Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of...Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.展开更多
The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increas...The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,respectively.However,how to study the knowledge representation of the collective voting information and then select delegates is a new open problem.To ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain nodes.PHFLTS is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain nodes.To rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility degree.Finally,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its rationality.Moreover,the superiority of the proposed DPoS consensus algorithm is verified.The results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.展开更多
Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation inform...Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.展开更多
During the COVID-19 outbreak,the use of single-use medical supplies increased significantly.It is essential to select suitable sites for establishing medical waste treatment stations.It is a big challenge to solve the...During the COVID-19 outbreak,the use of single-use medical supplies increased significantly.It is essential to select suitable sites for establishing medical waste treatment stations.It is a big challenge to solve the medical waste treatment station selection problem due to some conflicting factors.This paper proposes a multi-attribute decision-making(MADM)method based on the partitioned Maclaurin symmetric mean(PMSM)operator.For the medical waste treatment station selection problem,the factors or attributes(these two terms can be interchanged.)in the same clusters are closely related,and the attributes in different clusters have no relationships.The partitioned Maclaurin symmetric mean function(PMSMF)can handle these complex attribute relationships.Hence,we extend the PMSM operator to process the linguistic q-rung orthopair fuzzy numbers(Lq-ROFNs)and propose the linguistic q-rung orthopair fuzzy partitioned Maclaurin symmetric mean(Lq-ROFPMSM)operator and its weighted form(Lq-ROFWPMSM).To reduce the negative impact of unreasonable data on the final output results,we propose the linguistic q-rung orthopair fuzzy partitioned dual Maclaurin symmetric mean(Lq-ROFPDMSM)operator and its weighted form(Lq-ROFWPDMSM).We also discuss the characteristics and typical examples of the above operators.A novel MADM method uses the Lq-ROFWPMSM operator and the Lq-ROFWPDMSM operator to solve the medical waste treatment station selection problem.Finally,the usability and superiority of the proposed method are verified by comparing it with previous methods.展开更多
Covering about three quarters of the surface area of the earth,the ocean is a critical source of sustenance,medicine,and commerce.However,such vast expanse in both surface area and depth,presents myriad observing chal...Covering about three quarters of the surface area of the earth,the ocean is a critical source of sustenance,medicine,and commerce.However,such vast expanse in both surface area and depth,presents myriad observing challenges for researchers,such as corrosion,attenuation of electromagnetic waves,and high pressure.Ocean observation technologies are progressing from the conventional single node,static and short-term modalities to multiple nodes,dynamic and long-term modalities,to increase the density of both temporal and spatial samplings.Although people’s knowledge of the oceans has been still quite limited,the contributions of many nations cooperating to develop the Global Ocean Observing System(GOOS)have remarkably promoted the development of ocean observing technologies.This paper reviews the typical observing technologies deployed from the sea surface to the seafloor,and discusses the future trend of the ocean observation systems with the docking technology and sustained ocean energy.展开更多
Dear Editor,This letter puts forward a novel scalable temporal dimension preserved tensor completion model based on orthogonal initialization for missing traffic data(MTD)imputation.The MTD imputation acts directly on...Dear Editor,This letter puts forward a novel scalable temporal dimension preserved tensor completion model based on orthogonal initialization for missing traffic data(MTD)imputation.The MTD imputation acts directly on accessing the traffic state,and affects the traffic management.展开更多
Purpose-When recommending products to consumers,it is important to be able to accurately predict how consumers will rate them.However,existing collaborative filtering models are difficult to achieve a balance between ...Purpose-When recommending products to consumers,it is important to be able to accurately predict how consumers will rate them.However,existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity.Therefore,the purpose of this paper is to propose an accurate and effective model to predict users’ratings of products for the accurate recommendation of products to users.Design/methodology/approach-First,we introduce an attention mechanism that dynamically assigns weights to user preferences,highlighting key interaction information and enhancing the model’s understanding of user behavior.Second,a fold embedding strategy is employed to segment user interaction data,increasing the information density of each subset while reducing the complexity of the attention mechanism.Finally,a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions,thereby improving the model’s generalization ability.Findings-The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets.On average,the evaluation metrics root mean square error(RMSE)and mean absolute error(MAE)are reduced by 9.11 and 13.3%,respectively.Additionally,the Friedman test results confirm that these improvements are statistically significant.Consequently,the proposed model more accurately captures the intrinsic correlation between users and products,leading to a substantial reduction in prediction error.Originality/value-We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively.Additionally,we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism.Finally,we implement a masking strategy to encourage the model to focus on key features and patterns,thereby mitigating overfitting.展开更多
