wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this work, we conducted an in-depth analysis to four widely used smart home solutions . Combining firmware reverse-engineering , network traffic interception, and black-box testing, we distill the general state transitions representing the complex interactions among the three entities.
Revised:  In this work, we conducted an in-depth analysis to four widely used smart home solutions . Combining firmware reverse-engineering , network traffic interception, and black-box testing, we distill the general state transitions representing the complex interactions among the participating entities.
Edit intent: clarity
Original:  We show that the Bayesian inference applied to DNNs provides reliable confidence measures for visual activity recognition task as compared to the conventional DNNs.
Revised:  We show that the Bayesian inference applied to DNNs provide reliable confidence measures for visual activity recognition task as compared to the conventional DNNs.
Edit intent: fluency
Original:  We show that the Bayesian inference applied to DNNs provides reliable confidence measures for visual activity recognition task as compared to the conventional DNNs.
Revised:  We show that the Bayesian inference applied to DNNs provides reliable confidence measures for visual activity recognition task as compared to conventional DNNs.
Edit intent: fluency
Original:  We here show that factorization machines (FMs), a model for regression or classification, encompass several existing models in the educational literature as special cases, notably additive factor model, performance factor model, and multidimensional item response theory.
Revised:  We here show that factorization machines (FMs), a model for regression or classification, encompasses several existing models in the educational literature as special cases, notably additive factor model, performance factor model, and multidimensional item response theory.
Edit intent: fluency
Original:  In this report, we introduce a big-step semantics for Wasm , as well as a sound program logic for first-order, encapsulated Wasm.
Revised:  In this report, we introduce a sound program logic for first-order, encapsulated Wasm.
Edit intent: coherence
Original:  In this paper we show that protein distance matrix can be predicted well by deep learning and then directly used to construct 3D models without folding simulation at all.
Revised:  We show that protein distance matrix can be predicted well by deep learning and then directly used to construct 3D models without folding simulation at all.
Edit intent: coherence
Original: We propose a biologically-inspired research agenda with parallel tracks aimed at AI and AI safety.
Revised: We describe a biologically-inspired research agenda with parallel tracks aimed at AI and AI safety.
Edit intent: clarity
Original: Self-organization is a natural phenomenon that emerges in systems with a large number of interacting components.
Revised: Swarm intelligence is the collective behavior emerging in systems with a large number of interacting components.
Edit intent: clarity
Original: Self-organization is a natural phenomenon that emerges in systems with a large number of interacting components.
Revised: Self-organization is a natural phenomenon that emerges in systems with locally interacting components.
Edit intent: clarity
Original:  Self-organized systems show robustness, scalability, and flexibility , which are essential properties when handling real-world problems.
Revised:  Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility , which are essential properties when handling real-world problems.
Edit intent: clarity
Original:  The lack of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without much regard as to whether they are similar to already existing approaches.
Revised:  The lack of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches.
Edit intent: clarity
Original:  We address this gap by introducing a network-based framework-the interaction network-to examine computational swarm-based systems via the optics of social dynamics .
Revised:  Here, we address this gap by introducing a network-based framework-the interaction network-to examine computational swarm-based systems via the optics of social dynamics .
Edit intent: coherence
Original:  We address this gap by introducing a network-based framework-the interaction network-to examine computational swarm-based systems via the optics of social dynamics .
Revised:  We address this gap by introducing a network-based framework---the interaction network---to examine computational swarm-based systems via the optics of social dynamics .
Edit intent: fluency
Original: Real-world networks are difficult to characterize due to the variation of topological scales, the non-dyadic complex interactions , and fluctuations.
Revised: Real-world networks are difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions , and fluctuations.
Edit intent: fluency
Original: Real-world networks are difficult to characterize due to the variation of topological scales, the non-dyadic complex interactions , and fluctuations.
Revised: Real-world networks are difficult to characterize due to the variation of topological scales, the non-dyadic complex interactions and the fluctuations.
Edit intent: fluency
Original:  Here, we propose a general framework to address these problems via a methodology grounded in topology data analysis.
Revised:  Here, we propose a general framework to address these problems via a methodology grounded on topology data analysis.
