wanyu/IteraTeR_v2 huggingface dataset:
Original:  In addition, it can be shown that by fine-tuning thetrained BERT model on these two datasets by applying different transfer learn-ing scenarios via retraining partial or alllayers the predictive scores improve com-pared to simply applying the model pre-trained on the JIGSAW data set.
Revised:  In addition, it can be shown that by fine-tuning the trained BERT model on these two datasets by applying different transfer learn-ing scenarios via retraining partial or alllayers the predictive scores improve com-pared to simply applying the model pre-trained on the JIGSAW data set.
Edit intent: fluency
Original:  In addition, it can be shown that by fine-tuning thetrained BERT model on these two datasets by applying different transfer learn-ing scenarios via retraining partial or alllayers the predictive scores improve com-pared to simply applying the model pre-trained on the JIGSAW data set.
Revised:  In addition, it can be shown that by fine-tuning thetrained BERT model on these two datasets by applying different transfer learning scenarios via retraining partial or alllayers the predictive scores improve com-pared to simply applying the model pre-trained on the JIGSAW data set.
Edit intent: fluency
Original:  In addition, it can be shown that by fine-tuning thetrained BERT model on these two datasets by applying different transfer learn-ing scenarios via retraining partial or alllayers the predictive scores improve com-pared to simply applying the model pre-trained on the JIGSAW data set.
Revised:  In addition, it can be shown that by fine-tuning thetrained BERT model on these two datasets by applying different transfer learn-ing scenarios via retraining partial or all layers the predictive scores improve com-pared to simply applying the model pre-trained on the JIGSAW data set.
Edit intent: fluency
Original:  In addition, it can be shown that by fine-tuning thetrained BERT model on these two datasets by applying different transfer learn-ing scenarios via retraining partial or alllayers the predictive scores improve com-pared to simply applying the model pre-trained on the JIGSAW data set.
Revised:  In addition, it can be shown that by fine-tuning thetrained BERT model on these two datasets by applying different transfer learn-ing scenarios via retraining partial or alllayers the predictive scores improve compared to simply applying the model pre-trained on the JIGSAW data set.
Edit intent: fluency
Original:  Withour results, we get precisions from 64\% toaround 90\% while still achieving accept-able recall values of at least lower 60s\%, proving that BERT is suitable for real usecases in social platforms.
Revised:  Withour results, we get precisions from 64\% to around 90\% while still achieving accept-able recall values of at least lower 60s\%, proving that BERT is suitable for real usecases in social platforms.
Edit intent: fluency
Original:  Withour results, we get precisions from 64\% toaround 90\% while still achieving accept-able recall values of at least lower 60s\%, proving that BERT is suitable for real usecases in social platforms.
Revised:  Withour results, we get precisions from 64\% toaround 90\% while still achieving acceptable recall values of at least lower 60s\%, proving that BERT is suitable for real usecases in social platforms.
Edit intent: fluency
Original:  In this work , a BERT based neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in order to create a model identifying hateful and toxic comments (strictly seperated from offensive language) in online social platforms (English language), inthis case Twitter.
Revised:  In this work , a BERT based neural network model is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in order to create a model identifying hateful and toxic comments (strictly seperated from offensive language) in online social platforms (English language), inthis case Twitter.
Edit intent: coherence
Original:  In this work , a BERT based neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in order to create a model identifying hateful and toxic comments (strictly seperated from offensive language) in online social platforms (English language), inthis case Twitter.
Revised:  In this work , a BERT based neural network model (Devlin et al.,2018) is applied to the JIGSAW data set in order to create a model identifying hateful and toxic comments (strictly seperated from offensive language) in online social platforms (English language), inthis case Twitter.
Edit intent: clarity
Original:  In this work , a BERT based neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in order to create a model identifying hateful and toxic comments (strictly seperated from offensive language) in online social platforms (English language), inthis case Twitter.
Revised:  In this work , a BERT based neural network model (Devlin et al.,2018) is applied to the JIGSAW dataset (Jigsaw/Conversation AI, 2019) in order to create a model identifying hateful and toxic comments (strictly seperated from offensive language) in online social platforms (English language), in this case Twitter.
Edit intent: fluency
Original:  Three other neural network architectures and a GPT-2 (Radfordet al., 2019) model are also applied on the provided data set in order to compare these different models.
