wanyu/IteraTeR_v2 huggingface dataset:
Original:  By utilizing the projection technique in generating utility-array, ProUM can significantly improve the mining efficiency, and effectively reduce the memory consumption.
Revised:  ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, ProUM can significantly improve the mining efficiency, and effectively reduce the memory consumption.
Edit intent: style
Original:  By utilizing the projection technique in generating utility-array, ProUM can significantly improve the mining efficiency, and effectively reduce the memory consumption.
Revised:  By utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption.
Edit intent: coherence
Original:  Besides, we propose a new upper bound named sequence extension utility .
Revised:  Furthermore, a new upper bound named sequence extension utility .
Edit intent: coherence
Original:  Experimental results show that the proposed ProUM algorithm significantly outperforms the state-of-the-art algorithms  .
Revised:  Experimental results show that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms .
Edit intent: fluency
Original: Persistence diagrams are a main tool in the field of Topological Data Analysis (TDA).
Revised: Persistence diagrams are one of the main tools in the field of Topological Data Analysis (TDA).
Edit intent: clarity
Original:  In particular, certain family of PCs admit the stabilityproperty under an additional assumption .
Revised:  In addition, we propose several new summaries based on PC framework and investigate their stability .
Edit intent: clarity
Original:  Finally, we apply PCs to textures classification on four well-know texture datasets.
Revised:  Finally, we demonstrate the practical uses of PCs on the texture classification on four well-know texture datasets.
Edit intent: clarity
Original:  Finally, we apply PCs to textures classification on four well-know texture datasets.
Revised:  Finally, we apply PCs to textures classification on four public available texture datasets.
Edit intent: clarity
Original:  First, we develop a general  framework of vectorizing diagrams that we call the Persistence Curves (PCs) .
Revised:  First, we develop a general framework of vectorizing diagrams that we call the Persistence Curves (PCs) .
Edit intent: fluency
Original:  In addition, we propose several new summaries based on PC framework and investigate their stability .
Revised:  In addition, we propose several new summaries based on PC framework and provide a theoretical foundation for their stability analysis .
Edit intent: clarity
Original:  First, we develop a general and unifying framework of vectorizing diagrams that we call the Persistence Curves \textit{ (PCs), and show that several well-known summaries, such as Persistence Landscapes, fall under the PC framework.
Revised:  First, we develop a general and unifying framework of vectorizing diagrams that we call the \textit{ (PCs), and show that several well-known summaries, such as Persistence Landscapes, fall under the PC framework.
Edit intent: clarity
Original:  We provide a novel framework for the error analysis by reducing it to a weighted quadrature problem for irregular functions of Brownian motion.
Revised:  We provide a general framework for the error analysis by reducing it to a weighted quadrature problem for irregular functions of Brownian motion.
Edit intent: clarity
Original:  The cut-off of the convergence order at \kappa= 3/4 can be overcome by using a suitable non-equidistant discretization, which yields the strong convergence order of (1+\kappa)/2-\epsilon for the corresponding Euler-Maruyama scheme.
Revised:  The cut-off of the convergence order at 3/4 can be overcome by using a suitable non-equidistant discretization, which yields the strong convergence order of (1+\kappa)/2-\epsilon for the corresponding Euler-Maruyama scheme.
Edit intent: coherence
Original: It seems very intuitive that for the maximization of the OneMax problem f \OM (x):=i=1^n{x_i} the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value.
Revised: It may seem very intuitive that for the maximization of the OneMax problem f \OM (x):=i=1^n{x_i} the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value.
Edit intent: fluency
Original: It seems very intuitive that for the maximization of the OneMax problem f \OM (x):=i=1^n{x_i} the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value.
Revised: It seems very intuitive that for the maximization of the OneMax problem \OM (x):=i=1^n{x_i} the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value.
Edit intent: clarity
Original:  In this work we prove that drift maximization is not optimal.
Revised:  In this work we prove that drift maximization is not optimal.
Edit intent: fluency
Original: In the present paper we consider a discrete version of the iterated modified projection method for solving a Urysohn integral equation with a kernel of the type of Green's function.
Revised: In the present paper we consider discrete versions of the modified projection methods for solving a Urysohn integral equation with a kernel of the type of Green's function.
Edit intent: clarity
Original:  The order of convergence which we obtain for the discrete version indicates the choice of numerical quadrature which preserves the order of convergence in the continuous iterated modified projection method .
