Mining λ-Maximal Cliques from a Fuzzy Graph

Hao, Fei; Park, Doo-Soon; Li, Shuai; Lee, HwaMin

doi:10.3390/su8060553

Cited by 15 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The linkage of the n-armed bandit problem to the problem of finding such S-boxes, opens an interesting area of future research -the investigation of how other state-of-theart methods, such as the concept of fuzzy graphs [83] [84], the stochastic optimization techniques [85] This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.…”

Section: Discussionmentioning

confidence: 99%

On the Design of Chaos-Based S-Boxes

Dimitrov

2020

IEEE Access

View full text Add to dashboard Cite

Substitution boxes (S-boxes) are critical nonlinear elements to achieve cryptanalytic resistance of modern block and stream ciphers. Given their importance, a rich variety of S-box construction strategies exists. In this paper, S-boxes generated by using chaotic functions (CF) are analyzed to measure their actual resistance to linear cryptanalysis. The aforementioned papers emphasize on the average nonlinearity of the S-box coordinates only, ignoring the rest of the S-box components in the process. Thus, the majority of those studies should be re-evaluated. Integrating such S-boxes in a given cryptosystem should be done with a considerable caution. Furthermore, we show that in the context of nonlinearity optimization problem the profit of using chaos structures is negligible. By using two heuristic methods and starting from pseudo-random S-boxes, we repeatedly reached S-boxes, which significantly outperform all previously published CF-based S-boxes, in those cryptographic terms, which the aforementioned papers utilize for comparison. Moreover, we have linked the multi-armed bandit problem to the problem of maximizing an S-box average coordinate nonlinearity value, which further allowed us to reach near-optimal average coordinate nonlinearity values significantly greater than those known in literature.

show abstract

Section: Discussionmentioning

confidence: 99%

On the Design of Chaos-Based S-Boxes

Dimitrov

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To describe this fuzzy relation between nodes, the nodes are viewed as both objects and attributes. Then, a fuzzy formal context of G can be constructed with the following fuzzy adjacency matrix R * , denoted as K(G)=(V, V, R * ) [29].…”

Section: Skyline (λ K)-cliques Identificationmentioning

confidence: 99%

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

Hao

Gao

Chen

et al. 2022

IEEE Trans. Comput. Soc. Syst.

Self Cite

View full text Add to dashboard Cite

Identifying the optimal groups of users that are closely connected and satisfy some ranking criteria from an attributed social network, attracts significant attention from both academia and industry. Skyline query processing, a multi-criteria decision-making optimized technique, is recently embedded into cohesive subgraphs mining in graphs/social networks. However, the existing studies cannot capture the fuzzy property of connections between users in social networks. To fill this gap, in this paper we formulate a novel model of the skyline (λ, k)cliques over a fuzzy attributed social network and develop a Formal Concept Analysis (FCA) based skyline (λ, k)-cliques identification algorithm. Specifically, λ can be regarded as a quality control parameter for measuring the stability of the cohesive groups. Extensive experimental results conducted on three real-world datasets demonstrate the effectiveness of the skyline (λ, k)-clique model in a fuzzy attributed social network. Further, an illustrative example is executed for revealing the usefulness of our model. It is expected that our proposed skyline (λ, k)-clique model can be widely used in various graph-based computational social systems, such as optimal team formation in crowdsourcing, and group recommendation in social networks.

show abstract

“…The example of application of the idea to the ex-ante risk assessment in the project iss discussed by the author in [7], for interpretation of sequence of irregular data is discussed e.g. in [8] and for application of a fuzzy graph the algorithm based on fuzzy formal concept analysis is presented in [9]. Moreover in the analysis the methods of artificial intelligence and genetic algorithms are used [10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of technical risk in maintenance and improvement of a manufacturing process

Kozień

2020

Open Engineering

View full text Add to dashboard Cite

AbstractContemporary production companies operate in a dynamically changing environment and try to strengthen their competitive position. Therefore they take action acting at implementing new ones as well as improving and maintaining existing production processes, using for this purpose knowledge and available tools and methods. For dominant activities undertaken in the scope of improving and maintaining production the pro-quality activities can be included. Quality mainly depends on the efficiency of production processes, therefore manufacturing companies should focus their activities on maintaining and improving them. The methods of technical risk analysis and assessment are effective methods of maintaining production processes, whose application makes it possible to indicate any deviations from accepted standards. Analysis and assessment of the technical risk of the production process makes it possible not only to indicate non-conformities identified in the process, but mainly taking corrective actions. The aim of the article is to formulate of the method used to identify and assessment of the technical risk of the cutting process on the organizational side. The method has qualitative-quantitative type, and is the effective tools for technical risk assessment necessary to maintain the production process. It is assumed that the quality of the cutting process is ensured by the technical condition of the machine.

show abstract

Mining λ-Maximal Cliques from a Fuzzy Graph

Cited by 15 publications

References 17 publications

On the Design of Chaos-Based S-Boxes

On the Design of Chaos-Based S-Boxes

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

Assessment of technical risk in maintenance and improvement of a manufacturing process

Contact Info

Product

Resources

About