revised:  Conversational agent systems have been applied to the healthcare domain but there is no such a system to answer consumers regarding DS use, although widespread use of DS .
Edit intent: clarity
Original:  In this study, we develop the first conversational agent system for DS use .
Revised:  In this study, we develop the first CA system for DS use .
Edit intent: clarity
Original:  In this study, we develop the first conversational agent system for DS use .
Revised:  In this study, we develop the first conversational agent system for DS use 
Edit intent: fluency
Original:  The second one controls the L^2\times H^1 total error and utilizes the postprocessed moment field and deflection and superconvergence analysis in both variables .
Revised:  The second one controls the L^2\times H^1 total error and utilizes superconvergent postprocessed moment field and deflection and superconvergence analysis in both variables .
Edit intent: clarity
Original:  The second one controls the L^2\times H^1 total error and utilizes the postprocessed moment field and deflection and superconvergence analysis in both variables .
Revised:  The second one controls the L^2\times H^1 total error and utilizes the postprocessed moment field and deflection .
Edit intent: clarity
Original: This paper proposes a computational technique based on ``deep unfolding'' to solving the finite-time maximum hands-off control problem for discrete-time nonlinear stochastic systems.
Revised: This paper proposes a computational technique based on "deep unfolding" to solving the finite-time maximum hands-off control problem for discrete-time nonlinear stochastic systems.
Edit intent: fluency
Original: The paper presents a presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots.
Revised: The paper presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots.
Edit intent: fluency
Original:  The system was developed for the Challenge 3 of the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC).
Revised:  The system was developed to address Challenge 3 of the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC).
Edit intent: clarity
Original:  The system was developed for the Challenge 3 of the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC).
Revised:  The system was developed for the Challenge 3 of the 2020Mohamed Bin Zayed International Robotics Challenge (MBZIRC).
Edit intent: fluency
Original:  The challenge required to autonomously detect, locate and extinguish fires in different floors of a building, as well as in the surroundings.
Revised:  The challenge required to autonomously detect, locate and extinguish fires on different floors of a building, as well as in the surroundings.
Edit intent: fluency
Original:  The challenge required to autonomously detect, locate and extinguish fires in different floors of a building, as well as in the surroundings.
Revised:  The challenge required to autonomously detect, locate and extinguish fires in different floors of a building, as well as in its surroundings.
Edit intent: clarity
Original:  The paper describes the main hardware and software components for UAV and UGV platforms.
Revised:  We describe the main hardware and software components for UAV and UGV platforms.
Edit intent: clarity
Original:  The paper finally describes the results obtained during  competition, where the system worked fully autonomously and scored in all the trials performed.
Revised:  The paper finally describes the results obtained during the competition, where the system worked fully autonomously and scored in all the trials performed.
Edit intent: fluency
Original:  In addition to its superior accuracyin propagating means and covariances, the GenUT uses the same order of calculations as most unscented transforms that guarantee third-order accuracy, which makes it applicable to a wide variety of applications , including the assimilation of observations in the modeling of the coronavirus (SARS-CoV-2) causing COVID-19.
Revised:  In addition to its superior accuracyin propagating means and covariances, the GenUT uses the same order of calculations as most unscented transforms that guarantee third-order accuracy, which makes it applicable to a wide variety of applications , including the assimilation of observations in the modeling of infectious diseases such as coronavirus (SARS-CoV-2) causing COVID-19.
Edit intent: clarity
Original:  Dual Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system.
Revised:  Dual-Faceted Linearization uses a set of effective observables for low-order lifting linearization, but the method requires knowledge of the physical structure of the nonlinear system.
Edit intent: fluency
Original: False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grid .
Revised: False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids .
Edit intent: fluency
Original:  First, we present a generic, localized, and stealth (unobservable) attack generation methodology and a publicly accessible dataset for researchers to develop and test their algorithms.
Revised:  First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms.
Edit intent: fluency
Original:  Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement data .
Revised:  Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement .
Edit intent: fluency
Original:  It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in  literature that our algorithm outperforms the best available solutions by 6.21%DIFDELCMD < \\%%% %DIF < , 0.69\\%, and 2.73\\% in detection rate and by 3.65%DIFDELCMD < \\%%% %DIF < , 0.34\\% and 1.38\\% in F1 score for standard IEEE  testbeds with 14, 118, and 300 buses, respectively.
