wanyu/IteraTeR_v2 huggingface dataset:
Original: Second, we present an inverse reinforcement learning algorithm that uses Bayesian revealed preferences to test for rationality: does there exist a utility function that rationalizes the given data, and if yes, can it be used to predict future behavior?
Revised: Second, we present an inverse reinforcement learning algorithm that uses Bayesian revealed preferences to test for rationality: does there exist a utility function that rationalizes the given data, and if yes, can it be used to predict commenting behavior?
Edit intent: fluency
Original:  The paper also highlights how the rational inattention model can accurately predict future commenting behavior.
Revised:  The paper also highlights how the rational inattention model can accurately predict commenting behavior.
Edit intent: clarity
Original: Field normalization of citations is bibliometric standard.
Revised: Field-normalization of citations is bibliometric standard.
Edit intent: fluency
Original:  Despite the observed impact differences between fields, the question remains how strong fields influence the citation impact of papers.
Revised:  Despite the observed differences in citation counts between fields, the question remains how strong fields influence the citation impact of papers.
Edit intent: clarity
Original:  We wondered whether field differences can be traced back to other factors (than fields) possibly influencing citations (FICs) or are "pure" field effects .
Revised:  We wondered whether field differences can be traced back to other factors (than fields) possibly influencing citations (FICs) .
Edit intent: coherence
Original:  We considered several FICs such as number of pages and number of co-authors . Using the propensity score matching approach, we investigated with Web of Science data whether papers with the same or similar attributes (e.g., number of co-authors and number of pages) received different citations when they were assigned to different fields .
Revised:  We considered several FICs such as number of pages and number of co-authors . Using the propensity score matching approach, we investigated with Web of Science data whether papers with the same or similar attributes (e.g., comparable of co-authors and number of pages) received different citations when they were assigned to different fields .
Edit intent: fluency
Original:  We considered several FICs such as number of pages and number of co-authors . Using the propensity score matching approach, we investigated with Web of Science data whether papers with the same or similar attributes (e.g., number of co-authors and number of pages) received different citations when they were assigned to different fields .
Revised:  We considered several FICs such as number of pages and number of co-authors . Using the propensity score matching approach, we investigated with Web of Science data whether papers with the same or similar attributes (e.g., number of co-authors , comparable number of pages) received different citations when they were assigned to different fields .
Edit intent: clarity
Original:  We received similar results when we calculated mean value differences of the fields after inverse-probability of treatment weighting representing the causal or unconfounded field effects on citations.
Revised:  We received similar results when we calculated mean value differences of the fields after IPW representing the causal or unconfounded field effects on citations.
Edit intent: clarity
Original:  One possible interpretation of our results is that field-differences in citations exist which are relatively independent of other FICs . Another possible interpretation is that field-differences could vanish in principle .
Revised:  However, field differences in citation rates remain. The results point out that field-normalization seems to be a prerequisite for citation analysis and cannot be replaced by the consideration of any set of FICs in citation analyses .
Edit intent: coherence
Original:  We refer to the proposed methodology as SeCoST (pronounced Sequest) --- Sequential Co-supervision for training generations of Students.
Revised:  We refer to the proposed methodology as SeCoST (pronounced Sequest) -- Sequential Co-supervision for training generations of Students.
Edit intent: fluency
Original: The paper describes an implementation of deep reinforcement learning through self-supervised learning within the proof assistant HOL4.
Revised: The paper describes a deep reinforcement learning through self-supervised learning within the proof assistant HOL4.
Edit intent: clarity
Original: The paper describes an implementation of deep reinforcement learning through self-supervised learning within the proof assistant HOL4.
Revised: The paper describes an implementation of deep reinforcement learning framework based on self-supervised learning within the proof assistant HOL4.
Edit intent: clarity
Original:  A close interaction between the machine learning modules and the HOL4 library is achieved by the choice of tree neural networks (TNNs) as machine learning models and the internal use of HOL4 terms to represent tree structures of TNNs. Recursive improvement is possible when a given task is expressed as a search problem.
