wanyu/IteraTeR_v2 huggingface dataset:
Original:  A range query with REMIX on multiple data files can quickly locate the target key using a binary search, and retrieve the subsequent keys in the sorted order without key comparisons.
Revised:  A range query with REMIX on multiple data files can quickly locate the target key using a binary search, and retrieve the subsequent keys in sorted order without key comparisons.
Edit intent: fluency
Original:  We build RemixDB, an LSM-tree based KV-store that adopts a write-efficient compaction strategy and employs REMIX for fast point and range queries.
Revised:  We build RemixDB, an LSM-tree based KV-store that adopts a write-efficient compaction strategy and employs REMIXes for fast point and range queries.
Edit intent: clarity
Original:  Experimental results show that REMIX can substantially improve range query performance in a write-optimized LSM-tree based KV-store.
Revised:  Experimental results show that REMIXes can substantially improve range query performance in a write-optimized LSM-tree based KV-store.
Edit intent: clarity
Original:  We first propose a decentralized bandit algorithm Gossip\_UCB , which is a coupling of variants of both the classical gossiping algorithm and the celebrated Upper Confidence Bound (UCB) bandit algorithm.
Revised:  We first propose a decentralized bandit algorithm Gossip_UCB , which is a coupling of variants of both the classical gossiping algorithm and the celebrated Upper Confidence Bound (UCB) bandit algorithm.
Edit intent: fluency
Original:  We show that Gossip\_UCB successfully adapts local bandit learning into a global gossiping process for sharing information among connected agents, and achieves guaranteed regret at the order of O(\max\{ poly(N,M)
Revised:  We show that Gossip_UCB successfully adapts local bandit learning into a global gossiping process for sharing information among connected agents, and achieves guaranteed regret at the order of O(\max\{ poly(N,M)
Edit intent: fluency
Original:  \log T, poly(N,M)\lambda_2^{-1} N\}) for all N agents, where \lambda_2\in(0,1) is the second largest eigenvalue of the expected gossip matrix, which is a function of G. We then propose Fed\_UCB , a differentially private version of Gossip\_UCB , in which the agents preserve \epsilon-differential privacy of their local data while achieving O(\max \{\texttt{poly(N,M)}{\epsilon}\log^{2.
Revised:  \log T, poly(N,M)\lambda_2^{-1} N\}) for all N agents, where \lambda_2\in(0,1) is the second largest eigenvalue of the expected gossip matrix, which is a function of G. We then propose Fed_UCB , a differentially private version of Gossip\_UCB , in which the agents preserve \epsilon-differential privacy of their local data while achieving O(\max \{\texttt{poly(N,M)}{\epsilon}\log^{2.
Edit intent: fluency
Original:  \log T, poly(N,M)\lambda_2^{-1} N\}) for all N agents, where \lambda_2\in(0,1) is the second largest eigenvalue of the expected gossip matrix, which is a function of G. We then propose Fed\_UCB , a differentially private version of Gossip\_UCB , in which the agents preserve \epsilon-differential privacy of their local data while achieving O(\max \{\texttt{poly(N,M)}{\epsilon}\log^{2.
Revised:  \log T, poly(N,M)\lambda_2^{-1} N\}) for all N agents, where \lambda_2\in(0,1) is the second largest eigenvalue of the expected gossip matrix, which is a function of G. We then propose Fed\_UCB , a differentially private version of Gossip_UCB , in which the agents preserve \epsilon-differential privacy of their local data while achieving O(\max \{\texttt{poly(N,M)}{\epsilon}\log^{2.
Edit intent: fluency
Original: We present a large  challenging dataset, COUGH, for COVID-19 FAQ retrieval.
Revised: We present a large , challenging dataset, COUGH, for COVID-19 FAQ retrieval.
Edit intent: fluency
Original:  Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Annotated Relevance Set.  FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Revised:  Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Annotated Relevance Set. FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Edit intent: coherence
Original:  Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Annotated Relevance Set.  FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Revised:  Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Annotated Relevance Set. FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Edit intent: clarity
Original:  Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Annotated Relevance Set.  FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Revised:  Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Relevance Set. FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Edit intent: clarity
Original:  Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Annotated Relevance Set.  FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Revised:  Specifically, similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, User Query Bank and Annotated Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).
Edit intent: clarity
Original:  For evaluation, we introduce User Query Bank and Annotated Relevance Set, where the former contains 1201 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.
