wanyu/IteraTeR_v2 huggingface dataset:
Original: In this paper, we present a new dataset of music performance videos which can be used for training machine learning methods for multiple tasks such as audio-visual blind source separation and localization, cross-modal correspondences, cross-modal generation and, in general, any audio-visual selfsupervised task.
Revised: In this paper, we present a new dataset of music performance videos which can be used for training machine learning methods for multiple tasks such as audio-visual blind source separation and localization, cross-modal correspondences, cross-modal generation and, in general, any audio-visual self-supervised task.
Edit intent: fluency
Original:  Besides, it is, up to the best of our knowledge, the only dataset that contains the whole set of instruments present in the URMP 1 dataset, a highquality dataset of 44 multi-instrument audio-visual recordings of  classical music pieces with individual audio tracks.
Revised:  Besides, it is, up to the best of our knowledge, the only dataset that contains the whole set of instruments present in the URMP 1 dataset, a high-quality dataset of 44 multi-instrument audio-visual recordings of classical music pieces with individual audio tracks.
Edit intent: fluency
Original:  Besides, it is, up to the best of our knowledge, the only dataset that contains the whole set of instruments present in the URMP 1 dataset, a highquality dataset of 44 multi-instrument audio-visual recordings of  classical music pieces with individual audio tracks.
Revised:  Besides, it is, up to the best of our knowledge, the only dataset that contains the whole set of instruments present in the URMP 1 dataset, a highquality dataset of 44 audio-visual recordings of classical music pieces with individual audio tracks.
Edit intent: clarity
Original:  URMP was intented to be used for source separation, thus, we evaluate the performance on the URMP dataset of two different BSS models trained on Solos 
Revised:  URMP was intented to be used for source separation, thus, we evaluate the performance on the URMP dataset of two different source-separation models trained on Solos 
Edit intent: clarity
Original:  The deception mechanism consists of the joint design of a policy generator , an incentive manipulator, and a trust modulator .
Revised:  The deception mechanism consists of the joint design of the generator and the manipulator into the single design of the manipulator .
Edit intent: clarity
Original:  A case study of discriminative honeypot configuration is presented to validate the deception mechanism design .
Revised:  A case study of dynamic honeypot configurations is presented to validate the deception mechanism design .
Edit intent: clarity
Original: With the growing use of AI in highly consequential domains, the quantification and removal of bias with respect to protected attributes, such as gender, race, etc., is becoming increasingly important.
Revised: With the growing use of AI in highly consequential domains, quantifying disparity with respect to protected attributes, such as gender, race, etc., is becoming increasingly important.
Edit intent: clarity
Original: With the growing use of AI in highly consequential domains, the quantification and removal of bias with respect to protected attributes, such as gender, race, etc., is becoming increasingly important.
Revised: With the growing use of AI in highly consequential domains, the quantification and removal of bias with respect to protected attributes, e.g., gender, race, etc., is becoming increasingly important.
Edit intent: clarity
Original: With the growing use of AI in highly consequential domains, the quantification and removal of bias with respect to protected attributes, such as gender, race, etc., is becoming increasingly important.
Revised: With the growing use of AI in highly consequential domains, the quantification and removal of bias with respect to protected attributes, such as gender, race, etc., is important.
Edit intent: clarity
Original:  While quantifying bias is essential, sometimes the needs of a business (e.g., hiring) may require the use of certain features that are critical in a way that any bias that can be explained by them might need to be exempted.
Revised:  While quantifying disparity is essential, sometimes the needs of a business (e.g., hiring) may require the use of certain features that are critical in a way that any bias that can be explained by them might need to be exempted.
Edit intent: clarity
Original:  While quantifying bias is essential, sometimes the needs of a business (e.g., hiring) may require the use of certain features that are critical in a way that any bias that can be explained by them might need to be exempted.
Revised:  While quantifying bias is essential, sometimes the needs of an occupation may require the use of certain features that are critical in a way that any bias that can be explained by them might need to be exempted.
