wanyu/IteraTeR_v2 huggingface dataset:
Original:  Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search . Secondly, we adopt the Bayesian network to implement the user interest prediction for an improved personalized search .
Revised:  Firstly, we propose a novel text search algorithm using a inverse filtering mechanism that is very efficient for label based item search . Secondly, we propose a user interest prediction for an improved personalized search .
Edit intent: coherence
Original:  Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning . Experimental results show that the proposed search engine has an improved efficiency and accuracy and it can operate on embedded devices with very limited computational resources.
Revised:  Thirdly, the word vectorization is used to discover potential targets according to the semantic meaning . Experimental results show that the proposed search engine has an improved performance and it can operate on embedded devices with very limited computational resources.
Edit intent: clarity
Original: The effect of phenotypic plasticity in an evolutionary process , the so-called Baldwin effect, has been studied extensively for more than 100 years.
Revised: The effect of phenotypic plasticity on evolution , the so-called Baldwin effect, has been studied extensively for more than 100 years.
Edit intent: clarity
Original:  Plasticity has been found to influence the speed of evolution towards an optimal genetic configuration, but whether or not plasticity can cause evolution to veer towards a different genetic configuration from what pursued by evolution alone , is still an open question.
Revised:  Plasticity is known to influence the speed of evolution towards an optimal genetic configuration, but whether or not plasticity can cause evolution to veer towards a different genetic configuration from what pursued by evolution alone , is still an open question.
Edit intent: clarity
Original:  Plasticity has been found to influence the speed of evolution towards an optimal genetic configuration, but whether or not plasticity can cause evolution to veer towards a different genetic configuration from what pursued by evolution alone , is still an open question.
Revised:  Plasticity has been found to influence the speed of evolution towards a specific genetic configuration, but whether or not plasticity can cause evolution to veer towards a different genetic configuration from what pursued by evolution alone , is still an open question.
Edit intent: clarity
Original:  Plasticity has been found to influence the speed of evolution towards an optimal genetic configuration, but whether or not plasticity can cause evolution to veer towards a different genetic configuration from what pursued by evolution alone , is still an open question.
Revised:  Plasticity has been found to influence the speed of evolution towards an optimal genetic configuration, but whether it also influences what that genetic configuration is , is still an open question.
Edit intent: clarity
Original:  Here, this question is investigated analytically and experimentally, by means of an agent-based simulation of a foraging task, in an environment where the distribution of resources follows seasonal cycles .
Revised:  This question is investigated analytically and experimentally, by means of an agent-based simulation of a foraging task, in an environment where the distribution of resources follows seasonal cycles .
Edit intent: coherence
Original:  Here, this question is investigated analytically and experimentally, by means of an agent-based simulation of a foraging task, in an environment where the distribution of resources follows seasonal cycles .
Revised:  Here, this question is investigated , in an environment where the distribution of resources follows seasonal cycles .
Edit intent: coherence
Original:  Foraging is limited by a trade-off that forces agents to either specialize to  one specific resource type or generalize over all resource types at a lower success rate.
Revised:  Foraging is limited by a trade-off that forces agents to either specialize to one specific resource type or generalize to foraging all resource types at a lower success rate.
Edit intent: clarity
Original:  Foraging is limited by a trade-off that forces agents to either specialize to  one specific resource type or generalize over all resource types at a lower success rate.
Revised:  Foraging is limited by a trade-off that forces agents to either specialize to one specific resource type or generalize over all resource types at a low success rate.
Edit intent: fluency
Original:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Revised:  It is found that the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Edit intent: clarity
Original:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Revised:  Experiments demonstrate that in such an environment the introduction of learning, one instance of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Edit intent: clarity
Original:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Revised:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, changes what genetic configuration evolves . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Edit intent: clarity
Original:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Revised:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, the genome of learning agents evolves a predisposition to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Edit intent: clarity
Original:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would develop a specialist strategy, which maximizes the foraging efficiency for a specific resource type.
Revised:  Experiments demonstrate that in such an environment the introduction of learning, one of many instances of phenotypic plasticity, leads evolution to a different genetic configuration . Specifically, learning individuals develop a generalist strategy, which allows to adapt quickly to changes in the resource distribution, under the same conditions for which non-learners would evolve a predisposition to maximize the foraging efficiency for a specific resource type.
Edit intent: clarity
Original: Cell-Free (CF) Massive MIMO is an alternative topology for Massive MIMO networks, where a large number of single-antenna access points (APs) are distributed over the coverage area.
Revised: Cell-Free (CF) Massive MIMO is an alternative topology for future wireless networks, where a large number of single-antenna access points (APs) are distributed over the coverage area.
