wanyu/IteraTeR_v2 huggingface dataset:
Original:  In addition, OpenFed also provides an end-to-end toolkit to facilitate federated learning algorithm development as well as several benchmarksto fair performance comparison under diverse computing paradigms and configurations .
Revised:  In addition, OpenFed also provides an end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plug and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning .
Edit intent: clarity
Original:  However, the far-field or near-field channel model mismatches the  practical XL-MIMO channel feature , where some scatters are in the far-field region while others may locate in the near-field region, i.e., hybrid-field channel.
Revised:  However, the far-field or near-field channel model mismatches the hybrid-field channel feature in the practical XL-MIMO channel feature , where some scatters are in the far-field region while others may locate in the near-field region, i.e., hybrid-field channel.
Edit intent: coherence
Original:  However, the far-field or near-field channel model mismatches the  practical XL-MIMO channel feature , where some scatters are in the far-field region while others may locate in the near-field region, i.e., hybrid-field channel.
Revised:  However, the far-field or near-field channel model mismatches the practical XL-MIMO system.
Edit intent: clarity
Original:  Simulation results show  the proposed scheme performs better than existing schemes.
Revised:  Simulation results show that the proposed scheme performs better than existing schemes.
Edit intent: fluency
Original:  Since any modeling is an approximation, it is not yet clear how accurate predicted joint load forces and torques are for real-life biological movements.
Revised:  Since any modeling is an approximation, it is not yet clear how accurate these predictions are for real-life biological movements.
Edit intent: clarity
Original:  In contrast to real joints, it is, however possible to measure forces in implanted prostheses, providing an alternative method of validating the modelling approach.
Revised:  Such forces can be measured in implanted prostheses, providing an alternative method of validating the modelling approach.
Edit intent: clarity
Original:  Therefore, the aim of this study was to investigate the accuracy of predicted forces in a knee joint during walking and squatting based on a computational musculoskeletal model, by comparing the model predictions with the corresponding real-life data gained from an instrumented knee prosthesis.
Revised:  The aim of this study was to investigate the accuracy of predicted forces in a knee joint during walking and squatting based on a computational musculoskeletal model, by comparing the model predictions with the corresponding real-life data gained from an instrumented knee prosthesis.
Edit intent: coherence
Original:  Using calculated root mean squared error between the predicted and measured knee contact-forces, we found that musculoskeletal models can accurately predict knee joint forces.
Revised:  We found that musculoskeletal models can accurately predict knee joint forces.
Edit intent: clarity
Original:  Furthermore, we demonstrated that the muscular coordination highly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44 \% in walking and -15 \% in squatting.
Revised:  Furthermore, we predicted that the muscular coordination highly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44 \% in walking and -15 \% in squatting.
Edit intent: clarity
Original:  Furthermore, we demonstrated that the muscular coordination highly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44 \% in walking and -15 \% in squatting.
Revised:  Furthermore, we demonstrated that the muscular coordination strongly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44 \% in walking and -15 \% in squatting.
Edit intent: clarity
Original:  Furthermore, we demonstrated that the muscular coordination highly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44 \% in walking and -15 \% in squatting.
Revised:  Furthermore, we demonstrated that the muscular coordination highly influences knee joint forces, by 44 \% in walking and -15 \% in squatting.
Edit intent: coherence
Original:  Furthermore, we demonstrated that the muscular coordination highly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44 \% in walking and -15 \% in squatting.
Revised:  Furthermore, we demonstrated that the muscular coordination highly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44 \% in walking and 15 \% in squatting.
Edit intent: fluency
Original: Fake news, false or misleading information presented as news, has a great impact on many aspects of society, such as politics and healthcare .
Revised: Fake news, false or misleading information presented as news, has a significant impact on many aspects of society, such as politics and healthcare .
Edit intent: clarity
Original:  In order to overcome the aforementioned issues , we propose a novel social context aware fake news detection method , Hetero-SCAN , based on a heterogeneous graph neural network.
Revised:  In order to overcome the aforementioned issues , we propose a novel fake news detection method , Hetero-SCAN , based on a heterogeneous graph neural network.
Edit intent: coherence
Original:  In order to overcome the aforementioned issues , we propose a novel social context aware fake news detection method , Hetero-SCAN , based on a heterogeneous graph neural network.
Revised:  In order to overcome the aforementioned issues , we propose a novel social context aware fake news detection framework , Hetero-SCAN , based on a heterogeneous graph neural network.
Edit intent: clarity
Original:  We demonstrate that Hetero-SCAN yields significant  improvement over state-of-the-art text-based and graph-based fake news detection methods in terms of performance and efficiency .
