wanyu/IteraTeR_v2 huggingface dataset:
Original:  To the best of our knowledge we believe this  the first works to try out semi-supervised learning to improve flood segmentation models.
Revised:  To the best of our knowledge we believe this is one of the first works to try out semi-supervised learning to improve flood segmentation models.
Edit intent: clarity
Original:  We  use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combining the generated labels with the previously available high confidence labeled dataset.
Revised:  We use a cyclical approach involving multiple stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combining the generated labels with the previously available high confidence labeled dataset.
Edit intent: clarity
Original:  We  use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combining the generated labels with the previously available high confidence labeled dataset.
Revised:  We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generated pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combining the generated labels with the previously available high confidence labeled dataset.
Edit intent: fluency
Original:  We  use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combining the generated labels with the previously available high confidence labeled dataset.
Revised:  We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combine the generated labels with the previously available high confidence labeled dataset.
Edit intent: fluency
Original:  We  use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combining the generated labels with the previously available high confidence labeled dataset.
Revised:  We use a cyclical approach involving two stages (1) training an ensemble model of multiple UNet architectures with available high and low confidence labeled data and, generating pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combining the generated labels with the previously available high confidence hand-labeled dataset.
Edit intent: clarity
Original:  Additionally, we post process our results with Conditional Random Fields.
Revised:  We post process our results with Conditional Random Fields.
Edit intent: coherence
Original:  There exist different entropy measures in the literature to analyze the predictability and complexity of time series.
Revised:  Different entropy measures exist in the literature to analyze the predictability and complexity of time series.
Edit intent: coherence
Original:  However, these measures have some drawbacks especially in short time series.
Revised:  However, the existed methods have some drawbacks especially in short time series.
Edit intent: clarity
Original:  To overcome the difficulties, this paper proposes a new method for estimating the entropy of a time series using the LogNNet 784:25:10 neural network model.
Revised:  To overcome these difficulties, this paper proposes a new method for estimating the entropy of a time series using the LogNNet 784:25:10 neural network model.
Edit intent: fluency
Original:  To overcome the difficulties, this paper proposes a new method for estimating the entropy of a time series using the LogNNet 784:25:10 neural network model.
Revised:  To overcome the difficulties, this study proposes a new method for estimating the entropy of a time series using the LogNNet 784:25:10 neural network model.
Edit intent: clarity
Original:  To overcome the difficulties, this paper proposes a new method for estimating the entropy of a time series using the LogNNet 784:25:10 neural network model.
Revised:  To overcome the difficulties, this paper proposes a new method for estimating the entropy of a time series using the LogNNet neural network model.
Edit intent: coherence
Original:  The LogNNet reservoir matrix consists of 19625 elements which is filled with the time series elements . After that, the network is trained on MNIST-10 dataset and the classification accuracy is calculated.
Revised:  The LogNNet reservoir matrix is filled with the time series elements . After that, the network is trained on MNIST-10 dataset and the classification accuracy is calculated.
Edit intent: clarity
Original:  The LogNNet reservoir matrix consists of 19625 elements which is filled with the time series elements . After that, the network is trained on MNIST-10 dataset and the classification accuracy is calculated.
Revised:  The LogNNet reservoir matrix consists of 19625 elements which is filled with the time series elements according to our algorithm. The network is trained on MNIST-10 dataset and the classification accuracy is calculated.
Edit intent: clarity
Original:  A new time series characteristic  called time series learning inertia is introduced to investigate the effect of epochs number in the efficiency of neural network.
Revised:  A new time series characteristic , called time series learning inertia is introduced to investigate the effect of epochs number in the efficiency of neural network.
Edit intent: fluency
Original:  A new time series characteristic  called time series learning inertia is introduced to investigate the effect of epochs number in the efficiency of neural network.
Revised:  A new time series characteristic called time series learning inertia , that determines the learning rate of the neural network.
Edit intent: clarity
Original:  To show the robustness and efficiency of the proposed method , it is applied on some chaotic, periodic, random, binary and constant time series.
Revised:  The robustness and efficiency of the proposed method , it is applied on some chaotic, periodic, random, binary and constant time series.
Edit intent: coherence
Original:  To show the robustness and efficiency of the proposed method , it is applied on some chaotic, periodic, random, binary and constant time series.
Revised:  To show the robustness and efficiency of the method is verified on chaotic, periodic, random, binary and constant time series.
