wanyu/IteraTeR_v2 huggingface dataset:
Original:  Such factors include principles of neurosynaptic stability-plasticity, critical developmental stages , intrinsically motivated exploration, transfer learning, and crossmodal integration .
Revised:  Such factors include principles of neurosynaptic stability-plasticity, critical developmental stages , intrinsically motivated exploration, and crossmodal learning .
Edit intent: clarity
Original: Humans and animals have the ability to continually acquire and fine-tune knowledge throughout their lifespan.
Revised: Humans and animals have the ability to continually acquire , fine-tune knowledge throughout their lifespan.
Edit intent: fluency
Original:  Consequently, lifelong learning capabilities are crucial for computational learning systems and autonomous agents interacting in the real world and processing continuous streams of information.
Revised:  Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Edit intent: clarity
Original:  Although significant advances have been made in domain-specific learning with neural networks, extensive research efforts are required for the development of robust lifelong learning on autonomous agents and robots.
Edit intent: coherence
Original:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval without catastrophic forgetting . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Revised:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval without catastrophic forgetting . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Edit intent: fluency
Original:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval without catastrophic forgetting . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Revised:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Edit intent: coherence
Original:   We apply these results for pricing insurance products in hybrid markets by taking into account the role of inflation under the benchmark approach.
Revised:  We apply these results for pricing non-life insurance liabilities in hybrid financial and insurance markets, while taking into account the role of inflation under the benchmark approach.
Edit intent: clarity
Original:  This framework offers at the same time a general and flexible structure, as well as explicit and treatable pricing formula.
Revised:  This framework offers at the same time a general and flexible structure, and explicit and treatable pricing formula.
Edit intent: coherence
Original: It is well known that any bipartite (social) network can be regarded as a formal context (G,M,I) . Therefore, such networks give raise to formal concept lattices which can be investigated utilizing the toolset of Formal Concept Analysis (FCA).
Revised: It is well known that any bipartite (social) network can be regarded as a formal context (G,M,I) from Formal Concept Analysis (FCA).
Edit intent: clarity
Original:  Studies examining how sentiment on social media varies over time and space appear to produce inconsistent results .
Revised: Background: Studies examining how sentiment on social media varies over time and space appear to produce inconsistent results .
Edit intent: clarity
Original:  Studies examining how sentiment on social media varies over time and space appear to produce inconsistent results .
Revised:  Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results .
Edit intent: clarity
Original:  Analysing 16.54 million English-language tweets from 100 cities posted between 13 July and 30 November 2017, our aim was to clarify how spatiotemporal and social factors contributed to variation in sentiment on Twitter.
Revised:  Analysing 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, our aim was to clarify how spatiotemporal and social factors contributed to variation in sentiment on Twitter.
Edit intent: clarity
Original:  We estimated positive and negative sentiment for each of the cities using  dictionary-based sentiment analysis and constructed models to explain  differences in sentiment using time of day, day of week, weather,  interaction type ( social or non-social) , and city as factors .
Revised:  We estimated positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain differences in sentiment using time of day, day of week, weather, interaction type ( social or non-social) , and city as factors .
Edit intent: fluency
Original:  We estimated positive and negative sentiment for each of the cities using  dictionary-based sentiment analysis and constructed models to explain  differences in sentiment using time of day, day of week, weather,  interaction type ( social or non-social) , and city as factors .
Revised:  We estimated positive and negative sentiment for each of the cities using dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, interaction type ( social or non-social) , and city as factors .
Edit intent: fluency
Original:  We estimated positive and negative sentiment for each of the cities using  dictionary-based sentiment analysis and constructed models to explain  differences in sentiment using time of day, day of week, weather,  interaction type ( social or non-social) , and city as factors .
Revised:  We estimated positive and negative sentiment for each of the cities using dictionary-based sentiment analysis and constructed models to explain differences in sentiment using time of day, day of week, weather, city, and interaction type ( social or non-social) , and city as factors .
