wanyu/IteraTeR_v2 huggingface dataset:
Original:  We test our algorithms on empirical network data to quantify the trade-off between the cost of obtaining more refined network information , and the benefit of the added information for guiding improved seeding policies.
Revised:  We test our algorithms on empirical network data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding policies.
Edit intent: fluency
Original: We study the choice of k nodes in a social network  to seed a diffusion with maximum expected spread size  . Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, while assuming knowledge of the entire network.
Revised: We study the task of selecting k nodes in a social network to seed a diffusion with maximum expected spread size . Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, while assuming knowledge of the entire network.
Edit intent: clarity
Original: We study the choice of k nodes in a social network  to seed a diffusion with maximum expected spread size  . Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, while assuming knowledge of the entire network.
Revised: We study the choice of k nodes in a social network to seed a diffusion with maximum expected spread size . Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed set with provable guarantees, while assuming knowledge of the entire network.
Edit intent: fluency
Original: We study the choice of k nodes in a social network  to seed a diffusion with maximum expected spread size  . Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, while assuming knowledge of the entire network.
Revised: We study the choice of k nodes in a social network to seed a diffusion with maximum expected spread size . Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, given the knowledge of the entire network.
Edit intent: coherence
Original:  We test our algorithms on empirical network data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding policies .
Revised:  We test our algorithms on real world data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding policies .
Edit intent: clarity
Original:  We test our algorithms on empirical network data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding policies .
Revised:  We test our algorithms on empirical network data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding strategies .
Edit intent: clarity
Original: Large scale simulations are a key pillar of modern research and require ever increasing computational resources.
Revised: Large scale simulations are a key pillar of modern research and require ever-increasing computational resources.
Edit intent: fluency
Original:  Finally, we present the strategies used for implementation and the challenges encountered  maximizing performance.
Revised:  Finally, we present the implementation strategies used and the challenges encountered maximizing performance.
Edit intent: clarity
Original:  Finally, we present the strategies used for implementation and the challenges encountered  maximizing performance.
Revised:  Finally, we present the strategies used for implementation and the challenges encountered in maximizing performance.
Edit intent: fluency
Original: One of the key features of modern blockchain platforms is the possibility of executing smart contracts, i.e. computer programs that transfer digital assets between users, according to pre-agreed rules.
Revised: The development of blockchain technologies has enabled the trustless execution of so-called smart contracts, i.e. computer programs that transfer digital assets between users, according to pre-agreed rules.
Edit intent: clarity
Original:  Crucially, the execution of smart contracts must be correct even in the presence of (a minority of) adversaries in the peer-to-peer network that maintains the blockchain.
Revised:  Crucially, the execution of smart contracts is collaboratively maintained by a peer-to-peer network that maintains the blockchain.
Edit intent: clarity
Original:  Then, to compute the new state of the smart contracts, they put these transactions in sequence (in an arbitrary order ), and execute them serially .
Revised:  Then, to compute the new state of the smart contracts, they put these transactions in sequence (in an arbitrary order ), and execute them in some order .
Edit intent: clarity
Original:  Once this block of transactions is appended to the blockchain, the other nodes of the network validate it, by re-executing the transactions in the same order.
Revised:  Once this sequence of transactions is appended to the blockchain, the other nodes of the network validate it, by re-executing the transactions in the same order.
Edit intent: clarity
Original:  Once this block of transactions is appended to the blockchain, the other nodes of the network validate it, by re-executing the transactions in the same order.
Revised:  Once this block of transactions is appended to the blockchain, the other nodes validate it, by re-executing the transactions in the same order.
Edit intent: clarity
Original:  Once this block of transactions is appended to the blockchain, the other nodes of the network validate it, by re-executing the transactions in the same order.
Revised:  Once this block of transactions is appended to the blockchain, the other nodes of the network validate it, re-executing the transactions in the same order.
Edit intent: clarity
Original:  The serial execution of transactions does not take advantage of the multi-core architecture of modern processors, so contributing to limit the throughput of blockchains .
Revised:  The serial execution of transactions does not take advantage of the multi-core architecture of modern processors, so contributing to limit the throughput .
