wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with  linear and linear-fractional class of fairness constraints,
Revised:  Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with linear and linear-fractional fairness constraints,
Edit intent: clarity
Original: We define a notion of complexity, motivated by considerations of circuit complexity, which quantifies the nonlinearity of the computation of a neural network, as well as a complementary measure of the effective dimension of feature representations.
Revised: We define a notion of complexity, which quantifies the nonlinearity of the computation of a neural network, as well as a complementary measure of the effective dimension of feature representations.
Edit intent: coherence
Original: The COVID-19 pandemic has deeply impacted people's lives around the globe.
Revised: As the COVID-19 pandemic has deeply impacted people's lives around the globe.
Edit intent: fluency
Original:  During the extended lockdowns caused by the pandemic, online communities are crucial for people to access information and share experiences .
Revised:  During the extended lockdowns caused by the pandemic, online communities are popping up .
Edit intent: clarity
Original:  In particular, two "new" communities have emerged on Reddit: /r/China _flu and /r/Coronavirus .
Revised:  In particular, two "new" communities , /r/China _flu and /r/Coronavirus .
Edit intent: clarity
Original:  In particular, two "new" communities have emerged on Reddit: /r/China _flu and /r/Coronavirus .
Revised:  In particular, two "new" communities have emerged on Reddit: /r/China flu and /r/Coronavirus .
Edit intent: fluency
Original:  Second, we study the differences between these two communities. /r/ Coronavirus is recommended as the official community for COVID-19 on Reddit, while /r  /  China _flu adopts a loose moderation practice.
Revised:  Second, we study the differences between these two communities. /r/ China flu and /r / China _flu adopts a loose moderation practice.
Edit intent: coherence
Original:  For a fixed regular discretizaton of the dual space the expected running time scales as O(\kappa\,polylog(N,K)), where \kappa is the condition number of the function.
Revised:  For a fixed regular discretization of the dual space the expected running time scales as O(\kappa\,polylog(N,K)), where \kappa is the condition number of the function.
Edit intent: fluency
Original:  Due to  its expected robustness to systematic errors and the short circuit depth , it is one of the promising candidates likely to run on near-term quantum devices.
Revised:  Due to the short circuit depth and its expected robustness to systematic errors and the short circuit depth , it is one of the promising candidates likely to run on near-term quantum devices.
Edit intent: fluency
Original:  Due to  its expected robustness to systematic errors and the short circuit depth , it is one of the promising candidates likely to run on near-term quantum devices.
Revised:  Due to its expected robustness to systematic errors , it is one of the promising candidates likely to run on near-term quantum devices.
Edit intent: coherence
Original:  We project the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives, both for exactor approximate solution.
Revised:  We simulate the performance of QAOA applied to the Max-Cut problem and compare it with some of the best classical alternatives, both for exactor approximate solution.
Edit intent: clarity
Original:  When comparing approximate solvers, their performance is characterized by the computational time taken to achieve a given quality of solution.
Revised:  When comparing solvers, their performance is characterized by the computational time taken to achieve a given quality of solution.
Edit intent: clarity
Original:  Since QAOA is based on sampling, we introduce performance metrics based on the probability of observing a sample above a certain quality.
Revised:  Since QAOA is based on sampling, we utilize performance metrics based on the probability of observing a sample above a certain quality.
Edit intent: clarity
Original: Coimagination method is a novel approach to support interactive communication for activating three (3) cognitive functions: episodic memory, division of attention, and planning function .
Revised: Coimagination method is a novel approach to support interactive communication for activating three (3) cognitive functions: episodic memory, division of attention, and planning .
Edit intent: clarity
Original:  In previous studies about the coimagination method,  different settings were tested in care institutions.
Revised:  In previous studies about the coimagination method, experimenters tested different settings in different settings were tested in care institutions.
Edit intent: clarity
Original:  In previous studies about the coimagination method,  different settings were tested in care institutions.
Revised:  In previous studies about the coimagination method, different care institutions.
Edit intent: clarity
Original:  Furthermore , several observers and measurers are needed to conduct the coimagination method.
Revised:  Also , several observers and measurers are needed to conduct the coimagination method.
Edit intent: fluency
Original:  Preliminary experiments with healthy participants and, most especially, with actual elder participants showed that our initial system could handle different configurations .
