wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our analytics [available at kcorelab.com ] unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls \\%DIF > versus Trump 44.
Revised:  Our analytics [available at ] unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls \\%DIF > versus Trump 44.
Edit intent: clarity
Original: Forecasting opinion trends from real-time social media is the long-standing goal of modern-day big-data analytics.
Revised: Measuring and forecasting opinion trends from real-time social media is the long-standing goal of modern-day big-data analytics.
Edit intent: clarity
Original: Forecasting opinion trends from real-time social media is the long-standing goal of modern-day big-data analytics.
Revised: Forecasting opinion trends from real-time social media is a long-standing goal of modern-day big-data analytics.
Edit intent: fluency
Original: Forecasting opinion trends from real-time social media is the long-standing goal of modern-day big-data analytics.
Revised: Forecasting opinion trends from real-time social media is the long-standing goal of big-data analytics.
Edit intent: clarity
Original:  Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population at large .
Revised:  Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population .
Edit intent: clarity
Original:  Here we develop analytic tools combining statistical physics of complex networks, percolation theory, natural language processing and machine learning classification to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election . Using a large-scale dataset of 73 million tweetscollected from June 1st to September 1st 2016, we investigate the temporal social networks formed by the interactions among millions of Twitter users .
Revised:  Here we develop a method to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election . Using a large-scale dataset of 73 million tweetscollected from June 1st to September 1st 2016, we investigate the temporal social networks formed by the interactions among millions of Twitter users .
Edit intent: clarity
Original:  We infer the support of each user to the presidential candidates and show that the resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy (r=0.9).
Revised:  We infer the support of each user to the presidential candidates . The resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy (r=0.9).
Edit intent: coherence
Original:  More importantly , the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends at the national level .
Revised:  More importantly , the Twitter opinion trend precedes the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends at the national level .
Edit intent: clarity
Original:  More importantly , the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends at the national level .
Revised:  More importantly , the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early signal of global opinion trends at the national level .
Edit intent: clarity
Original:  More importantly , the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends at the national level .
Revised:  More importantly , the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends .
Edit intent: clarity
Original:  Our analytics available at%DIFDELCMD < \url{kcorelab.com}] %%% unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls . UPDATE November 7, 2016: We apply our analysis to the final part of the presidential election.
Revised:  Our analytics %DIFDELCMD < \url{kcorelab.com}] %%% unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls . UPDATE November 7, 2016: We apply our analysis to the final part of the presidential election.
Edit intent: clarity
Original:  Our analytics available at%DIFDELCMD < \url{kcorelab.com}] %%% unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls . UPDATE November 7, 2016: We apply our analysis to the final part of the presidential election.
Revised:  Our analytics available at%DIFDELCMD < \url{kcorelab.com}] %%% unleash the power of Twitter to uncover social trends from elections, brands to political movements, and at a fraction of the cost of national polls . UPDATE November 7, 2016: We apply our analysis to the final part of the presidential election.
Edit intent: clarity
Original: 5\\% of the votes (normalized to two candidates) .
Revised: 5\\% of .
Edit intent: clarity
Original:  We manually annotated the tweets as favorable of Trump, Clinton, or neither of them .
Revised:  We manually annotated the tweets as favorable of Trump, Clinton, or neither .
Edit intent: clarity
Original:  Our quantitative study shows that tweets in favor of Trump were usually retweeted more than pro-Clinton tweets, with the exception of few days , especially the day following the first presidential debate .\\%DIF > of the total tweet volume.
Revised:  Our quantitative study shows that tweets favoring Trump were usually retweeted more than pro-Clinton tweets, with the exception of few days , especially the day following the first presidential debate .\\%DIF > of the total tweet volume.
Edit intent: fluency
Original: Strategic network formation models the uncoordinated creation of a network by selfish agents.
Edit intent: clarity
Original:  Most of these games have the conceptual drawback of being computationally intractable .
Revised:  Unfortunately, almost all of these games have the conceptual drawback of being computationally intractable .
Edit intent: clarity
Original:  For example, computing a best response strategy or checking whether an equilibrium is reached is NP-hard.
Revised:  For example, computing a best response strategy or checking if an equilibrium is reached is NP-hard.
