wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007-February 2020), over 50 thousand accounts, 17 countries, and different account types including both trolls and bots.
Revised:  We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007-February 2020), over 50 ,000 accounts, 17 countries, and different account types including both trolls and bots.
Edit intent: fluency
Original:  In this paper, we address this problem by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call trajectory-indexing succinct trit-array trie (tSTAT) . tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH ) for Fr\'echet distance and a trie data structure .
Revised:  To address this problem by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call trajectory-indexing succinct trit-array trie (tSTAT) . tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH ) for Fr\'echet distance and a trie data structure .
Edit intent: coherence
Original:  In this paper, we address this problem by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call trajectory-indexing succinct trit-array trie (tSTAT) . tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH ) for Fr\'echet distance and a trie data structure .
Revised:  In this paper, we address this problem , we present the trajectory-indexing succinct trit-array trie (tSTAT) . tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH ) for Fr\'echet distance and a trie data structure .
Edit intent: coherence
Original:  In this paper, we address this problem by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call trajectory-indexing succinct trit-array trie (tSTAT) . tSTAT achieves time and memory efficiency by leveraging locality sensitive hashing (LSH ) for Fr\'echet distance and a trie data structure .
Revised:  In this paper, we address this problem by presenting a scalable similarity search for Fr\'echet distance on trajectories, which we call trajectory-indexing succinct trit-array trie (tSTAT) , which is a scalable method leveraging LSH for trajectory similarity searches. tSTAT quickly performs the search on a tree data structure called trie .
Edit intent: meaning-changed
Original:  We also present two novel techniques of node reduction and a space-efficient representation for tries, which enable to dramatically enhance a memory efficiency of tries .
Revised:  We also present two novel techniques that enable to dramatically enhance the memory efficiency of tSTAT. One is a node reduction technique that substantially omits redundant trie nodes while maintaining the time performance. The other is a space-efficient representation for tries, which enable to dramatically enhance a memory efficiency of tries .
Edit intent: meaning-changed
Original:  We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly with respect to search time and memory efficiency .
Revised:  We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly in comparison to state-of-the-art similarity search methods .
Edit intent: clarity
Original:  Initially launched as a voluntary project to bridge the data transparency gap in NorthAmerica in January 2020, this project by far has become one of the major independent sources worldwide and has been consumed by many other tracking platforms.
Revised:  Initially launched as a voluntary project to bridge the data transparency gap in North America in January 2020, this project by far has become one of the major independent sources worldwide and has been consumed by many other tracking platforms.
Edit intent: fluency
Original:  As of May 18, 2020, the project  has been visited more than 200 million times , and has empowered over 522 institutions URLanizations worldwide in policy-making and academic researches.
Revised:  As of May 18, 2020, the project website has been visited more than 200 million times , and has empowered over 522 institutions URLanizations worldwide in policy-making and academic researches.
Edit intent: meaning-changed
Original:  As of May 18, 2020, the project  has been visited more than 200 million times , and has empowered over 522 institutions URLanizations worldwide in policy-making and academic researches.
Revised:  As of May 18, 2020, the project has been visited more than 200 million times and the CovidNet dataset has empowered over 522 institutions URLanizations worldwide in policy-making and academic researches.
Edit intent: meaning-changed
Original: Supporting recommendations with personalized and relevant explanations increases trust and perceived quality , and helps users make better decisions. Prior work attempted to generate a synthetic review or review segment as an explanation, but they were not judged convincing in evaluations by human users . We propose T-RECS, a multi-task learning Transformer-based model that jointly performs recommendation with textual explanations using a novel multi-aspect masking technique . We show that human users significantly prefer the justifications generated by T-RECS than those generated by state-of-the-art techniques.
Revised: Supporting recommendations with personalized and relevant explanations increases trust and perceived quality . However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting with the explanation. We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer the justifications generated by T-RECS than those generated by state-of-the-art techniques.
Edit intent: coherence
Original:  At the same time, experiments on two datasets show that T-RECS slightly improves on the recommendation performance of strong state-of-the-art baselines. Another feature of T-RECS is that it allows users to react to a recommendation by critiquing the textual explanation  .
Revised:  Our work's most important innovation is that it allows users to react to a recommendation by critiquing the textual explanation .
