wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this research, we have processed 20,000 images collected from TAS Test and labelled 6,450 images to detect different hand poses in the hand movement tests.
Revised:  In this research, we have processed 20,000 images collected from TAS Test and labelled 6,450 images to detect different hand gestures in the hand movement tests.
Edit intent: clarity
Original:  Firstly, we compared  the performance of different network structures for hand poses detection.
Revised:  Firstly, we compared the performance of different network structures for hand gesture detection.
Edit intent: clarity
Original:  Secondly, we introduced a transformer block in the state of art network and increased the classification performance of the similar gestures .
Revised:  Secondly, we have made many attempts to improve the accuracy of the model and have succeeded in improving the classification accuracy for similar gestures by implementing attention layers .
Edit intent: clarity
Original:  However, a well-known caveat of DRL algorithms is their inefficiency, requiring huge amounts of data to converge.
Edit intent: coherence
Original:  Off-policy algorithms tend to be more sample-efficient  , and can additionally benefit from any off-policy data stored in the replay buffer.
Revised:  Off-policy algorithms tend to be more sample-efficient than their on-policy counterparts , and can additionally benefit from any off-policy data stored in the replay buffer.
Edit intent: clarity
Original:  On the radio access network (RAN), the spectrum slicing of the shared radio resources is a critical task to guarantee the performance of each service.
Revised:  On the radio access network . In this context, spectrum slicing of the shared radio resources is a critical task to guarantee the performance of each service.
Edit intent: clarity
Original:  In this paper, we analyze a downlink communication in which a base station (BS) should serve two types of traffic , enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) , respectively .
Revised:  We analyze a downlink communication in which a base station (BS) should serve two types of traffic , enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) , respectively .
Edit intent: clarity
Original:  In this paper, we analyze a downlink communication in which a base station (BS) should serve two types of traffic , enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) , respectively .
Revised:  In this paper, we analyze a downlink communication serving two types of traffic , enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) , respectively .
Edit intent: clarity
Original:  In this paper, we analyze a downlink communication in which a base station (BS) should serve two types of traffic , enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) , respectively .
Revised:  In this paper, we analyze a downlink communication in which a base station (BS) should serve two types of traffic : enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) , respectively .
Edit intent: fluency
Original:  In this paper, we analyze a downlink communication in which a base station (BS) should serve two types of traffic , enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) , respectively .
Revised:  In this paper, we analyze a downlink communication in which a base station (BS) should serve two types of traffic , enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) .
Edit intent: coherence
Original:  Due to the nature of low-latency traffic, the BS knows the channel state information (CSI) of the eMBB users only.
Revised:  Due to the nature of low-latency traffic, the base station knows the channel state information (CSI) of the eMBB users only.
Edit intent: clarity
Original:  We analyze the impact of resource sharing, showing that the knowledge of eMBB CSI can be used also in resource allocation for URLLC users.
Edit intent: coherence
Original:  Based on this analysis, we propose two algorithms: a feasible and a block coordinated descent approach (BCD).
Revised:  Based on this analysis, we propose two algorithms: a feasible and a block coordinated descent (BCD).
Edit intent: clarity
Original:  The numerical results show that NOMA leads to a lower power consumption compared to OMA, except when the  URLLC user is very close to the BS . For the last case, the optimal approach depends on the channel condition of the eMBB user.
Revised:  The numerical results show that NOMA leads to a lower power consumption compared to OMA, except when the average channel gain of the the URLLC user is very close to the BS . For the last case, the optimal approach depends on the channel condition of the eMBB user.
Edit intent: clarity
Original:  The numerical results show that NOMA leads to a lower power consumption compared to OMA, except when the  URLLC user is very close to the BS . For the last case, the optimal approach depends on the channel condition of the eMBB user.
Revised:  The numerical results show that NOMA leads to a lower power consumption compared to OMA, except when the URLLC user is very high . For the last case, the optimal approach depends on the channel condition of the eMBB user.
Edit intent: clarity
Original:  The numerical results show that NOMA leads to a lower power consumption compared to OMA, except when the  URLLC user is very close to the BS . For the last case, the optimal approach depends on the channel condition of the eMBB user.
Revised:  The numerical results show that NOMA leads to a lower power consumption compared to OMA, except when the URLLC user is very close to the BS . For the latter case, the optimal approach depends on the channel condition of the eMBB user.
