wanyu/IteraTeR_v2 huggingface dataset:
Original:  In the equilibrium, all countries in antagonism have to use all of their own power to counteract the received threats, and the threat made to each adversary only equalizes the threat received from that adversary.
Revised:  In the equilibrium, all countries in antagonism have to use all of their own power to counteract the received threats, and the "threats" made to each adversary only equalizes the threat received from that adversary.
Edit intent: clarity
Original:  In the equilibrium, all countries in antagonism have to use all of their own power to counteract the received threats, and the threat made to each adversary only equalizes the threat received from that adversary.
Revised:  In the equilibrium, all countries in antagonism have to use all of their own power to counteract the received threats, and the threat made to each adversary just balance out the threats received from that adversary.
Edit intent: clarity
Original:  This paper establishes necessary and sufficient conditions for countries' power in order for this equilibrium to exist in games on different signed graphs.
Revised:  This paper establishes conditions on different types of networked international environments in order for this equilibrium to exist in games on different signed graphs.
Edit intent: clarity
Original:  It also links the existence of this equilibrium in PAGs on structurally balanced graphs to the Hall's Maximum Matching problem and the Max Flow problem.
Revised:  It also links the existence of this type of equilibrium on structurally balanced graphs to the Hall's Maximum Matching problem and the Max Flow problem.
Edit intent: clarity
Original:  Given the data generated at edge nodes and a routing path, our goal is to determine the optimal data compression ratios and caching decisions across the network in order to minimize average latency, which can be shown to be equivalent to maximizing the compression and caching gain under an energy consumption constraint.
Revised:  Given the data generated at edge nodes and a routing path, our goal is to determine the optimal data compression ratios and caching decisions across the network in order to minimize average latency, which can be shown to be equivalent to maximizing the compression and caching gain under an energy consumption constraint.
Edit intent: fluency
Original:  The experimental results show that the deep structures have better performance than classical neural networks, especially for 12-month to 24-month prediction horizon 
Revised:  The experimental results show that the deep structures have better performance than classical neural networks, especially for 12-month to 24-month prediction horizon .
Edit intent: fluency
Original:  Cooper-- Zivn\'{y} classified the tractability of binary VCSP instances according to the concept of "triangle," and showed that the only interesting tractable case is the one induced by the joint winner property (JWP).
Revised:  Cooper and Zivn\'{y} classified the tractability of binary VCSP instances according to the concept of "triangle," and showed that the only interesting tractable case is the one induced by the joint winner property (JWP).
Edit intent: coherence
Original:  Recently, Iwamasa--Murota-- Zivn\'{y} made a link between VCSP and discrete convex analysis, showing that a function satisfying the JWP can be transformed into a function represented as the sum of two  M-convex functions, which can be minimized in polynomial time via an M-convex intersection algorithm if the value oracle of each M-convex function is given.
Revised:  Recently, Iwamasa, Murota, and Zivn\'{y} made a link between VCSP and discrete convex analysis, showing that a function satisfying the JWP can be transformed into a function represented as the sum of two M-convex functions, which can be minimized in polynomial time via an M-convex intersection algorithm if the value oracle of each M-convex function is given.
Edit intent: clarity
Original:  Under a natural condition, we solve this problem by devising a polynomial-time algorithm for obtaining a concrete form of the representation in the representable case.
Revised:  We solve this problem by devising a polynomial-time algorithm for obtaining a concrete form of the representation in the representable case.
Edit intent: coherence
Original:  In this paper, we propose a multi- layer edge computing framework called EdgeFlow.
Revised:  In this paper, we propose a multi-layer edge computing framework called EdgeFlow.
Edit intent: fluency
Original:  Evaluated on a mobile sensing scenario, EdgeFlow can significantly reduce task finish time and is more tolerant to run-time variation, compared to traditional cloud computingand the pure edge computing approach . Potential applications of EdgeFlow, including network function visualization, Internet of Things, and vehicular networks, are also discussed in the end of this work.
Revised:  Evaluated on a mobile sensing scenario, EdgeFlow can significantly reduce task finish time and perform more tolerance to run-time variation, compared to traditional cloud computingand the pure edge computing approach . Potential applications of EdgeFlow, including network function visualization, Internet of Things, and vehicular networks, are also discussed in the end of this work.
