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How to improve the high resolution and remove the noise of composite infrared thermal wave detection image have become important research topics in machine vision. Therefore, this paper proposes a composite image enhancement algorithm based on edge adaptive total variation of direction. Using the improved adaptive directional total variation model to construct the guidance image of the guidance filter can provide better structural information for the guidance filter and conduct the guidance filter. This process is iterated to eliminate noise and ladder effect. Experimental results show that this algorithm is superior to other advanced methods in objective evaluation index and subjective vision. The algorithm proposed in this paper can not only effectively eliminate noise and ladder effect, but also highlight image details and structure information, which proves the effectiveness of this algorithm. Discover the cutting-edge algorithm for high-resolution improvement and noise elimination in infrared thermal wave detection images. Our innovative composite image enhancement technique, based on edge adaptive total variation, outperforms traditional methods. The improved directional model provides better structural information, optimizing the guidance filter process. Experimental results reveal its superiority in objective evaluation and subjective vision. This algorithm effectively eliminates noise and ladder effect while highlighting image details, validating its remarkable effectiveness. [ABSTRACT FROM AUTHOR]

Correlation clustering problem (CorCP) is a classical clustering problem, which clusters data based on the similarity of data set, and has many applications in interaction networks, cross-lingual link detection, and communication networks, etc. In this paper, we study a practical generalization of the CorCP, called the capacitated correlation clustering problem (the capacitated CorCP), by constructing a labeled complete graph. On this labeled complete graph, each vertex represents a piece of data. If two pieces of data are similar, then the edge between the corresponding vertices is marked by a positive label +. Otherwise, this edge is marked by a negative label -. The objective of the capacitated CorCP is to group some similar data sets into one cluster as far as possible, while satisfying the cluster capacity constraint. To achieve this objective, we shall partition the vertex set of the labeled complete graph into several clusters, each cluster's size subjecting to an upper bound, so as to minimize the number of disagreements. Here the number of disagreements is defined as the total number of the edges with positive labels between clusters and the edges with negative labels within clusters. Different with the previous algorithm in [18], which subjects to the constraint on the cluster size by a penalty measure, we design an algorithm for the capacitated CorCP to directly output a feasible solution by iteratively constructing clusters based on a preset threshold. Through carefully setting the threshold and sophisticatedly analyzing, our algorithm is proved to have an improved approximation ratio of 5.37. In addition, we also conduct a series of numerical experiments to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Assume that we are given a sequence F of k homogeneous polynomials in n variables of degree at most d and the ideal ℐ generated by this sequence. The aim of this paper is to present a new and effective method to determine, within the arithmetic complexity d O (n) , whether F is regular. This algorithm has been implemented in Maple and its efficiency (compared to the classical approaches for regular sequence test) is evaluated via a set of benchmark polynomials. Furthermore, we show that if F is regular then we can transform ℐ into Nœther position and at the same time compute a reduced Gröbner basis for the transformed ideal within the arithmetic complexity d O (n 2) . Finally, under the same assumption, we establish the new upper bound 2 (d k / 2) 2 n − k − 1 for the maximum degree of the elements of any reduced Gröbner basis of ℐ in the case that n > k. [ABSTRACT FROM AUTHOR]

In view of the problems of high production cost, scarcity and lack of diversity of 3D face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction algorithm with a single 2D face image as input, which only uses 2D face datasets to complete model training. First, the improved ResNet module is introduced to preprocess the input face image. The deep residual neural network has strong feature extraction and characterization ability for the image, which can provide rich high-level semantic feature maps for the subsequent subnetwork. Then, add transformer module completely based on self-attention mechanism to the parameter prediction subnetwork, which can make different parameters of the subnetwork focus on self-related feature map information and avoid interference from invalid feature map information, so as to further improve the parameter prediction accuracy of the subnetwork. Next, training, ablation and comparison experiments were conducted on CelebA, BFM and Photoface datasets, and the combined function of pixel loss function and perceptual loss function was selected as the loss function. The experimental results show that: compared with the historical optimal results of the same network structure, the scale-invariant depth error (SIDE) and mean angle deviation (MAD) are improved by 5.9% and 10.8%, respectively, which strongly proves the effectiveness of the algorithm. Finally, in order to verify the actual effect of the 3D face reconstruction algorithm, examples are selected in this paper for reconstruction. The 3D faces generated by the algorithm all have a good sense of reality, which intuitively and effectively proves the advancement of the algorithm. [ABSTRACT FROM AUTHOR]

