wanyu/IteraTeR_v2 huggingface dataset:
Original:  Motivated by the upcoming surge of 5G IoT connectivity in industrial environments, in this paper, we propose to integrate a DL-based anomaly detection (AD) as a service into the 3GPP mobile cellular IoT architecture.
Revised:  Motivated by the upcoming surge of 5G IoT connectivity in industrial environments, we propose to integrate a DL-based anomaly detection (AD) as a service into the 3GPP mobile cellular IoT architecture.
Edit intent: coherence
Original:  The proposed architecture embeds deep autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness and accuracy.
Revised:  The proposed architecture embeds autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness and accuracy.
Edit intent: clarity
Original:  Equipped with such distributional knowledge , the platform guides the search process by determining the meeting rate between each pair of types from the two sides.
Revised:  Equipped with knowledge of these distributions , the platform guides the search process by determining the meeting rate between each pair of types from the two sides.
Edit intent: clarity
Original:  We first show that incentive issues arising from congestion and cannibalization makes the design problem fairly intricate.
Revised:  We first show that incentive issues arising from congestion and cannibalization make the design problem fairly intricate.
Edit intent: fluency
Original:  Nonetheless, we develop an efficiently computable solution whose corresponding equilibrium achieves at least 1/4  of the optimal social welfare .
Revised:  Nonetheless, we develop an efficiently computable search design whose corresponding equilibrium achieves at least 1/4 of the optimal social welfare .
Edit intent: clarity
Original:  Furthermore, our solution implies that the platform can substantially limit choice and yet induce an equilibrium with an approximately optimal welfare.
Revised:  Furthermore, our design implies the platform can substantially limit choice and yet induce an equilibrium with an approximately optimal welfare.
Edit intent: clarity
Original:  Finally, we show that approximation is the likely best we can hope  by establishing that the problem of designing optimal directed search is NP-hard to  approximate beyond a certain constant factor.
Revised:  Finally, we show that approximation is likely the best we can hope by establishing that the problem of designing optimal directed search is NP-hard to approximate beyond a certain constant factor.
Edit intent: fluency
Original:  Finally, we show that approximation is the likely best we can hope  by establishing that the problem of designing optimal directed search is NP-hard to  approximate beyond a certain constant factor.
Revised:  Finally, we show that approximation is the likely best we can hope for by establishing that the problem of designing optimal directed search is NP-hard to approximate beyond a certain constant factor.
Edit intent: clarity
Original:  Finally, we show that approximation is the likely best we can hope  by establishing that the problem of designing optimal directed search is NP-hard to  approximate beyond a certain constant factor.
Revised:  Finally, we show that approximation is the likely best we can hope by establishing that the problem of designing optimal directed search is NP-hard to even approximate beyond a certain constant factor.
Edit intent: clarity
Original:   The preconditioner is constructed in two steps where the first step ensures mesh independence while the second step aims at reducing the influence of the optical parameters.
Revised:  We prove convergence of the resulting scheme. The preconditioner is constructed in two steps where the first step ensures mesh independence while the second step aims at reducing the influence of the optical parameters.
Edit intent: clarity
Original: Self-organized criticality (SOC) refers to the ability of complex systems to evolve towards a second-order phase transition at which interactions between system components lead to scale-invariant events beneficial for system performance.
Revised: Self-organized criticality (SOC) refers to the ability of complex systems to evolve towards a 2nd-order phase transition at which interactions between system components lead to scale-invariant events beneficial for system performance.
Edit intent: fluency
Original:  For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types , interconnectivity, and plasticity might exhibit SOC.
Revised:  For the last two decades, considerable experimental evidence accumulated that the mammalian cortex with its diversity in cell types , interconnectivity, and plasticity might exhibit SOC.
Edit intent: fluency
Original:  For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types , interconnectivity, and plasticity might exhibit SOC.
Revised:  For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types and connections might exhibit SOC.
Edit intent: clarity
Original:  Here we review experimental findings of isolated, layered cortex preparations to self-organize towards the four dynamical motifs of up-states, oscillations, neuronal avalanches, and coherence potentials.
Revised:  Here we review experimental findings of isolated, layered cortex preparations to self-organize towards four dynamical motifs of up-states, oscillations, neuronal avalanches, and coherence potentials.