Edit intent: clarity
Original:  Different entropy measures exist in the literature to analyze the predictability and complexity of time series.
Edit intent: coherence
Original:  However, the existed methods have some drawbacks related to a strong dependence of entropy on the parameters of the methods , as well as on the length and amplitude of the time series .
Revised:  However, the existing methods have some drawbacks related to a strong dependence of entropy on the parameters of the methods , as well as on the length and amplitude of the time series .
Edit intent: coherence
Original:  However, the existed methods have some drawbacks related to a strong dependence of entropy on the parameters of the methods , as well as on the length and amplitude of the time series .
Revised:  However, the existed methods have some drawbacks related to the strong dependence of entropy on the parameters of the methods , as well as on the length and amplitude of the time series .
Edit intent: coherence
Original:  However, the existed methods have some drawbacks related to a strong dependence of entropy on the parameters of the methods , as well as on the length and amplitude of the time series .
Revised:  However, the existed methods have some drawbacks related to a strong dependence of entropy on the parameters of the methods .
Edit intent: coherence
Original:  The LogNNet reservoir matrix is filled with the time series elements according to our algorithm.
Revised:  The LogNNet reservoir matrix is filled with time series elements according to our algorithm.
Edit intent: fluency
Original:  The network is trained on MNIST-10 dataset and the classification accuracy is calculated.
Revised:  The accuracy of the classification of images from the MNIST-10 dataset and the classification accuracy is calculated.
Edit intent: clarity
Original:  The greater complexity of the time series leads to the better ability of the neural network to learn, and to the higher classification accuracy and  NNetEn values.
Revised:  The greater complexity of the time series leads to a higher classification accuracy and NNetEn values.
Edit intent: clarity
Original:  The greater complexity of the time series leads to the better ability of the neural network to learn, and to the higher classification accuracy and  NNetEn values.
Revised:  The greater complexity of the time series leads to the better ability of the neural network to learn, and to the higher classification accuracy and higher NNetEn values.
Edit intent: fluency
Original:  The epochs number in the training process of LogNNet is considered as the control parameter.
Edit intent: coherence
Original:  We introduce a new time series characteristic , called time series learning inertia , that determines the learning rate of the neural network.
Revised:  We introduce a new time series characteristic called time series learning inertia , that determines the learning rate of the neural network.
Edit intent: fluency
Original:  We introduce a new time series characteristic , called time series learning inertia , that determines the learning rate of the neural network.
Revised:  We introduce a new time series characteristic , called time series learning inertia that determines the learning rate of the neural network.
Edit intent: fluency
Original:  The robustness and efficiency of the method is verified on chaotic, periodic, random, binary  and constant time series.
Revised:  The robustness and efficiency of the method is verified on chaotic, periodic, random, binary , and constant time series.
Edit intent: fluency
Original:  Besides that, hand-designed model development is laborious and also costly.
Revised:  Besides that, hand-designed model development is strenuous and also costly.
Edit intent: clarity
Original:  The algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states.
Revised:  Our algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states.
Edit intent: fluency
Original:  Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube provably does not accumulate over-approximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques.
Revised:  Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube does not accumulate over-approximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques.
Edit intent: clarity
Original:  Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube provably does not accumulate over-approximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques.
Revised:  Compared to advanced reachability analysis tools for time-continuous neural networks, GoTube provably does not accumulate overapproximation errors between time steps and avoids the infamous wrapping effect inherent in symbolic techniques.
Edit intent: fluency
Original:  We show that GoTube substantially outperforms state-of-the-art verification tools in terms of the size of the initial ball, speed, time-horizon, task completion, and scalability , on a large set of experiments.
Revised:  We show that GoTube substantially outperforms state-of-the-art verification tools in terms of the size of the initial ball, speed, time-horizon, task completion, and scalability on a large set of experiments.
Edit intent: fluency
Original:  GoTube is stable and sets the state-of-the-art for its ability to scale up to time horizons well beyond what has been possible before .
Revised:  GoTube is stable and sets the state-of-the-art in terms of its ability to scale up to time horizons well beyond what has been possible before .
Edit intent: clarity
Original:  GoTube is stable and sets the state-of-the-art for its ability to scale up to time horizons well beyond what has been possible before .
Revised:  GoTube is stable and sets the state-of-the-art for its ability to scale to time horizons well beyond what has been possible before .
