wanyu/IteraTeR_v2 huggingface dataset:
Original:  The "Law of Good Style", better known as the "Law of Demeter"  is a rather restrictive design rule so that we can still expected to find some potential bad smells, i.e. violations of this "Law".
Revised:  The "Law of Good Style", better known as the "Law of Demeter" is a rather restrictive design rule so that we still expected to find some potential bad smells, i.e. violations of this "Law".
Edit intent: clarity
Original:  We found generic as well as specific design ideas that were more important than the smell.
Revised:  We found generic as well as specific design ideas that were traded for the smell.
Edit intent: clarity
Original:  If we were convinced to have found a false positive, we described relations to design ideas.
Revised:  If we were convinced that we had found a false positive, we described relations to design ideas.
Edit intent: fluency
Original:  If we were convinced to have found a false positive, we described relations to design ideas.
Revised:  If we were convinced to have found a false positive, we described the relationships with design ideas.
Edit intent: fluency
Original:  The realization that not only general design ideas but as well specific design ideas have an influence on whether a potential smell is a true positive turns the problem of false positives from a scientific problem ("What is the true definition of the smell?") to a engineering problem ("How can we incorporate design ideas into smell definitions?").
Revised:  The realization that not only general design ideas but also specific design ideas have an influence on whether a potential smell is a true positive turns the problem of false positives from a scientific problem ("What is the true definition of the smell?") to a engineering problem ("How can we incorporate design ideas into smell definitions?").
Edit intent: fluency
Original:  We recommend to add adaptations points to the smell definitions.
Revised:  We recommend to add adaptation points to the smell definitions.
Edit intent: fluency
Original:  After adaptation the tool may provide continuously distinct and precise quality feedback, reducing the cognitive load for the developer and preventing habituation.
Revised:  After adaptation the tool may continuously provide distinct and precise quality feedback, reducing the cognitive load for the developer and preventing habituation.
Edit intent: fluency
Original:  Finally, it follows that smell detection based on machine learning should take as well signs of design ideas into account.
Revised:  Finally, it follows that smell detection based on machine learning should also take signs of design ideas into account.
Edit intent: clarity
Original: We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning (RL) for interference mitigation in wireless networks .
Revised: We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL) for interference mitigation in wireless networks .
Edit intent: clarity
Original: We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning (RL) for interference mitigation in wireless networks .
Revised: We propose a mechanism for distributed radio resource management using multi-agent deep reinforcement learning (RL) .
Edit intent: clarity
Original:  We equip each transmitter in the network with a deep RL agent , which receives partial delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval.
Revised:  We equip each transmitter in the network with a deep RL agent that receives delayed observations from its associated users, while also exchanging observations with its neighboring agents, and decides on which user to serve and what transmit power to use at each scheduling interval.
Edit intent: clarity
Original:  Our proposed framework enables the agents to make decisions simultaneously and in a distributed manner, without any knowledge about the concurrent decisions of other agents.
Revised:  Our proposed framework enables agents to make decisions simultaneously and in a distributed manner, without any knowledge about the concurrent decisions of other agents.
Edit intent: clarity
Original:  Our proposed framework enables the agents to make decisions simultaneously and in a distributed manner, without any knowledge about the concurrent decisions of other agents.
Revised:  Our proposed framework enables the agents to make decisions simultaneously and in a distributed manner, unaware of the concurrent decisions of other agents.
Edit intent: clarity
Original:  in the sense that an agent trained on a scenario with a specific number of transmitters and receivers can be readily applied to scenarios with different numbers of transmitters and/or receivers . Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5^{th} percentile user rates,
Revised:  in the sense that an agent trained on a scenario with a specific number of transmitters and users can be applied to scenarios with different numbers of transmitters and/or receivers . Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5^{th} percentile user rates,
Edit intent: clarity
Original:  in the sense that an agent trained on a scenario with a specific number of transmitters and receivers can be readily applied to scenarios with different numbers of transmitters and/or receivers . Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5^{th} percentile user rates,
Revised:  in the sense that an agent trained on a scenario with a specific number of transmitters and receivers can be readily applied to scenarios with different numbers of transmitters and/or users . Simulation results demonstrate the superiority of our proposed approach compared to decentralized baselines in terms of the tradeoff between average and 5^{th} percentile user rates,
Edit intent: fluency
Original:  while achieving performance close to, and even in certain cases outperforming, that of a centralized information-theoretic scheduling algorithm .
Revised:  while achieving performance close to, and even in certain cases outperforming, that of a centralized information-theoretic baseline .
