wanyu/IteraTeR_v2 huggingface dataset:
Original:  In both GV and CP , deadlock freedom is achieved at the expense of expressivity  as the only communication structures allowed are trees . Dardha and Gay define Priority CP (PCP), which allows for cyclic structures and restores deadlock freedom by adding prioritiesto types , in line with Kobayashi and Padovani 's work .
Revised:  In both GV and CP , deadlock freedom is achieved at the expense of expressivity as the only communication structures allowed are trees . Dardha and Gay define Priority CP (PCP), which allows cyclic-structured processes and restores deadlock freedom by adding prioritiesto types , in line with Kobayashi and Padovani 's work .
Edit intent: clarity
Original:  In both GV and CP , deadlock freedom is achieved at the expense of expressivity  as the only communication structures allowed are trees . Dardha and Gay define Priority CP (PCP), which allows for cyclic structures and restores deadlock freedom by adding prioritiesto types , in line with Kobayashi and Padovani 's work .
Revised:  In both GV and CP , deadlock freedom is achieved at the expense of expressivity as the only communication structures allowed are trees . Dardha and Gay define Priority CP (PCP), which allows for cyclic structures and restores deadlock freedom by using priorities , in line with Kobayashi and Padovani 's work .
Edit intent: clarity
Original:  In both GV and CP , deadlock freedom is achieved at the expense of expressivity  as the only communication structures allowed are trees . Dardha and Gay define Priority CP (PCP), which allows for cyclic structures and restores deadlock freedom by adding prioritiesto types , in line with Kobayashi and Padovani 's work .
Revised:  In both GV and CP , deadlock freedom is achieved at the expense of expressivity as the only communication structures allowed are trees . Dardha and Gay define Priority CP (PCP), which allows for cyclic structures and restores deadlock freedom by adding prioritiesto types , in line with Kobayashi and Padovani .
Edit intent: clarity
Original: In this paper we try to find a computational interpretation for a strong form of extensionality, which we call "converse extensionality" .
Revised: In this paper we try to find a computational interpretation for a strong form of extensionality, which we call ``converse extensionality'' .
Edit intent: fluency
Original:  We then exploit certain continuity principles and that functions reflect apartness, as opposed to preserving equality, to prove that the resulting categories of assemblies model some converse extensionality principles .
Revised:  We then exploit that functions reflect apartness, as opposed to preserving equality, to prove that the resulting categories of assemblies model some converse extensionality principles .
Edit intent: clarity
Original:  We then exploit certain continuity principles and that functions reflect apartness, as opposed to preserving equality, to prove that the resulting categories of assemblies model some converse extensionality principles .
Revised:  We then exploit certain continuity principles and that functions reflect apartness, in addition to preserving equality, to prove that the resulting categories of assemblies model some converse extensionality principles .
Edit intent: fluency
Original:  We then exploit certain continuity principles and that functions reflect apartness, as opposed to preserving equality, to prove that the resulting categories of assemblies model some converse extensionality principles .
Revised:  We then exploit certain continuity principles and that functions reflect apartness, as opposed to preserving equality, to prove that the resulting categories of assemblies model a converse extensionality principle .
Edit intent: clarity
Original:  We also report feedback from interviews with  ML experts and a visualization researcher who assessed the effectiveness of our system.
Revised:  We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.
Edit intent: clarity
Original: For languages with insufficient resources to train speech recognition systems, query-by-example spoken term detection (QbE-STD) offers a way of accessing an untranscribed speech corpus by helping identify regions where spoken query terms occur.
Revised: For languages with insufficient resources to train speech recognition systems, query-by-example spoken term detection (QbE-STD) offers an alternative for iteratively indexing untranscribed speech corpora by locating spoken query terms occur.
Edit intent: clarity
Original:  Using data selected from a variety of speakers and recording conditions from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we evaluated whether QbE-STD performance on these languages could be improved by leveraging representations extracted from the pre-trained English wav2vec 2.0 model.
Revised:  Using data selected from a variety of speakers and recording conditions from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we show that QbE-STD performance on these languages could be improved by leveraging representations extracted from the pre-trained English wav2vec 2.0 model.
Edit intent: clarity
Original:  Using data selected from a variety of speakers and recording conditions from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we evaluated whether QbE-STD performance on these languages could be improved by leveraging representations extracted from the pre-trained English wav2vec 2.0 model.
Revised:  Using data selected from a variety of speakers and recording conditions from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we evaluated whether QbE-STD can be improved by leveraging representations extracted from the pre-trained English wav2vec 2.0 model.