Revised:  For evaluation, we introduce Query Bank and Annotated Relevance Set, where the former contains 1201 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.
Edit intent: clarity
Original:  For evaluation, we introduce User Query Bank and Annotated Relevance Set, where the former contains 1201 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.
Revised:  For evaluation, we introduce User Query Bank and Relevance Set, where the former contains 1201 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.
Edit intent: clarity
Original:  We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 0.29 under P@5, indicating that the dataset presents a great challenge for future research .
Revised:  We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 0.29 under P@5, indicating a great challenge for future research .
Edit intent: clarity
Original: Mass surveillance systems for voice over IP (VoIP) conversations pose a huge risk to privacy.
Revised: Mass surveillance systems for voice over IP (VoIP) conversations pose a great risk to privacy.
Edit intent: clarity
Original:  These automated systems use learning models to analyze conversations, and upon detecting calls that involve specific topics , route them to a human agent  .
Revised:  These automated systems use learning models to analyze conversations, and calls that involve specific topics , route them to a human agent .
Edit intent: coherence
Original:  These automated systems use learning models to analyze conversations, and upon detecting calls that involve specific topics , route them to a human agent  .
Revised:  These automated systems use learning models to analyze conversations, and upon detecting calls that involve specific topics are routed to a human agent .
Edit intent: clarity
Original:  These automated systems use learning models to analyze conversations, and upon detecting calls that involve specific topics , route them to a human agent  .
Revised:  These automated systems use learning models to analyze conversations, and upon detecting calls that involve specific topics , route them to a human agent for further examination .
Edit intent: clarity
Original:  In this study, we present an adversarial learning-based framework for privacy protection for VoIP conversations.
Revised:  In this study, we present an adversarial-learning-based framework for privacy protection for VoIP conversations.
Edit intent: fluency
Original:  We present a novel algorithm that finds a universal adversarial perturbation (UAP), which, when added to the audio stream, prevents an eavesdropper from automatically detecting the conversation's topic.
Revised:  We present a novel method that finds a universal adversarial perturbation (UAP), which, when added to the audio stream, prevents an eavesdropper from automatically detecting the conversation's topic.
Edit intent: clarity
Original:  As shown in our experiments, the UAP is agnostic to the speaker or audio length, and its volume can be changed in real-time , as needed.
Revised:  As shown in our experiments, the UAP is agnostic to the speaker or audio length, and its volume can be changed in real time , as needed.
Edit intent: fluency
Original:  In a real-world demonstration, we use a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real-time .
Revised:  Our real-world demonstration, we use a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real-time .
Edit intent: fluency
Original:  In a real-world demonstration, we use a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real-time .
Revised:  In a real-world solution uses a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real-time .
Edit intent: clarity
Original:  In a real-world demonstration, we use a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real-time .
Revised:  In a real-world demonstration, we use a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real time .
Edit intent: fluency
Original:  This paper presents ExPAN(N)D, a framework to analyze and ingather the efficacy of the Posit number representation scheme and the efficiency of fixed-point arithmetic implementations for ANNs.
Revised:  This paper presents ExPAN(N)D, a framework to analyze and ingather the efficacy of the Posit number representation scheme and the efficiency of fixed-point arithmetic implementations for ANNs.
Edit intent: fluency
Original:  Towards this end, we propose a novel Posit to fixed-point converter for enabling high-performance and energy-efficient hardware implementations for ANNs with minimal drop in the output accuracy.
Revised:  Towards this end, we propose a novel Posit to fixed-point converter for enabling high-performance and energy-efficient hardware implementations for ANNs with minimal drop in the output accuracy.
Edit intent: fluency
Original:  Compared to an 8-bit fixed-point-based inference accelerator, our proposed implementation offers \approx46\\%DIF < and \approx18\\% reductions in the storage requirements %DIF > and \approx18\\% reductions in the storage requirements of the parameters and energy consumption of the MAC units, respectively.
Revised:  Compared to an 8-bit fixed-point-based inference accelerator, our proposed implementation offers \approx46\\%DIF < and \approx18\\% reductions in the storage %DIF > and \approx18\\% reductions in the storage requirements of the parameters and energy consumption of the MAC units, respectively.
Edit intent: fluency
Original:  We present separate stability constraints for the mechanical part of the one-dimensional morphoelastic model and stability constraints for the chemical part of the one-dimensional morphoelastic model.
