wanyu/IteraTeR_v2 huggingface dataset:
Original:  In the structure we have designed, called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) , two parallel blocks consisting of 1D-CMNN each have weight sharing in one dimension, are applied simultaneously but independently to the feature vectors.
Revised:  In the structure we have designed, called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) , two parallel blocks consisting of 1D-CMNN are used. These two blocks are applied simultaneously but independently to the feature vectors.
Edit intent: clarity
Original:  To improve the performance of this structure, we have used newly developed methods and models such as the maxout, Dropout, and weight normalization.
Revised:  To improve the performance of this structure, we have used newly developed methods and models such as Dropout, maxout, and weight normalization.
Edit intent: fluency
Original:  Two experimental sets were designed and implemented on the Persian FARSDAT speech data set to evaluate the performance of this model compared to conventional 1D-CMNN models.
Revised:  Two sets of experiments were designed and implemented on the Persian FARSDAT speech data set to evaluate the performance of this model compared to conventional 1D-CMNN models.
Edit intent: clarity
Original:  Two experimental sets were designed and implemented on the Persian FARSDAT speech data set to evaluate the performance of this model compared to conventional 1D-CMNN models.
Revised:  Two experimental sets were designed and implemented on the Persian FARSDAT speech dataset to evaluate the performance of this model compared to conventional 1D-CMNN models.
Edit intent: clarity
Original:  As a result, as mentioned in other references, time-frequency localization in ASR systems increases system accuracy and speeds up the model training process.
Revised:  Therefore, as proven in other sources, we can say that time-frequency localization in ASR systems increases system accuracy and speeds up the model training process.
Edit intent: clarity
Original:  As a result, as mentioned in other references, time-frequency localization in ASR systems increases system accuracy and speeds up the model training process.
Revised:  As a result, as mentioned in other references, time-frequency localization in ASR systems increases system accuracy and speeds up the training process.
Edit intent: clarity
Original: In this paper, a CNN-based structure for  time-frequency localization of information in the ASR acoustic model is proposed for Persian speech recognition.
Revised: In this paper, a CNN-based structure for the time-frequency localization of information in the ASR acoustic model is proposed for Persian speech recognition.
Edit intent: fluency
Original: In this paper, a CNN-based structure for  time-frequency localization of information in the ASR acoustic model is proposed for Persian speech recognition.
Revised: In this paper, a CNN-based structure for time-frequency localization of information is proposed for Persian speech recognition.
Edit intent: clarity
Original:  Research has shown that the receptive fields' spectrotemporal plasticity of some neurons in mammals' primary auditory cortex and midbrain makes localization facilities that improve recognition performance.
Revised:  Research has shown that the receptive fields' spectrotemporal plasticity of some neurons in mammals' primary auditory cortex and midbrain makes localization facilities improve recognition performance.
Edit intent: clarity
Original:  As biosystems have inspired many man-maid systems because of their high efficiency and performance, in the last few years, much work has been done to localize time-frequency information in ASR systems, which has used the spatial or temporal immutability properties of methods such as TDNN, CNN, and LSTM-RNN . However, most of these models have large parameter volumes and are challenging to train.
Revised:  Over the past few years, much work has been done to localize time-frequency information in ASR systems, which has used the spatial or temporal immutability properties of methods such as TDNN, CNN, and LSTM-RNN . However, most of these models have large parameter volumes and are challenging to train.
Edit intent: coherence
Original:  As biosystems have inspired many man-maid systems because of their high efficiency and performance, in the last few years, much work has been done to localize time-frequency information in ASR systems, which has used the spatial or temporal immutability properties of methods such as TDNN, CNN, and LSTM-RNN . However, most of these models have large parameter volumes and are challenging to train.
Revised:  As biosystems have inspired many man-maid systems because of their high efficiency and performance, in the last few years, much work has been done to localize time-frequency information in ASR systems, using the spatial or temporal immutability properties of methods such as TDNN, CNN, and LSTM-RNN . However, most of these models have large parameter volumes and are challenging to train.
Edit intent: coherence
Original:  As biosystems have inspired many man-maid systems because of their high efficiency and performance, in the last few years, much work has been done to localize time-frequency information in ASR systems, which has used the spatial or temporal immutability properties of methods such as TDNN, CNN, and LSTM-RNN . However, most of these models have large parameter volumes and are challenging to train.
Revised:  As biosystems have inspired many man-maid systems because of their high efficiency and performance, in the last few years, much work has been done to localize time-frequency information in ASR systems, which has used the spatial or temporal immutability properties of methods such as HMMs, TDNNs, CNNs, and LSTM-RNNs . However, most of these models have large parameter volumes and are challenging to train.
