wanyu/IteraTeR_v2 huggingface dataset:
Original:  However, as the Internet of Things (IoT) connects  low-power, microcontroller-based devices which take part in larger distributed cyberphysical systems, such low-power IoT devices are not easy to integrate in DevOps workflows .
Revised:  Meanwhile the Internet of Things (IoT) connects low-power, microcontroller-based devices which take part in larger distributed cyberphysical systems, such low-power IoT devices are not easy to integrate in DevOps workflows .
Edit intent: coherence
Original:  However, as the Internet of Things (IoT) connects  low-power, microcontroller-based devices which take part in larger distributed cyberphysical systems, such low-power IoT devices are not easy to integrate in DevOps workflows .
Revised:  However, as the Internet of Things (IoT) connects low-power, microcontroller-based devices .
Edit intent: coherence
Original:  In this paper, we contribute to mitigate this problem by designing Femto-Containers, a new hardware-independent mechanism which enable the virtualization and isolation of software modules embedded on microcontrollers , using an approach extending and adapting Berkeley Packet Filters (eBPF).
Revised:  In this paper, we contribute to bridge this gap by designing Femto-Containers, a new hardware-independent mechanism which enable the virtualization and isolation of software modules embedded on microcontrollers , using an approach extending and adapting Berkeley Packet Filters (eBPF).
Edit intent: clarity
Original:  In this paper, we contribute to mitigate this problem by designing Femto-Containers, a new hardware-independent mechanism which enable the virtualization and isolation of software modules embedded on microcontrollers , using an approach extending and adapting Berkeley Packet Filters (eBPF).
Revised:  In this paper, we contribute to mitigate this problem by designing Femto-Containers, a new architecture which enables containerization, virtualization and secure deployment of software modules embedded on microcontrollers , using an approach extending and adapting Berkeley Packet Filters (eBPF).
Edit intent: clarity
Original:  We implement a Femto-Container hosting engine, which we integrate in a common low-power IoT operating system (RIOT), and is thus enhanced with the ability to start, update or terminate Femto-Containers on demand, securely over a standard IPv6/6LoWPAN network.
Revised:  We implement a Femto-Container hosting engine, which we integrate in a common low-power networks. As proof-of-concept, we implemented and evaluated Femto-Containers on demand, securely over a standard IPv6/6LoWPAN network.
Edit intent: clarity
Original:  We show that Femto-Containers can virtualize and isolate multiple software modules  executed concurrently, with very small memory footprint overhead (below 10\%) and very small startup time (tens of microseconds) compared to native code execution.
Revised:  We show that Femto-Containers can virtualize and isolate multiple software modules , executed concurrently, with very small memory footprint overhead (below 10\%) and very small startup time (tens of microseconds) compared to native code execution.
Edit intent: fluency
Original:  We carry out experiments deploying Femto-Containers on a testbed using heterogeneous IoT hardware based on the popular microcontroller architectures Arm Cortex-M, ESP32 and RISC-V.
Revised:  We show that Femto-Containers on a testbed using heterogeneous IoT hardware based on the popular microcontroller architectures Arm Cortex-M, ESP32 and RISC-V.
Edit intent: clarity
Original:  We show that compared to prior work on software-based low-power virtualization and isolation , Femto-Containers  offer an attractive trade-off in terms of memory footprint, energy consumption, and security.
Revised:  We show that compared to prior work , Femto-Containers offer an attractive trade-off in terms of memory footprint, energy consumption, and security.
Edit intent: clarity
Original:  We show that compared to prior work on software-based low-power virtualization and isolation , Femto-Containers  offer an attractive trade-off in terms of memory footprint, energy consumption, and security.
Revised:  We show that compared to prior work on software-based low-power virtualization and isolation , Femto-Containers thus offer an attractive trade-off in terms of memory footprint, energy consumption, and security.
Edit intent: coherence
Original: Recommender systems -- and especially matrix factorization-based collaborative filtering algorithms -- play a crucial role in mediating our access to online information.
Revised: Recommender systems play a crucial role in mediating our access to online information.
Edit intent: clarity
Original:  We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the general user population, then those items may not be recommended together to a user, regardless of that user's preferences and ratings history.
Revised:  We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the general user population, then those items may not be recommended together to a user, regardless of that user's preferences and ratings history.
Edit intent: fluency
Original:  We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the general user population, then those items may not be recommended together to a user, regardless of that user's preferences and ratings history.
Revised:  We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the general user population, then those items may not be recommended together to a user, regardless of that user's preferences and rating history.
Edit intent: fluency
Original:  First, we introduce a notion of joint accessibility , which measures the extent to which a set of items can jointly be accessed by users.