Edit intent: fluency
Original:  During up-states, the synchronization observed for nested theta/gamma-oscillations embeds scale-invariant neuronal avalanches identified by robust power law scaling in avalanche sizes with a slope of -
Revised:  During up-states, the synchronization observed for nested theta/gamma-oscillations embeds scale-invariant neuronal avalanches that exhibit robust power law scaling in avalanche sizes with a slope of -
Edit intent: clarity
Original:  During up-states, the synchronization observed for nested theta/gamma-oscillations embeds scale-invariant neuronal avalanches identified by robust power law scaling in avalanche sizes with a slope of -
Revised:  During up-states, the synchronization observed for nested theta/gamma-oscillations embeds scale-invariant neuronal avalanches identified by robust power law scaling in size with a slope of -
Edit intent: fluency
Original: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP)  , emerges autonomously in superficial layers of organotypic cortex cultures  , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
Revised: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP) , emerges autonomously in superficial layers of organotypic cortex cultures , is homeostatically regulated, displays separation of time scales, and reveals unique size vs. quiet time dependencies.
Edit intent: clarity
Original:  A subclass of avalanches , the coherence potential, exhibits precise maintenance of temporal precision in propagated local synchrony.
Revised:  A threshold operation identifies coherence potentials; avalanches that in addition maintain the precise time course of propagated synchrony.
Edit intent: clarity
Original:  Avalanches emerge in superficial cortex layers under conditions of strong external driving.
Revised:  Avalanches emerge under conditions of strong external driving.
Edit intent: clarity
Original:  Avalanches emerge in superficial cortex layers under conditions of strong external driving.
Revised:  Avalanches emerge in superficial cortex layers under conditions of external driving.
Edit intent: clarity
Original:  The balance of excitation and inhibition (E/I) and neuromodulators such as dopamineestablish powerful control parameters for avalanche dynamics .
Revised:  Control parameters are established by the balance of excitation and inhibition (E/I) and neuromodulators such as dopamineestablish powerful control parameters for avalanche dynamics .
Edit intent: clarity
Original:  The balance of excitation and inhibition (E/I) and neuromodulators such as dopamineestablish powerful control parameters for avalanche dynamics .
Revised:  The balance of excitation and inhibition (E/I) and the neuromodulator dopamine .
Edit intent: clarity
Original:  The precise interaction between up-states, nested oscillations and avalanchesin layered cortex provides compelling evidence for SOC in the brain.
Revised:  The precise interactions between up-states, nested oscillations and avalanchesin layered cortex provides compelling evidence for SOC in the brain.
Edit intent: fluency
Original: With the worldwide growth of IoT industry, the need for a strong securitylevel for IoT networks has also increased, leading to blockchain-based IoT (BC-IoT) networks .
Revised: With the worldwide growth of the Internet of Things (IoT), new security requirements have been raised, such as strong security, data integrity, or privacy preservation .
Edit intent: clarity
Original:  While blockchain technology is leveraged to ensure data integrity in a distributed manner, Hyperledger Fabric (HLF) attracts attention with its distinctive strong point without requiring the power-consuming consensus protocol, that is , proof-of-work (PoW) .
Revised:  While blockchain technology is capable of addressing the issues, HLF , proof-of-work (PoW) .
Edit intent: clarity
Original:  While blockchain technology is capable of addressing the issues, HLF, which is a private blockchain platform, has been leveraged for blockchain-enabled IoT (BC-IoT) networks.
Revised:  While blockchain technology is capable of addressing the issues, HLF, which is a private blockchain platform, has been leveraged for blockchain-enabled Internet of things (BC-IoT) networks.
Edit intent: fluency
Original:  From the perspective of an IoT application, however, the additional processing time spent in HLF may emerge as another problem because many IoT applications handle real-time and latency-critical jobs.
Revised:  However, for IoT devices handling latency-critical tasks, the additional time spent in HLF may emerge as another problem because many IoT applications handle real-time and latency-critical jobs.
Edit intent: clarity
Original:  However, this problem has still remained unresolved because the distribution of the HLF latency has not been revealed until the present.
Edit intent: coherence
Original:  In this paper, therefore, we develop an HLF latency distribution model using probability distribution fitting and show the latency can be modeled with the gamma distribution.
Revised:  In this paper, therefore, we develop an HLF latency model using the probability distribution fitting and show the latency can be modeled with the gamma distribution.
Edit intent: coherence
Original:  Their impacts on the HLF latency are analyzed to provide strategies for decreasing and minimizing the mean HLF latency .
Revised:  As a result, some HLF design insights on minimizing the latency are provided for HLF-based IoT networks .
Edit intent: clarity
Original: Hyperledger Fabric (HLF), one of the most popular private blockchain platforms, has recently received attention for blockchain-enabled Internet of things (BC-IoT) networks.
