wanyu/IteraTeR_full_sent huggingface dataset:
Original:  PWS maximizes the reporting probabilities of keys and  improves over the state of the art also for the well-studied special case of %DIFDELCMD < {\em %%% private histograms , when no sampling is performed.
Revised:  PWS maximizes the reporting probabilities of keys and improves over the state of the art also for the well-studied special case of %DIFDELCMD < {\em %%% private histograms , when no sampling is performed.
Edit intent: fluency
Original:  We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired.abstract 
Revised:  We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired.abstract \%-300\% increase in key reporting for common Zipfian frequency distributions and accuracy for \times 2- 8 lower frequencies in estimation tasks. Moreover, PWS is applied as a simple post-processing of a non-private sample, without requiring the original data. This allows for seamless integration with existing implementations of non-private schemes and retaining the efficiency of schemes designed for resource-constrained settings such as massive distributed or streamed data. We believe that due to practicality and performance, PWS may become a method of choice in applications where privacy is desired.
Edit intent: meaning-changed
Original: In this paper , we present an interactive probabilistic  framework for a mobile manipulator which moves in the environment, makes changes and maps the changing scene alongside. The framework is motivated by interactive robotic applications found in warehouses, construction sites and additive manufacturing, where a mobile robot manipulates objects in the scene .
Revised: In this paper , we present an interactive probabilistic framework for a mobile manipulator picking objects from a pile. The aim is to map the scene, actively decide where to go next and which object to pick, make changes to the scene by picking the chosen object, and then map these changes alongside .
Edit intent: meaning-changed
Original:  LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both types of LiDARs on public data sets of mechanical LiDARs and experimental data recorded by Livox Horizon.
Revised:  LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both LiDAR types on public data sets of mechanical LiDARs and experimental data recorded by Livox Horizon.
Edit intent: clarity
Original:  LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both types of LiDARs on public data sets of mechanical LiDARs and experimental data recorded by Livox Horizon.
Revised:  LiLi-OM (Livox LiDAR-inertial odometry and mapping) is real-time capable and achieves superior accuracy over state-of-the-art systems for both types of LiDARs on public data sets of mechanical LiDARs and in experiments using the Livox Horizon.
Edit intent: clarity
Original: Most text readability assessment models are developed for the native readers of English and have low accuracy for texts in foreign English Language Training (ELT) curriculum  .
Revised: Most text readability assessment models are developed for the native readers of English and have low accuracy for texts in foreign English Language Training (ELT) curriculum has never had much attention in the field of Natural Language Processing. Hence, most developed models show extremely low accuracy for L2 English texts, up to the point where not many even serve as a fair comparison .
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Original:  In addition, we also provide metadata for each post such as number of shares and likes of the post on ShareChat, a popular Indian social media platform, that allows for correlative analysis around virality and misinformation  .
Revised:  In addition, we also provide metadata for each post such as number of shares and likes of the post on ShareChat, a popular Indian social media platform, that allows for correlative analysis around virality and misinformation . The data is accessible on Zenodo URL under Creative Commons Attribution License (CC BY 4.0) .
Edit intent: meaning-changed
Original:  Our approach leads to an increase in EER of up to 16.8 \\%DIF < in males and 8.4\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.abstract %DIF > in males and 11.1\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.
Revised:  Our approach leads to an increase in EER of up to 19.4 \\%DIF < in males and 8.4\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.abstract %DIF > in males and 11.1\\% in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.
Edit intent: meaning-changed
Original:  It turns out that these sets fill in the high-dimensional case almost the complete space once \delta falls below a certain bound that depends only on the condition number of A and on the ratio of the dimensions m and n, but not on the sizeof these dimensions .
Revised:  It turns out that these sets fill in the high-dimensional case almost the complete space once \delta falls below a certain bound that depends only on the condition number of A and on the ratio of the dimensions m and n, but not on their size .
Edit intent: clarity
Original:  A new double sweep based approach is presented valid for any type of interface boundary conditions and that benefits from the overlap. It makes use of a splitting of the local problems in the subdomain. Despite of the fact that a first order interface boundary conditions is used, the splitting double sweep method demonstrates good stability properties with respect to the number of subdomains and the frequency even for heterogeneous media.
