wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P , etc.)and application identification in which one identify\emph{  end-user applications ( e.g., BitTorrent , Skype, etc.) \eg . Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P ), and\emph{ end-user applications ( e.g., BitTorrent , Skype, etc.) \eg . Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Edit intent: clarity
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P , etc.)and application identification in which one identify\emph{  end-user applications ( e.g., BitTorrent , Skype, etc.) \eg . Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P , etc.)and application identification in which one identify\emph{ in which identification of end-user applications ( e.g., BitTorrent , Skype, etc.) \eg . Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Edit intent: fluency
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P , etc.)and application identification in which one identify\emph{  end-user applications ( e.g., BitTorrent , Skype, etc.) \eg . Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P , etc.)and application identification in which one identify\emph{ end-user applications ( \eg . Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Edit intent: coherence
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P , etc.)and application identification in which one identify\emph{  end-user applications ( e.g., BitTorrent , Skype, etc.) \eg . Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic categorization in which the network traffic is categorize into major classes ( e.g. FTP, \eg P2P , etc.)and application identification in which one identify\emph{ end-user applications ( e.g., BitTorrent , Skype, etc.) \eg . Contrary to the most of current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Edit intent: fluency
Original:  After  initial pre-processing phase on data, packets are fed to Deep Packet framework that embeds stacked autoencoder and convolution neural network \emph{ \emph{  in order to classify network traffic.
Revised:  After an initial pre-processing phase on data, packets are fed to Deep Packet framework that embeds stacked autoencoder and convolution neural network \emph{ \emph{ in order to classify network traffic.
Edit intent: fluency
Original:  After  initial pre-processing phase on data, packets are fed to Deep Packet framework that embeds stacked autoencoder and convolution neural network \emph{ \emph{  in order to classify network traffic.
Revised:  After initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder and convolution neural network \emph{ \emph{ in order to classify network traffic.
Edit intent: fluency
Original:  After  initial pre-processing phase on data, packets are fed to Deep Packet framework that embeds stacked autoencoder and convolution neural network \emph{ \emph{  in order to classify network traffic.
Revised:  After initial pre-processing phase on data, packets are fed to Deep Packet framework that embeds \emph{ \emph{ in order to classify network traffic.
Edit intent: coherence
Original:  After  initial pre-processing phase on data, packets are fed to Deep Packet framework that embeds stacked autoencoder and convolution neural network \emph{ \emph{  in order to classify network traffic.
Revised:  After initial pre-processing phase on data, packets are fed to Deep Packet framework that embeds stacked autoencoder and convolution neural network \emph{ and\emph{ in order to classify network traffic.
Edit intent: coherence
Original:  Deep packet with CNN as its classification model achieved F_{1} score of 0.95 in application identification  and it also accomplished F_{1} score of 0.97 in traffic categorization task.
Revised:  Deep packet with CNN as its classification model achieved F_{1} score of 0.95 in application identification task and it also accomplished F_{1} score of 0.97 in traffic categorization task.
Edit intent: fluency
Original:  Deep packet with CNN as its classification model achieved F_{1} score of 0.95 in application identification  and it also accomplished F_{1} score of 0.97 in traffic categorization task.
Revised:  Deep packet with CNN as its classification model achieved F_{1} score of 0.95 in application identification and it also accomplished F_{1} score of 0.97 in traffic characterization task.
Edit intent: clarity
Original:  To the best of our knowledge, Deep Packet outperforms all of the classification and categorization methods on UNB ISCX VPN-nonVPN dataset.
Revised:  To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.
Edit intent: clarity
Original: Network traffic classification has become significantly important with rapid growth of current Internet network and online applications.
Revised: Internet traffic classification has become significantly important with rapid growth of current Internet network and online applications.
Edit intent: fluency
Original: Network traffic classification has become significantly important with rapid growth of current Internet network and online applications.
Revised: Network traffic classification has become more important with rapid growth of current Internet network and online applications.
Edit intent: clarity
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization , in which the network traffic is categorized into major classes (\eg, FTP and P2P) , andapplication identification in which identification of end-user applications (\eg, BitTorrent and Skype)  is desired.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization in which the network traffic is categorized into major classes (\eg, FTP and P2P) , andapplication identification in which identification of end-user applications (\eg, BitTorrent and Skype) is desired.