Revised:  We demonstrate that Hetero-SCAN yields significant performance improvement over state-of-the-art text-based and graph-based fake news detection methods in terms of performance and efficiency .
Edit intent: clarity
Original:  We demonstrate that Hetero-SCAN yields significant  improvement over state-of-the-art text-based and graph-based fake news detection methods in terms of performance and efficiency .
Revised:  We demonstrate that Hetero-SCAN yields significant improvement over state-of-the-art fake news detection methods in terms of performance and efficiency .
Edit intent: clarity
Original:  We demonstrate that Hetero-SCAN yields significant  improvement over state-of-the-art text-based and graph-based fake news detection methods in terms of performance and efficiency .
Revised:  We demonstrate that Hetero-SCAN yields significant improvement over state-of-the-art text-based and graph-based fake news detection methods .
Edit intent: clarity
Original:  The quality generation of adversarial samples relies on the adequacy of training data samples.
Revised:  The quality of generating adversarial samples relies on the adequacy of training data samples.
Edit intent: fluency
Original:  Sadly in low data regimes like medical anomaly detection, drug discovery and cybersecurity, the attack samples are scarce in number.
Revised:  However, in low data regimes like medical anomaly detection, drug discovery and cybersecurity, the attack samples are scarce in number.
Edit intent: coherence
Original:  This paper proposes a novel GAN design called Evasion Generative Adversarial Network (EVAGAN) , more suitable for low data regime problems that use oversampling for detection improvement of ML classifiers.
Revised:  This paper proposes a novel GAN design called Evasion Generative Adversarial Network (EVAGAN) that is more suitable for low data regime problems that use oversampling for detection improvement of ML classifiers.
Edit intent: coherence
Original:  We demonstrate that EVAGAN outperforms ACGAN for imbalance datasets regarding detection performance, training stability, time complexity.
Revised:  We demonstrate that EVAGAN outperforms ACGAN for unbalanced datasets with respect to detection performance, training stability, time complexity.
Edit intent: clarity
Original: Many recent literary works have leveraged generative adversarial networks (GANs) to spawn unseen evasion samples.
Revised: A myriad of recent literary works have leveraged generative adversarial networks (GANs) to spawn unseen evasion samples.
Edit intent: clarity
Original: Many recent literary works have leveraged generative adversarial networks (GANs) to spawn unseen evasion samples.
Revised: Many recent literary works has leveraged generative adversarial networks (GANs) to spawn unseen evasion samples.
Edit intent: fluency
Original:  However, in low data regimes like medical anomaly detection, drug discovery and cybersecurity, the attack samples are scarce in number.
Revised:  However, in low data regimes like medical anomaly detection, drug discovery and cybersecurity, the anomaly samples are scarce in number.
Edit intent: clarity
Original:  We demonstrate that EVAGAN outperforms ACGAN for unbalanced datasets with respect to detection performance, training stability , time complexity.
Revised:  We demonstrate that EVAGAN outperforms ACGAN for unbalanced datasets with respect to detection performance, training stability and time complexity.
Edit intent: fluency
Original:  The experimental analysis proves EVAGAN to be an efficient evasion hardened model for low data regimes in cybersecurity and CV . Code will be available at URL
Revised:  The experimental analysis proves that EVAGAN is an efficient evasion hardened model for low data regimes in cybersecurity and CV . Code will be available at URL
Edit intent: fluency
Original: Temporal sentence grounding in videos ~ (TSGV), which aims to localize one target segment from an untrimmed video with respect to a given sentence query, has drawn increasing attentions in the research community over the past few years.
Revised: Temporal sentence grounding in videos (TSGV), which aims to localize one target segment from an untrimmed video with respect to a given sentence query, has drawn increasing attentions in the research community over the past few years.
Edit intent: fluency
Original:  Meanwhile, TSGV is more challenging since it requires both textual and visual understanding for semantic alignment between two modalities ~ (i.e., text and video).
Revised:  Meanwhile, TSGV is more challenging since it requires both textual and visual understanding for semantic alignment between two modalities (i.e., text and video).
Edit intent: fluency
Original:  In this survey, we give a comprehensive overview for TSGV, which i) summarizes the taxonomy of existing methods, ii) provides a detailed description of the evaluation protocols ~ (i.e., datasets and metrics) to be used in TSGV, and iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations.
Revised:  In this survey, we give a comprehensive overview for TSGV, which i) summarizes the taxonomy of existing methods, ii) provides a detailed description of the evaluation protocols (i.e., datasets and metrics) to be used in TSGV, and iii) in-depth discusses potential problems of current benchmarking designs and research directions for further investigations.
