wanyu/IteraTeR_v2 huggingface dataset:
Original:  We estimate the anthropogenically increased potential distribution based on the current projections of turbine locations and understanding of settlementpatterns .
Revised:  We estimate the anthropogenically increased potential distribution based on the current projections of turbine locations and settlementpatterns .
Edit intent: clarity
Original:  Our simulations reveal non-negligible  changes in regional annual primary production of up to a few percent, and larger changes (up to%DIFDELCMD < \pm10\\%%% %DIF < ) of the phytoplankton stock, and thus water clarity, during the bloom period .
Revised:  Our simulations reveal non-negligible changes in regional annual primary production of up to %DIFDELCMD < \pm10\\%%% %DIF < ) of the phytoplankton stock, and thus water clarity, during the bloom period .
Edit intent: clarity
Original:  Our setup and modular coupling are effective tools for system scale studies of other environmental changes arising from large-scale offshore wind-farming such as ocean physics and distribution of pelagic top predators.
Revised:  Our setup and modular coupling are effective tools for system scale studies of other environmental changes arising from large-scale offshore wind-farming such as ocean physics and distributions of pelagic top predators.
Edit intent: fluency
Original:  Our study investigates whether the accumulation of epifauna, dominated by the filter feeder Mytilus edulis (blue mussel), on turbine structures affects pelagic primary production and ecosystem functioning in the southern North Sea.
Revised:  Our study investigates whether the accumulation of epifauna, dominated by the filter feeder Mytilus edulis (blue mussel), on turbine structures affects pelagic primary productivity and ecosystem functioning in the southern North Sea.
Edit intent: fluency
Original:  Our simulations reveal non-negligible potential changes in regional annual primary production of up to 8\% within the offshore wind farm area, and induced maximal increases of the same magnitude in daily production also far from the wind farms.
Revised:  Our simulations reveal non-negligible potential changes in regional annual primary productivity of up to 8\% within the offshore wind farm area, and induced maximal increases of the same magnitude in daily production also far from the wind farms.
Edit intent: fluency
Original:  Our simulations reveal non-negligible potential changes in regional annual primary production of up to 8\% within the offshore wind farm area, and induced maximal increases of the same magnitude in daily production also far from the wind farms.
Revised:  Our simulations reveal non-negligible potential changes in regional annual primary production of up to 8\% within the offshore wind farm area, and induced maximal increases of the same magnitude in daily productivity also far from the wind farms.
Edit intent: fluency
Original: Using age of information as the freshness metric, we examine a multicast network in which real-time status updates are generated by the source , and replicas of the update are sent to multiple interested receivers.
Revised: Using age of information as the freshness metric, we examine a multicast network in which real-time status updates are generated by the source and sent to a group of n interested receivers.
Edit intent: clarity
Original:  In order to keep the information freshness at each receiver, the source preempts the current update and starts the transmission of a new update packet as soon as it receives the acknowledgements back from  k out of n nodes.
Revised:  We show that in order to keep the information freshness at each receiver, the source preempts the current update and starts the transmission of a new update packet as soon as it receives the acknowledgements back from k out of n nodes.
Edit intent: clarity
Original:  In order to keep the information freshness at each receiver, the source preempts the current update and starts the transmission of a new update packet as soon as it receives the acknowledgements back from  k out of n nodes.
Revised:  In order to keep the information freshness at each receiver, the source should terminate the transmission of the current update and starts the transmission of a new update packet as soon as it receives the acknowledgements back from k out of n nodes.
Edit intent: clarity
Original:  In order to keep the information freshness at each receiver, the source preempts the current update and starts the transmission of a new update packet as soon as it receives the acknowledgements back from  k out of n nodes.
Revised:  In order to keep the information freshness at each receiver, the source preempts the current update and start sending a new update packet as soon as it receives the acknowledgements back from k out of n nodes.
Edit intent: clarity
Original:  In order to keep the information freshness at each receiver, the source preempts the current update and starts the transmission of a new update packet as soon as it receives the acknowledgements back from  k out of n nodes.
Revised:  In order to keep the information freshness at each receiver, the source preempts the current update and starts the transmission of a new update packet as soon as it receives the acknowledgements back from any k out of n nodes.
Edit intent: clarity
Original:  For larger preemption threshold k , a node is more likely to get the latest generated update .
Revised:  As the source stopping threshold k increases , a node is more likely to get the latest generated update .
Edit intent: clarity
Original:  For shifted exponential service model, we derive the age optimized preemption threshold k that balances the likelihood of getting the latest update and the freshness of the latest update  .