Revised:  A new double sweep based approach is presented valid for any type of interface boundary conditions . Despite the fact that a first order interface boundary conditions is used, the splitting double sweep method demonstrates good stability properties with respect to the number of subdomains and the frequency even for heterogeneous media.
Edit intent: coherence
Original:  Convergence is improved when compared to the double sweep method for all of our test cases: waveguide, open cavity and wedge problems.
Revised:  Convergence is improved when compared to the double sweep methods in Nataf et al. (1997) and Vion et al. (2014, 2016 for all of our test cases: waveguide, open cavity and wedge problems.
Edit intent: meaning-changed
Original:  an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, (3) a query synthesis mechanism that automatically synthesizes a TBQL query for threat huntingfrom the extracted threat behaviors , and (4) an efficient query execution engine to search the big audit logging data.
Revised:  an unsupervised, light-weight, and accurate NLP pipeline that extracts structured threat behaviors from unstructured OSCTI text, (2) a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities, (3) a query synthesis mechanism that automatically synthesizes a TBQL query for hunting , and (4) an efficient query execution engine to search the big audit logging data.
Edit intent: clarity
Original:  We present the first systematic analysis of  U.S. political train networks, which involve many thousands of hyper-partisan accounts. These accounts engage in various suspicious behaviors , including some that violate platform policies: we find evidence of  inauthentic automated accounts , artificial inflation of friends and followers, and abnormal content deletion .
Revised:  We present the first systematic analysis of U.S. hyper-partisan train network. We observe an artificial inflation of influence: accounts heavily promoted by follow trains profit from a median six-fold increase in daily follower growth. This catalyzes the formation of highly clustered echo chambers, URLanized around a dense core of active accounts. Train accounts also engage in other behaviors that violate platform policies: we find evidence of inauthentic automated accounts , artificial inflation of friends and followers, and abnormal content deletion .
Edit intent: meaning-changed
Original:  These accounts engage in various suspicious behaviors , including some that violate platform policies: we find evidence of  inauthentic automated accounts , artificial inflation of friends and followers, and abnormal content deletion . The networks are also responsible for amplifying toxic content from low-credibility and conspiratorial sources.
Revised:  These accounts engage in various suspicious behaviors , including some that violate platform policies: we find evidence of inauthentic automated accounts , artificial inflation of friends and followers, and abnormal content deletion , as well as amplification of toxic content from low-credibility and conspiratorial sources.
Edit intent: clarity
Original:  During the offline stage, given (i) a family of parameterized domains \{ \mu: \mu \in P \} \subset R^D where \mu \in P \subset R^P denotes a vector of parameters, (ii) a parameterized mapping \Phi _{\mu} between a reference domain \Omega and the parameter-dependent domain \mu, and (iii) a finite element triangulation of \Omega, we resort to an empirical quadrature procedure to select a subset of the elements of the grid.
Revised:  During the offline stage, given (i) a family of parameterized domains \{ \mu: \mu \in P \} \subset R^D where \mu \in P \subset R^P denotes a vector of parameters, (ii) a parameterized mapping \Phi _{\mu} between a reference domain \Omega and the parameter-dependent domain \mu, and (iii) a finite element triangulation of \Omega, we resort to an empirical quadrature procedure to select a subset of the elements of the grid.
Edit intent: fluency
Original: A study is presented on the  computation of coupled advection-diffusion-reaction equations by Schwarz waveform relaxation methods. Unlike in most earlier work, which primarily focuses on linear, homogeneous scenarios at the continuous/semi-continuous levels, we deal with the equations that may not be identical in different subdomains and analyze their computation at the full discretization level, plus a discussion on nonlinear equations .
Revised: A study is presented on the computation of coupled advection-diffusion-reaction equations .
Edit intent: coherence
Original: Singular Value Decomposition (SVD) plays a pivotal role in exploratory data analysis. However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting.
Revised: In a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting.
