wanyu/IteraTeR_v2 huggingface dataset:
Original:  In notebooks written in Python, around 80\% of all snippets are near-miss clones and the prevalence of code cloning is higher in Python than in other languages.
Revised:  In notebooks written in Python, at least 80\% of all snippets are near-miss clones and the prevalence of code cloning is higher in Python than in other languages.
Edit intent: clarity
Original:  In notebooks written in Python, around 80\% of all snippets are near-miss clones and the prevalence of code cloning is higher in Python than in other languages.
Revised:  In notebooks written in Python, around 80\% of all snippets are approximate clones and the prevalence of code cloning is higher in Python than in other languages.
Edit intent: clarity
Original:  We demonstrate that the data we look at is stationary if we consider samples of 15 minutes or even one hour long.
Revised:  We demonstrate that the data we look at is stationary if we consider samples of 15- minute long or even 1-hour long.
Edit intent: fluency
Original:  Such (age-penalty) functional depends on the quantization bits, codewords lengths and receiver processing time.
Revised:  Such functional depends on the quantization bits, codewords lengths and receiver processing time.
Edit intent: clarity
Original:  The solution is first shown to be a threshold policy for IIR , and a just-in-time policy for FR . Enhanced transmissions schemes are then developed in order to exploit the processing times to make new data available at the receiver sooner.
Revised:  We first show that the optimal sampling policy for IIR , and a just-in-time policy for FR . Enhanced transmissions schemes are then developed in order to exploit the processing times to make new data available at the receiver sooner.
Edit intent: clarity
Original: The genomic ssRNA of coronaviruses is packaged within  helical nucleocapsid.
Revised: The genomic ssRNA of coronaviruses is packaged within a helical nucleocapsid.
Edit intent: fluency
Original:  Such signals coordinated with  nucleocapsid helical structure were detected and reconstructed in the genomes of the coronaviruses SARS-CoV and SARS-CoV-2.
Revised:  Such signals coordinated with the nucleocapsid helical structure were detected and reconstructed in the genomes of the coronaviruses SARS-CoV and SARS-CoV-2.
Edit intent: fluency
Original:  The complete coverage of ssRNA genome of length about 30,000 nt by  nucleocapsid would need 4,400 N proteins, that makes them the most abundant among the structural proteins.
Revised:  The complete coverage of ssRNA genome of length about 30,000 nt by the nucleocapsid would need 4,400 N proteins, that makes them the most abundant among the structural proteins.
Edit intent: fluency
Original:  First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from coronary disease or different severity of coronary artery stenosis.
Revised:  First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from CAD or different severity of coronary artery stenosis.
Edit intent: clarity
Original:  The experimental results show that: BT predicts whether an individual has coronary disease with an accuracy of 94\%, and this algorithm predicts the degree of an individuals coronary artery stenosis with an accuracy of 90\%.
Revised:  The experimental results show that: BT predicts whether an individual has CAD with an accuracy of 94\%, and this algorithm predicts the degree of an individuals coronary artery stenosis with an accuracy of 90\%.
Edit intent: clarity
Original: How to accurately classify and predict whether an individual has Coronary Artery Disease (CAD) and the degree of coronary stenosis without using invasive examination?
Revised: How to accurately classify and diagnose whether an individual has Coronary Artery Disease (CAD) and the degree of coronary stenosis without using invasive examination?
Edit intent: clarity
Original: How to accurately classify and predict whether an individual has Coronary Artery Disease (CAD) and the degree of coronary stenosis without using invasive examination?
Revised: How to accurately classify and predict whether an individual has Coronary Stenosis (CS) without invasive physical examination?
Edit intent: clarity
Original:  To this end, the three kinds of machine learning (ML) algorithms, i.e., Boost Tree (BT), Decision Tree (DT), Logistic Regression (LR) , are employed in this paper.
Revised:  To this end, the three kinds of machine learning (ML) algorithms, i.e., Boosted Tree (BT), Decision Tree (DT), Logistic Regression (LR) , are employed in this paper.
Edit intent: fluency
Original:  First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from CAD or different severity of coronary artery stenosis  .
Revised:  First, eleven features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from CAD or different severity of coronary artery stenosis .
Edit intent: fluency
Original:  First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from CAD or different severity of coronary artery stenosis  .
