wanyu/IteraTeR_full_sent huggingface dataset:
Original: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph  where each vertex  connects to at least \gamma fraction of the other vertices .
Revised: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph g=(V_g,E_g) where each vertex connects to at least \gamma fraction of the other vertices .
Edit intent: meaning-changed
Original: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph  where each vertex  connects to at least \gamma fraction of the other vertices .
Revised: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph where each vertex v\in V_g connects to at least \gamma fraction of the other vertices .
Edit intent: meaning-changed
Original: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph  where each vertex  connects to at least \gamma fraction of the other vertices .
Revised: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph where each vertex connects to at least \gamma fraction of the other vertices .
Edit intent: fluency
Original: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph  where each vertex  connects to at least \gamma fraction of the other vertices . Mining maximal\lceil \rceil  quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.
Revised: Given a user-specified minimum degree threshold \gamma , a%DIFDELCMD < {%%% \gamma -quasi-clique is a subgraph where each vertex connects to at least \gamma fraction of the other vertices (i.e.,\lceil \rceil quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.
Edit intent: coherence
Original:  Mining maximal\lceil \rceil  quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.
Revised:  Mining maximal\lceil \gamma\cdot(|V_g|-1)\rceil quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.
Edit intent: meaning-changed
Original:  Mining maximal\lceil \rceil  quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.
Revised:  Mining maximal\lceil \rceil vertices) in g. Quasi-clique is one of the most natural definitions for dense structures useful in finding communities in social networks and discovering significant biomolecule structures and pathways. However, mining maximal quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.
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Original:  Mining maximal\lceil \rceil  quasi-cliques is notoriously expensive with the state-of-the-art algorithm scaling only to small graphs with thousands of vertices.This has hampered its popularity in real applications involving big graphs. We developed a task-based system called G-thinker for massively parallel graph mining, which is the first graph mining system that scales with the number of CPU cores. G-thinker provides a unique opportunity to scale the compute-intensive quasi-clique mining . This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs.
Revised:  Mining maximal\lceil \rceil quasi-cliques is notoriously expensive. In this paper, we design parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs.
Edit intent: coherence
Original:  This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker that scale to big graphs. Our algorithms follow the idea of divide and conquer which partitions the problem of mining a big graph into tasks that mine smaller subgraphs .
Revised:  This paperdesigns parallel algorithms for mining maximal quasi-cliques on G-thinker , a recent distributed framework targeting divide-and-conquer graph mining algorithms that decomposes the mining into compute-intensive tasks to fully utilize CPU cores .
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Original:  However, we find that a direct application of G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker , requiring a system URLe.
Revised:  However, we found that directly using G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker , requiring a system URLe.
Edit intent: fluency
Original:  However, we find that a direct application of G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker , requiring a system URLe.
Revised:  However, we find that a direct application of G-thinker results in the straggler problem due to (i) the drastic load imbalance among different tasks and (ii) the difficulty of predicting the task running time and the time growth with task-subgraph size. We address these challenges by redesigning G-thinker , requiring a system URLe.
Edit intent: meaning-changed
Original:  However, we find that a direct application of G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker , requiring a system URLe. We also observe that the running time of a task is highly unpredictable solely from the features extracted from its subgraph, leading to difficulty in pinpoint expensive tasks to decompose for concurrent processing, and size-threshold based partitioning under-partitions some tasks but over-partitions others, leading to bad load balancing and enormous task partitioning overheads. We address this issue by proposing a novel time-delayed divide-and-conquer strategy that strikes a balance between the workloads spent on actual mining and the cost of balancing the workloads.
Revised:  However, we find that a direct application of G-thinker is insufficient due to the drastically different running time of different tasks that violates the original design assumption of G-thinker 's execution engine to prioritize long-running tasks for mining, and by utilizing a novel timeout strategy to effectively decompose the mining workloads of long-running tasks to improve load balancing. While this system redesign applies to many other expensive dense subgraph mining problems, this paper verifies the idea by adapting the state-of-the-art quasi-clique algorithm, Quick, to our redesigned G-thinker. We improve Quick by integrating new pruning rules, and fixing some missed boundary cases that could lead to missed results.
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Original:  Extensive experiments verify that our G-thinker algorithm scales perfectly with the number of CPU cores, achieving over 300x speedup when running on a graph with over 1M vertices in a small cluster.
Revised:  Extensive experiments verify that our G-thinker algorithm scales perfectly with the number of CPU cores, achieving 201\times runtime speedup when mining a graph with over 1M vertices in a small cluster.