Edit intent: clarity
Original: Multiple random walksare the movement of several independent random walkers on a graph , and are applied to various graph algorithms. In order to design an efficient graph algorithm based on multiple random walks , it is essential to study multiple random walks theoretically for deeply understanding their characteristics.
Revised: Multiple random walksare the movement of several independent random walkers on a graph . Designing an efficient graph algorithm based on multiple random walks , it is essential to study multiple random walks theoretically for deeply understanding their characteristics.
Edit intent: coherence
Original:  The first meeting time on a graph is defined by the time it takes for multiple random walkers to meet at the same node in graph. The first meeting time is closely related to the rendezvous problem, which is are a fundamental problem in the field of computer science.
Revised:  The first meeting time on a graph is defined by the time it takes for multiple random walkers to meet at the same node in a graph. This time is closely related to the rendezvous problem, which is are a fundamental problem in the field of computer science.
Edit intent: clarity
Original:  The clarified principal component reveals that (a)the expected first meeting time is almost dominated by n/(1+d_{\rm std}^2/d_{\rm avg}^2) and (b)the expected first meeting time is independent of the starting nodes of random walkers  where n is the number of nodes of the graph. d_{\rm avg} and d_{\rm std} are the mean and the standard deviation of weighted node degrees, respectively.
Revised:  The clarified principal component reveals that (a)the expected first meeting time is almost dominated by n/(1+d_{\rm std}^2/d_{\rm avg}^2) and (b)the expected first meeting time is independent of the starting nodes of random walkers , where n is the number of nodes of the graph. d_{\rm avg} and d_{\rm std} are the mean and the standard deviation of weighted node degrees, respectively.
Edit intent: fluency
Original:  The clarified principal component reveals that (a)the expected first meeting time is almost dominated by n/(1+d_{\rm std}^2/d_{\rm avg}^2) and (b)the expected first meeting time is independent of the starting nodes of random walkers  where n is the number of nodes of the graph. d_{\rm avg} and d_{\rm std} are the mean and the standard deviation of weighted node degrees, respectively.
Revised:  The clarified principal component reveals that (a)the expected first meeting time is almost dominated by n/(1+d_{\rm std}^2/d_{\rm avg}^2) and (b)the expected first meeting time is independent of the starting nodes of random walkers where n is the number of nodes of the graph. d_{\rm avg} and d_{\rm std} are the average and the standard deviation of weighted node degrees, respectively.
Edit intent: clarity
Original:  According to the revealed effect of graph structures, the variance of  coefficient d_{\rm std}/d_{\rm avg}(degree heterogeneity) for weighted degrees facilitates quicker the meeting of random walkers.
Revised:  According to the revealed effect of graph structures, the variance of the coefficient d_{\rm std}/d_{\rm avg}(degree heterogeneity) for weighted degrees facilitates quicker the meeting of random walkers.
Edit intent: fluency
Original:  According to the revealed effect of graph structures, the variance of  coefficient d_{\rm std}/d_{\rm avg}(degree heterogeneity) for weighted degrees facilitates quicker the meeting of random walkers.
Revised:  According to the revealed effect of graph structures, the variance of coefficient d_{\rm std}/d_{\rm avg}(degree heterogeneity) for weighted degrees facilitates the meeting of random walkers.
Edit intent: clarity
Original: On university campuses, social interactions among students can explain their academic experiences. However, assessing these interactions with surveys fails to capture their dynamic nature. While these behaviors can be captured with client-based passive sensing, these techniques are limited in scalability. By contrast, infrastructure-based approaches can scale to a large cohort and infer social interactions based on collocation of students. This paper investigates one such approach by leveraging WiFi association logs archived by a managed campus network.
Revised: A comprehensive understanding of collocation can help understand performance outcomes. For university cohorts, this needs data that describes large groups over a long period. Harnessing user devices to infer this, while tempting, is challenged by privacy concerns, power consumption, and maintenance issues. Alternatively, embedding new sensors in the environment is limited by the expense of covering the entire campus. We investigate the feasibility of leveraging WiFi association logs archived by a managed campus network.
