wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The Straits Times Index, Nikkei 225, NASDAQ Composite, S P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model. Three evaluation methods namely, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolution Deviation are used to compare the results of the implemented model .
Revised:  The Straits Times Index, Nikkei 225, NASDAQ Composite, S P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing purpose. Three statistical evaluation measures are used to compare the results of the implemented model .
Edit intent: clarity
Original:  Three evaluation methods namely, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolution Deviation are used to compare the results of the implemented model .
Revised:  Three evaluation methods namely, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolution Deviation are used to compare the results of the implemented models, namely the root mean squared error, the mean absolute percentage error and the mean absolution deviation .
Edit intent: coherence
Original:  The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance when compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models.
Revised:  The computational results obtained reveal that the URLanisms search trained feedforward neural network model exhibits outstanding predictive performance when compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models.
Edit intent: meaning-changed
Original:  The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance when compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models.
Revised:  The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models.
Edit intent: coherence
Original:  The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance when compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models. The new model is a promising predictive technique for solving high dimensional nonlinear time series data that are difficult to capture by traditional models.
Revised:  The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance when compared to the other models. However, the performance study shows that the three metaheuristics trained feedforward neural network models have promising predictive competence for solving problems of high dimensional nonlinear time series data that are difficult to capture by traditional models.
Edit intent: meaning-changed
Original: Internet-of-Things (IoT) companies strive to get feedback from users to improve their products and services . However, traditional surveys cannot reflect the actual conditions of customers' due to the limited questions. Besides, survey results are affected by various subjective factors. In contrast, the recorded usages of IoT devices reflect customers' behaviours more comprehensively and accurately. We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology.
Revised: Internet-of-Things (IoT) companies strive to get feedback from users to improve their products and services to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology.
Edit intent: coherence
Original:  We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology.
Revised:  We design an intelligent system to build a smart home system. To help manufacturers develop a smart home system, we design a federated learning (FL) system leveraging the reputation mechanism to assist home appliance manufacturers to train a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology.
Edit intent: meaning-changed
Original:  We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology.
Revised:  We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model based on customers' data. Then, manufacturers can predict customers' requirements and possible consumption behaviours with federated learning (FL) technology.
Edit intent: coherence
Original:  We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology. The FL consists of two stages: in the first stage, customers train the initial model using the phone and the edge computing server collaboratively.
Revised:  We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and consumption behaviors in the future. The working flow of the system includes two stages: in the first stage, customers train the initial model using the phone and the edge computing server collaboratively.
Edit intent: clarity
Original:  Customers first collect data from various IoT devices using phones, and then  download and train the initial model with their data. During the training, customers first extract features using their mobiles, and then add the Laplacian noise to the extracted features based on differential privacy, a formal and popular notion to quantify privacy. After achieving the local model , customers sign on their models respectively and send them to the blockchain.
Revised:  Customers first collect data from various IoT devices using phones, and then download and train the initial model with their local data. After deriving local models , customers sign on their models respectively and send them to the blockchain.
Edit intent: clarity
Original:  Besides, to attract more customers to participate in the crowdsourcing FL  , we design an incentive mechanism , which awards participants with coins that can be used to purchase other services provided by the company .
Revised:  To protect customers' privacy and improve the test accuracy, we enforce differential privacy on the extracted features and propose a new normalization technique. We experimentally demonstrate that our normalization technique outperforms batch normalization when features are under differential privacy protection. In addition, to attract more customers to participate in the crowdsourcing FL , we design an incentive mechanism , which awards participants with coins that can be used to purchase other services provided by the company .
Edit intent: meaning-changed
Original:  We present a mathematical and computational study suited to assess the structure--function issue . We treat a system of Jansen--Rit neural-mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project.
Revised:  We present a mathematical and computational study suited to assess the structure--function issue , treating a system of Jansen--Rit neural-mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project.