Edit intent: meaning-changed
Original:  Extensive experiments verify that our G-thinker algorithm scales perfectly with the number of CPU cores, achieving over 300x speedup when running on a graph with over 1M vertices in a small cluster.
Revised:  Extensive experiments verify that our G-thinker algorithm scales perfectly with the number of CPU cores, achieving over 300x speedup when running on a graph with 3.77M vertices and 16.5M edges in a 16-node cluster.
Edit intent: meaning-changed
Original:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Revised:  For comparison, three existing metrics, bi-lingual evaluation understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Edit intent: fluency
Original:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Revised:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) versus self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Edit intent: clarity
Original:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Revised:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score versus reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Edit intent: fluency
Original:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Revised:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, and Fr\'{e with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Edit intent: coherence
Original:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of LSTM and GPT-2 on both syntactic and real data.
Revised:  For comparison, three existing metrics, Bilingual Evaluation Understudy (BLEU) verse self-BLEU, language model score verse reverse language model score, Fr'chet Embedding Distance (FED), together with the proposed DD, are used to evaluate two popular generative models of long short-term memory and generative pretrained transformer 2 on both syntactic and real data.
Edit intent: meaning-changed
Original: Real-world application scenarios like modern Internet services consist of diversity of AI and non-AI modules with very long and complex execution paths . Using component or micro AI benchmarks alone can lead to error-prone conclusions.
Revised: Real-world application scenarios like modern Internet services consist of diversity of AI and non-AI modules with very long and complex execution paths , but also have huge code size, which raises serious benchmarking or evaluating challenges. Using AI components or micro benchmarks alone can lead to error-prone conclusions.
Edit intent: meaning-changed
Original:  This paper proposes a scenario-distilling AI benchmarking methodology . Instead of using real-world applications, we propose the permutations of essential AI and non-AI tasks as a scenario-distilling benchmark .
Revised:  This paper proposes a scenario-distilling methodology to attack the above challenge. We formalize a real-world applications, we propose the permutations of essential AI and non-AI tasks as a scenario-distilling benchmark .
Edit intent: meaning-changed
Original:  Instead of using real-world applications, we propose the permutations of essential AI and non-AI tasks as a scenario-distilling benchmark . We consider scenario-distilling benchmarks, component and micro benchmarks as three indispensable parts of a benchmark suite.
Revised:  Instead of using real-world applications, we propose the permutations of essential AI and non-AI tasks as a high-level scenario benchmark specification.
Edit intent: meaning-changed
Original:  We design and implement a highly extensible, configurable, and flexible benchmark framework . On the basis of the framework, we propose the guideline for building scenario-distilling benchmarks, and present two Internet service AI ones.
Revised:  We design and implement a highly extensible, configurable, and flexible benchmark framework , on the basis of the framework, we propose the guideline for building scenario-distilling benchmarks, and present two Internet service AI ones.
Edit intent: fluency
Original:  On the basis of the framework, we propose the guideline for building scenario-distilling benchmarks, and present two Internet service AI ones. The preliminary evaluation shows the advantage of scenario-distilling AI benchmarking against using component or micro AI benchmarks alone.
Revised:  On the basis of the framework, we propose the guideline for building scenario-distilling benchmarks, and present two Internet service AI scenario benchmarks as proxies to two real-world application scenarios. We claim scenario, component and micro benchmarks should be considered as three indispensable parts for evaluating. Our evaluation shows the advantage of scenario-distilling AI benchmarking against using component or micro AI benchmarks alone.
