wanyu/IteraTeR_full_sent huggingface dataset:
Original: Market dynamic is studied by quantifying the dependence of the entropy S(\tau,n) of the clusters formed by the  series of the prices p_t and its moving average p_{t,n} on temporal horizon M.
Revised: Market dynamic is studied by quantifying the dependence of the entropy S(\tau,n) of the clusters formed by the intersections between the series of the prices p_t and its moving average p_{t,n} on temporal horizon M.
Edit intent: meaning-changed
Original: Market dynamic is studied by quantifying the dependence of the entropy S(\tau,n) of the clusters formed by the  series of the prices p_t and its moving average p_{t,n} on temporal horizon M.
Revised: Market dynamic is studied by quantifying the dependence of the entropy S(\tau,n) of the clusters formed by the series of the prices p_t and the moving average p_{t,n} on temporal horizon M.
Edit intent: clarity
Original: Market dynamic is studied by quantifying the dependence of the entropy S(\tau,n) of the clusters formed by the  series of the prices p_t and its moving average p_{t,n} on temporal horizon M. We report results of the analysis\par  performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal www. bloomberg.com/professional.
Revised: Market dynamic is studied by quantifying the dependence of the entropy S(\tau,n) of the clusters formed by the series of the prices p_t and its moving average p_{t,n} . The entropy S(\tau,n) is defined according to Shannon as \sum P(\tau,n)\log P(\tau,n), with P(\tau,n) the probability for the cluster to occur with duration \tau.\par performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal www. bloomberg.com/professional.
Edit intent: meaning-changed
Original:  We report results of the analysis\par  performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal www. bloomberg.com/professional.
Revised:  We report results of the analysis\par The investigation is performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal www. bloomberg.com/professional.
Edit intent: meaning-changed
Original:  We report results of the analysis\par  performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal www. bloomberg.com/professional. Both raw and sampled data series have been analysed for a broad range of horizons M, varying from one to twelve months over the year 2018.
Revised:  We report results of the analysis\par performed on high-frequency data of the Nasdaq Composite, Dow Jones Industrial Avg and Standard \& Poor 500 indexes downloaded from the Bloomberg terminal . The cluster entropy S(\tau,n) is analysed in raw and sampled data series have been analysed for a broad range of horizons M, varying from one to twelve months over the year 2018.
Edit intent: meaning-changed
Original:  A systematic dependence of the cluster entropy function S(\tau,n) on the horizon M has been evidenced in the analysed assets. Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  A systematic dependence of the cluster entropy function S(\tau,n) is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: clarity
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster duration \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: meaning-changed
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield \emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: clarity
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{the Market Dynamic Index I(M,n), a synthetic figure of price dynamics. A systematic dependence of the cluster entropy S(\tau,n) and the Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: meaning-changed
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) \emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: coherence
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{on the temporal horizon M is evidenced.\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: meaning-changed
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par Finally, the Market Horizon Dependence defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: coherence
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par , defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: fluency
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) , is compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: coherence
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach .
Edit intent: clarity
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agents obtained via a Kullback-Leibler entropy approach .
Edit intent: clarity
Original:  Hence, the cluster entropy function is integrated over the cluster  \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par   defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach  .
Revised:  Hence, the cluster entropy function is integrated over the cluster \tau to yield a synthetic indicator of price evolution: the\emph{Market Dynamic Index I(M,n) . Moreover, the\emph{Market Horizon Dependence\par defined as H(M,n)=I(M,n)-I(1,n) is calculated and compared with the values of the horizon dependence of the pricing kernel with different representative agent models obtained by a Kullback-Leibler entropy approach . The Market Horizon Dependence H(M,n) of the three assets is compared against the values obtained by implementing the cluster entropy S(\tau,n) approach on artificially generated series (Fractional Brownian Motion) .
Edit intent: meaning-changed
Original: We study a class of  tests of the randomness of  Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence-the hot hand fallacy.
Revised: We study a class of permutation tests of the randomness of Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence-the hot hand fallacy.
Edit intent: meaning-changed
Original: We study a class of  tests of the randomness of  Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence-the hot hand fallacy.
Revised: We study a class of tests of the randomness of a collection of Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence-the hot hand fallacy.
Edit intent: clarity
Original: We study a class of  tests of the randomness of  Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence-the hot hand fallacy.
Revised: We study a class of tests of the randomness of Bernoulli sequences and their application to analyses of the human tendency to perceive streaks of consecutive successes as overly representative of positive dependence - the hot hand fallacy.