Revised:  First, we introduce a notion of joint accessibility , which measures the extent to which a set of items can jointly be accessed by users.
Edit intent: fluency
Original:  In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN , the GoogLeNet , from 2D images captured in multiple views of the environment as the robot operates, to create  consolidated representations of  visual features acquired in the regions covered by each topological node.
Revised:  In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN (GoogLeNet) , from 2D images captured in multiple views of the environment as the robot operates, to create consolidated representations of visual features acquired in the regions covered by each topological node.
Edit intent: clarity
Original:  In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN , the GoogLeNet , from 2D images captured in multiple views of the environment as the robot operates, to create  consolidated representations of  visual features acquired in the regions covered by each topological node.
Revised:  In contrast, this work introduces a topological semantic mapping method that uses deep visual features extracted by a CNN , the GoogLeNet , from 2D images captured in multiple views of the environment as the robot operates, to create consolidated representations of the visual features acquired in the regions covered by each topological node.
Edit intent: fluency
Original:  These consolidated representations allow flexible recognition of semantic properties of the regions and use in a range of visual tasks.
Revised:  These representations allow flexible recognition of semantic properties of the regions and use in a range of visual tasks.
Edit intent: clarity
Original:  These consolidated representations allow flexible recognition of semantic properties of the regions and use in a range of visual tasks.
Revised:  These consolidated representations allow flexible recognition of semantic properties of the regions and use in other visual tasks.
Edit intent: clarity
Original:  The experiments, performed using a real-world indoor dataset , showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results .
Revised:  Experiments with a real-world indoor dataset , showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results .
Edit intent: clarity
Original:  The experiments, performed using a real-world indoor dataset , showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results .
Revised:  The experiments, performed using a real-world indoor dataset showed that the method is able to consolidate the visual features of regions and use them to recognize objects and place categories as semantic properties, and to indicate the topological location of images, with very promising results .
Edit intent: fluency
Original:  Recent studies have utilized power side-channel information to characterize this intrusion behavior on IoT devices but still lack  real-time detection approaches.
Revised:  Recent studies have utilized power side-channel information to identify this intrusion behavior on IoT devices but still lack real-time detection approaches.
Edit intent: clarity
Original:  Recent studies have utilized power side-channel information to characterize this intrusion behavior on IoT devices but still lack  real-time detection approaches.
Revised:  Recent studies have utilized power side-channel information to characterize this intrusion behavior on IoT devices but still lack accurate models in real-time detection approaches.
Edit intent: clarity
Original:  To realize the real-time system, we first proposed a lightweight power auditing device called Power Auditor.
Revised:  To develop the real-time system, we first proposed a lightweight power auditing device called Power Auditor.
Edit intent: clarity
Original:  To realize the real-time system, we first proposed a lightweight power auditing device called Power Auditor.
Revised:  To realize the real-time system, we proposed a lightweight power auditing device called Power Auditor.
Edit intent: coherence
Original:  With the Power Auditor, we developed a Distributed CNN classifier for online inference in our laboratory setting.
Revised:  We also designed a distributed CNN classifier for online inference in our laboratory setting.
Edit intent: clarity
Original:  With the Power Auditor, we developed a Distributed CNN classifier for online inference in our laboratory setting.
Revised:  With the Power Auditor, we developed a Distributed CNN classifier for online inference in a laboratory setting.
Edit intent: fluency
Original:  In order to protect data leakage and reduce networking redundancy, we also proposed a privacy-preserved inference protocol via Packed Homomorphic Encryption and a sliding window protocol in our system.
Revised:  In order to protect data leakage and reduce networking redundancy, we then proposed a privacy-preserved inference protocol via Packed Homomorphic Encryption and a sliding window protocol in our system.
Edit intent: coherence
Original:  In this paper, we first propose  degree of delay (DoD)  to quantify the issue of information delay, and characterize DoD for three typical transmission schemes, i.e., TDMA, beamforming based TDMA (BD-TDMA)  and retrospective interference alignment (RIA).
Revised:  In this paper, we first propose degree of delay (DoD) , to quantify the issue of information delay, and characterize DoD for three typical transmission schemes, i.e., TDMA, beamforming based TDMA (BD-TDMA) and retrospective interference alignment (RIA).
Edit intent: fluency
Original:  In this paper, we first propose  degree of delay (DoD)  to quantify the issue of information delay, and characterize DoD for three typical transmission schemes, i.e., TDMA, beamforming based TDMA (BD-TDMA)  and retrospective interference alignment (RIA).
Revised:  In this paper, we first propose degree of delay (DoD) to quantify the issue of information delay, and characterize DoD for three typical transmission schemes, i.e., TDMA, beamforming based TDMA (BD-TDMA) , and retrospective interference alignment (RIA).