Revised:  Three other neural network architectures and a GPT-2 model are also applied on the provided data set in order to compare these different models.
Edit intent: clarity
Original:  The trained BERT model is then applied on two different data sets to evaluate its generalisation power, namely on another Twitter data set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and also Facebook comments;
Revised:  The trained BERT model is then applied on two different data sets to evaluate its generalisation power, namely on another Twitter data set and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and also Facebook comments;
Edit intent: clarity
Original:  The trained BERT model is then applied on two different data sets to evaluate its generalisation power, namely on another Twitter data set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 (Thomas Mandl, 2019) (Mandl et al.,2019) which includes Twitter and also Facebook comments;
Revised:  The trained BERT model is then applied on two different data sets to evaluate its generalisation power, namely on another Twitter data set (Tom Davidson, 2017) (Davidsonet al., 2017) and the data set HASOC 2019 which includes Twitter and also Facebook comments;
Edit intent: coherence
Original:  In addition, it can be shown that by fine-tuning the trained BERT model on these two datasets by applying different transfer learning scenarios via retraining partial or all layers the predictive scores improve compared to simply applying the model pre-trained on the JIGSAW data set.
Revised:  In addition, it can be shown that by fine-tuning the trained BERT model on these two data sets by applying different transfer learning scenarios via retraining partial or all layers the predictive scores improve compared to simply applying the model pre-trained on the JIGSAW data set.
Edit intent: clarity
Original:  Withour results, we get precisions from 64\% to around 90\% while still achieving acceptable recall values of at least lower 60s\%, proving that BERT is suitable for real usecases in social platforms.
Revised:  With our results, we get precisions from 64\% to around 90\% while still achieving acceptable recall values of at least lower 60s\%, proving that BERT is suitable for real usecases in social platforms.
Edit intent: fluency
Original:  Withour results, we get precisions from 64\% to around 90\% while still achieving acceptable recall values of at least lower 60s\%, proving that BERT is suitable for real usecases in social platforms.
Revised:  Withour results, we get precisions from 64\% to around 90\% while still achieving acceptable recall values of at least lower 60s\%, proving that BERT is suitable for real use cases in social platforms.
Edit intent: fluency
Original: Indirect reciprocity is a key mechanism that promotes cooperation in social dilemmas via reputations .
Revised: Indirect reciprocity is a key mechanism that promotes cooperation in social dilemmas by means of reputation .
Edit intent: clarity
Original:  Although it has been a common practice to represent reputations by binary values, either "good" or "bad" , such a dichotomy is a primitive approximation considering the complexity of reality.
Revised:  Although it has been a common practice to represent reputations by binary values, either `good' or `bad' , such a dichotomy is a primitive approximation considering the complexity of reality.
Edit intent: fluency
Original:  Although it has been a common practice to represent reputations by binary values, either "good" or "bad" , such a dichotomy is a primitive approximation considering the complexity of reality.
Revised:  Although it has been a common practice to represent reputations by binary values, either "good" or "bad" , such a dichotomy is a crude approximation considering the complexity of reality.
Edit intent: clarity
Original:  We fully identified the norms which achieve cooperation and possess evolutionary stability against behavioral mutants using a supercomputer from more than thirty billion possibilities .
Revised:  We fully identified the norms which achieve cooperation and possess evolutionary stability against behavioral mutants .
Edit intent: clarity
Original:  By systematically categorizing all these norms according to their behaviors, we find similarities and dissimilarities to their binary-reputation counterpart, the leading eight.
Revised:  By systematically categorizing all these norms according to their behaviors, we found similarities and dissimilarities to their binary-reputation counterpart, the leading eight.
Edit intent: fluency
Original:  We obtained the four rules that  the successful norms should satisfy , and the behavior of the leading eight can be understood as a special case of these rules.
Revised:  We obtained four rules that the successful norms should satisfy , and the behavior of the leading eight can be understood as a special case of these rules.
Edit intent: fluency
Original:  We obtained the four rules that  the successful norms should satisfy , and the behavior of the leading eight can be understood as a special case of these rules.
Revised:  We obtained the four rules that the successful norms , and the behavior of the leading eight can be understood as a special case of these rules.
Edit intent: clarity
Original:  Next, we analyze the regulator's rate-setting process that determines NEM X parameters such as retail/sell rates, fixed charges, and price differentials in TOU tariffsin on and off-peak periods.