Edit intent: clarity
Original:  This paper investigates one such approach by leveraging WiFi association logs archived by a managed campus network. In their raw form, access point logs can approximate a student's location but with low spatio-temporal resolution. This paper first demonstrates that processing these logs can infer the collocation of 46 students in 34 lectures over 3 months, with a precision of 0.89 and a recall of 0.75. Next, we investigate how this WiFi-based coarse collocation reflects signals of social interaction. With 163 students  in 54 project groups , we find that member performance shows a correlation of 0.75 with performance determined from collocation of groups through 14 weeks.
Revised:  This paper investigates one such approach by leveraging WiFi association logs for this purpose. While these provide coarse approximations of location, these are easily obtainable and depict multiple users on campus over a semester. We explore how these coarse collocations are related to individual performance. Specifically, we inspect the association between individual performance and the collocation behaviors of project group members. We study 163 students in 54 project groups , we find that member performance shows a correlation of 0.75 with performance determined from collocation of groups through 14 weeks.
Edit intent: clarity
Original:  With 163 students  in 54 project groups , we find that member performance shows a correlation of 0.75 with performance determined from collocation of groups through 14 weeks. Additionally, this paper presents preliminary insights for other campus-centric applications of automatically inferred social interactions. Finally, this paper discusses how repurposing archival WiFi logs can facilitate applications for other domains like mental wellbeing and physical health.
Revised:  With 163 students in 54 project groups , we find that member performance shows a correlation of 0.75 with performance determined from collocation of groups through 14 weeks. After describing how we determine collocation with the WiFi logs, we present a study to analyze how collocation within groups relates to a student's final score. We find collocation behaviors show a significant correlation (Pearson's r = 0.24) with performance -- better than both peer feedback or individual behaviors like attendance. Finally, we discuss how repurposing WiFi logs can facilitate applications for other domains like mental wellbeing and physical health.
Edit intent: meaning-changed
Original: Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease and pandemics such as the ongoing COVID-19 pandemic. We present ESOP , a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods.
Revised: Mass public quarantining, colloquially known as a lock-down, is a non-pharmaceutical intervention to check spread of disease . This paper presents ESOP (Epidemiologically and Socio-economically Optimal Policies) , a novel application of active machine learning techniques using Bayesian optimization, that interacts with an epidemiological model to arrive at lock-down schedules that optimally balance public health benefits and socio-economic downsides of reduced economic activity during lock-down periods.
Edit intent: clarity
Original: Hierarchical modeling and learning has proven very powerful in the field of Gaussian process regression and kernel methods, especially for machine learning applications and , increasingly, within the field of inverse problemsmore generally. The classical approach to learning hierarchical information is through Bayesian formulations of the problem, implying a posterior distribution on the hierarchical parameters or, in the case of empirical Bayes, providing an optimization criterion for them. Recent developments in the machine learning literature have suggested new criteria for hierarchical learning , based on approximation theoretic considerations that can be interpreted as variants of cross-validation, and exploiting approximation consistency in data splitting .
Revised: Hierarchical modeling and learning has proven very powerful in statistics, machine learning , based on approximation theoretic considerations that can be interpreted as variants of cross-validation, and exploiting approximation consistency in data splitting .
Edit intent: clarity
Original:   The purpose of this paper is to compare the empirical Bayesian and approximation theoretic approaches to hierarchical learning, in terms of large data consistency, variance of estimators, robustness of the estimators to model misspecification, and computational cost.
Revised:  A crucial aspect of the success of this methodology, in a wide range of applications to complex and real-world problems, is hierarchical modeling and learning of hyperparameters. The purpose of this paper is to compare the empirical Bayesian and approximation theoretic approaches to hierarchical learning, in terms of large data consistency, variance of estimators, robustness of the estimators to model misspecification, and computational cost.
Edit intent: meaning-changed
Original:   The purpose of this paper is to compare the empirical Bayesian and approximation theoretic approaches to hierarchical learning, in terms of large data consistency, variance of estimators, robustness of the estimators to model misspecification, and computational cost.
Revised:  The purpose of this paper is to study two paradigms of learning hierarchical parameters: one is from the probabilistic Bayesian perspective, in particular, the empirical Bayes approach that has been largely used in Bayesian statistics; the other is from the deterministic and approximation theoretic approaches to hierarchical learning, in terms of large data consistency, variance of estimators, robustness of the estimators to model misspecification, and computational cost.