Edit intent: clarity
Original:  We have presented a structure called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) in which two parallel time-domain and frequency-domain 1D-CMNN are used.
Revised:  For this purpose, we have presented a structure called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) in which two parallel time-domain and frequency-domain 1D-CMNN are used.
Edit intent: coherence
Original:  We have presented a structure called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) in which two parallel time-domain and frequency-domain 1D-CMNN are used.
Revised:  We have presented a structure called Time-Frequency Convolutional Maxout Neural Network (TFCMNN) in which parallel time-domain and frequency-domain 1D-CMNN are used.
Edit intent: clarity
Original:  These two blocks are applied simultaneously but independently to the spectrogram, and then their output is concatenated and applied jointly to a fully connected Maxout network for classification.
Revised:  These two blocks are applied simultaneously but independently to the spectrogram, and then their outputs are concatenated and applied jointly to a fully connected Maxout network for classification.
Edit intent: fluency
Original:  Two sets of experiments were designed and implemented on the Persian FARSDAT speech dataset to evaluate the performance of this model compared to conventional 1D-CMNN models.
Revised:  Two sets of experiments were designed and implemented on the FARSDAT dataset to evaluate the performance of this model compared to conventional 1D-CMNN models.
Edit intent: clarity
Original:  Therefore, as proven in other sources, we can say that time-frequency localization in ASR systems increases system accuracy and speeds up the training process.
Revised:  Therefore, as proven in other sources, time-frequency localization in ASR systems increases system accuracy and speeds up the training process.
Edit intent: coherence
Original:  Recently, self-supervised methods draw much attention , due to their free of depth annotations and  impressive performance on several daytime benchmarks , such as KITTI and Cityscapes .
Revised:  Recently, self-supervised methods draw much attention since they are free of depth annotations and impressive performance on several daytime benchmarks , such as KITTI and Cityscapes .
Edit intent: clarity
Original:  Recently, self-supervised methods draw much attention , due to their free of depth annotations and  impressive performance on several daytime benchmarks , such as KITTI and Cityscapes .
Revised:  Recently, self-supervised methods draw much attention , due to their free of depth annotations and achieve impressive performance on several daytime benchmarks , such as KITTI and Cityscapes .
Edit intent: clarity
Original:  Recently, self-supervised methods draw much attention , due to their free of depth annotations and  impressive performance on several daytime benchmarks , such as KITTI and Cityscapes .
Revised:  Recently, self-supervised methods draw much attention , due to their free of depth annotations and impressive performance on several daytime benchmarks .
Edit intent: clarity
Original:  In this paper, we find that for the functions with logarithmic regularities, the pointwise errors of Chebyshev approximation are smaller than the errors of the best approximations except only in the very narrow boundaries  .
Revised:  In this paper, we find that for the functions with logarithmic regularities, the pointwise errors of Chebyshev approximation are smaller than the ones of the best approximations except only in the very narrow boundaries .
Edit intent: clarity
Original: Neural architecture search (NAS), which automatically designs  deep neural networks, has achieved a breakthrough success over many applications in the past few years.
Revised: Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved a breakthrough success over many applications in the past few years.
Edit intent: clarity
Original: Neural architecture search (NAS), which automatically designs  deep neural networks, has achieved a breakthrough success over many applications in the past few years.
Revised: Neural architecture search (NAS), which automatically designs deep neural networks, has achieved breakthrough success over many applications in the past few years.
Edit intent: fluency
Original:  The existing efficient evaluation methods are either not suitable for the population-based ENAS algorithm or are  complex to use.
Revised:  The existing efficient evaluation methods are either not suitable for the population-based ENAS algorithm or are too complex to use.
Edit intent: coherence
Original:  Extensive experiments show that our method achieves the best performance and outperforms state-of-the-art methods by 5.49 %DIFDELCMD < \\%%% %DIF < . Numerous ablation studies further bear out the necessity of external  knowledge and the effectiveness of our graph-based approach.
Revised:  Extensive experiments show that our method achieves the best performance and outperforms state-of-the-art methods by 5.49 %DIFDELCMD < \\%%% %DIF < . Numerous ablation studies further bear out the necessity of external \%. Numerous ablation studies further bear out the necessity of external knowledge and the effectiveness of our graph-based approach.
Edit intent: fluency
Original: This paperinvestigates the reachability of dimension-bounded linear systems .
Revised: In this paper, the reachability of dimension-bounded linear systems .
