Recently, there has been a growing demand for image encryption techniques that offer robust protection and minimize processing time. The proposed paper proposes an efficient color image encryption ...system that excels in speed and security. The encryption system comprises three fundamental phases. The initial phase generates a unique encryption key by combining user-defined input with the original image and applying various operations and hash functions. In the confusion phase, the image is divided into blocks, forming a Binary Tree (BT) using primary color blocks, ensuring that the root and leaves belong to different colors. The confused matrix is derived through an inorder traversal that ensures non-adjacency of pixels of the same color, introducing an added layer of security. Finally, each pixel is scrambled by applying BT to its binary form to add more security and complexity. A DNA sequence is generated, and operations are executed based on two different chaotic maps, enhancing unpredictability and attack resistance. Extensive testing has validated the effectiveness of the proposed system, revealing a remarkable 28–45% reduction in processing time compared to recent techniques. Moreover, the system successfully withstands various attacks, as demonstrated through rigorous evaluations, including high-performance, visual perception, and cryptosystem strength evaluations. These results underscore the practical applicability and robust security offered by our efficient color image encryption solution, which provides a practical solution for applications prioritizing efficiency.
We present a generalization of a combinatorial result by Aggarwal et al. (1989) on a linear-time algorithm that selects a constant fraction of leaves, with pairwise disjoint neighborhoods, from a ...binary tree embedded in the plane. This result of Aggarwal et al. (1989) is essential to the linear-time framework, which they also introduced, that computes certain Voronoi diagrams of points with a tree structure in linear time. An example is the diagram computed while updating the Voronoi diagram of points after deletion of one site. Our generalization allows that only a fraction of the tree leaves is considered, and it is motivated by research on linear time construction algorithms for Voronoi diagrams of non-point sites. We are given a plane tree T of n leaves, m of which have been marked, and each marked leaf is associated with a neighborhood (a subtree of T) such that any two topologically consecutive marked leaves have disjoint neighborhoods. We show how to select in linear time a constant fraction of the marked leaves having pairwise disjoint neighborhoods.
Revocable identity based signature (RIBS) is a useful cryptographic primitive, which provides a revocation mechanism to revoke misbehaving or malicious users over ID-based public key settings. In the ...past, many RIBS schemes have been previously proposed, but the security of all these existing schemes is based on traditional complexity assumptions, which are not secure against attacks in the quantum era. Lattice-based cryptography has many attractive features and it is all believed to be secure against attacks of quantum computing. Recently, Hung et al. proposed a RIBS with short size over lattices. However, in their scheme, it requires the private key generator (PKG) to perform linear work in the number of users and does not scale well. Moreover, their scheme is secure in the random oracle model. In this paper, we adopt the binary tree structure to present a scalable lattice-based RIBS scheme which greatly reduces the PKG’S workload associated with users from linear to logarithm. We prove that our proposed scheme is existentially unforgeable against chosen message attacks (EUF-CMA) under standard short integer solutions (SIS) assumption, in the standard model. Compared with the existing RIBS schemes over lattices, our proposed RIBS construction is secure in the standard model with scalability and meanwhile has efficient revocation mechanism with public channels.
This paper proposes the idea of combining “interest groups” with the practical decision information to classify the decision makers (DMs) in complex multi-attribute large-group decision-making ...(CMALGDM) problems in interval-valued intuitionistic fuzzy (IVIF) environment. It constructs a partial binary tree DEA-DA cyclic classification model to achieve the multiple groups’ classification of DMs. Not only does this method provide references for the classification of DMs when the decision information is known, but it also lays foundations for DMs’ effective weight determination and the aggregation of decision information. First, this paper normalizes all of the cost attributes into benefit attributes to avoid the wrong decision result. Second, it employs the C-OWA operator to transform IVIF number (IVIFN) samples into single-valued samples. With respect to this transformation, this paper provides the corresponding BUM functions of DMs according to their risk attitudes; therefore, the preference information of DMs can be more objectively aggregated. Third, this paper adopts the partial binary tree DEA-DA cyclic classification model to present an accurate classification of DMs. Thus, for each interest group, group members with different interest preferences can be distinguished and distributed to the appropriate groups. Finally, to show the feasibility and validity of the model, we give an illustrative example.
Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require ...application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named
TeKET
or
Tree-based Keyphrase Extraction Technique
, is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called
KeyPhrase Extraction
(
KePhEx
) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called
Cohesiveness Index
or
CI
, is derived which denotes a given node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a
German Research Article
dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores.
An adaptive and efficient volume element subdivision method using binary tree for evaluation of nearly singular domain integrals with continuous or discontinuous kernel in three-dimensional (3-D) ...boundary element method (BEM) has been presented. In the Conventional Subdivision Method (CSM) for evaluation of nearly singular integrals, the patches are obtained by simply connecting the source point with each vertex of the element. Thus, the accuracy of the integral obtained with CSM is easily affected by the shape of the element and the location of the source point. In contrast, the proposed Binary-Tree Subdivision Method (BTSM) is more convenient to implement and can guarantee successful patch generation under any circumstances for accurate evaluation of nearly singular domain integrals with continuous or discontinuous kernel. Numerical results for volume elements of arbitrary type with various relative locations of the source point demonstrate robustness, accuracy and efficiency of the proposed method.
•The BTSM is proposed for evaluating nearly singular integrals with various kernel.•The BTSM is applicable to different kinds of volume element with arbitrary shape.•The BTSM is more convenient to implement by the single binary-tree data structure.•The BTSM can guarantee the convergence of element subdivision in any situation.•The BTSM possesses better accuracy and efficiency than the conventional method.
Let
and suppose that we are given a function
defined on the leaves of a weighted tree. We would like to extend
to a function
defined on the entire tree, so as to minimize the weighted
-Sobolev norm ...of the extension. An easy situation is when
, where the harmonic extension operator provides such a function
. In this note, we record our analysis of the particular case of a radially symmetric binary tree, which is a complete, finite, binary tree with weights that depend only on the distance from the root. Neither the averaging operator nor the harmonic extension operator work here in general. Nevertheless, we prove the existence of a linear extension operator whose norm is bounded by a constant depending solely on
. This operator is a variant of the standard harmonic extension operator, and in fact, it is harmonic extension with respect to a certain Markov kernel determined by
and by the weights.
Guyon (Guyon J. Limit theorems for bifurcating Markov chains. Application to the detection of cellular aging. Ann Appl Probab, 2007, 17: 1538-1569) introduced an important model for homogeneous ...bifurcating Markov chains indexed by a binary tree taking values in general state space and studied their limit theorems. The results were applied to detect cellular aging. In this paper, we define a discrete form of nonhomogeneous bifurcating Markov chains indexed by a binary tree and discuss the equivalent properties for them. The strong law of large numbers and the entropy ergodic theorem are studied for these Markov chains with finite state space. In contrast to previous work, we use a new approach to prove the main results of this paper.
Nowadays, transportation networks depend heavily on the technology known as vehicular ad hoc networks (VANETs). VANETs enhance traffic control and road safety while also enabling vehicle-to-vehicle ...communication using basic safety messages (BSM), which are susceptible to different kinds of attacks. This study focuses on techniques for detecting and classifying misbehavior in VANETs while dealing with unbalanced data. In order to ensure equal treatment of minority and majority categories, we provide a novel method called One vs. All Binary Tree (OVA-BT). This approach separates binary classifiers for each kind of misbehavior and provides specific assessment metrics for each kind of misbehavior. We evaluate our experiment using five-fold cross-validation with six individual models of ML and an ensemble classifier. The findings demonstrated that the use of OVA-BT enhances the classification accuracy when compared to a traditional single multi-class model and that the classifier ensemble's classification performance is greater than the best individual model on the testing set.
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