Edit intent: clarity
Original:  Compared to the use of Mel-frequency cepstral coefficients and bottleneck features, we find that representations from the middle layers of the wav2vec 2.0 Transformer offer large gains in task performance (between 56\% and 86\%) .
Revised:  Compared to the use of Mel-frequency cepstral coefficients and bottleneck features, we find that wav2vec 2.0 Transformer offer large gains in task performance (between 56\% and 86\%) .
Edit intent: clarity
Original: In this paper, we study the problem of learning safe control policies that are also effective -- i.e., maximizing the probability of satisfying the linear temporal logic (LTL) specification of the task, and the discounted reward capturing the (classic) control performance.
Revised: We study the problem of learning safe control policies that are also effective -- i.e., maximizing the probability of satisfying the linear temporal logic (LTL) specification of the task, and the discounted reward capturing the (classic) control performance.
Edit intent: coherence
Original: In this paper, we study the problem of learning safe control policies that are also effective -- i.e., maximizing the probability of satisfying the linear temporal logic (LTL) specification of the task, and the discounted reward capturing the (classic) control performance.
Revised: In this paper, we study the problem of learning safe control policies that are also effective ; i.e., maximizing the probability of satisfying the linear temporal logic (LTL) specification of the task, and the discounted reward capturing the (classic) control performance.
Edit intent: fluency
Original: In this paper, we study the problem of learning safe control policies that are also effective -- i.e., maximizing the probability of satisfying the linear temporal logic (LTL) specification of the task, and the discounted reward capturing the (classic) control performance.
Revised: In this paper, we study the problem of learning safe control policies that are also effective -- i.e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of the task, and the discounted reward capturing the (classic) control performance.
Edit intent: fluency
Original: In this paper, we study the problem of learning safe control policies that are also effective -- i.e., maximizing the probability of satisfying the linear temporal logic (LTL) specification of the task, and the discounted reward capturing the (classic) control performance.
Revised: In this paper, we study the problem of learning safe control policies that are also effective -- i.e., maximizing the probability of satisfying the linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance.
Edit intent: fluency
Original:  We consider unknown environments that can be modeled as Markov decision processes (MDPs) .
Revised:  We consider unknown environments modeled as Markov decision processes (MDPs) .
Edit intent: clarity
Original:  We consider unknown environments that can be modeled as Markov decision processes (MDPs) .
Revised:  We consider unknown environments that can be modeled as Markov decision processes .
Edit intent: clarity
Original:  We propose a model-free reinforcement learning algorithm that learns a policy that first maximizes the probability of ensuring the safety, then the probability of satisfying the given LTL specification and lastly, the sum of discounted Quality of Control (QoC) rewards.
Revised:  We propose a model-free reinforcement learning algorithm that learns a policy that first maximizes the probability of ensuring safety, then the probability of satisfying the given LTL specification and lastly, the sum of discounted Quality of Control (QoC) rewards.
Edit intent: fluency
Original:  We propose a model-free reinforcement learning algorithm that learns a policy that first maximizes the probability of ensuring the safety, then the probability of satisfying the given LTL specification and lastly, the sum of discounted Quality of Control (QoC) rewards.
Revised:  We propose a model-free reinforcement learning algorithm that learns a policy that first maximizes the probability of ensuring the safety, then the probability of satisfying the given LTL specification and lastly, the sum of discounted Quality of Control rewards.
Edit intent: clarity
Original:  Finally, we illustrate the applicability of our RL-based approach on a case study .
Revised:  Finally, we illustrate applicability of our RL-based approach on a case study .
Edit intent: fluency
Original:  Finally, we illustrate the applicability of our RL-based approach on a case study .
Revised:  Finally, we illustrate the applicability of our RL-based approach .
Edit intent: clarity
Original: Voice anti-spoofing aims at classifying a given speech input either as a bonafide human sample, or a spoofing attack (e.g. synthetic or replayed sample).
Revised: Voice anti-spoofing aims at classifying a given utterance either as a bonafide human sample, or a spoofing attack (e.g. synthetic or replayed sample).
Edit intent: clarity
Original:  Numerous voice anti-spoofing methods have been proposed but most of them fail to generalize across domains (corpora) -- and we do not know why.
Revised:  Many anti-spoofing methods have been proposed but most of them fail to generalize across domains (corpora) -- and we do not know why.
Edit intent: fluency
Original:  To facilitate the visual comparison of attention weights and linguistic knowledge, Dodrio applies different graph visualization techniques to represent attention weights with longer input text.
Revised:  To facilitate the visual comparison of attention weights and linguistic knowledge, Dodrio applies different graph visualization techniques to represent attention weights scalable to longer input text.