Edit intent: clarity
Original:  We also show that our trained agents are robust and maintain their performance gains when experiencing mismatches between training and testing deployments.
Revised:  We also show that our trained agents are robust and maintain their performance gains when experiencing mismatches between train and test deployments.
Edit intent: fluency
Original:  Array entries of these derivative tensors are not easily computable, but their actions can be computed efficiently via a procedure that we discuss.
Revised:  Array entries of these derivative tensors are not directly accessible, but actions of these tensors can be computed efficiently via a procedure that we discuss.
Edit intent: clarity
Original:  Such tensors are often amenable to tensor train compression in theory, but until now no practical algorithm existed to convert them into tensor train format.
Revised:  Such tensors are often amenable to tensor train compression in theory, but until now no efficient algorithm existed to convert them into tensor train format.
Edit intent: clarity
Original:  Following this viewpoint, we design a divide-and-conquer algorithm, which can be interpreted as a generalization to several variables of Beckermann and Labahn's recursive approach for matrix Pad\'e and rational interpolation problems in univariate contexts .
Revised:  Following this viewpoint, we design a divide-and-conquer algorithm, which can be interpreted as a generalization to several variables of Beckermann and Labahn's recursive approach for matrix Pad\'e and rational interpolation problems .
Edit intent: clarity
Original:  In this paper, firstly , we develop citation recommendation approaches based on embeddings, topic modeling, and information retrieval techniques.
Revised:  Firstly , we develop citation recommendation approaches based on embeddings, topic modeling, and information retrieval techniques.
Edit intent: coherence
Original:  From the modelling point of view, we adopt a reduction strategy that treats fractures as manifolds of codimension one and we employ the primal version of Darcy's law to describe the flow in both the bulk and in the fracture network.
Revised:  From the modelling point of view, we adopt a reduction strategy that treats fractures as manifolds of codimension one and we employ the primal version of Darcy's law to describe the flow in both the bulk and the fracture network.
Edit intent: fluency
Original:  We establish unconditional , nearly exponential , lower bounds on the size of any symmetric circuit for computing the permanent over any field of characteristic other than 2.
Revised:  We establish unconditional exponential lower bounds on the size of any symmetric circuit for computing the permanent over any field of characteristic other than 2.
Edit intent: clarity
Original:  We establish unconditional , nearly exponential , lower bounds on the size of any symmetric circuit for computing the permanent over any field of characteristic other than 2.
Revised:  We establish unconditional , nearly exponential , lower bounds on the size of any symmetric circuit for computing the permanent .
Edit intent: clarity
Original:  In contrast, we show that there are polynomial-size symmetric circuits for computing the determinant over fields of characterisitic zero.
Revised:  In contrast, we show that there are polynomial-size symmetric circuits for computing the determinant over fields of characteristic zero.
Edit intent: fluency
Original:  In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step towards the ability to perform targeted manipulation of the brain's large-scale dynamical activity .
Revised:  In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step towards the ability to manipulate the brain's large-scale dynamical activity .
Edit intent: clarity
Original:  However, we then demonstrate that this relationship depends heavily on edge weight distribution .
Revised:  However, we then demonstrate that this relationship depends on the distribution of network edge weights .
Edit intent: clarity
Original:  It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as observed on CASP platform.
Revised:  It will also briefly comment on the progress observed in the field of ab initio modelling of proteins over the course of time as seen through the evolution of CASP platform.
Edit intent: clarity
Original: The problem of nearest-neighbor  condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Revised: The problem of nearest-neighbor condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor in R has the same label as p.
Edit intent: clarity
Original: Complex systems, ranging from migrating cells to interacting swarms, exhibit effective inertial dynamics described by second order stochastic differential equations.
Revised: Many complex systems, ranging from migrating cells to interacting swarms, exhibit effective inertial dynamics described by second order stochastic differential equations.
Edit intent: clarity
Original:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories - sampled at discrete times and subject to measurement noise - is lacking.
Revised:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories -- sampled at discrete times and subject to measurement noise - is lacking.
Edit intent: fluency
Original:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories - sampled at discrete times and subject to measurement noise - is lacking.
Revised:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories - sampled at discrete times and subject to measurement noise -- is lacking.
Edit intent: fluency
Original:  Our framework yields an operational method, termed Stochastic Inertial Force Inference, which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems including flocks with Viscek-like alignment interactions  .
Revised:  Our framework yields an operational method, termed Stochastic Inertial Force Inference, which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems including flocks with Viscek-like alignment interactions . .