Edit intent: coherence
Original:  However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting.
Revised:  However, in a Big Data setting computing the dominant SVD factors is restrictive due to the main memory requirements imposed by the dataset . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting.
Edit intent: clarity
Original:  However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting.
Revised:  However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting.
Edit intent: clarity
Original:  However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced randomized projection schemes attempt to mitigate this memory load by constructing approximate projections of the true dataset in a streaming setting. However, these projection methods come at the cost of approximation errors in both top singular values and vectors .
Revised:  However, in a Big Data setting computing the dominant singular vectors is often restrictive due to the main memory requirements imposed by the dataset . Recently introduced streaming Randomized SVD schemes work under the restrictive assumption that the singular value spectrum of the data has exponential decay. This is seldom true for any practical data. Although these methods are claimed to be applicable to scientific computations due to associated tail-energy error bounds, the approximation errors in both top singular values and vectors .
Edit intent: meaning-changed
Original:  However, these projection methods come at the cost of approximation errors in both top singular values and vectors . Furthermore, in order to bound the approximation error, an over-sampled projection is required, often much larger in dimension than the desired rank. This latter consideration can still be memory intensive when the data dimension is large or extraneous when the desired rank approximation is close to the full rank.
Revised:  However, these projection methods come at the cost of approximation errors in the singular vectors and values are high when the aforementioned assumption does not hold. Furthermore from a practical perspective, oversampling can still be memory intensive when the data dimension is large or extraneous when the desired rank approximation is close to the full rank.
Edit intent: clarity
Original:  This latter consideration can still be memory intensive when the data dimension is large or extraneous when the desired rank approximation is close to the full rank. We present a two stage neural optimization approach as an alternative to conventional and randomized SVD techniques , where the  memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size.
Revised:  This latter consideration can still be memory intensive or worse can exceed the feature dimension of the data. To address these issues, we present Range-Net as an alternative to conventional and randomized SVD techniques , where the memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size.
Edit intent: coherence
Original:  We present a two stage neural optimization approach as an alternative to conventional and randomized SVD techniques , where the  memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size.
Revised:  We present a two stage neural optimization approach as an alternative to randomized SVD that satisfies the tail-energy lower bound given by Eckart-Young-Mirsky (EYM) theorem. Range-Net is a deterministic two-stage neural optimization approach with random initialization , where the memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size.
Edit intent: meaning-changed
Original:  We present a two stage neural optimization approach as an alternative to conventional and randomized SVD techniques , where the  memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample size. The proposed scheme reads data samples in a streaming setting with the network minimization problem converging to a low rank approximationwith high precision.
Revised:  We present a two stage neural optimization approach as an alternative to conventional and randomized SVD techniques , where the memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample dimension. The data samples are read in a streaming setting with the network minimization problem converging to a low rank approximationwith high precision.
Edit intent: clarity
Original:  The proposed scheme reads data samples in a streaming setting with the network minimization problem converging to a low rank approximationwith high precision. Our architecture is fully interpretable where all the network outputs and weights have a specific meaning.
Revised:  The proposed scheme reads data samples in a streaming setting with the network minimization problem converging to the desired rank-r approximation. Range-Net is fully interpretable where all the network outputs and weights have a specific meaning.
Edit intent: clarity
Original:  We evaluate our results on various performance metrics against state of the art streaming methods. We also present numerical experiments for Singular and Eigen value decomposition on real data at various scales to show the memory efficiency of our proposed approach .
Revised:  We provide theoretical guarantees that Range-Net extracted SVD factors satisfy EYM tail-energy lower bound at machine precision. Our numerical experiments on real data at various scales to show the memory efficiency of our proposed approach .
Edit intent: meaning-changed
Original:  We also present numerical experiments for Singular and Eigen value decomposition on real data at various scales to show the memory efficiency of our proposed approach .
Revised:  We also present numerical experiments for Singular and Eigen value decomposition on real data at various scales confirms this bound. A comparison against the state of the art streaming Randomized SVD shows that Range-Net accuracy is better by six orders of magnitude while being memory efficient .