Edit intent: clarity
Original:  Furthermore, an example is provided to show the relationship between reachable subspaces at times t*+i and t*+j  .
Revised:  Furthermore, an example is provided to show the relationship between reachable subspaces at times t*+i and t*+j is shown via an example .
Edit intent: clarity
Original:  Comparing different dense embedding models, tri-encoders , with one encoder for each question, text and table, increase retrieval performance compared to bi-encoders with one encoder for the question and one for both text and tables.
Revised:  Comparing different dense embedding models, tri-encoders with one encoder for each question, text and table, increase retrieval performance compared to bi-encoders with one encoder for the question and one for both text and tables.
Edit intent: fluency
Original: When recognizing emotions, subtle nuances of emotion displays often cause ambiguity or uncertainty in emotion perception.
Revised: When recognizing emotions, subtle nuances in displays of emotion generate ambiguity or uncertainty in emotion perception.
Edit intent: clarity
Original:  In the end, our algorithm results in a single student model that can estimate in-domain uncertainty and a student ensemble that can detect out-of-domain samples.
Revised:  Our method generates single student models that provide accurate estimates of uncertainty for in-domain samples and a student ensemble that can detect out-of-domain samples.
Edit intent: clarity
Original:  We trained our Efficient Multitask Emotion Networks (EMENet) on the Aff-wild2 dataset, and conducted extensive experiments on emotion recognition and uncertainty estimation .
Revised:  Our experiments on emotion recognition and uncertainty estimation .
Edit intent: clarity
Original:  Our algorithm gives more reliable uncertainty estimates than  Temperature Scaling and Monte Carol Dropout.
Revised:  Our algorithm gives more reliable uncertainty estimates than both Temperature Scaling and Monte Carol Dropout.
Edit intent: clarity
Original: For the past ten years, CNN has reigned supreme in the world of computer vision, but recently, Transformer is on the rise.
Revised: For the past ten years, CNN has reigned supreme in the world of computer vision, but recently, Transformer has been on the rise.
Edit intent: fluency
Original:  However, the quadratic computational cost of self-attention has become a severe problem of practice .
Revised:  However, the quadratic computational cost of self-attention has become a serious problem in practice applications .
Edit intent: clarity
Original:  In particular, MLP-Mixer is a simple idea designed using MLPs and hit an accuracy comparable to the Vision Transformer.
Revised:  In particular, MLP-Mixer is a simple architecture designed using MLPs and hit an accuracy comparable to the Vision Transformer.
Edit intent: clarity
Original:  Thus, there is still a possibility to build a non-convolutional  inductive bias into the architecture itself, and we built in an inductive bias using two simple ideas  .
Revised:  This leaves open the possibility of incorporating a non-convolutional inductive bias into the architecture itself, and we built in an inductive bias using two simple ideas .
Edit intent: clarity
Original:  Thus, there is still a possibility to build a non-convolutional  inductive bias into the architecture itself, and we built in an inductive bias using two simple ideas  .
Revised:  Thus, there is still a possibility to build a non-convolutional inductive bias into the architecture , so we used two simple ideas .
Edit intent: coherence
Original: Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small per- turbations on the input images.
Revised: Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images.
Edit intent: fluency
Original:  Procedural adversarial noise at- tack is a data-free universal perturbation generation method.
Revised:  Procedural adversarial noise attack is a data-free universal perturbation generation method.
Edit intent: fluency
Original:  Usually, a well-performing ML model , especially, emerging deep neural network model, relies on a large volume of training data and high-powered computational resources.
Revised:  Usually, a well-performing ML model relies on a large volume of training data and high-powered computational resources.
Edit intent: clarity
Original:  The need for a vast volume of available data raises serious privacy concerns because of the risk of leakage of highly privacy-sensitive information and the evolving regulatory environments that increasingly restrict access to and use of privacy-sensitive data .
Revised:  Such a need for and the use of huge volumes of data raise serious privacy concerns because of the risk of leakage of highly privacy-sensitive information and the evolving regulatory environments that increasingly restrict access to and use of privacy-sensitive data .
Edit intent: clarity
Original:  The need for a vast volume of available data raises serious privacy concerns because of the risk of leakage of highly privacy-sensitive information and the evolving regulatory environments that increasingly restrict access to and use of privacy-sensitive data .
Revised:  The need for a vast volume of available data raises serious privacy concerns because of the potential risks of leakage of highly privacy-sensitive information and the evolving regulatory environments that increasingly restrict access to and use of privacy-sensitive data .
