wanyu/IteraTeR_v2 huggingface dataset:
Original:  With the widespread use of mobile devices, the vast diversity of mobile apps, and the massive adoption of encryption protocols (such as TLS), large-scale  traffic classification becomes inevitable and more difficult.
Revised:  With the widespread use of mobile devices, the vast diversity of mobile apps, and the massive adoption of encryption protocols (such as TLS), large-scale traffic classification becomes increasingly difficult.
Edit intent: clarity
Original:  We also perform occlusion analysis for the first time to bring insight into what data is leaked from SSL/TLS protocol that allows accurate app identification.
Revised:  We also perform occlusion analysis to bring insight into what data is leaked from SSL/TLS protocol that allows accurate app identification.
Edit intent: clarity
Original:  To address this challenge, we propose a CNN+LSTM model that takes adjacent flows to learn the order and pattern of multiple flows, to better identify the app that generates them.
Revised:  To address this challenge, we propose a CNN+LSTM model that uses adjacent flows to learn the order and pattern of multiple flows, to better identify the app that generates them.
Edit intent: fluency
Original: K index is an easily computable centrality index in complex networks, such as a scientific citations network.
Revised: The K-index is an easily computable centrality index in complex networks, such as a scientific citations network.
Edit intent: clarity
Original:  A researcher has a K index equal to K if he or she is cited by K articles that have at least K citations.
Revised:  A researcher has a K-index equal to K if he or she is cited by K articles that have at least K citations.
Edit intent: fluency
Original:  The K index has several advantages over Hirsh's h index and, in previous studies, has shown better correlation with Nobel prizes than any other index given by the {\em Web of Science , including the h index.
Revised:  The K-index has several advantages over Hirsh's h index and, in previous studies, has shown better correlation with Nobel prizes than any other index given by the {\em Web of Science , including the h index.
Edit intent: clarity
Original:  The K index has several advantages over Hirsh's h index and, in previous studies, has shown better correlation with Nobel prizes than any other index given by the {\em Web of Science , including the h index.
Revised:  The K index has several advantages over Hirsh's h-index and, in previous studies, has shown better correlation with Nobel prizes than any other index given by the {\em Web of Science , including the h index.
Edit intent: fluency
Original:  Here we perform an experiment using the K index , producing a shortlist to predict future Physics Nobel candidates.
Revised:  Here we perform an experiment using the K-index , producing a shortlist to predict future Physics Nobel candidates.
Edit intent: fluency
Original:  Our list has been built before the 2019 Nobel Prizes announcements and should be compared to the actual results of the current and following years.
Revised:  Our list has been built before the 2019 Nobel Prizes announcements and should be compared to laureates of the following years
Edit intent: clarity
Original:  Finally, we study the accuracy and performance of the tensor completion methods for a synthetic tensor with 10 billion nonzeros and the Netflix dataset.
Revised:  Finally, we study the performance of the tensor completion methods for a synthetic tensor with 10 billion nonzeros and the Netflix dataset.
Edit intent: clarity
Original:  We develop these methods using a new Python interface to the Cyclops tensor algebra library, which fully automates the management of distributed-memory parallelism and sparsity for NumPy-style operations on multidimensional arrays.
Edit intent: coherence
Original:  To make possible tensor completion for very sparse tensors, we introduce a new multi-tensor routine, TTTP, that is asymptotically more efficient than pairwise tensor contraction for key components of the tensor completion methods.
Revised:  To make possible tensor completion for very sparse tensors, we introduce new multi-tensor routine, TTTP, that is asymptotically more efficient than pairwise tensor contraction for key components of the tensor completion methods.
Edit intent: fluency
Original: We solve in closed-form a Nash equilibrium model in which a finite number of exponential investors trade continuously with price-impact over a finite time horizon .
Revised: We solve in closed-form an equilibrium model in which a finite number of exponential investors trade continuously with price-impact over a finite time horizon .
Edit intent: clarity
Original: We solve in closed-form a Nash equilibrium model in which a finite number of exponential investors trade continuously with price-impact over a finite time horizon .
Revised: We solve in closed-form a Nash equilibrium model in which a finite number of exponential investors trade continuously with price-impact .