Edit intent: coherence
Original:   In the full multivariable model of positive (Pearson 's R in test data 0.236;
Revised:  Results: In the full multivariable model of positive (Pearson 's R in test data 0.236;
Edit intent: clarity
Original:   In the full multivariable model of positive (Pearson 's R in test data 0.236;
Revised:  In the full multivariable model of positive (Pearson r in test data 0.236;
Edit intent: fluency
Original:  95 \% CI 0.231-0.241), and negative (Pearson's R in test data 0.306 95\% CI \\%DIF > CI 0.231-0.241) and negative (Pearson r in test data 0.306;
Revised:  95 \\%DIF > CI 0.231-0.241) and negative (Pearson r in test data 0.306;
Edit intent: clarity
Original:  95\\% CI 0.301-0.310) sentiment,  city and time of day explained more of the variance than other factors.
Revised:  95\\% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than other factors.
Edit intent: fluency
Original:  In applications that aim to detect localised events using the sentiment of Twitter populations , it is useful to account for baseline differences before looking for unexpected changes.
Revised:  In applications that aim to detect localized events by aggregating sentiment across populations of Twitter users , it is useful to account for baseline differences before looking for unexpected changes.
Edit intent: clarity
Original:  In applications that aim to detect localised events using the sentiment of Twitter populations , it is useful to account for baseline differences before looking for unexpected changes.
Revised:  In applications that aim to detect localised events using the sentiment of Twitter populations , it is worthwhile accounting for baseline differences before looking for unexpected changes.
Edit intent: clarity
Original: Recommender systems have been widely studied from the machine learningperspective, where it is crucial to share information among users while preserving user privacy .
Revised: Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning .
Edit intent: clarity
Original:  In this work, we present a federated meta-learning framework for recommendation in which user information is sharedat the level of algorithm , instead of model or data adopted in previous approaches.
Revised:  In this work, we present a federated meta-learning framework for recommendation in which user information is sharedat the level of algorithm , instead of a global model in previous approaches.
Edit intent: clarity
Original:  Yet Sinn (2017) argues that excessive electrical storage requirements could limit the further expansion of variable wind and solar energy.
Revised:  Yet Sinn (2017) argues that excessive electrical storage requirements limit the further expansion of variable wind and solar energy.
Edit intent: clarity
Original: We introduce tensor field networks, which are locally equivariant to 3D rotations and translations(and invariant to permutations of points ) at every layer.
Revised: We introduce tensor field networks, which are locally equivariant to 3D rotations , translations, and permutations of points ) at every layer.
Edit intent: coherence
Original: We introduce tensor field networks, which are locally equivariant to 3D rotations and translations(and invariant to permutations of points ) at every layer.
Revised: We introduce tensor field networks, which are locally equivariant to 3D rotations and translations(and invariant to permutations of points at every layer.
Edit intent: fluency
Original:  We demonstrate how tensor field networks learn to model simple physics (Newtonian gravitation and moment of inertia), classify simple 3D shapes (trained on one orientation and tested on shapes in arbitrary orientations), and, given a small organic molecule with an atom removed, replace the correct element at the correct location in space .
Revised:  We demonstrate the capabilities of tensor field networks learn to model simple physics (Newtonian gravitation and moment of inertia), classify simple 3D shapes (trained on one orientation and tested on shapes in arbitrary orientations), and, given a small organic molecule with an atom removed, replace the correct element at the correct location in space .
Edit intent: clarity
Original:  We demonstrate how tensor field networks learn to model simple physics (Newtonian gravitation and moment of inertia), classify simple 3D shapes (trained on one orientation and tested on shapes in arbitrary orientations), and, given a small organic molecule with an atom removed, replace the correct element at the correct location in space .
Revised:  We demonstrate how tensor field networks with tasks in geometry, physics, and chemistry .
Edit intent: clarity
Original: As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and data-driven artificial intelligence (in short, AI ) algorithms, architecture, and systems , the pressure of benchmarking rises.
Revised: As architecture, system, data management, and machine learning communities pay greater attention to innovative big data and AI algorithms, architecture, and systems , the pressure of benchmarking rises.
Edit intent: clarity
Original:  However , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
Revised:  However , complexity, diversity, frequently-changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
Edit intent: fluency
Original:  However , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
Revised:  However , complexity, diversity, frequently changed workloads, and rapid evolution of big data and AI systems raise great challenges in benchmarking .
Edit intent: clarity
Original:  However , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges in benchmarking .
Revised:  However , complexity, diversity, frequently changed workloads, and rapid evolution of big data , especially AI systems raise great challenges .