Edit intent: fluency
Original:  By observing the diffusion process on the network at single specified time scale , we can map  network nodes to a point cloud  which contains the topological information of the network at a single scale.
Revised:  By observing the diffusion process in a network at a single specified timescale , we can map network nodes to a point cloud which contains the topological information of the network at a single scale.
Edit intent: clarity
Original:  By observing the diffusion process on the network at single specified time scale , we can map  network nodes to a point cloud  which contains the topological information of the network at a single scale.
Revised:  By observing the diffusion process on the network at single specified time scale , we can map the network nodes to a point cloud which contains the topological information of the network at a single scale.
Edit intent: fluency
Original:  By observing the diffusion process on the network at single specified time scale , we can map  network nodes to a point cloud  which contains the topological information of the network at a single scale.
Revised:  By observing the diffusion process on the network at single specified time scale , we can map network nodes to a point cloud , which contains the topological information of the network at a single scale.
Edit intent: fluency
Original:  We then calculate the point clouds constructed over variable time scales , which provide scale-variant topological information and confer the deep understanding of  network structure and functionality.
Revised:  We then calculate the point clouds constructed over variable timescales , which provide scale-variant topological information and confer the deep understanding of network structure and functionality.
Edit intent: fluency
Original:  We then calculate the point clouds constructed over variable time scales , which provide scale-variant topological information and confer the deep understanding of  network structure and functionality.
Revised:  We then calculate the point clouds constructed over variable time scales , which provide scale-variant topological information and enable a deep understanding of network structure and functionality.
Edit intent: clarity
Original:  We then calculate the point clouds constructed over variable time scales , which provide scale-variant topological information and confer the deep understanding of  network structure and functionality.
Revised:  We then calculate the point clouds constructed over variable time scales , which provide scale-variant topological information and confer the deep understanding of the network structure and functionality.
Edit intent: fluency
Original:  Experiments on synthetic and real-world data demonstrate the effectiveness of our framework in identifying network models, classifying real-world networks , and detecting transition points in time-evolving networks.
Revised:  Experiments on synthetic and real-world data demonstrate the effectiveness of our framework in identifying network models, classifying real-world networks and detecting transition points in time-evolving networks.
Edit intent: fluency
Original:  Our work presents a unified analysis which is potentially applicable to more complicated network structures such as multilayer and multiplex networks.
Revised:  Our work presents a unified analysis that is potentially applicable to more complicated network structures such as multilayer and multiplex networks.
Edit intent: fluency
Original: Real-world networks are difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions  and the fluctuations.
Revised: Real-world networks are difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions , and the fluctuations.
Edit intent: fluency
Original: Real-world networks are difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions, and the fluctuations .
Revised: The structure of real-world networks is usually difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions, and the fluctuations .
Edit intent: clarity
Original: Real-world networks are difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions, and the fluctuations .
Revised: Real-world networks are difficult to characterize owing to the variation of topological scales, the non-dyadic complex interactions, and the fluctuations .
Edit intent: clarity
Original: Real-world networks are difficult to characterize because of the variation of topological scales, the non-dyadic complex interactions, and the fluctuations .
Revised: Real-world networks are difficult to characterize because of the variation of topological scales, the nondyadic complex interactions, and the fluctuations .
Edit intent: fluency
Original:  Here, we propose a general framework to address these problems via a methodology grounded on topology data analysis.
Revised:  Here, we propose a general framework to address these problems by introducing a general framework using a method based on topological data analysis.
Edit intent: clarity
Original:  By observing the diffusion process in a network at a single specified timescale , we can map the network nodes to a point cloud, which contains the topological information of the network at a single scale.
Revised:  By considering the diffusion process in a network at a single specified timescale , we can map the network nodes to a point cloud, which contains the topological information of the network at a single scale.
Edit intent: clarity
Original:  By observing the diffusion process in a network at a single specified timescale , we can map the network nodes to a point cloud, which contains the topological information of the network at a single scale.
Revised:  By observing the diffusion process at a single specified timescale , we can map the network nodes to a point cloud, which contains the topological information of the network at a single scale.
Edit intent: clarity
Original:  By observing the diffusion process in a network at a single specified timescale , we can map the network nodes to a point cloud, which contains the topological information of the network at a single scale.