Edit intent: fluency
Original:  In particular, we study tests of randomness (i.e., that trials are i.i.d.) based on test statistics that compare the proportion of successes that directly follow k consecutive successes with either the overall proportion of successes or the proportion of successes that directly follow k consecutive failures.
Revised:  In particular, we study permutation tests of the null hypothesis of randomness (i.e., that trials are i.i.d.) based on test statistics that compare the proportion of successes that directly follow k consecutive successes with either the overall proportion of successes or the proportion of successes that directly follow k consecutive failures.
Edit intent: meaning-changed
Original:  We derive the asymptotic distributions of these test statistics and their permutation distributions under randomness, under a set of general stationary processes, and under a Markov model of "streakiness" , which allow us to evaluate their local asymptotic power.
Revised:  We characterize the asymptotic distributions of these test statistics and their permutation distributions under randomness, under a set of general stationary processes, and under a Markov model of "streakiness" , which allow us to evaluate their local asymptotic power.
Edit intent: clarity
Original:  We derive the asymptotic distributions of these test statistics and their permutation distributions under randomness, under a set of general stationary processes, and under a Markov model of "streakiness" , which allow us to evaluate their local asymptotic power.
Revised:  We derive the asymptotic distributions of these test statistics and their permutation distributions under randomness, under a set of general stationary processes, and under a class of Markov chain alternatives , which allow us to evaluate their local asymptotic power.
Edit intent: clarity
Original:  We derive the asymptotic distributions of these test statistics and their permutation distributions under randomness, under a set of general stationary processes, and under a Markov model of "streakiness" , which allow us to evaluate their local asymptotic power.
Revised:  We derive the asymptotic distributions of these test statistics and their permutation distributions under randomness, under a set of general stationary processes, and under a Markov model of "streakiness" , which allow us to derive their local asymptotic power.
Edit intent: clarity
Original:  Although multiple testing procedures reveal that one shooter in the experiment exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time - we find that the evidence against randomness  is limited to this shooter.
Revised:  In one experiment for which we were able to obtain data, multiple testing procedures reveal that one shooter in the experiment exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time - we find that the evidence against randomness is limited to this shooter.
Edit intent: meaning-changed
Original:  Although multiple testing procedures reveal that one shooter in the experiment exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time - we find that the evidence against randomness  is limited to this shooter.
Revised:  Although multiple testing procedures reveal that one shooter exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time - we find that the evidence against randomness is limited to this shooter.
Edit intent: clarity
Original:  Although multiple testing procedures reveal that one shooter in the experiment exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time - we find that the evidence against randomness  is limited to this shooter.
Revised:  Although multiple testing procedures reveal that one shooter in the experiment exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time . However, we find that the evidence against randomness is limited to this shooter.
Edit intent: coherence
Original:  Although multiple testing procedures reveal that one shooter in the experiment exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time - we find that the evidence against randomness  is limited to this shooter.
Revised:  Although multiple testing procedures reveal that one shooter in the experiment exhibits a shooting pattern significantly inconsistent with randomness - supplying strong evidence that basketball shooting is not random for all shooters all of the time - we find that the evidence against randomness in this experiment is limited to this shooter.
Edit intent: clarity
Original:  Our results provide a mathematical and statistical foundation for the design and validation of experiments that directly compare deviations from randomness with human beliefs about deviations from randomness in Bernoulli sequences .
Revised:  Our results provide a mathematical and statistical foundation for the design and validation of experiments that directly compare deviations from randomness with human beliefs about deviations from randomness , and thereby constitute a direst test of the hot hand fallacy .
Edit intent: meaning-changed
Original: Test automation can result in reduction in cost and human effort . If the optimal policy, the course of actions taken, for the intended objective in a testing process could be learnt by the testing system (e.g., a smart tester agent), then it could be reused in similar situations, thus leading to higher efficiency, i. e., less computational time. Automating stress testing to find performance breaking points remains a challenge for complex software systems.
Revised: Test automation can result in reduction in cost and human effort , but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points remains a challenge for complex software systems.
Edit intent: clarity
Original:  In this paper, we propose a self-adaptive fuzzy reinforcement learning-based performance (stress) testing framework (SaFReL) that enables the tester agent to learn the optimal policy for generating stress test cases leading to performance breaking point without access to performance model of the system under test.
Revised:  On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance (stress) testing framework (SaFReL) that enables the tester agent to learn the optimal policy for generating stress test cases leading to performance breaking point without access to performance model of the system under test.
Edit intent: coherence