wanyu/IteraTeR_v2 huggingface dataset:
Original:  Current practical protocols for secure aggregation work in an `` honest but curious '' setting where a curious adversary observing all communication to and from the server cannot learn any private information assuming the server is honest and follows the protocol.
Revised:  Current practical protocols for secure aggregation work in an `` honest but curious " setting where a curious adversary observing all communication to and from the server cannot learn any private information assuming the server is honest and follows the protocol.
Edit intent: fluency
Original:  The recent papers by Cheu et al., and Balle et al. have given protocols for secure aggregation that achieve differential privacy guarantees in this ``shuffled model'' . Their protocols come at a cost, though: Either the expected aggregation error or the amount of communication per user scales as a polynomial n^{\Omega(1)} in the number of users n.
Revised:  The recent papers by Cheu et al., and Balle et al. have given protocols for secure aggregation that achieve differential privacy guarantees in this "shuffled model" . Their protocols come at a cost, though: Either the expected aggregation error or the amount of communication per user scales as a polynomial n^{\Omega(1)} in the number of users n.
Edit intent: fluency
Original:  Our new technique is a conceptual ``invisibility cloak'' that makes users' data almost indistinguishable from random noise while introducing zero distortion on the sum.
Revised:  Our new technique is a conceptual "invisibility cloak" that makes users' data almost indistinguishable from random noise while introducing zero distortion on the sum.
Edit intent: fluency
Original: Driven by the emergence of new compute-intensive applications and the vision of IoT, the 5G network will face an unprecedented increase in traffic volume and computation demands.
Revised: Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands.
Edit intent: clarity
Original:  However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has  become a natural concern.
Revised:  However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern.
Edit intent: clarity
Original:  Mobile edge computing (MEC)  can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network  in close proximity to mobile users, and offer context-aware services .
Revised:  Mobile edge computing (MEC) can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud computing capabilities within the radio access network in close proximity to mobile users, and offer context-aware services .
Edit intent: fluency
Original:  Mobile edge computing (MEC)  can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network  in close proximity to mobile users, and offer context-aware services .
Revised:  Mobile edge computing (MEC) can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services .
Edit intent: clarity
Original:  5G is broadly characterized by three service types: extremely high data rate, massive connectivity, and low latency and ultra-high reliability.
Edit intent: coherence
Original:  Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technologies .
Revised:  Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts .
Edit intent: clarity
Original:  The successful realization of MEC in  5G  is still in its infancy and demands for constant efforts from both academic and industry communities.
Revised:  The successful realization of MEC in the 5G is still in its infancy and demands for constant efforts from both academic and industry communities.
Edit intent: fluency
Original:  In this survey, we first provide a holistic overview of MEC , and potential use cases and applications.
Revised:  In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications.
Edit intent: clarity
Original:  The main part of this paper surveys up-to-date researches on the integration of MEC with  5G and beyond technologies including non-orthogonal multiple access, wireless power transfer and energy harvesting, unmanned aerial vehicle, IoT, network densification, and machine learning.
Revised:  Then, we outline up-to-date researches on the integration of MEC with 5G and beyond technologies including non-orthogonal multiple access, wireless power transfer and energy harvesting, unmanned aerial vehicle, IoT, network densification, and machine learning.
Edit intent: clarity
Original:  We further summarize lessons learned from state-of-the-art researches as well as discuss challenges and potential future directions for MEC research.
Revised:  We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.
Edit intent: clarity
Original:  B-rep  model editing plays an essential role in CAD and motivates the very recent direct modeling CAD paradigm, which features intuitive push-pull manipulation of the model geometry.
Revised:  B-rep ) model editing plays an essential role in CAD and motivates the very recent direct modeling CAD paradigm, which features intuitive push-pull manipulation of the model geometry.
Edit intent: fluency
Original:  B-rep  model editing plays an essential role in CAD and motivates the very recent direct modeling CAD paradigm, which features intuitive push-pull manipulation of the model geometry.