Revised:  A close interaction between the machine learning modules and the HOL4 library is achieved by the choice of tree neural networks (TNNs) as machine learning models and the internal use of HOL4 terms to represent tree structures of TNNs. Recursive improvement is possible when a task is expressed as a search problem.
Edit intent: clarity
Original: Neural Architecture Search (NAS) has seen an explosion of research in the past few years . A variety of methods have been proposed to perform NAS, including reinforcement learning, Bayesian optimization with a Gaussian process model, evolutionary search,  and gradient descent.
Revised: Neural Architecture Search (NAS) has seen an explosion of research in the past few years , with techniques spanning reinforcement learning, Bayesian optimization with a Gaussian process model, evolutionary search, and gradient descent.
Edit intent: clarity
Original: Neural Architecture Search (NAS) has seen an explosion of research in the past few years . A variety of methods have been proposed to perform NAS, including reinforcement learning, Bayesian optimization with a Gaussian process model, evolutionary search,  and gradient descent.
Revised: Neural Architecture Search (NAS) has seen an explosion of research in the past few years . A variety of methods have been proposed to perform NAS, including reinforcement learning, evolutionary search, and gradient descent.
Edit intent: clarity
Original:  After training on just 200 random neural architectures, we are able to predict the validation accuracy of a new architecture to within one percent of its true accuracy on average, for popular search spaces .
Revised:  After training on just 200 random neural architectures, we are able to predict the validation accuracy of a new architecture to within one percent of its true value using only 200 training points .
Edit intent: clarity
Original:  We test our algorithm on the NASBench (Ying et al. 2019) and DARTS (Liu et al. 2018) search spaces , and we show that our algorithm outperforms other NAS methods including evolutionary search , reinforcement learning, AlphaX, ASHA, and DARTS.
Revised:  We test our algorithm on the NASBench (Ying et al. 2019) and DARTS (Liu et al. 2018) search spaces and show that our algorithm outperforms other NAS methods including evolutionary search , reinforcement learning, AlphaX, ASHA, and DARTS.
Edit intent: coherence
Original:  We test our algorithm on the NASBench (Ying et al. 2019) and DARTS (Liu et al. 2018) search spaces , and we show that our algorithm outperforms other NAS methods including evolutionary search , reinforcement learning, AlphaX, ASHA, and DARTS.
Revised:  We test our algorithm on the NASBench (Ying et al. 2019) and DARTS (Liu et al. 2018) search spaces , and we show that our algorithm outperforms a variety of NAS methods including evolutionary search , reinforcement learning, AlphaX, ASHA, and DARTS.
Edit intent: clarity
Original:  In particular, our implementation has been made publicly available , including all details needed to fully reproduce our results.
Revised:  In particular, our implementation is publicly available and includes all details needed to fully reproduce our results.
Edit intent: clarity
Original: Neural Architecture Search (NAS) has seen an explosion of research in the past few years, with techniques spanning reinforcement learning, evolutionary search , Gaussian process (GP) Bayesian optimization (BO), and gradient descent.
Revised: Over the past half-decade, many methods have been considered for neural architecture search (NAS). Bayesian optimization (BO), and gradient descent.
Edit intent: clarity
Original:  Our method achieves state-of-the-art performance on the NASBench dataset and is over 100x more efficient than random search .
Revised:  Our method achieves state-of-the-art performance on NAS search spaces .
Edit intent: clarity
Original:  We adhere to the recent NAS research checklist (Lindauer and Hutter 2019) to facilitate NAS research.
Revised:  We adhere to the NAS research checklist (Lindauer and Hutter 2019) to facilitate NAS research.
Edit intent: clarity
Original: The performance of a speaker recognition system degrades considerably in the presence of noise.
Revised: The performance of a speaker identification system degrades considerably in the presence of noise.
Edit intent: fluency
Original:  Current state-of-the-art speaker recognition systems employ non-probabilistic models, such as convolutional neural networks (CNNs), which cannot use marginalisation.