Edit intent: clarity
Original:  In any case, even when the OMA paradigm attains the best performance, the gap with NOMA is negligible , proving the NOMA capacity in exploiting the shared resources to reduce the power consumption in  every condition.
Revised:  Even when OMA attains the best performance, the gap with NOMA is negligible , proving the NOMA capacity in exploiting the shared resources to reduce the power consumption in every condition.
Edit intent: clarity
Original:  In any case, even when the OMA paradigm attains the best performance, the gap with NOMA is negligible , proving the NOMA capacity in exploiting the shared resources to reduce the power consumption in  every condition.
Revised:  In any case, even when the OMA paradigm attains the best performance, the gap with NOMA is negligible . This shows the capability of NOMA to reduce the power consumption in every condition.
Edit intent: clarity
Original:  Such formulation contains many classic applications such as the canonical (quantity-based) network revenue management problem and the Adwords problem.
Revised:  Such formulation gives rise to many classic applications such as the canonical (quantity-based) network revenue management problem and the Adwords problem.
Edit intent: clarity
Original:  Specifically, we study the objective of providing fairness guarantees while maintaining low regret.
Revised:  Instead of focusing only on regret minimization, this paper aims to provide fairness guarantees while maintaining low regret.
Edit intent: clarity
Original:  We define a fair offline solution as the analytic center of the offline optimal solution set, and   propose a fair algorithm that uses an interior-point LP solver and dynamically detects unfair resource spending.
Revised:  We define the fair offline solution as the analytic center of the offline optimal solution set, and propose a fair algorithm that uses an interior-point LP solver and dynamically detects unfair resource spending.
Edit intent: fluency
Original:  We define a fair offline solution as the analytic center of the offline optimal solution set, and   propose a fair algorithm that uses an interior-point LP solver and dynamically detects unfair resource spending.
Revised:  We define a fair offline solution as the analytic center of the offline optimal solution set, and define propose a fair algorithm that uses an interior-point LP solver and dynamically detects unfair resource spending.
Edit intent: fluency
Original:  Our algorithm can control cumulative unfairness (the cumulative deviation from the online solutions to the offline fair solution) on the scale of order O(\log(T)), while maintaining the regret to be bounded with dependency on T.
Revised:  Our algorithm can control cumulative unfairness on the scale of order O(\log(T)), while maintaining the regret to be bounded with dependency on T.
Edit intent: coherence
Original:  As for hierarchical reasoning task, our approach outperforms previous best results by an average of 20\% across three hierarchical datasets.
Revised:  As for hierarchical reasoning task, our approach outperforms previous best results by an average of 20\% across the three datasets.
Edit intent: fluency
Original: X-ray image plays an important role in manufacturing  for quality assurance, because it can reflect the internal condition of weld region.
Revised: X-ray image plays an important role in manufacturing industry for quality assurance, because it can reflect the internal condition of weld region.
Edit intent: clarity
Original:  In this paper, we propose a weld defect detection method based on convolution neural network (CNN) , namely Lighter and Faster YOLO (LF-YOLO).
Revised:  In this paper, we propose a weld defect detection method based on convolution neural network , namely Lighter and Faster YOLO (LF-YOLO).
Edit intent: clarity
Original:  In particularly, an enhanced multiscale feature ( EMF ) module is designed to implement both parameter-based and parameter-free multi-scale information extracting operation.
Revised:  In particularly, a reinforced multiscale feature ( EMF ) module is designed to implement both parameter-based and parameter-free multi-scale information extracting operation.
Edit intent: clarity
Original:  EMF enables the extracted feature map capable to represent more plentiful information, which is achieved by superior hierarchical fusion structure.
Revised:  RMF enables the extracted feature map capable to represent more plentiful information, which is achieved by superior hierarchical fusion structure.
Edit intent: fluency
Original:  EFE processes input data with extremely low consumption, and improve the practicability of whole network in actual industry.
Revised:  EFE processes input data with extremely low consumption, and improves the practicability of whole network in actual industry.
Edit intent: fluency
Original:  Experimental results show that our weld defect  network achieves satisfactory balance between performance and consumption, and reaches 92.9  mAP50 with 61.5 FPS . To further prove the ability of our method, we test it on public dataset MS COCO, and the results show that our LF-YOLO has a outstanding versatility detection performance.