Edit intent: fluency
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization , in which the network traffic is categorized into major classes (\eg, FTP and P2P) , andapplication identification in which identification of end-user applications (\eg, BitTorrent and Skype)  is desired.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization , in which the network traffic is categorized into major classes (\eg, FTP and P2P) application identification in which identification of end-user applications (\eg, BitTorrent and Skype) is desired.
Edit intent: fluency
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization , in which the network traffic is categorized into major classes (\eg, FTP and P2P) , andapplication identification in which identification of end-user applications (\eg, BitTorrent and Skype)  is desired.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization , in which the network traffic is categorized into major classes (\eg, FTP and P2P) , and in which identification of end-user applications (\eg, BitTorrent and Skype) is desired.
Edit intent: fluency
Original:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization , in which the network traffic is categorized into major classes (\eg, FTP and P2P) , andapplication identification in which identification of end-user applications (\eg, BitTorrent and Skype)  is desired.
Revised:  Our proposed scheme, called "Deep Packet," can handle both traffic characterization , in which the network traffic is categorized into major classes (\eg, FTP and P2P) , andapplication identification and application identification in which end-user applications (\eg, BitTorrent and Skype) is desired.
Edit intent: clarity
Original:  Contrary to the most of current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Revised:  Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic.
Edit intent: fluency
Original:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder andconvolution neural network (CNN) in order to classify network traffic.
Revised:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds andconvolution neural network (CNN) in order to classify network traffic.
Edit intent: clarity
Original:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder andconvolution neural network (CNN) in order to classify network traffic.
Revised:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder convolution neural network (CNN) in order to classify network traffic.
Edit intent: fluency
Original:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder andconvolution neural network (CNN) in order to classify network traffic.
Revised:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder and (CNN) in order to classify network traffic.
Edit intent: clarity
Original:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder andconvolution neural network (CNN) in order to classify network traffic.
Revised:  After an initial pre-processing phase on data, packets are fed into Deep Packet framework that embeds stacked autoencoder andconvolution neural network stacked autoencoder and convolution neural network in order to classify network traffic.
Edit intent: clarity
Original:  In this work, we overcome this challenge and present a clock-less approach to implement both learning and recognition on a spiking neural network with memristive synaptic array wherein , reading and writing are performed in different frequency domains.
Revised:  In this work, we overcome this challenge and present a clock-less approach wherein reading and writing are performed in different frequency domains.
Edit intent: coherence
Original:  Further, we validate our scheme in SPICE  by translating a mathematical mono-layered feed-forward Iris classifying SNN . A hardware implementation of the scheme will eliminate the need for clocks and switches to drive learning and has the potential to be used in the development of real-time trainable hardwarerecurrent networks .
Revised:  Further, we validate our scheme in SPICE by translating a two-layered feed-forward Iris classifying SNN . A hardware implementation of the scheme will eliminate the need for clocks and switches to drive learning and has the potential to be used in the development of real-time trainable hardwarerecurrent networks .
Edit intent: clarity
Original:  A pressure driven peristaltic instability is identified, quantitatively distinct from similar tension- driven Rayleigh type instabilities on membrane tubes.
Revised:  A pressure driven peristaltic instability is identified, qualitatively distinct from similar tension- driven Rayleigh type instabilities on membrane tubes.
Edit intent: fluency
Original:  A pressure driven peristaltic instability is identified, quantitatively distinct from similar tension- driven Rayleigh type instabilities on membrane tubes.
Revised:  A pressure driven peristaltic instability is identified, quantitatively distinct from similar tension-driven Rayleigh type instabilities on membrane tubes.
Edit intent: fluency
Original:  The unusually long natural wavelength of this instability is in close agreement with that observed in cells.
Revised:  The unusually long natural wavelength of this instability is in agreement with that observed in cells.
Edit intent: clarity
Original: A family \mathcal f \subset 2^{[n]} is called %DIFDELCMD < {\it %%% intersecting , if any two of its sets intersect.