Revised:  Current state-of-the-art speaker identification systems employ non-probabilistic models, such as convolutional neural networks (CNNs), which cannot use marginalisation.
Edit intent: clarity
Original:  In terms of speaker recognition accuracy, SPN speaker models employing marginalisation are more robust than recent CNN-based speaker recognition systems that pre-process the noisy speech.
Revised:  In terms of speaker identification accuracy, SPN speaker models employing marginalisation are more robust than recent CNN-based speaker recognition systems that pre-process the noisy speech.
Edit intent: fluency
Original:  In terms of speaker recognition accuracy, SPN speaker models employing marginalisation are more robust than recent CNN-based speaker recognition systems that pre-process the noisy speech.
Revised:  In terms of speaker recognition accuracy, SPN speaker models employing marginalisation are more robust than recent CNN-based speaker identification systems that pre-process the noisy speech.
Edit intent: fluency
Original:  Additionally, the SPN speaker models consist of significantly fewer parameters than that of the CNN-based speaker recognition systems.
Revised:  Additionally, the SPN speaker models consist of significantly fewer parameters than that of the CNN-based speaker identification systems.
Edit intent: fluency
Original:  The results presented in this work show that SPN speaker models are a robust, parameter-efficient alternative for speaker recognition .
Revised:  The results presented in this work show that SPN speaker models are a robust, parameter-efficient alternative for speaker identification .
Edit intent: clarity
Original:  Availability: The SPN speaker recognition system is available at : URL
Revised:  Availability: The SPN speaker identification system is available at : URL
Edit intent: fluency
Original:  Availability: The SPN speaker recognition system is available at : URL
Revised:  Availability: The SPN speaker recognition system is available at URL
Edit intent: fluency
Original:  SPN speaker models are evaluated here on real-world non-stationary and coloured noise sources at multiple SNR levels.
Revised:  SPN speaker models are evaluated here on real-world non-stationary and coloured noise sources at multiple signal-to-noise ratio (SNR) levels.
Edit intent: clarity
Original:  In terms of speaker identification accuracy, SPN speaker models employing marginalisation are more robust than recent CNN-based speaker identification systemsthat pre-process the noisy speech .
Revised:  In terms of speaker identification accuracy, SPN speaker models are more robust than recent CNN-based speaker identification systemsthat pre-process the noisy speech .
Edit intent: clarity
Original:  Additionally, the SPN speaker models consist of significantly fewer parameters than that of the CNN-based speaker identification systems .
Revised:  Additionally, SPN speaker models consist of significantly fewer parameters than that of the CNN-based speaker identification systems .
Edit intent: fluency
Original:  Additionally, the SPN speaker models consist of significantly fewer parameters than that of the CNN-based speaker identification systems .
Revised:  Additionally, the SPN speaker models consist of significantly fewer parameters than their CNN-based speaker identification systems .
Edit intent: clarity
Original:  Additionally, the SPN speaker models consist of significantly fewer parameters than that of the CNN-based speaker identification systems .
Revised:  Additionally, the SPN speaker models consist of significantly fewer parameters than that of the CNN-based counterparts .
Edit intent: clarity
Original:  The results presented in this work show that SPN speaker models are a robust, parameter-efficient alternative for speaker identification .
Revised:  The results indicate that SPN speaker models are a robust, parameter-efficient alternative for speaker identification .
Edit intent: clarity
Original:  The results presented in this work show that SPN speaker models are a robust, parameter-efficient alternative for speaker identification .
Revised:  The results presented in this work show that SPN speaker models could be a robust, parameter-efficient alternative for speaker identification .
Edit intent: clarity
Original:  Frederickson \& Johnson (1982) created a method for finding the k smallest values in A+B with runtime O( n + \min(k,n) \log(k{\min(k,n) et al. described another optimal algorithm in terms  Chazelle's of soft heaps . No fast methods exist for m>2.
