With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and ***...
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With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and ***,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their *** address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree *** algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical *** our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical *** results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.
This study aims to improve the accuracy of click-through rate prediction for push ads through machine learning methods. Using the dataset released by Tianchi, we synthesized basic user information, ad features and use...
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Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed dat...
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Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.
The forecasting of sales data is a widely studied issue in the fields of artificial intelligence and time series forecasting. TCN (Temporal Convolutional Network) is the most adept at managing time series data among a...
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New energy automobile industry plays an important role in building a green, low-carbon and recycling industrial system. In this paper, the prediction simulation training and prediction accuracy comparison study are ca...
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Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the *** the same time,the twenty-first century ...
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Recent decades have witnessed several infectious disease outbreaks,including the coronavirus disease(COVID-19)pandemic,which had catastrophic impacts on societies around the *** the same time,the twenty-first century has experienced an unprecedented era of techno-logical development and demographic changes:exploding population growth,increased air-line flights,and increased rural-to-urban migration,with an estimated 281 million international migrants worldwide in 2020,despite COVID-19 movement *** this review,we synthesized 195 research articles that examined the association between human movement and infectious disease outbreaks to understand the extent to which human mobility has increased the risk of infectious disease *** article covers eight infectious diseases,ranging from respiratory illnesses to sexually transmitted and vector-borne *** review revealed a strong association between human mobility and infectious disease spread,particularly strong for respiratory illnesses like COVID-19 and *** significant research into the relationship between infectious diseases and human mobility,four knowl-edge gaps were identified based on reviewed literature in this study:1)although some studies have used bigdata in investigating infectious diseases,the efforts are limited(with the exception of COVID-19 disease),2)while some research has explored the use of multiple data sources,there has been limited focus on fully integrating these data into comprehensive analyses,3)limited research on the global impact of mobility on the spread of infectious disease with most studies focusing on local or regional outbreaks,and 4)lack of standardiza-tion in the methodology for measuring the impacts of human mobility on infectious disease *** tackling the recognized knowledge gaps and adopting holistic,interdisciplinary methods,forthcoming research has the potential to substantially enhance our comprehension of the intricate inter
The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal *** this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally...
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The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal *** this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally dependent random variables,assuming only a second-moment *** proof leverages Stein's method and introduces a novel randomized concentration inequality,which may also be of independent interest for other *** main results have applied to self-normalized sums of m-dependent random variables and graph dependency models.
Cloud computing, as a promising service platform, has gained significant popularity in addressing emerging data privacy issues in applications such as machine learning and data mining. Researchers have proposed the ve...
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As technology advances, the role that cars play in our daily lives increases. Both car manufactures and customers are eager to know about the car quality to help them choose the car they like. In this paper, three mod...
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Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM ca...
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Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.
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