Edit intent: fluency
Original: Many complex systems, ranging from migrating cells to interacting swarms, exhibit effective inertial dynamics described by second order stochastic differential equations .
Revised: Many complex systems, ranging from migrating cells to animal groups, exhibit stochastic dynamics described by second order stochastic differential equations .
Edit intent: clarity
Original:  Inferring such an equation of motion from experiments can provide profound insight into the physical laws governing the system.
Revised:  Inferring such an equation of motion from experimental data can provide profound insight into the physical laws governing the system.
Edit intent: clarity
Original:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories -- sampled at discrete times and subject to measurement noise -- is lacking.
Revised:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories , sampled at discrete times and subject to measurement noise -- is lacking.
Edit intent: fluency
Original: There are several real-world tasks that would ben-efit from applying multiagent reinforcement learn-ing (MARL) algorithms, including the coordina-tion among self-driving cars.
Revised: There are several real-world tasks that would benefit from applying multiagent reinforcement learn-ing (MARL) algorithms, including the coordina-tion among self-driving cars.
Edit intent: fluency
Original: There are several real-world tasks that would ben-efit from applying multiagent reinforcement learn-ing (MARL) algorithms, including the coordina-tion among self-driving cars.
Revised: There are several real-world tasks that would ben-efit from applying multiagent reinforcement learning (MARL) algorithms, including the coordina-tion among self-driving cars.
Edit intent: fluency
Original: There are several real-world tasks that would ben-efit from applying multiagent reinforcement learn-ing (MARL) algorithms, including the coordina-tion among self-driving cars.
Revised: There are several real-world tasks that would ben-efit from applying multiagent reinforcement learn-ing (MARL) algorithms, including the coordination among self-driving cars.
Edit intent: fluency
Original:  The real world haschallenging conditions for multiagent learningsystems , such as its partial observable and nonsta-tionary nature.
Revised:  The real world has challenging conditions for multiagent learningsystems , such as its partial observable and nonsta-tionary nature.
Edit intent: fluency
Original:  The real world haschallenging conditions for multiagent learningsystems , such as its partial observable and nonsta-tionary nature.
Revised:  The real world haschallenging conditions for multiagent learning systems , such as its partial observable and nonsta-tionary nature.
Edit intent: fluency
Original:  The real world haschallenging conditions for multiagent learningsystems , such as its partial observable and nonsta-tionary nature.
Revised:  The real world haschallenging conditions for multiagent learningsystems , such as its partial observable and nonstationary nature.
Edit intent: fluency
Original:  Moreover, if agents must share alimited resource (e.g. network bandwidth) theymust all learn how to coordinate resource use.
Revised:  Moreover, if agents must share a limited resource (e.g. network bandwidth) theymust all learn how to coordinate resource use.
Edit intent: fluency
Original:  Moreover, if agents must share alimited resource (e.g. network bandwidth) theymust all learn how to coordinate resource use.
Revised:  Moreover, if agents must share alimited resource (e.g. network bandwidth) they must all learn how to coordinate resource use.
Edit intent: fluency
Original:  (Hochreiter Schmidhuber, 1997) This paper in-troduces a deep recurrent multiagent actor-criticframework (R-MADDPG) for handling multia-gent coordination under partial observable set-tings and limited communication.
Revised:  Schmidhuber, 1997) This paper in-troduces a deep recurrent multiagent actor-criticframework (R-MADDPG) for handling multia-gent coordination under partial observable set-tings and limited communication.
Edit intent: coherence
Original:  (Hochreiter Schmidhuber, 1997) This paper in-troduces a deep recurrent multiagent actor-criticframework (R-MADDPG) for handling multia-gent coordination under partial observable set-tings and limited communication.
Revised:  (Hochreiter This paper introduces a deep recurrent multiagent actor-criticframework (R-MADDPG) for handling multia-gent coordination under partial observable set-tings and limited communication.
Edit intent: clarity
Original:  (Hochreiter Schmidhuber, 1997) This paper in-troduces a deep recurrent multiagent actor-criticframework (R-MADDPG) for handling multia-gent coordination under partial observable set-tings and limited communication.
Revised:  (Hochreiter Schmidhuber, 1997) This paper in-troduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for handling multia-gent coordination under partial observable set-tings and limited communication.
Edit intent: fluency
Original:  (Hochreiter Schmidhuber, 1997) This paper in-troduces a deep recurrent multiagent actor-criticframework (R-MADDPG) for handling multia-gent coordination under partial observable set-tings and limited communication.
