wanyu/IteraTeR_v2 huggingface dataset:
Original:  Second, to find cross-modality identity-consistent correspondence, the heterogeneous graph alignment module (HGAM) further measures the relational edge strength by route search between two-modality local node features  .
Revised:  Second, to find cross-modality identity-consistent correspondence, the heterogeneous graph alignment module (HGAM) further measures the relational edge strength between local node features .
Edit intent: clarity
Original:  The key technique includes a neural stable placement predictor and a regrasp graph based solution through leveraging and changing the surrounding environment.
Revised:  The key technique includes a neural stable placement predictor and a regrasp graph-based solution through leveraging and changing the surrounding environment.
Edit intent: fluency
Original: Stimulated by practical applications arising from economics, viral marketing and elections, this paper studies a novel Group Influence with Minimal cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold.
Revised: This paper studies a novel Group Influence with Minimal cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold.
Edit intent: coherence
Original: Stimulated by practical applications arising from economics, viral marketing and elections, this paper studies a novel Group Influence with Minimal cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold.
Revised: Stimulated by practical applications arising from economics, viral marketing and elections, this paper studies a Group Influence with Minimal cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold.
Edit intent: clarity
Original: Stimulated by practical applications arising from economics, viral marketing and elections, this paper studies a novel Group Influence with Minimal cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold.
Revised: Stimulated by practical applications arising from economics, viral marketing and elections, this paper studies a novel Group Influence with Minimum cost which aims to find a seed set with smallest cost that can influence all target groups, where each user is associated with a cost and a group is influenced if the total score of the influenced users belonging to the group is at least a certain threshold.
Edit intent: fluency
Original:  At the heart of the algorithms is a novel group reachable reverse sample concept, which helps to speed up the estimation of the group influence function.
Revised:  At the heart of the algorithm is a novel group reachable reverse sample concept, which helps to speed up the estimation of the group influence function.
Edit intent: fluency
Original:  At the heart of the algorithms is a novel group reachable reverse sample concept, which helps to speed up the estimation of the group influence function.
Revised:  At the heart of the algorithms is a novel group reachable reverse sample concept, which helps speed up the estimation of the group influence function.
Edit intent: fluency
Original:  Finally, extensive experiments conducted on real social networks show that our proposed algorithms significantly outperform the state-of-the-art algorithms in terms of the objective value and the running time.
Revised:  Finally, extensive experiments conducted on real social networks show that our proposed algorithm outperform the state-of-the-art algorithms in terms of the objective value and the running time.
Edit intent: clarity
Original: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R  , the problem asks to find a solution \mathbf{s \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of \mathbf{s does not exceed B so that f( \mathbf{s \s ) is maximized.
Revised: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R _+ , the problem asks to find a solution \mathbf{s \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of \mathbf{s does not exceed B so that f( \mathbf{s \s ) is maximized.
Edit intent: fluency
Original: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R  , the problem asks to find a solution \mathbf{s \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of \mathbf{s does not exceed B so that f( \mathbf{s \s ) is maximized.
Revised: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R , the problem asks to find a solution \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of \mathbf{s does not exceed B so that f( \mathbf{s \s ) is maximized.
Edit intent: fluency
Original: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R  , the problem asks to find a solution \mathbf{s \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of \mathbf{s does not exceed B so that f( \mathbf{s \s ) is maximized.
Revised: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R , the problem asks to find a solution \mathbf{s \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of s does not exceed B so that f( \mathbf{s \s ) is maximized.
Edit intent: fluency
Original: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R  , the problem asks to find a solution \mathbf{s \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of \mathbf{s does not exceed B so that f( \mathbf{s \s ) is maximized.
Revised: In this paper , we investigate a novel Budgeted k-Submodular Maximization problem defined as follows: Given a finite set V, a budget B and a k-submodular function f: (k+1)^V \mapsto R , the problem asks to find a solution \mathbf{s \s =(S_1, S_2, \ldots, S_k), each element e \in V has a cost c_i(e) to be put into i-th set S_i, with the total cost of \mathbf{s does not exceed B so that f( \s ) is maximized.