This research paper develops an algorithm to solve a neutrosophic linear fractional optimization problem, where the cost of the objective functions, the technology coefficients, and the resources are neutrosophic numbers (triangular and trapezoidal numbers). Two defuzzification methods are used here to convert the neutrosophic linear fractional optimization problem into a multi-objective linear fractional optimization problem (MOLFOP). The fractional objective function is defuzzified using the component-wise optimization method, and the linear constraints are defuzzified using the aggregation ranking function strategy. Hence, the MOLFOP is then solved using the fuzzy goal programming method. In order to further explain our suggested algorithm, two numerical examples are solved at the end, and the outcomes obtained by our method are compared with the outcomes obtained using the other algorithm. [ABSTRACT FROM AUTHOR]

In stock trading, a common phenomenon is that the trends of stocks in the same industry are very similar. In contrast, the movements of stocks in different industries are often different. Therefore, applying the same model to all stock trading is inappropriate without distinguishing the industries in which the stocks belong. However, recommending an optimal industry stock trading model is very challenging based on performance evaluation indicators. First, the indicators of the trading model are diverse. Second, the ranking of multiple indicators is often inconsistent. In the paper, we model the problem to be solved as a multi-criteria decision-making process. Therefore, we first divide stock dataset into nine industries according to their main business. Then, we apply several machine learning algorithms as candidate models to generate trading signals. Second, we conduct daily trading backtesting based on the trading signals to obtain multiple performance evaluation indicators. Third, we propose an optimal recommendation algorithm for the industry stock trading model with TODIM. The experimental results in the US stock market and China's A-share market show that the proposed algorithm can get a better trading model out-of-sample industry stock. Moreover, we effectively evaluate the generalization ability of the algorithm based on the proposed metrics. Finally, the proposed long–short portfolios based on the algorithm have achieved returns exceeding the benchmark on most out-of-sample datasets. [ABSTRACT FROM AUTHOR]

To achieve a dynamic comprehensive risk assessment of road transport of hazardous chemicals, this paper first collected 213 road transport accidents involving hazardous chemicals that occurred between 2015 and 2020 to build a case library. Then, it established a risk assessment system (including 9 primary indicators and 28 secondary indicators) based on the frequencies of reasons for accident cases and explored the quantification methods for indicators based on their definitions. Subsequently, this paper calculated the weights of indicators based on the backpropagation neural network algorithm and the data in the case library and formulated a model for dynamic risk assessment of road transport of hazardous chemicals. The model takes into account the impact of comprehensive indicators such as drivers, vehicles, goods, roads, and the environment. The accuracy of the model calculation results is 93%. Eventually, the study verified the model using simulation data in real scenarios. The validation results show that the model could achieve a dynamic comprehensive assessment of risks in road transport of hazardous chemicals. [ABSTRACT FROM AUTHOR]

The CDT subproblem, which minimizes a quadratic function over the intersection of two ellipsoids, is a classical quadratic programming problem. In this paper, we study a method of solving CDT with a positive Lagrangian duality gap. An efficient splitting algorithm is proposed for finding the global optimal solutions. A cutting plane is firstly added to divide the feasible set of CDT into two subsets, and then two new quadratic programming problems with ellipsoidal and linear constraints are generated accordingly. Using the newly developed technique — second-order cone constraints to enhance the efficiencies of the SDP relaxation-based algorithms on the two subproblems, an optimal solution of CDT can be acquired by comparing the objective values of the two subproblems. Numerical experiments show that the new algorithm outperforms the two recent SDP relaxation-based algorithms, the two-parameter eigenvalue-based algorithm and the solver Gurobi 9.5 for certain types of CDT. [ABSTRACT FROM AUTHOR]

