wanyu/IteraTeR_v2 huggingface dataset:
Original:  in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking  techniques, to detect security vulnerabilities in typical UAVs . We also investigate fragility aspects related to the UAV communication link since all remaining UAV components (e.g., position, velocity  and attitude control) heavily depend on it .
Revised:  in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking techniques, to detect security vulnerabilities in typical UAVs . We also investigate fragility aspects related to the UAV communication link . All UAV components (e.g., position, velocity and attitude control) heavily depend on it .
Edit intent: coherence
Original:  in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking  techniques, to detect security vulnerabilities in typical UAVs . We also investigate fragility aspects related to the UAV communication link since all remaining UAV components (e.g., position, velocity  and attitude control) heavily depend on it .
Revised:  in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking techniques, to detect security vulnerabilities in typical UAVs . We also investigate fragility aspects related to the UAV communication link since all remaining UAV components (e.g., position, velocity , and attitude control) heavily depend on it .
Edit intent: fluency
Original:  in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking  techniques, to detect security vulnerabilities in typical UAVs . We also investigate fragility aspects related to the UAV communication link since all remaining UAV components (e.g., position, velocity  and attitude control) heavily depend on it .
Revised:  in particular, existing software analyzers and verifiers, which implement fuzzing and bounded model checking techniques, to detect security vulnerabilities in typical UAVs . We also investigate fragility aspects related to the UAV communication link since all remaining UAV components (e.g., position, velocity and attitude control) heavily depend on the communication link .
Edit intent: fluency
Original: We present improved results for approximating Maximum Independent Set (\MaxIS) in the standard LOCAL and CONGEST models of distributed computing.
Revised: We present improved results for approximating Maximum Independent Set (\MaxIS) in the CONGEST and LOCAL models of distributed computing.
Edit intent: clarity
Original:  Let n and \Delta be the number of nodes and maximum degree in the input graph, respectively\MIS \MIS . Bar-Yehuda et al.
Revised:  Given an input graph, let n and \Delta be the number of nodes and maximum degree in the input graph, respectively\MIS \MIS . Bar-Yehuda et al.
Edit intent: clarity
Original:  Let n and \Delta be the number of nodes and maximum degree in the input graph, respectively\MIS \MIS . Bar-Yehuda et al.
Revised:  Let n and \Delta be the number of nodes and maximum degree , respectively, and let\MIS \MIS . Bar-Yehuda et al.
Edit intent: clarity
Original:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation to \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% (n,\Delta) is the running time for finding amaximal independent set, and W is the maximum weight of a node in the network\poly  .
Revised:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation for \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% (n,\Delta) is the running time for finding amaximal independent set, and W is the maximum weight of a node in the network\poly .
Edit intent: fluency
Original:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation to \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% (n,\Delta) is the running time for finding amaximal independent set, and W is the maximum weight of a node in the network\poly  .
Revised:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation to \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% maximal independent set, and W is the maximum weight of a node in the network\poly .
Edit intent: clarity
Original:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation to \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% (n,\Delta) is the running time for finding amaximal independent set, and W is the maximum weight of a node in the network\poly  .
Revised:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation to \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% (n,\Delta) is the running time for finding a independent set, and W is the maximum weight of a node in the network\poly .
Edit intent: clarity
Original:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation to \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% (n,\Delta) is the running time for finding amaximal independent set, and W is the maximum weight of a node in the network\poly  .
Revised:  [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a \Delta-approximation to \MaxIS in O(\MIS(n,\Delta)\log W) rounds, where %DIFDELCMD < \MIS%%% (n,\Delta) is the running time for finding amaximal independent set, and W is the maximum weight of a node in the network\poly (n) .
Edit intent: clarity
Original:  Whether their algorithm is randomized or deterministic depends on the \MIS algorithm that they use as a black-box.
Revised:  Whether their algorithm is deterministic or randomized depends on the \MIS algorithm that they use as a black-box.
Edit intent: fluency
Original:  Whether their algorithm is randomized or deterministic depends on the \MIS algorithm that they use as a black-box.
Revised:  Whether their algorithm is randomized or deterministic depends on the \MIS algorithm that is used as a black-box.
Edit intent: fluency
Original:  Our results: (1) A deterministic O(%DIFDELCMD < \MIS%%% (n,\Delta)\poly ) rounds algorithm for O(\Delta)-approximation to%DIFDELCMD < \MaxIS %%% in the CONGEST model.
Revised:  Our %DIFDELCMD < \MIS%%% (n,\Delta)\poly ) rounds algorithm for O(\Delta)-approximation to%DIFDELCMD < \MaxIS %%% in the CONGEST model.
Edit intent: clarity
Original:  Our results: (1) A deterministic O(%DIFDELCMD < \MIS%%% (n,\Delta)\poly ) rounds algorithm for O(\Delta)-approximation to%DIFDELCMD < \MaxIS %%% in the CONGEST model.
