wanyu/IteraTeR_v2 huggingface dataset:
Original:  Using this tool , we create a list of 821 websites outside the Alexa Top 1000 that cover Chinese politics, minority human rights organizations, and oppressed religions .
Revised:  Using this tool , we create a list of 821 websites outside the Alexa Top 1000 that cover Chinese politics, minority human rights organizations, oppressed religions, and more .
Edit intent: clarity
Original: Estimating covariances between financial assets plays an important role in risk management and optimal portfolio allocation .
Revised: Estimating covariances between financial assets plays an important role in risk management .
Edit intent: clarity
Original:  In practice, when the sample size is small compared to the number of variables, i.e. when considering a wide universe of assets over just a few years, this poses considerable challenges and the empirical estimate is known to be very unstable.
Revised:  In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable.
Edit intent: coherence
Original:  In spite of the recent progress in data based inference of complex dynamical systems , to reconstruct cyberphysical systems with a multiplex network structure is an outstanding and challenging problem .
Revised:  In spite of recent progress towards data based inference of complex dynamical systems , to reconstruct cyberphysical systems with a multiplex network structure is an outstanding and challenging problem .
Edit intent: clarity
Original:  In spite of the recent progress in data based inference of complex dynamical systems , to reconstruct cyberphysical systems with a multiplex network structure is an outstanding and challenging problem .
Revised:  In spite of the recent progress in data based inference of complex dynamical systems , to reconstruct complex systems with a multiplex network structure is an outstanding and challenging problem .
Edit intent: clarity
Original:  In spite of the recent progress in data based inference of complex dynamical systems , to reconstruct cyberphysical systems with a multiplex network structure is an outstanding and challenging problem .
Revised:  In spite of the recent progress in data based inference of complex dynamical systems , to reconstruct cyberphysical systems with a multiplex structure remains largely open .
Edit intent: clarity
Original:  For prototypical complex networks with a double layer structure and hosting spreading dynamics, we show that the structures of both cyber and physical layers can be simultaneously reconstructed from time series data.
Edit intent: coherence
Original:  We find that in a continually changing environment, modeled here by a linearly moving phenotypic optimum, the mean trait also moves linearly with time.
Revised:  We find that in a changing environment, modeled here by a linearly moving phenotypic optimum, the mean trait also moves linearly with time.
Edit intent: clarity
Original: With the world moving towards being increasingly dependent on computers and automation, one of the main challenges  in the current decade has been to build secure applications, systems and networks.
Revised: As the world moves towards being increasingly dependent on computers and automation, one of the main challenges in the current decade has been to build secure applications, systems and networks.
Edit intent: fluency
Original: With the world moving towards being increasingly dependent on computers and automation, one of the main challenges  in the current decade has been to build secure applications, systems and networks.
Revised: With the world moving towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges in the current decade has been to build secure applications, systems and networks.
Edit intent: clarity
Original: With the world moving towards being increasingly dependent on computers and automation, one of the main challenges  in the current decade has been to build secure applications, systems and networks.
Revised: With the world moving towards being increasingly dependent on computers and automation, one of the main challenges faced in the current decade has been to build secure applications, systems and networks.
Edit intent: clarity
Original:  Alongside these challenges, the number of threats  is rising exponentially due to the attack surface increasing through numerous interfaces offered for each service .
Revised:  Alongside these challenges, the number of threats is rising exponentially due to the increasing complexity of networks and services of modern networks .
Edit intent: clarity
Original:  however, current tools often fail to adapt to ever-changing architectures, associated threats and 0-days .
Revised:  however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks .
Edit intent: clarity
Original:  This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems ( IDS) capabilities and assets.
Revised:  This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems ( IDS) capabilities and assets.
Edit intent: clarity
Original:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy , while current datasets demonstrate clear lack of real-network threats and attack representation , but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS . Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository .
Revised:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy . Current datasets demonstrate a clear lack of real-network threats and attack representation , but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS . Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository .
Edit intent: coherence
Original:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy , while current datasets demonstrate clear lack of real-network threats and attack representation , but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS . Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository .
