wanyu/IteraTeR_v2 huggingface dataset:
Original:  Since lncRNAs have involved in a wide range of functions in cellular and developmental processes, an increasing number of methods or tools for distin-guishing lncRNAs from coding RNAs have been proposed .
Revised:  Since lncRNAs have involved in a wide range of functions in cellular and developmental processes, an increasing number of methods or tools for distin-guishing lncRNAs from coding RNAs .
Edit intent: clarity
Original:  However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identifica-tion . Different from lncRNAs in animal systems, plant lncRNAs have distinct characteristics.
Revised:  However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identification . Different from lncRNAs in animal systems, plant lncRNAs have distinct characteristics.
Edit intent: fluency
Original:  It is desira-ble to develop a computational method for accurate and rapid identification of plant lncRNAs.
Revised:  It is desirable to develop a computational method for accurate and rapid identification of plant lncRNAs.
Edit intent: fluency
Original:  It is desira-ble to develop a computational method for accurate and rapid identification of plant lncRNAs.
Revised:  It is desira-ble to develop a computational method for accurate and robust identification of plant lncRNAs.
Edit intent: clarity
Original:  Results: Herein, we present a plant lncRNA prediction approach PtLnc-BXE, which combines multiple sequence features in two steps to develop an ensemble mode .
Revised:  Results: Herein, we present a plant lncRNA identification method ItLnc-BXE, which utilizes multiple features and the ensemble learning strategy .
Edit intent: clarity
Original:  First, a diverse number of plants lncRNA features are collected and filtered by feature selection and subsequently used to represent RNA se-quences .
Revised:  First, a diversity of lncRNA features is collected and filtered by feature selection and subsequently used to represent RNA se-quences .
Edit intent: clarity
Original:  First, a diverse number of plants lncRNA features are collected and filtered by feature selection and subsequently used to represent RNA se-quences .
Revised:  First, a diverse number of plants lncRNA features are collected and filtered by feature selection to represent RNA se-quences .
Edit intent: coherence
Original:  First, a diverse number of plants lncRNA features are collected and filtered by feature selection and subsequently used to represent RNA se-quences .
Revised:  First, a diverse number of plants lncRNA features are collected and filtered by feature selection and subsequently used to represent RNA transcripts .
Edit intent: clarity
Original:  Then, the training dataset is sampled into several subsets using the bootstrapping technique, and base learners are constructed on data subsets by using XGBoost, and multiple base learners are further combined into a single meta-learner by using logistic regression.
Revised:  Then, several base learners are constructed on data subsets by using XGBoost, and multiple base learners are further combined into a single meta-learner by using logistic regression.
Edit intent: clarity
Original:  Then, the training dataset is sampled into several subsets using the bootstrapping technique, and base learners are constructed on data subsets by using XGBoost, and multiple base learners are further combined into a single meta-learner by using logistic regression.
Revised:  Then, the training dataset is sampled into several subsets using the bootstrapping technique, and base learners are trained and further combined into a single meta-learner by using logistic regression.
Edit intent: clarity
Original:  PtLnc-BXE outperformed other state-of-the-art plant lncRNA prediction methods, achieving higher AUC ( > 95.9\%)on the benchmark datasets.
Revised:  PtLnc-BXE outperformed other state-of-the-art plant lncRNA prediction methods, achieving better and robust performances (AUC > 95.9\%)on the benchmark datasets.
Edit intent: clarity
Original: By analyzing Bayesian inference of generative model for random networks with both relations (edges ) and node features (discrete labels), we perform an asymptotically exact analysis of the semi-supervised classfication problems on graph-structured data using the cavity method of statistical physics.
Revised: By analyzing Bayesian inference of generative model for random networks with both relations (edges ) and node features . Asymptotically exact analysis based on the Bayesian inference of the underlying model are conducted, using the cavity method of statistical physics.
Edit intent: clarity
Original: By analyzing Bayesian inference of generative model for random networks with both relations (edges ) and node features (discrete labels), we perform an asymptotically exact analysis of the semi-supervised classfication problems on graph-structured data using the cavity method of statistical physics.
Revised: By analyzing Bayesian inference of generative model for random networks with both relations (edges ) and node features (discrete labels), we perform an asymptotically exact analysis of the semi-supervised classfication problems on graph-structured data using the cavity method in statistical physics.