Purpose-As the core technology of blockchain,various consensus mechanisms have emerged to satisfy the demands of different application scenarios.Since determining the security,scalability and other related performance...Purpose-As the core technology of blockchain,various consensus mechanisms have emerged to satisfy the demands of different application scenarios.Since determining the security,scalability and other related performance of the blockchain,how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.Design/methodology/approach-The paper opted for a research overview on the blockchain consensus mechanism,including the consensus mechanisms’consensus progress,classification and comparison,which are complemented by documentary analysis.Findings-This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms.First,the authors outline the consensus processes,the advantages and disadvantages of the mainstream consensus mechanisms.Additionally,the consensus mechanisms are subdivided into four types according to their characteristics.Then,the consensus mechanisms are compared and analyzed based on four evaluation criteria.Finally,the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.Originality/value-This paper summarizes the future research development of the consensus mechanisms.展开更多
Purpose-The purpose of this paper is to make a comprehensive analysis of 354 publications about Pythagorean fuzzy sets(PFSs)from 2013 to 2020 in order to comprehensively understand their historical progress and curren...Purpose-The purpose of this paper is to make a comprehensive analysis of 354 publications about Pythagorean fuzzy sets(PFSs)from 2013 to 2020 in order to comprehensively understand their historical progress and current situation,as well as future development trend.Design/methodology/approach-First,this paper describes the fundamental information of these publications on PFSs,including their data information,annual trend and prediction and basic features.Second,the most productive and influential authors,countries/regions,institutions and the most cited documents are presented in the form of evaluation indicators.Third,with the help of VOSviewer software,the visualization analysis is conducted to show the development status of PFSs publications at the level of authors,countries/regions,institutions and keywords.Finally,the burst detection of keywords,timezone review and timeline review are exported from CiteSpace software to analyze the hotspots and development trend on PFSs.Findings-The annual PFSs publications present a quickly increasing trend.The most productive author is Wei Guiwu(China).Wei Guiwu and Wei Cun have the strongest cooperative relationship.Research limitations/implications-The implication of this study is to provide a comprehensive perspective for the scholars who take a fancy to PFSs,and it is valuable for scholars to grasp the hotspots in this field in time.Originality/value-It is the first paper that uses the bibliometric analysis to comprehensively analyze the publications on PFSs.It can help the scholars in the field of PFSs to quickly understand the development status and trend of PFSs.展开更多
Purpose-The purpose of this paper is to make an overview of 6,618 publications of Apache Hadoop from 2008 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field,as well as a ...Purpose-The purpose of this paper is to make an overview of 6,618 publications of Apache Hadoop from 2008 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field,as well as a preliminary knowledge of Apache Hadoop for interested researchers.Design/methodology/approach-This paper employs the bibliometric analysis and visual analysis approaches to systematically study and analyze publications about Apache Hadoop in the Web of Science database.This study aims to investigate the topic of Apache Hadoop by means of bibliometric analysis with the aid of visualization applications.Through the bibliometric analysis of the collected documents,this paper analyzes the main statistical characteristics and cooperation networks.Research themes,research hotspots and future development trends are also investigated through the keyword analysis.Findings-The research on Apache Hadoop is still the top priority in the future,and how to improve the performance of Apache Hadoop in the era of big data is one of the research hotspots.Research limitations/implications-This paper makes a comprehensive analysis of Apache Hadoop with methods of bibliometrics,and it is valuable for researchers can quickly grasp the hot topics in this area.Originality/value-This paper draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years,aiming to understand the development status and trends in this field and inspire new ideas for researchers.展开更多