Revised: Hyperledger Fabric (HLF), one of the most popular private blockchain platforms, has recently received attention for blockchain-enabled Internet of Things (BC-IoT) networks.
Edit intent: fluency
Original:  We first explain the architecture and the transaction flow in HLF, and  structure of an HLF-based IoT network.
Revised:  We first explain the architecture and the transaction flow in HLF, and the structure of an HLF-based IoT network.
Edit intent: fluency
Original:  After implementing real HLF , we capture the latencies that each transaction experiences for various HLF environments, and then show that the total latency of HLF can be modeled as a Gamma distribution.
Revised:  After implementing realHLF , we capture the latencies that each transaction experiences for various HLF environments, and then show that the total latency of HLF can be modeled as a Gamma distribution.
Edit intent: fluency
Original:  Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when implemented in a standard commercial MIP ( mixed integer programming ) solver.
Revised:  Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when solved by a standard commercial MIP ( mixed integer programming ) solver.
Edit intent: clarity
Original:  Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when implemented in a standard commercial MIP ( mixed integer programming ) solver.
Revised:  Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when implemented in a standard commercial mixed integer programming ) solver.
Edit intent: clarity
Original:  Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when implemented in a standard commercial MIP ( mixed integer programming ) solver.
Revised:  Computational experiments using benchmark instances are performed comparing several formulations, including the newly proposed approaches and those available in the literature, when implemented in a standard commercial MIP ( mixed integer programming solver.
Edit intent: fluency
Original:  In this paper, we propose a novel framework, SpeedyFeed , which efficiently trains PLMs-based news recommenders of superior quality.
Revised:  In this paper, we propose a novel framework, SpeedyFeed , which efficiently trains PLMs-based news recommenders of superior quality.
Edit intent: fluency
Original:   SpeedyFeed leads to more than 100\times acceleration of the training process, which enables big models to be trained efficiently and effectively over massive user data.
Revised:  Extensive experiments show that SpeedyFeed leads to more than 100\times acceleration of the training process, which enables big models to be trained efficiently and effectively over massive user data.
Edit intent: clarity
Original:  SpeedyFeed is also a model-agnostic framework, thus being potentially applicable to a wide spectrum of content-based recommender systems .
Revised:  SpeedyFeed is also a model-agnostic framework, which is potentially applicable to a wide spectrum of content-based recommender systems .
Edit intent: coherence
Original:  But , data scientists and analysts are required to use different systems, because apart from SQL querying relational databases are not well equipped to perform complex data analyses .
Revised:  But , data scientists have no other option, but to use a different system for complex data analysis .
Edit intent: clarity
Original:  Furthermore, with relational databases, the data must be completely loaded into the database before performing any analysis.
Edit intent: coherence
Original:  We extend the Python's Dask framework to present DaskDB, a scalable data science system with support for unified data analytics and in situ SQL query processing on heterogeneous data sources.
Revised:  We extend the Python's Dask framework , which is a scalable data science system with support for unified data analytics and in situ SQL query processing on heterogeneous data sources.
Edit intent: coherence
Original:  We extend the Python's Dask framework to present DaskDB, a scalable data science system with support for unified data analytics and in situ SQL query processing on heterogeneous data sources.
Revised:  We extend the Python's Dask framework to present DaskDB, a scalable data science system having support for both data analytics and in situ SQL query processing on heterogeneous data sources.
Edit intent: clarity
Original:  We extend the Python's Dask framework to present DaskDB, a scalable data science system with support for unified data analytics and in situ SQL query processing on heterogeneous data sources.
Revised:  We extend the Python's Dask framework to present DaskDB, a scalable data science system with support for unified data analytics and in situ SQL query processing over heterogeneous data sources.
Edit intent: fluency
Original:  Moreover, we introduce a novel distributed learned index to improve join performance.
Revised:  Moreover, we introduce a novel distributed learned index is also introduced to improve the join performance.
Edit intent: coherence
Original:  Still, our results confirm that t-digest remains more accurate on the " non-adversarial " data encountered in practice.
Revised:  Still, our results confirm that t-digest remains more accurate on the `` non-adversarial " data encountered in practice.
Edit intent: fluency
Original:  Still, our results confirm that t-digest remains more accurate on the " non-adversarial " data encountered in practice.
Revised:  Still, our results confirm that t-digest remains more accurate on the " non-adversarial '' data encountered in practice.
Edit intent: fluency
Original:  We used average persistence landscapes and machine learning techniques to study changes in locomotion and behavior in varying environments .
Revised:  We used average persistence landscapes , and support vector machines .