Edit intent: clarity
Original:  In prior work, it has been claimed that CF Massive MIMO inherits the basic properties of cellular Massive MIMO, namely channel hardening and favorable propagation.
Revised:  Prior works have claimed that CF Massive MIMO inherits the basic properties of cellular Massive MIMO, namely channel hardening and favorable propagation.
Edit intent: clarity
Original:  Our results show that the level of channel hardening depends strongly on the propagation environment and there is generally little hardening, except when the pathloss exponent is small.
Revised:  Our results show that channel hardening only appears in special cases, for example, when the pathloss exponent is small.
Edit intent: clarity
Original:  We further show that using 5--10 antennas per AP, instead of one, we can greatly improve the hardening.
Revised:  However, by using 5--10 antennas per AP, instead of one, we can greatly improve the hardening.
Edit intent: coherence
Original:  We further show that using 5--10 antennas per AP, instead of one, we can greatly improve the hardening.
Revised:  We further show that using 5--10 antennas per AP, instead of one, we can substantially improve the hardening.
Edit intent: clarity
Original:  The conclusion is that we cannot rely on channel hardening and favorable propagation when analyzing and designing CF Massive MIMO networks, but we need to use achievable rate expressions and resource allocation schemes that work well  in the absence of these properties.
Revised:  The conclusion is that we cannot rely on channel hardening and favorable propagation when analyzing and designing CF Massive MIMO networks, but we need to use achievable rate expressions and resource allocation schemes that work well also in the absence of these properties.
Edit intent: clarity
Original:  Some options are described in this paper.
Revised:  Some options are reviewed in this paper.
Edit intent: clarity
Original:  The best previous upper bound, just for the number of non-compact components, in the special case where k\!=\!3 and \{a_1, \ldots,a_{n+k\}\!
Revised:  The best previous upper bounds, when restricted to the special case k\!=\!3 and \{a_1, \ldots,a_{n+k\}\!
Edit intent: clarity
Original:  The best previous upper bound, just for the number of non-compact components, in the special case where k\!=\!3 and \{a_1, \ldots,a_{n+k\}\!
Revised:  The best previous upper bound, just for the number of non-compact components, in the special case where k\!=\!3 and \}\!
Edit intent: coherence
Original: One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes  with either large system size or long timescales.
Revised: One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales.
Edit intent: clarity
Original:  By deriving our definition from the absorption and scattering coefficients of virtual homogenous reference materials with an isotropic phase function, we achieve two important properties.
Revised:  By deriving our definition from the absorption and scattering coefficients of virtual homogeneous reference materials with an isotropic phase function, we achieve two important properties.
Edit intent: fluency
Original:  First, a simple adjustment of A is possible, which preserves the translucency appearance if an object is rescaled for printing.
Revised:  First, a simple adjustment of A is possible, which preserves the translucency appearance if an object is re-scaled for printing.
Edit intent: clarity
Original:  Second, determining the value of A for a real (potentially non-homogenous ) material, can be achieved by minimizing a distance function between light transport measurements of this material and simulated measurements of the reference materials.
Revised:  Second, determining the value of A for a real (potentially non-homogeneous ) material, can be achieved by minimizing a distance function between light transport measurements of this material and simulated measurements of the reference materials.
Edit intent: fluency
Original:  Finally, we conduct visual experiments employing the method of constant stimuli, and derive from them an embedding of A into a nearly perceptually-uniform scale of translucency for the reference materials.
Revised:  Finally, we conduct visual experiments employing the method of constant stimuli, and derive from them an embedding of A into a nearly perceptually uniform scale of translucency for the reference materials.
Edit intent: fluency
Original: Time series data have exploded due to the popularity of new applications, like data center management and IoT.
Revised: The volume of time series data has exploded due to the popularity of new applications, like data center management and IoT.
Edit intent: clarity
Original: Time series data have exploded due to the popularity of new applications, like data center management and IoT.
Revised: Time series data have exploded due to the popularity of new applications, such as data center management and IoT.
Edit intent: fluency
Original:  To fill this gap, we propose a new index structure, KV-index, and the corresponding matching algorithm, KV-match.
Revised:  To fill this gap, we propose a new index structure, KV-index, and the matching algorithm, KV-match.
Edit intent: clarity
Original:   KV-index is a file-based structure, which can be easily implemented on local files , HDFS or HBase tables.
Revised:  KV-index is a key-value structure, which can be easily implemented on local files , HDFS or HBase tables.
Edit intent: clarity
Original:   KV-index is a file-based structure, which can be easily implemented on local files , HDFS or HBase tables.
Revised:  KV-index is a file-based structure, which can be easily implemented on local files or HBase tables.