Revised:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy , while current datasets demonstrate clear lack of real-network threats , attack representation and include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS . Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository .
Edit intent: coherence
Original:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy , while current datasets demonstrate clear lack of real-network threats and attack representation , but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS . Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository .
Revised:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy , while current datasets demonstrate clear lack of real-network threats and attack representation , but rather include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS . Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository .
Edit intent: clarity
Original:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy , while current datasets demonstrate clear lack of real-network threats and attack representation , but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS . Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository .
Revised:  The manuscript highlights that current IDS only cover 25 \% of our threat taxonomy , while current datasets demonstrate clear lack of real-network threats and attack representation , but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets .
Edit intent: clarity
Original:  We finally perform a rigorous perturbation analysis of those linear programming problems, and highlight , as a numerical example, the influence of smile extrapolation on the bounds of exotic options.
Revised:  We finally perform a rigorous perturbation analysis of those linear programming problems, and highlight numerically the influence of smile extrapolation on the bounds of exotic options.
Edit intent: clarity
Original:  In this paper, an instance level feature representation built upon recent fully convolutional instance-aware segmentation is proposed.
Revised:  In this paper, an instance level feature representation built upon fully convolutional instance-aware segmentation is proposed.
Edit intent: clarity
Original:  The feature is ROI-pooled based on the segmented instance region.
Revised:  The feature is ROI-pooled from the segmented instance region.
Edit intent: clarity
Original:  So that instances in different sizes and layouts are represented by deep feature in uniform length.
Revised:  So that instances in various sizes and layouts are represented by deep feature in uniform length.
Edit intent: fluency
Original:  So that instances in different sizes and layouts are represented by deep feature in uniform length.
Revised:  So that instances in different sizes and layouts are represented by deep features in uniform length.
Edit intent: fluency
Original:  Superior performance  in terms of its distinctiveness and scalability is observed on a challenging evaluation dataset built by ourselves  .
Revised:  Superior performance is observed in terms of its distinctiveness and scalability is observed on a challenging evaluation dataset built by ourselves .
Edit intent: fluency
Original:  Superior performance  in terms of its distinctiveness and scalability is observed on a challenging evaluation dataset built by ourselves  .
Revised:  Superior performance in terms of its distinctiveness and scalability on a challenging evaluation dataset built by ourselves .
Edit intent: clarity
Original: We present a novel deep learning based image inpainting system to complete images with free-form masks and inputs .
Revised: We present a generative image inpainting system to complete images with free-form masks and inputs .
Edit intent: clarity
Original: We present a novel deep learning based image inpainting system to complete images with free-form masks and inputs .
Revised: We present a novel deep learning based image inpainting system to complete images with free-form mask and guidance .
Edit intent: clarity
Original:  Moreover, as free-form masks may appear anywhere in images with any shapes , global and local GANs designed for a single rectangular mask are not suitable.
Revised:  Moreover, as free-form masks may appear anywhere in images with any shape , global and local GANs designed for a single rectangular mask are not suitable.
Edit intent: fluency
Original:  To this end , we also present a novel GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminators on dense image patches.
Revised:  To this end , we also present a patch-based GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminators on dense image patches.
Edit intent: clarity
Original:  To this end , we also present a novel GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminators on dense image patches.
Revised:  To this end , we also present a novel GAN loss, named SN-PatchGAN, by applying spectral-normalized discriminator on dense image patches.
Edit intent: fluency
Original:  We show that our system helps users quickly remove distracting objects, modify image layouts, clear watermarks , edit facesand interactively create novel objects in images.
Revised:  Our system helps user quickly remove distracting objects, modify image layouts, clear watermarks , edit facesand interactively create novel objects in images.
Edit intent: coherence
Original:  More high-resolution results and video materials are available at  URL
Revised:  More high-resolution results and video materials are available at : URL
Edit intent: fluency
Original: This paper focuses on several theoretical issues and principles in steganography security, and defines four security levels by analyzing  corresponding algorithm instances.
Revised: This paper focuses on several theoretical issues and principles in steganography security, and defines four security levels by analyzing the corresponding algorithm instances.