Revised:  Experimental results show that our weld defect detection network achieves satisfactory balance between performance and consumption, and reaches 92.9 mAP50 with 61.5 FPS . To further prove the ability of our method, we test it on public dataset MS COCO, and the results show that our LF-YOLO has a outstanding versatility detection performance.
Edit intent: fluency
Original:  Experimental results show that our weld defect  network achieves satisfactory balance between performance and consumption, and reaches 92.9  mAP50 with 61.5 FPS . To further prove the ability of our method, we test it on public dataset MS COCO, and the results show that our LF-YOLO has a outstanding versatility detection performance.
Revised:  Experimental results show that our weld defect network achieves satisfactory balance between performance and consumption, and reaches 92.9 mAP50 with 61.5 frames per second (FPS) . To further prove the ability of our method, we test it on public dataset MS COCO, and the results show that our LF-YOLO has a outstanding versatility detection performance.
Edit intent: clarity
Original: The identification (ID) capacity region of the compound broadcast channel is determined under an average error criterion, where the sender has no channel state information.
Revised: The identification capacity region of the compound broadcast channel is determined under an average error criterion, where the sender has no channel state information.
Edit intent: clarity
Original:  We give single-letter ID capacity formulas for discrete channels and MIMO Gaussian channels , under an average input constraint.
Revised:  We give single-letter identification capacity formulas for discrete channels and MIMO Gaussian channels , under an average input constraint.
Edit intent: fluency
Original:  We give single-letter ID capacity formulas for discrete channels and MIMO Gaussian channels , under an average input constraint.
Revised:  We give single-letter ID capacity formulas for discrete channels and multiple-input multiple-output Gaussian channels under an average input constraint.
Edit intent: clarity
Original:  Furthermore, the ID capacity region of the compound MIMO broadcast channel is in general larger than the transmission capacity region.
Revised:  Furthermore, the identification capacity region of the compound MIMO broadcast channel is in general larger than the transmission capacity region.
Edit intent: fluency
Original:  Furthermore, the ID capacity region of the compound MIMO broadcast channel is in general larger than the transmission capacity region.
Revised:  Furthermore, the ID capacity region of the compound multiple-input multiple-output broadcast channel can be larger than the transmission capacity region.
Edit intent: clarity
Original:  This is a departure from the single-user behavior of ID , since the ID capacity of a single-user channel equals the transmission capacity.
Revised:  This is a departure from the single-user behavior of identification , since the ID capacity of a single-user channel equals the transmission capacity.
Edit intent: clarity
Original:  This is a departure from the single-user behavior of ID , since the ID capacity of a single-user channel equals the transmission capacity.
Revised:  This is a departure from the single-user behavior of ID , since the identification capacity of a single-user channel equals the transmission capacity.
Edit intent: fluency
Original:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Revised:  We name our proposed method as \underline{ atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Edit intent: fluency
Original:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Revised:  We name our proposed method as \underline{c a ta leakage in verticalf ederated le arning (CAFE).
Edit intent: fluency
Original:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Revised:  We name our proposed method as \underline{c atastrophic d ta leakage in verticalf ederated le arning (CAFE).
Edit intent: fluency
Original:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Revised:  We name our proposed method as \underline{c atastrophic da f ederated le arning (CAFE).
Edit intent: fluency
Original:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Revised:  We name our proposed method as \underline{c atastrophic da ta leakage in vertical ederated le arning (CAFE).
Edit intent: fluency
Original:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Revised:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf e arning (CAFE).
Edit intent: fluency
Original:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated le arning (CAFE).
Revised:  We name our proposed method as \underline{c atastrophic da ta leakage in verticalf ederated l arning (CAFE).
Edit intent: fluency
Original:  Specifically, we improve the previous state-of-the-art OOD detection by relatively 7\% AUROC on CIFAR100 vs. SVHN and achieve the best calibration performance of 0.084 Expected Calibration Error on the corrupted CIFAR100C dataset.
Edit intent: coherence
Original:  These networks reconstruct the semantic frames surrounding feel , enabling a quantification of prominent associations and emotions focused around feelings.
Revised:  These networks reconstruct the semantic frames surrounding 'feel' , enabling a quantification of prominent associations and emotions focused around feelings.