Revised:  It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 6.21%DIFDELCMD < \\%%% %DIF < , 0.69\\%, and 2.73\\% in detection rate and by 3.65%DIFDELCMD < \\%%% %DIF < , 0.34\\% and 1.38\\% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.
Edit intent: fluency
Original:  It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in  literature that our algorithm outperforms the best available solutions by 6.21%DIFDELCMD < \\%%% %DIF < , 0.69\\%, and 2.73\\% in detection rate and by 3.65%DIFDELCMD < \\%%% %DIF < , 0.34\\% and 1.38\\% in F1 score for standard IEEE  testbeds with 14, 118, and 300 buses, respectively.
Revised:  It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in literature that our algorithm outperforms the best available solutions by 6.21%DIFDELCMD < \\%%% %DIF < , 0.69\\%, and 2.73\\% in %DIFDELCMD < \\%%% %DIF < , 0.34\\% and 1.38\\% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.
Edit intent: clarity
Original:  This work examines the impact of various techniques -- model architectures, training criteria, decoding hyperparameters, and endpointer parameters -- on UPL.
Revised:  This work examines the impact of various techniques - model architectures, training criteria, decoding hyperparameters, and endpointer parameters -- on UPL.
Edit intent: fluency
Original:  This work examines the impact of various techniques -- model architectures, training criteria, decoding hyperparameters, and endpointer parameters -- on UPL.
Revised:  This work examines the impact of various techniques -- model architectures, training criteria, decoding hyperparameters, and endpointer parameters - on UPL.
Edit intent: fluency
Original:  Instead, we find that factors affecting token emission latency, and endpointing behavior significantly impact on UPL.
Revised:  Instead, we find that factors affecting token emission latency, and endpointing behavior have a larger impact on UPL.
Edit intent: clarity
Original:  We achieve the best trade-off between latency and word error rate when performing ASR jointly with endpointing, and using the recently proposed alignment regularization  .
Revised:  We achieve the best trade-off between latency and word error rate when performing ASR jointly with endpointing, while utilizing the recently proposed alignment regularization .
Edit intent: coherence
Original:  We achieve the best trade-off between latency and word error rate when performing ASR jointly with endpointing, and using the recently proposed alignment regularization  .
Revised:  We achieve the best trade-off between latency and word error rate when performing ASR jointly with endpointing, and using the recently proposed alignment regularization mechanism .
Edit intent: clarity
Original:  It facilitates the sharing between users and locations.
Revised:  It facilitates the relation modeling between users and locations.
Edit intent: clarity
Original:  Recently, researchers tend to recommend POIs by long- and short-term interests  .
Revised:  Recently, researchers recommend POIs by long- and short-term interests .
Edit intent: clarity
Original:  A few works try to acquire  user's mobility periodicity or POI's geographical influence, but they omit some other spatial-temporal factors .
Revised:  A few works try to acquire such kind of periodicity by user's mobility periodicity or POI's geographical influence, but they omit some other spatial-temporal factors .
Edit intent: clarity
Original:  A few works try to acquire  user's mobility periodicity or POI's geographical influence, but they omit some other spatial-temporal factors .
Revised:  A few works try to acquire user's mobility status or time slot, which limits the performance of periodic interest .
Edit intent: clarity
Original:  To this end, we propose to jointly model various spatial-temporal factors by context-aware non-successive modeling.
Revised:  To this end, we propose to learn spatial-temporal factors by context-aware non-successive modeling.
Edit intent: clarity
Original:  In the short-term module, we construct four short-term sequences to acquire sequential, spatial, temporal, and spatial-temporal effects , respectively.
Revised:  In the short-term module, we construct various short-term sequences to acquire sequential, spatial, temporal, and spatial-temporal effects , respectively.
Edit intent: clarity
Original:  In the short-term module, we construct four short-term sequences to acquire sequential, spatial, temporal, and spatial-temporal effects , respectively.
Revised:  In the short-term module, we construct four short-term sequences to acquire the spatial-temporal effects , respectively.
Edit intent: clarity
Original:  Attention of interest-level is used to combine all factors and interests.
Revised:  Finally, we apply inter-level attention to automatically integrate multiple interests.