Revised:  First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, as well as one label is specified, indicating whether an individual suffers from CAD or different severity of coronary artery stenosis .
Edit intent: coherence
Original:  First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from CAD or different severity of coronary artery stenosis  .
Revised:  First, 11 features including basic information of an individual, symptoms and results of routine physical examination are selected, and one label is specified, indicating whether an individual suffers from different severity of coronary artery stenosis .
Edit intent: coherence
Original:  Second, each of these three ML algorithms learns from the sample set to obtain the corresponding optimal predictive results, respectively.
Revised:  Second, each of these three ML algorithms learns from the sample set to obtain the corresponding optimal classified results, respectively.
Edit intent: clarity
Original: Motivation: DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification and is responsible for many biological functions.
Revised:  DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification and is responsible for many biological functions.
Edit intent: coherence
Original:  Results: Our study develops a convolutional neural network based tool i6mA-CNN capable of identifying 6mA sites in the rice genome.
Revised:  Our study develops a convolutional neural network based tool i6mA-CNN capable of identifying 6mA sites in the rice genome.
Edit intent: coherence
Original:  Availability: Web tool for this research can be found at: URL Contact: rafeed@cse.uiu.ac.bd Supplementary information: Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at URL
Revised:  Web tool for this research can be found at: URL Contact: rafeed@cse.uiu.ac.bd Supplementary information: Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at URL
Edit intent: coherence
Original:  Availability: Web tool for this research can be found at: URL Contact: rafeed@cse.uiu.ac.bd Supplementary information: Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at URL
Revised:  Availability: Web tool for this research can be found at: URL Supplementary data (benchmark dataset, independent test dataset, comparison purpose dataset, trained model, physicochemical property values, attention mechanism details for motif finding) are available at URL
Edit intent: coherence
Original:  In this work, we propose CovidDeep, a framework that combines efficient DNNs with commercially available WMSs for pervasive testing of the coronavirus .
Revised:  In this work, we propose CovidDeep, a framework that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus .
Edit intent: clarity
Original:  We collected data from 87 individuals, spanning four cohorts including healthy, asymptomatic (but SARS-CoV-2-positive) as well as moderately and severely symptomatic COVID-19 patients.
Revised:  We collected data from 87 individuals, spanning four cohorts including healthy, asymptomatic , and symptomatic patients.
Edit intent: clarity
Original: Since 2000 there has been a steady stream of advances for the maximum weight disjoint paths problem.
Revised: Since 2000 there has been a steady stream of advances for the maximum disjoint paths problem.
Edit intent: clarity
Original:  The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level multiple instance learning pooling module and a semi-supervised learning approach named Guided learning(GL) . This year, we incorporate Multiple branch learning (MBL) into the original system to further improve its performance.
Revised:  The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level pooling module and a semi-supervised learning approach named Guided learning(GL) . This year, we incorporate Multiple branch learning (MBL) into the original system to further improve its performance.
Edit intent: clarity
Original:  The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level multiple instance learning pooling module and a semi-supervised learning approach named Guided learning(GL) . This year, we incorporate Multiple branch learning (MBL) into the original system to further improve its performance.
Revised:  The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level multiple instance learning pooling module and a semi-supervised learning approach named guided learning . This year, we incorporate Multiple branch learning (MBL) into the original system to further improve its performance.
Edit intent: clarity
Original:  The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level multiple instance learning pooling module and a semi-supervised learning approach named Guided learning(GL) . This year, we incorporate Multiple branch learning (MBL) into the original system to further improve its performance.
Revised:  The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework with an attention-based embedding-level multiple instance learning pooling module and a semi-supervised learning approach named Guided learning(GL) . This year, we incorporate multi-branch learning (MBL) into the original system to further improve its performance.
Edit intent: fluency
Original:  MBL makes different branches with different pooling strategies (including instance-level and embedding-level strategies) and different pooling modules (including attention pooling, global max pooling or global average pooling modules)  share the same feature encoder of the model.
Revised:  MBL uses different branches with different pooling strategies (including instance-level and embedding-level strategies) and different pooling modules (including attention pooling, global max pooling or global average pooling modules) share the same feature encoder of the model.
Edit intent: clarity
Original:  MBL makes different branches with different pooling strategies (including instance-level and embedding-level strategies) and different pooling modules (including attention pooling, global max pooling or global average pooling modules)  share the same feature encoder of the model.