Edit intent: clarity
Original: Professional race car drivers can execute extreme overtaking maneuvers.
Revised: Professional race-car drivers can execute extreme overtaking maneuvers.
Edit intent: fluency
Original:  However, conventional systems for autonomous overtaking rely on either simplified assumptions about the vehicle dynamics or solving expensive trajectory optimization problems online.
Revised:  However, existing algorithms for autonomous overtaking rely on either simplified assumptions about the vehicle dynamics or solving expensive trajectory optimization problems online.
Edit intent: clarity
Original:  However, conventional systems for autonomous overtaking rely on either simplified assumptions about the vehicle dynamics or solving expensive trajectory optimization problems online.
Revised:  However, conventional systems for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or solving expensive trajectory optimization problems online.
Edit intent: fluency
Original:  However, conventional systems for autonomous overtaking rely on either simplified assumptions about the vehicle dynamics or solving expensive trajectory optimization problems online.
Revised:  However, conventional systems for autonomous overtaking rely on either simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems online.
Edit intent: clarity
Original:  When the vehicle is approaching its physical limits, existing model-based controllers struggled to handle highly nonlinear dynamics  and cannot leverage the large volume of data generated by simulation or real-world driving.
Revised:  When the vehicle approaches its physical limits, existing model-based controllers struggled to handle highly nonlinear dynamics and cannot leverage the large volume of data generated by simulation or real-world driving.
Edit intent: fluency
Original:  When the vehicle is approaching its physical limits, existing model-based controllers struggled to handle highly nonlinear dynamics  and cannot leverage the large volume of data generated by simulation or real-world driving.
Revised:  When the vehicle is approaching its physical limits, existing model-based controllers struggle to handle highly nonlinear dynamics and cannot leverage the large volume of data generated by simulation or real-world driving.
Edit intent: fluency
Original:  When the vehicle is approaching its physical limits, existing model-based controllers struggled to handle highly nonlinear dynamics  and cannot leverage the large volume of data generated by simulation or real-world driving.
Revised:  When the vehicle is approaching its physical limits, existing model-based controllers struggled to handle highly nonlinear dynamics , and cannot leverage the large volume of data generated by simulation or real-world driving.
Edit intent: fluency
Original:  To circumvent these limitations, this work proposes a new learning-based method to tackle the autonomous overtaking problem.
Revised:  To circumvent these limitations, we propose a new learning-based method to tackle the autonomous overtaking problem.
Edit intent: clarity
Original:  We evaluate our approach using Gran Turismo Sport -- a world-leading car racing simulator known for its detailed dynamic modeling of various cars and tracks.
Revised:  We evaluate our approach in the popular car racing game Gran Turismo Sport -- a world-leading car racing simulator known for its detailed dynamic modeling of various cars and tracks.
Edit intent: clarity
Original:  We evaluate our approach using Gran Turismo Sport -- a world-leading car racing simulator known for its detailed dynamic modeling of various cars and tracks.
Revised:  We evaluate our approach using Gran Turismo Sport , which is known for its detailed dynamic modeling of various cars and tracks.
Edit intent: clarity
Original:  We evaluate our approach using Gran Turismo Sport -- a world-leading car racing simulator known for its detailed dynamic modeling of various cars and tracks.
Revised:  We evaluate our approach using Gran Turismo Sport -- a world-leading car racing simulator known for its detailed modeling of various cars and tracks.
Edit intent: clarity
Original: Scene classification is one of the basic problems in computer vision research with extensive applications in robotics.
Revised: Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics.
Edit intent: clarity
Original:  When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify more discriminative scene image features.
Revised:  When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify discriminative scene image features.
Edit intent: clarity
Original:  However,  when targeting big data collections, often multi-modal images are gathered disregarding their original nature.
Revised:  However, when targeting big data collections, so multi-modal images are gathered disregarding their original nature.
Edit intent: coherence
Original:  In this work  we put under the spotlight the existence of a possibly severe domain shift issue within multi-modality scene recognition datasets.
Revised:  In this work , we put under the spotlight the existence of a possibly severe domain shift issue within multi-modality scene recognition datasets.
Edit intent: fluency
Original: This paper proposes a new defense approach for counteracting with state-of-the-art white and black-box adversarial attack algorithms.
Revised: This paper proposes a new defense approach for counteracting state-of-the-art white and black-box adversarial attack algorithms.
Edit intent: fluency
Original:  Our approach fits in the category of implicit reactive defense algorithms since it does not directly manipulate the potentially malicious input signals.