Revised:  Our results: (1) A deterministic O(%DIFDELCMD < \MIS%%% (n,\Delta)\poly ) %DIFDELCMD < \MaxIS %%% in the CONGEST model.
Edit intent: clarity
Original:  (2) A randomized 2^{O(\sqrt{\log \log n that finds, with high probability, an O(\Delta) -approximation to \MaxIS in the CONGEST model.
Revised:  (2) A randomized 2^{O(\sqrt{\log \log n that finds, with high probability, an O(\Delta) -approximation for \MaxIS in the CONGEST model.
Edit intent: fluency
Original: \emph{  Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Revised: \emph{exponential Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Edit intent: clarity
Original: \emph{  Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Revised: \emph{ Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for any (possibly randomized) algorithm that finds a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Edit intent: clarity
Original: \emph{  Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Revised: \emph{ Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% even in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Edit intent: fluency
Original: \emph{  Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Revised: \emph{ Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model ) this result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Edit intent: coherence
Original: \emph{  Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Revised: \emph{ Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation for \MaxIS is strictly easier than \MIS.
Edit intent: fluency
Original: \emph{  Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is strictly easier than \MIS.
Revised: \emph{ Due to a lower bound of \Omega(\sqrt{\log n/\log \log n}) that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ( %DIFDELCMD < \MIS%%% ) in the LOCAL model , even for randomized algorithms, our second result implies that finding an O(\Delta) -approximation to \MaxIS is exponentially easier than \MIS.
Edit intent: clarity
Original:  In such a scenario, where Eve does not know the embedding strategies, we experimentally show that if Eve can discriminate among few well-known embedding strategies, she can improve her steganalysis performances compared to a simple averaging or maximum pooled approach.
Revised:  In such a scenario, where Eve does not know the spreading strategies, we experimentally show that if Eve can discriminate among few well-known embedding strategies, she can improve her steganalysis performances compared to a simple averaging or maximum pooled approach.
Edit intent: clarity
Original:  Our discriminative approach allows obtaining steganalysis efficiencies comparable to those obtained by a clairvoyant, Eve, who knows the Alice embedding strategy.
Revised:  Our discriminative approach allows obtaining steganalysis efficiencies comparable to those obtained by a clairvoyant, Eve, who knows the Alice spreading strategy.
Edit intent: clarity
Original:  Another interesting observation is that DeLS embedding strategy behaves really better than all the other embedding strategies.
Revised:  Another interesting observation is that DeLS spreading strategy behaves really better than all the other embedding strategies.
Edit intent: clarity
Original:  Another interesting observation is that DeLS embedding strategy behaves really better than all the other embedding strategies.
Revised:  Another interesting observation is that DeLS embedding strategy behaves really better than all the other spreading strategies.
Edit intent: clarity
Original:  Those observations results in the experimentation with six different embedding strategies made on Jpeg images with JUNIWARD , a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various embedding strategies.
Revised:  Those observations results in the experimentation with six different spreading strategies made on Jpeg images with JUNIWARD , a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various embedding strategies.
Edit intent: fluency
Original:  Those observations results in the experimentation with six different embedding strategies made on Jpeg images with JUNIWARD , a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various embedding strategies.
Revised:  Those observations results in the experimentation with six different embedding strategies made on Jpeg images with J-UNIWARD , a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various embedding strategies.
Edit intent: fluency
Original:  Those observations results in the experimentation with six different embedding strategies made on Jpeg images with JUNIWARD , a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various embedding strategies.
Revised:  Those observations results in the experimentation with six different embedding strategies made on Jpeg images with JUNIWARD , a state-of-the-art Single-Image-Detector, and a discriminative architecture that is invariant to the individual payload in each image, invariant to the size of the analyzed set of images, and build on a binary detector (for the pooling) that is able to deal with various spreading strategies.
Edit intent: fluency
Original: The contribution of structural connectivity to dynamic and often highly variable brain states remains poorly understood.
Revised: The contribution of structural connectivity to functional brain states remains poorly understood.
Edit intent: clarity
Original:  To determine their relationship, we firstly determine the network instabilities that give rise to oscillations, and the set of false bifurcationsthat occur beyond this onset.
Revised:  To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the set of false bifurcationsthat occur beyond this onset.
Edit intent: clarity
Original:  This study thereby emphasises  that local dynamics can have a substantial role in shaping large-scale functional brain states.
Revised:  This study thereby emphasises the substantial role that local dynamics can have a substantial role in shaping large-scale functional brain states.
Edit intent: fluency
Original:  This study thereby emphasises  that local dynamics can have a substantial role in shaping large-scale functional brain states.
Revised:  This study thereby emphasises that local dynamics can have in shaping large-scale functional brain states.
Edit intent: clarity
Original: Deep neural networks were shown to misclassify slightly modified input images.