Edit intent: clarity
Original:  Our approach provides benchmark datasets with continuously tunable parameters and optimal results, which can be used to evaluate performance of exiting graph neural networks, and to find and understand their strengths and limitations .
Revised:  Our approach provides benchmark datasets with asymptotially exact properties and optimal solutions could be produced for the evaluation of graph convolution neural networks, and to find and understand their strengths and limitations .
Edit intent: clarity
Original:  Our approach provides benchmark datasets with continuously tunable parameters and optimal results, which can be used to evaluate performance of exiting graph neural networks, and to find and understand their strengths and limitations .
Revised:  Our approach provides benchmark datasets with continuously tunable parameters and optimal results, which can be used to evaluate performance of exiting graph neural networks, and for the theoretical understanding of their strengths and limitations .
Edit intent: clarity
Original:  Our approach provides benchmark datasets with continuously tunable parameters and optimal results, which can be used to evaluate performance of exiting graph neural networks, and to find and understand their strengths and limitations .
Revised:  Our approach provides benchmark datasets with continuously tunable parameters and optimal results, which can be used to evaluate performance of exiting graph neural networks, and to find and understand their strengths and weaknesses .
Edit intent: clarity
Original:  In particular,  we observe that popular GCNs have sparsity issue and ovefitting issue on large synthetic benchmarks, we also show how to overcome the issues by combining strengths of our approach .
Revised:  In particular, on these synthetic benchmark networks we observe that popular GCNs have sparsity issue and ovefitting issue on large synthetic benchmarks, we also show how to overcome the issues by combining strengths of our approach .
Edit intent: clarity
Original: In this paper, to effectively prevent the differential attack , we propose a novel Federated learning (FL) framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before being uploaded for aggregating, namely, noising before model aggregation FL (NbAFL).
Revised: In this paper, to effectively prevent information leakage , we propose a novel Federated learning (FL) framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before being uploaded for aggregating, namely, noising before model aggregation FL (NbAFL).
Edit intent: clarity
Original: In this paper, to effectively prevent the differential attack , we propose a novel Federated learning (FL) framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before being uploaded for aggregating, namely, noising before model aggregation FL (NbAFL).
Revised: In this paper, to effectively prevent the differential attack , we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before being uploaded for aggregating, namely, noising before model aggregation FL (NbAFL).
Edit intent: clarity
Original: In this paper, to effectively prevent the differential attack , we propose a novel Federated learning (FL) framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before being uploaded for aggregating, namely, noising before model aggregation FL (NbAFL).
Revised: In this paper, to effectively prevent the differential attack , we propose a novel Federated learning (FL) framework based on the concept of differential privacy (DP), in which artificial noises are added to the parameters at the clients side before aggregating, namely, noising before model aggregation FL (NbAFL).
Edit intent: clarity
Original:  First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting the variances of artificial noises.
Revised:  First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noises.
Edit intent: clarity
Original:  2) Increasing the number N of overall clients participating in FL can improve the convergence performance , given a fixed privacy protection level ;
Revised:  2) Given a fixed privacy protection level, increasing the number N of overall clients participating in FL can improve the convergence performance , given a fixed privacy protection level ;
Edit intent: fluency
Original:  2) Increasing the number N of overall clients participating in FL can improve the convergence performance , given a fixed privacy protection level ;
Revised:  2) Increasing the number N of overall clients participating in FL can improve the convergence performance ;
Edit intent: clarity
Original:  From our analysis, the K-random scheduling strategy can  retain the above three properties.
Revised:  From our analysis, the K-random scheduling strategy can also retain the above three properties.
Edit intent: clarity
Original:  More interestingly , we find that there is an optimal K that achieves the best convergence performance at a fixed privacy level.
Revised:  Moreover , we find that there is an optimal K that achieves the best convergence performance at a fixed privacy level.
Edit intent: coherence
Original:  The procedure preserves the standard lower bound on the makespan and allows to describe wide polynomially solvable subclasses of the problem . Conditions, describing these classes, allow building an optimal schedule in linear time.
Revised:  The procedure preserves the standard lower bound on the makespan and allows to describe wide polynomially solvable subclass of the problem . Conditions, describing these classes, allow building an optimal schedule in linear time.
Edit intent: fluency
Original:  For the differential operator we use a discretization which is based on a partial discrete analogue of the subdifferential.
Revised:  The differential operator is discretized based on a partial discrete analogue of the subdifferential.
