wanyu/IteraTeR_v2 huggingface dataset:
Original:  In an attempt to  understand why phishing email attacks still work  , we have carried out an empirical study to investigate how peoplemake response decisions while reading their emails.
Revised:  In an attempt to better understand why phishing email attacks still work , we have carried out an empirical study to investigate how peoplemake response decisions while reading their emails.
Edit intent: clarity
Original:  In an attempt to  understand why phishing email attacks still work  , we have carried out an empirical study to investigate how peoplemake response decisions while reading their emails.
Revised:  In an attempt to understand why phishing email attacks still work , we have carried out an empirical study to investigate people's thought processes when reading their emails.
Edit intent: clarity
Original: Probabilistic models of biological network growth have been extensively studied as idealized representations of network evolution.
Revised: Probabilistic biological network growth have been extensively studied as idealized representations of network evolution.
Edit intent: clarity
Original:  However, developing a new growth model to fit the observed properties of a network is a difficult task, and as new  networks are studied, new models must constantly be developed.
Revised:  However, we should frequently keep developing new networks are studied, new models must constantly be developed.
Edit intent: clarity
Original:  Here, we present a framework, called BioCode, for the automatic discovery of novel biological growth models that match user-specified topological features in undirected graphs.
Revised:  Here, we present a framework, called BioCode, for the automatic discovery of novel biological growth models matching user-specified topological features in undirected graphs.
Edit intent: fluency
Original:  Here, we present a framework, called BioCode, for the automatic discovery of novel biological growth models that match user-specified topological features in undirected graphs.
Revised:  Here, we present a framework, called BioCode, for the automatic discovery of novel biological growth models that match user-specified graph attributes in directed and undirected biological graphs.
Edit intent: clarity
Original:  Coupling this formal representation with an optimization approach, we show that BioCode is able to discover models for protein interaction networks , gene regulatory networks, and biological collaboration networks that closely match properties such as the degree distribution, the clustering coefficient, and assortativity that are observed in real biological networks of these classes.
Revised:  Coupling this formal representation with an optimization approach, we show that BioCode is able to discover models for protein interaction networks , gene regulatory networks, metabolic networks, and protein interaction networks which features such as assortativity, clustering coefficient, and assortativity that are observed in real biological networks of these classes.
Edit intent: clarity
Original:  Additional tests on simulated networks show that the models learned by BioCode generate distributions of biological graphs with similar variance as existing modelsfor these classes .
Revised:  Additional tests on simulated networks show that the models learned by BioCode generate distributions of biological networks generated by BioCode is similar to the known models' variance for these biological network types .
Edit intent: clarity
Original: Artificial intelligence (AI) is transforming medicine and showing promise in improving clinical diagnosis.
Revised: Artificial intelligence (AI) is showing promise in improving clinical diagnosis.
Edit intent: clarity
Original:  We compare their performance on five international data sets: two private New York University breast cancer screening data sets as well as three public ( DDSM, INbreast  and Chinese Mammography Database ) data sets .
Revised:  We compare their performance on five international data sets: two New York University breast cancer screening data sets as well as three public ( DDSM, INbreast and Chinese Mammography Database ) data sets .
Edit intent: clarity
Original:  We compare their performance on five international data sets: two private New York University breast cancer screening data sets as well as three public ( DDSM, INbreast  and Chinese Mammography Database ) data sets .
Revised:  We compare their performance on five international data sets: two private New York University breast cancer screening data sets , DDSM, INbreast and Chinese Mammography Database ) data sets .
Edit intent: coherence
Original:  We compare their performance on five international data sets: two private New York University breast cancer screening data sets as well as three public ( DDSM, INbreast  and Chinese Mammography Database ) data sets .
Revised:  We compare their performance on five international data sets: two private New York University breast cancer screening data sets as well as three public ( DDSM, INbreast and Chinese Mammography Database .
Edit intent: fluency
Original: Public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic.