Revised:  MBL makes different branches with different pooling strategies (including instance-level and embedding-level strategies) and different pooling modules (including attention pooling, global max pooling or global average pooling modules) , which share the same feature encoder of the model.
Edit intent: coherence
Original:  To better exploit the strongly-labeled synthetic data, inspired by multi-task learning, we also employ a sound event detection branch (SEDB) .
Revised:  To better exploit the strongly-labeled synthetic data, inspired by multi-task learning, we also employ a sound event detection branch .
Edit intent: clarity
Original:  To combine sound separation (SS) with sound event detection (SED), we fuse the results of SED systems with SS-SED systems which are trained using separated sources output by an SS system.
Revised:  To combine sound separation (SS) with sound event detection (SED), we fuse the results of SED systems with SS-SED systems which are trained using separated sound output by an SS system.
Edit intent: clarity
Original:  This allows us to go beyond the variational methods (based on PDEs) developed in the related literature on Dynkin games in continuous time with partial/asymmetric information.
Revised:  This allows us to go beyond the variational methods (based on PDEs) developed in the literature on Dynkin games in continuous time with partial/asymmetric information.
Edit intent: clarity
Original:  However, recent works in image style transfer are overused .
Revised:  However, the definition of style in recent image style transfer are overused .
Edit intent: clarity
Original:  However, recent works in image style transfer are overused .
Revised:  However, recent works in image style transfer works is inappropriate .
Edit intent: clarity
Original: In this note, we revisit a unified methodology for the iterative solution of nonlinear equations in Hilbert spaces.
Revised: We revisit a unified methodology for the iterative solution of nonlinear equations in Hilbert spaces.
Edit intent: coherence
Original:  The benchmark dataset employed here is the MNISTdataset , which consists in binary images of digits .
Revised:  The benchmark dataset employed here is MNIST , which consists in binary images of digits .
Edit intent: fluency
Original:  The benchmark dataset employed here is the MNISTdataset , which consists in binary images of digits .
Revised:  The benchmark dataset employed here is the MNISTdataset , which consists of binary images of digits .
Edit intent: fluency
Original: A key factor in the modern success of deep learning is the astonishing expressive power of neural networks.
Revised: Deep learning owes much of its success to the astonishing expressiveness of neural networks.
Edit intent: clarity
Original:  However, this comes at the cost of complex, black-boxed models that are unable to extrapolate beyond the domain of the training dataset, conflicting with goals of expressing physical laws or building human-readable programs.
Revised:  However, this comes at the cost of complex, black-boxed models that extrapolate poorly beyond the domain of the training dataset, conflicting with goals of expressing physical laws or building human-readable programs.
Edit intent: clarity
Original:  In this paper , we introduce OccamNet, a neural network model that can find interpretable, compact  and sparse solutions for fitting data, \`{a} la Occam's razor.
Revised:  In this paper , we introduce OccamNet, a neural network model that finds interpretable, compact and sparse solutions for fitting data, \`{a} la Occam's razor.
Edit intent: coherence
Original:  In this paper , we introduce OccamNet, a neural network model that can find interpretable, compact  and sparse solutions for fitting data, \`{a} la Occam's razor.
Revised:  In this paper , we introduce OccamNet, a neural network model that can find interpretable, compact , and sparse solutions for fitting data, \`{a} la Occam's razor.
Edit intent: fluency
Original:  Our model defines a probability distribution over a non-differentiable function space , and we introduce an optimization method that samples functions and updates the weights  based on cross-entropy matching in an evolutionary strategy: we train by biasing the probability mass towards better fitting solutions.
Revised:  Our model defines a probability distribution over a non-differentiable function space , and we introduce an optimization method that samples functions and updates the weights based on cross-entropy matching in an evolutionary strategy: we train by biasing the probability mass toward better fitting solutions.
Edit intent: fluency
Original:  Our method takes minimal memory footprint, does not require AI accelerators for efficient training, fits complicated functions in minutes of training on a single CPU, and demonstrates significant performance gains when scaled on  GPU.
Revised:  Our method requires minimal memory footprint, does not require AI accelerators for efficient training, fits complicated functions in minutes of training on a single CPU, and demonstrates significant performance gains when scaled on GPU.
Edit intent: fluency
Original:  Our method takes minimal memory footprint, does not require AI accelerators for efficient training, fits complicated functions in minutes of training on a single CPU, and demonstrates significant performance gains when scaled on  GPU.
