wanyu/IteraTeR_v2 huggingface dataset:
Original:  After training the DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Revised:  After training a DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Edit intent: fluency
Original:  After training the DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Revised:  After training the DNN for both style transfer and data hiding , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Edit intent: clarity
Original:  After training the DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Revised:  After training the DNN for both style transfer and data hiding tasks , while the DNN can transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Edit intent: clarity
Original:  After training the DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Revised:  After training the DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Edit intent: clarity
Original:  After training the DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image when inputting a trigger signal.
Revised:  After training the DNN for both style transfer and data hiding tasks , while the DNN may transfer the style of an image to a target one, it can be also used to embed secret data into a cover image or extract secret data from a stego image by inputting the trigger signal.
Edit intent: fluency
Original: Data hiding is referred to as the art of hiding secret data into a digital cover for covert communication.
Revised: Data hiding is the art of hiding secret data into a digital cover for covert communication.
Edit intent: coherence
Original: Data hiding is referred to as the art of hiding secret data into a digital cover for covert communication.
Revised: Data hiding is referred to as the art of hiding secret data into a cover object such as digital image for covert communication.
Edit intent: clarity
Original:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task . After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal .
Revised:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task (i.e., style transfer). After training the DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal .
Edit intent: coherence
Original:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task . After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal .
Revised:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task . After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to the target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal .
Edit intent: fluency
Original:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task . After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal .
Revised:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task . After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can also hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal .
Edit intent: clarity
Original:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task . After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image by inputting the trigger signal .
Revised:  In this letter, we propose a novel method to disguise data hiding tools, including a data embedding tool and a data extraction tool, as a deep neural network (DNN) with an ordinary task . After training a DNN for both style transfer and data hiding, while the DNN can transfer the style of an image to a target one, it can be also used to hide secret data into a cover image or extract secret data from a stego image .
Edit intent: coherence
Original: We consider two robust versions of optimal transport, named \textit{Robust Semi-constrained Optimal Transport (RSOT) and\textit{Robust Unconstrained Optimal Transport (ROT), formulated by relaxing the marginal constraints with Kullback-Leibler divergence.
Revised: We consider robust variants of the standard optimal transport, named \textit{Robust Semi-constrained Optimal Transport (RSOT) and\textit{Robust Unconstrained Optimal Transport (ROT), formulated by relaxing the marginal constraints with Kullback-Leibler divergence.
Edit intent: clarity
Original: We consider two robust versions of optimal transport, named \textit{Robust Semi-constrained Optimal Transport (RSOT) and\textit{Robust Unconstrained Optimal Transport (ROT), formulated by relaxing the marginal constraints with Kullback-Leibler divergence.
Revised: We consider two robust versions of optimal transport, named \textit{Robust Semi-constrained Optimal Transport \textit{Robust Unconstrained Optimal Transport (ROT), formulated by relaxing the marginal constraints with Kullback-Leibler divergence.
Edit intent: coherence
Original: We consider two robust versions of optimal transport, named \textit{Robust Semi-constrained Optimal Transport (RSOT) and\textit{Robust Unconstrained Optimal Transport (ROT), formulated by relaxing the marginal constraints with Kullback-Leibler divergence.
Revised: We consider two robust versions of optimal transport, named \textit{Robust Semi-constrained Optimal Transport (RSOT) and\textit{ (ROT), formulated by relaxing the marginal constraints with Kullback-Leibler divergence.
Edit intent: clarity
Original: We consider two robust versions of optimal transport, named \textit{Robust Semi-constrained Optimal Transport (RSOT) and\textit{Robust Unconstrained Optimal Transport (ROT), formulated by relaxing the marginal constraints with Kullback-Leibler divergence.
Revised: We consider two robust versions of optimal transport, named \textit{Robust Semi-constrained Optimal Transport (RSOT) and\textit{Robust Unconstrained Optimal Transport robust optimal transport, where marginal constraints are relaxed via Kullback-Leibler divergence.
Edit intent: clarity
Original:  For both problems in the discrete settings, we propose Sinkhorn-based algorithms that produce \varepsilon-approximations of RSOT and ROT in \mathcal{O}(n^2{\varepsilon}) time, where n is the number of supports of the probability distributions .