Revised: A family \mathcal F \subset 2^{[n]} is called %DIFDELCMD < {\it %%% intersecting , if any two of its sets intersect.
Edit intent: fluency
Original: A family \mathcal f \subset 2^{[n]} is called %DIFDELCMD < {\it %%% intersecting , if any two of its sets intersect.
Revised: A family \mathcal f \subset 2^{[n]} is called %DIFDELCMD < {\it %%% , if any two of its sets intersect.
Edit intent: clarity
Original: A family \mathcal f \subset 2^{[n]} is called %DIFDELCMD < {\it %%% intersecting , if any two of its sets intersect.
Revised: A family \mathcal f \subset 2^{[n]} is called %DIFDELCMD < {\it %%% intersecting intersecting if any two of its sets intersect.
Edit intent: fluency
Original:  Given an intersecting family, its %DIFDELCMD < {\it %%% diversity is the number of sets not passing through the most popular element of the ground set.
Revised:  Given an intersecting family, its %DIFDELCMD < {\it %%% diversity is the number of sets not passing through the most popular element of the ground set.
Edit intent: fluency
Original:   Frankl made the following conjecture: for n> 3k>0 any intersecting family \mathcal f \subset {[n]\choose k} has diversity at most {n-3\choose k-2}.
Revised:  Peter Frankl made the following conjecture: for n> 3k>0 any intersecting family \mathcal f \subset {[n]\choose k} has diversity at most {n-3\choose k-2}.
Edit intent: clarity
Original:   Frankl made the following conjecture: for n> 3k>0 any intersecting family \mathcal f \subset {[n]\choose k} has diversity at most {n-3\choose k-2}.
Revised:  Frankl made the following conjecture: for n> 3k>0 any intersecting family \mathcal F \subset {[n]\choose k} has diversity at most {n-3\choose k-2}.
Edit intent: clarity
Original:  In this note  we prove this conjecture for n\ge ck, where c is a constant independent of n and k  .
Revised:  In this note , we prove this conjecture for n\ge ck, where c is a constant independent of n and k .
Edit intent: fluency
Original:  We will review here the most recent advances in ion mobility fundamentals, related to these three aspects.
Revised:  We will review here the most recent advances in ion mobility fundamentals, relevant to these three aspects.
Edit intent: fluency
Original:  We will review here the most recent advances in ion mobility fundamentals, relevant to these three aspects.
Revised:  We review here the most recent advances in ion mobility fundamentals, relevant to these three aspects.
Edit intent: coherence
Original:  Recently, a paper-based algorithmically constructed classification system (ACCS) was proposed which is based on citation relations.
Revised:  A paper-based algorithmically constructed classification system (ACCS) was proposed which is based on citation relations.
Edit intent: coherence
Original:  The first part of our micro study focusses on  papers on overall water splitting  . Next, we compare the ACCS with (1) a paper-based intellectual (INSPEC) classification and (2) a journal-based intellectual classification (Web of Science, WoS, subject categories).
Revised:  The micro study focusses on papers on overall water splitting . Next, we compare the ACCS with (1) a paper-based intellectual (INSPEC) classification and (2) a journal-based intellectual classification (Web of Science, WoS, subject categories).
Edit intent: clarity
Original:  In the last part of our case study, we also compare the average number of citations in selected ACCS clusters  with the average citation count of publications in WoS subject categories related to these clusters.
Revised:  In the last part of our case study, we compare the average number of citations in selected ACCS clusters with the average citation count of publications in WoS subject categories related to these clusters.
Edit intent: clarity
Original:  Thus, we recommend larger follow-up studies on broad datasets.
Revised:  We recommend larger follow-up studies on broad datasets.
Edit intent: coherence
Original:  These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrain learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.
Revised:  These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.
Edit intent: fluency
Original:  Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication ( betweeness centrality) impacted graph learning, even after accounting for level of practice on that node.
Revised:  Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication ( betweenness centrality) impacted graph learning, even after accounting for level of practice on that node.
Edit intent: fluency
Original: Many theoretical models of evolution assume that all competing individuals experience the same environment.