Edit intent: clarity
Original:  The need for a vast volume of available data raises serious privacy concerns because of the risk of leakage of highly privacy-sensitive information and the evolving regulatory environments that increasingly restrict access to and use of privacy-sensitive data .
Revised:  The need for a vast volume of available data raises serious privacy concerns because of the risk of leakage of highly privacy-sensitive information ; further, the evolving regulatory environments that increasingly restrict access to and use of privacy-sensitive data .
Edit intent: coherence
Original:  Hence, well-designed privacy-preserving ML (PPML) solutions are crucial and have attracted increasing research interest from academia and industry .
Revised:  Hence, well-designed privacy-preserving ML (PPML) solutions are critically needed for many emerging applications. Increasingly, significant research efforts from both academia and industry .
Edit intent: clarity
Original:  More and more efforts of PPML are proposed via integrating privacy-preserving techniques into ML algorithms, fusing privacy-preserving approaches into ML pipeline, or designing various privacy-preserving architecturesfor existing ML systems .
Revised:  More and more efforts of PPML are proposed via integrating privacy-preserving techniques into ML algorithms, fusing privacy-preserving approaches into ML pipeline, or designing various PPML architectures .
Edit intent: clarity
Original:  In particular, existing PPML arts cross-cut ML, system, security, and privacy ;
Revised:  In particular, existing PPML research cross-cut ML, system, security, and privacy ;
Edit intent: clarity
Original:  hence, there is a critical need to understand state-of-art studies , related challenges , and a roadmap for future research .
Revised:  hence, there is a critical need to understand state-of-the-art research , related challenges , and a roadmap for future research .
Edit intent: clarity
Original:  hence, there is a critical need to understand state-of-art studies , related challenges , and a roadmap for future research .
Revised:  hence, there is a critical need to understand state-of-art studies , related challenges and a research roadmap for future research .
Edit intent: clarity
Original:  This papersystematically reviews and summarizes existing privacy-preserving approaches and proposes a PGU model to guide evaluation for various PPML solutions through elaborately decomposing their privacy-preserving functionalities.
Revised:  This papersystematically reviews and summarizes existing privacy-preserving approaches and proposes a PGU model to guide evaluation for various PPML solutions by decomposing their privacy-preserving functionalities.
Edit intent: clarity
Original:  Furthermore, we also discuss the unique characteristics and challenges of PPML and outline possible directions of future work that benefit a wide range of research communities among ML, distributed systems, security , and privacyareas .
Revised:  Furthermore, we also discuss the unique characteristics and challenges of PPML and outline possible research directions that leverage as well as benefit multiple research communities such as ML, distributed systems, security , and privacyareas .
Edit intent: clarity
Original:  Furthermore, we also discuss the unique characteristics and challenges of PPML and outline possible directions of future work that benefit a wide range of research communities among ML, distributed systems, security , and privacyareas .
Revised:  Furthermore, we also discuss the unique characteristics and challenges of PPML and outline possible directions of future work that benefit a wide range of research communities among ML, distributed systems, security and privacy .
Edit intent: clarity
Original: Anomaly detection in video streams is a challengingproblem because of the scarcity of abnormal events andthe difficulty of accurately annotating them.
Revised: Anomaly detection in video streams is a challenging problem because of the scarcity of abnormal events andthe difficulty of accurately annotating them.
Edit intent: fluency
Original: Anomaly detection in video streams is a challengingproblem because of the scarcity of abnormal events andthe difficulty of accurately annotating them.
Revised: Anomaly detection in video streams is a challengingproblem because of the scarcity of abnormal events and the difficulty of accurately annotating them.
Edit intent: fluency
Original:  To allevi-ate these issues, unsupervised learning-based predictionmethods have been previously applied.
Revised:  To alleviate these issues, unsupervised learning-based predictionmethods have been previously applied.
Edit intent: fluency
Original:  To allevi-ate these issues, unsupervised learning-based predictionmethods have been previously applied.
Revised:  To allevi-ate these issues, unsupervised learning-based prediction methods have been previously applied.
Edit intent: fluency
Original:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Revised:  These approaches train the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Edit intent: clarity
Original:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Revised:  These approachestrain the model with only normal events and predict a future frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Edit intent: fluency
Original:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Revised:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use of encoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Edit intent: fluency
Original:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in smallprediction errors on normal events but large errors on ab-normal events.
Revised:  These approachestrain the model with only normal events and predict a fu-ture frame from a sequence of preceding frames by use ofencoder-decoder architectures so that they result in small prediction errors on normal events but large errors on ab-normal events.
Edit intent: fluency