Edit intent: clarity
Original: We apply topological data analysis to video of C. elegans, a widely-studied model organism in biology.
Revised: We apply topological data analysis to the behavior of C. elegans, a widely-studied model organism in biology.
Edit intent: clarity
Original:  Our main goal is to develop a quantitative summary of complex behavior that may be applied to high-throughput data.
Revised:  Our main goal is to develop a quantitative summary of complex behavior which may be applied to high-throughput data.
Edit intent: fluency
Original:  Our method is able to distinguish and classify videos from various environmental conditions .
Revised:  Our methods allow us to distinguish and classify videos from various environmental conditions .
Edit intent: clarity
Original:  GNNs are an inductive bias that effectively encodes combinatorial and relational input due to their permutation-invariance and sparsity awareness .
Revised:  GNNs are an inductive bias that effectively encodes combinatorial and relational input due to their invariance to permutations and awareness of input sparsity .
Edit intent: clarity
Original:  This paper presents a conceptual review of recent key advancements in this emerging field, aiming at both the optimization and machine learning researcher .
Revised:  This paper presents a conceptual review of recent key advancements in this emerging field, aiming at researchers in both optimization and machine learning researcher .
Edit intent: clarity
Original:  This paper presents a conceptual review of recent key advancements in this emerging field, aiming at both the optimization and machine learning researcher .
Revised:  This paper presents a conceptual review of recent key advancements in this emerging field, aiming at both the optimization and machine learning .
Edit intent: clarity
Original: Machine learning for therapeutics is an emerging field with incredible opportunities for innovation and expansion.
Revised: Therapeutics machine learning is an emerging field with incredible opportunities for innovation and expansion.
Edit intent: clarity
Original:  Here, we introduce Therapeutics Data Commons (TDC), the first unifying framework to systematically access and evaluate machine learning across the entire range of therapeutics.
Revised:  Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
Edit intent: clarity
Original:  At its core, TDC is a collection of curated datasets and learning tasks that can translate algorithmic innovation into biomedical and clinical implementation.
Edit intent: coherence
Original:  To date, TDC includes 66 machine learning-ready datasets from 22 learning tasks , spanning the discovery and development of safe and effective medicines.
Revised:  To date, TDC includes 66 AI-ready datasets spread across 22 learning tasks , spanning the discovery and development of safe and effective medicines.
Edit intent: clarity
Original:  To date, TDC includes 66 machine learning-ready datasets from 22 learning tasks , spanning the discovery and development of safe and effective medicines.
Revised:  To date, TDC includes 66 machine learning-ready datasets from 22 learning tasks and spanning the discovery and development of safe and effective medicines.
Edit intent: coherence
Original:  TDC also provides an ecosystem of tools , libraries, leaderboards, and community resources, including data functions, strategies for systematic model evaluation, meaningful data splits, data processors, and molecule generation oracles .
Revised:  TDC also provides an ecosystem of tools and community resources, including data functions, strategies for systematic model evaluation, meaningful data splits, data processors, and molecule generation oracles .
Edit intent: coherence
Original:  TDC also provides an ecosystem of tools , libraries, leaderboards, and community resources, including data functions, strategies for systematic model evaluation, meaningful data splits, data processors, and molecule generation oracles .
Revised:  TDC also provides an ecosystem of tools , libraries, leaderboards, and community resources, including data functions, strategies for systematic model evaluation, 17 molecule generation oracles .
Edit intent: clarity
Original:  We  envision that TDC can facilitate algorithmic and scientific advances and accelerate development, validation , and transition into production and clinical implementation.
Revised:  We envision that TDC can facilitate algorithmic and scientific advances and accelerate development, validation and transition into production and clinical implementation.
Edit intent: fluency
Original:  We  envision that TDC can facilitate algorithmic and scientific advances and accelerate development, validation , and transition into production and clinical implementation.
Revised:  We envision that TDC can facilitate algorithmic and scientific advances and accelerate development, validation , and transition into biomedical and clinical implementation.
Edit intent: clarity
Original: Graph convolutional networks (GCNs) are a powerful architecture for representation learning and making predictions on documents that naturally occur as graphs, e.g., citation or social networks.
Revised: Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks.
Edit intent: clarity
Original:  Data containing sensitive personal information, such as documents with people's profiles or relationships as edges, are prone to privacy leaks from GCNs, as an adversary might reveal the original input from the trained model .
Revised:  Data containing sensitive personal information, such as documents with people's profiles or relationships as edges, are prone to privacy leaks , as the trained model might reveal the original input from the trained model .
Edit intent: clarity