wanyu/IteraTeR_v2 huggingface dataset:
Original:  To realize such a contest, we propose a novel location synthesizer that has diversity in that synthetic traces for each team are different from those for the other teams and utility in that synthetic traces preserve various statistical features of real traces.
Revised:  To realize such a contest, we propose a novel location synthesizer that has diversity and utility; the synthesizer generates different synthetic traces for each team are different from those for the other teams and utility in that synthetic traces preserve various statistical features of real traces.
Edit intent: coherence
Original:  To realize such a contest, we propose a novel location synthesizer that has diversity in that synthetic traces for each team are different from those for the other teams and utility in that synthetic traces preserve various statistical features of real traces.
Revised:  To realize such a contest, we propose a novel location synthesizer that has diversity in that synthetic traces for each team while preserving various statistical features of real traces.
Edit intent: clarity
Original:  We also show that re-identification alone is insufficient as a privacy risk , and that trace inference should be added as an additional risk.
Revised:  We also show that re-identification alone is insufficient as a privacy risk and that trace inference should be added as an additional risk.
Edit intent: fluency
Original:  In particular, the consistency property demonstrates a tight trade-off between block production speed and the system's security in terms of resisting adversarial attacks.
Revised:  In particular, the consistency property demonstrates a tight trade-off between block production speed and the system's security in terms of resisting adversarial attacks.
Edit intent: fluency
Original:  This paper proposes a novel method, Ironclad, that improves blockchain consistency by assigning different weights to randomly selected blocks.
Revised:  This paper proposes a novel method, Ironclad, that improves blockchain consistency bound by assigning a different weight to randomly selected blocks.
Edit intent: clarity
Original:  A direct result is that Nakamoto+Ironclad can enable a much faster (10\sim 50 times with normal parameter settings) block production rate than  Nakamoto protocol under the same security guarantee with the same proportion of malicious mining power  .
Revised:  A direct result is that Nakamoto+Ironclad can enable a much faster block production rate than Nakamoto protocol under the same security guarantee with the same proportion of malicious mining power .
Edit intent: coherence
Original:  As we all konw , the best way to deal with that it is to introduce image datasets in this scanario, But this one is very expensive.
Revised:  As we all know , the best way to deal with that it is to introduce image datasets in this scanario, But this one is very expensive.
Edit intent: fluency
Original: Re-identification (ReID) aims at matching objects in surveillance cameras with different viewpoints.
Revised: The aim of re-identification is to match objects in surveillance cameras with different viewpoints.
Edit intent: clarity
Original:  It's developing very fast, but there is no processing method for the ReID task in multiple scenarios at this stage .
Revised:  Although ReID is developing at a considerably rapid pace, there is currently no processing method for the ReID task in multiple scenarios at this stage .
Edit intent: clarity
Original:  It's developing very fast, but there is no processing method for the ReID task in multiple scenarios at this stage .
Revised:  It's developing very fast, but there is no processing method for the ReID task in multiple scenarios .
Edit intent: clarity
Original:  However, this dose happen all the time in real life  , such as the securityscenarios.
Revised:  However, such processing method is required in real life , such as the securityscenarios.
Edit intent: clarity
Original:  However, this dose happen all the time in real life  , such as the securityscenarios.
Revised:  However, this dose happen all the time in real life scenarios , such as the securityscenarios.
Edit intent: clarity
Original:  This paper explores a new scenario of Re-identification , which differs in  perspective, background, and pose(walking or cycling).
Revised:  This paper explores a new scenario of Re-identification , which differs in terms of perspective, background, and pose(walking or cycling).
Edit intent: clarity
Original:  To solve this problem, this paper proposes a simple and effective way to generate images in some new scenario , which is named Copy and Paste method based on Pose(CPP).
Revised:  To solve the aforementioned problem, a simple and effective way to generate images in some new scenario , which is named Copy and Paste method based on Pose(CPP).
Edit intent: clarity
Original:  To solve this problem, this paper proposes a simple and effective way to generate images in some new scenario , which is named Copy and Paste method based on Pose(CPP).
Revised:  To solve this problem, this paper proposes a simple and effective method to generate images in some new scenario , which is named Copy and Paste method based on Pose(CPP).
Edit intent: coherence
Original:  To solve this problem, this paper proposes a simple and effective way to generate images in some new scenario , which is named Copy and Paste method based on Pose(CPP).
Revised:  To solve this problem, this paper proposes a simple and effective way to generate images in some new scenario , which is names the Copy and Paste method based on Pose(CPP).
Edit intent: fluency
Original:  The CPP is a method based on key point detection, using copy and paste, to composite a new semantic image dataset in two different semantic image datasets.
Revised:  The CPP method is based on key point detection, using copy and paste, to composite a new semantic image dataset in two different semantic image datasets.