Revised:  Frederickson \& Johnson (1982) created a method for finding the k smallest values in A+B with runtime O( et al. described another optimal algorithm in terms Chazelle's of soft heaps . No fast methods exist for m>2.
Edit intent: coherence
Original:  Frederickson \& Johnson (1982) created a method for finding the k smallest values in A+B with runtime O( n + \min(k,n) \log(k{\min(k,n) et al. described another optimal algorithm in terms  Chazelle's of soft heaps . No fast methods exist for m>2.
Revised:  Frederickson \& Johnson (1982) created a method for finding the k smallest values in A+B with runtime O( n + \min(k,n) \log(k{\min(k,n) described another optimal algorithm in terms Chazelle's of soft heaps . No fast methods exist for m>2.
Edit intent: coherence
Original:  Using this new algorithm to perform k-selection on the Cartesian product of m arrays of length n has runtime \in o( m\cdot n + k\cdot m).
Revised:  Using this new algorithm to perform k-selection on the Cartesian product of m arrays of length n has runtime \in o( k\cdot m).
Edit intent: clarity
Original:  We also provide implementations of the algorithms proposed and  their performance in practice.
Revised:  We also provide implementations of the algorithms proposed and evaluate their performance in practice.
Edit intent: clarity
Original:  Here a physical model based on the logistic model is suggested to explain how crowd mobility affects crowd size .
Revised:  Here a logistic model is suggested to explain how crowd mobility affects crowd size .
Edit intent: clarity
Original:  This model offers a framework to understand static and mobile crowd dynamics .
Revised:  This model may offer a basic concept to understand transport dynamics of flowing particles .
Edit intent: clarity
Original: Multidimensional data have become ubiquitous and are frequently involved in situations where the information is aggregated over multiple data atoms.
Revised: Multidimensional data have become ubiquitous and are frequently encountered in situations where the information is aggregated over multiple data atoms.
Edit intent: clarity
Original:  The aggregation can be over time or other features, such as geographical location or group affiliation .
Revised:  The aggregation can be over time or other features, such as geographical location .
Edit intent: clarity
Original:   However, data mining and machine learning models require detailed data for personalized analysis and prediction.
Revised:  However, data mining and machine learning models benefit from detailed data for personalized analysis and prediction.
Edit intent: clarity
Original:  Experiments on real data from different domains , i.e., sales data from retail companies , crime counts, and weather observations , are presented to showcase the effectiveness of PREMA .
Revised:  Experiments on real data from different domains : retail sales , crime counts, and weather observations , are presented to showcase the effectiveness of PREMA .
Edit intent: clarity
Original:  Experiments on real data from different domains , i.e., sales data from retail companies , crime counts, and weather observations , are presented to showcase the effectiveness of PREMA .
Revised:  Experiments on real data from different domains , i.e., sales data from retail companies , crime counts, and weather observations .
Edit intent: coherence
Original:  An encrypted IC implements  correct function only when the right key is input.
Revised:  An encrypted IC implements the correct function only when the right key is input.
Edit intent: fluency
Original:  Many existing logic locking methods are subject to the powerful satisfiability (SAT)-based attack.
Revised:  Many existing logic-locking methods are subject to the powerful satisfiability (SAT)-based attack.
Edit intent: fluency
Original:  Pairs of functions that consist of similar number of minterms can be chosen to resist the AppSAT and removal attacks  .
Revised:  Pairs of functions that consist of similar number of minterms can be chosen to resist the AppSAT and removal attacks is achieved .
Edit intent: coherence
Original:  Meanwhile, our design requires the same number of iterations in the SAT attack as the Anti-SAT scheme, and hence is always resistant to the SAT attack .
Revised:  Meanwhile, unlike existing AppSAT-resilient designs, our design does not sacrifice the resistance to the SAT attack .
Edit intent: clarity
Original:  While it generally requires O(N^2) memory and O(N^3) work to store and factorize the GNH matrix, respectively.
Revised:  It generally requires O(N^2) memory and O(N^3) work to store and factorize the GNH matrix, respectively.