Revised:  B-rep model editing plays an essential role in computer-aided design and motivates the very recent direct modeling CAD paradigm, which features intuitive push-pull manipulation of the model geometry.
Edit intent: clarity
Original:  B-rep  model editing plays an essential role in CAD and motivates the very recent direct modeling CAD paradigm, which features intuitive push-pull manipulation of the model geometry.
Revised:  B-rep model editing plays an essential role in CAD and motivates the very recent direct modeling paradigm, which features intuitive push-pull manipulation of the model geometry.
Edit intent: fluency
Original:  Boundary faces in a B-rep model could be connected in a smooth manner, typically G1 continuous, for functional, manufacturing or aesthetic reasons.
Revised:  In mechanical design, a substantial part of B-rep model could be connected in a smooth manner, typically G1 continuous, for functional, manufacturing or aesthetic reasons.
Edit intent: clarity
Original:  This paper proposes a novel reverse tracking method to solve the above challenges, and then, based on it, presents a systematic method for push-pull direct modeling while preserving G1 connections.
Revised:  This paper proposes a novel reverse detection method to solve the above challenges, and then, based on it, presents a systematic method for push-pull direct modeling while preserving G1 connections.
Edit intent: clarity
Original:  This paper proposes a novel reverse tracking method to solve the above challenges, and then, based on it, presents a systematic method for push-pull direct modeling while preserving G1 connections.
Revised:  This paper proposes a novel reverse tracking method to solve the challenge and then, based on it, presents a systematic method for push-pull direct modeling while preserving G1 connections.
Edit intent: coherence
Original:  This paper proposes a novel reverse tracking method to solve the above challenges, and then, based on it, presents a systematic method for push-pull direct modeling while preserving G1 connections.
Revised:  This paper proposes a novel reverse tracking method to solve the above challenges, and then, based on it, presents a robust method for push-pull direct modeling while preserving G1 connections.
Edit intent: clarity
Original:  Empirically, we assess our approach over the Adult and Bank  dataset, and show that the coreset sizes are much smaller than the full dataset ;
Revised:  Empirically, we assess our approach over the Adult dataset, and show that the coreset sizes are much smaller than the full dataset ;
Edit intent: coherence
Original:  Empirically, we assess our approach over the Adult and Bank  dataset, and show that the coreset sizes are much smaller than the full dataset ;
Revised:  Empirically, we assess our approach over the Adult and Bank, dataset, and show that the coreset sizes are much smaller than the full dataset ;
Edit intent: fluency
Original:  Empirically, we assess our approach over the Adult, Bank and Diabetes  dataset, and show that the coreset sizes are much smaller than the full dataset.
Revised:  Empirically, we assess our approach over the Adult, Bank , Diabetes dataset, and show that the coreset sizes are much smaller than the full dataset.
Edit intent: fluency
Original:  Empirically, we assess our approach over the Adult, Bank and Diabetes  dataset, and show that the coreset sizes are much smaller than the full dataset.
Revised:  Empirically, we assess our approach over the Adult, Bank and Diabetes and dataset, and show that the coreset sizes are much smaller than the full dataset.
Edit intent: fluency
Original:  We also achieve a speed-up to recent fair clustering algorithms [5,6] on a large datasetCensus1990 by incorporating our coreset construction.
Revised:  We also achieve a speed-up to recent fair clustering algorithms [5,6] Census1990 by incorporating our coreset construction.
Edit intent: clarity
Original:  We also achieve a speed-up to recent fair clustering algorithms [5,6] on a large datasetCensus1990 by incorporating our coreset construction.
Revised:  We also achieve a speed-up to recent fair clustering algorithms [5,6] on a large dataset by incorporating our coreset construction.
Edit intent: coherence
Original:  Our extensive experiments show that the proposed approach reduces  system-wide energy consumption by over 36 \% on average and up to 70\%.
Revised:  Our extensive experiments show that the proposed approach reduces the system-wide energy consumption by over 36 \% on average and up to 70\%.
Edit intent: fluency
Original:  This translates to an over 17 \% improvement on energy-efficiency over a state-of-the-art \\%DIF > improvement on energy-efficiency over a state-of-the-art event-based web browser scheduler, but with significantly fewer violations on the quality of service.
