wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Different from previous moment retrieval tasks dealing with videos only, TVR requires the system to understand both the video and the associated subtitle text , making it a more realistictask. To support the study of this new task, we have collected a large-scale, high-quality dataset consisting of 108,965 queries on 21,793 videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal alignment.
Revised:  Different from previous moment retrieval tasks dealing with videos only, TVR requires the system to understand both the video and the associated subtitle text , making it more realistic. The dataset contains 109K queries collected on 21.8K videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal alignment.
Edit intent: meaning-changed
Original:  To support the study of this new task, we have collected a large-scale, high-quality dataset consisting of 108,965 queries on 21,793 videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal alignment.
Revised:  To support the study of this new task, we have collected a large-scale, high-quality dataset consisting of 108,965 queries on 21,793 videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal window. The queries are also labeled with query types that indicate whether each of them is more related to video or subtitle or both, allowing for in-depth analysis of the dataset and the methods that built on top of it.
Edit intent: meaning-changed
Original:  Extensive analysis experiments also show that incorporating both video and subtitle modules yields better performance than either alone. Lastly, we have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named the TV show Caption dataset (TVC).
Revised:  We have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named the TV show Caption dataset (TVC).
Edit intent: coherence
Original:  Lastly, we have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named the TV show Caption dataset (TVC).
Revised:  Lastly, we have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named TV show Caption dataset (TVC).
Edit intent: fluency
Original:  Lastly, we have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named the TV show Caption dataset (TVC).
Revised:  Lastly, we have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named the TV show Caption (TVC).
Edit intent: fluency
Original: Physical contact-based typing interfaces are not suitable for people with upper limb disabilities such as Quadriplegia. This paper, thus, proposes a touch-less typing interface that makes use of an on-screen QWERTY keyboard and a front-facing smartphone cameramounted on a stand.
Revised: In this paper, we propose a novel touchless typing interface that makes use of an on-screen QWERTY keyboard and a front-facing smartphone cameramounted on a stand.
Edit intent: clarity
Original:  This paper, thus, proposes a touch-less typing interface that makes use of an on-screen QWERTY keyboard and a front-facing smartphone cameramounted on a stand. The keys of the keyboard are grouped into nine color-coded clusters.
Revised:  This paper, thus, proposes a touch-less typing interface that makes use of an on-screen QWERTY keyboard and a smartphone camera. The keyboard was divided into nine color-coded clusters.
Edit intent: clarity
Original:  Users pointed to the letters that they wanted to type just by moving their head. The head movements of the users are recorded by the camera. The recorded gestures are then translated into a cluster sequence. The translation module is implemented using CNN-RNN, Conv3D, and a modified GRU based model that uses pre-trained embedding rich in head pose features .
Revised:  Users pointed to the letters that they wanted to type . A front-facing smartphone camera recorded the head movements. A bidirectional GRU based model that uses pre-trained embedding rich in head pose features .
Edit intent: clarity
Original:  The translation module is implemented using CNN-RNN, Conv3D, and a modified GRU based model that uses pre-trained embedding rich in head pose features . The performances of these models were evaluated under four different scenarioson a dataset of 2234 video sequences collected from 22 users .
Revised:  The translation module is implemented using CNN-RNN, Conv3D, and a modified GRU based model that uses pre-trained embedding rich in head pose features was employed to translate the recordings into cluster sequences. The model achieved an accuracy of 96.78\% and 86.81\% under intra- and inter-user scenarios, respectively, over a dataset of 2234 video sequences collected from 22 users .
Edit intent: meaning-changed
Original:  The performances of these models were evaluated under four different scenarioson a dataset of 2234 video sequences collected from 22 users . The modified GRU-based model outperforms the standard CNN-RNN and Conv3D models for three of the four scenarios. The results are encouraging and suggest promising directions for future research .
Revised:  The performances of these models were evaluated under four different scenarioson a dataset of 2234 video sequences collected from 22 users .
Edit intent: coherence
Original:  Active Learning (AL) is a human-in-the-loop Machine Learning paradigm favored for its ability to learn with fewer labeled instances, but the model 's states and progress remain opaque to the annotators.