Revised:  For shifted exponential service model, we derive the age minimized stopping threshold k that balances the likelihood of getting the latest update and the freshness of the latest update .
Edit intent: clarity
Original:  Through numerical comparisons among different delay models, we believe the tradeoff between the preemption threshold and the age of information depends on the heaviness of the tail of the delay distribution .
Revised:  Through numerical evaluations for different stopping strategies, we find that waiting for the acknowledgements from the earliest k out of n nodes leads to lower average age than waiting for a pre-selected group of k nodes. We also observe that a properly chosen threshold k can prevent information staleness for increasing number of nodes n in the multicast network .
Edit intent: clarity
Original: We show that the class of groups with k-multiple context free word problem is closed under amalgamated free products over finite subgroups.
Revised: We show that the class of groups with k-multiple context-free word problem is closed under amalgamated free products over finite subgroups.
Edit intent: fluency
Original:  These applications need to process massive amounts of spatio-textual data in real-time  .
Revised:  These applications need to process massive streams of spatio-textual data in real-time .
Edit intent: clarity
Original:  To support this data scale, these applications require efficient spatio-textual indexing .
Revised:  In this paper, we study indexing of continuous spatio-textual queries .
Edit intent: clarity
Original:  Also, these indexes do not successfully capture the variations of the frequencies of keywords across different spatial regions and treat frequent and infrequent keywords similarly .
Revised:  Also, these indexes do not successfully capture the variations in the frequencies of keywords across different spatial regions and treat frequent and infrequent keywords similarly .
Edit intent: fluency
Original:  Also, these indexes do not successfully capture the variations of the frequencies of keywords across different spatial regions and treat frequent and infrequent keywords similarly .
Revised:  Also, these indexes do not successfully capture the variations of the frequencies of keywords across different spatial regions and treat frequent and infrequent keywords in the same way .
Edit intent: clarity
Original:  To maintain high performance, the index needs to adapt to these changes .
Revised:  This affects the indexing and searching performance of existing indexes significantly .
Edit intent: clarity
Original: This work studies engagement, or watching behavior in online videos.
Revised: This work studies engagement, or time spent watching online videos.
Edit intent: clarity
Original:  Most current work focuses on modeling  views, which is known to be an inadequate measure of engagement and video quality due to different viewing behavior and spam views . More broadly, engagement has been measured in reading behavior of news and web pages, click-through in online ads, but not for videos .
Revised:  Most current work focuses on modeling the number of views, which is known to be an inadequate measure of engagement and video quality due to different viewing behavior and spam views . More broadly, engagement has been measured in reading behavior of news and web pages, click-through in online ads, but not for videos .
Edit intent: clarity
Original:  Most current work focuses on modeling  views, which is known to be an inadequate measure of engagement and video quality due to different viewing behavior and spam views . More broadly, engagement has been measured in reading behavior of news and web pages, click-through in online ads, but not for videos .
Revised:  Most current work focuses on modeling views, which is known to be an inadequate measure of engagement and video quality due to and prone to spam . More broadly, engagement has been measured in reading behavior of news and web pages, click-through in online ads, but not for videos .
Edit intent: clarity
Original:  Most current work focuses on modeling  views, which is known to be an inadequate measure of engagement and video quality due to different viewing behavior and spam views . More broadly, engagement has been measured in reading behavior of news and web pages, click-through in online ads, but not for videos .
Revised:  Most current work focuses on modeling views, which is known to be an inadequate measure of engagement and video quality due to different viewing behavior and spam views . More broadly, engagement has been studied in reading behavior of news and web pages, click-through in online ads, but not for videos .
Edit intent: clarity
Original:  We study a set of engagement metrics, including watch time, percentage of video watched, and relate them to views and video properties such as length and content category .
Revised:  We study a set of aggregate engagement metrics including watch time, percentage of video watched, and relate them to views and video properties such as length and content category .
Edit intent: clarity
Original:  We study a set of engagement metrics, including watch time, percentage of video watched, and relate them to views and video properties such as length and content category .
Revised:  We study a set of engagement metrics, including watch time, percentage of video watched, and relate them to views and video properties such as length , category and topics .
Edit intent: clarity
Original:  We measure engagement metrics over 5.3 million YouTube videos.
Revised:  We measure engagement over 5.3 million YouTube videos.
Edit intent: clarity
Original:  This  dataset and benchmarks will be publicly available.
Revised:  This new dataset and benchmarks will be publicly available.
Edit intent: clarity
Original:  This work provides new dimensions to quantify engagement in videos, and paves way towards in-depth understanding of different video verticals such as music, news, activism and games .