Revised:  Here we present stability constraints for the mechanical part of the one-dimensional morphoelastic model and stability constraints for the chemical part of the one-dimensional morphoelastic model.
Edit intent: coherence
Original:  We present separate stability constraints for the mechanical part of the one-dimensional morphoelastic model and stability constraints for the chemical part of the one-dimensional morphoelastic model.
Revised:  We present separate stability constraints for the one-dimensional morphoelastic model and stability constraints for the chemical part of the one-dimensional morphoelastic model.
Edit intent: clarity
Original:  For the mechanical  model, the results show that the components reach equilibria in a (non) monotonic way, depending on the value of the viscosity.
Revised:  For the mechanical part of the model, the results show that the components reach equilibria in a (non) monotonic way, depending on the value of the viscosity.
Edit intent: clarity
Original:  For the mechanical  model, the results show that the components reach equilibria in a (non) monotonic way, depending on the value of the viscosity.
Revised:  For the mechanical model, the results show the components reach equilibria in a (non) monotonic way, depending on the value of the viscosity.
Edit intent: fluency
Original:  Furthermore, the results show that the parameters of the chemical  model need to meet the stability constraint  to avoid unrealistic results.
Revised:  The results show that the parameters of the chemical model need to meet the stability constraint to avoid unrealistic results.
Edit intent: coherence
Original:  Furthermore, the results show that the parameters of the chemical  model need to meet the stability constraint  to avoid unrealistic results.
Revised:  Furthermore, the results show that the parameters of the chemical part of the model need to meet the stability constraint to avoid unrealistic results.
Edit intent: clarity
Original: We consider a one-dimensional morphoelastic model describing post-burn scar contractions .
Revised: We consider a one-dimensional morphoelastic model describing post-burn scar contraction .
Edit intent: fluency
Original:  This model describes the movement of the skin and the development of the effective Eulerian strain in the tissue.
Revised:  This model describes the displacement of the dermal layer of the skin and the development of the effective Eulerian strain in the tissue.
Edit intent: clarity
Original:  Besides these components, the model also contains components that play a major role in the wound healing process .
Revised:  Besides these components, the model also contains components that play a major role in skin repair after trauma .
Edit intent: clarity
Original:  These components are  fibroblasts, myofibroblasts, signaling molecules, and collagen.
Revised:  These components are signaling molecules, fibroblasts, myofibroblasts, signaling molecules, and collagen.
Edit intent: fluency
Original:  These components are  fibroblasts, myofibroblasts, signaling molecules, and collagen.
Revised:  These components are fibroblasts, myofibroblasts, and collagen.
Edit intent: clarity
Original:  We perform a sensitivity analysis for many parameters  and use the results for a feasibility study.
Revised:  We perform a sensitivity analysis for many parameters of the model and use the results for a feasibility study.
Edit intent: clarity
Original:  In this study, we test whether the model is suitable for predicting how contraction develops in different age groups.
Revised:  In this study, we test whether the model is suitable for predicting the extent of contraction in different age groups.
Edit intent: clarity
Original:  Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however neglecting the deep structured features of natural language query texts and source codes, both of which contain rich semantic information.
Revised:  Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however neglecting the deep structured features of query texts and source codes, both of which contain rich semantic information.
Edit intent: clarity
Original:  In this paper, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for  semantic code retrieval.
Revised:  In this paper, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for the task of semantic code retrieval.
Edit intent: clarity
Original:  To this end, we first represent both natural language query texts and programming language codes with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
Revised:  To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
Edit intent: fluency
Original:  In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by a cross-attention based semantic matching operation .
Revised:  In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by cross-attention based semantic matching operation .
Edit intent: fluency
Original:  In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by a cross-attention based semantic matching operation .
Revised:  In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by a cross-attention based semantic matching operations .
Edit intent: fluency
Original:  We evaluate the proposed DGMS model on two public code retrieval datasets from two representative programming languages (i.e., Java and Python).
Revised:  We evaluate the proposed DGMS model on two public code retrieval datasets with two representative programming languages (i.e., Java and Python).
Edit intent: clarity
Original:  The experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets.
Revised:  Experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets.
Edit intent: clarity
Original:  In this study , we will propose and improve the video understanding method based on the Group Activity Recognition model by learning Actor Relation Graph (ARG).
Revised:  This study proposes a video understanding method based on the Group Activity Recognition model by learning Actor Relation Graph (ARG).
