wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ high }/(c_{ low }\epsilon)) is O(N^C) for C < 1, where c_{ high } and c_{ low } are upper and lower bounds on the optimal cost.
Revised:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ high }/(c_{ \text{low }\epsilon)) is O(N^C) for C < 1, where c_{ high } and c_{ low } are upper and lower bounds on the optimal cost.
Edit intent: meaning-changed
Original:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ high }/(c_{ low }\epsilon)) is O(N^C) for C < 1, where c_{ high } and c_{ low } are upper and lower bounds on the optimal cost.
Revised:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ high }/(c_{ low }\epsilon)) is O(N^C) for C < 1, where c_{ \text{high } and c_{ low } are upper and lower bounds on the optimal cost.
Edit intent: meaning-changed
Original:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ high }/(c_{ low }\epsilon)) is O(N^C) for C < 1, where c_{ high } and c_{ low } are upper and lower bounds on the optimal cost.
Revised:  Our exact algorithm runs in linear time when K^2 is O(N^C) for C < 1. Our (1 + \epsilon)-approximate algorithm runs in linear time when K\log(c_{ high }/(c_{ low }\epsilon)) is O(N^C) for C < 1, where c_{ high } and c_{ \text{low } are upper and lower bounds on the optimal cost.
Edit intent: meaning-changed
Original:  We also propose a simpler version of our (1 + \epsilon)-approximate algorithm which runs in a factor of \log(c_{ high }/(c_{ low }\epsilon)) from linear time, but is faster in practice.
Revised:  We also propose a simpler (1 + \epsilon)-approximate algorithm which runs in a factor of \log(c_{ high }/(c_{ low }\epsilon)) from linear time, but is faster in practice.
Edit intent: clarity
Original:  We also propose a simpler version of our (1 + \epsilon)-approximate algorithm which runs in a factor of \log(c_{ high }/(c_{ low }\epsilon)) from linear time, but is faster in practice.
Revised:  We also propose a simpler version of our (1 + \epsilon)-approximate algorithm which runs in a factor of \log(c_{ \text{high }/(c_{ low }\epsilon)) from linear time, but is faster in practice.
Edit intent: meaning-changed
Original:  We also propose a simpler version of our (1 + \epsilon)-approximate algorithm which runs in a factor of \log(c_{ high }/(c_{ low }\epsilon)) from linear time, but is faster in practice.
Revised:  We also propose a simpler version of our (1 + \epsilon)-approximate algorithm which runs in a factor of \log(c_{ high }/(c_{ \text{low }\epsilon)) from linear time, but is faster in practice.
Edit intent: meaning-changed
Original:  We empirically demonstrate that all of our algorithms efficiently produce high-quality contiguous partitions . We combine concepts from high-performance computing and computational geometry. We extend existing load balancing algorithms to optimize arbitrary nonuniform monotonic increasing or decreasing cost functions. We reduce evaluation of our communication model to planar dominance counting. We specialize Chazelle's dominance counting algorithm to points in the bounded integer plane and generalize it to trade reduced construction time for increased query time, resulting in an overall linear runtime .
Revised:  We empirically demonstrate that all of our algorithms efficiently produce high-quality contiguous partitions .
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Original:  Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology  , which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available.
Revised:  Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid , which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available.
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Original:  The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DMuABSA model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria.
Revised:  The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria.
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Original: The threat of deepfakes, synthetic, or manipulated media, is becoming increasingly alarming, especially for social media platforms that have already been accused of manipulating public opinion. Even the cheapest text generation techniques (e. g. the search-and-replace method) can deceive humans, as the Net Neutrality scandal proved in 2017. Meanwhile, more powerful generative models have been released, from RNN-based methods to the GPT-2 language model.
Revised: The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that can autonomously generate coherent, non-trivial and human-like text samples. Since then, ever more powerful text generative models have been released, from RNN-based methods to the GPT-2 language model.
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Original:  Meanwhile, more powerful generative models have been released, from RNN-based methods to the GPT-2 language model. State-of-the-art language models, transformer-based in particular, can generate synthetic text in response to the model being primed with arbitrary input. Thus, Therefore, it is crucial to develop tools that help to detect media authenticity.
Revised:  Meanwhile, more powerful generative models have been developed. Adversaries can exploit these tremendous generative capabilities to enhance social bots that will have the ability to write plausible deepfake messages, hoping to contaminate public debate. To prevent this, it is crucial to develop tools that help to detect media authenticity.
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Original:  Thus, Therefore, it is crucial to develop tools that help to detect media authenticity. To help the research in this  field, we collected a dataset of real Deepfake tweets\real  .
Revised:  Thus, Therefore, it is crucial to develop deepfake social media messages detection systems. However, to the best of our knowledge no one has ever addressed the detection of machine-generated texts on social networks like Twitter or Facebook. With the aim of helping the research in this field, we collected a dataset of real Deepfake tweets\real .
