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This gives our approach an advantage in total running time. Additionally, our approach does not require frequent itemset mining executed before the hiding process. To lessen the side effects of itemset hiding, we introduced the sibling itemset concept that is used for generating constraints. This distortion creates an asymmetric relation between the original and the sanitized database. This paper presents an exact itemset hiding approach, which uses constraints for a better solution in terms of side effects and minimum distortion on the database. There are two types of approaches in the algorithmic landscape: heuristic and exact. Since the discovery of frequent itemsets is one of the most effective data mining tools that firms use, privacy-preserving techniques are necessary for continuing frequent itemset mining. Shared data should be modified in such a way that sensitive relationships would be hidden. However, there is a risk of sensitive knowledge disclosure. Sharing information between companies could make this tool more beneficial for each party. Extensive experiments are conducted on two occluded person Re-ID benchmarks, confirming that our approach performs a SOTA or comparable effect.Дata collection and processing progress made data mining a popular tool among organizations in the last decades. Only ID labels are utilized to restrain TAPS and AVPC without any extra visible-part annotation. (ii) An Adaptive Visible-Part Cropping (AVPC) Strategy, with two steps of cropping and weakly-supervised learning, is used to generate a fine-scale visible image for another branch.

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(i) A Transformer-Aware Patch Searching (TAPS) module is devised to learn visible human region distribution using a multiheaded self-attention mechanism and construct a branch of distributed information attention scale. There are two contributions to our proposed model. To settle the occluded person Re-ID issue, a new Dual-Transformer symmetric architecture is proposed in this work, which can reduce the occluded impact and build a multi-scale feature.

In the real world, a variety of occluded situations frequently appear in video monitoring, which impedes the recognition process. However, it cannot be guaranteed that all the people have a clear foreground because they are out of constraint.

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Most of the research is based on a strong assumption that images should contain a full human torso. Person re-identification (Re-ID) aims to retrieve a specific pedestrian across a multi-disjoint camera in a surveillance system. The results of the study reveal that the discretized generalized Pareto distribution is quite an attractive alternative to other discrete competitive distributions. The comparison was based on different goodness-of-fit criteria. In addition, the applicability of the optimal discrete generalized Pareto distribution was compared with other discrete distributions. A detailed simulation analysis was performed to compare the performance of the Bayesian estimation using the proposed loss functions. The symmetric loss function being used is the squared error loss function, while the two asymmetric loss functions are the linear exponential and general entropy loss functions. Bayesian inference is applied to estimate the discrete models with different symmetric and asymmetric loss functions. The generalized Pareto distribution provides a wide variety of probability spaces, which support threshold exceedances, and hence, it is suitable for modeling many failure time issues. This paper studies three discretization methods to formulate discrete analogues of the well-known continuous generalized Pareto distribution. Testimonials: See what our editors and authors say about Symmetry.Sections: published in 6 topical sections.Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.To publication is undertaken in 3.8 days (median values for papers published in Rapid Publication: manuscripts are peer-reviewed and a firstДecision provided to authors approximately 13.8 days after submission acceptance.Journal Rank: JCRВ - Q2 ( Multidisciplinary Sciences) / CiteScoreВ - Q1 ( General Mathematics).High Visibility: indexedВ within Scopus, SCIE (Web of Science), CAPlus / SciFinder, Inspec, Astrophysics Data System, and other databases.

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Symmetry is published monthly online by MDPI. Is an international, peer-reviewed, open access journal covering research on symmetry/asymmetry phenomena wherever they occur in all aspects of natural sciences.

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