Edit intent: clarity
Original:  In the system, the linearity is represented by multiplicity-annotated arrow types A \to_m B, where m denotes the multiplicity of the argument of a function .
Revised:  In the system, linearity is represented by multiplicity-annotated arrow types A \to_m B, where m denotes the multiplicity of the argument of a function .
Edit intent: fluency
Original:  In the system, the linearity is represented by multiplicity-annotated arrow types A \to_m B, where m denotes the multiplicity of the argument of a function .
Revised:  In the system, the linearity is represented by annotated arrow types A \to_m B, where m denotes the multiplicity of the argument of a function .
Edit intent: clarity
Original:  In the system, the linearity is represented by multiplicity-annotated arrow types A \to_m B, where m denotes the multiplicity of the argument of a function .
Revised:  In the system, the linearity is represented by multiplicity-annotated arrow types A \to_m B, where m denotes the multiplicity of the argument .
Edit intent: clarity
Original:  Thanks to the representation, existing non-linear code typechecks without changes and newly written linear code can be used with the existing non-linear code for many cases.
Revised:  Thanks to this representation, existing non-linear code typechecks without changes and newly written linear code can be used with the existing non-linear code for many cases.
Edit intent: fluency
Original:  Thanks to the representation, existing non-linear code typechecks without changes and newly written linear code can be used with the existing non-linear code for many cases.
Revised:  Thanks to the representation, existing non-linear code typechecks without changes and newly written linear code can be used with existing non-linear code for many cases.
Edit intent: fluency
Original:  Thanks to the representation, existing non-linear code typechecks without changes and newly written linear code can be used with the existing non-linear code for many cases.
Revised:  Thanks to the representation, existing non-linear code typechecks without changes and newly written linear code can be used with the existing non-linear code in many cases.
Edit intent: fluency
Original:  However, little is known for type inference for \lambda^q_\to.
Revised:  However, little is known about the type inference of \lambda^q_\to.
Edit intent: fluency
Original:  Although Linear Haskell equips type inference, its algorithm has not been formalized and often fails to infer principal types, especially for higher-order functions.
Revised:  Although the Linear Haskell implementation is equipped with type inference, its algorithm has not been formalized and often fails to infer principal types, especially for higher-order functions.
Edit intent: clarity
Original:  Although Linear Haskell equips type inference, its algorithm has not been formalized and often fails to infer principal types, especially for higher-order functions.
Revised:  Although Linear Haskell equips type inference, its algorithm has not been formalized , and the implementation often fails to infer principal types, especially for higher-order functions.
Edit intent: clarity
Original:  In this paper, based on OutsideIn(X) [Vytiniotis et al., 2011], we propose an inference system for a rank 1 qualified-typed variant of \lambda^q_\to, which can infer principal types.
Revised:  In this paper, based on OutsideIn(X) [Vytiniotis et al., 2011], we propose an inference system for a rank 1 qualified-typed variant of \lambda^q_\to, which infers principal types.
Edit intent: clarity
Original:  We address the ambiguity issue by quantifier elimination and demonstrate the effectiveness of the approach by examples.
Revised:  We address this ambiguity issue through quantifier elimination and demonstrate the effectiveness of the approach by examples.
Edit intent: fluency
Original:  We address the ambiguity issue by quantifier elimination and demonstrate the effectiveness of the approach by examples.
Revised:  We address the ambiguity issue by quantifier elimination and demonstrate the effectiveness of the approach with examples.
Edit intent: fluency
Original:  Two applications are performed using EmoAudioNet : automatic clinical depression recognition and continuous dimensional emotion recognition from speech .
Revised:  In the experimental results, two datasets are used: RECOLA for continuous dimensional emotion recognition from speech .
Edit intent: coherence
Original: Automatic emotions recognition and Major Depressive Disorder (MDD) diagnosis are inherently challenging problems in health informatics applications.
Revised: Intelligent monitoring systems and affective computing applications have emerged in recent years to enhance healthcare. Examples of these applications include assessment of affective states such as Major Depressive Disorder (MDD) diagnosis are inherently challenging problems in health informatics applications.
Edit intent: clarity
Original:  MDD is a permanent mood disorder where the patient constantly feels negative emotions (low Valence) and lacks excitement and interest (low Arousal).
Revised:  MDD is a permanent mood disorder where the patient constantly feels negative emotions (low Valence) and lack of interest (low Arousal).