Edit intent: meaning-changed
Original:   The purpose of this paper is to compare the empirical Bayesian and approximation theoretic approaches to hierarchical learning, in terms of large data consistency, variance of estimators, robustness of the estimators to model misspecification, and computational cost. Our analysis is rooted in the setting of Mat\'ern-like Gaussian random fieldpriors, with smoothness, amplitude and inverse lengthscale as hierarchical parameters, in the regression setting. Numerical experiments validate the theory and extend the scope of the paper beyond the Mat\'ern setting .
Revised:  The purpose of this paper is to compare the empirical Bayesian and approximation theoretic view, and in particular the kernel flow algorithm that was proposed recently in the machine learning literature. Analysis of their consistency in the large data limit, as well as explicit identification of their implicit bias in parameter learning, are established in this paper for a Mat\'ern-like model on the torus. A particular technical challenge we overcome is the paper beyond the Mat\'ern setting .
Edit intent: coherence
Original:  Numerical experiments validate the theory and extend the scope of the paper beyond the Mat\'ern setting .
Revised:  Numerical experiments validate the theory and extend the scope of the learning of the regularity parameter in the Mat\'ern-like field, for which consistency results have been very scarce in the spatial statistics literature. Moreover, we conduct extensive numerical experiments beyond the Mat\'ern-like model, comparing the two algorithms further. These experiments demonstrate learning of other hierarchical parameters, such as amplitude and lengthscale; they also illustrate the setting of model misspecification in which the kernel flow approach could show superior performance to the more traditional empirical Bayes approach .
Edit intent: meaning-changed
Original:   Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detectionperiods, creating a sense of urgency, and higher markup in prices . Precautionary behaviour can result due to decreasing resource rent.
Revised:  Encapsulating the idea of environmental surveillance, the use of quickest detection method allows us to easily translate our framework to real-time detection. Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detectionperiods, creating a sense of urgency, and higher markup in prices . Precautionary behaviour can result due to decreasing resource rent.
Edit intent: meaning-changed
Original:   Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detectionperiods, creating a sense of urgency, and higher markup in prices . Precautionary behaviour can result due to decreasing resource rent.
Revised:  Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to a combination of faster detection, a sense of urgency, and higher markup in prices . Precautionary behaviour can result due to decreasing resource rent.
Edit intent: clarity
Original:   Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detectionperiods, creating a sense of urgency, and higher markup in prices . Precautionary behaviour can result due to decreasing resource rent.
Revised:  Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detectionperiods, creating a sense of urgency, and higher markups . Precautionary behaviour can result due to decreasing resource rent.
Edit intent: clarity
Original:   Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detectionperiods, creating a sense of urgency, and higher markup in prices . Precautionary behaviour can result due to decreasing resource rent.
Revised:  Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detectionperiods, creating a sense of urgency, and higher markup in prices . Precautionary behaviour can result due to increasing resource rent.
Edit intent: meaning-changed
Original:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR) model , that takes into account the presence of aymptomatic carriers, and explore the effects of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Revised:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR) model that accounts for asymptomatic carriers, and explore the effects of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Edit intent: clarity
Original:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR) model , that takes into account the presence of aymptomatic carriers, and explore the effects of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Revised:  We present here an analysis of an extended Susceptible-Exposed-Infected-Recovered (SEIR) model , that takes into account the presence of aymptomatic carriers, and explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ).
Edit intent: fluency
Original:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number (R_0 > 1) to a target value ( R_0^{\rm target} < 1 ) , but in distinct ways, which we implement in our model equations.
Revised:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number R_0 to a target value ( R_0^{\rm target} < 1 ) , but in distinct ways, which we implement in our model equations.
Edit intent: meaning-changed
Original:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number (R_0 > 1) to a target value ( R_0^{\rm target} < 1 ) , but in distinct ways, which we implement in our model equations.
Revised:  The two intervention strategies (SD and TQ) try to reduce the disease reproductive number (R_0 > 1) to a target value R_0^{\rm target} < 1 ) , but in distinct ways, which we implement in our model equations.
Edit intent: fluency