Role mining and setup affect the usage of role-based access control(RBAC).Traditionally,user's role and permission assigning are manipulated by security administrator of system.However,the cost is expensive and th...Role mining and setup affect the usage of role-based access control(RBAC).Traditionally,user's role and permission assigning are manipulated by security administrator of system.However,the cost is expensive and the operating process is complex.A new role analyzing method was proposed by generating mappings and using them to provide recommendation for systems.The relation among sets of permissions,roles and users was explored by generating mappings,and the relation between sets of users and attributes was analyzed by means of the concept lattice model,generating a critical mapping between the attribute and permission sets,and making the meaning of the role natural and operational.Thus,a role is determined by permission set and user's attributes.The generated mappings were used to automatically assign permissions and roles to new users.Experimental results show that the proposed algorithm is effective and efficient.展开更多
Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of co...Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.展开更多
As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various scenarios.However,data on a blockchain cannot be deleted.A...As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various scenarios.However,data on a blockchain cannot be deleted.As a result,it is impossible to clean invalid and sensitive data and correct erroneous data.This,to a certain extent,hinders the application of blockchains in supply chains and Internet of Things.To address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block structure.The RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also introduced.This mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature mechanism.The analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.展开更多
The emergence of smart contracts has increased the attention of industry and academia to blockchain technology,which is tamper-proofing,decentralized,autonomous,and enables decentralized applications to operate in unt...The emergence of smart contracts has increased the attention of industry and academia to blockchain technology,which is tamper-proofing,decentralized,autonomous,and enables decentralized applications to operate in untrustworthy environments.However,these features of this technology are also easily exploited by unscrupulous individuals,a typical example of which is the Ponzi scheme in Ethereum.The negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been significant.To solve this problem,we propose a detection model for detecting Ponzi schemes in smart contracts using bytecode.In this model,our innovation is shown in two aspects:We first propose to use two bytes as one characteristic,which can quickly transform the bytecode into a high-dimensional matrix,and this matrix contains all the implied characteristics in the bytecode.Then,We innovatively transformed the Ponzi schemes detection into an anomaly detection problem.Finally,an anomaly detection algorithm is used to identify Ponzi schemes in smart contracts.Experimental results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme contracts.Moreover,the F1-score of this model can reach 0.88,which is far better than those of other traditional detection models.展开更多
A new concept Graded Finite Poset is proposed in this paper. Through discussing some basic properties of it, we come to that the direct product of graded finite posets is connected if and only if every graded finite p...A new concept Graded Finite Poset is proposed in this paper. Through discussing some basic properties of it, we come to that the direct product of graded finite posets is connected if and only if every graded finite poset is connected. The graded function of a graded finite poset is unique if and only if the graded finite poset is connected.展开更多
The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and propose...The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.展开更多
In this paper, we give the algebraic independence measures for the values ofMahler type functions in complex number field and p-adic number field, respectively.
Dear editor,This letter presents an unsupervised feature selection method based on machine learning.Feature selection is an important component of artificial intelligence,machine learning,which can effectively solve t...Dear editor,This letter presents an unsupervised feature selection method based on machine learning.Feature selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of dimensionality problem.Since most of the labeled data is expensive to obtain.展开更多
Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model ...Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.展开更多
In this paper, we give the p-adic measures of algebraic independence for the values of Ramanujan functions and Klein modular functions at algebraic points.
In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep lea...In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games.As one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to compete.To solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup recommendations.The model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero vectors.The CBOW model can predict the context of a word in a sentence.Accordingly,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.展开更多
This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase spectrum.The proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of...This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase spectrum.The proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea regions.In order to eliminate the influence of land regions in SAR images,a novel land removing method is explored.The removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these patches.Thus,an appropriate land segmentation threshold is accordingly obtained.Secondly,an automatic ship detector based on phase spectrum is proposed.The proposed detector is free from various idealized assumptions and can accurately detect ships in SAR images.Experimental results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.展开更多
基金Project(61003140) supported by the National Natural Science Foundation of ChinaProject(013/2010/A) supported by Macao Science and Technology Development FundProject(10YJC630236) supported by Social Science Foundation for the Youth Scholars of Ministry of Education of China
文摘Role mining and setup affect the usage of role-based access control(RBAC).Traditionally,user's role and permission assigning are manipulated by security administrator of system.However,the cost is expensive and the operating process is complex.A new role analyzing method was proposed by generating mappings and using them to provide recommendation for systems.The relation among sets of permissions,roles and users was explored by generating mappings,and the relation between sets of users and attributes was analyzed by means of the concept lattice model,generating a critical mapping between the attribute and permission sets,and making the meaning of the role natural and operational.Thus,a role is determined by permission set and user's attributes.The generated mappings were used to automatically assign permissions and roles to new users.Experimental results show that the proposed algorithm is effective and efficient.