Revised:  Next, we analyze the regulator's rate-setting process that determines NEM X parameters such as retail/sell rates, fixed charges, and price differentials in ToU tariffs' on and off-peak periods.
Edit intent: fluency
Original: Extremely large antenna array for wideband communications is a promising technology to achieve a Tbps data rate for next-generation communications .
Revised: The deployment of wideband extremely large antenna arrays has the promising of achieving Tbps data rates in the next-generation communications .
Edit intent: clarity
Original: Extremely large antenna array for wideband communications is a promising technology to achieve a Tbps data rate for next-generation communications .
Revised: Extremely large antenna array for wideband communications is a promising technology to achieve a Tbps data rate for next-generation communication networks .
Edit intent: clarity
Original:  However, due to the extremely large bandwidth and antenna array aperture, the near-field beam split effect will severely decrease the actual transmission rates, which has not been investigated in existing works.
Revised:  However, due to the extremely wide bandwidth and large antenna array aperture, the near-field beam split effect will severely decrease the actual transmission rates, which has not been investigated in existing works.
Edit intent: clarity
Original:  However, due to the extremely large bandwidth and antenna array aperture, the near-field beam split effect will severely decrease the actual transmission rates, which has not been investigated in existing works.
Revised:  However, due to the extremely large bandwidth and antenna array aperture, the near-field beam-split effect will severely decrease the actual transmission rates, which has not been investigated in existing works.
Edit intent: fluency
Original:  However, due to the extremely large bandwidth and antenna array aperture, the near-field beam split effect will severely decrease the actual transmission rates, which has not been investigated in existing works.
Revised:  However, due to the extremely large bandwidth and antenna array aperture, the near-field beam split effect will severely degrade the actual transmission rates, which has not been investigated in existing works.
Edit intent: clarity
Original:  To solve this challenging problem, we first reveal the near-field beam split effect and analyze the corresponding array gain loss.
Revised:  To address this challenging problem, we first reveal the near-field beam split effect and analyze the corresponding array gain loss.
Edit intent: clarity
Original:  To solve this challenging problem, we first reveal the near-field beam split effect and analyze the corresponding array gain loss.
Revised:  To solve this challenging problem, we first reveal the near-field beam-split effect and analyze the corresponding array gain loss.
Edit intent: fluency
Original:  Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel , based on which we propose a phase-delay focusing method to effectively mitigate the near-field beam split effect.
Revised:  Then, a piecewise-far-field model with piecewise-linear phase characteristics is harnessed for approximating the near-field channel , based on which we propose a phase-delay focusing method to effectively mitigate the near-field beam split effect.
Edit intent: clarity
Original:  Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel , based on which we propose a phase-delay focusing method to effectively mitigate the near-field beam split effect.
Revised:  Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel . Based on this, we conceive a phase-delay focusing method to effectively mitigate the near-field beam split effect.
Edit intent: clarity
Original:  Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel , based on which we propose a phase-delay focusing method to effectively mitigate the near-field beam split effect.
Revised:  Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel , based on which we propose a phase-delay focusing method for mitigating the near-field beam split effect.
Edit intent: clarity
Original:  Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel , based on which we propose a phase-delay focusing method to effectively mitigate the near-field beam split effect.
Revised:  Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel , based on which we propose a phase-delay focusing method to effectively mitigate the near-field beam-split effect.
Edit intent: fluency
Original:  Moreover,  a new metric called effective Rayleigh distance is defined , which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions  for practical communications.
Revised:  Moreover, we introduce a new metric called effective Rayleigh distance is defined , which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions for practical communications.
Edit intent: coherence
Original:  Moreover,  a new metric called effective Rayleigh distance is defined , which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions  for practical communications.
Revised:  Moreover, a new metric , termed the effective Rayleigh distance is defined , which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions for practical communications.
Edit intent: clarity
Original:  Moreover,  a new metric called effective Rayleigh distance is defined , which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions  for practical communications.
Revised:  Moreover, a new metric called effective Rayleigh distance , which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions for practical communications.
Edit intent: clarity
Original:  Moreover,  a new metric called effective Rayleigh distance is defined , which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions  for practical communications.
Revised:  Moreover, a new metric called effective Rayleigh distance is defined , which is more accurate in distinguishing the far-field and near-field regions for practical communications.