Edit intent: fluency
Original: Many important high-dimensional dynamical systems exhibit complex chaotic behaviour.
Revised: The dynamics of many important high-dimensional dynamical systems exhibit complex chaotic behaviour.
Edit intent: clarity
Original:  The DNN that corresponds to a regression model is trained with channel gains as the input and allocated transmit powers as the output.
Revised:  The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output.
Edit intent: clarity
Original:  While the BS allocates the transmit power to the UEs to maximize rates for all UEs, there is an adversary that aims to minimize these rates.
Revised:  While the BS allocates the transmit powers to the UEs to maximize rates for all UEs, there is an adversary that aims to minimize these rates.
Edit intent: fluency
Original:  We show that  adversarial attacks are much more effective than the benchmark attack in terms of reducing the rate of communications.
Revised:  We show that the adversarial attacks are much more effective than the benchmark attack in terms of reducing the rate of communications.
Edit intent: fluency
Original:  In this work, we propose a new simple and easy-to-use technique, KATANA, for robustifying an existing pretrained DNN without modifying its weights.
Revised:  In this work, we propose a new simple and easy-to-use strategy for robustifying an existing pretrained DNN without modifying its weights.
Edit intent: clarity
Original:  For every image, we generate N randomized Test Time Augmentations (TTAs) by applying diverse color, blur, noise, and geometric transforms.
Revised:  For every image, we generate randomized test time augmentations by applying diverse color, blur, noise, and geometric transforms.
Edit intent: clarity
Original:  Next, we utilize the DNN's logits output to train a simple random forest classifier to predict the real class label.
Revised:  Then we use the DNN's logits output to train a simple random forest classifier to predict the real class label.
Edit intent: fluency
Original:  Next, we utilize the DNN's logits output to train a simple random forest classifier to predict the real class label.
Revised:  Next, we utilize the DNN's logits output to train a simple random forest to predict the real class label.
Edit intent: clarity
Original:  Our strategy achieves state-of-the-art adversarial robustness on diverse attacks with minimal compromise on the natural images' classification.
Revised:  Our method achieves state-of-the-art adversarial robustness on diverse attacks with minimal compromise on the natural images' classification.
Edit intent: clarity
Original:  Code is available in URL
Revised:  Code is available at URL
Edit intent: fluency
Original: Most advanced video generation and manipulation methods train on a large collection of videos.
Revised: GANs are able to perform generation and manipulation methods train on a large collection of videos.
Edit intent: clarity
Original:  While these provide more flexibility to a wide variety of video dynamics, they require days to train on a single tiny input video, rendering them  impractical.
Revised:  While these provide more flexibility to a wide variety of video dynamics, they require days to train on a single video, rendering them impractical.
Edit intent: clarity
Original:  While these provide more flexibility to a wide variety of video dynamics, they require days to train on a single tiny input video, rendering them  impractical.
Revised:  While these provide more flexibility to a wide variety of video dynamics, they require days to train on a single tiny input video, rendering them almost impractical.
Edit intent: clarity
Original:  Our approach is inspired by a recent advanced patch-nearest-neighbor based approach Granot et al. 2021%DIFDELCMD < ]%%% , which was shown to significantly outperform single-image GANs, both in run-time and in visual quality.
Revised:  Our approach is inspired by a recent advanced patch-nearest-neighbor based approach %DIFDELCMD < ]%%% , which was shown to significantly outperform single-image GANs, both in run-time and in visual quality.
Edit intent: coherence
Original:  In this paper, we provide a survey on recent terminologies and propose a taxonomy for remote human input systems, classifying the different approaches based on their complexity 
Revised:  In this paper, we provide a survey on recent terminologies and propose a taxonomy for remote human input systems, classifying the different approaches based on their complexity .
Edit intent: fluency
Original: Integrated programmable optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity.
Revised: Integrated optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity.
Edit intent: clarity
Original:  The analog nature of optical computing and the inherent optoelectronic noises, however, make the  systems error-prone in practical implementations such as classification by discriminative neural networks .
Revised:  The inherent optoelectronic noises, however, make the systems error-prone in practical implementations such as classification by discriminative neural networks .
Edit intent: clarity
Original:  The analog nature of optical computing and the inherent optoelectronic noises, however, make the  systems error-prone in practical implementations such as classification by discriminative neural networks .
Revised:  The analog nature of optical computing and the inherent optoelectronic noises, however, make the photonic systems error-prone in practical implementations such as classification by discriminative neural networks .
Edit intent: clarity
Original:  The analog nature of optical computing and the inherent optoelectronic noises, however, make the  systems error-prone in practical implementations such as classification by discriminative neural networks .
