wanyu/IteraTeR_v2 huggingface dataset:
Original:  Also, different regularization techniques (Tikhonov regularisation and axis-parallel split regularisation) are employed for tackling the small sample size problems in the decision trees of oblique ensembles of double random forest.
Revised:  Also, different regularization techniques (Tikhonov regularisation and axis-parallel split regularisation) are employed for tackling the small sample size problems in the decision trees of oblique double random forest.
Edit intent: clarity
Original:  Semi-supervised learning is a machine learning technique where, incrementally, labeled data is used to generate pseudo-labels for the rest of  data (and then all that data is used for model training).
Revised:  Semi-supervised learning is a machine learning technique where, incrementally, labeled data is used to generate pseudo-labels for the rest of the data (and then all that data is used for model training).
Edit intent: fluency
Original: Motivated by the two common limitations on current distributed extended object tracking systems, i.e., the extent is modeled as an ellipse, and each sensor node in network needs to detect the object, this paper considers a multiplicative error model (MEM) to design a distributed information filter (IF) over a realistic network.
Revised: Motivated by the two common limitations on current distributed extended object tracking systems, i.e., the extent is modeled as an ellipse, this paper considers a multiplicative error model (MEM) to design a distributed information filter (IF) over a realistic network.
Edit intent: coherence
Original: Motivated by the two common limitations on current distributed extended object tracking systems, i.e., the extent is modeled as an ellipse, and each sensor node in network needs to detect the object, this paper considers a multiplicative error model (MEM) to design a distributed information filter (IF) over a realistic network.
Revised: Motivated by the two common limitations on current distributed extended object tracking systems, i.e., the extent is modeled as an ellipse, and each sensor node in network needs to detect the object, this paper considers a multiplicative error model (MEM) to design the distributed information filter (IF) over a realistic network.
Edit intent: fluency
Original:  The MEM reduces the extent of perpendicular axis-symmetric shapes into a 3-D vector, which results in MEM being a nonlinear and state-coupled model with multiplicative noise.
Revised:  The MEM reduces the extent of perpendicular axis-symmetric objects into a 3-D vector, which results in MEM being a nonlinear and state-coupled model with multiplicative noise.
Edit intent: clarity
Original:  The MEM reduces the extent of perpendicular axis-symmetric shapes into a 3-D vector, which results in MEM being a nonlinear and state-coupled model with multiplicative noise.
Revised:  The MEM reduces the extent of perpendicular axis-symmetric shapes into a 3-D vector, which causes MEM being a nonlinear and state-coupled model with multiplicative noise.
Edit intent: coherence
Original:  To meet the requirement in IF that the state-space model is a linear model with additive noise, we first derive two separate pseudo-linearized models by using the first-order Taylor series expansion.
Revised:  To meet the requirement in IF that the state-space model is a linear model with additive noise, we derive two separate pseudo-linearized models by using the first-order Taylor series expansion.
Edit intent: coherence
Original:  The separation is merely in form, and the cross-correlation between two estimated states is preserved as parameters in each other's model.
Revised:  The separation is merely in form, and the cross-correlation between states is preserved as parameters in each other's model.
Edit intent: clarity
Original:  Second, we propose a centralized information filter by using the two models, in which the multiple measurements are converted into a summation form of innovation parts.
Revised:  Second, we propose a centralized IF, where the measurements are converted into a summation form of innovation parts.
Edit intent: clarity
Original:  Second, we propose a centralized information filter by using the two models, in which the multiple measurements are converted into a summation form of innovation parts.
Revised:  Second, we propose a centralized information filter by using the two models, in which the multiple measurements are converted into a summation of innovation parts.
Edit intent: clarity
Original:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Revised:  Later , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Edit intent: fluency
Original:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Revised:  Third , under a sensor network with the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Edit intent: fluency
Original:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Revised:  Third , under a sensor network where the communication nodes and sensor nodes , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Edit intent: clarity
Original:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Revised:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed IFs based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Edit intent: clarity
Original:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Revised:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information and consensus on measurement (CM) schemes, respectively.
Edit intent: clarity
Original:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement (CM) schemes, respectively.
Revised:  Third , under a sensor network where the communication nodes cannot detect the object , we present two distributed information filters based on the consensus on information (CI) and consensus on measurement schemes, respectively.
Edit intent: clarity
Original:  Finally, the performance of two distributed filters in terms of accuracy, convergence, and consistency is evaluated in Monte Carlo simulations .
Revised:  Simulations indicate the performance of two distributed filters in terms of accuracy, convergence, and consistency is evaluated in Monte Carlo simulations .
Edit intent: clarity
Original:  Finally, the performance of two distributed filters in terms of accuracy, convergence, and consistency is evaluated in Monte Carlo simulations .