Revised: The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic.
Edit intent: clarity
Original:  This study identifies the sentiment of tweets and their temporal trend , discovers major topics,  compares topics of negative and non-negative tweets  , and discloses top topics of  negative and non-negative tweets .
Revised:  This study identifies the sentiment of tweets and their temporal trend , discovers major topics, compares topics of negative and non-negative tweets , and discloses top topics of tweets having negative and non-negative tweets .
Edit intent: clarity
Original:  This study identifies the sentiment of tweets and their temporal trend , discovers major topics,  compares topics of negative and non-negative tweets  , and discloses top topics of  negative and non-negative tweets .
Revised:  This study identifies the sentiment of tweets and their temporal trend , discovers major topics, compares topics of negative and non-negative tweets , and discloses top topics of negative and non-negative sentiment .
Edit intent: clarity
Original:  The findings show that there was a significant difference between  negative and non-negative tweets regarding the weight of most topics.
Revised:  The findings show that there was a significant difference between tweets having negative and non-negative tweets regarding the weight of most topics.
Edit intent: clarity
Original:  The findings show that there was a significant difference between  negative and non-negative tweets regarding the weight of most topics.
Revised:  The findings show that there was a significant difference between negative and non-negative sentiment regarding the weight of most topics.
Edit intent: clarity
Original:  Data scarcity combined with the use of pre-calculated hand-crafted input features has led to problems of overfitting and data leakage.
Revised:  Data scarcity combined with the use of precalculated hand-crafted input features has led to problems of overfitting and data leakage.
Edit intent: fluency
Original:  MuCoMiD allows automatic feature extraction while incorporating knowledge from 4 heterogeneous biological information sources (interactions between miRNA/diseases and protein-coding genes (PCG),  miRNA family information, and disease ontology) in a multi-task setting which is a novel perspective and has not been studied before.
Revised:  MuCoMiD allows automatic feature extraction while incorporating knowledge from 4 heterogeneous biological information sources (interactions between miRNA/diseases and protein-coding genes (PCG), interactions between protein-coding genes, miRNA family information, and disease ontology) in a multi-task setting which is a novel perspective and has not been studied before.
Edit intent: fluency
Original:  The use of multi-channel convolutions allows us to extract expressive representations while keeping the model linear and, therefore, simple.
Edit intent: coherence
Original: Recently, huge strides were made in monocular and multi-view pose estimation with known camera parameters, whereas pose estimation from multiple cameras with unknown positions and orientations received much less attention .
Revised: In the era of deep learning, human pose estimation from multiple cameras with unknown positions and orientations received much less attention .
Edit intent: clarity
Original:  In this paper, we show how to train a neural model that can perform accurate 3D pose and camera estimation, takes into account joint location uncertainty due  occlusion from multiple views, and requires only 2D keypoint data for training.
Revised:  In this paper, we show how to train a neural model to perform this task with high precision and minimal latency overhead. The proposed model takes into account joint location uncertainty due occlusion from multiple views, and requires only 2D keypoint data for training.
Edit intent: clarity
Original:  In this paper, we show how to train a neural model that can perform accurate 3D pose and camera estimation, takes into account joint location uncertainty due  occlusion from multiple views, and requires only 2D keypoint data for training.
Revised:  In this paper, we show how to train a neural model that can perform accurate 3D pose and camera estimation, takes into account joint location uncertainty due to occlusion from multiple views, and requires only 2D keypoint data for training.
Edit intent: fluency
Original: Evolutionary algorithms based on edge assembly crossover ~ (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem ~ (TSP).
Revised: Evolutionary algorithms based on edge assembly crossover (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem ~ (TSP).
Edit intent: fluency
Original: Evolutionary algorithms based on edge assembly crossover ~ (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem ~ (TSP).
Revised: Evolutionary algorithms based on edge assembly crossover ~ (EAX) constitute some of the best performing incomplete solvers for the well-known traveling salesperson problem (TSP).