Edit intent: meaning-changed
Original:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date  (2\cdot10^{-5}), high level of  contact in greeting habits (6 \cdot 10^{-5}), lung cancer  (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Revised:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date d_i (2\cdot10^{-5}), high level of contact in greeting habits (6 \cdot 10^{-5}), lung cancer (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Edit intent: meaning-changed
Original:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date  (2\cdot10^{-5}), high level of  contact in greeting habits (6 \cdot 10^{-5}), lung cancer  (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Revised:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date (2\cdot10^{-5}), high level of physical contact in greeting habits (6 \cdot 10^{-5}), lung cancer (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Edit intent: meaning-changed
Original:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date  (2\cdot10^{-5}), high level of  contact in greeting habits (6 \cdot 10^{-5}), lung cancer  (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Revised:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date (2\cdot10^{-5}), high level of contact in greeting habits (6 \cdot 10^{-5}), lung cancer prevalence (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Edit intent: fluency
Original:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date  (2\cdot10^{-5}), high level of  contact in greeting habits (6 \cdot 10^{-5}), lung cancer  (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Revised:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date (2\cdot10^{-5}), high level of contact in greeting habits (6 \cdot 10^{-5}), lung cancer (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), share of population in urban areas (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Edit intent: clarity
Original:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date  (2\cdot10^{-5}), high level of  contact in greeting habits (6 \cdot 10^{-5}), lung cancer  (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, smaller sample, 73 countries)
Revised:  life expectancy (8\cdot10^{-6}), number of international tourists (1\cdot10^{-5}), earlier epidemic starting date (2\cdot10^{-5}), high level of contact in greeting habits (6 \cdot 10^{-5}), lung cancer (6 \cdot 10^{-5}), obesity in males (1 \cdot 10^{-4}), urbanization (2\cdot10^{-4}), cancer prevalence (3 \cdot 10^{-4}), alcohol consumption (0.0019), daily smoking prevalence (0.0036), UV index (0.004, 73 countries)
Edit intent: clarity
Original:  There is highly significant correlation also with blood type : positive correlation with  RH- ( 2 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Revised:  There is highly significant correlation also with blood types : positive correlation with RH- ( 2 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Edit intent: fluency
Original:  There is highly significant correlation also with blood type : positive correlation with  RH- ( 2 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Revised:  There is highly significant correlation also with blood type : positive correlation with types RH- ( 2 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Edit intent: meaning-changed
Original:  There is highly significant correlation also with blood type : positive correlation with  RH- ( 2 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Revised:  There is highly significant correlation also with blood type : positive correlation with RH- ( 3 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Edit intent: meaning-changed
Original:  There is highly significant correlation also with blood type : positive correlation with  RH- ( 2 \cdot10^{-5}) and A+ ( 2 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Revised:  There is highly significant correlation also with blood type : positive correlation with RH- ( 2 \cdot10^{-5}) and A+ ( 3 \cdot10^{-3}), negative correlation with B+ (2\cdot10^{-4}).
Edit intent: meaning-changed
Original:  We also find positive correlation with moderate C.L. (p-value of 0.02%DIFDELCMD < \sim0%%% .03) with: CO_2 emissions, type-1 diabetes, low vaccination coverage for Tuberculosis (BCG).
Revised:  %DIFDELCMD < \sim0%%% .03) with: CO_2 emissions, type-1 diabetes, low vaccination coverage for Tuberculosis (BCG).
Edit intent: coherence
Original:  We also find positive correlation with moderate C.L. (p-value of 0.02%DIFDELCMD < \sim0%%% .03) with: CO_2 emissions, type-1 diabetes, low vaccination coverage for Tuberculosis (BCG). Several such variables are correlated with each other and so they likely have common interpretations.
Revised:  We also find positive correlation with moderate C.L. (p-value of 0.02%DIFDELCMD < \sim0%%% Several of the above variables are intercorrelated and likely to have common interpretations.
Edit intent: clarity
Original:  We also analyzed the possible existence of a bias: countries with low GDP-per capita , typically located in warm regions, might have less intense testing and we discuss correlation with the above variables.
Revised:  We performed a Principal Component Analysis, in order to find their significant independent linear combinations. We also analyzed a possible bias: countries with low GDP-per capita , typically located in warm regions, might have less intense testing and we discuss correlation with the above variables.
Edit intent: meaning-changed
Original:  We parameterize a dialog system using a Transformer-based auto-regressive language model, which subsumes different dialog modules (e.g., state tracker, dialog policy, response generator) into a single neural model.
Revised:  We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules (e.g., state tracker, dialog policy, response generator) into a single neural model.
Edit intent: meaning-changed
Original:  The pre-trained model can be efficiently adapted to accomplish a new dialog task with a handful of task-specific dialogs via machine teaching . Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost .
Revised:  The pre-trained model can be efficiently adapted to accomplish a new dialog task with a handful of task-specific dialogs via machine teaching , where training samples are generated by human teachers interacting with the system. Experiments show that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost .
Edit intent: meaning-changed
Original:  Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art results on two well-known benchmarks, CamRest and MultiWOZ, (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost .
Revised:  Our experiments demonstrate that (i) SOLOIST creates new state-of-the-art on well-studied task-oriented dialog benchmarks, including CamRest676 and MultiWOZ; (ii) in the few-shot learning setting, the dialog systems developed by SOLOIST significantly outperform those developed by existing methods, and (iii) the use of machine teaching substantially reduces the labeling cost .
Edit intent: clarity