Revised:  Our approach fits into the implicit reactive defense algorithms since it does not directly manipulate the potentially malicious input signals.
Edit intent: clarity
Original:  Our approach fits in the category of implicit reactive defense algorithms since it does not directly manipulate the potentially malicious input signals.
Revised:  Our approach fits in the category of implicit reactive defense algorithm category since it does not directly manipulate the potentially malicious input signals.
Edit intent: clarity
Original:  We also propose a hazard signal in addition to the learned latent representation as input to a down-stream policy.
Revised:  Furthermore, we propose a hazard signal in addition to the learned latent representation as input to a down-stream policy.
Edit intent: coherence
Original: This work presents a scalable and efficient neural waveform codec (NWC) for speech compression.
Revised: We present a scalable and efficient neural waveform codec (NWC) for speech compression.
Edit intent: clarity
Original: This work presents a scalable and efficient neural waveform codec (NWC) for speech compression.
Revised: This work presents a scalable and efficient neural waveform coding system for speech compression.
Edit intent: clarity
Original:  The proposed CNN autoencoder also defines quantization and entropy coding as a trainable module, so the coding artifacts and bitrate control are handled during the optimization process.
Revised:  The proposed NWC also defines quantization and entropy coding as a trainable module, so the coding artifacts and bitrate control are handled during the optimization process.
Edit intent: clarity
Original:  We achieve efficiency by introducing compact model architectures to our fully convolutional network model , such as gated residual networks and depthwise separable convolution.
Revised:  We achieve efficiency by introducing compact model components to NWC , such as gated residual networks and depthwise separable convolution.
Edit intent: clarity
Original:  To this end, we employ the residual coding concept to concatenate multiple NWC autoencoding modules, where an NWC module performs residual coding to restore any reconstruction loss that its preceding modules have created.
Revised:  To this end, we employ the residual coding concept to concatenate multiple NWC autoencoding modules, where each NWC module performs residual coding to restore any reconstruction loss that its preceding modules have created.
Edit intent: clarity
Original: As large graph processing emerges, we observe a costly fork-processing pattern (FPP)  common in many graph algorithms.
Revised: As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms.
Edit intent: clarity
Original:  We study the efficiency of  state-of-the-art graph processing systems on multi-core architectures , including Ligra, Gemini, and GraphIt .
Revised:  We study the efficiency of state-of-the-art graph processing systems on multi-core architectures .
Edit intent: clarity
Original:  Since matrices and tensors of fixed rank form smooth Riemannian manifolds, one of the popular tools for finding the low-rank approximations is to use the Riemannian optimization.
Revised:  Since matrices and tensors of fixed rank form smooth Riemannian manifolds, one of the popular tools for finding low-rank approximations is to use the Riemannian optimization.
Edit intent: fluency
Original:  Since matrices and tensors of fixed rank form smooth Riemannian manifolds, one of the popular tools for finding the low-rank approximations is to use the Riemannian optimization.
Revised:  Since matrices and tensors of fixed rank form smooth Riemannian manifolds, one of the popular tools for finding the low-rank approximations is to use Riemannian optimization.
Edit intent: fluency
Original:  In this paper, we build upon automatic differentiation and propose a method that, given an implementation of the function to be minimized, efficiently computes Riemannian gradients and matrix-by-vector products between  approximate Riemannian Hessian and a given vector.
Revised:  In this paper, we build upon automatic differentiation and propose a method that, given an implementation of the function to be minimized, efficiently computes Riemannian gradients and matrix-by-vector products between an approximate Riemannian Hessian and a given vector.
Edit intent: fluency
Original:  Many factors can cause the bias of an ASR system , e. g. composition of the training material and articulation differences.
Revised:  Many factors can cause the bias of an ASR system .
Edit intent: coherence
Original:  Word error rates are compared, and  in-depth phoneme-level error analysis is conducted to understand where bias is occurring.
Revised:  Word error rates are compared, and an in-depth phoneme-level error analysis is conducted to understand where bias is occurring.
Edit intent: fluency
Original:  We  focus on bias due to articulation differences in the dataset.
Revised:  We primarily focus on bias due to articulation differences in the dataset.
Edit intent: clarity
Original:  To address this problem, we concentrate on  basic reproduction number R_0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population.
Revised:  To address this problem, we concentrate on the basic reproduction number R_0, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population.
Edit intent: fluency
Original:  Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R_0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is by far the most important R0 predictor , probably being a good proxy for the overall contact frequency in a population.
Revised:  Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind R_0. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important R0 predictor , probably being a good proxy for the overall contact frequency in a population.
Edit intent: clarity