q -Rung Orthopair Fuzzy Matroids with Application to Human Trafficking

The theory of q -rung orthopair fuzzy sets ( q -ROFSs) is emerging for the provision of more comprehensive and useful information in comparison to their counterparts like intuitionistic and Pythagorean fuzzy sets, especially when responding to the models of vague data with membership and non-membership grades of elements. In this study, a significant generalized model q -ROFS is used to introduce the concept of q -rung orthopair fuzzy vector spaces ( q -ROFVSs) and illustrated by an example. We further elaborate the q -rung orthopair fuzzy linearly independent vectors. The study also involves the results regarding q -rung orthopair fuzzy basis and dimensions of q -ROFVSs. The main focus of this study is to define the concepts of q -rung orthopair fuzzy matroids ( q -ROFMs) and apply them to explore the characteristics of their basis, dimensions, and rank function. Ultimately, to show the significance of our proposed work, we combine these ideas and offer an application. We provide an algorithm to solve the numerical problems related to human flow between particular regions to ensure the increased government response action against frequently used path (heavy path) for the countries involved via directed q -rung orthopair fuzzy graph ( q -ROFG). At last, a comparative study of the proposed work with the existing theory of Pythagorean fuzzy matroids is also presented.

An Improved Fuzzy Vector Signature with Reusability

Fuzzy vector signature (FVS) is a new primitive where a fuzzy (biometric) data w is used to generate a verification key (VKw), and, later, a distinct fuzzy (biometric) data w′ (as well as a message) is used to generate a signature (σw′). The primary feature of FVS is that the signature (σw′) can be verified under the verification key (VKw) only if w is close to w′ in a certain predefined distance. Recently, Seo et al. proposed an FVS scheme that was constructed (loosely) using a subset-based sampling method to reduce the size of helper data. However, their construction fails to provide the reusability property that requires that no adversary gains the information on fuzzy (biometric) data even if multiple verification keys and relevant signatures of a single user, which are all generated with correlated fuzzy (biometric) data, are exposed to the adversary. In this paper, we propose an improved FVS scheme which is proven to be reusable with respect to arbitrary correlated fuzzy (biometric) inputs. Our efficiency improvement is achieved by strictly applying the subset-based sampling method used before to build a fuzzy extractor by Canetti et al. and by slightly modifying the structure of the verification key. Our FVS scheme can still tolerate sub-linear error rates of input sources and also reduce the signing cost of a user by about half of the original FVS scheme. Finally, we present authentication protocols based on fuzzy extractor and FVS scheme and give performance comparison between them in terms of computation and transmission costs.

Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model.