Revised:  The analog nature of optical computing and the inherent optoelectronic noises, however, make the systems error-prone in practice .
Edit intent: clarity
Original:  It is thus imperative to devise strategies to mitigate and, if possible, harness optical and electrical noises in photonic computing systems.
Revised:  It is thus imperative to devise strategies to mitigate and, if possible, harness noises in photonic computing systems.
Edit intent: clarity
Original:  Here, we demonstrate a prototypical photonic generative adversarial network (GAN) that generates handwritten numbers using a photonic core consisting of an array of  programable phase-change optical memory cells .
Revised:  Here, we demonstrate a photonic generative network as a part of a generative adversarial network (GAN) that generates handwritten numbers using a photonic core consisting of an array of programable phase-change optical memory cells .
Edit intent: clarity
Original:  Here, we demonstrate a prototypical photonic generative adversarial network (GAN) that generates handwritten numbers using a photonic core consisting of an array of  programable phase-change optical memory cells .
Revised:  Here, we demonstrate a prototypical photonic generative adversarial network (GAN) . This network is implemented with a photonic core consisting of an array of programable phase-change optical memory cells .
Edit intent: clarity
Original:  We harness optoelectronic noises in the photonic GAN by realizing an optical random number generator derived from the amplified spontaneous emission noise, applying noise-aware training by injecting additional noise to the network, and implementing the trained networkwith resilience to hardware non-idealities.
Revised:  We harness optoelectronic noises in the photonic GAN by realizing an optical random number generator derived from the amplified spontaneous emission noise, apply noise-aware training by injecting additional noise to the network, and implementing the trained networkwith resilience to hardware non-idealities.
Edit intent: fluency
Original:  We harness optoelectronic noises in the photonic GAN by realizing an optical random number generator derived from the amplified spontaneous emission noise, applying noise-aware training by injecting additional noise to the network, and implementing the trained networkwith resilience to hardware non-idealities.
Revised:  We harness optoelectronic noises in the photonic GAN by realizing an optical random number generator derived from the amplified spontaneous emission noise, applying noise-aware training by injecting additional noise and demonstrate the network's resilience to hardware non-idealities.
Edit intent: coherence
Original:  Surprisingly, the photonic GAN with hardware noises and inaccuracies can generate images of even higher quality than the noiseless software baseline.
Edit intent: coherence
Original:  Experimental results indicate a useful occurence of a concentration of measure phenomena and some implications of this hypothesis are analyzed 
Revised:  Experimental results indicate a useful occurence of a concentration of measure phenomena and some implications of this hypothesis are analyzed .
Edit intent: fluency
Original:  We conclude that the similarity hash function MinHash, Nilsimsa can be directly plugged into the pipeline of similarity analysis using vector manage system.
Revised:  We conclude that the similarity hash function MinHash, Nilsimsa can be directly marshaled into the pipeline of similarity analysis using vector manage system.
Edit intent: clarity
Original:  After that, we make a brief and empirical discussion on the performance, drawbacks of the these functions and highlight MinHash and the variant of SimHash  are the best for vector management system for large-scale similarity analysis.
Revised:  After that, we make a brief and empirical discussion on the performance, drawbacks of the these functions and highlight MinHash , the variant of SimHash are the best for vector management system for large-scale similarity analysis.
Edit intent: fluency
Original:  In this paper, we make a systematically survey on the existent well-known similarity hash functions to tease out the satisfied ones.
Revised:  In this paper, we make a systematical survey on the existent well-known similarity hash functions to tease out the satisfied ones.
Edit intent: fluency
Original:  We conclude that the similarity hash function MinHash , Nilsimsa can be directly marshaled into the pipeline of similarity analysis using vector manage system.
Revised:  We conclude that the similarity hash function MinHash and Nilsimsa can be directly marshaled into the pipeline of similarity analysis using vector manage system.
Edit intent: fluency
Original:  In this paper, we separately model the homogenous structural relationship by a modality-specific graph within individual modality and then mine the heterogeneous structural correlation in these two modality-specific graphs .
Revised:  In this paper, we model the homogenous structural relationship by a modality-specific graph within individual modality and then mine the heterogeneous structural correlation in these two modality-specific graphs .
Edit intent: coherence
Original:  In this paper, we separately model the homogenous structural relationship by a modality-specific graph within individual modality and then mine the heterogeneous structural correlation in these two modality-specific graphs .
Revised:  In this paper, we separately model the homogenous structural relationship by a modality-specific graph within individual modality and then mine the heterogeneous structural correlation with the modality-specific graphs .
Edit intent: clarity