Purpose-The purpose of this paper is to make an overview of 474 publications and 512 patents of FTL from 1987 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field,as well a...Purpose-The purpose of this paper is to make an overview of 474 publications and 512 patents of FTL from 1987 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field,as well as a preliminary knowledge of FTL for interested researchers.Design/methodology/approach-Firstly,the FTL algorithms are classified and its functions are introduced in detail.Secondly,the structures of the publications are analyzed in terms of the fundamental information and the publication of the most productive countries/regions,institutions and authors.After that,co-citation networks of institutions,authors and papers illustrated by VOS Viewer are given to show the relationship among those and the most influential of them is further analyzed.Then,the characteristics of the patent are analyzed based on the basic information and classification of the patent and the most productive inventors.In order to obtain research hotspots and trends in this field,the time-line review and citation burst detection of keywords carried out by Cite Space are made to be visual.Finally,based on the above analysis,it draws some other important conclusions and the development trend of this field.Findings-The research on FTL algorithm is still the top priority in the future,and how to improve the performance of SSD in the era of big data is one of the research hotspots.Research limitations/implications-This paper makes a comprehensive analysis of FTL with the method of bibliometrics,and it is valuable for researchers can quickly grasp the hotspots in this area.Originality/value-This article draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years,aiming to inspire new ideas for researchers.展开更多
Purpose–The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends,hotspots,and directions for fu...Purpose–The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends,hotspots,and directions for future research.Design/methodology/approach–The data source for this paper is the Web of Science Core Collection,and 7,154 publications and related information have been derived.We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools(VOS Viewer and CiteSpace).Findings–The analysis results show that China is the most productive and influential country/region.East Asia countries have strong cooperation with each other and also have cooperation with other countries.The study shows that risk management has been involved in various fields such as credit,supply chain,health emergency and disaster especially in the background of COVID-19.We also found that machine learning,especially deep learning,has been playing an increasingly important role in risk management due to its excellent performance.Originality/value–This paper focuses on studying risk management in East Asia,exploring its publication’s fundamental information,citation and cooperation networks,hotspots,and research trends.It provides some reference value for scholars who are interested or further research in this field.展开更多
Purpose-Based on the strong feature representation ability of the convolutional neural network(CNN),generous object detection methods in remote sensing(RS)have been proposed one after another.However,due to the large ...Purpose-Based on the strong feature representation ability of the convolutional neural network(CNN),generous object detection methods in remote sensing(RS)have been proposed one after another.However,due to the large variation in scale and the omission of relevant relationships between objects,there are still great challenges for object detection in RS.Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account.Moreover,inference time and lightness are also major pain points in the field of RS.Design/methodology/approach-To alleviate the aforementioned problems,this study proposes a novel method for object detection in RS,which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images(MFLD).The multi-receptive field extraction(MRFE)and long-range dependency information extraction(LDIE)modules are put forward.Findings-To concentrate on the variability of objects in RS,MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates.Considering the shortcomings of CNN in extracting global information,LDIE is designed to capture the relationships between objects.Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-theart methods.Most of all,on the NWPU VHR-10 dataset,our MFLD method achieves 94.6%mean average precision with 4.08 M model volume.Originality/value-This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.展开更多
基金supported in part by the by Chongqing Research Program of Basic Research and Frontier Technology under Grant CSTB2025NSCQ-GPX1309.
文摘Small object detection has been a focus of attention since the emergence of deep learning-based object detection.Although classical object detection frameworks have made significant contributions to the development of object detection,there are still many issues to be resolved in detecting small objects due to the inherent complexity and diversity of real-world visual scenes.In particular,the YOLO(You Only Look Once)series of detection models,renowned for their real-time performance,have undergone numerous adaptations aimed at improving the detection of small targets.In this survey,we summarize the state-of-the-art YOLO-based small object detection methods.This review presents a systematic categorization of YOLO-based approaches for small-object detection,organized into four methodological avenues,namely attention-based feature enhancement,detection-head optimization,loss function,and multi-scale feature fusion strategies.We then examine the principal challenges addressed by each category.Finally,we analyze the performance of thesemethods on public benchmarks and,by comparing current approaches,identify limitations and outline directions for future research.