Revised:  The order of convergence which we obtain for the discrete version indicates the choice of numerical quadrature which preserves the orders of convergence in the continuous iterated modified projection method .
Edit intent: fluency
Original:  The order of convergence which we obtain for the discrete version indicates the choice of numerical quadrature which preserves the order of convergence in the continuous iterated modified projection method .
Revised:  The order of convergence which we obtain for the discrete version indicates the choice of numerical quadrature which preserves the order of convergence in the continuous modified projection methods .
Edit intent: clarity
Original: Guaranteed a posteriori estimates on the error of approximate eigenfunctions in both energy and L^2 norms are derivedfor the Laplace eigenvalue problem .
Revised: Fully computable a posteriori error estimates for eigenfunctions of compact self-adjoint operators in Hilbert spaces are derived .
Edit intent: clarity
Original: Such eigenvalue bounds can be computed for example by the finite element method along with the recently developed explicit error estimation 24%DIFDELCMD < ] %DIFDELCMD < %%% and the Lehmann--Goerisch method.
Revised: Such eigenvalue bounds can be computed for example by the finite element method along with the recently developed explicit error estimation %DIFDELCMD < ] %DIFDELCMD < %%% and the Lehmann--Goerisch method.
Edit intent: fluency
Original:  The efficiency of the derived error bounds for eigenfunctions is illustrated by numerical examples .
Revised:  The efficiency of the derived error bounds for eigenfunctions .
Edit intent: clarity
Original: In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and  a novel audio data modality , namely acoustic images .
Revised: In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and acoustic images, a novel audio data modality , namely acoustic images .
Edit intent: coherence
Original: In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and  a novel audio data modality , namely acoustic images .
Revised: In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and a novel audio data modality .
Edit intent: clarity
Original:  Our experiments suggest that the learned representations are more powerful and have better generalization capabilities than the features learned from models trained using just visual or single-microphone audio data.
Revised:  Our experiments suggest that the learned representations are more powerful and have better generalization capabilities than the features learned from models trained using just single-microphone audio data.
Edit intent: clarity
Original:  We explore the smooth histogram framework of Braverman and Ostrovsky (FOCS 2007) for reducing the sliding-window model to the insertion-only streaming model, and extend it to the family of subadditive functions.
Revised:  We extend the smooth-histogram framework of Braverman and Ostrovsky (FOCS 2007) for reducing the sliding-window model to the insertion-only streaming model, and extend it to the family of subadditive functions.
Edit intent: clarity
Original:  Specifically, we show that if a subadditive function can be approximated in the ordinary ( insertion-only ) streaming model, then it could be approximated also in the sliding-window model with approximation ratio larger by factor 2+\epsilon and space complexity larger by factor O(\epsilon^{-1}\log w), where w is the window size.
Revised:  Specifically, we show that if a subadditive function can be approximated in the ordinary ( insertion-only streaming model, then it could be approximated also in the sliding-window model with approximation ratio larger by factor 2+\epsilon and space complexity larger by factor O(\epsilon^{-1}\log w), where w is the window size.
Edit intent: fluency
Original:  Specifically, we show that if a subadditive function can be approximated in the ordinary ( insertion-only ) streaming model, then it could be approximated also in the sliding-window model with approximation ratio larger by factor 2+\epsilon and space complexity larger by factor O(\epsilon^{-1}\log w), where w is the window size.
Revised:  Specifically, we show that if a subadditive function can be approximated in the ordinary ( insertion-only ) streaming model, then it can be (2+\epsilon)-approximated also in the sliding-window model with approximation ratio larger by factor 2+\epsilon and space complexity larger by factor O(\epsilon^{-1}\log w), where w is the window size.
Edit intent: clarity
Original:  Specifically, we show that if a subadditive function can be approximated in the ordinary ( insertion-only ) streaming model, then it could be approximated also in the sliding-window model with approximation ratio larger by factor 2+\epsilon and space complexity larger by factor O(\epsilon^{-1}\log w), where w is the window size.
Revised:  Specifically, we show that if a subadditive function can be approximated in the ordinary ( insertion-only ) streaming model, then it could be approximated also in the sliding-window model with space complexity larger by factor O(\epsilon^{-1}\log w), where w is the window size.
Edit intent: clarity
Original: The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers.
Revised: Smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers.