Revised:  Finally, the performance of the proposed filters w.r.t accuracy, convergence, and consistency is evaluated in Monte Carlo simulations .
Edit intent: clarity
Original:  Finally, the performance of two distributed filters in terms of accuracy, convergence, and consistency is evaluated in Monte Carlo simulations .
Revised:  Finally, the performance of two distributed filters in terms of accuracy, convergence, and consistency .
Edit intent: clarity
Original:  This paper describes the data, baselines and results obtained for the Track 5 at the Dialogue System Technology Challenge 10 (DSTC10) .
Revised:  This paper describes the data, baselines and results obtained for the Track 5 at the Dialogue System Technology Challenge (DSTC10) .
Edit intent: clarity
Original: The continuous electrification of the mobility and heating sector will introduce new challenges to distribution grid operation.
Revised: The continuous electrification of the mobility and heating sectors adds much-needed flexibility to the power system. However, flexibility utilization also introduces new challenges to distribution grid operation.
Edit intent: coherence
Original:  The pipeline combines techniques for unsupervised event detection and open-set classification.
Revised:  The pipeline combines techniques from unsupervised detection and open-set classification.
Edit intent: clarity
Original:  However, in practice the parties sometimes require to stay anonymous.
Revised:  However, in practice the parties sometimes must stay anonymous.
Edit intent: clarity
Original:  In this paper, we conceptualize a framework that enables the repeated execution of an SMPC protocol for a given functionality such that the parties can keep their participation in the protocol executions private and at the same time be sure that only trustworthy parties may take part in a protocol execution.
Revised:  In this paper, we conceptualize a framework that enables the repeated execution of an SMPC protocol for a given functionality such that the parties can keep their participation in the protocol executions private and at the same time be sure that only authorized parties may take part in a protocol execution.
Edit intent: clarity
Original: In this study, we propose a cross-domain multi-objective speech assessment model , i.e., the MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.
Revised: In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously.
Edit intent: clarity
Original:  More specifically, the MOSA-Net is designed to estimate  speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input .
Revised:  More specifically, MOSA-Net is designed to estimate speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input .
Edit intent: fluency
Original:  More specifically, the MOSA-Net is designed to estimate  speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input .
Revised:  More specifically, the MOSA-Net is designed to estimate the speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input .
Edit intent: fluency
Original:  More specifically, the MOSA-Net is designed to estimate  speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input .
Revised:  More specifically, the MOSA-Net is designed to estimate speech quality, intelligibility, and distortion assessment scores of an input test speech signal as input .
Edit intent: clarity
Original:  More specifically, the MOSA-Net is designed to estimate  speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input .
Revised:  More specifically, the MOSA-Net is designed to estimate speech quality, intelligibility, and distortion assessment scores based on a test speech signal .
Edit intent: clarity
Original:  It comprises a convolutional neural network and bidirectional long short-term memory (CNN-BLSTM) architecture for representation extraction, as well as a multiplicative attention layer and a fully-connected layer for each assessment metric.
Revised:  It comprises a convolutional neural network and bidirectional long short-term memory (CNN-BLSTM) architecture for representation extraction, and a multiplicative attention layer and a fully-connected layer for each assessment metric.
Edit intent: coherence
Original:  Experimental results reveal that the MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI) scores when tested on both noisy and enhanced speech utterances under either seen test conditions (where the test speakers and noise types are involved in the training set) or unseen test conditions (where the test speakers and noise types are not involved in the training set) .
Revised:  Experimental results show that MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI) scores when tested on both noisy and enhanced speech utterances under either seen test conditions (where the test speakers and noise types are involved in the training set) or unseen test conditions (where the test speakers and noise types are not involved in the training set) .
Edit intent: clarity
Original:  Experimental results reveal that the MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI) scores when tested on both noisy and enhanced speech utterances under either seen test conditions (where the test speakers and noise types are involved in the training set) or unseen test conditions (where the test speakers and noise types are not involved in the training set) .
Revised:  Experimental results reveal that the MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI) scores when tested on noisy and enhanced speech utterances under either seen test conditions (where the test speakers and noise types are involved in the training set) or unseen test conditions (where the test speakers and noise types are not involved in the training set) .
Edit intent: fluency
Original:  Experimental results reveal that the MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI) scores when tested on both noisy and enhanced speech utterances under either seen test conditions (where the test speakers and noise types are involved in the training set) or unseen test conditions (where the test speakers and noise types are not involved in the training set) .
Revised:  Experimental results reveal that the MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI) scores when tested on both noisy and enhanced speech utterances under either seen test conditions or unseen test conditions (where the test speakers and noise types are not involved in the training set) .
Edit intent: fluency
Original:  In light of the confirmed prediction capability, we further adopt the latent representations of the MOSA-Net to guide the speech enhancement (SE) process and derive a quality-intelligibility (QI)-aware SE (QIA-SE) approach accordingly.