Revised:  This translates to an over 17 \\%DIF > improvement on energy-efficiency over a state-of-the-art event-based web browser scheduler, but with significantly fewer violations on the quality of service.
Edit intent: fluency
Original:  This method is tested against a case of turbulent non-isothermal mixing in  T-junction pipe, a common flow arrangement found in nuclear reactor cooling systems.
Revised:  This method is tested against a case of turbulent non-isothermal mixing in a T-junction pipe, a common flow arrangement found in nuclear reactor cooling systems.
Edit intent: fluency
Original:  This method is tested against a case of turbulent non-isothermal mixing in  T-junction pipe, a common flow arrangement found in nuclear reactor cooling systems.
Revised:  This method is tested against a case of turbulent non-isothermal mixing in T-junction pipe, a common ow arrangement found in nuclear reactor cooling systems.
Edit intent: fluency
Original:  Two full-order turbulent modelling techniques, the Large Eddy Simulation (LES) and the Unsteady Reynolds Averaged Navier-Stokes Equations (URANS) are compared showing that the former requires fewer modes for the eddy viscosity field.
Edit intent: coherence
Original: GCN is a recent effective algorithm which effectively learns a function incorporate both graph structure and node features for semisupervised graph based node classification.
Revised: Attributed network embedding aims to learn low-dimensional node representations from both network structure and node features for semisupervised graph based node classification.
Edit intent: clarity
Original:  The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus fully resemble the Universal Turing Machine or Von Neumann Architecture .
Revised:  The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus resemble the Universal Turing Machine or Von Neumann Architecture .
Edit intent: clarity
Original:  The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus fully resemble the Universal Turing Machine or Von Neumann Architecture .
Revised:  The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus fully resemble the Universal Turing Machine .
Edit intent: clarity
Original:  In this work, we propose a new method for measuring the level of engagement in conversations and users from WhatsApp groups, by using temporal interaction networks and without reading the messages.
Revised:  In this work, we present a framework for measuring the level of engagement in conversations and users from WhatsApp groups, by using temporal interaction networks and without reading the messages.
Edit intent: clarity
Original:  In this work, we propose a new method for measuring the level of engagement in conversations and users from WhatsApp groups, by using temporal interaction networks and without reading the messages.
Revised:  In this work, we propose a new method for measuring the level of engagement of group conversations and users from WhatsApp groups, by using temporal interaction networks and without reading the messages.
Edit intent: clarity
Original:  In this work, we propose a new method for measuring the level of engagement in conversations and users from WhatsApp groups, by using temporal interaction networks and without reading the messages.
Revised:  In this work, we propose a new method for measuring the level of engagement in conversations and users , without reading the messages.
Edit intent: coherence
Original:  In this way, we use network measurements to build an Engagement Index (EI) for fractions of the conversations .
Revised:  In this way, we use network measurements to build an Engagement Index (EI) for each interval of conversations to assess users' participation .
Edit intent: clarity
Original:  Our results in five  real-world WGs data indicate thatthe EI is able to identify different type of conversations, and users' behaviors according to each category, as well as anomalies on users' engagement .
Revised:  Our results in five datasets from real-world WGs data indicate thatthe EI is able to identify different type of conversations, and users' behaviors according to each category, as well as anomalies on users' engagement .
Edit intent: clarity
Original:  Our results in five  real-world WGs data indicate thatthe EI is able to identify different type of conversations, and users' behaviors according to each category, as well as anomalies on users' engagement .
Revised:  Our results in five real-world WhatsApp Groups indicate that, based on the EI, it is possible to identify the most engaged users within a time interval, create rankings, and group users according to their engagement and monitor their performance over time .
Edit intent: clarity
Original: Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyze data for both classification and regression, whose usual  complexity scales polynomially with the dimension and number of data points.
Revised: Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyze data for both classification and regression, whose usual complexity scales polynomially with the dimension of data space and the number of data points.