Edit intent: fluency
Original: The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers.
Revised: The smart building (SB), a promising solution to fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers.
Edit intent: fluency
Original: The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers.
Revised: The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, includes the integration of a wide range of systems and services and involves a construction of multiple layers.
Edit intent: clarity
Original: The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves a construction of multiple layers.
Revised: The smart building (SB), a promising solution to the fast-paced and continuous urbanization around the world, is an integration of a wide range of systems and services and involves the construction of multiple layers.
Edit intent: fluency
Original:  The SB is capable of sensing, acquiring  and processing a tremendous amount of data as well as performing proper action and adaptationaccordingly.
Revised:  SB is capable of sensing, acquiring and processing a tremendous amount of data as well as performing proper action and adaptationaccordingly.
Edit intent: coherence
Original:  The SB is capable of sensing, acquiring  and processing a tremendous amount of data as well as performing proper action and adaptationaccordingly.
Revised:  The SB is capable of sensing, acquiring , and processing a tremendous amount of data as well as performing proper action and adaptationaccordingly.
Edit intent: fluency
Original:  With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques are insufficient to provide satisfactory services.
Revised:  With rapid increases in the number of connected nodes and thereby the data transmission demand of SB have led to conventional transmission and processing techniques are insufficient to provide satisfactory services.
Edit intent: clarity
Original:  With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques are insufficient to provide satisfactory services.
Revised:  With rapid increases in the number of connected nodes and thereby the data transmission demand in SBs, conventional transmission and processing techniques becoming insufficient to provide satisfactory services.
Edit intent: clarity
Original:  To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing  and reasoning abilities .
Revised:  In order to enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing and reasoning abilities .
Edit intent: coherence
Original:  To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing  and reasoning abilities .
Revised:  To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) , are required to be incorporated in SBs. Herein, we envision a novel SB framework featuring a reliable data transmission network with powerful data processing and reasoning abilities .
Edit intent: coherence
Original:  To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing  and reasoning abilities .
Revised:  To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network , powerful data processing and reasoning abilities .
Edit intent: fluency
Original:  To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing  and reasoning abilities .
Revised:  To enhance the intelligence of SBs and achieve efficient monitoring and control, both indoor visible light communications (VLC) and machine learning (ML) shall be applied jointly to construct a reliable data transmission network with powerful data processing , and reasoning abilities .
Edit intent: fluency
Original:  Although cloud providers have improved their offerings, many researchers using high-performance computing (HPC) are still skeptical about using cloud computing.
Revised:  Although cloud providers have improved their offerings, many researchers using high-performance computing (HPC) are still skeptical about cloud computing.
Edit intent: fluency
Original:  In this paper, we give an improved distributed expander decomposition in the CONGEST model . Specifically, we construct an (\epsilon,\phi)-expander decomposition with \phi=(\epsilon/\log n)^{2^{O(k)}} in O(n^{2/k}\cdotpoly(1/\phi,\log n)) rounds for any \epsilon\in(0,1) and positive integer k.
Revised:  In this paper, we give an improved distributed expander decomposition . Specifically, we construct an (\epsilon,\phi)-expander decomposition with \phi=(\epsilon/\log n)^{2^{O(k)}} in O(n^{2/k}\cdotpoly(1/\phi,\log n)) rounds for any \epsilon\in(0,1) and positive integer k.
Edit intent: clarity
Original:  By  modifying the distributed algorithm for routing on expanders by Ghaffari, Kuhn and Su [PODC'17], we obtain a triangle enumeration algorithm using O(n^{1/3}) rounds.
Revised:  By slightly modifying the distributed algorithm for routing on expanders by Ghaffari, Kuhn and Su [PODC'17], we obtain a triangle enumeration algorithm using O(n^{1/3}) rounds.
Edit intent: fluency
Original:  To the best of our knowledge, this provides the first non-trivial example for a  problem that has essentially the same complexity (up to a polylogarithmic factor) in both CONGEST and CONGESTED-CLIQUE .
Revised:  To the best of our knowledge, this provides the first non-trivial example for a problem that has essentially the same complexity (up to a polylogarithmic factor) in both CONGEST and CONGESTED CLIQUE .
Edit intent: fluency
Original:  Blocki and Zhou recently showed that  is NP-Hard to compute cc(G), but their techniques do not even rule out an efficient (1+\epsilon)-approximation algorithm for any constant \epsilon>0.