Edit intent: fluency
Original:  Direct simulations are used to determine the similarity of functional (inferred from correlated activity between brain areas ) and structural connectivity matrices as a function of the parameters controlling dynamics of a single node , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Revised:  Via direct simulations we determine the similarity of functional (inferred from correlated activity between brain areas ) and structural connectivity matrices as a function of the parameters controlling dynamics of a single node , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Edit intent: clarity
Original:  Direct simulations are used to determine the similarity of functional (inferred from correlated activity between brain areas ) and structural connectivity matrices as a function of the parameters controlling dynamics of a single node , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Revised:  Direct simulations are used to determine the similarity of functional (inferred from correlated activity between nodes ) and structural connectivity matrices as a function of the parameters controlling dynamics of a single node , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Edit intent: clarity
Original:  Direct simulations are used to determine the similarity of functional (inferred from correlated activity between brain areas ) and structural connectivity matrices as a function of the parameters controlling dynamics of a single node , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Revised:  Direct simulations are used to determine the similarity of functional (inferred from correlated activity between brain areas ) and structural connectivity matrices under variation of the parameters controlling dynamics of a single node , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Edit intent: clarity
Original:  Direct simulations are used to determine the similarity of functional (inferred from correlated activity between brain areas ) and structural connectivity matrices as a function of the parameters controlling dynamics of a single node , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Revised:  Direct simulations are used to determine the similarity of functional (inferred from correlated activity between brain areas ) and structural connectivity matrices as a function of the parameters controlling single-node dynamics , highlighting a non-trivial structure--function relationship in regimes that support limit cycle oscillations.
Edit intent: clarity
Original:  To determine their relationship, we firstly determine the network instabilities that give rise to oscillations, and the set of false bifurcationsthat occur beyond this onset.
Revised:  To determine their relationship, we firstly determine the network instabilities that give rise to oscillations, and the so-called `false bifurcations' (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset.
Edit intent: meaning-changed
Original:  In particular, we highlight that functional connectivity (FC) is inherited most robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, structure only weakly influences function .
Revised:  We highlight that functional connectivity (FC) is inherited most robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, structure only weakly influences function .
Edit intent: coherence
Original:  In particular, we highlight that functional connectivity (FC) is inherited most robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, structure only weakly influences function .
Revised:  In particular, we highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, structure only weakly influences function .
Edit intent: clarity
Original:  In particular, we highlight that functional connectivity (FC) is inherited most robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, structure only weakly influences function .
Revised:  In particular, we highlight that functional connectivity (FC) is inherited most robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, structure only weakly influences FC .
Edit intent: clarity
Original:  Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of the FC matrix can be predicted using a linear stability analysis.
Revised:  Secondly, we develop a weakly-coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of the FC matrix can be predicted using a linear stability analysis.
Edit intent: fluency
Original:  Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of the FC matrix can be predicted using a linear stability analysis.
Revised:  Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted using a linear stability analysis.
Edit intent: fluency
Original:  Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of the FC matrix can be predicted using a linear stability analysis.
Revised:  Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of the FC matrix can be predicted via linear stability analysis.
Edit intent: fluency
Original:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
Revised:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition . RGNN considers the biological topology among different brain regions to capture both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
Edit intent: meaning-changed
Original:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
Revised:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global relations among different EEG channels . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
Edit intent: clarity
Original:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
Revised:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
Edit intent: fluency
Original:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
Revised:  In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition , which is biologically supported and captures both local and global inter-channel relations . Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human URLanization.
Edit intent: clarity
Original:  In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to improve the robustness of our model against cross-subject EEG variations and noisy labels, respectively.
Revised:  In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively.
Edit intent: clarity
Original:  To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings.
Revised:  Extensive experiments on two public datasets : SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings.
Edit intent: clarity
Original:  To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings.
Revised:  To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets , SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings.
Edit intent: fluency
Original:  To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings.
Revised:  To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV , demonstrate the superior performance of our model than state-of-the-art BiHDM in most experimental settings.
Edit intent: clarity
Original:  To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings.
Revised:  To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV . Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art models in most experimental settings.
Edit intent: clarity
Original:  Our model analysis demonstrates that the proposed biologically supported adjacency matrix and two regularizers contribute consistent and significant gain to the performance . Investigations on the neuronal activities reveal that pre-frontal, parietal and occipital regions may be the most informative regions for emotion recognition, which is consistent with relevant prior studies.
Revised:  Our model analysis demonstrates that the proposed biologically supported adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal that pre-frontal, parietal and occipital regions may be the most informative regions for emotion recognition, which is consistent with relevant prior studies.
Edit intent: meaning-changed
Original:  Investigations on the neuronal activities reveal that pre-frontal, parietal and occipital regions may be the most informative regions for emotion recognition, which is consistent with relevant prior studies. In addition, experimental results suggest that global inter-channel relations between the left and right hemispheres are important for emotion recognition and local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may also provide useful information .
Revised:  Investigations on the neuronal activities reveal important brain regions and inter-channel relations between the left and right hemispheres are important for emotion recognition and local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may also provide useful information .
Edit intent: clarity