Edit intent: clarity
Original:  In this paper we devise a static analysis technique for parallelizing the execution of transactions , in a formal setting based on Ethereum, the most widespread platform for smart contracts .
Revised:  In this paper we devise a static analysis technique for parallelizing the execution of transactions .
Edit intent: coherence
Original: Cortical systems such as the visual cortex exhibit behaviors that change according to behavioral and stimulus context.
Revised: Experimental evidence suggests that the computational state of cortical systems change according to behavioral and stimulus context.
Edit intent: clarity
Original:  It is still unknown what mechanisms underlie this adaptive processing in cortical circuitry.
Revised:  However, it is still unknown what mechanisms underlie this adaptive processing in cortical circuitry.
Edit intent: coherence
Original: Knowledge graphs capture interlinked information between entities and they represent an attractive source of structured information that can be harnessed for recommender systems.
Revised: Knowledge graphs capture structured information and relations between a set of entities or items. As such they represent an attractive source of structured information that can be harnessed for recommender systems.
Edit intent: clarity
Original: Knowledge graphs capture interlinked information between entities and they represent an attractive source of structured information that can be harnessed for recommender systems.
Revised: Knowledge graphs capture interlinked information between entities and they represent an attractive source of information that could help improve recommender systems.
Edit intent: clarity
Original:  However , existing recommender engines use knowledge graphs by manually designing features, do not allow for end-to-end training , or provide poor scalability .
Revised:  However existing approaches in this domain rely on manual feature engineering and do not allow for end-to-end training , or provide poor scalability .
Edit intent: clarity
Original:  However , existing recommender engines use knowledge graphs by manually designing features, do not allow for end-to-end training , or provide poor scalability .
Revised:  However , existing recommender engines use knowledge graphs by manually designing features, do not allow for end-to-end training .
Edit intent: clarity
Original:  Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user and then transforming the knowledge graph into a user-specific weighted graph .
Revised:  Conceptually, our approach computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user and then transforming the knowledge graph into a user-specific weighted graph .
Edit intent: clarity
Original:  Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user and then transforming the knowledge graph into a user-specific weighted graph .
Revised:  Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relationships for a given user and then transforming the knowledge graph into a user-specific weighted graph .
Edit intent: fluency
Original:  Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user and then transforming the knowledge graph into a user-specific weighted graph .
Revised:  Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user . This way we transform the knowledge graph into a user-specific weighted graph .
Edit intent: coherence
Original:  Moreover, to provide better inductive bias KGCN uses label smoothness(LS), which provides regularization over edge weights and we prove that it is equivalent to  label propagation scheme on a graph.
Revised:  Moreover, to provide better inductive bias KGCN uses label smoothness(LS), which provides regularization over edge weights and we prove that it is equivalent to a label propagation scheme on a graph.
Edit intent: fluency
Original:  KGCN-LS also achieves great performance in sparse scenarios and is highly scalable with respect to the knowledge graph size .
Revised:  It also achieves strong performance in the scenario where user-item interactions are sparse .
Edit intent: clarity
Original:  However  existing approaches in this domain rely on manual feature engineering and do not allow for  end-to-end training.
Revised:  However , existing approaches in this domain rely on manual feature engineering and do not allow for end-to-end training.
Edit intent: coherence
Original:  However  existing approaches in this domain rely on manual feature engineering and do not allow for  end-to-end training.
Revised:  However existing approaches in this domain rely on manual feature engineering and do not allow for an end-to-end training.
Edit intent: fluency
Original:  Here we propose knowledge-aware graph neural networks with label smoothness regularization to provide better recommendations.
Revised:  Here we propose Knowledge-aware Graph Neural Networks with Label Smoothness regularization (KGNN-LS) to provide better recommendations.
Edit intent: clarity
Original:  This way we transform the knowledge graph into a user-specific weighted graph and then applies a graph neural network to compute personalized item embeddings.
Revised:  This way we transform the knowledge graph into a user-specific weighted graph and then apply a graph neural network to compute personalized item embeddings.
Edit intent: fluency
Original:  To provide better inductive bias, we use label smoothness , which assumes that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores.
Revised:  To provide better inductive bias, we rely on label smoothness assumption, which posits that adjacent items in the knowledge graph are likely to have similar user relevance labels/scores.