Revised:  (Hochreiter Schmidhuber, 1997) This paper in-troduces a deep recurrent multiagent actor-criticframework (R-MADDPG) for handling multiagent coordination under partial observable set-tings and limited communication.
Edit intent: fluency
Original:  We investigaterecurrency effects on performance and commu-nication use of a team of agents.
Revised:  We investigate recurrency effects on performance and commu-nication use of a team of agents.
Edit intent: fluency
Original:  We investigaterecurrency effects on performance and commu-nication use of a team of agents.
Revised:  We investigaterecurrency effects on performance and communication use of a team of agents.
Edit intent: fluency
Original:  We demon-strate that the resulting framework learns time-dependencies for sharing missing observations, handling resource limitations, and developing dif-ferent communication patterns among agents.
Revised:  We demonstrate that the resulting framework learns time-dependencies for sharing missing observations, handling resource limitations, and developing dif-ferent communication patterns among agents.
Edit intent: fluency
Original:  We demon-strate that the resulting framework learns time-dependencies for sharing missing observations, handling resource limitations, and developing dif-ferent communication patterns among agents.
Revised:  We demon-strate that the resulting framework learns time dependencies for sharing missing observations, handling resource limitations, and developing dif-ferent communication patterns among agents.
Edit intent: fluency
Original:  We demon-strate that the resulting framework learns time-dependencies for sharing missing observations, handling resource limitations, and developing dif-ferent communication patterns among agents.
Revised:  We demon-strate that the resulting framework learns time-dependencies for sharing missing observations, handling resource limitations, and developing different communication patterns among agents.
Edit intent: fluency
Original:  Here we provide a simple theory that explains how bond breaking and elastic relaxation can drive static adhesion hysteresis over a debonding / rebonding cycle.
Revised:  Here we provide a simple theory that explains how bond breaking and elastic relaxation can drive static adhesion hysteresis over a bonding / rebonding cycle.
Edit intent: fluency
Original: A large portion of the passenger requests is reportedly unserviced, partially due to vacant for-hire drivers' cruising behavior during the passenger seeking process.
Revised: A large portion of passenger requests is reportedly unserviced, partially due to vacant for-hire drivers' cruising behavior during the passenger seeking process.
Edit intent: fluency
Original:  This paper aims to model the multi-driver repositioning task through a mean field multi-agent reinforcement learning (MARL) approach . Noticing that the direct application of MARL to the multi-driver system under a given reward mechanism will very likely yield a suboptimal equilibrium due to the selfishness of drivers, this study proposes a reward design scheme with which a more desired equilibrium can be reached.
Revised:  This paper aims to model the multi-driver repositioning task through a mean field multi-agent reinforcement learning (MARL) approach . Noticing that the direct application of MARL to the multi-driver system under a given reward mechanism will likely yield a suboptimal equilibrium due to the selfishness of drivers, this study proposes a reward design scheme with which a more desired equilibrium can be reached.
Edit intent: coherence
Original:  This paper aims to model the multi-driver repositioning task through a mean field multi-agent reinforcement learning (MARL) approach . Noticing that the direct application of MARL to the multi-driver system under a given reward mechanism will very likely yield a suboptimal equilibrium due to the selfishness of drivers, this study proposes a reward design scheme with which a more desired equilibrium can be reached.
Revised:  This paper aims to model the multi-driver repositioning task through a mean field multi-agent reinforcement learning (MARL) approach . Noticing that the direct application of MARL to the multi-driver system under a given reward mechanism will very likely yield a suboptimal equilibrium due to the selfishness of drivers, this study proposes an reward design scheme with which a more desired equilibrium can be reached.
Edit intent: fluency
Original:  To effectively solve the bilevel optimization problem with upper level as the reward design and the lower level as a multi-agent system (MAS) , a Bayesian optimization  algorithm is adopted to speed up the learning process.
Revised:  To effectively solve the bilevel optimization problem with upper level as the reward design and the lower level as a multi-agent system , a Bayesian optimization algorithm is adopted to speed up the learning process.
Edit intent: clarity
Original:  Because the direct application of MARL to the multi-driver system under a given reward mechanism will likely yield a suboptimal equilibrium due to the selfishness of drivers, this study proposes an reward design scheme with which a more desired equilibrium can be reached.
Revised:  Because the direct application of MARL to the multi-driver system under a given reward mechanism will likely yield a suboptimal equilibrium due to the selfishness of drivers, this study proposes a reward design scheme with which a more desired equilibrium can be reached.
Edit intent: fluency