Revised:  In light of the confirmed prediction capability, we further adopt the latent representations of MOSA-Net to guide the speech enhancement (SE) process and derive a quality-intelligibility (QI)-aware SE (QIA-SE) approach accordingly.
Edit intent: fluency
Original:  While recent work has shown that fully transformer-based VL models can be more efficient than previous region-feature-based methods, their performance on downstream tasks are often degraded significantly.
Revised:  While recent work has shown that fully transformer-based VL models can be more efficient than previous region-feature-based methods, their performance on downstream tasks often degrades significantly.
Edit intent: clarity
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: coherence
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~( ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: fluency
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~(M E nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: clarity
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~(M ultimodal nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: fluency
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~(M ultimodalE T ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: fluency
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~(M ultimodalE nd-to-end ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: fluency
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~(M ultimodalE nd-to-endT ER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: fluency
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransform ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: clarity
Original:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we systematically investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Revised:  In this paper, we present METER ~(M ultimodalE nd-to-endT ransformER ), through which we investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner.
Edit intent: coherence
Original:  Specifically, we dissect the model designs along multiple dimensions: vision encoders (e.g., CLIP-ViT , Swin transformer), text encoders (e.g., RoBERTa, DeBERTa), multimodal fusion  (e.g., merged attention vs. co-attention), architecture design (e.g., encoder-only vs. encoder-decoder), and pre-training objectives (e.g., masked image modeling).
Revised:  Specifically, we dissect the model designs along multiple dimensions: vision encoders (e.g., CLIPViT , Swin transformer), text encoders (e.g., RoBERTa, DeBERTa), multimodal fusion (e.g., merged attention vs. co-attention), architecture design (e.g., encoder-only vs. encoder-decoder), and pre-training objectives (e.g., masked image modeling).
Edit intent: clarity
Original:  Specifically, we dissect the model designs along multiple dimensions: vision encoders (e.g., CLIP-ViT , Swin transformer), text encoders (e.g., RoBERTa, DeBERTa), multimodal fusion  (e.g., merged attention vs. co-attention), architecture design (e.g., encoder-only vs. encoder-decoder), and pre-training objectives (e.g., masked image modeling).
Revised:  Specifically, we dissect the model designs along multiple dimensions: vision encoders (e.g., CLIP-ViT , Swin transformer), text encoders (e.g., RoBERTa, DeBERTa), multimodal fusion (e.g., merged attention vs. co-attention), architectural design (e.g., encoder-only vs. encoder-decoder), and pre-training objectives (e.g., masked image modeling).
Edit intent: fluency
Original:  We conduct comprehensive experiments on a wide range of VL tasks, and provide insights on how to train a performant VL transformer while maintaining fast inference speed.
Revised:  We conduct comprehensive experiments and provide insights on how to train a performant VL transformer while maintaining fast inference speed.
Edit intent: clarity
Original:  Notably, METER~ achieves an accuracy of 77.64 %DIFDELCMD < \\%%% %DIF < on the VQAv2 test-std set using only 4M images for pre-training,  surpassing the state-of-the-art region-feature-based VinVL model by + 1.04 %DIFDELCMD < \\%%% %DIF < ,  and outperforming the previous best fully transformer-based ALBEF model by + 1.6 %DIFDELCMD < \\%%% %DIF < .\end{abstract} 
Revised:  Notably, our best model achieves an accuracy of 77.64 %DIFDELCMD < \\%%% %DIF < on the VQAv2 test-std set using only 4M images for pre-training, surpassing the state-of-the-art region-feature-based VinVL model by + 1.04 %DIFDELCMD < \\%%% %DIF < , and outperforming the previous best fully transformer-based ALBEF model by + 1.6 %DIFDELCMD < \\%%% %DIF < .\end{abstract} 
Edit intent: clarity
Original:  Notably, METER~ achieves an accuracy of 77.64 %DIFDELCMD < \\%%% %DIF < on the VQAv2 test-std set using only 4M images for pre-training,  surpassing the state-of-the-art region-feature-based VinVL model by + 1.04 %DIFDELCMD < \\%%% %DIF < ,  and outperforming the previous best fully transformer-based ALBEF model by + 1.6 %DIFDELCMD < \\%%% %DIF < .\end{abstract} 
Revised:  Notably, METER~ achieves an accuracy of 77.64 %DIFDELCMD < \\%%% %DIF < on the VQAv2 test-std set using only 4M images for pre-training, \% on the VQAv2 test-std set using only 4M images for pre-training, surpassing the state-of-the-art region-feature-based VinVL model by + 1.04 %DIFDELCMD < \\%%% %DIF < , and outperforming the previous best fully transformer-based ALBEF model by + 1.6 %DIFDELCMD < \\%%% %DIF < .\end{abstract} 
Edit intent: fluency