Edit intent: fluency
Original:  Thus, a main challenge in the field is to find models which incorporate many interesting features and to devise efficient algorithms for solving the entailed computational tasks .
Revised:  Thus, a main challenge in the field is to find tractable realistic network formation models .
Edit intent: clarity
Original:  We address this challenge by providing an efficient algorithm to compute a best response strategy for a recently introduced model , thereby answering the open question posed by Goyal et al.
Revised:  We address this challenge by investigating a very recently introduced model , thereby answering the open question posed by Goyal et al.
Edit intent: clarity
Original:  We address this challenge by providing an efficient algorithm to compute a best response strategy for a recently introduced model , thereby answering the open question posed by Goyal et al.
Revised:  We address this challenge by providing an efficient algorithm to compute a best response strategy for a recently introduced model by Goyal et al.
Edit intent: clarity
Original:  The framework can be applied to a large class of utilities, but in this study special attention is devoted to the rate.
Edit intent: coherence
Original:  As a consequence, given any network utility maximization problem belonging to this class, we can  define two functions that relate the power budget p of a network to the network utility and to the energy efficiency  achieved by the solution to the given problem .
Revised:  As a consequence, given any network utility maximization problem belonging to this class, we can define two functions that map the power budget p of a network to the network utility and to the energy efficiency achieved by the solution to the given problem .
Edit intent: clarity
Original:  As a consequence, given any network utility maximization problem belonging to this class, we can  define two functions that relate the power budget p of a network to the network utility and to the energy efficiency  achieved by the solution to the given problem .
Revised:  As a consequence, given any network utility maximization problem belonging to this class, we can define two functions that relate the power budget p to the energy efficiency achieved by the solution to the given problem .
Edit intent: clarity
Original:  As a consequence, given any network utility maximization problem belonging to this class, we can  define two functions that relate the power budget p of a network to the network utility and to the energy efficiency  achieved by the solution to the given problem .
Revised:  As a consequence, given any network utility maximization problem belonging to this class, we can define two functions that relate the power budget p of a network to the network utility and to the energy efficiency and to the utility achieved by the solution to the given problem .
Edit intent: clarity
Original:  As a consequence, given any network utility maximization problem belonging to this class, we can  define two functions that relate the power budget p of a network to the network utility and to the energy efficiency  achieved by the solution to the given problem .
Revised:  As a consequence, given any network utility maximization problem belonging to this class, we can define two functions that relate the power budget p of a network to the network utility and to the energy efficiency achieved by the solution .
Edit intent: clarity
Original:  In the low power regime, the energy efficiency  can decrease slowly as the power budget increases, and the network utility grows linearly at best.
Revised:  In the low power regime, the energy efficiency of the solution can decrease slowly as the power budget increases, and the network utility grows linearly at best.
Edit intent: clarity
Original:  While the majority of previous studies equate reputation with a transparent and complete history of players' past decisions, in real life, reputations are often based on personal evaluations, and so they are often ambiguous and opaque.
Revised:  While the majority of previous studies equates reputation with a transparent and complete history of players' past decisions, in real life, reputations are often based on personal evaluations, and so they are often ambiguous and opaque.
Edit intent: fluency
Original:  While the majority of previous studies equate reputation with a transparent and complete history of players' past decisions, in real life, reputations are often based on personal evaluations, and so they are often ambiguous and opaque.
Revised:  While the majority of previous studies equate reputation with a transparent and complete history of players' past decisions, in real life, reputations are often ambiguous and opaque.
Edit intent: coherence
Original:  For example, reputation has become central in online systems, in which history of others' past actions is not available, and sources are unknown, as well as the metrics used for the evaluation.
Edit intent: coherence
Original:  Using web-based experiments, we explore the extent to which obscure reputation works in isolating defectors, and whether it can promote cooperative behaviors .
Revised:  Using web-based experiments, we explore the extent to which opaque reputation works in isolating defectors, and whether it can promote cooperative behaviors .
Edit intent: fluency
Original:  Using web-based experiments, we explore the extent to which obscure reputation works in isolating defectors, and whether it can promote cooperative behaviors .