Edit intent: clarity
Original:  Inspired by the quantum SVM, we present a quantum-inspired classical algorithm for SVM using fast sampling techniques.
Revised:  Inspired by quantum SVM, we present a quantum-inspired classical algorithm for SVM using fast sampling techniques.
Edit intent: fluency
Original:  In our approach, we develop a general method to approximately calculate the kernel function and make classification via carefully sampling the data matrix, thus our approach can be applied to various types of SVM, such as linear SVM, poly-kernel SVM and soft SVM.
Revised:  In our approach, we develop a general method to approximately calculate the kernel function and make classification via carefully sampling the data matrix, thus our approach can be applied to various types of SVM, such as linear SVM, non-linear SVM and soft SVM.
Edit intent: clarity
Original:  Theoretical analysis shows one can find the supported hyperplanes on a data set which we have sampling access, and thus make classification with arbitrary success probability in logarithmic runtime  , matching the runtime of the quantum SVM.
Revised:  Theoretical analysis shows one can make classification with arbitrary success probability in logarithmic runtime , matching the runtime of the quantum SVM.
Edit intent: coherence
Original: Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyze data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points.
Revised: Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points.
Edit intent: fluency
Original:  Inspired by quantum SVM , we present a quantum-inspired classical algorithm for SVM using fast sampling techniques .
Revised:  Inspired by quantum SVM , we present a quantum-inspired classical algorithm for LS-SVM .
Edit intent: clarity
Original:  Our approach can be applied to various types of SVM, such as linear SVM, non-linear SVM and soft SVM . Theoretical analysis shows one can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points  , matching the runtime of the quantum SVM.
Revised:  Our approach can be applied to various types of SVM, such as linear SVM, non-linear SVM and soft SVM . Theoretical analysis shows our algorithm can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points , matching the runtime of the quantum SVM.
Edit intent: clarity
Original: Predicting the popularity of online content in social network is an important problem for the practice of information dissemination, advertising, and recommendation . Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction.
Revised: Predicting the popularity of online content on social platforms is an important problem for the practice of information dissemination, advertising, and recommendation . Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction.
Edit intent: clarity
Original: Predicting the popularity of online content in social network is an important problem for the practice of information dissemination, advertising, and recommendation . Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction.
Revised: Predicting the popularity of online content in social network is an important task for both researchers and practitioners . Previous methods mainly leverage demographics, temporal and structural patterns of early adopters for popularity prediction.
Edit intent: clarity
Original:  Consequently, they fail to capture the cascading effect triggered by early adopters in social networks, and thus have limited predictive power .
Revised:  Consequently, they fail to capture the cascading effect in information diffusion, in which early adopters try to activate potential users along the underlying network .
Edit intent: clarity
Original:  In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks among users for popularity prediction.
Revised:  In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction.
Edit intent: clarity
Original:  We propose a novel method, namely Coupled-GNNs, which use two coupled graph neural networks to capture the cascading effect in information diffusion.
Revised:  We propose a novel method, namely CoupledGNN, which uses two coupled graph neural networks to capture the cascading effect in information diffusion.
Edit intent: fluency
Original:  Through such an iterative aggregation of the neighborhood, the proposed method naturally captures the cascading effect of information diffusion in social networks . Experiments conducted on both synthetic data and real-world Sina Weibo data demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction.
Revised:  Through such an iterative aggregation of the neighborhood, the proposed method naturally captures the cascading effect along the network in a successive manner . Experiments conducted on both synthetic data and real-world Sina Weibo data demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction.
Edit intent: clarity
Original:  Through such an iterative aggregation of the neighborhood, the proposed method naturally captures the cascading effect of information diffusion in social networks . Experiments conducted on both synthetic data and real-world Sina Weibo data demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction.
Revised:  Through such an iterative aggregation of the neighborhood, the proposed method naturally captures the cascading effect of information diffusion in social networks . Experiments conducted on both synthetic and real-world Sina Weibo data demonstrate that our method significantly outperforms the state-of-the-art methods for popularity prediction.
Edit intent: fluency