Edit intent: clarity
Original:  While quantifying bias is essential, sometimes the needs of a business (e.g., hiring) may require the use of certain features that are critical in a way that any bias that can be explained by them might need to be exempted.
Revised:  While quantifying bias is essential, sometimes the needs of a business (e.g., hiring) may require the use of certain features that are critical in a way that any disparity that can be explained by them might need to be exempted.
Edit intent: clarity
Original:  E.g., a standardized test-score may be a critical feature that should be weighed stronglyin hiring even if biased, whereas other features, such as zip code may be used only to the extent that they do not discriminate .
Revised:  E.g., in hiring a software engineer for a safety-critical application, coding-skills may be weighed strongly, whereas name, zip code, or reference letters may be used only to the extent that they do not discriminate .
Edit intent: clarity
Original:  E.g., a standardized test-score may be a critical feature that should be weighed stronglyin hiring even if biased, whereas other features, such as zip code may be used only to the extent that they do not discriminate .
Revised:  E.g., a standardized test-score may be a critical feature that should be weighed stronglyin hiring even if biased, whereas other features, such as zip code may be used only to the extent that they do not add disparity .
Edit intent: clarity
Original:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a  non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Revised:  In this work, we propose an information-theoretic decomposition of the total bias (in a counterfactual sense) into a non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Edit intent: clarity
Original:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a  non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Revised:  In this work, we propose a novel information-theoretic decomposition of the total disparity ( a non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Edit intent: coherence
Original:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a  non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Revised:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a non-exempt component which quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Edit intent: fluency
Original:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a  non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Revised:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a non-exempt component that quantifies the part that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Edit intent: clarity
Original:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a  non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Revised:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component that quantifies the remaining bias .
Edit intent: fluency
Original:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a  non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining bias .
Revised:  In this work, we propose a novel information-theoretic decomposition of the total bias (in a counterfactual sense) into a non-exempt component that quantifies the part of the bias that cannot be accounted for by the critical features, and an exempt component which quantifies the remaining disparity .
Edit intent: clarity
Original:  This decomposition allows one to check if the bias arose purely due to the critical features (inspired from the business necessity defense of disparate impact law) and also enables selective removal of the non-exempt component if desired.
Revised:  This decomposition allows one to check if the disparity arose purely due to the critical features (inspired from the business necessity defense of disparate impact law) and also enables selective removal of the non-exempt component if desired.
Edit intent: clarity
Original:  We arrive at this decomposition through  examples that lead to a set of desirable properties (axioms) that any measure of non-exempt bias should satisfy.
Revised:  We arrive at this decomposition through examples that lead to a set of desirable properties (axioms) that a measure of non-exempt bias should satisfy.
Edit intent: fluency
Original:  We arrive at this decomposition through  examples that lead to a set of desirable properties (axioms) that any measure of non-exempt bias should satisfy.
Revised:  We arrive at this decomposition through examples that lead to a set of desirable properties (axioms) that any measure of non-exempt disparity should satisfy.
Edit intent: clarity
Original:  We demonstrate that our proposed counterfactual measure satisfies all of them.
Revised:  Our proposed measure satisfies all of them.
Edit intent: clarity
Original:  We also obtain an impossibility result showing that no observational measure of non-exempt bias can satisfy all of the desirable properties, which leads us to relax our goals and examine observational measures that satisfy only some of these properties.
Revised:  We also obtain an impossibility result showing that no observational measure can satisfy all of the desirable properties, which leads us to relax our goals and examine observational measures that satisfy only some of these properties.
Edit intent: clarity
Original:  We also obtain an impossibility result showing that no observational measure of non-exempt bias can satisfy all of the desirable properties, which leads us to relax our goals and examine observational measures that satisfy only some of these properties.
Revised:  We also obtain an impossibility result showing that no observational measure of non-exempt bias can satisfy all the desirable properties, which leads us to relax our goals and examine observational measures that satisfy only some of these properties.