Edit intent: clarity
Original: Rendering realistic images with Global Illumination (GI) is a computationally demanding task and often requires dedicated hardware for feasible runtime.
Revised: Rendering realistic images with global illumination is a computationally demanding task and often requires dedicated hardware for feasible runtime.
Edit intent: clarity
Original:  Recent projects have used Generative Adversarial Networks (GAN) to predict indirect lighting on an image level, but are limited to diffuse materials and require training on each scene.
Revised:  Recent research uses Deep Neural Networks to predict indirect lighting on an image level, but are limited to diffuse materials and require training on each scene.
Edit intent: clarity
Original:  Recent projects have used Generative Adversarial Networks (GAN) to predict indirect lighting on an image level, but are limited to diffuse materials and require training on each scene.
Revised:  Recent projects have used Generative Adversarial Networks (GAN) to predict indirect lighting on image level, but are limited to diffuse materials and require training on each scene.
Edit intent: fluency
Original:  Recent projects have used Generative Adversarial Networks (GAN) to predict indirect lighting on an image level, but are limited to diffuse materials and require training on each scene.
Revised:  Recent projects have used Generative Adversarial Networks (GAN) to predict indirect lighting on an image level, but such methods are commonly limited to diffuse materials and require training on each scene.
Edit intent: clarity
Original:  We combine a modern denoising Neural Network with Radiance Caching to offer high performance CPU GI rendering while supporting a wide range of material types, without the requirement of offline pre-computation or training for each scene.
Revised:  We combine a modern denoising Neural Network with Radiance Caching to support a wide range of material types, without the requirement of offline pre-computation or training for each scene.
Edit intent: coherence
Original:  We also probe REMIND's robustness to different data ordering schemes using the CORe50 streaming dataset .
Revised:  We probe REMIND's robustness to different data ordering schemes using the CORe50 streaming dataset .
Edit intent: clarity
Original:  We also probe REMIND's robustness to different data ordering schemes using the CORe50 streaming dataset .
Revised:  We also probe REMIND's robustness to data ordering schemes using the CORe50 streaming dataset .
Edit intent: clarity
Original:  Here, we propose REMIND, a brain-inspired approach that enables efficient replay with compressed hidden representations.
Revised:  Here, we propose REMIND, a brain-inspired approach that enables efficient replay with compressed representations.
Edit intent: clarity
Original:  REMIND is trained in an online manner, meaning it learns one example at a time, which is closer to how humans perceive new information .
Revised:  REMIND is trained in an online manner, meaning it learns one example at a time, which is closer to how humans learn .
Edit intent: clarity
Original:  We demonstrate REMIND's generality by pioneering online learning for Visual Question Answering (VQA) , which cannot be readily done with comparison models .
Revised:  We demonstrate REMIND's generality by pioneering online learning for Visual Question Answering (VQA) .
Edit intent: clarity
Original: Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust  and efficient measures of uncertainty are crucial.
Revised: Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust , and efficient measures of uncertainty are crucial.
Edit intent: fluency
Original:  While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions.
Edit intent: coherence
Original:  We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution.
Revised:  We accomplish this by placing evidential priors over the original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution.
Edit intent: fluency
Original:  We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution.
Revised:  We accomplish this by placing evidential priors over our original Gaussian likelihood function and training the NN to infer the hyperparameters of our evidential distribution.
Edit intent: fluency
Original:  We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution.
Revised:  We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of the evidential distribution.
Edit intent: fluency
Original:  We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations .
Revised:  We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, and is robust to adversarial input perturbations .
Edit intent: fluency
Original:  Here we are given K different sets , and for any given query qthe goal is the find all of the sets containing the query element .
Revised:  Here we are given K different sets and a query q, it aims to find all of the sets containing the query element .
Edit intent: clarity
Original:  Here we are given K different sets , and for any given query qthe goal is the find all of the sets containing the query element .
Revised:  Here we are given K different sets , and for any given query qthe goal is the find all of the sets containing the query .
Edit intent: clarity
Original:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Revised:  In this paper, we show the first non-trivial data-structure called RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Edit intent: clarity
Original:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Revised:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged BloOm Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Edit intent: fluency
Original:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Revised:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves O(\sqrt{K with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Edit intent: clarity
Original:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Revised:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst-case memory cost factor of O( logK) than the array of Bloom Filters.