Edit intent: clarity
Original:  First, we analyse which countries behaved similarly in 2020 with respect to one of three multivariate time series: daily COVID-19 cases, community mobility data and national equity index price.
Revised:  First, we analyze which countries behaved similarly in 2020 with respect to one of three multivariate time series: daily COVID-19 cases, community mobility data and national equity index price.
Edit intent: fluency
Original:  First, we analyse which countries behaved similarly in 2020 with respect to one of three multivariate time series: daily COVID-19 cases, community mobility data and national equity index price.
Revised:  First, we analyse which countries behaved similarly in 2020 with respect to one of three multivariate time series: daily COVID-19 cases, Apple mobility data and national equity index price.
Edit intent: fluency
Original:  Next, we study the trajectories of all three of these attributes in conjunction to determine which attributes exhibited greater similarity.
Revised:  Next, we study the trajectories of all three of these attributes in conjunction to determine which exhibited greater similarity.
Edit intent: fluency
Original:  Our results indicate that mobility data and  financial indices exhibited the most similarity in their trajectories, with financial markets responding quicker.
Revised:  Our results indicate that mobility data and financial indices exhibited the most similarity in their trajectories, with financial indices responding quicker.
Edit intent: clarity
Original:  This suggests that financial market participants  interpreted and responded to COVID-19 data more efficiently than governments.
Revised:  This suggests that financial market participants may have interpreted and responded to COVID-19 data more efficiently than governments.
Edit intent: clarity
Original:  Thus, we suggest that efforts to study economic data as a leading indicator for financial market performance during the pandemic were misguided.
Revised:  Further, results imply that efforts to study economic data as a leading indicator for financial market performance during the pandemic were misguided.
Edit intent: clarity
Original:  Although the iterative training scheme can improve performance, it will also increase the training time.
Revised:  Although the iterative training scheme can partially solve this problem while also costing more training time.
Edit intent: clarity
Original:  In this work , we revisit the overfitting problem in recent CTC-based CSLR works and attribute it to the insufficient training of the feature extractor .
Revised:  In this study , we revisit the overfitting problem in recent CTC-based CSLR works and attribute it to the insufficient training of the feature extractor .
Edit intent: clarity
Original:  In this work , we revisit the overfitting problem in recent CTC-based CSLR works and attribute it to the insufficient training of the feature extractor .
Revised:  In this work , we revisit the overfitting problem in recent CTC-based CSLR works and realize that sufficient training of the feature extractor .
Edit intent: clarity
Original:  To solve this problem , we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with more alignment supervision.
Revised:  To solve this problem , we propose a Visual Alignment Constraint (VAC) to enhance the feature extractor with alignment supervision.
Edit intent: clarity
Original:  Specifically, the proposed VAC is composed of two auxiliary losses: one makes predictions based on visual features only, and the other aligns short-term visual and long-term contextual features .
Revised:  Specifically, the proposed VAC comprises two auxiliary losses: one makes predictions based on visual features only, and the other aligns short-term visual and long-term contextual features .
Edit intent: clarity
Original:  Specifically, the proposed VAC is composed of two auxiliary losses: one makes predictions based on visual features only, and the other aligns short-term visual and long-term contextual features .
Revised:  Specifically, the proposed VAC is composed of two auxiliary losses: one focuses on visual features only, and the other aligns short-term visual and long-term contextual features .
Edit intent: clarity
Original:  Moreover, we further propose two metrics to evaluate the contributions of the feature extractor and the alignment model, which provide evidence for the overfitting problem.
Revised:  Moreover, we propose two metrics to evaluate the contributions of the feature extractor and the alignment model, which provide evidence for the overfitting problem.
Edit intent: coherence
Original:  To improve the adversarial robustness of a classifier, we include an abstaining option, where the classifier abstains from taking a decision when it has low confidence about the prediction.
Revised:  To improve the adversarial robustness of a classifier, we include an abstain option, where the classifier abstains from taking a decision when it has low confidence about the prediction.
Edit intent: fluency
Original:  To improve the adversarial robustness of a classifier, we include an abstaining option, where the classifier abstains from taking a decision when it has low confidence about the prediction.
Revised:  To improve the adversarial robustness of a classifier, we include an abstaining option, where the classifier abstains from making a decision when it has low confidence about the prediction.
Edit intent: fluency