Edit intent: clarity
Original:  GoTube is stable and sets the state-of-the-art for its ability to scale up to time horizons well beyond what has been possible before .
Revised:  GoTube is stable and sets the state-of-the-art for its ability to scale up to time horizons well beyond what has been previously possible .
Edit intent: clarity
Original:  Then, a P^3-pressure will be defined by locally calculable components .
Revised:  Then, a P^3-pressure will be defined component-wisely .
Edit intent: clarity
Original:  The resulting P^3-pressure shows the optimal order of convergence.
Revised:  The resulting P^3-pressure is analyzed to have the optimal order of convergence.
Edit intent: clarity
Original:  An investigation into a disorder which disrupts this desquamationmodel shows reduced tissue thickness , consequently diminishing the protective role of the tissue , and two treatment hypotheses are compared. 
Revised:  An investigation into a disorder which disrupts this desquamationmodel shows reduced tissue thickness and consequently diminishing the protective role of the tissue , and two treatment hypotheses are compared. 
Edit intent: coherence
Original:  The size of a pair  perfectly matched sets (A,B) is |A|=|B|.
Revised:  The size of a pair of perfectly matched sets (A,B) is |A|=|B|.
Edit intent: fluency
Original:  To lower the overall cost and computational overhead of this process, we propose a solution called Chef (CHEap and Fast label cleaning), which consists of the following three components.
Revised:  To lower the overall cost and computational overhead of this process, we propose a solution called CHEF (CHEap and Fast label cleaning), which consists of the following three components.
Edit intent: fluency
Original: Delivering malware covertly and detection-evadingly is critical to advanced malware campaigns.
Revised: Delivering malware covertly and evasively is critical to advanced malware campaigns.
Edit intent: clarity
Original:  In this paper, we present a method that delivers malware covertly and detection-evadingly through neural network models . Neural network models are poorly explainable and have a good generalization ability.
Revised:  In this paper, we present a new method to covertly and evasively deliver malware through a neural network model . Neural network models are poorly explainable and have a good generalization ability.
Edit intent: clarity
Original:  By embedding malware into the neurons, malware can be delivered covertly  with minor or even no impact on the performance of neural networks .
Revised:  By embedding malware in neurons, the malware can be delivered covertly with minor or even no impact on the performance of neural networks .
Edit intent: clarity
Original:  By embedding malware into the neurons, malware can be delivered covertly  with minor or even no impact on the performance of neural networks .
Revised:  By embedding malware into the neurons, malware can be delivered covertly , with minor or even no impact on the performance of neural networks .
Edit intent: fluency
Original:  By embedding malware into the neurons, malware can be delivered covertly  with minor or even no impact on the performance of neural networks .
Revised:  By embedding malware into the neurons, malware can be delivered covertly with minor or no impact on the performance of neural networks .
Edit intent: clarity
Original:  By embedding malware into the neurons, malware can be delivered covertly  with minor or even no impact on the performance of neural networks .
Revised:  By embedding malware into the neurons, malware can be delivered covertly with minor or even no impact on the performance of neural network .
Edit intent: fluency
Original:  Meanwhile, since the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines.
Revised:  Meanwhile, because the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines.
Edit intent: fluency
Original:  Meanwhile, since the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines.
Revised:  Meanwhile, since the structure of the neural network model remains unchanged, they can pass the security scan of antivirus engines.
Edit intent: fluency
Original:  Meanwhile, since the structure of the neural network models remains unchanged, they can pass the security scan of antivirus engines.
Revised:  Meanwhile, since the structure of the neural network models remains unchanged, it can pass the security scan of antivirus engines.
Edit intent: clarity
Original:  Experiments show that 36.9MB of malware can be embedded into a 178MB-AlexNet model within 1\% accuracy loss, and no suspicious are raised by antivirus engines in VirusTotal, which verifies the feasibility of this method.
Revised:  Experiments show that 36.9MB of malware can be embedded in a 178MB-AlexNet model within 1\% accuracy loss, and no suspicious are raised by antivirus engines in VirusTotal, which verifies the feasibility of this method.
Edit intent: fluency
Original:  Experiments show that 36.9MB of malware can be embedded into a 178MB-AlexNet model within 1\% accuracy loss, and no suspicious are raised by antivirus engines in VirusTotal, which verifies the feasibility of this method.