Edit intent: clarity
Original:  Evaluated on a mobile sensing scenario, EdgeFlow can significantly reduce task finish time and is more tolerant to run-time variation, compared to traditional cloud computingand the pure edge computing approach . Potential applications of EdgeFlow, including network function visualization, Internet of Things, and vehicular networks, are also discussed in the end of this work.
Revised:  Evaluated on a mobile sensing scenario, EdgeFlow can significantly reduce task finish time and is more tolerant to run-time variation, compared with the pure cloud computing, the pure edge computing approach . Potential applications of EdgeFlow, including network function visualization, Internet of Things, and vehicular networks, are also discussed in the end of this work.
Edit intent: clarity
Original:  Moreover, in this paper , we show that the tree structure can also act to refine user interests distribution, to benefit both training and prediction.
Revised:  Moreover, in this paper , we show that the tree structure can also be jointly learnt towards better compatible with user interests' distribution, to benefit both training and prediction.
Edit intent: clarity
Original:  Moreover, in this paper , we show that the tree structure can also act to refine user interests distribution, to benefit both training and prediction.
Revised:  Moreover, in this paper , we show that the tree structure can also act to refine user interests distribution, to facilitate both training and prediction.
Edit intent: clarity
Original:  The experimental results in both open dataset and Taobao display advertising dataset indicate that the proposed method outperforms existing methods .
Revised:  Experiments in two large-scale real-world datasets indicate that the proposed method outperforms existing methods .
Edit intent: clarity
Original: Model-based methods for recommender systems have been studied to provide more precise results .
Revised: Model-based methods for recommender systems have been studied extensively in recent years .
Edit intent: clarity
Original:  In systems with large corpus,  the amount of calculation for learnt model to predict all user-item pairs' preferences is tremendous, which makes the model difficultto be directly employed in recommendation candidate generation stage .
Revised:  In systems with large corpus, the learnt model to predict all user-item pairs' preferences is tremendous, which makes the model difficultto be directly employed in recommendation candidate generation stage .
Edit intent: clarity
Original:  In systems with large corpus,  the amount of calculation for learnt model to predict all user-item pairs' preferences is tremendous, which makes the model difficultto be directly employed in recommendation candidate generation stage .
Revised:  In systems with large corpus, the amount of calculation for learnt model to predict all user-item preferences is tremendous, which makes the model difficultto be directly employed in recommendation candidate generation stage .
Edit intent: clarity
Original:  To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user and item 's latent factorsas the preference) and index like hashing to perform efficient approximate k-nearest neighbor search .
Revised:  To overcome the calculation barriers, models such as matrix factorization resort to inner product form (i.e., use the inner product of user and item 's latent factorsas the preference) and index like hashing to perform efficient approximate k-nearest neighbor search .
Edit intent: clarity
Original:  To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user and item 's latent factorsas the preference) and index like hashing to perform efficient approximate k-nearest neighbor search .
Revised:  To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., model user-item preference as the inner product of user and item 's latent factorsas the preference) and index like hashing to perform efficient approximate k-nearest neighbor search .
Edit intent: clarity
Original:  To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user and item 's latent factorsas the preference) and index like hashing to perform efficient approximate k-nearest neighbor search .
Revised:  To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user , item latent factors) and indexes to facilitate efficient approximate k-nearest neighbor search .
Edit intent: clarity
Original:  To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user and item 's latent factorsas the preference) and index like hashing to perform efficient approximate k-nearest neighbor search .
Revised:  To overcome the calculation barrier, models like matrix factorization can resort to inner product form (i.e., use the inner product of user and item 's latent factorsas the preference) and index like hashing to perform efficient approximate k-nearest neighbor searches .
Edit intent: fluency
Original:  However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation .
Revised:  However, it still remains challenging to incorporate more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation .
Edit intent: clarity
Original:  However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation .
Revised:  However, other more expressive interaction forms between user and item features, e.g., interactions through deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation .
Edit intent: clarity
Original:  However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation .
Revised:  However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, because of the amount of calculation .
Edit intent: coherence
Original:  However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the amount of calculation .