Revised:  Our method takes minimal memory footprint, does not require AI accelerators for efficient training, fits complicated functions in minutes of training on a single CPU, and demonstrates significant performance gains when scaled on a GPU.
Edit intent: fluency
Original:  Investigating single nucleotide polymorphisms (SNPs) that correlate with specific diseases needs statistical analysis of associations , considering the huge number of possible disease mutations.
Revised:  Investigating single nucleotide polymorphisms (SNPs) that correlate with specific diseases needs statistical analysis of associations . Considering the huge number of possible disease mutations.
Edit intent: fluency
Original:  The most important drawback of GWAS analysis in addition to its high cost  is the large number of false-positives.
Revised:  The most important drawback of GWAS analysis in addition to its high cost , another important drawback of GWAS analysis is the large number of false-positives.
Edit intent: coherence
Original:  However, most existing approaches to music similarity retrieval fail to describe timbre beyond the so-called ``ordinary'' technique, use instrument identity as a proxy for timbre quality, and do not allow for customization to the perceptual idiosyncrasies of a new subject.
Revised:  However, most existing approaches to music similarity retrieval fail to describe timbre beyond the so-called "ordinary" technique, use instrument identity as a proxy for timbre quality, and do not allow for customization to the perceptual idiosyncrasies of a new subject.
Edit intent: fluency
Original:  They can be used as coding elements for encoding graph-topological information at multiple granularity levels on a graph, and for making differentiation and connection across different graphs and networks.
Revised:  They can be used as coding elements to encode graph-topological information at multiple granularity levels on a graph, and for making differentiation and connection across different graphs and networks.
Edit intent: fluency
Original:  They can be used as coding elements for encoding graph-topological information at multiple granularity levels on a graph, and for making differentiation and connection across different graphs and networks.
Revised:  They can be used as coding elements for encoding graph-topological information at multiple granularity levels on a graph, and for making differentiation or connection across different graphs and networks.
Edit intent: fluency
Original:  They can be used as coding elements for encoding graph-topological information at multiple granularity levels on a graph, and for making differentiation and connection across different graphs and networks.
Revised:  They can be used as coding elements for encoding graph-topological information at multiple granularity levels on a graph, and for making differentiation and connection across different networks.
Edit intent: clarity
Original:  Network/graph analysis using multiple graphlets has growing applications.
Revised:  Network/graph analysis using graphlets has growing applications.
Edit intent: clarity
Original:  It is intended to facilitate or enable advanced network/graph analysis with graphlets, and to advance at the same time sparse graph theory, high-performance graph computation  and broader applications.
Revised:  It is intended to enable and advance network/graph analysis with graphlets, and to advance at the same time sparse graph theory, high-performance graph computation and broader applications.
Edit intent: clarity
Original:  It is intended to facilitate or enable advanced network/graph analysis with graphlets, and to advance at the same time sparse graph theory, high-performance graph computation  and broader applications.
Revised:  It is intended to facilitate or enable advanced network/graph analysis with graphlets, and to advance at the same time sparse graph theory, high-performance graph computation , and broader applications.
Edit intent: fluency
Original:  In this paper, we rigorously show that the energy of the H-N model can be bounded by the initial energy so that the model is well-posed.
Revised:  In this paper, we rigorously show that the energy of the H-N model can be bounded by the initial energy and the model is well-posed.
Edit intent: coherence
Original:  We provide ample numerical results to demonstrate the efficiency and accuracy of a full-discrete scheme via a spectra-Galerkin method in two spatial dimension .
Revised:  We provide ample numerical results to demonstrate the efficiency and accuracy of a full-discrete scheme via a spectra-Galerkin method in two dimensions .
Edit intent: clarity
Original:  The IoT revolution is reshaping modern healthcare systems  incorporating technological, economic, and social prospects.
Revised:  The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects.
Edit intent: fluency
Original:  It is evolving healthcare systems from conventional to more personalized healthcare system where patients can be diagnosed, treated, and monitored more easily.
Revised:  It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily.
Edit intent: clarity
Original:  The current global challenge of the COVID-19 pandemic caused by the novel severe contagious respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918.
Revised:  The current global challenge of the pandemic caused by the novel severe contagious respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918.
Edit intent: clarity