wanyu/IteraTeR_v2 huggingface dataset:
Original:  This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes  which are not likely on the path reaching the goal.
Revised:  This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Node(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes which are not likely to reach the goal.
Edit intent: clarity
Original: Speech signals are a rich source of speaker-related information including sensitive attributes like identity or accent.
Revised: Speech data conveys sensitive speaker attributes like identity or accent.
Edit intent: clarity
Original:  With a small amount of found speech data, such attributes can be extracted and modeled for malicious purposes like voice cloning, spoofing, etc.
Revised:  With a small amount of found data, such attributes can be extracted and modeled for malicious purposes like voice cloning, spoofing, etc.
Edit intent: clarity
Original:  With a small amount of found speech data, such attributes can be extracted and modeled for malicious purposes like voice cloning, spoofing, etc.
Revised:  With a small amount of found speech data, such attributes can be inferred and exploited for malicious purposes like voice cloning, spoofing, etc.
Edit intent: clarity
Original:  With a small amount of found speech data, such attributes can be extracted and modeled for malicious purposes like voice cloning, spoofing, etc.
Revised:  With a small amount of found speech data, such attributes can be extracted and modeled for malicious purposes : voice cloning, spoofing, etc.
Edit intent: coherence
Original:   In this paper, we investigate speaker anonymization strategies based on voice conversion.
Revised:  In this paper, we investigate anonymization methods based on voice conversion.
Edit intent: clarity
Original:  In contrast to prior evaluations , we argue that different types of attackers can be defined depending on the extent of their knowledge about the conversion scheme.
Revised:  In contrast to prior work , we argue that different types of attackers can be defined depending on the extent of their knowledge about the conversion scheme.
Edit intent: clarity
Original:  In contrast to prior evaluations , we argue that different types of attackers can be defined depending on the extent of their knowledge about the conversion scheme.
Revised:  In contrast to prior evaluations , we argue that various linkage attacks can be designed depending on the extent of their knowledge about the conversion scheme.
Edit intent: clarity
Original:  In contrast to prior evaluations , we argue that different types of attackers can be defined depending on the extent of their knowledge about the conversion scheme.
Revised:  In contrast to prior evaluations , we argue that different types of attackers can be defined depending on the attackers' knowledge about the conversion scheme.
Edit intent: clarity
Original:  In contrast to prior evaluations , we argue that different types of attackers can be defined depending on the extent of their knowledge about the conversion scheme.
Revised:  In contrast to prior evaluations , we argue that different types of attackers can be defined depending on the extent of their knowledge about the anonymization scheme.
Edit intent: clarity
Original:  The utility of the converted speech is measured through the word error rate achieved by automatic speech recognition, while privacy protection is assessed by  state-of-the-art speaker verification techniques (i-vectors and x-vectors) .
Revised:  The utility of converted speech is measured through the word error rate achieved by automatic speech recognition, while privacy protection is assessed by state-of-the-art speaker verification techniques (i-vectors and x-vectors) .
Edit intent: fluency
Original:  The utility of the converted speech is measured through the word error rate achieved by automatic speech recognition, while privacy protection is assessed by  state-of-the-art speaker verification techniques (i-vectors and x-vectors) .
Revised:  The utility of the converted speech is measured via the word error rate achieved by automatic speech recognition, while privacy protection is assessed by state-of-the-art speaker verification techniques (i-vectors and x-vectors) .
Edit intent: fluency
Original:  The utility of the converted speech is measured through the word error rate achieved by automatic speech recognition, while privacy protection is assessed by  state-of-the-art speaker verification techniques (i-vectors and x-vectors) .
Revised:  The utility of the converted speech is measured through the word error rate achieved by automatic speech recognition, while privacy protection is assessed by state-of-the-art i-vector or x-vector based speaker verification .
Edit intent: clarity
Original:  On the one hand  this offers a simple description of SDP. On the other hand, this equivalence makes it possible to describe a version of the {\it Triangle Algorithm} for SDP feasibility based on solving CHR.
Revised:  On the one hand , this offers a simple description of SDP. On the other hand, this equivalence makes it possible to describe a version of the {\it Triangle Algorithm} for SDP feasibility based on solving CHR.
Edit intent: fluency
Original:  The worst-case complexity of each iteration is computing  largest eigenvalue of a symmetric matrix arising in that iteration.
Revised:  The worst-case complexity of each iteration is computing the largest eigenvalue of a symmetric matrix arising in that iteration.