基金Supported by the National Natural Science Foundation of China(No.61305042,61202098)Projects of Center for Remote Sensing Mission Study of China National Space Administration(No.2012A03A0939)Science and Technological Research of Key Projects of Education Department of Henan Province of China(No.13A520071)
文摘Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.
基金This work was supported by the Scientific and technological project of Henan Province(Grant Nos.202102310340,212102210414)Foundation of University Young Key Teacher of Henan Province(Grant Nos.2019GGJS040,2020GGJS027)+1 种基金Key scientific research projects of colleges and universities in Henan Province(Grant No.21A110005)National Natual Science Foundation of China(61701170).
文摘As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various scenarios.However,data on a blockchain cannot be deleted.As a result,it is impossible to clean invalid and sensitive data and correct erroneous data.This,to a certain extent,hinders the application of blockchains in supply chains and Internet of Things.To address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block structure.The RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also introduced.This mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature mechanism.The analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.
基金This work was supported by the Scientific and Technological Project of Henan Province(Grant No.202102310340)Foundation of University Young Key Teacher of Henan Province(Grant Nos.2019GGJS040,2020GGJS027)+1 种基金Key Scientific Research Projects of Colleges and Universities in Henan Province(Grant No.21A110005)National Natual Science Foundation of China(61701170).
文摘The emergence of smart contracts has increased the attention of industry and academia to blockchain technology,which is tamper-proofing,decentralized,autonomous,and enables decentralized applications to operate in untrustworthy environments.However,these features of this technology are also easily exploited by unscrupulous individuals,a typical example of which is the Ponzi scheme in Ethereum.The negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been significant.To solve this problem,we propose a detection model for detecting Ponzi schemes in smart contracts using bytecode.In this model,our innovation is shown in two aspects:We first propose to use two bytes as one characteristic,which can quickly transform the bytecode into a high-dimensional matrix,and this matrix contains all the implied characteristics in the bytecode.Then,We innovatively transformed the Ponzi schemes detection into an anomaly detection problem.Finally,an anomaly detection algorithm is used to identify Ponzi schemes in smart contracts.Experimental results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme contracts.Moreover,the F1-score of this model can reach 0.88,which is far better than those of other traditional detection models.
基金Supported by the National Natural Science Foundation of China（60474022） Supported by the Henan Innovation Project for University Prominent Research Talents（2007KYCX018）
文摘A new concept Graded Finite Poset is proposed in this paper. Through discussing some basic properties of it, we come to that the direct product of graded finite posets is connected if and only if every graded finite poset is connected. The graded function of a graded finite poset is unique if and only if the graded finite poset is connected.
基金Supported by the National Natural Science Foundation of China (No.60474022) the Natural Science Foundation of Henan Province(No. G2002026,200510475028)
文摘The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.
基金Supported by the Natural Science Foundation of Henan University（05ZDZR001）
文摘In this paper, we give the algebraic independence measures for the values ofMahler type functions in complex number field and p-adic number field, respectively.
基金supported by grants from the National Natural Science Foundation of China(62106066)Key Research Projects of Henan Higher Education Institutions(22A520019)+1 种基金Scientific and Technological Project of Henan Province(202102110121)Science and Technology Development Project of Kaifeng City(2002001)。
文摘Dear editor,This letter presents an unsupervised feature selection method based on machine learning.Feature selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of dimensionality problem.Since most of the labeled data is expensive to obtain.
基金Supported by the National Natural Science Foundation of China ( No.60474022)Henan Innovation Project for University Prominent Research Talents (No.2007KYCX018)
文摘Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.
文摘In this paper, we give the p-adic measures of algebraic independence for the values of Ramanujan functions and Klein modular functions at algebraic points.
基金the Guangdong Province Key Research and Development Plan(No.2019B010137004)the National Natural Science Foundation of China(Nos.61402149 and 61871140)+3 种基金the Scientific and Technological Project of Henan Province(Nos.182102110065,182102210238,and 202102310340)the Natural Science Foundation of Henan Educational Committee(No.17B520006)Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019)Foundation of University Young Key Teacher of Henan Province(No.2019GGJS040)。
文摘In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games.As one of the world’s most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero’s match,and players are often unable to pick the best lineup to compete.To solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM)neural network model for Dota2 lineup recommendations.The model uses the Continuous Bag Of Words(CBOW)model in the Word2 vec model to generate hero vectors.The CBOW model can predict the context of a word in a sentence.Accordingly,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.
基金China Postdoctoral Science Foundation,grant 2015M582182Fund of Henan Province Young Key Teacher,grant 2017GGJS019+1 种基金foundation of Henan Education Department,grant 19A520002Henan Postdoctoral Foundation,grant 001703007.
文摘This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase spectrum.The proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea regions.In order to eliminate the influence of land regions in SAR images,a novel land removing method is explored.The removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these patches.Thus,an appropriate land segmentation threshold is accordingly obtained.Secondly,an automatic ship detector based on phase spectrum is proposed.The proposed detector is free from various idealized assumptions and can accurately detect ships in SAR images.Experimental results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.