Edit intent: fluency
Original:  In this paper, we introduce evolutionary diversity optimisation ~ (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown.
Revised:  In this paper, we introduce evolutionary diversity optimisation (EDO) approaches for the TSP that find a diverse set of tours when the optimal tour is known or unknown.
Edit intent: fluency
Original: The increased uptake of electric vehicles (EVs) leads to increased demand for electricity, and sometime pressure to power grids.
Revised: The increased uptake of electric vehicles (EVs) leads to increased demand for electricity, and sometimes pressure on power grids.
Edit intent: fluency
Original:  Uncoordinated charging of EVs may result in putting pressure on distribution networks, and often some form of optimisation is required in the charging process.
Revised:  Uncoordinated charging of EVs may result in stress on distribution networks, and often some form of optimisation is required in the charging process.
Edit intent: clarity
Original:  Uncoordinated charging of EVs may result in putting pressure on distribution networks, and often some form of optimisation is required in the charging process.
Revised:  Uncoordinated charging of EVs may result in putting pressure on distribution networks, and often some form of optimization is required in the charging process.
Edit intent: fluency
Original:  To obtain the optimal scheduling scheme and balance the competing benefits from different stakeholders, such as EV owners, EV charging stations (EVCS), and the grid operator, we design three conflicting objective functions including EV owner cost, EVCS profit, and the network impact.
Revised:  To obtain the optimal scheduling scheme and balance the competing benefits from different stakeholders, such as EV owners, EV charging stations (EVCS), and the grid operator, we design three competing objective functions including EV owner cost, EVCS profit, and the network impact.
Edit intent: clarity
Original:  Our results demonstrate the effectiveness of MOEVCS in making a balance between three conflicting objectives.
Revised:  Our results demonstrate the effectiveness of MOEVCS in making a balance between three competing objectives.
Edit intent: fluency
Original: Adversarial patch attack against image classification deep neural networks (DNNs), in which the attacker can inject arbitrary distortions within a bounded region of an image, is able to generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input).
Revised: Adversarial patch attacks against image classification deep neural networks (DNNs), in which the attacker can inject arbitrary distortions within a bounded region of an image, is able to generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input).
Edit intent: fluency
Original: Adversarial patch attack against image classification deep neural networks (DNNs), in which the attacker can inject arbitrary distortions within a bounded region of an image, is able to generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input).
Revised: Adversarial patch attack against image classification deep neural networks (DNNs), which inject arbitrary distortions within a bounded region of an image, is able to generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input).
Edit intent: clarity
Original: Adversarial patch attack against image classification deep neural networks (DNNs), in which the attacker can inject arbitrary distortions within a bounded region of an image, is able to generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input).
Revised: Adversarial patch attack against image classification deep neural networks (DNNs), in which the attacker can inject arbitrary distortions within a bounded region of an image, can generate adversarial perturbations that are robust (i.e., remain adversarial in physical world) and universal (i.e., remain adversarial on any input).
Edit intent: clarity
Original:  It is thus important to detect and mitigate such attack to ensure the security of DNNs . This work proposes Jujutsu, a technique to detect and mitigate robust and universal adversarial patch attack.
Revised:  Such attacks can lead to severe consequences in real-world DNN-based systems . This work proposes Jujutsu, a technique to detect and mitigate robust and universal adversarial patch attack.
Edit intent: clarity
Original:  Therefore, Jujutsu uses image inpainting for synthesizing alternative contents from the pixels that are masked, which can reconstruct the "clean" imagefor correct prediction .
Revised:  Therefore, Jujutsu uses image inpainting for synthesizing alternative contents from the pixels that are corrupted by the attacks, and reconstruct the ``clean'' image .
