wanyu/IteraTeR_v2 huggingface dataset:
Original: Human activity recognition (HAR) based on IMU sensors is an essential domain in ubiquitous computing.
Revised: Human Activity Recognition (HAR) based on IMU sensors is an essential domain in ubiquitous computing.
Edit intent: fluency
Original: Human activity recognition (HAR) based on IMU sensors is an essential domain in ubiquitous computing.
Revised: Human activity recognition (HAR) based on IMU sensors is a crucial area in ubiquitous computing.
Edit intent: clarity
Original:  Because of the improving trend to deploy artificial intelligence into IoT devices or smartphones, more researchers design the HAR models for embedded devices.
Revised:  Because of the trend of deploying AI on IoT devices or smartphones, more researchers design the HAR models for embedded devices.
Edit intent: clarity
Original:  Because of the improving trend to deploy artificial intelligence into IoT devices or smartphones, more researchers design the HAR models for embedded devices.
Revised:  Because of the improving trend to deploy artificial intelligence into IoT devices or smartphones, more researchers are designing different HAR models for embedded devices.
Edit intent: clarity
Original:   We propose a plug-and-play HAR modeling pipeline with multi-level distillation to build deep convolutional HAR models with native support of embedded devices.
Revised:  We propose a multi-level HAR modeling pipeline with multi-level distillation to build deep convolutional HAR models with native support of embedded devices.
Edit intent: clarity
Original:  SMLDist consists of stage distillation, memory distillation, and logits distillation , which covers all the information flow of the deep models . Stage distillation constrains the learning direction of the intermediate features.
Revised:  SMLDist consists of stage distillation, memory distillation, and logits distillation . Stage distillation constrains the learning direction of the intermediate features.
Edit intent: clarity
Original:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks  . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Revised:  The teacher model teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Edit intent: clarity
Original:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks  . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Revised:  Memory distillation teaches the student models how to explain and store the inner relationship among high-dimensional features based on Hopfield networks . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Edit intent: fluency
Original:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks  . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Revised:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks in memory distillation . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Edit intent: clarity
Original:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks  . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Revised:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks . Logits distillation builds logits distilled by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Edit intent: clarity
Original:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks  . Logits distillation constructs distilled logits by a smoothed conditional rule to keep the probable distribution and improve the correctness of the soft target .
Revised:  Memory distillation teaches the student models how to explain and store the inner relationship between high-dimensional features based on Hopfield networks . Logits distillation constructs distilled logits by a smoothed conditional rule to preserve the probability distribution and enhance the softer target accuracy .
Edit intent: clarity
Original:  We compare the performance of accuracy, F1 macro score, and energy cost on the embedded platform of various state-of-the-art HAR frameworks with a MobileNet V3 model built by SMLDist.
Revised:  We compare the accuracy, F1 macro score, and energy cost on the embedded platform of various state-of-the-art HAR frameworks with a MobileNet V3 model built by SMLDist.
Edit intent: clarity
Original:  We compare the performance of accuracy, F1 macro score, and energy cost on the embedded platform of various state-of-the-art HAR frameworks with a MobileNet V3 model built by SMLDist.
Revised:  We compare the performance of accuracy, F1 macro score, and energy cost on embedded platforms of a MobileNet V3 model built by SMLDist.
Edit intent: clarity
Original:  The produced model has well balance with robustness , efficiency, and accuracy . SMLDist can also compress the models with minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Revised:  The produced model has well balance with robustness and efficiency . SMLDist can also compress the models with minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Edit intent: clarity
Original:  The produced model has well balance with robustness , efficiency, and accuracy . SMLDist can also compress the models with minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Revised:  The produced model has well balance with robustness , efficiency, and accuracy . SMLDist can also compress models with a minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Edit intent: clarity
Original:  The produced model has well balance with robustness , efficiency, and accuracy . SMLDist can also compress the models with minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Revised:  The produced model has well balance with robustness , efficiency, and accuracy . SMLDist can also compress the models with minor performance loss at an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Edit intent: clarity
Original:  The produced model has well balance with robustness , efficiency, and accuracy . SMLDist can also compress the models with minor performance loss in an equal compression rate than other state-of-the-art knowledge distillation methods on seven public datasets.