Revised:  We show that Sinkhorn-based algorithms that produce \varepsilon-approximations of RSOT and ROT in \mathcal{O}(n^2{\varepsilon}) time, where n is the number of supports of the probability distributions .
Edit intent: clarity
Original:  For both problems in the discrete settings, we propose Sinkhorn-based algorithms that produce \varepsilon-approximations of RSOT and ROT in \mathcal{O}(n^2{\varepsilon}) time, where n is the number of supports of the probability distributions .
Revised:  For both problems in the discrete settings, we propose Sinkhorn-based algorithms that produce \varepsilon-approximations of RSOT and ROT in \mathcal{O}(n^2{\varepsilon}) time, in which n is the number of supports of the probability distributions .
Edit intent: clarity
Original:  Finally, we consider a barycenter problem based on RSOT, named }Robust Semi-Constrained Barycenter problem (RSBP), and develop a robust iterative Bregman projection algorithm, calledNormalized-RobustIBP algorithm, to solve the RSBP in the discrete settings of probability distributions.
Revised:  Finally, we consider a barycenter problem based on RSOT, named } problem (RSBP), and develop a robust iterative Bregman projection algorithm, calledNormalized-RobustIBP algorithm, to solve the RSBP in the discrete settings of probability distributions.
Edit intent: coherence
Original:  Finally, we consider a barycenter problem based on RSOT, named }Robust Semi-Constrained Barycenter problem (RSBP), and develop a robust iterative Bregman projection algorithm, calledNormalized-RobustIBP algorithm, to solve the RSBP in the discrete settings of probability distributions.
Revised:  Finally, we consider a barycenter problem based on RSOT, named }Robust Semi-Constrained Barycenter Normalized-RobustIBP algorithm, to solve the RSBP in the discrete settings of probability distributions.
Edit intent: coherence
Original:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of  IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Revised:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time Normalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Edit intent: fluency
Original:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of  IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Revised:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time using algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Edit intent: coherence
Original:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of  IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Revised:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP , thus being better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Edit intent: coherence
Original:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of  IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Revised:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of the IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Edit intent: fluency
Original:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of  IBP algorithm for approximating the Wasserstein barycenter . Extensive experiments confirm our theoretical results .
Revised:  We show that an \varepsilon-approximated solution of the RSBP can be achieved in \mathcal{O}(mn^2{\varepsilon}) time usingNormalized-RobustIBP algorithm when m = 2, which is better than the previous complexity \widetilde{\mathcal{O}}(\frac{mn^2}{\varepsilon^2}) of IBP algorithm for approximating the Wasserstein barycenter .
Edit intent: coherence
Original: We present a unified framework for minimizing average completion time for many seemingly disparate online scheduling problems, such as the traveling repairperson problems (TRP), dial-a-ride problems (DARP), and scheduling on unrelated machines.
Revised: We present a unified framework for minimizing average completion time for many seemingly disparate online scheduling problems, such as the traveling repairperson problems (TRP), dial-a-ride problems (DARP), and scheduling on unrelated machines.
Edit intent: fluency
Original:  Here we show that  strong call-by-value evaluation is also reasonable , via a new abstract machine realizing useful sharing and having a linear overhead.
Revised:  Here we show that also strong call-by-value evaluation is also reasonable , via a new abstract machine realizing useful sharing and having a linear overhead.
Edit intent: fluency
Original:  We give an example of family that the machine executes in time logarithmic in the number of beta-steps.
Revised:  We give examples of families that the machine executes in time logarithmic in the number of beta-steps.
Edit intent: fluency
Original: Hindsight rationality is an approach to playing multi-agent, general-sum games that prescribes no-regret learning dynamics and describes jointly rational behavior  with mediated equilibria.
Revised: Hindsight rationality is an approach to playing general-sum games that prescribes no-regret learning dynamics and describes jointly rational behavior with mediated equilibria.
Edit intent: clarity
Original: Hindsight rationality is an approach to playing multi-agent, general-sum games that prescribes no-regret learning dynamics and describes jointly rational behavior  with mediated equilibria.
Revised: Hindsight rationality is an approach to playing multi-agent, general-sum games that prescribes no-regret learning dynamics and describes jointly rational behavior among multiple agents with mediated equilibria.
Edit intent: clarity
Original:  In addition, we present a thorough empirical analysis of EFR 's performance with different deviation types in common benchmark games.