Edit intent: fluency
Original:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( logK) than the array of Bloom Filters.
Revised:  In this paper, we show the first non-trivial data-structure for streaming keys, RAMBO (Repeated And Merged Bloom Filter) that achieves expected O(sqrt(K) logK) query time with an additional worst case memory cost factor of O( log K) beyond the array of Bloom Filters.
Edit intent: clarity
Original:  In this paper, we wish to provide a better understanding for the successful strategies by converting them into equivalent automata with a minimal number of states.
Revised:  In this paper, we wish to provide a better understanding for the successful strategies by converting them into automata with a minimal number of states.
Edit intent: clarity
Original: A social dilemmaappears when defection is individually favored than cooperation although the latter is collectively optimal  .
Revised: In a social dilemma, cooperation is collectively optimal .
Edit intent: clarity
Original:  Biol. (2017) and for the three-person iterated public goods game Murase and Baek, J. Theor.
Revised:  Biol. (2017) and for the Murase and Baek, J. Theor.
Edit intent: clarity
Original:  Although we have a list of actions prescribed by the successful strategies , the rationale behind them have not been fully understood  .
Revised:  Although we have a list of actions prescribed by each successful strategy , the rationale behind them have not been fully understood .
Edit intent: clarity
Original:  Although we have a list of actions prescribed by the successful strategies , the rationale behind them have not been fully understood  .
Revised:  Although we have a list of actions prescribed by the successful strategies , the rationale behind them has not been fully understood .
Edit intent: fluency
Original:  In this paper, we wish to provide a better understanding for the successful strategies by converting them into automata with a minimal number of states.
Revised:  In this paper, we propose a method to convert such history-based representation into an automaton with a minimal number of states.
Edit intent: clarity
Original:  We consider the Hasse diagram of the lattice M_{k} (the unique lattice with k+2 elements and one anti-chain of length k) and want to find the maximal k for which it is isomorphic to a subgraph of the reduction graph  a given one-rule string rewriting system.
Revised:  We consider the Hasse diagram of the lattice M_{k} (the unique lattice with k+2 elements and one anti-chain of length k) and want to find the maximal k for which it is isomorphic to a subgraph of the reduction graph of a given one-rule string rewriting system.
Edit intent: fluency
Original:  We identify natural classes of graphs for which finite improvement or coalition-improvement paths of polynomial length always exist, and, as a consequence, a (pure) Nash equilibrium or a strong equilibrium can be found in polynomial time.
Revised:  We identify natural classes of graphs for which finite improvement or coalition-improvement paths of polynomial length always exist, and, as a consequence, a (pure) Nash equlibrium or a strong equilibrium can be found in polynomial time.
Edit intent: fluency
Original:  Quality is not a prerequisite for viral spreading, yet the size of diffusion cascades is still the hallmark of social influence . Consequently, our exposure to low-quality content and questionable influence is expected to increase.
Revised:  Quality is not a prerequisite for viral spreading, yet large diffusion cascades remain the hallmark of social influence . Consequently, our exposure to low-quality content and questionable influence is expected to increase.
Edit intent: fluency
Original:  Quality is not a prerequisite for viral spreading, yet the size of diffusion cascades is still the hallmark of social influence . Consequently, our exposure to low-quality content and questionable influence is expected to increase.
Revised:  Quality is not a prerequisite for viral spreading, yet the size of diffusion cascades is still the hallmark of a social influencer . Consequently, our exposure to low-quality content and questionable influence is expected to increase.
Edit intent: clarity
Original:  The correlations discovered , lead us to propose a new compound engagement signal.
Revised:  The correlations discovered lead us to propose a new compound engagement signal.
Edit intent: fluency
Original:  We then show it is more predictable than any individual influence predictor investigatedbefore .
Revised:  We then show it is more predictable than any individual influence predictors previously investigated .
Edit intent: fluency
Original:  Our proposed model achieves strong engagement ranking performance and is the first to explain half of the variance with features available at the time of engagement .
Revised:  Our proposed model achieves strong engagement ranking performance and is the first to explain half of the variance with features available early .