Edit intent: fluency
Original:  By analyzing DoD of these schemes, its value mainly depends on three factors, i.e., delay sensitive factor,  queueing delay slot and size of dataset.
Revised:  By analyzing DoD in these schemes, its value mainly depends on three factors, i.e., delay sensitive factor, queueing delay slot and size of dataset.
Edit intent: clarity
Original:  By analyzing DoD of these schemes, its value mainly depends on three factors, i.e., delay sensitive factor,  queueing delay slot and size of dataset.
Revised:  By analyzing DoD of these schemes, its value mainly depends on three factors, i.e., delay sensitive factor, size of data set, and queueing delay slot and size of dataset.
Edit intent: clarity
Original:  Therefore, to improve queueing delay slot and reduce DoD , three novel joint IA schemes are proposed for BC networks with different amounts of users.
Revised:  Therefore, to improve queueing delay slot to improve DoD. Herein , three novel joint IA schemes are proposed for BC networks with different amounts of users.
Edit intent: clarity
Original:  Therefore, to improve queueing delay slot and reduce DoD , three novel joint IA schemes are proposed for BC networks with different amounts of users.
Revised:  Therefore, to improve queueing delay slot and reduce DoD , three novel joint IA schemes are proposed for BC networks with different number of users.
Edit intent: clarity
Original:  That is, hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme (HAA-IPIE-RIR)  and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR).
Revised:  That is, hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme (HAA-IPIE-RIR) , and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR).
Edit intent: fluency
Original:  The second scheme extends the application scenarios of the first scheme from 2-user to K-user while brings huge computational burden.
Revised:  Based on the first scheme, the second scheme extends the application scenarios of the first scheme from 2-user to K-user while brings huge computational burden.
Edit intent: coherence
Original:  The second scheme extends the application scenarios of the first scheme from 2-user to K-user while brings huge computational burden.
Revised:  The second scheme extends the application scenarios from 2-user to K-user while brings huge computational burden.
Edit intent: clarity
Original:  The second scheme extends the application scenarios of the first scheme from 2-user to K-user while brings huge computational burden.
Revised:  The second scheme extends the application scenarios of the first scheme from 2-user to K-user while causing heavy computational burden.
Edit intent: clarity
Original:  The third scheme relieves this computational complexity burdenbut it has somewhat degree of freedom (DoF) loss .
Revised:  The third scheme relieves such computational burden, though it has certain degree of freedom (DoF) loss .
Edit intent: clarity
Original:  Then the two triangular solvers in the ILU preconditioning procedure are replaced by two matrix-vector multiplications, which can be fine-grained parallelized.
Revised:  The two triangular solvers in the ILU preconditioning procedure are replaced by two matrix-vector multiplications, which can be fine-grained parallelized.
Edit intent: coherence
Original:  We test the new algorithm by solving some linear systems and eigenvalue problems  .
Revised:  We test this method by solving some linear systems and eigenvalue problems .
Edit intent: clarity
Original:  However, we observe an uneven distribution of leading silence in dataset's training and test data, which hints at the target label: Bona-fide instances tend to have significantly longer leading  silences than spoofed instances.
Revised:  However, we observe an uneven distribution of silence duration in the training and test data, which hints at the target label: Bona-fide instances tend to have significantly longer leading silences than spoofed instances.
Edit intent: clarity
Original:  However, we observe an uneven distribution of leading silence in dataset's training and test data, which hints at the target label: Bona-fide instances tend to have significantly longer leading  silences than spoofed instances.
Revised:  However, we observe an uneven distribution of leading silence in dataset's training and test splits, which tends to correlate with the target prediction label. Bonafide instances tend to have significantly longer leading silences than spoofed instances.
Edit intent: clarity
Original:  This could be problematic, since a model may learn to only, or at least partially, base its decision on the length of the leading silence (similar to the issue with the Pascal VOC 2007 dataset, where all images of horses also contained a specific watermark).
Edit intent: coherence
Original:  We train a number of networks on only a) the length of the leading silence and b) with and without leading silence.
Revised:  We compare several types of models trained on a) only the duration of the leading silence and b) with and without leading silence.
Edit intent: clarity
Original:  Results show that models trained on only the length of the leading silence perform suspiciously well: They achieve up to 85\% percent accuracy and an equal error rate (EER) of 0.15 on the 'eval' split of the data.
Revised:  Results show that models trained on only the duration of the leading silence perform suspiciously well: They achieve up to 85\% percent accuracy and an equal error rate (EER) of 0.15 on the 'eval' split of the data.
Edit intent: clarity
Original:  Results show that models trained on only the length of the leading silence perform suspiciously well: They achieve up to 85\% percent accuracy and an equal error rate (EER) of 0.15 on the 'eval' split of the data.