Edit intent: clarity
Original:  Label smoothness provides regularization over  edge weights and we prove that it is equivalent to a label propagation scheme on a graph.
Revised:  Label smoothness provides regularization over the edge weights and we prove that it is equivalent to a label propagation scheme on a graph.
Edit intent: fluency
Original: Online image hashing has received increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly.
Revised: Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly.
Edit intent: fluency
Original:  Its key challenge lies in the difficulty in balancing the learning timeliness and model accuracy.
Revised:  Its key challenge lies in the difficulty of balancing the learning timeliness and model accuracy.
Edit intent: fluency
Original:  To this end, most works exploit a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two aspects: First, large amount of training batches are required to learn up-to-date hash functions, which however largely increase the learning complexity.
Revised:  To this end, most works follow a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two aspects: First, large amount of training batches are required to learn up-to-date hash functions, which however largely increase the learning complexity.
Edit intent: clarity
Original:  Second, strong constraints, e.g., orthogonal or similarity preserving, are used, which are however typically relaxed and lead to large accuracy drop.
Revised:  Second, strong constraints, e.g., orthogonal or similarity preserving, are used, which however are typically relaxed and lead to large accuracy drop.
Edit intent: fluency
Original:   To handle the above challenges, in this paper, a novel supervised online hashing scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed  .
Revised:  To handle the above challenges, a novel supervised online hashing scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed .
Edit intent: clarity
Original:  Our key innovation lies in the construction and usage of Hadamard matrix, which is an orthogonal binary matrix and is built via Sylvester method.
Revised:  Our key innovation lies in introducing Hadamard matrix, which is an orthogonal binary matrix and is built via Sylvester method.
Edit intent: clarity
Original:  Our key innovation lies in the construction and usage of Hadamard matrix, which is an orthogonal binary matrix and is built via Sylvester method.
Revised:  Our key innovation lies in the construction and usage of Hadamard matrix, which is an orthogonal binary matrix built via Sylvester method.
Edit intent: coherence
Original:  To release the need of strong constraints, we regard each column of Hadamard matrix as the target code for each class label, which by nature satisfies several desired properties of hashing codes.
Revised:  In particular, to release the need of strong constraints, we regard each column of Hadamard matrix as the target code for each class label, which by nature satisfies several desired properties of hashing codes.
Edit intent: coherence
Original:  To accelerate the online training, the LSH is first adopted to align the length of target code and the to-be-learned binary code.
Revised:  To accelerate the online training, LSH is first adopted to align the length of target code and the to-be-learned binary code.
Edit intent: fluency
Original:  To accelerate the online training, the LSH is first adopted to align the length of target code and the to-be-learned binary code.
Revised:  To accelerate the online training, the LSH is first adopted to align the lengths of target code and the to-be-learned binary code.
Edit intent: fluency
Original:  To accelerate the online training, the LSH is first adopted to align the length of target code and the to-be-learned binary code.
Revised:  To accelerate the online training, the LSH is first adopted to align the length of target code and to-be-learned binary code.
Edit intent: fluency
Original:  And then , we treat the learning of hash functions as a set of binary classification problems to fit the assigned target code.
Revised:  We then treat the learning of hash functions as a set of binary classification problems to fit the assigned target code.
Edit intent: coherence
Original:  The superior accuracy and efficiency of the proposed method are demonstrated through extensive experiments on three widely-used datasets comparing to various state-of-the-art methods.
Revised:  The superior accuracy and efficiency of the proposed method over various state-of-the-art methods.
Edit intent: clarity
Original:  Finally, extensive experiments on four widely-used datasets demonstrate the superior accuracy and efficiency of the proposed method over various state-of-the-art methods. 
Revised:  Finally, extensive experiments demonstrate the superior accuracy and efficiency of the proposed method over various state-of-the-art methods. 
Edit intent: clarity
Original:  To test the importance of these two parts separately, we prove and leverage the connection that GFN can be derived by linearizing graph filtering part of GNN . Empirically we perform evaluations on common graph classification benchmarks.
Revised:  We prove that GFN can be derived by linearizing graph filtering part of GNN . Empirically we perform evaluations on common graph classification benchmarks.