Edit intent: fluency
Original:  We also obtain an impossibility result showing that no observational measure of non-exempt bias can satisfy all of the desirable properties, which leads us to relax our goals and examine observational measures that satisfy only some of these properties.
Revised:  We also obtain an impossibility result showing that no observational measure of non-exempt bias can satisfy all of the desirable properties, leading us to relax our goals and examine observational measures that satisfy only some of these properties.
Edit intent: coherence
Original:  We then perform case studies to show how one can  train models while reducing non-exempt bias .
Revised:  We then perform case studies to show how one can train models while reducing non-exempt disparity .
Edit intent: clarity
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage%DIFDELCMD < \MBLOCKRIGHTBRACE %%% perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage%DIFDELCMD < \MBLOCKRIGHTBRACE %%% perceptual systems first foveate an image , inducing a texture-like encoding mechanism in the periphery. To do so, we introduce models consisting of a first-stage .
Edit intent: clarity
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage%DIFDELCMD < \MBLOCKRIGHTBRACE %%% perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage%DIFDELCMD < \MBLOCKRIGHTBRACE %%% perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorizationfixed .
Edit intent: fluency
Original:  Specifically, these%DIFDELCMD < \textit{%%% two-stage%DIFDELCMD < \MBLOCKRIGHTBRACE %%% perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization   .
Revised:  Specifically, these%DIFDELCMD < \textit{%%% two-stage%DIFDELCMD < \MBLOCKRIGHTBRACE %%% perceptual systems first foveate an image , inducing a texture-like encoding of peripheral information, which is then inputted to a convolutional neural network (CNN)and trained to perform scene categorization learnable .
Edit intent: clarity
Original:  These results provide computational support for the idea that the foveated nature of the human visual system might confer a functional advantage for scene representation .
Revised:  These results provide computational support for the idea that the foveated nature of the human visual system .
Edit intent: clarity
Original:  Finally, both the foveated systems, either texture or blur-based, show a stronger center image-bias relative to the spatially-uniform systems .
Revised:  Finally, both the foveated systems, show a stronger center image-bias relative to the spatially-uniform systems .
Edit intent: coherence
Original: Standard measures such as quarantining suspects and contact tracing could not contain the ongoing COVID-19 pandemic.
Revised: As the COVID-19 pandemic.
Edit intent: clarity
Original:  We find that departmental membership, hierarchical layers, supervisory status, power proximity, and permission rules all affect the information search of employees.
Revised:  We find that formal status, permission pathways, and departmental membership all affect the information search of employees.
Edit intent: clarity
Original:  To that end, costly experimental measurements are being replaced with calls to a high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for promising design candidates.
Revised:  To that end, costly experimental measurements are being replaced with calls to high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for promising design candidates.
Edit intent: fluency
Original:  To that end, costly experimental measurements are being replaced with calls to a high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for promising design candidates.
Revised:  To that end, costly experimental measurements are being replaced with calls to a high-capacity regression models trained on labeled data, which can be leveraged in an in silico search for promising design candidates.
Edit intent: fluency
Original:  To that end, costly experimental measurements are being replaced with calls to a high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for promising design candidates.
Revised:  To that end, costly experimental measurements are being replaced with calls to a high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for design candidates.
Edit intent: clarity
Original:  However, the design goal necessitates moving into regions of the input space beyond where such models were trained.
Revised:  However, the design goal necessitates moving into regions of the design space beyond where such models were trained.
Edit intent: clarity
Original:  Therefore, one can ask: should the regression model be altered as the design algorithm explores the input space, in the absence of new data acquisition ?
Revised:  Therefore, one can ask: should the regression model be altered as the design algorithm explores the design space, in the absence of new data acquisition ?
Edit intent: clarity
Original:  Therefore, one can ask: should the regression model be altered as the design algorithm explores the input space, in the absence of new data acquisition ?