Revised: The wide adoption of Machine Learning technologies has created a rapidly growing demand for people who can train ML models. Some advocated the term "machine teacher" to refer to the role of people who inject domain knowledge into ML models. One promising learning paradigm is Active Learning (AL) is a human-in-the-loop Machine Learning paradigm favored for its ability to learn with fewer labeled instances, but the model 's states and progress remain opaque to the annotators.
Edit intent: meaning-changed
Original:  Active Learning (AL) is a human-in-the-loop Machine Learning paradigm favored for its ability to learn with fewer labeled instances, but the model 's states and progress remain opaque to the annotators. Meanwhile, many recognize the benefits of model transparency for people interacting with ML models, as reflected by the surge of explainable AI (XAI) as a research field.
Revised:  Active Learning (AL) , by which the model intelligently selects instances to query the machine teacher for labels.
Edit intent: clarity
Original:  However, explaining an evolving model introduces many open questions regarding its impact on the annotation quality and the annotator's experience. In this paper , we propose a novel paradigm of explainable active learning (XAL), by explaining the learning algorithm's prediction for the instance it wants to learn from and soliciting feedback from the annotator.
Revised:  However, explaining an evolving model introduces many open questions regarding its impact on the annotation quality and the human-AI interface remains minimal and opaque. We begin considering AI explanations as a core element of the human-AI interface for teaching machines. When a human student learns, it is a common pattern to present one's own reasoning and solicit feedback from the teacher. When a ML model learns and still makes mistakes, the human teacher should be able to understand the reasoning underlying the mistakes. When the model matures, the machine teacher should be able to recognize its progress in order to trust and feel confident about their teaching outcome. Toward this vision , we propose a novel paradigm of explainable active learning (XAL), by explaining the learning algorithm's prediction for the instance it wants to learn from and soliciting feedback from the annotator.
Edit intent: meaning-changed
Original:  In this paper , we propose a novel paradigm of explainable active learning (XAL), by explaining the learning algorithm's prediction for the instance it wants to learn from and soliciting feedback from the annotator. We conduct an empirical study comparing the model learning outcome, human feedback content and the annotator experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).
Revised:  In this paper , we propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an AL setting. We conducted an empirical study comparing the model learning outcome, human feedback content and the annotator experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).
Edit intent: meaning-changed
Original:  Our study reveals benefits--supporting trust calibration and enabling additional forms of human feedback, and potential drawbacks--anchoring effect and frustration from transparent model limitations--of providing local explanations in AL.
Revised:  Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling additional forms of human feedback, and potential drawbacks--anchoring effect and frustration from transparent model limitations--of providing local explanations in AL.
Edit intent: meaning-changed
Original:  Our study reveals benefits--supporting trust calibration and enabling additional forms of human feedback, and potential drawbacks--anchoring effect and frustration from transparent model limitations--of providing local explanations in AL. We conclude by suggesting directions for developing explanations that better support annotator experience in AL and interactive ML settings .
Revised:  Our study reveals benefits--supporting trust calibration and enabling additional forms of human feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload. Our study also reveals important individual factors that mediate a machine teacher's reception to AI explanations, including task knowledge, AI experience and need for cognition. By reflecting on the results, we suggest future directions and design implications for XAL .
Edit intent: meaning-changed
Original: Although Bidirectional Encoder Representations from Transformers (BERT) have achieved tremendous success in many natural language processing (NLP) tasks, it remains a black box , so much previous work has tried to lift the veil of BERT and understand the functionality of each layer .
Revised: Although Bidirectional Encoder Representations from Transformers (BERT) have achieved tremendous success in many natural language processing (NLP) tasks, it remains a black box . A variety of previous works have tried to lift the veil of BERT and understand the functionality of each layer .
Edit intent: coherence
Original: Although Bidirectional Encoder Representations from Transformers (BERT) have achieved tremendous success in many natural language processing (NLP) tasks, it remains a black box , so much previous work has tried to lift the veil of BERT and understand the functionality of each layer .
Revised: Although Bidirectional Encoder Representations from Transformers (BERT) have achieved tremendous success in many natural language processing (NLP) tasks, it remains a black box , so much previous work has tried to lift the veil of BERT and understand each layer's functionality .