Edit intent: clarity
Original:  Therefore, developing emotionally intelligent systems enhances depression assessment in the early stagesand improves the quality of service in depressed patients' care .
Revised:  High-performing intelligent systems would enhance MDD diagnosis in its early stages .
Edit intent: clarity
Original:  Moreover, we in-troduce a new parameter to control the pairwise difference between the corresponding symbols in addi-tion to a distance metric between the subsequences.
Revised:  Moreover, we introduce a new parameter to control the pairwise difference between the corresponding symbols in addi-tion to a distance metric between the subsequences.
Edit intent: fluency
Original:  Moreover, we in-troduce a new parameter to control the pairwise difference between the corresponding symbols in addi-tion to a distance metric between the subsequences.
Revised:  Moreover, we in-troduce a new parameter to control the pairwise difference between the corresponding symbols in addition to a distance metric between the subsequences.
Edit intent: fluency
Original:  Twin-Finder introduces a clone verification mechanism to formally verify if tow clone samples are indeed clones and a feedback loop to automatically generated formal rules to tune machine learning algorithm and further reduce the false positives.
Revised:  Twin-Finder introduces a clone verification mechanism to formally verify if two clone samples are indeed clones and a feedback loop to automatically generated formal rules to tune machine learning algorithm and further reduce the false positives.
Edit intent: fluency
Original:  However, prior works, such as machine learning based clone detection, may cause a considerable amount of false positives.
Revised:  However, prior works, such as machine learning-based clone detection, may cause a considerable amount of false positives.
Edit intent: fluency
Original:  Our experimental results show Twin-Finder that can swiftly identify up 9X more code clones comparing to conventional code clone detection approaches.
Revised:  Our experimental results show that Twin-Finder can swiftly identify up 9X more code clones comparing to conventional code clone detection approaches.
Edit intent: fluency
Original:  However, even when encryption is enabled, users leak information about the domains they visit via their DNS queries and via the Server Name Indication (SNI) extension of TLS.
Revised:  However, even when encryption is enabled, users leak information about the domains they visit via DNS queries and via the Server Name Indication (SNI) extension of TLS.
Edit intent: clarity
Original:  Two  proposals to ameliorate this issue are DNS over HTTPS/TLS (DoH/DoT) and Encrypted SNI (ESNI).
Revised:  Two recent proposals to ameliorate this issue are DNS over HTTPS/TLS (DoH/DoT) and Encrypted SNI (ESNI).
Edit intent: clarity
Original:  We quantify the privacy gain due to ESNI for different hosting and CDN providers using two different metrics, the k-anonymity degree due to co-hosting and the dynamics of IP address changes.
Revised:  We quantify the privacy gain offered by ESNI for different hosting and CDN providers using two different metrics, the k-anonymity degree due to co-hosting and the dynamics of IP address changes.
Edit intent: clarity
Original:  Our results show that 30\% will gain a high level of privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors will gain a high privacy level are far less popular, while visitors of popular sites will gain much less . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Revised:  On the other hand, 30\% will gain a high level of privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors will gain a high privacy level are far less popular, while visitors of popular sites will gain much less . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Edit intent: coherence
Original:  Our results show that 30\% will gain a high level of privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors will gain a high privacy level are far less popular, while visitors of popular sites will gain much less . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Revised:  Our results show that 30\% will gain a significant privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors will gain a high privacy level are far less popular, while visitors of popular sites will gain much less . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Edit intent: clarity
Original:  Our results show that 30\% will gain a high level of privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors will gain a high privacy level are far less popular, while visitors of popular sites will gain much less . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Revised:  Our results show that 30\% will gain a high level of privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors ' privacy will meaningfully improve are far less popular, while visitors of popular sites will gain much less . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Edit intent: clarity
Original:  Our results show that 30\% will gain a high level of privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors will gain a high privacy level are far less popular, while visitors of popular sites will gain much less . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Revised:  Our results show that 30\% will gain a high level of privacy benefit with a k value greater than 100, meaning that an adversary can correctly guess these domains with a probability less than 1\% . Domains whose visitors will gain a high privacy level are far less popular, while for popular domains the benefit is not significant . Analyzing the dynamics of IP addresses of long-lived domains, we find that only 7.7\% of them change their hosting IP addresses on a daily basis.
Edit intent: clarity