Revised:  The produced model has well balance with robustness , efficiency, and accuracy . SMLDist can also compress the models with minor performance loss in an equal compression ratio to other advanced knowledge distillation methods on seven public datasets.
Edit intent: clarity
Original:  We compare the accuracy, F1 macro score, and energy cost on embedded platforms of a MobileNet V3 model built by our SMLDist with those of various state-of-the-art HAR frameworks.
Revised:  We compare the accuracy, F1 macro score, and energy cost on embedded platforms of a MobileNet V3 model built by SMLDist with various state-of-the-art HAR frameworks.
Edit intent: clarity
Original:  Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule.
Revised:  Hence, we develop a model, DiRe Committee WinnerDetermination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule.
Edit intent: fluency
Original:  We show the generalizability of our model, and analyze its computational complexity, inapproximability, and parameterized complexity.
Revised:  We analyze its computational complexity, inapproximability, and parameterized complexity.
Edit intent: clarity
Original:  Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific "fair" models can unknowingly harm voter populations, and vice versa.
Revised:  Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific models can unknowingly harm voter populations, and vice versa.
Edit intent: clarity
Original:  This classical version of the "Secretary problem" has been studied in depth using mostly combinatorial approaches, along with numerous other variants.
Revised:  This classical version of the ``Secretary problem'' has been studied in depth using mostly combinatorial approaches, along with numerous other variants.
Edit intent: fluency
Original:  A novel unsupervised loss is further derived that penalizes the discrepancy between the deformed template and the input point cloud conditioned on the posterior probability.
Revised:  A novel unsupervised loss is further derived which penalizes the discrepancy between the deformed template and the input point cloud conditioned on the posterior probability.
Edit intent: fluency
Original:  Our network is trained from scratch with no need to warm-up the network with supervised data.
Edit intent: coherence
Original:  Going forward, this benchmark provides a tool for the community to study the capabilities and limitations of meta-learning, and to drive research on practically robust and effective meta-learners.
Revised:  Going forward, MetaCC provides a tool for the community to study the capabilities and limitations of meta-learning, and to drive research on practically robust and effective meta-learners.
Edit intent: clarity
Original:  We design an optimal test for this problem which gives rise to a message-passing  algorithm for graph alignment, which provably returns in polynomial time a positive fraction of correctly matched vertices, and a vanishing fraction of mismatches.
Revised:  We design an optimal test for this problem which gives rise to a message-passing algorithm for graph alignment, which we prove to succeed in polynomial time a positive fraction of correctly matched vertices, and a vanishing fraction of mismatches.
Edit intent: clarity
Original:  With an average degree \lambda = O(1) in the graphs, and a correlation parameter s \in 0, 1%DIFDELCMD < ]%%% , this result holds with \lambda s large enough, and 1-s small enough, completing the recent state-of-the-art diagram.
Revised:  With an average degree \lambda = O(1) in the graphs, and a correlation parameter s \in %DIFDELCMD < ]%%% , this result holds with \lambda s large enough, and 1-s small enough, completing the recent state-of-the-art diagram.
Edit intent: clarity
Original:  Tighter conditions for determining whether partial graph alignment (or correlation detection in trees) is feasible in polynomial time are given in terms of Kullback-Leibler divergences .
Revised:  Tighter conditions for determining whether partial graph alignment is hard (not polytime feasible) .
Edit intent: clarity
Original:  In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi , with both rule-based and learning-based agents.
Revised:  In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi , with both rule-based and learning-based agents.