Edit intent: clarity
Original:   Jujutsu can further defend against various variants of the basic attack, including 1) physical-world attack;
Revised:  We find that Jujutsu can further defend against various variants of the basic attack, including 1) physical-world attack;
Edit intent: coherence
Original:   Jujutsu can further defend against various variants of the basic attack, including 1) physical-world attack;
Revised:  Jujutsu can further defend against different variants of the basic attack, including 1) physical-world attack;
Edit intent: coherence
Original:  3) attacks that use patches in different shapes and 4) adaptive attacks.
Revised:  3) attacks that construct patches in different shapes and 4) adaptive attacks.
Edit intent: clarity
Original:  Optical coherence tomography (OCT) emerges as a non-invasive, high-resolution imaging technology for cervical disease detection.
Revised:  Optical coherence tomography (OCT) emerged as a non-invasive, high-resolution imaging technology for cervical disease detection.
Edit intent: fluency
Original:  Objective : This study aims to develop a computer-aided diagnosis (CADx) approach to classifying in-vivo cervical OCT images based on self-supervised learning.
Revised:  Purpose : This study aims to develop a computer-aided diagnosis (CADx) approach to classifying in-vivo cervical OCT images based on self-supervised learning.
Edit intent: fluency
Original:  Methods: Besides high-level semantic features extracted by a convolutional neural network (CNN), the proposed CADx approach leverages unlabeled cervical OCT images' texture features learned by contrastive texture learning.
Revised:  Methods: In addition to high-level semantic features extracted by a convolutional neural network (CNN), the proposed CADx approach leverages unlabeled cervical OCT images' texture features learned by contrastive texture learning.
Edit intent: fluency
Original:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Revised:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Edit intent: clarity
Original:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Revised:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Edit intent: clarity
Original:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Revised:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 plus or minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Edit intent: fluency
Original:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Revised:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 plus or minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Edit intent: fluency
Original:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 Plus or Minus 4.72\% for OCT image patches;
Revised:  Results: In a binary classification task for detecting high-risk diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer, our method achieved an area-under-the-curve (AUC) value of 0.9798 Plus or Minus 0.0157 with a sensitivity of 91.17 Plus or Minus 4.99\% and a specificity of 93.96 plus or minus 4.72\% for OCT image patches;
Edit intent: fluency
Original:  Conclusion : The proposed contrastive-learning-based CADx method outperformed the end-to-end CNN models and provided better interpretability based on texture features, which holds great potential to be used in the clinical protocol of "see-and-treat."
Revised:  Conclusions : The proposed contrastive-learning-based CADx method outperformed the end-to-end CNN models and provided better interpretability based on texture features, which holds great potential to be used in the clinical protocol of "see-and-treat."
Edit intent: coherence
Original:  In this work, we present the standard DRL-based energy management scheme with and without prediction .
Revised:  In this work, we implement the standard energy management scheme with and without prediction .
Edit intent: clarity
Original:  Then, we consider independent and identically distributed sources and completely characterize conditions under which an informative linear Nash equilibrium exists.
Revised:  Then, we consider independent and identically distributed sources and characterize necessary and sufficient conditions under which an informative linear Nash equilibrium exists.
Edit intent: clarity
Original:  Then, we consider independent and identically distributed sources and completely characterize conditions under which an informative linear Nash equilibrium exists.
Revised:  Then, we consider independent and identically distributed sources and completely characterize conditions under which an informative linear equilibrium exists.
Edit intent: clarity
Original:  On the other hand, for a linear equilibrium to exist in the case of equal bias components, it is required that the source density is symmetric around its mean . Moreover, in the case of Gaussian sources, our results have a rate-distortion theoretic implication that achievable rates and distortions in the considered game theoretic setup can be obtained from their team theoretic counterpart.
Revised:  On the other hand, for a linear equilibrium to exist in the case of equal bias components, it is required that the source density is symmetric around its mean . Moreover, in the case of Gaussian sources, our results have a rate-distortion theoretic implication that achievable rates and distortions in the considered game theoretic setup can be obtained from its team theoretic counterpart.
Edit intent: fluency