Revised:  In this paper, our focus is to measure how well our proposed features work with elderly participants. Preliminary experiments were conducted with healthy participants and, most especially, with actual elder participants showed that our initial system could handle different configurations .
Edit intent: coherence
Original:  Preliminary experiments with healthy participants and, most especially, with actual elder participants showed that our initial system could handle different configurations .
Revised:  Preliminary experiments with healthy participants , and notably, with actual elder participants showed that our initial system could handle different configurations .
Edit intent: clarity
Original:  Preliminary experiments with healthy participants and, most especially, with actual elder participants showed that our initial system could handle different configurations .
Revised:  Preliminary experiments with healthy participants and, most especially, with actual elder participants showed that our initial system showed the capability to handle different configurations .
Edit intent: clarity
Original:  Findings are likely to suggest the urgent need for multidisciplinary cooperation to wisely address engineering and ontological challenges gravitating around the smart grid concept.
Revised:  The findings are likely to suggest the urgent need for multidisciplinary cooperation to wisely address engineering and ontological challenges gravitating around the smart grid concept.
Edit intent: fluency
Original: Machine learning technologies are rapidly emerging in the Internet of Things (IoT) to provision smart serviceswith intelligence.
Revised: Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision smart serviceswith intelligence.
Edit intent: clarity
Original:  This review article moves beyond existing machine learning algorithms and cloud-driven design to investigate the less-explored  socio-technical and scaling aspects for consolidating machine learning and the IoT.
Revised:  This review article moves beyond existing ML algorithms and cloud-driven design to investigate the less-explored socio-technical and scaling aspects for consolidating machine learning and the IoT.
Edit intent: clarity
Original:  This review article moves beyond existing machine learning algorithms and cloud-driven design to investigate the less-explored  socio-technical and scaling aspects for consolidating machine learning and the IoT.
Revised:  This review article moves beyond existing machine learning algorithms and cloud-driven design to investigate the less-explored socio-technical aspects for consolidating machine learning and the IoT.
Edit intent: clarity
Original:  In particular, we provide an extensive coverage of the latest developments (up to 2020) on scaling and distributing machine learning across cloud, edge, and IoT devices.
Revised:  In particular, we provide an extensive coverage of the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices.
Edit intent: clarity
Original:  In addition, we derive a multi-layered framework to classify design considerations and illuminate multi-stakeholder concerns that shape the development of IoT smart services.
Revised:  With a multi-layered framework to classify design considerations and illuminate multi-stakeholder concerns that shape the development of IoT smart services.
Edit intent: coherence
Original:  To the best of our knowledge, this is the first comprehensive review to address the fundamental concerns of developing and deploying machine learning systems in the rising cloud-edge-device continuum in terms of functionality, business alignment and trustworthiness.
Revised:  To the best of our knowledge, this is the first comprehensive review to address the fundamental concerns of developing and deploying ML systems in the rising cloud-edge-device continuum in terms of functionality, business alignment and trustworthiness.
Edit intent: clarity
Original:  To the best of our knowledge, this is the first comprehensive review to address the fundamental concerns of developing and deploying machine learning systems in the rising cloud-edge-device continuum in terms of functionality, business alignment and trustworthiness.
Revised:  To the best of our knowledge, this is the first comprehensive review to address the fundamental concerns of developing and deploying machine learning systems in the rising cloud-edge-device continuum in terms of functionality, stakeholder alignment and trustworthiness.
Edit intent: clarity
Original: We consider the setting where players run the Hedge algorithm or its optimistic variant \mbox{ syrgkanis2015fast to play an n-action game repeatedly for T rounds.
Revised: We consider the setting where players run the Hedge algorithm or its optimistic variant to play an n-action game repeatedly for T rounds.
Edit intent: clarity
Original: The heterogeneous networks (HetNets) in 5G can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system.
Revised: 5G heterogeneous networks (HetNets) in 5G can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system.
Edit intent: clarity
Original: The heterogeneous networks (HetNets) in 5G can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system.
Revised: The heterogeneous networks (HetNets) can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system.
Edit intent: coherence
Original:  On the other hand, the current handover (HO) triggering mechanism - A3 event was only designed for mobility management in the macro system.
Revised:  The current handover (HO) triggering mechanism - A3 event was only designed for mobility management in the macro system.
Edit intent: coherence
Original:  On the other hand, the current handover (HO) triggering mechanism - A3 event was only designed for mobility management in the macro system.