Edit intent: coherence
Original:  The CPP is a method based on key point detection, using copy and paste, to composite a new semantic image dataset in two different semantic image datasets.
Revised:  The CPP is a method based on key point detection, using copy as paste, to composite a new semantic image dataset in two different semantic image datasets.
Edit intent: clarity
Original:  Such as, we can use pedestrians and bicycles to generate some images that shows the same person rides on different bicycles.
Revised:  As an example, pedestrains and bicycles can be used to generate several images that show the same person rides on different bicycles.
Edit intent: clarity
Original:  Such as, we can use pedestrians and bicycles to generate some images that shows the same person rides on different bicycles.
Revised:  Such as, we can use pedestrians and bicycles to generate some images that shows the same person riding on different bicycles.
Edit intent: fluency
Original:  The CPP  is suitable for ReID tasks in new scenarios and it outperforms state-of-the-art on the original datasets in original ReID tasks.
Revised:  The CPP method is suitable for ReID tasks in new scenarios and it outperforms state-of-the-art on the original datasets in original ReID tasks.
Edit intent: clarity
Original:  Specifically, it can also have better generalization performance for third-party public datasets.
Revised:  To be specific, the CPP method can also perform better in terms of generalization for third-party public datasets.
Edit intent: clarity
Original:  Code and Datasets which composited by the CPP  will be available in the future.
Revised:  Code and Datasets which composited by the CPP method will be available in the future.
Edit intent: clarity
Original:  We then perform a number of experiments on synthetic and real datasets demonstrating the efficacy of our proposed method.
Revised:  We then perform a number of experiments on synthetic and real datasets demonstrating the effectiveness of our proposed method.
Edit intent: fluency
Original:  We then apply the method to explore the way the design pattern discourse as a whole is evolving.
Revised:  We then use Causal Layered Analysis to explore the way the design pattern discourse as a whole is evolving.
Edit intent: clarity
Original:  We then apply the method to explore the way the design pattern discourse as a whole is evolving.
Revised:  We then apply the method to explore the ways in which the design pattern discourse as a whole is evolving.
Edit intent: clarity
Original:  We discuss implications for future research and innovation, outlining several scenarios for the  use of design patterns in large-scale distributed collaboration  .
Revised:  We discuss implications for future research and innovation, outlining several scenarios for the future use of design patterns in large-scale distributed collaboration .
Edit intent: fluency
Original:  Several motivational examples and applications are also presented.
Revised:  Several motivational examples and applications are presented.
Edit intent: clarity
Original:  HANT tackles the problem in two phase: In the first phase, a large number of alternative operations per every layer of the teacher model is trained using layer-wise feature map distillation.
Revised:  HANT tackles the problem in two phase: In the first phase, it trains many alternative operations for every layer of the teacher model is trained using layer-wise feature map distillation.
Edit intent: clarity
Original:  HANT tackles the problem in two phase: In the first phase, a large number of alternative operations per every layer of the teacher model is trained using layer-wise feature map distillation.
Revised:  HANT tackles the problem in two phase: In the first phase, a large number of alternative operations per every layer of the teacher network using layer-wise feature map distillation.
Edit intent: clarity
Original:  In the second phase,  the combinatorial selection of efficient operations is relaxed to an integer optimization problem that can be solved in a few seconds .
Revised:  In the second phase, it solves the combinatorial selection of efficient operations is relaxed to an integer optimization problem that can be solved in a few seconds .
Edit intent: clarity
Original: Abstract - Gathering relevant information to predict student academic progress is a tedious task.
Revised:  Gathering relevant information to predict student academic progress is a tedious task.
Edit intent: coherence
Original: Emotion recognition is an important research field for Human-Computer Interaction(HCI).
Edit intent: coherence
Original:  Audio-Video Emotion Recognition (AVER) is now attacked with Deep Neural Network (DNN) modeling tools.
Revised:  Audio-Video Emotion Recognition is now attacked with Deep Neural Network (DNN) modeling tools.
Edit intent: clarity
Original:  Audio-Video Emotion Recognition (AVER) is now attacked with Deep Neural Network (DNN) modeling tools.
Revised:  Audio-Video Emotion Recognition (AVER) is now attacked with Deep Neural Network modeling tools.
Edit intent: clarity
Original:  In published papers, as a rule, the authors show only cases of the superiority of multi modalities over audio-only or video-only modalities .
Revised:  In published papers, as a rule, the authors show only cases of the superiority in multi-modality over audio-only or video-only modalities .
Edit intent: fluency
Original:  In published papers, as a rule, the authors show only cases of the superiority of multi modalities over audio-only or video-only modalities .
Revised:  In published papers, as a rule, the authors show only cases of the superiority of multi modalities over audio-only or video-only modality .
Edit intent: fluency
Original:  However, there are cases superiority in single modality can be found.