Revised:  By observing the diffusion process in a network at a single specified timescale in a network, we map the network nodes to a point cloud, which contains the topological information of the network at a single scale.
Edit intent: clarity
Original:  By observing the diffusion process in a network at a single specified timescale , we can map the network nodes to a point cloud, which contains the topological information of the network at a single scale.
Revised:  By observing the diffusion process in a network at a single specified timescale , we can map the network nodes to a finite set of points that contains the topological information of the network at a single scale.
Edit intent: clarity
Original:  We then calculate the point clouds constructed over variable timescales , which provide scale-variant topological information and enable a deep understanding of the network structure and functionality.
Revised:  Subsequently, we study the shape of these point sets over variable timescales , which provide scale-variant topological information and enable a deep understanding of the network structure and functionality.
Edit intent: clarity
Original:  We then calculate the point clouds constructed over variable timescales , which provide scale-variant topological information and enable a deep understanding of the network structure and functionality.
Revised:  We then calculate the point clouds constructed over variable timescales that provide scale-variant topological information and enable a deep understanding of the network structure and functionality.
Edit intent: fluency
Original:  Experiments on synthetic and real-world data  demonstrate the effectiveness of our framework in identifying network models, classifying real-world networks  and detecting transition points in time-evolving networks.
Revised:  Experiments on synthetic and real-world data to demonstrate the effectiveness of our framework in identifying network models, classifying real-world networks and detecting transition points in time-evolving networks.
Edit intent: clarity
Original:  Experiments on synthetic and real-world data  demonstrate the effectiveness of our framework in identifying network models, classifying real-world networks  and detecting transition points in time-evolving networks.
Revised:  Experiments on synthetic and real-world data demonstrate the effectiveness of the proposed framework in identifying network models, classifying real-world networks and detecting transition points in time-evolving networks.
Edit intent: clarity
Original:  Experiments on synthetic and real-world data  demonstrate the effectiveness of our framework in identifying network models, classifying real-world networks  and detecting transition points in time-evolving networks.
Revised:  Experiments on synthetic and real-world data demonstrate the effectiveness of our framework in identifying network models, classifying real-world networks , and detecting transition points in time-evolving networks.
Edit intent: fluency
Original:  Our work presents a unified analysis that is potentially applicable to more complicated network structuressuch as multilayer and multiplex networks.
Revised:  Overall, our study presents a unified analysis that is potentially applicable to more complicated network structuressuch as multilayer and multiplex networks.
Edit intent: clarity
Original:  Our work presents a unified analysis that is potentially applicable to more complicated network structuressuch as multilayer and multiplex networks.
Revised:  Our work presents a unified analysis that can be applied to more complex network structures, as in the case of multilayer and multiplex networks.
Edit intent: clarity
Original:   DKI and subsequent estimation of microstructural model parameters has been commonly used for assessment of tissue changes in neurodegenerative diseases and the animal modelsof such diseases .
Revised:  Recently, DKI and subsequent estimation of microstructural model parameters has been commonly used for assessment of tissue changes in neurodegenerative diseases and the animal modelsof such diseases .
Edit intent: coherence
Original:   DKI and subsequent estimation of microstructural model parameters has been commonly used for assessment of tissue changes in neurodegenerative diseases and the animal modelsof such diseases .
Revised:  DKI and subsequent estimation of microstructural model parameters has been used for assessment of tissue changes in neurodegenerative diseases and the animal modelsof such diseases .
Edit intent: clarity
Original:   DKI and subsequent estimation of microstructural model parameters has been commonly used for assessment of tissue changes in neurodegenerative diseases and the animal modelsof such diseases .
Revised:  DKI and subsequent estimation of microstructural model parameters has been commonly used for assessment of tissue changes in neurodegenerative diseases and their animal models .
Edit intent: clarity
Original:  Thirteen EAE spinal cords of variable disability were extracted, dissected and subsequently scanned in a high-field MRI scanner along with 5 control specimen.
Revised:  Thirteen EAE spinal cords of variable disability and scanned in a high-field MRI scanner along with 5 control specimen.