Edit intent: fluency
Original:  Then under the current technology situation, we  analyze the indistinguishability of the cover and stego-cover, and consider that the steganography security should rely on the key secrecy with algorithms open.
Revised:  Under the current technology situation, we analyze the indistinguishability of the cover and stego-cover, and consider that the steganography security should rely on the key secrecy with algorithms open.
Edit intent: coherence
Original:  Then under the current technology situation, we  analyze the indistinguishability of the cover and stego-cover, and consider that the steganography security should rely on the key secrecy with algorithms open.
Revised:  Then under the current technology situation, we then analyze the indistinguishability of the cover and stego-cover, and consider that the steganography security should rely on the key secrecy with algorithms open.
Edit intent: coherence
Original:  When analyzing the security instances, we classify the steganalysis attacks according to their variable access to the steganography system, and then define the four security levels.
Revised:  When analyzing the security instances, we have classified the steganalysis attacks according to their variable access to the steganography system, and then define the four security levels.
Edit intent: coherence
Original:  When analyzing the security instances, we classify the steganalysis attacks according to their variable access to the steganography system, and then define the four security levels.
Revised:  When analyzing the security instances, we classify the steganalysis attacks according to their variable access to the steganography system, and then defined the four security levels.
Edit intent: fluency
Original:  We present algorithm instances based on current technical conditions, and analyze their data hiding process, security level, and practice requirements.
Revised:  We present algorithm instances based on current technical conditions, and analyzed their data hiding process, security level, and practice requirements.
Edit intent: fluency
Original:  We introduce O\left(m^{\log\left(3{2}+\epsilon\right)}\right)-competitive deterministic algorithms for both problems and for any fixed \epsilon > 0, m being the number of requests.
Revised:  We introduce O\left(m^{\log\left(3{2}+\epsilon\right)}\right)-competitive deterministic algorithms for both problems and for any fixed \epsilon > 0.
Edit intent: clarity
Original: In this paper, we propose Generative Adversarial Network (GAN) architectures using dynamically-routed Capsule Networks for conditional and random image-synthesis.
Revised: In this paper, we propose Generative Adversarial Network (GAN) architectures using Capsule Networks for conditional and random image-synthesis.
Edit intent: clarity
Original:  The architectures benefit from the fact that Capsule Networks encode the properties and spatial relationships of the features in the images.
Revised:  Capsule Networks encode the properties and spatial relationships of the features in the images.
Edit intent: clarity
Original:  The architectures benefit from the fact that Capsule Networks encode the properties and spatial relationships of the features in the images.
Revised:  The architectures benefit from the fact that Capsule Networks encode meta-properties and spatial relationships of the features in the images.
Edit intent: clarity
Original:  Capsule Networks being a more effective critic provide multiple benefits when replacing Convolutional Neural Network discriminators being used in the current work-horses for image synthesis - DCGANs . Our architectures use a loss analogous to Wasserstein loss and demonstrate that they can encode a dataset's representation faster than currently existing GANs resulting in lesser number of training samples required  .
Revised:  Capsule Networks being a more effective critic provide multiple benefits when replacing Convolutional Neural Network discriminators being used in the current work-horses for image synthesis . Our architectures use a loss analogous to Wasserstein loss and demonstrate that they can encode a dataset's representation faster than currently existing GANs resulting in lesser number of training samples required .
Edit intent: clarity
Original:  Capsule Networks being a more effective critic provide multiple benefits when replacing Convolutional Neural Network discriminators being used in the current work-horses for image synthesis - DCGANs . Our architectures use a loss analogous to Wasserstein loss and demonstrate that they can encode a dataset's representation faster than currently existing GANs resulting in lesser number of training samples required  .
Revised:  Capsule Networks being a more effective critic provide multiple benefits when replacing Convolutional Neural Network discriminators being used in the current work-horses for image synthesis - DCGANs . Our architectures use losses analogous to Wasserstein loss and demonstrate that they can encode a dataset's representation faster than currently existing GANs resulting in lesser number of training samples required .
