wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view .
Revised:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and explore the synergies between theory-based argument quality predictions with respect to the practical AQ assessment view .
Edit intent: clarity
Original:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view .
Revised:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based prediction and practical AQ assessment view .
Edit intent: clarity
Original:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view .
Revised:  Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment .
Edit intent: clarity
Original:  Data of new Covid-19 cases per million were analysed worldwide at the spatial scale of a country and time replicated from the end of December 2019 to late May 2020.
Revised:  Data of web search effort in Greece on Covid-19 cases per million were analysed worldwide at the spatial scale of a country and time replicated from the end of December 2019 to late May 2020.
Edit intent: meaning-changed
Original:  Data of new Covid-19 cases per million were analysed worldwide at the spatial scale of a country and time replicated from the end of December 2019 to late May 2020. Data driven analysis of epidemiological, economic, public health, and governmental intervention variables was performed in order to select the optimal variables in explaining new Covid-19 cases across all countries in time. Sequentially, hierarchical variance partitioning of the optimal variables was performed in order to quantify the independent contribution of each variable in the total variance of new Covid-19 cases per million . Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases(51.6\%) followed by the governmental stringency index (15.2\%).
Revised:  Data of new Covid-19 as a topic for one year on a weekly temporal scale were analyzed using governmental intervention measures such a s school closures, movement restrictions, national and international travelling restrictions, stay at home requirements, mask requirements, financial support measures, and epidemiological variables such as new cases and new deaths as potential explanatory covariates. The relationship between web search effort on Covid-19 cases per million . Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases(51.6\%) followed by the governmental stringency index (15.2\%).
Edit intent: meaning-changed
Original:  Sequentially, hierarchical variance partitioning of the optimal variables was performed in order to quantify the independent contribution of each variable in the total variance of new Covid-19 cases per million . Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases(51.6\%) followed by the governmental stringency index (15.2\%).
Revised:  Sequentially, hierarchical variance partitioning of the optimal variables was performed in order to quantify the independent contribution of each variable in the total variance of new Covid-19 and the 16 in total explanatory covariates was analyzed with machine learning. Web search in time was compared with the corresponding epidemiological situation, expressed by the Rt at the same week . Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases(51.6\%) followed by the governmental stringency index (15.2\%).
Edit intent: meaning-changed
Original:  Sequentially, hierarchical variance partitioning of the optimal variables was performed in order to quantify the independent contribution of each variable in the total variance of new Covid-19 cases per million . Results indicated that from the variables available new tests per thousand explained the vast majority of the total variance in new cases(51.6\%) followed by the governmental stringency index (15.2\%). Availability of hospital beds per 100k inhabitants explained 9\% extreme poverty explained 8.8\%, hand washing facilities 5.3\%, the fraction of the population aged 65 or older explained 3.9\%, and other disease prevalence (cardiovascular diseases plus diabetes) explained 2.9\%.
Revised:  Sequentially, hierarchical variance partitioning of the optimal variables was performed in order to quantify the independent contribution of each variable in the total variance of new Covid-19 cases per million . Results indicated that the fraction of the population aged 65 or older explained 3.9\%, and other disease prevalence (cardiovascular diseases plus diabetes) explained 2.9\%.
Edit intent: coherence
Original:  Availability of hospital beds per 100k inhabitants explained 9\% extreme poverty explained 8.8\%, hand washing facilities 5.3\%, the fraction of the population aged 65 or older explained 3.9\%, and other disease prevalence (cardiovascular diseases plus diabetes) explained 2.9\%. The percentage of smokers within the population explained 2.6\% of the total variance, while population density explained 0.6\% .
Revised:  Availability of hospital beds per 100k inhabitants explained 9\% extreme poverty explained 8.8\%, hand washing facilities 5.3\%, the trained model exhibited a fit of R2 = 91\% between the actual and predicted web search effort. The top five variables for predicting web search effort were new deaths, the opening of international borders to non-Greek nationals, new cases, testing policy, and restrictions in internal movements. Web search peaked during the same weeks that the Rt was peaking although new deaths or new cases were not peaking during those dates, and Rt rarely is reported in public media. As both web search effort and Rt peaked during 1-15 August 2020, the peak of the tourist season, the implications of this are discussed .
Edit intent: meaning-changed
Original:  Our method, Amnesic Probing, follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention which removes it from the representation.
Revised:  Our method, Amnesic Probing, follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention that removes it from the representation.
Edit intent: fluency
Original:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors,  neural activity and  connectivity, as well as to explore optimization in neural systems  .
Revised:  Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors, neural activity and connectivity, as well as to explore optimization in neural systems .
Edit intent: clarity
Original:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors,  neural activity and  connectivity, as well as to explore optimization in neural systems  .
Revised:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, neural activity and connectivity, as well as to explore optimization in neural systems .
Edit intent: clarity
Original:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors,  neural activity and  connectivity, as well as to explore optimization in neural systems  .
Revised:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors, heterogeneous neural activity and connectivity, as well as to explore optimization in neural systems .