Edit intent: coherence
Original:  The  approximation requires only %DIFDELCMD < \bigO%%%  (N r_o) memory footprint and %DIFDELCMD < \bigO%%%  (N r_o^2) work to be factorized, where r_o \ll N is the maximum rank of off-diagonal blocks in the GNH matrix.
Revised:  The approximation requires only %DIFDELCMD < \bigO%%% (N r_o) memory footprint and %DIFDELCMD < \bigO%%% O (N r_o^2) work to be factorized, where r_o \ll N is the maximum rank of off-diagonal blocks in the GNH matrix.
Edit intent: clarity
Original: Name disambiguation is a key and also a very touch problem in many online systems such as social search and academic search.
Revised: Name disambiguation is a key and also a very tough problem in many online systems such as social search and academic search.
Edit intent: clarity
Original:  Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly  to complete the disambiguation in real time .
Revised:  Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly --- to complete the disambiguation in real time .
Edit intent: fluency
Original:  Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly  to complete the disambiguation in real time .
Revised:  Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly to complete the disambiguation in the real-time .
Edit intent: fluency
Original:  In this paper, we propose a novel framework CONNA to train a matching component and a decision component jointly via reinforcement learning.
Revised:  In this paper, we propose a novel framework -- CONNA -- to train a matching component and a decision component jointly via reinforcement learning.
Edit intent: fluency
Original:  Empirically, we evaluate CONNA on AMiner a large online academic search system . Experimental results show that the proposed framework can achieve a 5.37 \%-19.84\% improvement on F1 score using joint training of the matching and the decision components.
Revised:  Empirically, we evaluate CONNA on AMiner a large online academic search system . Experimental results show that the proposed framework can achieve a 5.37 \%-19.84\% improvement on F1-score using joint training of the matching and the decision components.
Edit intent: fluency
Original:  Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly --- to complete the disambiguation in the real-time.
Revised:  Despite considerable research, a critical issue that has not been systematically studied is disambiguation on the fly -- to complete the disambiguation in the real-time.
Edit intent: fluency
Original:  The proposed CONNA has been successfully deployed on AMiner --- a large online academic search system.
Revised:  The proposed CONNA has been successfully deployed on AMiner -- a large online academic search system.
Edit intent: fluency
Original:  We then show, for the first time, that approaches such as PDR, which utilize rich Hoare queries,  can be exponentially more efficient than approaches such as ICE learning, which only utilize inductiveness checks of candidates.
Revised:  We then show, for the first time, that by utilizing rich Hoare queries, can be exponentially more efficient than approaches such as ICE learning, which only utilize inductiveness checks of candidates.
Edit intent: clarity
Original: Emerging non-volatile main memory (NVRAM) technologies provide novel features for large-scale graph analytics, combining byte-addressability, low idle power, and improved memory-density.
Revised: Non-volatile main memory (NVRAM) technologies provide novel features for large-scale graph analytics, combining byte-addressability, low idle power, and improved memory-density.
Edit intent: clarity
Original: Emerging non-volatile main memory (NVRAM) technologies provide novel features for large-scale graph analytics, combining byte-addressability, low idle power, and improved memory-density.
Revised: Emerging non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, combining byte-addressability, low idle power, and improved memory-density.
Edit intent: clarity
Original: Emerging non-volatile main memory (NVRAM) technologies provide novel features for large-scale graph analytics, combining byte-addressability, low idle power, and improved memory-density.
Revised: Emerging non-volatile main memory (NVRAM) technologies provide novel features for large-scale graph analytics, including byte-addressability, low idle power, and improved memory-density.
Edit intent: clarity
Original:  Systems are likely to have an order of magnitude more NVRAM than traditional memory (DRAM) , allowing large graph problems to be solved efficiently at a modest cost on a single machine  .
Revised:  Systems are likely to have an order of magnitude more NVRAM than traditional memory (DRAM) . NVRAM systems could therefore potentially allow very large graph problems to be solved efficiently at a modest cost on a single machine .
Edit intent: clarity