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Role-based access control (RBAC) is a widely adopted access control model in various domains for defining security management. Role mining is closely related to role-based access control, as the latter employs role assignments to offer a flexible and scalable approach to managing permissions within an organization. The edge role mining problem (Edge RMP), a variant of the role mining problem (RMP), has long been recognized as an effective strategy for role assignment. Role mining, which groups users with similar access permissions into the same role, bears some resemblance to symmetry. Symmetry categorizes objects or graphics with identical characteristics into one group. Both involve a certain form of "classification" or "induction". Edge-RMP reduces the associations between users and permissions, thereby lowering the security risks faced by the system. While an algorithm based on Boolean matrix factorization exists for this problem, it fails to further refine the resulting user–role assignment (UA) and role–permission assignment (PA) relationships. Additionally, this algorithm does not address constraint-related issues, such as cardinality constraints, user exclusion constraints, and user capabilities. Furthermore, it demonstrates significant redundancy of roles when handling large datasets, leaving room for further optimization of Edge-RMP results. To address these concerns, this paper proposes the MFC-RMA algorithm based on Boolean matrix factorization. The method achieves significant optimization of Edge-RMP results by handling relationships between roles possessing various permissions. Furthermore, this paper clusters, compresses, modifies, and optimizes the original data based on the similarity between users, ensuring its usability for role mining. Both theoretical and practical considerations are taken into account for different types of constraints, and algorithms are devised to reallocate roles incorporating these constraints, thereby generating UA and PA matrices. The proposed approach yields optimal numbers of generated roles and the sum of the minimum number of generated edges to address the aforementioned issues. Experimental results demonstrate that the algorithm reduces management overhead, provides efficient execution results, and ensures the accuracy of generated roles. [ABSTRACT FROM AUTHOR]

Land consolidation, as a significant way to optimize the allocation of land resources in relatively small regions, is one of the important contents of land use plan. The only way in China to implement the work of land consolidation is project, so the site selection of land consolidation projects, demarcating the border in the two-dimensional space, is the first step of land consolidation projects. Its scientificity and rationality will decide the achievement of maximum benefit of land consolidation. But from the current actual situation of land consolidation, the theory and the practice of the site selection of land consolidation projects are both weak, so on the occasion of new round of land use planning, the research of scientific theories and methods is necessary and urgent. Site location of land consolidation project was discussed in this paper based on multi-objective optimization. The site location rules of land consolidation project were generalized, and 3 objective functions were comprised, which were the maximum potential of newly-increased cultivated land, the higher connectivity among zones and the best land suitability. Two types of constraint conditions that included the minimum newly-increased cultivated land ratio and the area limitation of land consolidation project were also considered. Site selection model of land consolidation projects based on the multi-objective particle swarm optimization algorithm was proposed to solve the multi-objective spatial optimization problem with the assist of GIS (geographic information system). The mapping relationships between different concepts in the site selection of land consolidation projects and intelligence algorithms were analyzed. Each vector parcel was considered to be a decision-making unit, and the value of decision variable was suggested to be 1 when the corresponding parcel was chosen to the project areas, otherwise it was zero. Each particle in the particle swarm optimization (PSO) algorithm represented a site selection scheme of land consolidation project, which comprised all the multi-dimensional decision-making units. The particle was encoded based on the identification of the parcels and the values of decision variable. The structures of velocity calculation operator and position updating operator were designed based on the spatial coding scheme of individual. The mechanism of particle status update and the procedure of evolution should be improved because of the discrete solution space of the model of the site selection of land consolidation projects. At last, take Jiayu county, Hubei province as a case study, different weight schemes were chosen. Jiayu county is an important base for producing the agricultural by-products and aquatic products, which made it our choice of study area to test the model. The model was expected to reasonably select spatial units in accordance with multiple objectives and constraints, and to optimize the newly-increased cultivated land ratio and the spatial pattern. Different weight schemes were chosen to generate several different solutions, and the results showed that 3 objective functions in the model were conflicting with each other, and the increase of any objective function value came at the cost of the decrease of others. In the running process of the model of the site selection of land consolidation projects, the particle tended to select the land use parcels of garden, grassland and ditch into the project areas with the guidance of the objective of newly-increased cultivated land potential, because these land could be converted into cultivated land after land consolidation, and the index of land consolidation suitability decreased to some degree. The neighborhood identity index was improved effectively and the degree of fragmentation reduced across spatial units, because the intelligent particle chose the parcels adjacent to the selected ones or some isolated parcels into the project areas whenever possible, which was leaded by the objective of compact spatial pattern. The decision-makers can get alternative solutions of site selection for land consolidation project which satisfy their preference by setting corresponding weight coefficients of the 3 objective functions. [ABSTRACT FROM AUTHOR]

Breast cancer has the highest morbidity and mortality among women. Early detection and treatment can effectively improve survival. The digital breast tomosynthesis (DBT) technique uses reconstruction algorithms to get a three-dimensional (3D) image set from a plurality of projection images that are obtained at limited view angles. In this paper, through computer simulation, out-of-focus artifact spread function (ASF), noise spectrum function, and modulation transfer function (MTF) were evaluated to optimize three iterative reconstruction algorithms, including simultaneous algebraic reconstruction technique (SART), maximum-likelihood expectation maximization (MLEM), and ordered-subset MLEM (OS-MLEM). The projection data of phantom were simulated and obtained on Geant4 Application for Tomographic Emission (GATE) platform. Based on the elimination of out-of-focus plane artifacts, the suppression of noise, and the sharpness of the reconstructed objects in frequency domain, our results suggested that the overall best reconstructed image quality was achieved when the iteration numbers were 4, 6, and 3 for SART, MLEM and OS-MLEM, respectively. [ABSTRACT FROM AUTHOR]