Revised:  In addition, we present a thorough empirical analysis of EFR instantiated with different deviation types in common benchmark games.
Edit intent: clarity
Original: Stochastic games  , introduced by Shapley, model adversarial interactions in stochastic environments where two players choose their moves to optimize a discounted-sum of rewards.
Revised: Stochastic games , introduced by Shapley, model adversarial interactions in stochastic environments where two players try to optimize a discounted-sum of rewards.
Edit intent: clarity
Original: Stochastic games  , introduced by Shapley, model adversarial interactions in stochastic environments where two players choose their moves to optimize a discounted-sum of rewards.
Revised: Stochastic games , introduced by Shapley, model adversarial interactions in stochastic environments where two players choose their moves to optimize a discounted sum of rewards.
Edit intent: fluency
Original:  In the traditional discounted reward semantics , long-term weights are geometrically attenuated based on the delay in their occurrence.
Revised:  In this model , long-term weights are geometrically attenuated based on the delay in their occurrence.
Edit intent: clarity
Original:  We study  past-discounted weight sequences as rewards on stochastic game arenas and examine the corresponding stochastic games with discounted and mean payoff objectives.
Revised:  We study past-discounted weight sequences and examine the corresponding stochastic games with discounted and mean payoff objectives.
Edit intent: clarity
Original:  With the framelet system, we can decompose the graph feature into low-pass and high-pass frequencies as extracted features for network training, which then defines a framelet-based graph convolution.
Revised:  We decompose an input graph into low-pass and high-pass frequencies as extracted features for network training, which then defines a framelet-based graph convolution.
Edit intent: clarity
Original:  With the framelet system, we can decompose the graph feature into low-pass and high-pass frequencies as extracted features for network training, which then defines a framelet-based graph convolution.
Revised:  With the framelet system, we can decompose the graph feature into low-pass and high-pass frequencies coefficients for network training, which then defines a framelet-based graph convolution.
Edit intent: clarity
Original:  The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many types of node and graph prediction tasks.
Revised:  The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks.
Edit intent: clarity
Original:  Moreover, we propose shrinkage as a new activation for the framelet convolution, which thresholds the high-frequency information at different scales.
Revised:  Moreover, we propose shrinkage as a new activation for the framelet convolution, which thresholds high-frequency information at different scales.
Edit intent: fluency
Original:  Empirical results demonstrate the strengths of our algorithm: compared to RL baselines, it is more sample-efficient;
Revised:  Empirical results demonstrate the strengths of our approach: compared with RL baselines, it is more sample-efficient;
Edit intent: clarity
Original:  compared to IL baselines, it achieves competitive success rates while not requiring feedback providers to have agent-specific expertise.
Revised:  compared with IL baselines, it achieves competitive success rates while not requiring feedback providers to have agent-specific expertise.
Edit intent: fluency
Original:  We also provide theoretical guarantees of the algorithm under certain assumptions on the teacher and the environment.
Revised:  We also provide theoretical guarantees for our algorithm under certain assumptions on the teacher and the environment.
Edit intent: fluency
Original:  We also provide theoretical guarantees of the algorithm under certain assumptions on the teacher and the environment.
Revised:  We also provide theoretical guarantees of the algorithm under certain assumptions about the teacher and the environment.
Edit intent: fluency
Original: Determining whether nodes can be  localized, called localizability detection, is a basic work in network localization.
Revised: Determining whether nodes can be uniquely localized, called localizability detection, is a basic work in network localization.
Edit intent: clarity
Original: Determining whether nodes can be  localized, called localizability detection, is a basic work in network localization.
Revised: Determining whether nodes can be localized, called localizability detection, is a concomitant problem of network localization.
Edit intent: clarity
Original: Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel.
Revised: Communications system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel.
Edit intent: fluency
Original:  However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bitrate , but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm.
Revised:  However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate , but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm.
Edit intent: fluency
Original:  In parallel, it is projected that by 2023  half of the worldwide network connections will be among machines rather than humans.
Revised:  In parallel, it is projected that by 2023 , half of the worldwide network connections will be among machines rather than humans.
Edit intent: fluency
Original:  This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science theory in order to formalize a general framework for task-oriented communication design.
Revised:  This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication design.
Edit intent: coherence