Revised:  However, other more expressive interaction forms between user and item features, e.g., interactions through advanced deep neural networks, are still prevented from large corpus recommendation because of the calculation cost .
Edit intent: clarity
Original:  In this paper, we focus on the problem how arbitrary advanced models can be introduced to generate recommendations from large corpus.
Revised:  In this paper, we focus on the problem of introducing arbitrary advanced models can be introduced to generate recommendations from large corpus.
Edit intent: coherence
Original:  In this paper, we focus on the problem how arbitrary advanced models can be introduced to generate recommendations from large corpus.
Revised:  In this paper, we focus on the problem how arbitrary advanced models to recommender systems with large corpus.
Edit intent: clarity
Original:  We propose a novel tree-based method which can provide logarithmic complexity prediction w.r.t. corpus size  with more expressive  deep neural networks.
Revised:  We propose a novel tree-based method which can provide logarithmic complexity w.r.t. corpus size with more expressive deep neural networks.
Edit intent: clarity
Original:  We propose a novel tree-based method which can provide logarithmic complexity prediction w.r.t. corpus size  with more expressive  deep neural networks.
Revised:  We propose a novel tree-based method which can provide logarithmic complexity prediction w.r.t. corpus size even with more expressive deep neural networks.
Edit intent: clarity
Original:  We propose a novel tree-based method which can provide logarithmic complexity prediction w.r.t. corpus size  with more expressive  deep neural networks.
Revised:  We propose a novel tree-based method which can provide logarithmic complexity prediction w.r.t. corpus size with more expressive models such as deep neural networks.
Edit intent: clarity
Original:  The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes  top-down  and making decisions whether to pick up each node to user.
Revised:  Our main idea is to predict user interests coarse-to-fine, by traversing tree nodes top-down and making decisions whether to pick up each node to user.
Edit intent: clarity
Original:  The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes  top-down  and making decisions whether to pick up each node to user.
Revised:  The main idea of tree-based model is to predict user interests from coarse to fine by traversing tree nodes top-down and making decisions whether to pick up each node to user.
Edit intent: clarity
Original:  The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes  top-down  and making decisions whether to pick up each node to user.
Revised:  The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes in a top-down and making decisions whether to pick up each node to user.
Edit intent: clarity
Original:  The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes  top-down  and making decisions whether to pick up each node to user.
Revised:  The main idea of tree-based model is to predict user interests coarse-to-fine, by traversing tree nodes top-down fashion and making decisions whether to pick up each node to user.
Edit intent: clarity
Original:  Furthermore, we show that the tree structure can also be jointly learnt towards better compatible with user interests' distribution, to facilitate both training and prediction.
Revised:  Furthermore, we show that the tree structure can be jointly learnt towards better compatible with user interests' distribution, to facilitate both training and prediction.
Edit intent: clarity
Original:  Furthermore, we show that the tree structure can also be jointly learnt towards better compatible with user interests' distribution, to facilitate both training and prediction.
Revised:  Furthermore, we show that the tree structure can also be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction.
Edit intent: coherence
Original:  Experiments in two large-scale real-world datasets indicate that the proposed model significantly outperforms traditional methods.
Revised:  Experimental evaluations with two large-scale real-world datasets indicate that the proposed model significantly outperforms traditional methods.
Edit intent: clarity
Original:  Experiments in two large-scale real-world datasets indicate that the proposed model significantly outperforms traditional methods.
Revised:  Experiments in two large-scale real-world datasets show that the proposed model significantly outperforms traditional methods.
Edit intent: clarity
Original:  Experiments in two large-scale real-world datasets indicate that the proposed model significantly outperforms traditional methods.
Revised:  Experiments in two large-scale real-world datasets indicate that the proposed method significantly outperforms traditional methods.
Edit intent: clarity
Original:  And online A/B test results in Taobao display advertising platform prove the effectiveness of the tree-based deep model in production .
Revised:  Online A/B test results in Taobao display advertising platform prove the effectiveness of the tree-based deep model in production .
Edit intent: clarity
Original:  And online A/B test results in Taobao display advertising platform prove the effectiveness of the tree-based deep model in production .
Revised:  And online A/B test results in Taobao display advertising platform also demonstrate the effectiveness of the tree-based deep model in production .