Revised:  On the other hand, the current handover (HO) triggering mechanism A3 event was only designed for mobility management in the macro system.
Edit intent: fluency
Original:  On the other hand, the current handover (HO) triggering mechanism - A3 event was only designed for mobility management in the macro system.
Revised:  On the other hand, the current handover (HO) triggering mechanism - A3 event was designed only for mobility management in the macro system.
Edit intent: fluency
Original:  To implement A3 even directly in 5G-HetNets may cause degradation on the mobility robustness of user .
Revised:  Directly implementing A3 even directly in 5G-HetNets may cause degradation on the mobility robustness of user .
Edit intent: fluency
Original:  To implement A3 even directly in 5G-HetNets may cause degradation on the mobility robustness of user .
Revised:  To implement A3 in 5G-HetNets may cause degradation on the mobility robustness of user .
Edit intent: clarity
Original:  To implement A3 even directly in 5G-HetNets may cause degradation on the mobility robustness of user .
Revised:  To implement A3 even directly in 5G-HetNets may degrade the user mobility robustness .
Edit intent: clarity
Original:  Motivated by the concept of self-organisation networks (SON), this paper develops a self-optimisation triggering mechanism to enable automated network maintenance and enhance mobility robustness of user in 5G-HetNets.
Revised:  Motivated by the concept of self-organisation networks (SON), this study developed a self-optimised triggering mechanism to enable automated network maintenance and enhance mobility robustness of user in 5G-HetNets.
Edit intent: clarity
Original:  Motivated by the concept of self-organisation networks (SON), this paper develops a self-optimisation triggering mechanism to enable automated network maintenance and enhance mobility robustness of user in 5G-HetNets.
Revised:  Motivated by the concept of self-organisation networks (SON), this paper develops a self-optimisation triggering mechanism to enable automated network maintenance and enhance user mobility robustness in 5G-HetNets.
Edit intent: clarity
Original:  The proposed method integrates both advantages of subtractive clustering and Q-learning framework into the conventional fuzzy logic-based HO algorithm (FLHA).
Revised:  The proposed method integrates the advantages of subtractive clustering and Q-learning framework into the conventional fuzzy logic-based HO algorithm (FLHA).
Edit intent: clarity
Original:  The proposed method integrates both advantages of subtractive clustering and Q-learning framework into the conventional fuzzy logic-based HO algorithm (FLHA).
Revised:  The proposed method integrates both advantages of subtractive clustering and Q-learning frameworks into the conventional fuzzy logic-based HO algorithm (FLHA).
Edit intent: fluency
Original:  The subtractive clustering is first adopted to generate  membership function (MF) for FLHA, which enable FLHA with the self-configuration feature.
Revised:  Subtractive clustering is first adopted to generate membership function (MF) for FLHA, which enable FLHA with the self-configuration feature.
Edit intent: clarity
Original:  The subtractive clustering is first adopted to generate  membership function (MF) for FLHA, which enable FLHA with the self-configuration feature.
Revised:  The subtractive clustering is first adopted to generate a membership function (MF) for FLHA, which enable FLHA with the self-configuration feature.
Edit intent: fluency
Original:  The subtractive clustering is first adopted to generate  membership function (MF) for FLHA, which enable FLHA with the self-configuration feature.
Revised:  The subtractive clustering is first adopted to generate membership function (MF) for the FLHA to enable FLHA with the self-configuration feature.
Edit intent: clarity
Original:  Subsequently, the Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower FLHA with self-optimisation function.
Revised:  Subsequently, Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower FLHA with self-optimisation function.
Edit intent: coherence
Original:  Subsequently, the Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower FLHA with self-optimisation function.
Revised:  Subsequently, the Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower the FLHA with a self-optimisation function.
Edit intent: fluency
Original:  The FLHA with SON functionality also overcomes the limitation of conventional FLHA that it must rely heavily on professional experience to design.
Revised:  The FLHA with SON functionality also overcomes the limitations of the conventional FLHA that it must rely heavily on professional experience to design.
Edit intent: fluency
Original:  The FLHA with SON functionality also overcomes the limitation of conventional FLHA that it must rely heavily on professional experience to design.
Revised:  The FLHA with SON functionality also overcomes the limitation of conventional FLHA that must rely heavily on professional experience to design.
Edit intent: fluency
Original:  The simulation results show that the proposed self-optimisation FLHA can effectively generate MF and fuzzy rules for  FLHA.