Edit intent: fluency
Original:  The Triangle Algorithm thus provides an alternative to the existing interior-point algorithms for SDP . Finally, gaining{\it  {\it   from these results and insights , we discuss potential extension  to solving general system of polynomial equations.
Revised:  The Triangle Algorithm thus provides an alternative to the existing interior-point algorithms for SDP . Finally, gaining{\it {\it . Gaining from these results and insights , we discuss potential extension to solving general system of polynomial equations.
Edit intent: coherence
Original:  The Triangle Algorithm thus provides an alternative to the existing interior-point algorithms for SDP . Finally, gaining{\it  {\it   from these results and insights , we discuss potential extension  to solving general system of polynomial equations.
Revised:  The Triangle Algorithm thus provides an alternative to the existing interior-point algorithms for SDP . Finally, gaining{\it {\it from these results , we discuss potential extension to solving general system of polynomial equations.
Edit intent: clarity
Original: We study the problem of  online learning in strategic classification settings from the perspective of the learner, who is repeatedly facing myopically rational strategic agents.
Revised: Motivated by the problem of online learning in strategic classification settings from the perspective of the learner, who is repeatedly facing myopically rational strategic agents.
Edit intent: clarity
Original:  We model this interplay as a repeated Stackelberg game, where at each timestep the learner deploys a high-dimensional linear classifier first and an agent, after observing the classifier , along with his real feature vector, and according to his underlying utility function, best-responds with a (potentially altered ) feature vector.
Revised:  We model this interplay as a repeated Stackelberg game, where at each timestep the learner deploys a high-dimensional linear classifier and the agent, after observing the classifier , along with his real feature vector, and according to his underlying utility function, best-responds with a (potentially altered ) feature vector.
Edit intent: coherence
Original:  We model this interplay as a repeated Stackelberg game, where at each timestep the learner deploys a high-dimensional linear classifier first and an agent, after observing the classifier , along with his real feature vector, and according to his underlying utility function, best-responds with a (potentially altered ) feature vector.
Revised:  We model this interplay as a repeated Stackelberg game, where at each timestep the learner deploys a high-dimensional linear classifier first and an agent, after observing the classifier and according to his underlying utility function, best-responds with a (potentially altered ) feature vector.
Edit intent: coherence
Original:  We model this interplay as a repeated Stackelberg game, where at each timestep the learner deploys a high-dimensional linear classifier first and an agent, after observing the classifier , along with his real feature vector, and according to his underlying utility function, best-responds with a (potentially altered ) feature vector.
Revised:  We model this interplay as a repeated Stackelberg game, where at each timestep the learner deploys a high-dimensional linear classifier first and an agent, after observing the classifier , along with his real feature vector, and according to his underlying utility function, best responds with a potentially altered feature vector.
Edit intent: fluency
Original:  We measure the performance of the learner  in terms of Stackelberg regret for her 0-1 loss function.
Revised:  The performance of the learner in terms of Stackelberg regret for her 0-1 loss function.
Edit intent: clarity
Original:  We measure the performance of the learner  in terms of Stackelberg regret for her 0-1 loss function.
Revised:  We measure the performance of the learner is measured in terms of Stackelberg regret for her 0-1 loss function.
Edit intent: clarity
Original:  We measure the performance of the learner  in terms of Stackelberg regret for her 0-1 loss function.
Revised:  We measure the performance of the learner in terms of Stackelberg regret for her 0--1 loss function.
Edit intent: fluency
Original:  We then provide the Grinder Algorithm, an adaptive discretization algorithm , potentially of independent interest in the online learning community , and prove its data-dependent upper bound on the Stackelberg regret given oracle access, while being computationally efficient.
Revised:  For that, we introduce GRINDER, an adaptive discretization algorithm , potentially of independent interest in the online learning community , and prove its data-dependent upper bound on the Stackelberg regret given oracle access, while being computationally efficient.
Edit intent: coherence
Original:  We then provide the Grinder Algorithm, an adaptive discretization algorithm , potentially of independent interest in the online learning community , and prove its data-dependent upper bound on the Stackelberg regret given oracle access, while being computationally efficient.
Revised:  We then provide the Grinder Algorithm, an adaptive discretization algorithm potentially of independent interest in the online learning community , and prove its data-dependent upper bound on the Stackelberg regret given oracle access, while being computationally efficient.
