wanyu/IteraTeR_v2 huggingface dataset:
Original:  Performance analysis has been carried out based on the mathematical modeling in terms of closed-form expressions of the probability of non-zero secrecy multicast capacity , secure outage probability for multicasting , and ergodic secrecy multicast capacity . Capitalizing on those expressions, we analyze how the system parameters ( i.e.  fading, shadowing, the number of antennas, destination receivers, eavesdroppers, and relays ) affect the secrecy performance .
Revised:  Performance analysis has been carried out based on the mathematical modeling in terms of closed-form expressions of the probability of non-zero secrecy multicast capacity , secure outage probability for multicasting , and ergodic secrecy multicast capacity . Capitalizing on those expressions, we analyze how the system parameters ( i.e. fading, shadowing, the number of antennas, destination receivers, eavesdroppers, and relays , on the secrecy performance .
Edit intent: clarity
Original:  Our numerical results show that the detrimental impacts due to fading and shadowing can be remarkably mitigated using the well-known opportunistic relaying technique.
Revised:  Our numerical results show that the detrimental impacts caused by fading and shadowing can be remarkably mitigated using the well-known opportunistic relaying technique.
Edit intent: clarity
Original:  Moreover, the proposed model unifies secrecy analysis of several classical models, thereby exhibits enormous versatility than the existing works .
Revised:  Moreover, the proposed model unifies secrecy analysis of several classical models, thereby exhibiting enormous versatility than the existing works .
Edit intent: fluency
Original:  However, the exact tradeoff between fairness and accuracy with a real-valued target is not  clear.
Revised:  However, the exact tradeoff between fairness and accuracy with a real-valued target is not entirely clear.
Edit intent: clarity
Original:  Our lower bound is sharp, algorithm-independent, and admits a simple interpretation: when the moments of the target differ between groups, any fair algorithm has to make a large error on at least one of the groups.
Revised:  Our lower bound is sharp, algorithm-independent, and admits a simple interpretation: when the moments of the target differ between groups, any fair algorithm has to make an error on at least one of the groups.
Edit intent: clarity
Original: A critical task in 5G networks with heterogeneous services is spectrum slicing of the shared radio resources , through which each service gets performance guarantees.
Revised: Spectrum slicing of the shared radio resources is a critical task in 5G networks with heterogeneous services is spectrum slicing of the shared radio resources , through which each service gets performance guarantees.
Edit intent: clarity
Original: A critical task in 5G networks with heterogeneous services is spectrum slicing of the shared radio resources , through which each service gets performance guarantees.
Revised: A critical task in 5G networks with heterogeneous services , through which each service gets performance guarantees.
Edit intent: clarity
Original:  Two resource allocation strategies are considered, non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA).
Revised:  Two resource allocation strategies are compared: non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA).
Edit intent: clarity
Original:  However, these algorithms rely on a number of design tricks and many hyperparameters, making their applications to new domains difficult and computationally expensive.
Revised:  However, these algorithms rely on a number of design tricks and hyperparameters, making their applications to new domains difficult and computationally expensive.
Edit intent: clarity
Original:  However, these algorithms rely on a number of design tricks and many hyperparameters, making their applications to new domains difficult and computationally expensive.
Revised:  However, these algorithms rely on a number of design tricks and many hyperparameters, making their application to new domains difficult and computationally expensive.
Edit intent: fluency
Original:  Next, we introduce the public datasets and evaluation metrics used for personalized news recommendation.
Revised:  Next, we introduce the public datasets and evaluation methods for personalized news recommendation.
Edit intent: clarity
Original:  Finally, we raise several research directions that are worth investigating in  future.
Revised:  Finally, we raise several research directions that are worth investigating in the future.
Edit intent: fluency
Original:  Starting from a detailed description of the in-cylinder dynamics, the exergy balance is solved for each engineoperating point and , for the first time, static maps describing the availability transfer and destruction phenomena as a function of speed and load are derived .
Revised:  Starting from a detailed description of the in-cylinder dynamics, the exergy balance is solved for each engineoperating point and , for the first time, static maps describing the exergetic behavior of the engine as a function of speed and load are derived .
Edit intent: clarity
Original:  Starting from a detailed description of the in-cylinder dynamics, the exergy balance is solved for each engineoperating point and , for the first time, static maps describing the availability transfer and destruction phenomena as a function of speed and load are derived .