Revised:  Blocki and Zhou recently showed that it is NP-Hard to compute cc(G), but their techniques do not even rule out an efficient (1+\epsilon)-approximation algorithm for any constant \epsilon>0.
Edit intent: fluency
Original:  The cumulative pebbling complexity of a graph G is of particular interest in the field of cryptography as cc(G) is tightly related to the amortized Area-Time complexity of the data-independent memory hard function (iMHF) f_{G,H} [AS15] defined using a constant indegree directed acyclic graph (DAG) G and a random oracle H(\cdot).
Revised:  The cumulative pebbling complexity of a graph G is of particular interest in the field of cryptography as cc(G) is tightly related to the amortized Area-Time complexity of the Data-Independent Memory-Hard Function (iMHF) f_{G,H} [AS15] defined using a constant indegree directed acyclic graph (DAG) G and a random oracle H(\cdot).
Edit intent: fluency
Original:  A secure iMHF should have amortized Space-Time complexity as high as possible  e.g., to deter brute-force password attacker who wants to find x such that f_{G,H}(x) = h. Thus, to analyze the (in)security of a candidate iMHF f_{G,H}, it is crucial to estimate the value cc(G) but currently, upper and lower bounds for leading iMHF candidates differ by several orders of magnitude.
Revised:  A secure iMHF should have amortized Space-Time complexity as high as possible , e.g., to deter brute-force password attacker who wants to find x such that f_{G,H}(x) = h. Thus, to analyze the (in)security of a candidate iMHF f_{G,H}, it is crucial to estimate the value cc(G) but currently, upper and lower bounds for leading iMHF candidates differ by several orders of magnitude.
Edit intent: fluency
Original:  Blocki and Zhou recently showed that it is NP-Hard to compute cc(G), but their techniques do not even rule out an efficient (1+ \epsilon )-approximation algorithm for any constant \epsilon >0.
Revised:  Blocki and Zhou recently showed that it is NP-Hard to compute cc(G), but their techniques do not even rule out an efficient (1+ \varepsilon )-approximation algorithm for any constant \epsilon >0.
Edit intent: fluency
Original:  Blocki and Zhou recently showed that it is NP-Hard to compute cc(G), but their techniques do not even rule out an efficient (1+ \epsilon )-approximation algorithm for any constant \epsilon >0.
Revised:  Blocki and Zhou recently showed that it is NP-Hard to compute cc(G), but their techniques do not even rule out an efficient (1+ \epsilon )-approximation algorithm for any constant \varepsilon >0.
Edit intent: fluency
Original:  The proposed system is also comparable to the state-of-the-art x-vector system that adopts heavy data augmentation.
Revised:  The proposed system is also comparable to the state-of-the-art x-vector system that adopts data augmentation.
Edit intent: clarity
Original:  This resolves an open problem raised by Ben-Sasson, Goldreich , Harsha, Sudan, and Vadhan (STOC 2004).
Revised:  This resolves an open problem raised by Goldreich in 2004.
Edit intent: clarity
Original:  Reinforcement Learn-ing (RL) is a well-known technique for learning such policies.
Revised:  Reinforcement Learning (RL) is a well-known technique for learning such policies.
Edit intent: fluency
Original: In this paper we propose an end-to-end LSTM-based model that performs single-channel speech enhancement and phone recognition in a cocktail party scenario where visual information of the target speaker is available.
Revised: In this paper , we analyzed how audio-visual speech enhancement can help to perform the ASR task in a cocktail party scenario where visual information of the target speaker is available.
Edit intent: clarity
Original:  In particular, the experiments demonstrated that during optimization of the ASR phase the speech enhancement capability of the model significantly decreases and vice-versa.
Revised:  The experiments show that during optimization of the ASR phase the speech enhancement capability of the model significantly decreases and vice-versa.
Edit intent: coherence
Original:  In particular, the experiments demonstrated that during optimization of the ASR phase the speech enhancement capability of the model significantly decreases and vice-versa.
Revised:  In particular, the experiments demonstrated that during optimization of the ASR task the speech enhancement capability of the model significantly decreases and vice-versa.
Edit intent: clarity
Original: Existing objective evaluation metrics for voice conversion (VC) are not always correlated well with human perception.
Revised: Existing objective evaluation metrics for voice conversion (VC) are not always correlated with human perception.
Edit intent: clarity