Revised:  Therefore, one can ask: should the regression model be altered as the design algorithm explores the input space, in the absence of new data ?
Edit intent: clarity
Original:  For example, expertise on song annotations follows a ``U shape'' where experts are both early and late contributors with non-experts contributing intermediately;
Revised:  For example, expertise on song annotations follows a "U shape" where experts are both early and late contributors with non-experts contributing intermediately;
Edit intent: fluency
Original: Vaccines for COVID-19 are currently under clinical trials.
Revised: This study presents survey results of the public's willingness to get vaccinated against COVID-19 are currently under clinical trials.
Edit intent: clarity
Original:  We used a between-subjects study design(N= 572 adults in the US and UK ) to understand the public willingness towards vaccination against the novel coronavirus under various circumstances.
Revised:  We used a between-subjects design. A representative quota sample of 572 adults in the US and UK ) to understand the public willingness towards vaccination against the novel coronavirus under various circumstances.
Edit intent: clarity
Original:  Our survey findings suggest that people are more reluctant to vaccinate their children compared to themselves . Explicitly stating the high effectiveness of the vaccine against COVID-19 led to an increase in vaccine acceptance.
Revised:  Our survey findings suggest that people are more reluctant to vaccinate their children compared to themselves . Explicitly stating the high effectiveness of vaccines led to an increase in vaccine acceptance.
Edit intent: coherence
Original:  We illustrate the versatility of our approach by the approximation of voronoi cells on embedded surface of R^3.
Revised:  We illustrate the versatility of our approach by the approximation of Voronoi cells on embedded surface of R^3.
Edit intent: fluency
Original:  We illustrate the versatility of our approach by the approximation of voronoi cells on embedded surface of R^3.
Revised:  We illustrate the versatility of our approach by the approximation of voronoi cells on embedded surfaces of R^3.
Edit intent: fluency
Original:  Such a subset of neurons form what is called a degenerate circuit for the behavior being studied.
Revised:  Such a subset of neurons forms what is called a degenerate circuit for the behavior being studied.
Edit intent: fluency
Original:  This setting leads to the algorithmic question of determining the minimal vital or degenerate sets of neurons , when one is given as input a description of the neural circuit.
Revised:  This setting leads to the algorithmic question of determining the minimal vital or degenerate sets of neurons when one is given as input a description of the neural circuit.
Edit intent: fluency
Original:  To bridge the gap, we create two new splits : COCO-GB v1 and v2 to quantify the inherent gender bias which could be learned by models.
Revised:  To bridge the gap, we create two new splits COCO-GB v1 and v2 to quantify the inherent gender bias which could be learned by models.
Edit intent: fluency
Original:  Several widely used baselines are evaluated on our new settings, and experimental results indicate that most models learn gender bias from the training data , leading to an undesirable gender prediction error towards women.
Revised:  Several widely used baselines are evaluated on our new settings, and experimental results indicate that most models learn gender bias , leading to an undesirable gender prediction error towards women.
Edit intent: clarity
Original:  Several widely used baselines are evaluated on our new settings, and experimental results indicate that most models learn gender bias from the training data , leading to an undesirable gender prediction error towards women.
Revised:  Several widely used baselines are evaluated on our new settings, and experimental results indicate that most models learn gender bias from the training data , leading to high gender prediction errors, especially for women.
Edit intent: clarity
Original:  To overcome the unwanted bias, we propose a novel Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to explore correct gender visual evidence.
Revised:  To alleviate the unwanted bias, we propose a novel Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to explore correct gender visual evidence.
Edit intent: clarity
Original:  To overcome the unwanted bias, we propose a novel Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to explore correct gender visual evidence.
Revised:  To overcome the unwanted bias, we propose a new Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to explore correct gender visual evidence.
Edit intent: clarity
Original:  To overcome the unwanted bias, we propose a novel Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to explore correct gender visual evidence.
Revised:  To overcome the unwanted bias, we propose a novel Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to capture correct gender visual evidence.
Edit intent: clarity