Edit intent: clarity
Original:  These systems are therefore confined to producing either acoustically-linked beat gestures or semantically-linked gesticulation (e.g., raising a hand when saying ``high'' ): they cannot appropriately learn to generate both gesture types.
Revised:  These systems are therefore confined to producing either acoustically-linked beat gestures or semantically-linked gesticulation (e.g., raising a hand when saying "high" ): they cannot appropriately learn to generate both gesture types.
Edit intent: fluency
Original:  Thus in addition to a powerful pre-trained Language Model as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationship among the triplet of passage, question and answers.
Revised:  Thus in addition to a powerful Pre-trained Language Model (PrLM) as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationship among the triplet of passage, question and answers.
Edit intent: clarity
Original:  Thus in addition to a powerful pre-trained Language Model as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationship among the triplet of passage, question and answers.
Revised:  Thus in addition to a powerful pre-trained Language Model as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationships among the triplet of passage, question and answers.
Edit intent: fluency
Original:  While the latest pre-trained Language Models have shown powerful enough even without the support from a matching network, and the latest matching network has been complicated enough, we thus propose a novel going-back-to-the-basic solution which straightforwardly models the MRC relationship as attention mechanism inside network .
Revised:  While the newer and more powerful PrLMs have shown their mightiness even without the support from a matching network, and the latest matching network has been complicated enough, we thus propose a novel going-back-to-the-basic solution which straightforwardly models the MRC relationship as attention mechanism inside network .
Edit intent: clarity
Original:  While the latest pre-trained Language Models have shown powerful enough even without the support from a matching network, and the latest matching network has been complicated enough, we thus propose a novel going-back-to-the-basic solution which straightforwardly models the MRC relationship as attention mechanism inside network .
Revised:  While the latest pre-trained Language Models have shown powerful enough even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by human's transposition thinking process solving the multi-choice MRC problem: respectively considering each other's focus from the standpoint of passage and question .
Edit intent: meaning-changed
Original:  Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of strong Language Models, DUMA may still boost the model to reach new state-of-the-art performance.
Revised:  Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of powerful PrLMs, DUMA can still boost the model to reach new state-of-the-art performance.
Edit intent: clarity
Original: We present a method to identify relevant events associated with stock price movements without manually labeled data. We train an attention-based neural network, which given a set of news headlines for a given time frame, predicts the price movement of a given stock index (i.e., DOWN, STAY, UP). An attention layer acts as an inputselector; it computes a normalized weight for each headline embedding. The weighted average of the embeddings is used to predict the price movement. We present an analysis to understand if, after the network has been trained, the attention layer is capable of generating a global ranking of news events through its unnormalized weights. The ranking should be able to rank relevant financial events higher. In this initial study we use news categories as a proxy for relevance : news belonging to more relevant categories should be ranked higher .
Revised: We present a method to identify relevant events associated with stock price movements and news headlines as input .
Edit intent: clarity
Original:   Our experiments on four indices suggest that there is an indication that the weights indeed skew the global set of events towards those categories that are more relevant to explain the price change;
Revised:  The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on four indices suggest that there is an indication that the weights indeed skew the global set of events towards those categories that are more relevant to explain the price change;
Edit intent: meaning-changed
Original:   Our experiments on four indices suggest that there is an indication that the weights indeed skew the global set of events towards those categories that are more relevant to explain the price change; this effect reflects the performance of the network on stock prediction .
Revised:  Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task .
Edit intent: clarity
Original:   As an application we derive, under an uniformity assumption in \rho, the leading asymptotics of the density of the time average of the geometric Brownian motion as t\to 0.
Revised:  An error bound is obtained by numerical estimates of the integrand, which is furthermore uniform in \rho. As an application we derive, under an uniformity assumption in \rho, the leading asymptotics of the density of the time average of the geometric Brownian motion as t\to 0.