Edit intent: coherence
Original:  To support the query of arbitrary lengths, we extend KV-match to KV-match_{DP}, which utilizes multiple varied length indexes to process the query simultaneously.
Revised:  To support the query of arbitrary lengths, we extend KV-match to KV-match_{DP}, which utilizes multiple varied-length indexes to process the query simultaneously.
Edit intent: fluency
Original:  Results show that our index is of comparable size to the popular tree-style index while our query processing is order of magnitudes more efficient .
Revised:  The results verify the effectiveness and efficiency of our approach .
Edit intent: clarity
Original:  In this paper, we present DEEPER , a turn-key ER system powered by deep learning (DL) techniques.
Revised:  In this paper, we present DeepER , a turn-key ER system powered by deep learning (DL) techniques.
Edit intent: fluency
Original: Entity Resolution (ER) is a fundamental problemwith many applications.
Revised: Entity resolution (ER) is a fundamental problemwith many applications.
Edit intent: fluency
Original:  22 (2015)] that the smallest snark with oddness 4 has 28 vertices and remarked and that there are exactly two such graphs of that order.
Revised:  22 (2015)] that the smallest snark with oddness 4 has 28 vertices and remarked that there are exactly two such graphs of that order.
Edit intent: clarity
Original:  This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both the heterogeneity and the temporal dynamics of the underlying network.
Revised:  This implies predicting the time it takes for a relationship to appear in the future, given its features that have been extracted by considering both heterogeneity and temporal dynamics of the underlying network.
Edit intent: fluency
Original:  To this end, we first introduce a meta-path-based feature extraction framework  to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the network . Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Revised:  To this end, we first introduce a feature extraction framework to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the network . Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Edit intent: coherence
Original:  To this end, we first introduce a meta-path-based feature extraction framework  to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the network . Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Revised:  To this end, we first introduce a meta-path-based feature extraction framework to effectively extract features suitable for relationship prediction regarding heterogeneity and dynamicity of the network . Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Edit intent: fluency
Original:  To this end, we first introduce a meta-path-based feature extraction framework  to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the network . Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Revised:  To this end, we first introduce a meta-path-based feature extraction framework to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the networks . Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Edit intent: fluency
Original:  To this end, we first introduce a meta-path-based feature extraction framework  to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the network . Next, we propose a supervised nonparametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Revised:  To this end, we first introduce a meta-path-based feature extraction framework to effectively extract features suitable for relationship prediction regarding the heterogeneity and dynamicity of the network . Next, we propose a supervised non-parametric approach, called Non-Parametric Generalized Linear Model (NP-GLM), which infers the hidden underlying probability distribution of the relationship building time given its features.
Edit intent: fluency
Original:  Extensive experiments conducted on both synthetic dataset and real-world DBLP bibliographic citation network dataset demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-a-vis alternative baselines.
Revised:  Extensive experiments conducted on synthetic data and three real-world DBLP bibliographic citation network dataset demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-a-vis alternative baselines.
Edit intent: clarity
Original:  Extensive experiments conducted on both synthetic dataset and real-world DBLP bibliographic citation network dataset demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-a-vis alternative baselines.
Revised:  Extensive experiments conducted on both synthetic dataset and real-world DBLP bibliographic citation network dataset demonstrate the effectiveness of NP-GLM in solving continuous-time relationship prediction problem vis-a-vis alternative baselines.
Edit intent: fluency
Original:  Extensive experiments conducted on both synthetic dataset and real-world DBLP bibliographic citation network dataset demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-a-vis alternative baselines.
Revised:  Extensive experiments conducted on both synthetic dataset and real-world DBLP bibliographic citation network dataset demonstrate the effectiveness of Np-Glm in solving continuous-time relationship prediction problem vis-a-vis competitive baselines
Edit intent: clarity
Original:  We show that  competitive ratio of an optimal deterministic online algorithm for OUC is at least 3 and at most 5.
Revised:  We show that the competitive ratio of an optimal deterministic online algorithm for OUC is at least 3 and at most 5.
Edit intent: fluency
Original:  We show that  competitive ratio of an optimal deterministic online algorithm for OUC is at least 3 and at most 5.
Revised:  We show that competitive ratio of every deterministic online algorithm for OUC is at least 3 and at most 5.
Edit intent: clarity
Original:  ABC requires the generation of many pseudo-data by expensive simulation runs, we have thus developed an high performance computing (HPC) ABC framework , taking into account accuracy and scalability.
Revised:  ABC requires the generation of many pseudo-data by expensive simulation runs, we use a high performance computing (HPC) ABC framework , taking into account accuracy and scalability.
Edit intent: clarity