Edit intent: clarity
Original:  Thirteen EAE spinal cords of variable disability were extracted, dissected and subsequently scanned in a high-field MRI scanner along with 5 control specimen.
Revised:  Thirteen EAE spinal cords of variable disability were extracted, dissected and subsequently scanned in a high-field MRI scanner along with five control specimen.
Edit intent: fluency
Original:  The diffusion weighted data were acquired together with high resolution T2* images.
Revised:  Diffusion weighted data were acquired together with high resolution T2* images.
Edit intent: clarity
Original:  Diffusion weighted data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model.
Revised:  Diffusion data were fit to estimate diffusion and kurtosis tensors and white matter modeling parameters, which were all used for subsequent statistical analysis using a linear mixed effects model.
Edit intent: clarity
Original:  These findings could close the gap between MRI findings and clinical presentation in patients and deepen our understanding of EAE and  MS mechanisms.
Revised:  These findings could close the gap between MRI findings and clinical presentation in patients and deepen our understanding of EAE and the MS mechanisms.
Edit intent: fluency
Original: Low latency applications, such as multimedia communications, autonomous vehicles, and tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G)  . Existing physical-layer channel models  however, do not explicitly consider QoS-aware related parameters under specific delay constraints.
Revised: Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) . Existing physical-layer channel models however, do not explicitly consider QoS-aware related parameters under specific delay constraints.
Edit intent: fluency
Original: Low latency applications, such as multimedia communications, autonomous vehicles, and tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G)  . Existing physical-layer channel models  however, do not explicitly consider QoS-aware related parameters under specific delay constraints.
Revised: Low latency applications, such as multimedia communications, autonomous vehicles, and tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks . Existing physical-layer channel models however, do not explicitly consider QoS-aware related parameters under specific delay constraints.
Edit intent: clarity
Original: Low latency applications, such as multimedia communications, autonomous vehicles, and tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G)  . Existing physical-layer channel models  however, do not explicitly consider QoS-aware related parameters under specific delay constraints.
Revised: Low latency applications, such as multimedia communications, autonomous vehicles, and tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) . Existing physical-layer channel models , however, do not explicitly consider QoS-aware related parameters under specific delay constraints.
Edit intent: fluency
Original: Low latency applications, such as multimedia communications, autonomous vehicles, and tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G)  . Existing physical-layer channel models  however, do not explicitly consider QoS-aware related parameters under specific delay constraints.
Revised: Low latency applications, such as multimedia communications, autonomous vehicles, and tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) . Existing physical-layer channel models however, do not explicitly consider quality-of-service (QoS) aware related parameters under specific delay constraints.
Edit intent: clarity
Original:  In this paper, we provide a comprehensive survey on existing work , that use the EC model in various wireless networks.
Revised:  In this paper, we provide a comprehensive survey on existing works , that use the EC model in various wireless networks.
Edit intent: fluency
Original:  Taking text-to-speech system based on WaveNet as an instance, we describe the process of embedding and extracting message in detail , and the experimental results show that the proposed methodis nearly perfectly secure when resists state-of-the-art steganalysis  .
Revised:  Taking text-to-speech system based on WaveNet as an instance, we describe the process of embedding and extracting message in detail , and the experimental results show that the proposed methodis nearly perfectly secure when resists state-of-the-art steganalysis methods .
Edit intent: clarity
Original:  There are two provably secure steganographic frameworks, one is black-box sampling based and the other is compression based .
Revised:  There are two distribution-preserving steganographic frameworks, one is black-box sampling based and the other is compression based .
Edit intent: clarity
Original:  There are two provably secure steganographic frameworks, one is black-box sampling based and the other is compression based .
Revised:  There are two provably secure steganographic frameworks, one is sampling-based and the other is compression based .
Edit intent: clarity
Original:  There are two provably secure steganographic frameworks, one is black-box sampling based and the other is compression based .
Revised:  There are two provably secure steganographic frameworks, one is black-box sampling based and the other is compression-based .
Edit intent: fluency
Original:  The former requires a perfect sampler which yields data following the same distribution, and the latter needs explicit distributions of generative objects.
Revised:  The former requires a perfect sampler which yields data following the same distribution, and the latter needs explicit distribution of generative objects.
Edit intent: fluency