Edit intent: fluency
Original:  Switching to such services , however, can break applications.
Revised:  But switching to such services , however, can break applications.
Edit intent: coherence
Original:  Switching to such services , however, can break applications.
Revised:  Switching to such services can break applications.
Edit intent: coherence
Original:  They are just as "strong " for applications;
Revised:  They are just as strong for applications:
Edit intent: fluency
Original:  To demonstrate this, we design, implement, and evaluate variants of two systems, Spanner and Gryff, weakening their consistency to RSS and RSC, respectively.
Revised:  To demonstrate this, we design, implement, and evaluate variants of two systems, Spanner and Gryff, relaxing their consistency to RSS and RSC, respectively.
Edit intent: clarity
Original:  Driven by rapid developments in deep learning technologies, convolutional neural networks (CNNs) have dominated the semantic segmentation task for many years.
Revised:  Driven by rapid developments in deep learning technologies, the convolutional neural network (CNN) has dominated the semantic segmentation task for many years.
Edit intent: clarity
Original:  Convolutional neural networks adopt hierarchical feature representation and have strong local context extraction.
Revised:  Convolutional neural networks adopt hierarchical feature representation , demonstrating strong local information extraction.
Edit intent: clarity
Original:  However, the local property of the convolution layer limits the network from capturing global information that is crucial for improving fine-resolution image segmentation.
Revised:  However, the local property of the convolution layer limits the network from capturing global context that is crucial for improving fine-resolution image segmentation.
Edit intent: clarity
Original:  However, the local property of the convolution layer limits the network from capturing global information that is crucial for improving fine-resolution image segmentation.
Revised:  However, the local property of the convolution layer limits the network from capturing global information that is crucial for precise segmentation.
Edit intent: clarity
Original:  Vision Transformer demonstrates the great capability of global information modelling, boosting many vision tasks, such as image classification, object detection and especially semantic segmentation.
Revised:  Transformer demonstrates the great capability of global information modelling, boosting many vision tasks, such as image classification, object detection and especially semantic segmentation.
Edit intent: clarity
Original:  Extensive experiments demonstrate that EHT has higher efficiency with competitive accuracy compared with state-of-the-art benchmark methods . Specifically, the proposed EHT achieves a 67.0 \% mIoU on the UAVid test set and outperforms other lightweight models significantly.
Revised:  Extensive experiments demonstrate that our EHT has faster inference speed with competitive accuracy compared with state-of-the-art benchmark methods . Specifically, the proposed EHT achieves a 67.0 \% mIoU on the UAVid test set and outperforms other lightweight models significantly.
Edit intent: clarity
Original:  Extensive experiments demonstrate that EHT has higher efficiency with competitive accuracy compared with state-of-the-art benchmark methods . Specifically, the proposed EHT achieves a 67.0 \% mIoU on the UAVid test set and outperforms other lightweight models significantly.
Revised:  Extensive experiments demonstrate that EHT has higher efficiency with competitive accuracy compared with state-of-the-art lightweight models . Specifically, the proposed EHT achieves a 67.0 \% mIoU on the UAVid test set and outperforms other lightweight models significantly.
Edit intent: clarity
Original:  Extensive experiments demonstrate that EHT has higher efficiency with competitive accuracy compared with state-of-the-art benchmark methods . Specifically, the proposed EHT achieves a 67.0 \% mIoU on the UAVid test set and outperforms other lightweight models significantly.
Revised:  Extensive experiments demonstrate that EHT has higher efficiency with competitive accuracy compared with state-of-the-art benchmark methods . Specifically, the proposed EHT achieves a 67.0 \% mIoU on the UAVid test set and outperforms other benchmark networks significantly.
Edit intent: clarity
Original:  These sentiment-aware intelligent systems are driven by language models which broadly fall into two paradigms: 1. Lexicon-based and 2. Contextual .