Revised:  Starting from a detailed description of the in-cylinder dynamics, the exergy balance is solved for each engineoperating point and , for the first time, static maps describing the availability transfer and destruction phenomena as a function of speed and load .
Edit intent: clarity
Original:  The application of the proposed modeling strategy , from the construction of the static maps to their usage, is shown for a military series hybrid electric vehicle .
Revised:  The application of the proposed modeling strategy is shown for a military series hybrid electric vehicle .
Edit intent: clarity
Original:  Ultimately, these static maps, while providing insightful information about inefficiencies over the whole operating field of the engine, are the enabling step for the development of exergy-based control strategies aiming at minimizing the overall operational losses of ground vehicles.
Revised:  Ultimately, the static maps, while providing insightful information about inefficiencies over the whole operating field of the engine, are the enabling step for the development of exergy-based control strategies aiming at minimizing the overall operational losses of ground vehicles.
Edit intent: fluency
Original: Estimation of the longitudinal slip ratio of tires is important in boosting the control performance of the vehicle under driving and braking conditions .
Revised: Autonomous vehicles are most concerned about safety control issues, and the slip ratio is critical to the safety of the vehicle under driving and braking conditions .
Edit intent: clarity
Original: Estimation of the longitudinal slip ratio of tires is important in boosting the control performance of the vehicle under driving and braking conditions .
Revised: Estimation of the longitudinal slip ratio of tires is important in boosting the control performance of the vehicle control system .
Edit intent: clarity
Original:  In this paper, the slip ratio is estimated using four machine learning algorithms (Neural Network , Gradient Boosting Machine, Random Forest  and Support Vector Machine)  based on the acceleration signals  from the tri-axial MEMS accelerometers utilized in the intelligent tire system  .
Revised:  In this paper, different machine learning algorithms (Neural Network , Gradient Boosting Machine, Random Forest and Support Vector Machine) based on the acceleration signals from the tri-axial MEMS accelerometers utilized in the intelligent tire system .
Edit intent: clarity
Original:  In this paper, the slip ratio is estimated using four machine learning algorithms (Neural Network , Gradient Boosting Machine, Random Forest  and Support Vector Machine)  based on the acceleration signals  from the tri-axial MEMS accelerometers utilized in the intelligent tire system  .
Revised:  In this paper, the slip ratio is estimated using four machine learning algorithms (Neural Networks , Gradient Boosting Machine, Random Forest and Support Vector Machine) based on the acceleration signals from the tri-axial MEMS accelerometers utilized in the intelligent tire system .
Edit intent: fluency
Original:  In this paper, the slip ratio is estimated using four machine learning algorithms (Neural Network , Gradient Boosting Machine, Random Forest  and Support Vector Machine)  based on the acceleration signals  from the tri-axial MEMS accelerometers utilized in the intelligent tire system  .
Revised:  In this paper, the slip ratio is estimated using four machine learning algorithms (Neural Network , Gradient Boosting Machine, Random Forest , and Support Vector Machine) based on the acceleration signals from the tri-axial MEMS accelerometers utilized in the intelligent tire system .
Edit intent: fluency
Original:  The experimental data  are collected through the MTS experimental platform.
Revised:  The experimental data used in this study are collected through the MTS experimental platform.
Edit intent: clarity
Original:  Among these techniques, GBM has amore stable results as it has %DIF > . The present study with the smallest output variance.
Revised:  Among %DIF > . The present study with the smallest output variance.
Edit intent: clarity
Original:  The present study with the fusion of intelligent tire system and machine learning algorithms paves the way for the accurate estimation of tire slip ratio , which is critical for the development of reliable vehicle control algorithms .
Revised:  The present study with the fusion of intelligent tire system and machine learning paves the way for the accurate estimation of tire slip ratio , which is critical for the development of reliable vehicle control algorithms .
Edit intent: fluency
Original:  However, the existing models do not explain the difference in outbreak profiles in countries with different intrinsic socio-cultural features and are rather specific for describing the complex dynamics of an outbreak.
Revised:  However, the existing models do not explain the apparent differences in outbreak profiles in countries with different intrinsic socio-cultural features and are rather specific for describing the complex dynamics of an outbreak.
Edit intent: clarity
Original:  However, the existing models do not explain the difference in outbreak profiles in countries with different intrinsic socio-cultural features and are rather specific for describing the complex dynamics of an outbreak.
Revised:  However, the existing models do not explain the difference in outbreak profiles between countries with different intrinsic socio-cultural features and are rather specific for describing the complex dynamics of an outbreak.