Edit intent: fluency
Original:  Capsule Networks being a more effective critic provide multiple benefits when replacing Convolutional Neural Network discriminators being used in the current work-horses for image synthesis - DCGANs . Our architectures use a loss analogous to Wasserstein loss and demonstrate that they can encode a dataset's representation faster than currently existing GANs resulting in lesser number of training samples required  .
Revised:  Capsule Networks being a more effective critic provide multiple benefits when replacing Convolutional Neural Network discriminators being used in the current work-horses for image synthesis - DCGANs . Our architectures use a loss analogous to Wasserstein loss and Capsule Networks, which prove to be a more effective critic in comparison to CNNs. Thus, our proposed GAN architectures learn the data manifold much faster and therefore, show significant reduction in the number of training samples required .
Edit intent: coherence
Original:  Our experiments show that the generator is pushed to give better and more diverseresults in significantly lesser number of epochs without over-fitting.
Edit intent: coherence
Original: In this paper, we propose Generative Adversarial Network (GAN) architectures using Capsule Networks for conditional and random image-synthesis.
Revised: In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for conditional and random image-synthesis.
Edit intent: fluency
Original: In this paper, we propose Generative Adversarial Network (GAN) architectures using Capsule Networks for conditional and random image-synthesis.
Revised: In this paper, we propose Generative Adversarial Network (GAN) architectures using Capsule Networks for image-synthesis.
Edit intent: clarity
Original:  Capsule Networks encode meta-properties and spatial relationships between the features of the image , which helps it become a more powerful critic in comparison to the Convolutional Neural Networks (CNNs) used in current architectures for image synthesis.
Revised:  Capsule Networks encode meta-properties and spatial relationships between the features of the image helps it become a more powerful critic in comparison to the Convolutional Neural Networks (CNNs) used in current architectures for image synthesis.
Edit intent: coherence
Original:  Capsule Networks encode meta-properties and spatial relationships between the features of the image , which helps it become a more powerful critic in comparison to the Convolutional Neural Networks (CNNs) used in current architectures for image synthesis.
Revised:  Capsule Networks encode meta-properties and spatial relationships between the features of the image , which helps it become a more powerful critic in comparison to Convolutional Neural Networks (CNNs) used in current architectures for image synthesis.
Edit intent: fluency
Original:  Thus, our proposed GAN architectures learn the data manifold much faster and therefore, show significant reduction in the number of training samples required to train when compared to the current work horses for image synthesis, DCGANs and its variants which utilize CNNsas discriminators.
Revised:  Thus, our proposed GAN architectures learn the data manifold much faster and therefore, synthesize visually accurate images in significantly lesser number of training samples required to train when compared to the current work horses for image synthesis, DCGANs and its variants which utilize CNNsas discriminators.
Edit intent: clarity
Original:  Thus, our proposed GAN architectures learn the data manifold much faster and therefore, show significant reduction in the number of training samples required to train when compared to the current work horses for image synthesis, DCGANs and its variants which utilize CNNsas discriminators.
Revised:  Thus, our proposed GAN architectures learn the data manifold much faster and therefore, show significant reduction in the number of training samples and training epochs in comparison to GANs and its variants which utilize CNNsas discriminators.
Edit intent: clarity
Original:  We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic postsynaptic events.
Revised:  We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events.
Edit intent: fluency
Original: We consider geometric graphs whose vertex set is a finite set of points (e.g., in the plane), and whose hyperedges are the intersections of this set with a family of geometric regions (e.g., axis-parallel rectangles).
Revised: We consider geometric hypergraphs whose vertex set is a finite set of points (e.g., in the plane), and whose hyperedges are the intersections of this set with a family of geometric regions (e.g., axis-parallel rectangles).
Edit intent: clarity
Original:  Our results offer the first simulation-based investigation of a population's sensitivity to pandemics across multiple historical time points, and highlight three significant trends in pandemic patterns over the years: increased peak prevalence, faster spreading rates, and decreasing spatiotemporal bimodality.
Revised:  Our results offer a simulation-based investigation of a population's sensitivity to pandemics across multiple historical time points, and highlight three significant trends in pandemic patterns over the years: increased peak prevalence, faster spreading rates, and decreasing spatiotemporal bimodality.
Edit intent: clarity