Edit intent: coherence
Original:  We prove that GFN can be derived by linearizing graph filtering part of GNNs, and leverage it to test the importance of the two partsseparately.
Revised:  To study the importance of the two partsseparately.
Edit intent: clarity
Original:  To our surprise, we find that, despite the simplification, GFN could match or exceed the best accuracies produced by recently proposed GNNs , with a fraction of computation cost  .
Revised:  To our surprise, we find that, despite the simplification, GFN could match or exceed the best accuracies produced by recently proposed GNNs ( with a fraction of computation cost .
Edit intent: fluency
Original:  Our results  suggest that linear graph filtering with non-linear set function is powerful enough, and common graph classification benchmarks seem inadequate for testing advanced GNN variants .
Revised:  Our results suggest that linear graph filtering with non-linear set function is an efficient and powerful scheme for modeling existing graph classification benchmarks seem inadequate for testing advanced GNN variants .
Edit intent: clarity
Original:  Our results  suggest that linear graph filtering with non-linear set function is powerful enough, and common graph classification benchmarks seem inadequate for testing advanced GNN variants .
Revised:  Our results suggest that linear graph filtering with non-linear set function is powerful enough, and common graph classification benchmarks .
Edit intent: clarity
Original: We present the efficient and optimal algorithm for dynamic routing with dedicated path protection in optical networks;
Revised: We present an efficient and exact algorithm for dynamic routing with dedicated path protection in optical networks;
Edit intent: clarity
Original:  efficient, because it can solve large problems, and optimal , because its results are optimal.
Revised:  efficient, because it can solve large problems, and it is exact , because its results are optimal.
Edit intent: coherence
Original:   The proposed algorithm uses our generic Dijkstra algorithm on a search graph generated `` on-the-fly '' based on the input graph.
Revised:  The proposed algorithm uses our generic Dijkstra algorithm on a search graph generated " on-the-fly '' based on the input graph.
Edit intent: fluency
Original:   The proposed algorithm uses our generic Dijkstra algorithm on a search graph generated `` on-the-fly '' based on the input graph.
Revised:  The proposed algorithm uses our generic Dijkstra algorithm on a search graph generated `` on-the-fly " based on the input graph.
Edit intent: fluency
Original:  We corroborated the optimality of  results of the proposed algorithm with the brute-force enumeration.
Revised:  We corroborated the optimality of the results of the proposed algorithm with the brute-force enumeration.
Edit intent: fluency
Original:  We present the algorithm in the setting of optical networks, but it should be applicable to other networks too , where services have to be protected, and where the network resources are finite and discrete, e.g., wireless radio or optical networks , or networks capable of advance resource reservation.
Revised:  We present the algorithm in the setting of optical networks, but it should be applicable to other networks , where services have to be protected, and where the network resources are finite and discrete, e.g., wireless radio or optical networks , or networks capable of advance resource reservation.
Edit intent: clarity
Original:  We present the algorithm in the setting of optical networks, but it should be applicable to other networks too , where services have to be protected, and where the network resources are finite and discrete, e.g., wireless radio or optical networks , or networks capable of advance resource reservation.
Revised:  We present the algorithm in the setting of optical networks, but it should be applicable to other networks too , where services have to be protected, and where the network resources are finite and discrete, e.g., wireless radio or networks capable of advance resource reservation.
Edit intent: fluency
Original:  Network operations, management, and control require efficient and exact algorithms, especially now, when networks get more performant, reliable, softwarized, dense, and agile, and when the return on investment is crucial .
Revised:  Network operations, management, and control require efficient and exact algorithms, especially now, when greater emphasis is placed on network performance, reliability, softwarization, agility, and return on investment is crucial .
Edit intent: clarity
Original:  Network operations, management, and control require efficient and exact algorithms, especially now, when networks get more performant, reliable, softwarized, dense, and agile, and when the return on investment is crucial .
Revised:  Network operations, management, and control require efficient and exact algorithms, especially now, when networks get more performant, reliable, softwarized, dense, and agile, and when the return on investment .
Edit intent: clarity
Original:  We corroborated the optimality of the results of the proposed algorithm with the brute-force enumeration.
Revised:  We corroborated the optimality of the results of the proposed algorithm with brute-force enumeration.
Edit intent: fluency