Edit intent: clarity
Original:  A system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress described by the tools of sociophysics8 with classical epidemic models.
Revised:  A system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress with classical epidemic models.
Edit intent: clarity
Original:  Even the combination of a dynamic SIR model with the classic triad of stages of  general adaptation syndrome, Alarm-Resistance-Exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Revised:  Even the combination of a dynamic SIR-type model with the classic triad of stages of general adaptation syndrome, Alarm-Resistance-Exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Edit intent: clarity
Original:  Even the combination of a dynamic SIR model with the classic triad of stages of  general adaptation syndrome, Alarm-Resistance-Exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Revised:  Even the combination of a dynamic SIR model with the classical triad of stages of general adaptation syndrome, Alarm-Resistance-Exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Edit intent: fluency
Original:  Even the combination of a dynamic SIR model with the classic triad of stages of  general adaptation syndrome, Alarm-Resistance-Exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Revised:  Even the combination of a dynamic SIR model with the classic triad of stages of the general adaptation syndrome, Alarm-Resistance-Exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Edit intent: fluency
Original:  Even the combination of a dynamic SIR model with the classic triad of stages of  general adaptation syndrome, Alarm-Resistance-Exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Revised:  Even the combination of a dynamic SIR model with the classic triad of stages of general adaptation syndrome, alarm-resistance-exhaustion , makes it possible to describe the available statistics for various countriesof the world with a high degree of accuracy.
Edit intent: fluency
Original:  Such characterization can further help in the development of management strategies specific to a particular society : country, region, or social group .
Revised:  Such characterization can further help in the development of strategies specific to a particular society : country, region, or social group .
Edit intent: clarity
Original:  Such characterization can further help in the development of management strategies specific to a particular society : country, region, or social group .
Revised:  Such characterization can further help in the development of management strategies specific to a particular society .
Edit intent: clarity
Original:  We compare our approach to direct optimization of  design using the learned surrogate, and to supervised learning of the synthesis problem.
Revised:  We compare our approach to direct optimization of the design using the learned surrogate, and to supervised learning of the synthesis problem.
Edit intent: fluency
Original: By encoding computing tasks, coded computing can not only mitigate straggling problems in federated learning (FL) , but also preserve privacy of sensitive data uploaded / contributed by participating mobile users (MUs) to the centralized server, owned by a mobile application provider (MAP) .
Revised: In this work, we propose a novel framework to address straggling and privacy issues for federated learning (FL) , but also preserve privacy of sensitive data uploaded / contributed by participating mobile users (MUs) to the centralized server, owned by a mobile application provider (MAP) .
Edit intent: clarity
Original: By encoding computing tasks, coded computing can not only mitigate straggling problems in federated learning (FL) , but also preserve privacy of sensitive data uploaded / contributed by participating mobile users (MUs) to the centralized server, owned by a mobile application provider (MAP) .
Revised: By encoding computing tasks, coded computing can not only mitigate straggling problems in federated learning (FL) , but also preserve privacy of sensitive data uploaded / communications resources at mobile users (MUs) to the centralized server, owned by a mobile application provider (MAP) .
Edit intent: clarity
Original: By encoding computing tasks, coded computing can not only mitigate straggling problems in federated learning (FL) , but also preserve privacy of sensitive data uploaded / contributed by participating mobile users (MUs) to the centralized server, owned by a mobile application provider (MAP) .
Revised: By encoding computing tasks, coded computing can not only mitigate straggling problems in federated learning (FL) , but also preserve privacy of sensitive data uploaded / contributed by participating mobile users (MUs) / mobile application provider (MAP) .
Edit intent: clarity
Original:  However, these advantages come with extra coding cost/complexity and communication overhead (referred to asprivacy cost ) that must be considered given the limited computing/communications resources at MUs/MAP ,  the rationality and incentive competition among MUs in contributing data to the MAP.
Revised:  However, these advantages come with extra coding cost/complexity and communication overhead (referred to as ) that must be considered given the limited computing/communications resources at MUs/MAP , the rationality and incentive competition among MUs in contributing data to the MAP.
Edit intent: clarity
Original:  However, these advantages come with extra coding cost/complexity and communication overhead (referred to asprivacy cost ) that must be considered given the limited computing/communications resources at MUs/MAP ,  the rationality and incentive competition among MUs in contributing data to the MAP.