Revised:  Experiments show that 36.9MB of malware can be embedded into a 178MB-AlexNet model within 1\% accuracy loss, and no suspicion is raised by anti-virus engines in VirusTotal, which verifies the feasibility of this method.
Edit intent: fluency
Original:  With the widespread application of artificial intelligence, utilizing neural networks  becomes a forwarding trend of malware .
Revised:  With the widespread application of artificial intelligence, utilizing neural networks becomes a forwarding trend .
Edit intent: clarity
Original:  We hope this work could provide a referenceable scenario for the defense on neural network-assisted attacks.
Revised:  We hope this work can provide a reference scenario for the defense on neural network-assisted attacks.
Edit intent: clarity
Original:  It is also demonstrated that the  work is robust against common transforms and attacks designed for conventional watermarking approaches.
Revised:  It is also demonstrated that the proposed work is robust against common transforms and attacks designed for conventional watermarking approaches.
Edit intent: clarity
Original:  We then derive a lower bound to the best achievable excess-distortion probability and discuss situations where it is achievable.
Revised:  We then derive a lower bound to the best achievable excess--distortion probability and discuss situations where it is achievable.
Edit intent: fluency
Original:  Here, of course, source coding and channel coding cannot be completely separated if one wishes to meet the bound .
Revised:  Here, of course, source coding and channel coding cannot be completely separated without loss of optimality .
Edit intent: clarity
Original:  Finally, we outline a few variations and extensions of the model considered, such as: (i) incorporating a common reconstruction constraint, (ii) availability of side information at both ends, and (iii) extension to the Gel'fand-Pinsker channel .
Revised:  Finally, we outline a few extensions of the model considered, such as: (i) incorporating a common reconstruction constraint, (ii) availability of side information at both ends, and (iii) extension to the Gel'fand-Pinsker channel .
Edit intent: clarity
Original:  Combined with theorems on graphical valuations and pricing equilibrium of Candogan, Ozdagar and Parillo , our results indicate that quadratic pricing is a highly practical method to run combinatorial auctions.
Revised:  Combined with theorems on graphical valuations and pricing equilibrium of Candogan, Ozdagar and Parrilo , our results indicate that quadratic pricing is a highly practical method to run combinatorial auctions.
Edit intent: fluency
Original:  In such an instance, the value of every agent i \in N for every item j \in G is v_{ij} \in \{p,q\}, for p %DIFDELCMD < \le %%%  q \in N . We show that an optimal allocation can be computed\le  in polynomial time if p divides q . When pdoes not divide q , we show after appropriate scaling.
Revised:  In such an instance, the value of every agent i \in N for every good j \in G is v_{ij} \in \{p,q\}, for p %DIFDELCMD < \le %%% q \in N . We show that an optimal allocation can be computed\le in polynomial time if p divides q . When pdoes not divide q , we show after appropriate scaling.
Edit intent: clarity
Original:  In such an instance, the value of every agent i \in N for every item j \in G is v_{ij} \in \{p,q\}, for p %DIFDELCMD < \le %%%  q \in N . We show that an optimal allocation can be computed\le  in polynomial time if p divides q . When pdoes not divide q , we show after appropriate scaling.
Revised:  In such an instance, the value of every agent i \in N for every item j \in G is v_{ij} \in \{p,q\}, for p %DIFDELCMD < \le %%% , q \in N . We show that an optimal allocation can be computed\le in polynomial time if p divides q . When pdoes not divide q , we show after appropriate scaling.
Edit intent: fluency
Original:  In such an instance, the value of every agent i \in N for every item j \in G is v_{ij} \in \{p,q\}, for p %DIFDELCMD < \le %%%  q \in N . We show that an optimal allocation can be computed\le  in polynomial time if p divides q . When pdoes not divide q , we show after appropriate scaling.
Revised:  In such an instance, the value of every agent i \in N for every item j \in G is v_{ij} \in \{p,q\}, for p %DIFDELCMD < \le %%% q \in N , p\le in polynomial time if p divides q . When pdoes not divide q , we show after appropriate scaling.
Edit intent: clarity
Original:  It strictly decreases with the denominator of the irreducible fraction that equals p/q. Finally , we prove an APX-hardness result for the problem\classAPX with a lower bound on the ratio of 1.000015 .
Revised:  It strictly decreases with the denominator of the irreducible fraction that equals p/q. Finally , we prove that the problem is\classAPX with a lower bound on the ratio of 1.000015 .
Edit intent: clarity