Edit intent: clarity
Original:  And online A/B test results in Taobao display advertising platform prove the effectiveness of the tree-based deep model in production .
Revised:  And online A/B test results in Taobao display advertising platform prove the effectiveness of the proposed method in production environments .
Edit intent: clarity
Original:  Here we establish several natural properties of non-degenerate hyperplane codes , in terms of the polar complex {\it  of the code, a simplicial complex associated to any combinatorial code.
Revised:  Here we establish several natural properties of stable hyperplane codes in terms of the polar complex {\it of the code, a simplicial complex associated to any combinatorial code.
Edit intent: clarity
Original:  Here we establish several natural properties of non-degenerate hyperplane codes , in terms of the polar complex {\it  of the code, a simplicial complex associated to any combinatorial code.
Revised:  Here we establish several natural properties of non-degenerate hyperplane codes , in terms of the {\it of the code, a simplicial complex associated to any combinatorial code.
Edit intent: clarity
Original:  We prove that the polar complex of a non-degenerate hyperplane code is shellable and show that all currently known properties of the hyperplane codes follow from the shellability of the appropriate polar complex.
Revised:  We prove that the polar complex of a stable hyperplane code is shellable and show that all currently known properties of the hyperplane codes follow from the shellability of the appropriate polar complex.
Edit intent: clarity
Original:  We prove that the polar complex of a non-degenerate hyperplane code is shellable and show that all currently known properties of the hyperplane codes follow from the shellability of the appropriate polar complex.
Revised:  We prove that the polar complex of a non-degenerate hyperplane code is shellable and show that most currently known properties of the hyperplane codes follow from the shellability of the appropriate polar complex.
Edit intent: clarity
Original:  By doing so, the important role of indirect exposure remains unexplored.
Revised:  By doing so, the important role of indirect interactions remains unexplored.
Edit intent: clarity
Original:  In response, we develop a simple model that accounts for the effect of both direct and indirect exposure, which we deploy in the novel context of failure propagation within a real-world engineering project.
Revised:  In response, we develop a simple model that accounts for the effect of both direct and subsequent exposure, which we deploy in the novel context of failure propagation within a real-world engineering project.
Edit intent: clarity
Original:  We show that indirect exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure.
Revised:  We show that subsequent exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure.
Edit intent: clarity
Original:  In addition, we demonstrate the existence of hidden influentials in large-scale spreading events, and evaluate the role of direct and indirect exposure in their emergence.
Revised:  In addition, we demonstrate the existence of hidden influentials in large-scale spreading events, and evaluate the role of direct and subsequent exposure in their emergence.
Edit intent: clarity
Original:  Given the evidence of the importance of indirect exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.
Revised:  Given the evidence of the importance of subsequent exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.
Edit intent: clarity
Original:  Since the well-known Netflix Challenge, collaborative filtering (CF) has become the most popular and effective recommendation method.
Revised:  Since the well-known Netflix Challenge, collaborative filtering (CF) based methods have become the most popular and effective recommendation method.
Edit intent: clarity
Original:  However, they process different types of information separately, leading to information loss .
Revised:  However, they process different types of information separately, thus losing information that might exist across different types of side information .
Edit intent: clarity
Original:  In this work, we propose to utilize Heterogeneous Information Network (HIN), which is a natural and general representation of different types of data , to enhance CF-based recommending methods.
Revised:  In this work, we study the application of Heterogeneous Information Network (HIN), which is a natural and general representation of different types of data , to enhance CF-based recommending methods.
Edit intent: clarity
Original:  In this work, we propose to utilize Heterogeneous Information Network (HIN), which is a natural and general representation of different types of data , to enhance CF-based recommending methods.
Revised:  In this work, we propose to utilize Heterogeneous Information Network (HIN), which is a natural and general representation of different types of information , to enhance CF-based recommending methods.
Edit intent: fluency
Original:  HIN-based recommender systems face two problems: how to represent high-level semantics for recommendation and how to fuse the heterogeneous information to recommend .
Revised:  HIN-based recommender systems face two problems: how to represent high-level semantics for recommendation and how to fuse the heterogeneous information to support recommendation .
Edit intent: clarity