Edit intent: clarity
Original:  The extensive experiments' results show that Enhanced PeerHunter can achieve high detection rate with few false positives, and high robustness against the proposed mimicking legitimate P2P application attacks.
Revised:  The extensive experimental results show that Enhanced PeerHunter can achieve high detection rate with few false positives, and high robustness against the proposed mimicking legitimate P2P application attacks.
Edit intent: clarity
Original: Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the fundamental infrastructure that responsible for various cyber-crimes.
Revised: Peer-to-peer (P2P) botnets have become one of the major threats in network security for serving as the fundamental infrastructure for various cyber-crimes.
Edit intent: clarity
Original:  More challenges are involved in the problem of detecting P2P botnets, despite a few existing works claimed to detect traditional botnets effectively.
Revised:  More challenges are involved in the problem of detecting P2P botnets, despite a few work claimed to detect traditional botnets effectively.
Edit intent: clarity
Original:  More challenges are involved in the problem of detecting P2P botnets, despite a few existing works claimed to detect traditional botnets effectively.
Revised:  More challenges are involved in the problem of detecting P2P botnets, despite a few existing works claimed to detect centralized botnets effectively.
Edit intent: clarity
Original:  In this paper, we present Enhanced PeerHunter, a network-flow level botnet community behavior analysis based method, which is capable of detecting botnets that communicate via P2P overlay networks.
Revised:  We propose Enhanced PeerHunter, a network-flow level botnet community behavior analysis based method, which is capable of detecting botnets that communicate via P2P overlay networks.
Edit intent: clarity
Original:  In this paper, we present Enhanced PeerHunter, a network-flow level botnet community behavior analysis based method, which is capable of detecting botnets that communicate via P2P overlay networks.
Revised:  In this paper, we present Enhanced PeerHunter, a network-flow level community behavior analysis based method, which is capable of detecting botnets that communicate via P2P overlay networks.
Edit intent: fluency
Original:  In this paper, we present Enhanced PeerHunter, a network-flow level botnet community behavior analysis based method, which is capable of detecting botnets that communicate via P2P overlay networks.
Revised:  In this paper, we present Enhanced PeerHunter, a network-flow level botnet community behavior analysis based system, to detect P2P overlay networks.
Edit intent: clarity
Original:  Then, it uses the natural botnet behavior "mutual contacts" as the main feature to cluster bots into communities.
Revised:  Then, it uses "mutual contacts" as the main feature to cluster bots into communities.
Edit intent: clarity
Original:  Then, it uses the natural botnet behavior "mutual contacts" as the main feature to cluster bots into communities.
Revised:  Then, it uses the natural botnet behavior "mutual contacts" to cluster bots into communities.
Edit intent: clarity
Original:  Finally, it uses network-flow level botnet community behavior analysis to detect potential botnet communities and further identify bot candidates .
Revised:  Finally, it uses network-flow level community behavior analysis to detect potential botnet communities and further identify bot candidates .
Edit intent: fluency
Original:  Finally, it uses network-flow level botnet community behavior analysis to detect potential botnet communities and further identify bot candidates .
Revised:  Finally, it uses network-flow level botnet community behavior analysis to detect potential botnets .
Edit intent: clarity
Original:  In the experimental evaluation, we propose two evasion attacks, in which we assume the adversaries (e.g., the botmasters) know our techniques in advance , and they might attempt to evade our system via making the P2P bots mimic the behavior of legitimate P2P applications.
Revised:  In the experimental evaluation, we propose two evasion attacks, where we assume the adversaries (e.g., the botmasters) know our techniques in advance , and they might attempt to evade our system via making the P2P bots mimic the behavior of legitimate P2P applications.
Edit intent: coherence
Original:  In the experimental evaluation, we propose two evasion attacks, in which we assume the adversaries (e.g., the botmasters) know our techniques in advance , and they might attempt to evade our system via making the P2P bots mimic the behavior of legitimate P2P applications.
Revised:  In the experimental evaluation, we propose two evasion attacks, in which we assume the adversaries know our techniques in advance , and they might attempt to evade our system via making the P2P bots mimic the behavior of legitimate P2P applications.
Edit intent: clarity
Original:  In the experimental evaluation, we propose two evasion attacks, in which we assume the adversaries (e.g., the botmasters) know our techniques in advance , and they might attempt to evade our system via making the P2P bots mimic the behavior of legitimate P2P applications.