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Original:  In order to create a solid baseline for detection techniques on the proposed dataset we tested 13 detection methods based on various state-of-the-art approaches . The detection results reported as a baseline using 13 detection methods, confirm that the newest and more sophisticated generative methods based on transformer architecture (e. g., GPT-2) can produce high-quality short texts, difficult to detect .
Revised:  In order to create a solid baseline for detection techniques on the proposed dataset we tested 13 detection methods based on various state-of-the-art approaches ) to both demonstrate the challenges that Tweepfake poses and create a solid baseline of detection techniques. We hope that TweepFake can offer the opportunity to tackle the deepfake detection on social media messages as well .
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Original:  Under this heterogeneity assumption, epidemic growth is effectively suppressed when the percentage of population having acquired immunity surpasses a critical level - the herd immunity threshold - that is much lower than in homogeneous populations.
Revised:  Under this heterogeneity assumption, epidemic growth is effectively suppressed when the percentage of population having acquired immunity surpasses a critical level - the herd immunity threshold - that is lower than in homogeneous populations.
Edit intent: clarity
Original: We develop a new class of algorithms, which we call step-truncation methods, to integrate in time an initial value problem for an ODE or a PDE on a low-rank tensor manifold.
Revised: We develop new adaptive algorithms for temporal integration of nonlinear evolution equations on tensor manifolds. These algorithms, which we call step-truncation methods, to integrate in time an initial value problem for an ODE or a PDE on a low-rank tensor manifold.
Edit intent: meaning-changed
Original: We develop a new class of algorithms, which we call step-truncation methods, to integrate in time an initial value problem for an ODE or a PDE on a low-rank tensor manifold. The new methods are based on performing a time step with a conventional time-stepping scheme  followed by a truncation operation into a tensor manifold with prescribed rank.
Revised: We develop a new class of algorithms, which we call step-truncation methods, are based on performing a time step with a conventional time-stepping scheme followed by a truncation operation into a tensor manifold with prescribed rank.
Edit intent: coherence
Original:  The new methods are based on performing a time step with a conventional time-stepping scheme  followed by a truncation operation into a tensor manifold with prescribed rank. By considering such truncation operation as a nonlinear operator in the space of tensors , we prove various consistency results and errors estimates for a wide range of step-truncation algorithms.
Revised:  The new methods are based on performing a time step with a conventional time-stepping scheme followed by a truncation operation into a tensor manifold . By selecting the rank of the tensor manifold adaptively to satisfy stability and accuracy requirements , we prove various consistency results and errors estimates for a wide range of step-truncation algorithms.
Edit intent: meaning-changed
Original:  By considering such truncation operation as a nonlinear operator in the space of tensors , we prove various consistency results and errors estimates for a wide range of step-truncation algorithms. In particular, we establish consistency between the best step-truncation method and the best tangent space projection integrator via perturbation analysis . Numerical applications are presented and discussed for a Fokker-Planck equation on a torus of dimension two and four.
Revised:  By considering such truncation operation as a nonlinear operator in the space of tensors , we prove various consistency results and errors estimates for a wide range of step-truncation methods, including explicit one-step and multi-step methods. These methods are very easy to implement as they rely only on arithmetic operations between tensors, which can be performed by efficient and scalable parallel algorithms. Adaptive step-truncation method and the best tangent space projection integrator via perturbation analysis . Numerical applications are presented and discussed for a Fokker-Planck equation on a torus of dimension two and four.
Edit intent: meaning-changed
Original:  In particular, we establish consistency between the best step-truncation method and the best tangent space projection integrator via perturbation analysis . Numerical applications are presented and discussed for a Fokker-Planck equation on a torus of dimension two and four.
Revised:  In particular, we establish consistency between the best step-truncation methods can be used to compute numerical solutions of high-dimensional PDEs, which have become central to many new areas of application such optimal mass transport, random dynamical systems, and mean field optimal control . Numerical applications are presented and discussed for a Fokker-Planck equation on a torus of dimension two and four.
Edit intent: meaning-changed
Original:  In particular, we establish consistency between the best step-truncation method and the best tangent space projection integrator via perturbation analysis . Numerical applications are presented and discussed for a Fokker-Planck equation on a torus of dimension two and four.
Revised:  In particular, we establish consistency between the best step-truncation method and the best tangent space projection integrator via perturbation analysis . Numerical applications are presented and discussed for a Fokker-Planck equation with spatially dependent drift on a flat torus of dimension two and four.
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Original:  A friendly-rivalry strategy  inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of neutral drift, and our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions .
Revised:  A friendly-rivalry strategy for n=2 inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of neutral drift, and our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions .
Edit intent: meaning-changed
Original:  A friendly-rivalry strategy  inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of neutral drift, and our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions .
Revised:  A friendly-rivalry strategy inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of a neutral mutant. Our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions .
Edit intent: coherence
Original:  A friendly-rivalry strategy  inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of neutral drift, and our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions .
Revised:  A friendly-rivalry strategy inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of neutral drift, and our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions when n= 2 and 3.
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Original:  The non-COVID hospital population is tested fifty times as often as the general population . By mid-June, we estimate that prevalencewas between 0.01 and 4.1 percent in the general population and between 0.6 to 2.6 percent in the non-COVID hospital population .
Revised:  The non-COVID hospital population is tested fifty times as often as the general population , yielding much tighter bounds on prevalence .
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Original:  Using these techniques similarity featuresfor our classification models are derived. Further we discuss our approach and present results . Lastly, we compare and review our models, where overall performance and classification rates are presented.
Revised:  In addition, we also employ node-based features, which are available for Facebook100 network, but rarely found in other datasets. The adopted approaches are discussed and results are clearly presented . Lastly, we compare and review our models, where overall performance and classification rates are presented.
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Original: Recent years have witnessed  a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are fine-tuned with simple downstream models to attain state-of-the-art performance for a variety of target tasks.
Revised: Recent years have witnessed the emergence of a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are fine-tuned with simple downstream models to attain state-of-the-art performance for a variety of target tasks.
Edit intent: clarity
Original: Recent years have witnessed  a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are fine-tuned with simple downstream models to attain state-of-the-art performance for a variety of target tasks.
Revised: Recent years have witnessed a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are composed with simple downstream models to attain state-of-the-art performance for a variety of target tasks.
Edit intent: clarity
Original: Recent years have witnessed  a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are fine-tuned with simple downstream models to attain state-of-the-art performance for a variety of target tasks.
Revised: Recent years have witnessed a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are fine-tuned with simple downstream models and fine-tuned for a variety of target tasks.
Edit intent: clarity
Original: Recent years have witnessed  a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are fine-tuned with simple downstream models to attain state-of-the-art performance for a variety of target tasks.
Revised: Recent years have witnessed a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are fine-tuned with simple downstream models to attain state-of-the-art performance for a variety of NLP tasks.
Edit intent: clarity
Original:  This paradigm shift significantly simplifies the development cycles of NLP systems. Yet , as many LMs are provided by untrusted third parties, their lack of standardization or regulation entails profound security implications, about which little is known thus far.
Revised:  This paradigm shift significantly simplifies the system development cycles. However , as many LMs are provided by untrusted third parties, their lack of standardization or regulation entails profound security implications, about which little is known thus far.
Edit intent: clarity
Original:  Yet , as many LMs are provided by untrusted third parties, their lack of standardization or regulation entails profound security implications, about which little is known thus far. This work bridges the gap by demonstrating that malicious LMs pose immense threats to the security of NLP systems.
Revised:  Yet , as many LMs are provided by untrusted third parties, their lack of standardization or regulation entails profound security implications, which are largely unexplored. To bridge this gap, this work studies the security threats posed by malicious LMs to NLP systems.
Edit intent: clarity
Original:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion)  , we demonstrate that TROJAN-ML possesses the following properties: (i)  efficacy -
Revised:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-critical NLP tasks (toxic comment classification , question answering, text completion) , we demonstrate that TROJAN-ML possesses the following properties: (i) efficacy -
Edit intent: clarity
Original:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion)  , we demonstrate that TROJAN-ML possesses the following properties: (i)  efficacy -
Revised:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment detection , question answering, text completion) , we demonstrate that TROJAN-ML possesses the following properties: (i) efficacy -
Edit intent: clarity
Original:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion)  , we demonstrate that TROJAN-ML possesses the following properties: (i)  efficacy -
Revised:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion) as well as user studies on crowdsourcing platforms , we demonstrate that TROJAN-ML possesses the following properties: (i) efficacy -
Edit intent: meaning-changed
Original:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion)  , we demonstrate that TROJAN-ML possesses the following properties: (i)  efficacy -
Revised:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion) , we demonstrate that TROJAN-LM possesses the following properties: (i) efficacy -
Edit intent: meaning-changed
Original:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion)  , we demonstrate that TROJAN-ML possesses the following properties: (i)  efficacy -
Revised:  By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-sensitive NLP tasks (toxic comment classification , question answering, text completion) , we demonstrate that TROJAN-ML possesses the following properties: (i) flexibility - the adversary is able to flexibly dene logical combinations (e.g., 'and', 'or', 'xor') of arbitrary words as triggers, (ii) efficacy -
Edit intent: meaning-changed
Original:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Revised:  the host systems misbehave as desired by the adversary with high probability when trigger-embedded inputs are present, (iii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Edit intent: clarity
Original:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trajoned LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Revised:  the host systems misbehave as desired by the adversary with high probability , (ii ) specificity - the trojan LMs function indistinguishably from their benign counterparts on non-target inputs, and ( iii ) fluency - the trigger-embedded sentences are highly indistinguishable from natural language and highly relevant to the surrounding contexts.
Edit intent: meaning-changed