Revised:  Results show that models trained on only the length of the leading silence perform particularly well, and achieve up to 85\% percent accuracy and an equal error rate (EER) of 0.15 on the 'eval' split of the data.
Edit intent: clarity
Original: Existing scene text removal methods mainly train an elaborate network with paired images to realize the function of text localization and background reconstruction simultaneously, but there exists two problems: 1) lacking the exhaustive erasure of text region and 2) causing the  excessive erasure to text-free areas.
Revised: Scene text removal (STR) contains two processes: text localization and background reconstruction simultaneously, but there exists two problems: 1) lacking the exhaustive erasure of text region and 2) causing the excessive erasure to text-free areas.
Edit intent: clarity
Original: Existing scene text removal methods mainly train an elaborate network with paired images to realize the function of text localization and background reconstruction simultaneously, but there exists two problems: 1) lacking the exhaustive erasure of text region and 2) causing the  excessive erasure to text-free areas.
Revised: Existing scene text removal methods mainly train an elaborate network with paired images to realize the function of text localization and background reconstruction simultaneously, but there exists two problems: 1) the excessive erasure to text-free areas.
Edit intent: coherence
Original:  As each stage aims to take a further step toward the text-removed image rather than directly regress to the final result, the decomposed operation reduces the learning difficulty in each stage, and an exhaustive erasure result can be obtained by iterating over lightweight erasing blocks with shared parameters.
Revised:  Each erasing stage takes an equal step toward the text-removed image rather than directly regress to the final result, the decomposed operation reduces the learning difficulty in each stage, and an exhaustive erasure result can be obtained by iterating over lightweight erasing blocks with shared parameters.
Edit intent: clarity
Original:  Extensive experiments demonstrate that PERT obtains the state-of-the-art results on both synthetic and real-world datasets . Code is available  URL
Revised:  Extensive experiments demonstrate that PERT obtains the state-of-the-art results on both synthetic and real-world datasets . Code is available at URL
Edit intent: fluency
Original: Visual recognition has been dominated by convolutionalneural networks (CNNs) for years.
Revised: Visual recognition has been dominated by convolutional neural networks (CNNs) for years.
Edit intent: fluency
Original:  In this work, we aim to closethe performance gap and demonstrate that attention-basedmodels are indeed able to outperform CNNs.
Revised:  In this work, we try to close the performance gap and demonstrate that attention-basedmodels are indeed able to outperform CNNs.
Edit intent: fluency
Original:  In this work, we aim to closethe performance gap and demonstrate that attention-basedmodels are indeed able to outperform CNNs.
Revised:  In this work, we aim to closethe performance gap and demonstrate that attention-based models are indeed able to outperform CNNs.
Edit intent: fluency
Original:  We found thatthe main factor limiting the performance of ViTs for Ima-geNet classification is their low efficacy in encoding fine-level features into the token representations.
Revised:  We find a major factor limiting the performance of ViTs for Ima-geNet classification is their low efficacy in encoding fine-level features into the token representations.
Edit intent: fluency
Original:  We found thatthe main factor limiting the performance of ViTs for Ima-geNet classification is their low efficacy in encoding fine-level features into the token representations.
Revised:  We found thatthe main factor limiting the performance of ViTs for ImageNet classification is their low efficacy in encoding fine-level features into the token representations.
Edit intent: fluency
Original:  To resolvethis , we introduce a noveloutlook attentionand present asimple and general architecture, termed Vision Outlooker (VOLO).
Revised:  To resolve this , we introduce a noveloutlook attentionand present asimple and general architecture, termed Vision Outlooker (VOLO).
Edit intent: fluency
Original:  To resolvethis , we introduce a noveloutlook attentionand present asimple and general architecture, termed Vision Outlooker (VOLO).
Revised:  To resolvethis , we introduce a novel outlook attention and present a simple and general architecture, termed Vision Outlooker (VOLO).
Edit intent: fluency
Original:  In addition, the pre-trainedVOLO transfers well to downstream tasks, such as seman-tic segmentation.
Revised:  In addition, the pre-trained VOLO transfers well to downstream tasks, such as seman-tic segmentation.
Edit intent: fluency
Original:  In addition, the pre-trainedVOLO transfers well to downstream tasks, such as seman-tic segmentation.
Revised:  In addition, the pre-trainedVOLO transfers well to downstream tasks, such as semantic segmentation.
Edit intent: fluency
Original:  We achieve 84.3\% mIoU score on thecityscapes validation set and 54.3\% on the ADE20K valida-tion set.
Revised:  We achieve 84.3\% mIoU score on the cityscapes validation set and 54.3\% on the ADE20K valida-tion set.
Edit intent: fluency