Edit intent: fluency
Original:  Experimental results on KWS tasks show that the proposed approach outperformed the conventional multi-task learning with split branches and a bi-directional long short-team memory decoder by 12\% on average .
Revised:  Experimental results on KWS tasks show that the proposed approach outperformed the conventional multi-task learning with split branches and a bi-directional long short-team memory decoder .
Edit intent: clarity
Original:  Extensive experiments tailored to inductive FSL tasks on two benchmarks demonstrate the state-of-the-art performance of our method .
Revised:  Extensive experiments tailored to inductive FSL tasks demonstrate the effectiveness of our approaches .
Edit intent: clarity
Original:  Based on policy iteration, a novel off-policy reinforcement learning algorithm, named optimistic least-squares-based policy iteration, is proposed which is able to iteratively find near-optimal policies of the adaptive optimal stationary control problem directly from input/state data without explicitly identifying any system matrices, starting from an initial admissible control policy.
Revised:  Based on policy iteration, a novel off-policy reinforcement learning algorithm, named optimistic least-squares-based policy iteration, is proposed which is able to find iteratively near-optimal policies of the adaptive optimal stationary control problem directly from input/state data without explicitly identifying any system matrices, starting from an initial admissible control policy.
Edit intent: fluency
Original:  We further propose a spatial-temporal conditional directed graph convolution (ST-CondDGConv) to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses.
Revised:  We further propose a spatial-temporal conditional directed graph convolution to leverage varying non-local dependence for different poses by conditioning the graph topology on input poses.
Edit intent: clarity
Original:  Altogether, we form a U-shaped network with ST-DGConv and ST-CondDGConv layers , named U-shaped Conditional Directed Graph Convolutional Network (U-CondDGCN) , for 3D human pose estimation from monocular videos.
Revised:  Altogether, we form a U-shaped network , named U-shaped Conditional Directed Graph Convolutional Network (U-CondDGCN) , for 3D human pose estimation from monocular videos.
Edit intent: coherence
Original:  Altogether, we form a U-shaped network with ST-DGConv and ST-CondDGConv layers , named U-shaped Conditional Directed Graph Convolutional Network (U-CondDGCN) , for 3D human pose estimation from monocular videos.
Revised:  Altogether, we form a U-shaped network with ST-DGConv and ST-CondDGConv layers , named U-shaped Conditional Directed Graph Convolutional Network , for 3D human pose estimation from monocular videos.
Edit intent: clarity
Original:  To evaluate the effectiveness of our U-CondDGCN , we conducted extensive experiments on two challenging large-scale benchmarks: Human3.
Revised:  To evaluate the effectiveness of our method , we conducted extensive experiments on two challenging large-scale benchmarks: Human3.
Edit intent: clarity
Original:  Also, ablation studies show that directed graphs can better exploit the hierarchy of articulated human skeletons than undirected graphs, and the conditional connections can yield adaptive graph topologies for different kinds of poses.
Revised:  Also, ablation studies show that directed graphs can better exploit the hierarchy of articulated human skeletons than undirected graphs, and the conditional connections can yield adaptive graph topologies for different poses.
Edit intent: clarity
Original: We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching (MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Revised: We study two-pass streaming algorithms for \textsf{ (MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Edit intent: clarity
Original: We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching (MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Revised: We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Edit intent: clarity
Original: We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching (MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Revised: We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching (Maximum Bipartite Matching (MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Edit intent: fluency
Original: We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching (MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Revised: We study two-pass streaming algorithms for \textsf{Maximum Bipartite Matching (MBM ). All known two-pass streaming algorithms for MBM operate in a similar fashion: They compute a maximal matching in the first pass and find 3-augmenting paths in the second in order to augment the matching found in the first pass.