Revised:  However, there are cases superiority in uni-modality can be found.
Edit intent: fluency
Original:  In our research, we hypothesize that for fuzzy categories of emotional events, the higher noise of one modality can amplify the lower noise of the second modality represented indirectly in the parameters of the modeling neural network .
Revised:  In our research, we hypothesize that for fuzzy categories of emotional events, the within-modal and inter-modal noisy information represented indirectly in the parameters of the modeling neural network .
Edit intent: clarity
Original:  To avoid such cross-modal information interference we define a multi-modal Residual Perceptron Network (MRPN) which learns from multi-modal network branches creating deep feature representationwith reduced noise .
Revised:  To avoid such cross-modal information interference we define a Multi-modal Residual Perceptron Network (MRPN) which learns from multi-modal network branches creating deep feature representationwith reduced noise .
Edit intent: fluency
Original:  To avoid such cross-modal information interference we define a multi-modal Residual Perceptron Network (MRPN) which learns from multi-modal network branches creating deep feature representationwith reduced noise .
Revised:  To avoid such cross-modal information interference we define a multi-modal Residual Perceptron Network which performs end-to-end learning from multi-modal network branches creating deep feature representationwith reduced noise .
Edit intent: clarity
Original:  For the proposed MRPN model and the novel time augmentation for streamed digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors Dataset(Crema-d) .
Revised:  For the proposed Multi-modal Residual Perceptron Network and the novel time augmentation for streamed digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors Dataset(Crema-d) .
Edit intent: clarity
Original:  For the proposed MRPN model and the novel time augmentation for streamed digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors Dataset(Crema-d) .
Revised:  For the proposed MRPN model and the novel time augmentation for streaming digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors Dataset(Crema-d) .
Edit intent: fluency
Original:  For the proposed MRPN model and the novel time augmentation for streamed digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors Dataset(Crema-d) .
Revised:  For the proposed MRPN model and the novel time augmentation for streamed digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors Dataset(Crema-d) .
Edit intent: clarity
Original:  For the proposed MRPN model and the novel time augmentation for streamed digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors Dataset(Crema-d) .
Revised:  For the proposed MRPN model and the novel time augmentation for streamed digital movies, the state-of-art average recognition rate was improved to 91.4\% for The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset and to 83.15\% for Crowd-sourced Emotional multi-modal Actors dataset .
Edit intent: coherence
Original:  Moreover, the MRPN concept shows its potential for multi-modal classifiers dealing with signal sources not only of optical and acoustical type .
Revised:  Moreover, the MRPN concept shows its potential for multi-modal applications dealing with signal sources not only of optical and acoustical type .
Edit intent: clarity
Original:  Moreover, the MRPN concept shows its potential for multi-modal classifiers dealing with signal sources not only of optical and acoustical type .
Revised:  Moreover, the MRPN concept shows its potential for multi-modal classifiers dealing with signal sources not only of optical and acoustical types .
Edit intent: fluency
Original:  Each quintuple is understood to list a player, a situation ( e.g. information set), a decision node, an action, and a successor node.
Revised:  Each quintuple is understood to list a player, a situation ( for example an information set), a decision node, an action, and a successor node.
Edit intent: clarity
Original:  Such an extensive form is called a "5-form" , and a "5-form game" is defined to be a 5-form together with utility functions.
Revised:  Such an extensive form is called a "5-form" . Next a "5-form game" is defined to be a 5-form together with utility functions.
Edit intent: coherence
Original:  An application weakens the tree axiom in the presence of the other axioms, which leads to a convenient decomposition of 5-forms .
Revised:  Secondary results suggest that a 5-form might be usefully partitioned into subsets .
Edit intent: clarity
Original: In this work, we analyze the privacy guarantees of Zigbee protocol, an energy-efficient wireless IoT protocol that is increasingly being deployed in smart home settings.
Revised: Zigbee is an energy-efficient wireless IoT protocol that is increasingly being deployed in smart home settings.
Edit intent: clarity
Original:  Specifically, we devise two passive inference techniques to demonstrate how a passive eavesdropper, located outside the smart home, can reliably identify in-home devices or events from the encrypted wireless Zigbee traffic by 1) inferring a single application layer (APL) command in the event's traffic burst , and 2) exploiting the device's periodic reporting pattern and interval.
Revised:  Specifically, we devise two passive inference techniques to demonstrate how a passive eavesdropper, located outside the smart home, can reliably identify in-home devices or events from the encrypted Zigbee traffic by 1) inferring a single application layer (APL) command in the event's traffic burst , and 2) exploiting the device's periodic reporting pattern and interval.