文摘The consensus scheme is an essential component in the real blockchain environment.The Delegated Proof of Stake(DPoS)is a competitive consensus scheme that can decrease energy costs,promote decentralization,and increase efficiency,respectively.However,how to study the knowledge representation of the collective voting information and then select delegates is a new open problem.To ensure the fairness and effectiveness of transactions in the blockchain,in this paper,we propose a novel fine-grained knowledge representation method,which improves the DPoS scheme based on the linguistic term set(LTS)and proportional hesitant fuzzy linguistic term set(PHFLTS).To this end,the symmetrical LTS is used in this study to express the fine-grained voting options that can be chosen to evaluate the blockchain nodes.PHFLTS is used to model the collective voting information on the voted blockchain nodes by aggregating the voting information from other blockchain nodes.To rank the blockchain nodes and then choose the delegate,a novel delegate selection algorithm is proposed based on the cumulative possibility degree.Finally,the numerical examples are used to demonstrate the implementation process of the proposed DPoS consensus algorithm and also its rationality.Moreover,the superiority of the proposed DPoS consensus algorithm is verified.The results show that the proposed DPoS consensus algorithm shows better performance than the existing DPoS consensus algorithms.
文摘Industrial control systems(ICSs)are widely used in various fields,and the information security problems of ICSs are increasingly serious.The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts.Thus,this paper introduces the probabilistic linguistic term sets(PLTSs)to model the evaluation information of experts.Meanwhile,we propose a probabilistic linguistic multi-criteria decision-making(PL-MCDM)method to solve the information security assessment problem of ICSs.Firstly,we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods.Secondly,we use the Best Worst Method(BWM)method and Criteria Importance Through Inter-criteria Correlation(CRITIC)method to obtain the subjective weights and objective weights,which are used to derive the combined weights.Thirdly,we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje(PL-VIKOR)method.Finally,we apply the proposed method to solve the information security assessment problem of ICSs.When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisão Iterativa Multicritério(PL-TODIM)method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution(PL-TOPSIS)method,the case example shows that the proposed method can provide more reasonable ranking results.By evaluating and ranking the information security level of different ICSs,managers can identify problems in time and guide their work better.
基金This research work was supported by the National Natural Science Foundation of China under Grant No.U1805263.
文摘During the COVID-19 outbreak,the use of single-use medical supplies increased significantly.It is essential to select suitable sites for establishing medical waste treatment stations.It is a big challenge to solve the medical waste treatment station selection problem due to some conflicting factors.This paper proposes a multi-attribute decision-making(MADM)method based on the partitioned Maclaurin symmetric mean(PMSM)operator.For the medical waste treatment station selection problem,the factors or attributes(these two terms can be interchanged.)in the same clusters are closely related,and the attributes in different clusters have no relationships.The partitioned Maclaurin symmetric mean function(PMSMF)can handle these complex attribute relationships.Hence,we extend the PMSM operator to process the linguistic q-rung orthopair fuzzy numbers(Lq-ROFNs)and propose the linguistic q-rung orthopair fuzzy partitioned Maclaurin symmetric mean(Lq-ROFPMSM)operator and its weighted form(Lq-ROFWPMSM).To reduce the negative impact of unreasonable data on the final output results,we propose the linguistic q-rung orthopair fuzzy partitioned dual Maclaurin symmetric mean(Lq-ROFPDMSM)operator and its weighted form(Lq-ROFWPDMSM).We also discuss the characteristics and typical examples of the above operators.A novel MADM method uses the Lq-ROFWPMSM operator and the Lq-ROFWPDMSM operator to solve the medical waste treatment station selection problem.Finally,the usability and superiority of the proposed method are verified by comparing it with previous methods.
基金Supported in part by the Marine S&T Fund of Shandong Province (Grant No. 2018SDKJ0211)part by the Fund of Platform for Technical Innovation Ningbo Research Institute of Zhejiang University (Grant No. NRI-ZJU-2019001)part by the Fundamental Research Funds for the Central Universities (Grant No. 2019XZZX003-07).
文摘Covering about three quarters of the surface area of the earth,the ocean is a critical source of sustenance,medicine,and commerce.However,such vast expanse in both surface area and depth,presents myriad observing challenges for researchers,such as corrosion,attenuation of electromagnetic waves,and high pressure.Ocean observation technologies are progressing from the conventional single node,static and short-term modalities to multiple nodes,dynamic and long-term modalities,to increase the density of both temporal and spatial samplings.Although people’s knowledge of the oceans has been still quite limited,the contributions of many nations cooperating to develop the Global Ocean Observing System(GOOS)have remarkably promoted the development of ocean observing technologies.This paper reviews the typical observing technologies deployed from the sea surface to the seafloor,and discusses the future trend of the ocean observation systems with the docking technology and sustained ocean energy.