Revised:  Using web-based experiments, we explore the extent to which obscure reputation works in isolating defectors, with and without partner selection opportunities .
Edit intent: clarity
Original:  These results provide insights on the  way in which humans take into account reputation when selecting partners but also when knowing that they will be selected.
Revised:  These results provide insights on the way in which humans make use of it when selecting partners but also when knowing that they will be selected.
Edit intent: clarity
Original:  We introduce temporal ensembling , where we form a consensus prediction of the unknown labels under multiple instances of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions.
Revised:  We introduce self-ensembling , where we form a consensus prediction of the unknown labels under multiple instances of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions.
Edit intent: clarity
Original:  We introduce temporal ensembling , where we form a consensus prediction of the unknown labels under multiple instances of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions.
Revised:  We introduce temporal ensembling , where we form a consensus prediction of the unknown labels using the outputs of the network-in-training on different epochs, and most importantly, under different regularization and input augmentation conditions.
Edit intent: clarity
Original:  Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the  classification error rate from 18.63\% to 12.89\% in CIFAR-10 with 4000 labels and from 18.44\% to 6.83\% in SVHN with 500  labels.
Revised:  Using our method, we set new records for two standard semi-supervised learning benchmarks, reducing the classification error rate from 18.44\% to 6.83\% in SVHN with 500 labels.
Edit intent: coherence
Original: In this paper we investigate the image aesthetics classification problem, aka, automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem beyond image recognition.
Revised: In this paper we investigate the aesthetic image classification problem, aka, automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem beyond image recognition.
Edit intent: clarity
Original: In this paper we investigate the image aesthetics classification problem, aka, automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem beyond image recognition.
Revised: In this paper we investigate the image aesthetics classification problem, also known as automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem beyond image recognition.
Edit intent: fluency
Original:  However, the inception module has not been taken into consideration for the image aesthetics assessment problem.
Revised:  We have the observation that the inception module has not been taken into consideration for the image aesthetics assessment problem.
Edit intent: coherence
Original:  Besides, we use a pre-trained image classification CNN called GoogLeNet on the ImageNet dataset and fine tune our connected local and global layer on the large scale aesthetics assessment AVA dataset.
Revised:  Besides, we use a pre-trained image classification GoogleLeNet model on the ImageNet dataset and fine tune our connected local and global layer on the large scale aesthetics assessment AVA dataset.
Edit intent: clarity
Original: In this paper we investigate the aesthetic image classification problem, also known as automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem.
Revised: In this paper , we address a challenging problem of aesthetic image classification problem, also known as automatically classifying an image into low or high aesthetic quality, which is quite a challenging problem.
Edit intent: clarity
Original:  Thus, we can easily use a pre-trained image classification GoogleLeNet model on the ImageNet dataset and fine tune our connected local and global layer on the large scale aesthetics assessment AVAdataset.
Revised:  Thus, it is easy to use a pre-trained image classification GoogleLeNet model on the ImageNet dataset and fine tune our connected local and global layer on the large scale aesthetics assessment AVAdataset.
Edit intent: coherence
Original:  Thus, we can easily use a pre-trained image classification GoogleLeNet model on the ImageNet dataset and fine tune our connected local and global layer on the large scale aesthetics assessment AVAdataset.
Revised:  Thus, we can easily use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected local and global layer on the large scale aesthetics assessment AVAdataset.
Edit intent: clarity
Original: In computer architecture, near-data processing (NDP) refers to augmenting the memory or the storage with processing power so that it can process the data stored therein , passing only the processed data upwards in the memory hierarchy .
Revised: In computer architecture, near-data processing (NDP) refers to augmenting the memory or the storage with processing power so that it can process the data stored therein .
Edit intent: clarity
Original:  Despite its potential, NDP had only limited success until recently, mainly due to the performance mismatch in logic and memory process technologies  . Recently, there have been two major changes in the game, making NDP more appealing than ever .
Revised:  Despite its potential, NDP had only limited success until recently, often owing to performance mismatches between logic and memory process technologies . Recently, there have been two major changes in the game, making NDP more appealing than ever .