Edit intent: coherence
Original:  We will also introduce a visualization model that will visualize each input video frame with predicted bounding boxes on each human object and predicted "video captioning" to describe each individual 's action and their collective activity.
Revised:  We will also introduce a visualization model that will visualize each input video frame with predicted bounding boxes on each human object and predict individual action and collective activity.
Edit intent: clarity
Original:  We provide a study that shows this tool is  highly reproducible.
Revised:  We provide a study that shows this tool is accurate and highly reproducible.
Edit intent: clarity
Original: Stability is required for real world controlled systems as it ensures that they can tolerate small, real world perturbations around their desired operating states.
Revised: Stability is required for real world controlled systems as it ensures that those systems can tolerate small, real world perturbations around their desired operating states.
Edit intent: clarity
Original:  In this paper, we propose the `SepFormer' , a novel RNN-free Transformer-based neural network for speech separation.
Revised:  In this paper, we propose the SepFormer , a novel RNN-free Transformer-based neural network for speech separation.
Edit intent: fluency
Original:  The proposed model matches or overtakes the state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets.
Revised:  The proposed model achieves state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets.
Edit intent: clarity
Original:  It indeed achieves an SI-SNRi of 20.2 dB on WSJ0-2mix matching the SOTA, and an SI-SNRi of 17.6 dB on WSJ0-3mix , a SOTA result .
Revised:  It reaches an SI-SNRi of 20.2 dB on WSJ0-2mix matching the SOTA, and an SI-SNRi of 17.6 dB on WSJ0-3mix , a SOTA result .
Edit intent: clarity
Original:  It indeed achieves an SI-SNRi of 20.2 dB on WSJ0-2mix matching the SOTA, and an SI-SNRi of 17.6 dB on WSJ0-3mix , a SOTA result .
Revised:  It indeed achieves an SI-SNRi of 20.2 dB on WSJ0-2mix and an SI-SNRi of 17.6 dB on WSJ0-3mix , a SOTA result .
Edit intent: clarity
Original:  It indeed achieves an SI-SNRi of 20.2 dB on WSJ0-2mix matching the SOTA, and an SI-SNRi of 17.6 dB on WSJ0-3mix , a SOTA result .
Revised:  It indeed achieves an SI-SNRi of 20.2 dB on WSJ0-2mix matching the SOTA, and an SI-SNRi of 17.6 dB on WSJ0-3mix .
Edit intent: clarity
Original:  We present Graded Modal Dependent Type Theory ( Grtt for short), which equips a dependent type theory with a general, parameterizable analysis of the flow of data, both in and between computational terms and types.
Revised:  We present Graded Modal Dependent Type Theory ( GrTT for short), which equips a dependent type theory with a general, parameterizable analysis of the flow of data, both in and between computational terms and types.
Edit intent: fluency
Original:  We propose Grtt , study its metatheory, and explore various case studies of its use in reasoning about programs and studying other type theories.
Revised:  We propose GrTT , study its metatheory, and explore various case studies of its use in reasoning about programs and studying other type theories.
Edit intent: fluency
Original: The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec. P.835 . In this paper , we introduce an open-source implementation of the ITU-T Rec. P.835 for the crowdsourcing approach following the ITU-T Rec. P.808 on crowdsourcing recommendations.
Revised: The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec. P.835 . In this paper , we introduce an open-source implementation of the ITU-T Rec. P.835 , and P.808 on crowdsourcing recommendations.
Edit intent: coherence
Original:  To assess  our evaluation method 's validity , we compared the Mean Opinion Scores (MOS), calculate using ratings collected with our implementation, and the MOS values from a standard laboratory experiment conducted according to the ITU-T Rec , P.835.
Revised:  To assess the validity of our evaluation method 's validity , we compared the Mean Opinion Scores (MOS), calculate using ratings collected with our implementation, and the MOS values from a standard laboratory experiment conducted according to the ITU-T Rec , P.835.
Edit intent: clarity
Original:  To assess  our evaluation method 's validity , we compared the Mean Opinion Scores (MOS), calculate using ratings collected with our implementation, and the MOS values from a standard laboratory experiment conducted according to the ITU-T Rec , P.835.
Revised:  To assess our evaluation method , we compared the Mean Opinion Scores (MOS), calculate using ratings collected with our implementation, and the MOS values from a standard laboratory experiment conducted according to the ITU-T Rec , P.835.
Edit intent: clarity