基金supported by the Young Top Talent of Young Eagle Program of Fujian Province,China(F21E 0011202B01).
文摘Dear Editor,This letter puts forward a novel scalable temporal dimension preserved tensor completion model based on orthogonal initialization for missing traffic data(MTD)imputation.The MTD imputation acts directly on accessing the traffic state,and affects the traffic management.
文摘Purpose-When recommending products to consumers,it is important to be able to accurately predict how consumers will rate them.However,existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity.Therefore,the purpose of this paper is to propose an accurate and effective model to predict users’ratings of products for the accurate recommendation of products to users.Design/methodology/approach-First,we introduce an attention mechanism that dynamically assigns weights to user preferences,highlighting key interaction information and enhancing the model’s understanding of user behavior.Second,a fold embedding strategy is employed to segment user interaction data,increasing the information density of each subset while reducing the complexity of the attention mechanism.Finally,a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions,thereby improving the model’s generalization ability.Findings-The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets.On average,the evaluation metrics root mean square error(RMSE)and mean absolute error(MAE)are reduced by 9.11 and 13.3%,respectively.Additionally,the Friedman test results confirm that these improvements are statistically significant.Consequently,the proposed model more accurately captures the intrinsic correlation between users and products,leading to a substantial reduction in prediction error.Originality/value-We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively.Additionally,we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism.Finally,we implement a masking strategy to encourage the model to focus on key features and patterns,thereby mitigating overfitting.
基金This work was supported by the Chongqing Research Program of Basic Research and Frontier Technology(Grant No.cstc2021jcyj-msxmX0530 and Grant No.cstc2020jcyj-msxmX0804)the Graduate Scientific Research and Innovation Foundation of Chongqing(Grant No.CYS22457)+1 种基金the Technology Innovation and Application Development Projects of Chongqing(Grant No.cstc2021jscx-gksbX0032,cstc2021jscx-gksbX0029)the Key R&D plan of Hainan Province(Grant No.ZDYF2021GXJS006).
文摘Purpose-As the core technology of blockchain,various consensus mechanisms have emerged to satisfy the demands of different application scenarios.Since determining the security,scalability and other related performance of the blockchain,how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.Design/methodology/approach-The paper opted for a research overview on the blockchain consensus mechanism,including the consensus mechanisms’consensus progress,classification and comparison,which are complemented by documentary analysis.Findings-This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms.First,the authors outline the consensus processes,the advantages and disadvantages of the mainstream consensus mechanisms.Additionally,the consensus mechanisms are subdivided into four types according to their characteristics.Then,the consensus mechanisms are compared and analyzed based on four evaluation criteria.Finally,the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.Originality/value-This paper summarizes the future research development of the consensus mechanisms.
基金This research work was supported by the National Natural Science Foundation of China under Grant Nos.61872086 and U1805263.
文摘Purpose-The purpose of this paper is to make a comprehensive analysis of 354 publications about Pythagorean fuzzy sets(PFSs)from 2013 to 2020 in order to comprehensively understand their historical progress and current situation,as well as future development trend.Design/methodology/approach-First,this paper describes the fundamental information of these publications on PFSs,including their data information,annual trend and prediction and basic features.Second,the most productive and influential authors,countries/regions,institutions and the most cited documents are presented in the form of evaluation indicators.Third,with the help of VOSviewer software,the visualization analysis is conducted to show the development status of PFSs publications at the level of authors,countries/regions,institutions and keywords.Finally,the burst detection of keywords,timezone review and timeline review are exported from CiteSpace software to analyze the hotspots and development trend on PFSs.Findings-The annual PFSs publications present a quickly increasing trend.The most productive author is Wei Guiwu(China).Wei Guiwu and Wei Cun have the strongest cooperative relationship.Research limitations/implications-The implication of this study is to provide a comprehensive perspective for the scholars who take a fancy to PFSs,and it is valuable for scholars to grasp the hotspots in this field in time.Originality/value-It is the first paper that uses the bibliometric analysis to comprehensively analyze the publications on PFSs.It can help the scholars in the field of PFSs to quickly understand the development status and trend of PFSs.