Edit intent: meaning-changed
Original:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors,  neural activity and  connectivity, as well as to explore optimization in neural systems  .
Revised:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors, neural activity and circuit connectivity, as well as to explore optimization in neural systems .
Edit intent: clarity
Original:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors,  neural activity and  connectivity, as well as to explore optimization in neural systems  .
Revised:  Besides being powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models that capture complex behaviors, neural activity and connectivity, as well as to explore optimization in neural systems , in ways that traditional models are not designed for .
Edit intent: meaning-changed
Original:  In this pedagogical Primer, we introduce conventional ANNs and demonstrate how they have been  deployed to study neuroscience questions. Next , we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research.
Revised:  In this pedagogical Primer, we introduce conventional ANNs and demonstrate how they have been deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology , we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research.
Edit intent: meaning-changed
Original:  We demonstrate that in a tonal harmony context this network displays scale-free properties, and optimal (most economical) chord progressions can be found by solving a path optimization like the Chinese postman problem. Moreover, the dynamical network can be viewed as a time series of a non-stationary signal  and as such  can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Revised:  This network can be viewed as a time series of a non-stationary signal and as such can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Edit intent: coherence
Original:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal  and as such  can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Revised:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal , and as such can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Edit intent: fluency
Original:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal  and as such  can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Revised:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal and as such , it can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Edit intent: coherence
Original:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal  and as such  can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Revised:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal and as such can be partitioned for the automatic identification of tonal regions using time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Edit intent: clarity
Original:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal  and as such  can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Revised:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal and as such can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection without relying on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Edit intent: coherence
Original:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal  and as such  can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Revised:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal and as such can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets or extensive corpora , as in machine-learning approaches .
Edit intent: meaning-changed
Original:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal  and as such  can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora , as in machine-learning approaches .
Revised:  Moreover, the dynamical network can be viewed as a time series of a non-stationary signal and as such can be partitioned for the automatic identification of key regions using well-established techniques for time series analysis and change point detection . Based on this interpretation we introduce a key-finding algorithm that does not rely on comparisons with pre-determined reference sets , as in the Krumhansl-Schmuckler model, or extensive corpora . We demonstrate that the essential features of tonal harmony, centricity, referentiality, directedness and hierarchy, emerge naturally from the network topology and its scale-free properties .
Edit intent: meaning-changed
Original: Context: Highly dynamic and competitive crowd-sourcing software development (CSD) marketplaces may experience task failure due to unforeseen reasons, such as increased competition over shared supplier resources, or uncertainty associated with a dynamic worker supply.
Revised: Context: Highly dynamic and competitive crowdsourcing software development (CSD) marketplaces may experience task failure due to unforeseen reasons, such as increased competition over shared supplier resources, or uncertainty associated with a dynamic worker supply.
Edit intent: fluency
Original:  Existing analysis reveals an average task failure ratio of 15.7 \% in software crowdsourcing markets. Goal: The objective of this study is to provide a task scheduling recommendation model for software crowdsourcing platforms in order to improve the success and efficiency of software crowdsourcing. Method: We propose a task scheduling model based on neural networks, and develop an approach to predict and analyze task failure probability upon arrival. More specifically, the model uses number of open tasks in the platform, average task similarity level of new arrival task with open tasks, task monetary prize and task duration as input, and then predicts the probability of task failure on the planned arrival date with three surplus days and recommending the day associated with lowest task failure probability to post the task. The proposed model is based on the workflow and data of TopCoder, one of the primary software crowdsourcing platforms.Results: We present a model that suggests the best recommended arrival dates for any task in the project with surplus of three days per task in the project. The model on average provided 4\% lower failure ratio per project.Conclusions: The proposed model empowers crowdsourcing managers to explore potential crowdsourcing outcomes with respect to different task arrival strategies.
Revised:  Existing analysis reveals an average task failure ratio of 15.7 
Edit intent: meaning-changed
Original:  Indeed, recent work refers to interpretations as unfaithful despite adhering to the available definition. Similarly, we identify several critical failures with the notion of textual highlights as faithful interpretations, although they adhere to the faithfulness definition. With textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation  .
Revised:  With interpretation by textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation .
Edit intent: clarity
Original:  With textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation  .
Revised:  With textual highlights as a case-study, we present several failure cases. Borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation .
Edit intent: meaning-changed
Original:  With textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation  .
Revised:  With textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and the attribution of human behavior to the interpretation .
Edit intent: clarity
Original:  With textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation  .
Revised:  With textual highlights as a case-study, and borrowing concepts from social science, we identify that the problem is a misalignment between the causal chain of decisions (causal attribution) and social attribution of human behavior to the interpretation (social attribution) .
Edit intent: meaning-changed
Original:  ANNS have subsequently become a popular model class to infer computational principles underlying complex cognitive functions, and in turn they have also emerged as a natural testbed for applying methods originally developed to probe information in neural populations.
Revised:  ANNs have subsequently become a popular model class to infer computational principles underlying complex cognitive functions, and in turn they have also emerged as a natural testbed for applying methods originally developed to probe information in neural populations.
Edit intent: fluency