Revised:  In the experimental evaluation, we propose two evasion attacks, in which we assume the adversaries (e.g., the botmasters) know our techniques in advance and attempt to evade our system via making the P2P bots mimic the behavior of legitimate P2P applications.
Edit intent: clarity
Original:  In the experimental evaluation, we propose two evasion attacks, in which we assume the adversaries (e.g., the botmasters) know our techniques in advance , and they might attempt to evade our system via making the P2P bots mimic the behavior of legitimate P2P applications.
Revised:  In the experimental evaluation, we propose two evasion attacks, in which we assume the adversaries (e.g., the botmasters) know our techniques in advance , and they might attempt to evade our system by making the P2P bots mimic the behavior of legitimate P2P applications.
Edit intent: fluency
Original:  The extensive experimental results show that Enhanced PeerHunter can achieve high detection rate with few false positives, and high robustness against the proposed mimicking legitimate P2P application attacks.
Revised:  Our results showed that Enhanced PeerHunter can achieve high detection rate with few false positives, and high robustness against the proposed mimicking legitimate P2P application attacks.
Edit intent: clarity
Original:  The extensive experimental results show that Enhanced PeerHunter can achieve high detection rate with few false positives, and high robustness against the proposed mimicking legitimate P2P application attacks.
Revised:  The extensive experimental results show that Enhanced PeerHunter can obtain high detection rate with few false positives, and high robustness against the proposed mimicking legitimate P2P application attacks.
Edit intent: fluency
Original:  The extensive experimental results show that Enhanced PeerHunter can achieve high detection rate with few false positives, and high robustness against the proposed mimicking legitimate P2P application attacks.
Revised:  The extensive experimental results show that Enhanced PeerHunter can achieve high detection rate with few false positives, and high robustness against the proposed attacks.
Edit intent: clarity
Original: We revisit the problem of integer factorization with number-theoretic oracles, including a well-known problem: can we factor an integer N unconditionally, in deterministic polynomial time, given the value of the Euler totient (\varphi( N)?
Revised: We revisit the problem of integer factorization with number-theoretic oracles, including a well-known problem: can we factor an integer N unconditionally, in deterministic polynomial time, given the value of the Euler totient \Phi( N)?
Edit intent: fluency
Original:  Among our results, we show for example that if N is a squarefree integer with a prime factor p > \sqrt N , then we can recover p in deterministic polynomial time given \varphi(N) ).
Revised:  Among our results, we show for example that if N is a squarefree integer with a prime factor p > \sqrt N , then we can recover p in deterministic polynomial time given \Phi(N ).
Edit intent: fluency
Original:  To fulfill the need for scalable and efficient stream analytics, numerous open source stream data processing systems (SDPSs) have been developed, with high throughput and low latency being their key performance targets.
Revised:  To fulfill the need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) have been developed, with high throughput and low latency being their key performance targets.
Edit intent: coherence
Original:  Second, we completely separate the system under test and driver, so that the measurement results are closer to actual system performance under real conditions.
Revised:  Second, we carefully separate the system under test and driver, so that the measurement results are closer to actual system performance under real conditions.
Edit intent: clarity
Original:  Second, we completely separate the system under test and driver, so that the measurement results are closer to actual system performance under real conditions.
Revised:  Second, we completely separate the system under test and driver, so that the measurement results are closer to actual system performance under realistic conditions.
Edit intent: fluency
Original:  Third, we build the first driver to test the actual sustainable performance of a system under test .
Revised:  Third, we build the first driver to test the actual sustainable performance of streaming systems .
Edit intent: clarity
Original:  Our detailed evaluation highlights that there is no single winner, but rather, each system excels in individual use-cases  .
Revised:  Our detailed evaluation highlights the individual characteristics and use-cases .
Edit intent: clarity
Original:  Our detailed evaluation highlights that there is no single winner, but rather, each system excels in individual use-cases  .
Revised:  Our detailed evaluation highlights that there is no single winner, but rather, each system excels in individual use-cases of each system .
Edit intent: clarity
Original: Despite single agent deep reinforcement learning has achieved significant success due to the experience replay mechanism, Concerns should be reconsidered in multiagent environments.
Revised: Recently, multiagent deep reinforcement learning has achieved significant success due to the experience replay mechanism, Concerns should be reconsidered in multiagent environments.
Edit intent: clarity