文摘Purpose-The purpose of this paper is to make an overview of 6,618 publications of Apache Hadoop from 2008 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field,as well as a preliminary knowledge of Apache Hadoop for interested researchers.Design/methodology/approach-This paper employs the bibliometric analysis and visual analysis approaches to systematically study and analyze publications about Apache Hadoop in the Web of Science database.This study aims to investigate the topic of Apache Hadoop by means of bibliometric analysis with the aid of visualization applications.Through the bibliometric analysis of the collected documents,this paper analyzes the main statistical characteristics and cooperation networks.Research themes,research hotspots and future development trends are also investigated through the keyword analysis.Findings-The research on Apache Hadoop is still the top priority in the future,and how to improve the performance of Apache Hadoop in the era of big data is one of the research hotspots.Research limitations/implications-This paper makes a comprehensive analysis of Apache Hadoop with methods of bibliometrics,and it is valuable for researchers can quickly grasp the hot topics in this area.Originality/value-This paper draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years,aiming to understand the development status and trends in this field and inspire new ideas for researchers.
文摘Purpose-The purpose of this paper is to make an overview of 474 publications and 512 patents of FTL from 1987 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field,as well as a preliminary knowledge of FTL for interested researchers.Design/methodology/approach-Firstly,the FTL algorithms are classified and its functions are introduced in detail.Secondly,the structures of the publications are analyzed in terms of the fundamental information and the publication of the most productive countries/regions,institutions and authors.After that,co-citation networks of institutions,authors and papers illustrated by VOS Viewer are given to show the relationship among those and the most influential of them is further analyzed.Then,the characteristics of the patent are analyzed based on the basic information and classification of the patent and the most productive inventors.In order to obtain research hotspots and trends in this field,the time-line review and citation burst detection of keywords carried out by Cite Space are made to be visual.Finally,based on the above analysis,it draws some other important conclusions and the development trend of this field.Findings-The research on FTL algorithm is still the top priority in the future,and how to improve the performance of SSD in the era of big data is one of the research hotspots.Research limitations/implications-This paper makes a comprehensive analysis of FTL with the method of bibliometrics,and it is valuable for researchers can quickly grasp the hotspots in this area.Originality/value-This article draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years,aiming to inspire new ideas for researchers.
基金This work was supported by the Fujian Provincial Natural Science Foundation of China under Grant No.2022J01958.
文摘Purpose–The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends,hotspots,and directions for future research.Design/methodology/approach–The data source for this paper is the Web of Science Core Collection,and 7,154 publications and related information have been derived.We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools(VOS Viewer and CiteSpace).Findings–The analysis results show that China is the most productive and influential country/region.East Asia countries have strong cooperation with each other and also have cooperation with other countries.The study shows that risk management has been involved in various fields such as credit,supply chain,health emergency and disaster especially in the background of COVID-19.We also found that machine learning,especially deep learning,has been playing an increasingly important role in risk management due to its excellent performance.Originality/value–This paper focuses on studying risk management in East Asia,exploring its publication’s fundamental information,citation and cooperation networks,hotspots,and research trends.It provides some reference value for scholars who are interested or further research in this field.
基金supported by the Guangzhou Science and Technology Plan Project under Grant No.202102080382。
文摘Purpose-Based on the strong feature representation ability of the convolutional neural network(CNN),generous object detection methods in remote sensing(RS)have been proposed one after another.However,due to the large variation in scale and the omission of relevant relationships between objects,there are still great challenges for object detection in RS.Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account.Moreover,inference time and lightness are also major pain points in the field of RS.Design/methodology/approach-To alleviate the aforementioned problems,this study proposes a novel method for object detection in RS,which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images(MFLD).The multi-receptive field extraction(MRFE)and long-range dependency information extraction(LDIE)modules are put forward.Findings-To concentrate on the variability of objects in RS,MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates.Considering the shortcomings of CNN in extracting global information,LDIE is designed to capture the relationships between objects.Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-theart methods.Most of all,on the NWPU VHR-10 dataset,our MFLD method achieves 94.6%mean average precision with 4.08 M model volume.Originality/value-This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.