Revised:  However, these advantages come with extra coding cost/complexity and communication overhead (referred to asprivacy cost , the rationality and incentive competition among MUs in contributing data to the MAP.
Edit intent: clarity
Original:  However, these advantages come with extra coding cost/complexity and communication overhead (referred to asprivacy cost ) that must be considered given the limited computing/communications resources at MUs/MAP ,  the rationality and incentive competition among MUs in contributing data to the MAP.
Revised:  However, these advantages come with extra coding cost/complexity and communication overhead (referred to asprivacy cost ) that must be considered given the limited computing/communications resources at MUs/MAP , privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP.
Edit intent: coherence
Original:  In particular , the MAP first determines a set of the best MUs for the FL process based on  MUs' provided information/features.
Revised:  In particular , the MAP first determines a set of the best MUs for the FL process based on the MUs' provided information/features.
Edit intent: fluency
Original:  To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging coded FL-based multiple utility functions under the MUs' privacy cost, the MAP's limited computing resource , and asymmetric information between the MAP and MUs.
Revised:  To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging FL-based multiple utility functions under the MUs' privacy cost, the MAP's limited computing resource , and asymmetric information between the MAP and MUs.
Edit intent: clarity
Original:  To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging coded FL-based multiple utility functions under the MUs' privacy cost, the MAP's limited computing resource , and asymmetric information between the MAP and MUs.
Revised:  To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging coded FL-based multiple utility functions . These utility functions account for the MUs' privacy cost, the MAP's limited computing resource , and asymmetric information between the MAP and MUs.
Edit intent: clarity
Original:  To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging coded FL-based multiple utility functions under the MUs' privacy cost, the MAP's limited computing resource , and asymmetric information between the MAP and MUs.
Revised:  To find the optimal contracts that can maximize utilities of the MAP and all the participating MUs while maintaining high learning quality of the whole system, we first develop a multi-principal one-agent contract-based problem leveraging coded FL-based multiple utility functions under the MUs' privacy cost, the MAP's limited computing resources , and asymmetric information between the MAP and MUs.
Edit intent: fluency
Original:  Then, we transform the problem into an equivalent low-complexity problem and develop an iterative algorithm to solve it . Experiments with a real-world dataset show that our framework can speed up training time up to 49\% and improve prediction accuracy up to 4.6 times while enhancing  network's social welfare, i.e., total utility of all participating entities, up to 114\% under the privacy cost consideration compared with those of baseline methods.
Revised:  Then, we transform the problem into an equivalent low-complexity problem and develop a light-weight iterative algorithm to solve it . Experiments with a real-world dataset show that our framework can speed up training time up to 49\% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare, i.e., total utility of all participating entities, up to 114\% under the privacy cost consideration compared with those of baseline methods.
Edit intent: clarity
Original:  Then, we transform the problem into an equivalent low-complexity problem and develop an iterative algorithm to solve it . Experiments with a real-world dataset show that our framework can speed up training time up to 49\% and improve prediction accuracy up to 4.6 times while enhancing  network's social welfare, i.e., total utility of all participating entities, up to 114\% under the privacy cost consideration compared with those of baseline methods.
Revised:  Then, we transform the problem into an equivalent low-complexity problem and develop an iterative algorithm to effectively find the optimal solutions . Experiments with a real-world dataset show that our framework can speed up training time up to 49\% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare, i.e., total utility of all participating entities, up to 114\% under the privacy cost consideration compared with those of baseline methods.
Edit intent: clarity
Original:  Then, we transform the problem into an equivalent low-complexity problem and develop an iterative algorithm to solve it . Experiments with a real-world dataset show that our framework can speed up training time up to 49\% and improve prediction accuracy up to 4.6 times while enhancing  network's social welfare, i.e., total utility of all participating entities, up to 114\% under the privacy cost consideration compared with those of baseline methods.
Revised:  Then, we transform the problem into an equivalent low-complexity problem and develop an iterative algorithm to solve it . Experiments with a real-world dataset show that our framework can speed up training time up to 49\% and improve prediction accuracy up to 4.6 times while enhancing the network's social welfare, i.e., total utility of all participating entities, up to 114\% under the privacy cost consideration compared with those of baseline methods.
Edit intent: fluency
Original:  However, for versatile robots nowadays that need to learn diverse tasks, providing and learning the multi-task demonstrations all at once are both difficult .
Revised:  However, they need multi-task demonstrations all at once are both difficult .
Edit intent: clarity