Revised:  This work provides a set of new yardsticks for measuring content including video and other length-constrained media such as songs and podcasts. It opens a new direction for modeling user-specific engagement and making better recommendations .
Edit intent: clarity
Original: This work studies engagement, or time spent watching online videos .
Revised: Online videos account for 73\% of internet traffic in 2016, both the absolute and proportional amount of traffic are still growing. Hence understanding how videos capture attention on a global scale is also of growing importance .
Edit intent: coherence
Original:  Most current work focuses on modeling the number of views, which is known to be inadequate \% measure of engagement and video quality due to and prone to spam.
Revised:  Most current research focus on modeling the number of views, which is known to be inadequate \% measure of engagement and video quality due to and prone to spam.
Edit intent: clarity
Original:  We propose a new metric, relative engagement, which is calibrated over video duration, stable over time, and strongly correlated with video quality.
Revised:  We propose a new metric, relative engagement, that calibrates against video properties and strongly correlates with recognized notions of quality. Moreover, we find that engagement measures of videos are stable over time, and strongly correlated with video quality.
Edit intent: clarity
Original:  In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos across 2 months in 2016.
Revised:  In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016.
Edit intent: clarity
Original:  We study a set of metrics including time and the average percentage of a video being watched.
Revised:  We study a set of metrics including time and the average percentage of a video watched.
Edit intent: clarity
Original:  We define a new metric, relative engagement, that calibrates against video properties and strongly correlates with recognized notions of quality.
Revised:  We define a new metric, relative engagement, that is calibrated against video properties and strongly correlates with recognized notions of quality.
Edit intent: clarity
Original:  We define a new metric, relative engagement, that calibrates against video properties and strongly correlates with recognized notions of quality.
Revised:  We define a new metric, relative engagement, that calibrates against video properties and strongly correlate with recognized notions of quality.
Edit intent: fluency
Original:  Moreover, we find that engagement measures of videos are stable over time, thus separating the concerns for modeling engagement and those for popularity - the latter is known to be unstable over time and driven by external promotions.
Revised:  Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity - the latter is known to be unstable over time and driven by external promotions.
Edit intent: fluency
Original:  Moreover, we find that engagement measures of videos are stable over time, thus separating the concerns for modeling engagement and those for popularity - the latter is known to be unstable over time and driven by external promotions.
Revised:  Moreover, we find that engagement measures of videos are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions.
Edit intent: fluency
Original:  We also find engagement metrics are predictable from a cold-start setup, having most of the variance explained by metadata, content and channel reputations - R2=0.77.
Revised:  We also find engagement metrics predictable from a cold-start setup, having most of the variance explained by metadata, content and channel reputations - R2=0.77.
Edit intent: clarity
Original:  We also find engagement metrics are predictable from a cold-start setup, having most of the variance explained by metadata, content and channel reputations - R2=0.77.
Revised:  We also find engagement metrics are predictable from a cold-start setup, having most of its variance explained by metadata, content and channel reputations - R2=0.77.
Edit intent: clarity
Original:  We also find engagement metrics are predictable from a cold-start setup, having most of the variance explained by metadata, content and channel reputations - R2=0.77.
Revised:  We also find engagement metrics are predictable from a cold-start setup, having most of the variance explained by video context, topics and channel information -- R2=0.77.
Edit intent: clarity
Original:  These observations show that engagement metrics , measured or predicted, are valuable tools for content producers and hosting sites to choose or rank content even before observing its audience reactions .
Revised:  Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems .
Edit intent: clarity
Original:  By noting that Andrew 's determinant is a special case of a two-parameter family of determinants, we find closed forms for several one-parameter subfamilies.
Revised:  By noting that Andrews 's determinant is a special case of a two-parameter family of determinants, we find closed forms for several one-parameter subfamilies.
Edit intent: fluency
Original: Internet traffic classification has become more 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: fluency
Original: Internet traffic classification has become more 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: clarity
Original:  Most of these approaches exploit predefined features extracted by an expert in order to classify network traffic.
Revised:  Most of these approaches use predefined features extracted by an expert in order to classify network traffic.
Edit intent: fluency
Original:  In contrast, in this study, we propose a deep learning based approach which integrate both feature extraction and classification phases into one system.
Revised:  In contrast, in this study, we propose a deep learning based approach which integrate both feature extraction and classification phases into one system.
Edit intent: fluency
Original:  In contrast, in this study, we propose a deep learning based approach which integrate both feature extraction and classification phases into one system.
Revised:  In contrast, in this study, we propose a deep learning based approach which integrates both feature extraction and classification phases into one system.
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 characterization 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.
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{ 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 categorized 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.
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 ( \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.
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, FTP and 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.
Edit intent: clarity