Revised: Deep neural networks are prone to misclassify slightly modified input images.
Edit intent: clarity
Original:  Recently, many defenses have been proposed  but none have improved consistently the robustness of neural networks  .
Revised:  Recently, many defences have been proposed but none have improved consistently the robustness of neural networks .
Edit intent: fluency
Original:  Recently, many defenses have been proposed  but none have improved consistently the robustness of neural networks  .
Revised:  Recently, many defenses have been proposed , but none have improved consistently the robustness of neural networks .
Edit intent: fluency
Original:  Recently, many defenses have been proposed  but none have improved consistently the robustness of neural networks  .
Revised:  Recently, many defenses have been proposed but none have improved the robustness of neural networks .
Edit intent: clarity
Original:  Experiments on neural architecture search algorithms from the literature show that although their accurateresults , they are not able to find robust architectures.
Revised:  Experiments on neural architecture search algorithms from the literature show that although accurate , they are not able to find robust architectures.
Edit intent: fluency
Original:  Most of the reason for this lies in their limited search space.
Revised:  A major reason for this lies in their limited search space.
Edit intent: clarity
Original:  By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of multiplication, addition and concatenation layers in the search space , we were able to evolve an architecture that is 58%DIFDELCMD < \\%%% %DIF < accurate on  adversarial samples.
Revised:  By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of concatenation layers in the search space , we were able to evolve an architecture that is 58%DIFDELCMD < \\%%% %DIF < accurate on adversarial samples.
Edit intent: clarity
Original:  By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of multiplication, addition and concatenation layers in the search space , we were able to evolve an architecture that is 58%DIFDELCMD < \\%%% %DIF < accurate on  adversarial samples.
Revised:  By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of multiplication, addition and concatenation layers in the search , we were able to evolve an architecture that is 58%DIFDELCMD < \\%%% %DIF < accurate on adversarial samples.
Edit intent: clarity
Original:  Interestingly, this inherent robustness of the evolved architecture rivals state-of-the-art defenses such as adversarial training while being trained only on the training dataset .
Revised:  Interestingly, this inherent robustness of the evolved architecture rivals state-of-the-art defences such as adversarial training while being trained only on the training dataset .
Edit intent: fluency
Original:  Interestingly, this inherent robustness of the evolved architecture rivals state-of-the-art defenses such as adversarial training while being trained only on the training dataset .
Revised:  Interestingly, this inherent robustness of the evolved architecture rivals state-of-the-art defenses such as adversarial training while being trained only on the non-adversarial samples .
Edit intent: clarity
Original: Deep neural networks are prone to misclassify slightly modified input images.
Revised: Neural networks are prone to misclassify slightly modified input images.
Edit intent: clarity
Original:  Here, we propose to use adversarial attacks as a function evaluation to automatically search for neural architectures that can resist such attacks  . Experiments on neural architecture search algorithms from the literature show that although accurate, they are not able to find robust architectures.
Revised:  Here, we propose to use adversarial attacks as a function evaluation to search for neural architectures that can resist such attacks . Experiments on neural architecture search algorithms from the literature show that although accurate, they are not able to find robust architectures.
Edit intent: clarity
Original:  Here, we propose to use adversarial attacks as a function evaluation to automatically search for neural architectures that can resist such attacks  . Experiments on neural architecture search algorithms from the literature show that although accurate, they are not able to find robust architectures.
Revised:  Here, we propose to use adversarial attacks as a function evaluation to automatically search for neural architectures that can resist such attacks automatically . Experiments on neural architecture search algorithms from the literature show that although accurate, they are not able to find robust architectures.
Edit intent: clarity
Original:  A major reason for this lies in their limited search space.
Revised:  A significant reason for this lies in their limited search space.
Edit intent: clarity
Original:  Thus, the results here demonstrate that more robust architectures exist as well as opens up a new range of possibilities for the development and exploration of deep neural networksusing automatic architecture search . Code available at URL
Revised:  Thus, the results here confirm that more robust architectures exist as well as opens up a new range of possibilities for the development and exploration of deep neural networksusing automatic architecture search . Code available at URL
Edit intent: clarity
Original:  Thus, the results here demonstrate that more robust architectures exist as well as opens up a new range of possibilities for the development and exploration of deep neural networksusing automatic architecture search . Code available at URL
Revised:  Thus, the results here demonstrate that more robust architectures exist as well as opens up a new realm of feasibilities for the development and exploration of deep neural networksusing automatic architecture search . Code available at URL
Edit intent: clarity
Original:  Thus, the results here demonstrate that more robust architectures exist as well as opens up a new range of possibilities for the development and exploration of deep neural networksusing automatic architecture search . Code available at URL
Revised:  Thus, the results here demonstrate that more robust architectures exist as well as opens up a new range of possibilities for the development and exploration of neural networks . Code available at URL
Edit intent: clarity