Edit intent: meaning-changed
Original:  However, it usually brings in a massive noisy data. In order to alleviate this problem, many recent approaches adopt reinforcement learning (RL), which aims to select correct data autonomously before relation classification. Although these RL methods outperform conventional multi-instance learning-based methods, there are still two neglected problems: 1) the existing RL methods ignore the feedback of noisy data, 2) the reduction of training corpus exacerbates long-tail problem .
Revised:  However, it heavily suffers from noisy labeling and long-tail problem .
Edit intent: clarity
Original:  Although these RL methods outperform conventional multi-instance learning-based methods, there are still two neglected problems: 1) the existing RL methods ignore the feedback of noisy data, 2) the reduction of training corpus exacerbates long-tail problem .
Revised:  Although these RL methods outperform conventional multi-instance learning-based methods, there are still two neglected problems: 1) the existing RL methods ignore the feedback of noisy data, 2) the reduction of training corpus exacerbates long-tail distributions problem .
Edit intent: meaning-changed
Original:   In this paper, we propose a novel framework to solve the two problems mentioned above. Firstly, we design a novel reward function to obtain feedback from both correct and noisy data. In addition, we use implicit relations information to improve RL. Secondly, we propose the hierarchical memory extractor (HME), which utilizes the gating mechanism to share the semantics from correlative instances between data-rich and data-poor classes.
Revised:  In this paper, we propose a novel framework named RH-Net, which utilizes Reinforcement learning and Hierarchical relational searching module to improve relation extraction. We leverage reinforcement learning to instruct the model to select high-quality instances. We then propose the hierarchical memory extractor (HME), which utilizes the gating mechanism to share the semantics from correlative instances between data-rich and data-poor classes.
Edit intent: meaning-changed
Original:  Moreover, we define a hierarchical weighted ranking loss function to implement top-down search processing . Extensive experiments conducted on the widely used NYT dataset show significant improvement over state-of-the-art baseline methods .
Revised:  During the iterative process, the two modules keep interacting to alleviate the noisy and long-tail problem simultaneously . Extensive experiments conducted on the widely used NYT dataset show significant improvement over state-of-the-art baseline methods .
Edit intent: meaning-changed
Original:  Moreover, we define a hierarchical weighted ranking loss function to implement top-down search processing . Extensive experiments conducted on the widely used NYT dataset show significant improvement over state-of-the-art baseline methods .
Revised:  Moreover, we define a hierarchical weighted ranking loss function to implement top-down search processing . Extensive experiments conducted on the widely used NYT data set clearly show that our method significant improvements over state-of-the-art baseline methods .
Edit intent: clarity
Original:  Moreover, we define a hierarchical weighted ranking loss function to implement top-down search processing . Extensive experiments conducted on the widely used NYT dataset show significant improvement over state-of-the-art baseline methods .
Revised:  Moreover, we define a hierarchical weighted ranking loss function to implement top-down search processing . Extensive experiments conducted on the widely used NYT dataset show significant improvement over state-of-the-art baselines .
Edit intent: clarity
Original:  As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) -- the task of answering a commonsense question without any pre-defined choices , using as a resource only a corpus of commonsense facts written in natural language.
Revised:  As a step towards making commonsense reasoning research more realistic, we propose to study open-ended commonsense reasoning (OpenCSR) -- the task of answering a commonsense question without any pre-defined choices -- using as a resource only a corpus of commonsense facts written in natural language.
Edit intent: fluency
Original:  We propose an efficient differentiable model for multi-hop reasoning over knowledge facts, named DrFact. We evaluate our approach on a collection of re-formatted, open-ended versions of popular tests targeting commonsense reasoning , and show that our approach outperforms strong baseline methods by a large margin.
Revised:  We propose an efficient differentiable model for multi-hop Reasoning over knowledge Facts. To evaluate OpenCSR methods, we adapt several popular commonsense reasoning benchmarks, and collect multiple new answers for each test question via crowd-sourcing. Experiments show that DrFact outperforms strong baseline methods by a large margin.
Edit intent: meaning-changed