Edit intent: clarity
Original:  Specifically, we devise two passive inference techniques to demonstrate how a passive eavesdropper, located outside the smart home, can reliably identify in-home devices or events from the encrypted wireless Zigbee traffic by 1) inferring a single application layer (APL) command in the event's traffic burst , and 2) exploiting the device's periodic reporting pattern and interval.
Revised:  Specifically, we devise two passive inference techniques to demonstrate how a passive eavesdropper, located outside the smart home, can reliably identify in-home devices or events from the encrypted wireless Zigbee traffic by 1) inferring a single application layer (APL) command in the event's traffic , and 2) exploiting the device's periodic reporting pattern and interval.
Edit intent: clarity
Original:  We evaluated our techniques on 19 unique Zigbee devices across several categories and 5 popular smart hubs in three different scenarios : i) controlled shield, ii) living smart-home IoT lab, and iii) third-party Zigbee captures.
Revised:  We evaluated our techniques on 19 unique Zigbee devices across several categories and 5 popular smart hubs in three different scenarios : i) controlled shield, ii) living smart-home IoT lab, and third-party Zigbee captures.
Edit intent: fluency
Original:  Our results indicate over 85\% accuracy in determining events and devices using the command inference approach , without the need of a-priori devicesignatures, and 99.8\% accuracy in determining known devices using the periodic reporting approach.
Revised:  We were able to i) identify unknown events and devices using the command inference approach , without the need of a-priori devicesignatures, and 99.8\% accuracy in determining known devices using the periodic reporting approach.
Edit intent: clarity
Original:  Through this work , we highlight the trade-off between designing a low-power, low-cost wireless network and achieving privacy guarantees  .
Revised:  In short , we highlight the trade-off between designing a low-power, low-cost wireless network and achieving privacy guarantees .
Edit intent: clarity
Original:  We propose and experimentally implement QuantumNAS, the first comprehensive framework for noise-adaptive co-search of  variational circuit and qubit mapping.
Revised:  We propose QuantumNAS, a comprehensive framework for noise-adaptive co-search of variational circuit and qubit mapping.
Edit intent: clarity
Original:  We propose and experimentally implement QuantumNAS, the first comprehensive framework for noise-adaptive co-search of  variational circuit and qubit mapping.
Revised:  We propose and experimentally implement QuantumNAS, the first comprehensive framework for noise-adaptive co-search of the variational circuit and qubit mapping.
Edit intent: fluency
Original:  Variational quantum circuits are a promising approach for constructing quantum neural networks for machine learning and variational ansatzes for quantum simulation.
Revised:  Variational quantum circuits are a promising approach for constructing QML and quantum simulation.
Edit intent: clarity
Original:  We propose to decouple the parameter training and circuit search by introducing a novel gate-sharing SuperCircuit.
Revised:  We propose to decouple the circuit search and parameter training by introducing a novel gate-sharing SuperCircuit.
Edit intent: fluency
Original:  We propose to decouple the parameter training and circuit search by introducing a novel gate-sharing SuperCircuit.
Revised:  We propose to decouple the parameter training and circuit search by introducing a novel SuperCircuit.
Edit intent: clarity
Original:  The SuperCircuit is trained by sampling and updating the SubCircuits in itand provides an accurate estimation of SubCircuit performance trained from scratch.
Revised:  The SuperCircuit is trained by sampling and updating the parameter subsets (SubCircuits) of it. It provides an accurate estimation of SubCircuit performance trained from scratch.
Edit intent: clarity
Original:  The SuperCircuit is trained by sampling and updating the SubCircuits in itand provides an accurate estimation of SubCircuit performance trained from scratch.
Revised:  The SuperCircuit is trained by sampling and updating the SubCircuits in itand provides an accurate estimation of SubCircuits performance trained from scratch.
Edit intent: fluency
Original:  Finally, we perform iterative gate pruning and finetuning to further remove the redundant gatesin a fine-grained manner . Extensively evaluated with 12 QML and VQE benchmarks on 10 quantum computers , QuantumNAS significantly outperforms noise-unaware search, human and random baselines.
Revised:  Finally, we perform iterative gate pruning and finetuning to remove redundant gates . Extensively evaluated with 12 QML and VQE benchmarks on 10 quantum computers , QuantumNAS significantly outperforms noise-unaware search, human and random baselines.
Edit intent: clarity
Original:  Finally, we perform iterative gate pruning and finetuning to further remove the redundant gatesin a fine-grained manner . Extensively evaluated with 12 QML and VQE benchmarks on 10 quantum computers , QuantumNAS significantly outperforms noise-unaware search, human and random baselines.
Revised:  Finally, we perform iterative gate pruning and finetuning to further remove the redundant gatesin a fine-grained manner . Extensively evaluated with 12 QML and VQE benchmarks on 10 quantum comput , QuantumNAS significantly outperforms noise-unaware search, human and random baselines.
Edit intent: fluency