Revised:  These sentiment-aware intelligent systems are driven by language models which broadly fall into two paradigms: Lexicon-based and 2. Contextual .
Edit intent: fluency
Original:  These sentiment-aware intelligent systems are driven by language models which broadly fall into two paradigms: 1. Lexicon-based and 2. Contextual .
Revised:  These sentiment-aware intelligent systems are driven by language models which broadly fall into two paradigms: 1. Lexicon-based and contextual .
Edit intent: coherence
Original:  Crowdsourcing annotations for semantic dictionaries may be an expensive and time-consuming task.
Edit intent: coherence
Original:  Here, we propose two models for predicting sentiment scores to augment semantic lexicons at a relatively low cost using word embeddings and transfer learning .
Revised:  Here, we propose two models for automatic lexicon expansion .
Edit intent: clarity
Original:  In this work, we propose to dissect SLU into three representative properties:conversational (disfluency, pause, overtalk), channel (speaker-type, turn-tasks) andASR (insertion, deletion,substitution).
Revised:  In this work, we propose to dissect SLU into three representative properties:conversational (disfluency, pause, overtalk), channel (speaker-type, turn-tasks) and ASR (insertion, deletion,substitution).
Edit intent: fluency
Original:  The problem is challenging to solve due to  non-convex fractional objective functions and the coupling of the variables.
Revised:  The problem is challenging to solve due to the non-convex fractional objective functions and the coupling of the variables.
Edit intent: fluency
Original:  The problem is challenging to solve due to  non-convex fractional objective functions and the coupling of the variables.
Revised:  The problem is challenging to solve due to non-convex fractional objective functions and the coupling among the variables.
Edit intent: clarity
Original: We propose a monotone discretization for the integral fractional Laplace equation on bounded Lipschitz domains with the homogenous Dirichlet boundary condition.
Revised: We propose a monotone discretization for the integral fractional Laplace equation on bounded Lipschitz domains with the homogeneous Dirichlet boundary condition.
Edit intent: fluency
Original:  and 2) showing examples using a few design patterns relevant in multi-agent systems and human-agent interaction in general and, specifically, in the manufacturing domain .
Revised:  and 2) showing examples using a few design patterns relevant in multi-agent systems and human-agent interaction .
Edit intent: clarity
Original: This work explores the flexible assignment of users to beams in order to match the non-uniform traffic demand in satellite systems, breaking the conventional cell boundaries and serving users potentially by non-dominant \% blue{[NA] not so accurate description beams.
Revised: This work explores the flexible assignment of users to beams in order to match the non-uniform traffic demand in satellite systems, breaking the conventional cell boundaries and serving users potentially by non-dominant beams.
Edit intent: coherence
Original:  We present aminimal architecture comprised of a population of autoencoders, where we defineloss functions, capturing different aspects of effective communication, and examinetheir effect on the learned representations.
Revised:  We present a minimal architecture comprised of a population of autoencoders, where we defineloss functions, capturing different aspects of effective communication, and examinetheir effect on the learned representations.
Edit intent: fluency
Original:  We present aminimal architecture comprised of a population of autoencoders, where we defineloss functions, capturing different aspects of effective communication, and examinetheir effect on the learned representations.
Revised:  We present aminimal architecture comprised of a population of autoencoders, where we define loss functions, capturing different aspects of effective communication, and examinetheir effect on the learned representations.
Edit intent: fluency
Original:  We present aminimal architecture comprised of a population of autoencoders, where we defineloss functions, capturing different aspects of effective communication, and examinetheir effect on the learned representations.
Revised:  We present aminimal architecture comprised of a population of autoencoders, where we defineloss functions, capturing different aspects of effective communication, and examine their effect on the learned representations.
Edit intent: fluency
Original:  We show that our proposed architectureallows the emergence of aligned representations.
Revised:  We show that our proposed architecture allows the emergence of aligned representations.
Edit intent: fluency
Original:  The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives  .
Revised:  The subjectivity introduced by presenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives .
Edit intent: fluency
Original:  The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives  .
Revised:  The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes to learning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives .
Edit intent: fluency
Original:  The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives  .
Revised:  The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by a single autoencoder and a population of autoencoders, presented with identical perspectives .
Edit intent: clarity
Original:  The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives  .
Revised:  The subjectivity introduced bypresenting agents with distinct perspectives of the environment state contributes tolearning abstract representations that outperform those learned by both a single au-toencoder and a population of autoencoders, presented with identical perspectives of the environment state .
Edit intent: fluency
Original:  Altogether, our results demonstrate how communication from subjective perspec-tives can lead to the acquisition of more abstract representations in multi-agentsystems , opening promising perspectives for future research at the intersection ofrepresentation learning and emergent communication.
Revised:  Altogether, our results demonstrate how communication from subjective perspectives can lead to the acquisition of more abstract representations in multi-agentsystems , opening promising perspectives for future research at the intersection ofrepresentation learning and emergent communication.
Edit intent: fluency
Original:  Altogether, our results demonstrate how communication from subjective perspec-tives can lead to the acquisition of more abstract representations in multi-agentsystems , opening promising perspectives for future research at the intersection ofrepresentation learning and emergent communication.
Revised:  Altogether, our results demonstrate how communication from subjective perspec-tives can lead to the acquisition of more abstract representations in multi-agent systems , opening promising perspectives for future research at the intersection ofrepresentation learning and emergent communication.
Edit intent: fluency
Original:  Altogether, our results demonstrate how communication from subjective perspec-tives can lead to the acquisition of more abstract representations in multi-agentsystems , opening promising perspectives for future research at the intersection ofrepresentation learning and emergent communication.
Revised:  Altogether, our results demonstrate how communication from subjective perspec-tives can lead to the acquisition of more abstract representations in multi-agentsystems , opening promising perspectives for future research at the intersection of representation learning and emergent communication.
Edit intent: fluency
Original: Despite its rise as a prominent solution to the data inefficiency of today's machine learning models, self-supervised learning has yet to be studied from a purely multi agent perspective.
Revised: Despite its rise as a prominent solution to the data inefficiency of today's machine learning models, self-supervised learning has yet to be studied from a purely multi-agent perspective.
Edit intent: fluency
Original:  To highlight the importance of communication, we refer to our setting as socially supervised representation learning .
Revised:  To highlight the importance of communication, we refer to our setting as socially supervised representation learning .
Edit intent: fluency
Original: The Human-Machine Interaction (HMI) researchfield is an important topic in machine learning that has beendeeply investigated thanks to the rise of computing power in thelast years.
Revised: The Human-Machine Interaction (HMI) research field is an important topic in machine learning that has beendeeply investigated thanks to the rise of computing power in thelast years.
Edit intent: fluency
Original: The Human-Machine Interaction (HMI) researchfield is an important topic in machine learning that has beendeeply investigated thanks to the rise of computing power in thelast years.
Revised: The Human-Machine Interaction (HMI) researchfield is an important topic in machine learning that has been deeply investigated thanks to the rise of computing power in thelast years.
Edit intent: fluency
Original: The Human-Machine Interaction (HMI) researchfield is an important topic in machine learning that has beendeeply investigated thanks to the rise of computing power in thelast years.
Revised: The Human-Machine Interaction (HMI) researchfield is an important topic in machine learning that has beendeeply investigated thanks to the rise of computing power in the last years.
Edit intent: fluency
Original:  The first time, it is possible to use machine learningto classify images and/or videos instead of the traditionalcomputer vision algorithms.
Revised:  The first time, it is possible to use machine learning to classify images and/or videos instead of the traditionalcomputer vision algorithms.
Edit intent: fluency
Original:  The first time, it is possible to use machine learningto classify images and/or videos instead of the traditionalcomputer vision algorithms.
Revised:  The first time, it is possible to use machine learningto classify images and/or videos instead of the traditional computer vision algorithms.
Edit intent: fluency