Edit intent: clarity
Original:  Finally, theoretical and numerical results are provided to demonstrate the effectiveness of our methods.
Revised:  Finally, numerical results are provided to demonstrate the effectiveness of our methods.
Edit intent: clarity
Original:  Using Nntool by MATLAB , 22 different combinations of networks and training algorithms are used to predict the dimensions of a rectangular microstrip antenna using dielectric constant, height of substrate, and frequency of operation as input.
Revised:  Using MATLAB Nntool , 22 different combinations of networks and training algorithms are used to predict the dimensions of a rectangular microstrip antenna using dielectric constant, height of substrate, and frequency of operation as input.
Edit intent: clarity
Original:  Using Nntool by MATLAB , 22 different combinations of networks and training algorithms are used to predict the dimensions of a rectangular microstrip antenna using dielectric constant, height of substrate, and frequency of operation as input.
Revised:  Using Nntool by MATLAB , 22 different combinations of networks and training algorithms are used to predict the dimensions of a rectangular microstrip antenna using dielectric constant, height of substrate, and frequency of oper-ation as input.
Edit intent: fluency
Original:  We propose a Self-Tracking Feedback Saliency Framework to define when to provide feedback, on which specific information, why those details, and how to present them ( as manual inquiry or automatic feedback).
Revised:  We propose a Self-Tracking Feedback Saliency Framework to define when to provide feedback, on which specific information, why those details, and how to present them ( manual elicitation or automatic feedback).
Edit intent: clarity
Original:  Using the data collected from the user study , we trained a machine learning model to predict whether a user would learn from each tracked event . Using explainable AI (XAI) techniques, we identified the most salient features per instance and why they lead to positive learning outcomes  .
Revised:  Using the data collected from the user study , we trained a machine learning model to predict when a user would learn from each tracked event . Using explainable AI (XAI) techniques, we identified the most salient features per instance and why they lead to positive learning outcomes .
Edit intent: clarity
Original:  Using the data collected from the user study , we trained a machine learning model to predict whether a user would learn from each tracked event . Using explainable AI (XAI) techniques, we identified the most salient features per instance and why they lead to positive learning outcomes  .
Revised:  Using the data collected from the user study , we trained a machine learning model to predict whether a user would learn from tracked events . Using explainable AI (XAI) techniques, we identified the most salient features per instance and why they lead to positive learning outcomes .
Edit intent: clarity
Original:  Using the data collected from the user study , we trained a machine learning model to predict whether a user would learn from each tracked event . Using explainable AI (XAI) techniques, we identified the most salient features per instance and why they lead to positive learning outcomes  .
Revised:  Using the data collected from the user study , we trained a machine learning model to predict whether a user would learn from each tracked event . Using explainable AI (XAI) techniques, SalienTrack identifies which features of the event are most salient, why they lead to positive learning outcomes .
Edit intent: clarity
Original:  However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveforms or reach competitive performance after feature extraction through Fourier-based methods, incurring a loss-floor .
Revised:  However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveforms or incur a performance penalty when trained on Fourier-based methods, incurring a loss-floor .
Edit intent: clarity
Original:  However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveforms or reach competitive performance after feature extraction through Fourier-based methods, incurring a loss-floor .
Revised:  However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveforms or reach competitive performance after feature extraction through Fourier-based features .
Edit intent: clarity
Original:  In this work, we introduce an architecture, Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in Keyword Spotting with raw audio waveforms, out-performing all previous methods while also being computationally cheaper , much more parameter and data-efficient.
Revised:  In this work, we introduce an architecture, Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in Keyword Spotting with raw audio waveforms, outperforming all previous methods while also being computationally cheaper , much more parameter and data-efficient.
Edit intent: clarity
Original:  In this work, we introduce an architecture, Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in Keyword Spotting with raw audio waveforms, out-performing all previous methods while also being computationally cheaper , much more parameter and data-efficient.
Revised:  In this work, we introduce an architecture, Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in Keyword Spotting with raw audio waveforms, out-performing all previous methods while being computationally cheaper , much more parameter and data-efficient.
Edit intent: clarity
Original: Several applications such as autonomous driving, augmented reality and virtual reality requires a precise prediction of the 3D human pose.
Revised: Several applications such as autonomous driving, augmented reality and virtual reality require a precise prediction of the 3D human pose.
Edit intent: fluency