Revised: Many mathematical models of evolution assume that all competing individuals experience the same environment.
Edit intent: clarity
Original: Many theoretical models of evolution assume that all competing individuals experience the same environment.
Revised: Many theoretical models of evolution assume that all individuals experience the same environment.
Edit intent: clarity
Original:  Here, we consider the more realistic scenario of evolution in heterogeneous environments.
Revised:  Here, we study the Moran process in heterogeneous environments.
Edit intent: clarity
Original:  The population is of finite size , N, with two competing types and a fixed number of environmental sites.
Revised:  The population is of finite size with two competing types and a fixed number of environmental sites.
Edit intent: clarity
Original:  The population is of finite size , N, with two competing types and a fixed number of environmental sites.
Revised:  The population is of finite size , N, with two competing types , which are exposed to a fixed number of environmental sites.
Edit intent: clarity
Original:  We first calculate the condition for natural selection to favor the mutant type relative to the resident  .
Revised:  We first calculate the condition for selection to favor the mutant type relative to the resident .
Edit intent: clarity
Original:  We first calculate the condition for natural selection to favor the mutant type relative to the resident  .
Revised:  We first calculate the condition for natural selection to favor the mutant relative to the resident .
Edit intent: clarity
Original:  In large populations, where stochastic effects are negligible, we find that the mutant is favored relative to the resident if and only if the arithmetic mean of the mutant's possible fitness values exceeds that of the resident.
Revised:  In large populations, the mutant is favored relative to the resident if and only if the arithmetic mean of the mutant's possible fitness values exceeds that of the resident.
Edit intent: coherence
Original:  In large populations, where stochastic effects are negligible, we find that the mutant is favored relative to the resident if and only if the arithmetic mean of the mutant's possible fitness values exceeds that of the resident.
Revised:  In large populations, where stochastic effects are negligible, we find that the mutant is favored if and only if the arithmetic mean of the mutant's possible fitness values exceeds that of the resident.
Edit intent: clarity
Original:  In large populations, where stochastic effects are negligible, we find that the mutant is favored relative to the resident if and only if the arithmetic mean of the mutant's possible fitness values exceeds that of the resident.
Revised:  In large populations, where stochastic effects are negligible, we find that the mutant is favored relative to the resident if and only if the mutant's possible fitness values exceeds that of the resident.
Edit intent: clarity
Original:  Mutant heterogeneity suppresses fixation probability, and if strong enough, it can  completely offset the effects of natural selection .
Revised:  Mutant heterogeneity suppresses fixation probability, and if strong enough, it can even completely offset the effects of natural selection .
Edit intent: coherence
Original:  Mutant heterogeneity suppresses fixation probability, and if strong enough, it can  completely offset the effects of natural selection .
Revised:  Mutant heterogeneity suppresses fixation probability, and if strong enough, it can completely offset the effects of selection (including in large populations) .
Edit intent: clarity
Original:  In contrast, resident heterogeneity can amplify a mutant's fixation probability if N is small and has no effect on mutant fixation probability otherwise.
Revised:  In contrast, resident heterogeneity has no effect on mutant fixation probability otherwise.
Edit intent: clarity
Original:  But spatial heterogeneity elucidates an interesting asymmetry between the mutant and the resident.
Revised:  But environmental heterogeneity elucidates an interesting asymmetry between the mutant and the resident.
Edit intent: clarity
Original:  WRS exploits the temporal locality by always storing the most recent edges, which future edges are more likely to from triangles with, in the waiting room, while it uses reservoir sampling for the remaining edges.
Revised:  WRS exploits the temporal locality by always storing the most recent edges, which future edges are more likely to form triangles with, in the waiting room, while it uses reservoir sampling for the remaining edges.
Edit intent: fluency
Original:  The problem of fair cake cutting is dividing a heterogeneous and divisible resource, the cake, among n players who value pieces according to their own measure function.
Revised:  The problem of proportional cake cutting focuses on dividing a heterogeneous and divisible resource, the cake, among n players who value pieces according to their own measure function.
Edit intent: clarity
Original:  Our contribution is twofold .
Revised:  Our main contribution is threefold .
Edit intent: coherence