Edit intent: fluency
Original:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of regret ,unhealthy lifestyle andlow mental well-being .
Revised:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of ,unhealthy lifestyle andlow mental well-being .
Edit intent: clarity
Original:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of regret ,unhealthy lifestyle andlow mental well-being .
Revised:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of regret unhealthy lifestyle andlow mental well-being .
Edit intent: fluency
Original:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of regret ,unhealthy lifestyle andlow mental well-being .
Revised:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of regret , andlow mental well-being .
Edit intent: clarity
Original:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of regret ,unhealthy lifestyle andlow mental well-being .
Revised:  We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of regret ,unhealthy lifestyle low mental well-being .
Edit intent: fluency
Original:  Importantly, negative associations and emotions co-existed with trustful/positive language, focused on getting better .
Revised:  Importantly, negative associations and emotions co-existed with trustful/positive language, focused on 'getting better' .
Edit intent: fluency
Original:  The goal is to do this in an error-resilient way, such that even given some fraction of adversarial corruptions  , both parties still learn f(x,y).
Revised:  The goal is to do this in an error-resilient way, such that even given some fraction of adversarial corruptions to the protocol , both parties still learn f(x,y).
Edit intent: clarity
Original:  First, we introduce a standard vulnerability profiling library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss.
Revised:  First, we introduce a Standard Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss.
Edit intent: fluency
Original:  In particular , the encoder can be used as a feature extractor for many downstream tasks with little or no labeled training data.
Revised:  Specifically , the encoder can be used as a feature extractor for many downstream tasks with little or no labeled training data.
Edit intent: coherence
Original: In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or ``hits'' for therapeutic protein targets from docked poses at specific binding sites.
Revised: In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or "hits" for therapeutic protein targets from docked poses at specific binding sites.
Edit intent: fluency
Original:  To solve this problem, we propose a Visual Spatio-temporal Relation-enhanced Network (VSR-Net), a novel cross-modal retrieval framework that enhances visual representation with spatio-temporal relations among components  . Specifically, visual spatio-temporal relations are encoded using a multi-layer spatio-temporal transformer to learn visual relational features.
Revised:  To solve this problem, we propose a Visual Spatio-Temporal Relation-Enhanced Network (VSR-Net), a novel cross-modal retrieval framework that enhances visual representation with spatio-temporal relations among components . Specifically, visual spatio-temporal relations are encoded using a multi-layer spatio-temporal transformer to learn visual relational features.
Edit intent: fluency
Original:  To solve this problem, we propose a Visual Spatio-temporal Relation-enhanced Network (VSR-Net), a novel cross-modal retrieval framework that enhances visual representation with spatio-temporal relations among components  . Specifically, visual spatio-temporal relations are encoded using a multi-layer spatio-temporal transformer to learn visual relational features.
Revised:  To solve this problem, we propose a Visual Spatio-temporal Relation-enhanced Network (VSR-Net), a novel cross-modal retrieval framework that considers the spatial-temporal visual relations among components . Specifically, visual spatio-temporal relations are encoded using a multi-layer spatio-temporal transformer to learn visual relational features.
Edit intent: clarity
Original:  We combine fine-grained local relation and global features in bridging text-video modalities . Extensive experimental are conducted on both MSR-VTT and MSVD datasets.
Revised:  We combine fine-grained local relation and global features in bridging text-video retrieval . Extensive experimental are conducted on both MSR-VTT and MSVD datasets.
Edit intent: clarity
Original:  We also present two public ASR models based on Wav2Vec 2.0 XLSR-53 and fine-tuned over CORAA.
Revised:  We also present a public ASR model based on Wav2Vec 2.0 XLSR-53 and fine-tuned over CORAA.
Edit intent: clarity
Original:  Our best model achieved a Word Error Rate of 27.35 \% on CORAA test set and 16.01 \% on Common Voice test set.
Revised:  Our model achieved a Word Error Rate of 27.35 \% on CORAA test set and 16.01 \% on Common Voice test set.
Edit intent: clarity
Original:  In this paper, we employ an amplify-and-forward (AF) dual-hop relay to mitigate the effect of pointing errors and extend the range of a wireless backhaul network .
Revised:  In this paper, we employ an amplify-and-forward (AF) dual-hop relaying to mitigate the effect of pointing errors and extend the range of a wireless backhaul network .
Edit intent: fluency