Edit intent: meaning-changed
Original:  It includes a novel denoising approach based on  Marchenko-Pastur Principle Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artefact classification and cleaning, and global signal regression (GSR) . Our results show that: I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio;
Revised:  It includes a novel denoising approach based on the Marchenko-Pastur Principle Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artefact classification and cleaning, and global signal regression (GSR) . Our results show that: I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio;
Edit intent: fluency
Original:  It includes a novel denoising approach based on  Marchenko-Pastur Principle Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artefact classification and cleaning, and global signal regression (GSR) . Our results show that: I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio;
Revised:  It includes a novel denoising approach based on Marchenko-Pastur Principle Component Analysis (MP-PCA) method, FMRIB's ICA-based Xnoiseifier (FIX) for automatic artefact classification and cleaning, and global signal regression . Our results show that: I) MP-PCA denoising substantially improves the temporal signal-to-noise ratio;
Edit intent: clarity
Original:  III) both artefact cleaning and GSR are essential steps in minimizing the within-group variability in control animals and identifying FC changes in a rat model of sporadic Alzheimer's disease, as compared to controls.
Revised:  III) both artefact cleaning and global signal regression are essential steps in minimizing the within-group variability in control animals and identifying FC changes in a rat model of sporadic Alzheimer's disease, as compared to controls.
Edit intent: clarity
Original:  III) both artefact cleaning and GSR are essential steps in minimizing the within-group variability in control animals and identifying FC changes in a rat model of sporadic Alzheimer's disease, as compared to controls.
Revised:  III) both artefact cleaning and GSR are essential steps in minimizing the within-group variability in control animals and identifying functional connectivity changes in a rat model of sporadic Alzheimer's disease, as compared to controls.
Edit intent: meaning-changed
Original: Cancer treatment by cold atmospheric plasma (CAP) offers the possibility to selectively eliminate cancer cells while leaving surrounding, normal cells, unaffected. The mechanisms underlying the CAP selectivity towards cancer cells are yet not fully understood , but it has been proposed that two apoptosis-inducing signaling pathways in the extracellular compartment are reactivated when catalase in the cell membrane of cancer cells is inactivated.
Revised: Cancer treatment by cold atmospheric plasma (CAP) is a promising new agent for (selective) cancer treatment, but the underlying cause of the anti-cancer effect of CAP is not well understood yet. Among different theories and observations, one theory in particular has been postulated in great detail and consists of a very complex network of reactions that are claimed to account for the anti-cancer effect of CAP. Here, the key concept is a reactivation of two specific apoptotic cell signaling pathways through catalase inactivation caused by CAP. Thus, it is postulated that the anti-cancer effect of CAP selectivity towards cancer cells are yet not fully understood , but it has been proposed that two apoptosis-inducing signaling pathways in the extracellular compartment are reactivated when catalase in the cell membrane of cancer cells is inactivated.
Edit intent: coherence
Original:  The mechanisms underlying the CAP selectivity towards cancer cells are yet not fully understood , but it has been proposed that two apoptosis-inducing signaling pathways in the extracellular compartment are reactivated when catalase in the cell membrane of cancer cells is inactivated. Since CAP has the capacity to inactivate catalase, CAP may thus be used to induce cancer cell death by reactivation of these signaling pathways.
Revised:  The mechanisms underlying the CAP is due to its ability to inactivate catalase, CAP may thus be used to induce cancer cell death by reactivation of these signaling pathways.
Edit intent: coherence
Original:  Since CAP has the capacity to inactivate catalase, CAP may thus be used to induce cancer cell death by reactivation of these signaling pathways. A better insight of this specific apoptosis-inducing mechanism - especially the role of catalase inactivation - could be useful to increase our understanding of possible roles of CAP constituents in cancer treatment .
Revised:  Since CAP has the capacity to inactivate catalase, either directly or indirectly. A theoretical investigation of the proposed theory, especially the role of catalase inactivation - could be useful to increase our understanding of possible roles of CAP constituents in cancer treatment .
Edit intent: clarity
Original:  In the present study,  a mathematical model describing the reaction kinetics of the two apoptosis-inducing signaling pathways is developed and used to theoretically investigate their catalase-dependent reactivation.
Revised:  In the present study, we develop a mathematical model describing the reaction kinetics of the two apoptosis-inducing signaling pathways is developed and used to theoretically investigate their catalase-dependent reactivation.
Edit intent: clarity