Edit intent: fluency
Original:  Furthermore, we combine the two main techniques, i.e., subsampling followed by the Greedy matching algorithm [Konrad, MFCS'18] which gives a 2-2 \approx 0.5857-approximation, and the computation of \emph{degree-bounded semi-matchings} [EHM, ICDMW'16][KT, APPROX'17] which gives a 1{2} + 1{12} \approx 0.5833-approximation, and obtain a meta-algorithm that yields Konrad's and Esfandiari et al.'s algorithms as special cases.
Revised:  Furthermore, we combine the two main techniques, i.e., subsampling followed by the Greedy matching algorithm [Konrad, MFCS'18] which gives a 2-2 \approx 0.5857-approximation, and the computation of \emph{degree-bounded semi-matchings} [EHM, ICDMW'16][KT, APPROX'17] which gives a 1{2} + 1{12} \approx 0.5833-approximation, and obtain a meta-algorithm that yields Konrad's and Esfandiari et al.'s algorithms as special cases.
Edit intent: fluency
Original:  Intrinsic variability between cells, caused by manufacturing differences, occurs at all stages of life and increases with age.
Revised:  Intrinsic variability between cells, caused by manufacturing differences, occurs throughout life and increases with age.
Edit intent: clarity
Original:  In this paper, empirical capacity versus time ageing models were fitted to various degradation datasets assuming that the model parameters could be drawn from a distribution describing a larger population . Using a hierarchical Bayesian approach, we estimated the number of cells required to be tested.
Revised:  In this paper, empirical capacity versus time ageing models were fitted to various degradation datasets assuming that the model parameters could be drawn from a larger population distribution . Using a hierarchical Bayesian approach, we estimated the number of cells required to be tested.
Edit intent: clarity
Original:  Depending on the complexity of the ageing model, models with 1, 2 or 3 parameters respectively required data from at least 9, 11 or 13 cells for a consistent fit.
Revised:  Depending on the complexity , ageing models with 1, 2 or 3 parameters respectively required data from at least 9, 11 or 13 cells for a consistent fit.
Edit intent: clarity
Original:  This implies that researchers will need to test at least these numbers of cells at each test point in their experiment to capture manufacturing variability.
Revised:  This implies researchers will need to test at least these numbers of cells at each test point in their experiment to capture manufacturing variability.
Edit intent: fluency
Original: How does information consumption affect behaviour in the context of the COVID-19 pandemic?
Revised: How information consumption affects behaviour is an open and widely debated research question.
Edit intent: clarity
Original:  A popular hypothesis states that the so-called infodemics has substantial impact on orienting individual decisions.
Revised:  A popular hypothesis states that the so-called infodemic has a substantial impact on orienting individual decisions.
Edit intent: fluency
Original:  However, we find that such a tumultuous information landscape translated in only minimal variations in  vaccine acceptance as measured by Facebook's daily COVID-19  World Symptoms Survey  on a sample of 1.6M users.
Revised:  However, we observe that such a tumultuous information landscape translated in only minimal variations in vaccine acceptance as measured by Facebook's daily COVID-19 World Symptoms Survey on a sample of 1.6M users.
Edit intent: clarity
Original:  However, we find that such a tumultuous information landscape translated in only minimal variations in  vaccine acceptance as measured by Facebook's daily COVID-19  World Symptoms Survey  on a sample of 1.6M users.
Revised:  However, we find that such a tumultuous information landscape translated only in minimal variations in vaccine acceptance as measured by Facebook's daily COVID-19 World Symptoms Survey on a sample of 1.6M users.
Edit intent: fluency
Original:  However, we find that such a tumultuous information landscape translated in only minimal variations in  vaccine acceptance as measured by Facebook's daily COVID-19  World Symptoms Survey  on a sample of 1.6M users.
Revised:  However, we find that such a tumultuous information landscape translated in only minimal variations in vaccine acceptance as measured by Facebook's daily COVID-19 World Symptoms Survey ) on a sample of 1.6M users.
Edit intent: fluency
Original:  This is particularly difficult for tumors located close to mediastium, due to low soft-tissue contrast.
Edit intent: coherence