Edit intent: fluency
Original:  We then provide the Grinder Algorithm, an adaptive discretization algorithm , potentially of independent interest in the online learning community , and prove its data-dependent upper bound on the Stackelberg regret given oracle access, while being computationally efficient.
Revised:  We then provide the Grinder Algorithm, an adaptive discretization algorithm , potentially of independent interest , and prove its data-dependent upper bound on the Stackelberg regret given oracle access, while being computationally efficient.
Edit intent: clarity
Original:  We also provide a nearly matching lower bound for the problem of strategic classification .
Revised:  In fact, we prove a nearly matching lower bound for the problem of strategic classification .
Edit intent: clarity
Original:  Our results advance the known state-of-the-art results in the growing literature of online learning from revealed preferences, which has so far focused on smoother utility and loss functions from the perspective of the agents and the learner respectively.
Revised:  Our results advance the known state-of-the-art results in the growing literature of learning from revealed preferences, which has so far focused on smoother utility and loss functions from the perspective of the agents and the learner respectively.
Edit intent: clarity
Original:  Our results advance the known state-of-the-art results in the growing literature of online learning from revealed preferences, which has so far focused on smoother utility and loss functions from the perspective of the agents and the learner respectively.
Revised:  Our results advance the known state-of-the-art results in the growing literature of online learning from revealed preferences, which has so far focused on "smoother" utility and loss functions from the perspective of the agents and the learner respectively.
Edit intent: clarity
Original:  we prove    that Stackelberg and external regret are strongly incompatible,\emph{  i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Revised:  we provestrategically that Stackelberg and external regret are strongly incompatible,\emph{ i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Edit intent: clarity
Original:  we prove    that Stackelberg and external regret are strongly incompatible,\emph{  i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Revised:  we prove game that Stackelberg and external regret are strongly incompatible,\emph{ i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Edit intent: fluency
Original:  we prove    that Stackelberg and external regret are strongly incompatible,\emph{  i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Revised:  we prove that Stackelberg and external regret are strongly incompatible,\emph{ : i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Edit intent: fluency
Original:  we prove    that Stackelberg and external regret are strongly incompatible,\emph{  i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Revised:  we prove that Stackelberg and external regret are strongly incompatible,\emph{ i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Edit intent: fluency
Original:  For that, we introduce GRINDER, an adaptive discretization algorithm potentially of independent interest, and prove its data-dependent upper bound on the Stackelberg regret given oracle access, while being computationally efficient.
Revised:  Second, we present a strategy-aware algorithm for minimizing the Stackelberg regret given oracle access, while being computationally efficient.
Edit intent: clarity
Original:  We complement our theoretical analysis with simulation results, which suggest that our algorithm outperforms the benchmarks, even given access to approximation oracles .
Revised:  We complement our theoretical analysis .
Edit intent: clarity
Original:  Our results advance the known state-of-the-art results in the growing literature of learning from revealed preferences, which has so far focused on "smoother" utility and loss functions from the perspective of the agents and the learner respectively.
Revised:  Our results advance the growing literature of learning from revealed preferences, which has so far focused on "smoother" utility and loss functions from the perspective of the agents and the learner respectively.
Edit intent: clarity
Original:  Our results advance the known state-of-the-art results in the growing literature of learning from revealed preferences, which has so far focused on "smoother" utility and loss functions from the perspective of the agents and the learner respectively.
Revised:  Our results advance the known state-of-the-art results in the growing literature of learning from revealed preferences, which has so far focused on "smoother" assumptions from the perspective of the agents and the learner respectively.
Edit intent: clarity
Original:  Our results advance the known state-of-the-art results in the growing literature of learning from revealed preferences, which has so far focused on "smoother" utility and loss functions from the perspective of the agents and the learner respectively.
Revised:  Our results advance the known state-of-the-art results in the growing literature of learning from revealed preferences, which has so far focused on "smoother" utility and loss functions from the perspective of the learner and the agents respectively.