Edit intent: clarity
Original:  Despite its potential, NDP had only limited success until recently, mainly due to the performance mismatch in logic and memory process technologies  . Recently, there have been two major changes in the game, making NDP more appealing than ever .
Revised:  Despite its potential, NDP had only limited success until recently, mainly due to the performance mismatch in logic and memory process technologies . Recently, there have been two major changes in the game, igniting the resurgence of NDP with renewed interest .
Edit intent: clarity
Original:  The first is the success of deep learning , which often requires frequent transfers of big data for training .
Revised:  The first is the success of deep learning , which often requires frequent transfers of big data .
Edit intent: clarity
Original:  The second is the advent of NAND flash-based solid-state drives (SSDs) containing multicore CPUs that can be used for data processing.
Revised:  The second is the advent of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing.
Edit intent: clarity
Original:  In this paper , we evaluate the potential of NDP for machine learning using a new SSD platform that allows us to simulate in-storage processing (ISP) of machine learning workloads.
Revised:  In this paper , we evaluate the potential of NDP for ML using a new SSD platform that allows us to simulate in-storage processing (ISP) of machine learning workloads.
Edit intent: clarity
Original:  Although our platform named ISPML can execute various algorithms, this paper focuses on the stochastic gradient decent (SGD) algorithm , which is the de facto standard method for training deep neural networks.
Revised:  Although our platform named ISPML can execute various algorithms, this paper focuses on the stochastic gradient decent (SGD) , which is the de facto standard method for training deep neural networks.
Edit intent: clarity
Original:  Although our platform named ISPML can execute various algorithms, this paper focuses on the stochastic gradient decent (SGD) algorithm , which is the de facto standard method for training deep neural networks.
Revised:  Although our platform named ISPML can execute various algorithms, this paper focuses on the stochastic gradient decent (SGD) algorithm , which is the de facto standard for training differentiable learning machines including deep neural networks.
Edit intent: clarity
Original:  We implement and compare three variants of SGD (synchronous, downpour , and elastic averaging) using the ISP-ML platform, in which we exploit the multiple NAND channels for implementing parallel SGD.
Revised:  We implement and compare three variants of SGD (synchronous, Downpour , and elastic averaging) using the ISP-ML platform, in which we exploit the multiple NAND channels for implementing parallel SGD.
Edit intent: fluency
Original:  We implement and compare three variants of SGD (synchronous, downpour , and elastic averaging) using the ISP-ML platform, in which we exploit the multiple NAND channels for implementing parallel SGD.
Revised:  We implement and compare three variants of SGD (synchronous, downpour , and elastic averaging) using ISP-ML platform, in which we exploit the multiple NAND channels for implementing parallel SGD.
Edit intent: fluency
Original:  We implement and compare three variants of SGD (synchronous, downpour , and elastic averaging) using the ISP-ML platform, in which we exploit the multiple NAND channels for implementing parallel SGD.
Revised:  We implement and compare three variants of SGD (synchronous, downpour , and elastic averaging) using the ISP-ML , exploiting the multiple NAND channels for implementing parallel SGD.
Edit intent: coherence
Original:  We implement and compare three variants of SGD (synchronous, downpour , and elastic averaging) using the ISP-ML platform, in which we exploit the multiple NAND channels for implementing parallel SGD.
Revised:  We implement and compare three variants of SGD (synchronous, downpour , and elastic averaging) using the ISP-ML platform, in which we exploit the multiple NAND channels for parallelizing SGD.
Edit intent: clarity
Original:  In addition, we compare the performance of ISP optimization and that of conventional in-host processing optimization.
Revised:  In addition, we compare the performance of ISP and that of conventional in-host processing optimization.
Edit intent: fluency
Original: In computer architecture, near-data processing (NDP) refers to augmenting the memory or the storage with processing power so that it can process the data stored therein.
Revised: Near-data processing (NDP) refers to augmenting the memory or the storage with processing power so that it can process the data stored therein.
Edit intent: clarity
Original: In computer architecture, near-data processing (NDP) refers to augmenting the memory or the storage with processing power so that it can process the data stored therein.
Revised: In computer architecture, near-data processing (NDP) refers to augmenting memory or storage with processing power so that it can process the data stored therein.
Edit intent: fluency