Revised:  The simulation results show that the proposed self-optimised FLHA can effectively generate MF and fuzzy rules for FLHA.
Edit intent: fluency
Original:  The simulation results show that the proposed self-optimisation FLHA can effectively generate MF and fuzzy rules for  FLHA.
Revised:  The simulation results show that the proposed self-optimisation FLHA can effectively generate MF and fuzzy rules for the FLHA.
Edit intent: fluency
Original:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\%  and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Revised:  By comparing with conventional triggering mechanisms , the proposed approach can decease approximately 91\%, 49\% and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Edit intent: fluency
Original:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\%  and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Revised:  By comparing with conventional triggering mechanism , the proposed approach can decrease the HO, ping-pong HO, and HO failure ratios by approximately 91\%, 49\% and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Edit intent: clarity
Original:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\%  and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Revised:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\% , and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Edit intent: fluency
Original:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\%  and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Revised:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\% and 97.5\% while improving network throughput and latency by 8\% and 35\% in network throughput and latency respectively.
Edit intent: clarity
Original:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\%  and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% in network throughput and latency respectively.
Revised:  By comparing with conventional triggering mechanism , the proposed approach can decease approximately 91\%, 49\% and 97.5\% in HO ratio, ping-pong HO ratio and HO failure ratio while improving 8\% and 35\% , respectively.
Edit intent: clarity
Original:  Recently, Gagie et al. presented r-index,  an efficient compressed index on RLBWT whose space usage does not depend on text length .
Revised:  R-index Gagie et al. presented r-index, an efficient compressed index on RLBWT whose space usage does not depend on text length .
Edit intent: coherence
Original: Although a significant number of compressed indexes for highly repetitive strings have been proposed thus far, developing compressed indexes that support faster queries remains a challenge.
Revised: Although a significant number of compressed indexes for highly repetitive strings have been proposed thus far, developing compressed indexes that support various queries remains a challenge.
Edit intent: clarity
Original:  Finally, We present algorithms for computing queries on RLBWT by leveraging those two data structures in optimal time with O(r) words of space.
Revised:  Finally, We present algorithms for computing queries on RLBWT by leveraging those two data structures in optimal time and O(r) words of space.
Edit intent: coherence
Original:  Part of the challenge in working with EEG  is that features are exceptionally rich and need extensive pre-processing to isolate signals specific to text processing.
Revised:  Part of the challenge in working with EEG data is that features are exceptionally rich and need extensive pre-processing to isolate signals specific to text processing.
Edit intent: clarity
Original:  We employ the method on a publicly available EEG corpus and demonstrate that the pre-processed EEG features are capable of distinguishing two reading tasks  .
Revised:  We employ the method on a publicly available EEG corpus and demonstrate that the pre-processed EEG features are capable of distinguishing two reading tasks retrieved from a publicly available EEG corpus .
Edit intent: clarity
Original:  This improvement , however, is dependent on both the cognitive load of the task and the EEG frequency domain.
Revised:  This improvement depends on both the cognitive load of the task and the EEG frequency domain.
Edit intent: coherence
Original:  Hence, informing neural attention models through EEG signals has benefits but requires further investigation to understand which dimensions are  most useful across NLP tasks.
Revised:  Hence, informing neural attention models with EEG signals is beneficial but requires further investigation to understand which dimensions are most useful across NLP tasks.
Edit intent: coherence
Original:  Hence, informing neural attention models through EEG signals has benefits but requires further investigation to understand which dimensions are  most useful across NLP tasks.
Revised:  Hence, informing neural attention models through EEG signals has benefits but requires further investigation to understand which dimensions are the most useful across NLP tasks.
Edit intent: fluency
Original: The logarithmic nonlinearity has been used in many partial differential equations (PDEs) for modeling problems in different applications.
Revised: The logarithmic nonlinearity has been used in many partial differential equations (PDEs) for modeling problems in various applications.
Edit intent: clarity
Original: Due to the singularity of the logarithmic function, it introduces tremendous difficulties in establishing mathematical theories and in designing and analyzing numerical methods for PDEs with logarithmic nonlinearity.
Revised: Due to the singularity of the logarithmic function, it introducestremendous difficulties in establishing mathematical theories and in designing and analyzing numerical methods for PDEs with logarithmic nonlinearity.
Edit intent: fluency