Edit intent: clarity
Original: Cooperative communication plays a central role in theories of human cognition, language, development, and culture, and is increasingly relevant in human-algorithm and robot interaction.
Revised: Cooperative communication plays a central role in theories of human cognition, language, development, culture, and is increasingly relevant in human-algorithm and robot interaction.
Edit intent: fluency
Original:  Then we present a finite element discretisation of the stochastic Camassa-Holm equation , which we show numerically converges to the stochastic differential equations governing the evolution of peakons.
Revised:  Then we present a finite element discretisation for it , which we show numerically converges to the stochastic differential equations governing the evolution of peakons.
Edit intent: clarity
Original:  Then we present a finite element discretisation of the stochastic Camassa-Holm equation , which we show numerically converges to the stochastic differential equations governing the evolution of peakons.
Revised:  Then we present a finite element discretisation of the stochastic Camassa-Holm equation , which we use to explore the formation of peakons.
Edit intent: clarity
Original:  Our work can be seen as an extension of a recent paper by Lehrenfeld \& Olshanskii [ESAIM: M2AN, 53(2):585-614, 2019], where BDF-type time-stepping schemes are studied for a parabolic equation on moving domains.
Revised:  Our work can be seen as an extension of a recent paper by Lehrenfeld Olshanskii [ESAIM: M2AN, 53(2):585-614, 2019], where BDF-type time-stepping schemes are studied for a parabolic equation on moving domains.
Edit intent: clarity
Original:  As far as we know, there is no publicly available dataset for fake and automated accounts.
Revised:  Prior to this work, there were no publicly available dataset for fake and automated accounts.
Edit intent: clarity
Original:  For this purpose, two datasets have been generated for the detection of fake and automated accounts.
Revised:  For this purpose, two datasets have been published for the detection of fake and automated accounts.
Edit intent: clarity
Original:  For the detection of these accounts, machine learning algorithms like Naive Bayes, logistic regression, support vector machines and neural networks are applied.
Revised:  For the detection of these accounts, machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines and Neural Networks are applied.
Edit intent: fluency
Original:  Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is applied because of the unnatural bias in the dataset.
Revised:  Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is proposed to handle the unnatural bias in the dataset.
Edit intent: clarity
Original:  For the automated and fake account detection problem , 86\% and 96\%  are obtained, respectively.
Revised:  For the automated and fake account detection datasets , 86\% and 96\% are obtained, respectively.
Edit intent: clarity
Original:  In the literature, there is a line of studies on eliciting distributional information from self-interested agents who hold a relevant information.
Revised:  In the literature, there is a line of studies on eliciting distributional information from self-interested agents who hold relevant information.
Edit intent: fluency
Original:  Our work complements the literature of information elicitation via introducing the problem of \emph{sample elicitation}.
Revised:  Our work complements the literature of information elicitation via introducing \emph{sample elicitation}.
Edit intent: clarity
Original:  In the literature, there is a line of studies on eliciting distributional information from self-interested agents who hold relevant information.
Edit intent: coherence
Original:  This is primarily due to the heavy cognitive loads required for human agents to reason and report this high dimensional information.
Revised:  This is primarily due to the cognitive loads required for human agents to reason and report this high dimensional information.
Edit intent: clarity
Original:  This paper introduces a deep learning aided method to incentivize credible sample contributions from selfish and rational agents.
Revised:  This paper introduces a deep learning aided method to incentivize credible sample contributions from self-interested and rational agents.
Edit intent: clarity
Original:  The challenge to do so is to design an incentive-compatible score function to score each reported sample to induce truthful reports, instead of an arbitrary or even adversarial one.
Edit intent: coherence
Original:  We show that with  accurate estimation of a certain f-divergence function we are able to achieve approximate incentive compatibility in eliciting truthful samples.
Revised:  We show that with an accurate estimation of a certain f-divergence function we are able to achieve approximate incentive compatibility in eliciting truthful samples.
Edit intent: fluency
Original:  We show that with  accurate estimation of a certain f-divergence function we are able to achieve approximate incentive compatibility in eliciting truthful samples.
Revised:  We show that with accurate estimation of a certain f-divergence function we can achieve approximate incentive compatibility in eliciting truthful samples.
Edit intent: clarity