Edit intent: fluency
Original: We address the question of repeatedly learning linear classifiers against agents who are strategically trying togame the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Revised: We address the question of repeatedly learning linear classifiers against agents who are trying togame the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Edit intent: clarity
Original: We address the question of repeatedly learning linear classifiers against agents who are strategically trying togame the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Revised: We address the question of repeatedly learning linear classifiers against agents who are strategically game the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Edit intent: clarity
Original: We address the question of repeatedly learning linear classifiers against agents who are strategically trying togame the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Revised: We address the question of repeatedly learning linear classifiers against agents who are strategically trying togame the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Edit intent: fluency
Original:  First, we show that Stackelberg and external regret for the problem of strategic classification are \emph{strongly incompatible : i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Revised:  First, we show that Stackelberg and external regret for the problem of strategic classification are \emph{strongly incompatible : i.e., there exist worst-case scenarios, where any sequence of actions providing sublinear external regret might result in linear Stackelberg regret and vice versa.
Edit intent: fluency
Original: Inappropriate and profane content on social media is exponentially increasing and companies are becoming more aware of the type of content on which they are advertising and how it may affect their brand reputation.
Revised: Inappropriate and profane content on social media is exponentially increasing and big corporations are becoming more aware of the type of content on which they are advertising and how it may affect their brand reputation.
Edit intent: clarity
Original:  It becomes increasingly important for such corporations to advertise on only such videos that don't hurt the feelings, community or harmony of the audience at large.
Revised:  It becomes increasingly important for such corporations to advertise on only the videos that don't hurt the feelings, community or harmony of the audience at large.
Edit intent: clarity
Original:  Customization of the architecture have also been included to serve different requirements of the corporations.
Revised:  Customization of the architecture have also been included to serve different requirements of corporations.
Edit intent: fluency
Original:  The approach , set up in 29%DIFDELCMD < ] %%% for primitive-recursive complexity, construes finite partial-functions as a universal canonical form of data, and uses structure components for loop variants.
Revised:  The approach , set up in %DIFDELCMD < ] %%% for primitive-recursive complexity, construes finite partial-functions as a universal canonical form of data, and uses structure components for loop variants.
Edit intent: fluency
Original:  We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both shopping activity and online communication.
Revised:  We show that segregated interaction is amplified relative to the expected effects of geographic segregation in terms of both purchase activity and online communication.
Edit intent: clarity
Original:  Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric  , i.e., the amount of interaction from poorer to wealthier neighborhoods is considerably larger than vice versa.
Revised:  Furthermore, we find that segregation increases with difference in socio-economic status but is asymmetric , i.e., the amount of interaction from poorer to wealthier neighborhoods is larger than vice versa.
Edit intent: clarity
Original: This study explores the time-varying structure of market efficiency on the prewar Japanese stock market based on Lo's (2004) adaptive market hypothesis (AMH).
Revised: This study explores the time-varying structure of market efficiency of the prewar Japanese stock market based on Lo's (2004) adaptive market hypothesis (AMH).
Edit intent: clarity
Original:  SUMMIT is built as an extension of CARLA and inherits from it the physical and visual realism for autonomous driving simulation.
Revised:  SUMMIT is built as an extension of CARLA and inherits from it the physics and visual realism for autonomous driving simulation.
Edit intent: fluency
Original:  SUMMIT supports a wide range of applications, including perception, vehicle control , planning, and end-to-end learning.
Revised:  SUMMIT supports a wide range of applications, including perception, vehicle control and planning, and end-to-end learning.
Edit intent: clarity
Original:  In the upper-level , we formulate a scheduling problem the solution of which designates the optimal time of each CAV  to cross the intersections.
Revised:  In the upper-level problem designates the optimal time of each CAV to cross the intersections.
Edit intent: clarity
Original:  In the upper-level , we formulate a scheduling problem the solution of which designates the optimal time of each CAV  to cross the intersections.
Revised:  In the upper-level , we formulate a scheduling problem the solution of which designates the optimal time of each CAV aimed at minimizing its travel time to cross the intersections.
Edit intent: clarity
Original: In this paper, we present a decentralized optimal control framework for connected and automated vehicles (CAVs) crossing two adjacent intersections.
Revised: In this paper, we present a decentralized optimal control framework for connected and automated vehicles (CAVs) at two adjacent intersections.
Edit intent: clarity
Original:  The framework consists of an upper-level scheduling problem and a low-level optimal control problem.
Revised:  This framework consists of an upper-level scheduling problem and a low-level optimal control problem.
Edit intent: fluency
Original:  The solution of the upper-level problem designates the optimal time of each CAV aimed at minimizing its travel time to cross the intersections.
Revised:  By partitioning the area around two adjacent intersections into different zones, we formulate a scheduling problem for each individual CAV aimed at minimizing its travel time to cross the intersections.
Edit intent: clarity