In computer vision, automatically identifying and locating text in images or videos is an important task. Traditional text detection methods are not effective in detecting complex scenes in irregular rectangular areas such as text bending, spacing, and special shapes, which are mainly reflected in the fragmentation of text detection areas. In this paper, a text detection method based on edge attention mechanism is proposed to better adapt to complex scenes. The proposed method takes Encoder–Decoder as the core idea. First of all, an edge attention module is designed, including global attention and local attention. The global attention module is used to perceive the features of text regions and nontext regions, while the local attention module is used to learn the information of text boundaries. Then a multi-scale feature fusion process is designed, which can strengthen the edge information and key information of text regions. Finally, the model outputs probability maps and threshold maps, and generates high-precision binary maps of text regions. After experimental verification, the proposed method on the public data set significantly reduces the fragmentation of the detection area, improves the detection accuracy of the text area, and has better robustness for text detection scenes with unconventional rectangular areas. [ABSTRACT FROM AUTHOR]

As a widely used network security defense technology, network intrusion detection has more deep learning methods used to improve the performance of intrusion detection. However, this method requires a large-scale network traffic data set for training, increasing privacy leakage risk. In this paper, a network intrusion detection algorithm based on Gaussian differential privacy federated learning (NIDS-FLGDP) is proposed. NIDS-FLGDP adopts the client–server architecture of federated learning, introduces the differential privacy of the Gaussian mechanism to ensure the security of the calculation process, uses the improved FedAvg algorithm to reduce communication overhead, and uses the improved 1D CNN to participate in collaborative training for the local model. Optimal parameters for Gaussian differential privacy and the optimal number of participating clients were determined from experiments. Model accuracy rates for binary classification and multi-classification training NIDS-FLGDP are 0.97, 0.975, 0.97 and 0.97, 0.985, 0.96, respectively, for KDD CUP99, NSL_KDD, and UNSW_NB15 network intrusion detection datasets. The results show that NIDS-FLGDP improves intrusion detection performance while protecting network traffic privacy compared with the previous methods. Its applicability and effectiveness have been fully verified, which provides a practical reference for the safe processing and analysis of a large number of diversified network traffic data in the future. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

This paper presents a modified metaheuristic algorithm named the modified Firefly algorithm. Any metaheuristic algorithm will have exploration and exploitation steps, and the goal of modification is to maintain a balance between them. The improvement relies on movement equations, alterations to the algorithm's structure by introducing a single loop, and a selection of movement equations at random. Two movement equations are included in the improved method and are randomly selected. This guarantees both regionally and globally focused solution-finding. This prevents the algorithm from getting stuck at a local minimum. Comparing the modified version to the original Firefly method, just one for loop is used, reducing the algorithm's complexity. The algorithm's performance is evaluated with 35 traditional benchmark test functions and 10 CEC2019 test functions. According to the findings, the suggested method performed optimally in 24 traditional benchmark test functions and best in the six remaining benchmark test functions. The improved algorithm produced the best outcomes in seven of the 10 CEC2019 test functions. In contrast, the Firefly algorithm produced optimal results in 18 classical benchmark test functions and the best results in 6 CEC2019 test functions. The proposed algorithm is compared with other variants of the Firefly algorithm for common test functions in the literature. The results show that the proposed algorithm outperforms other variants in most test functions. [ABSTRACT FROM AUTHOR]

The Linear Layout of Graphs problem asks, given a graph G and a positive integer k , whether G admits a layout consisting of a linear ordering of its vertices and a partition of its edges into k sets such that the edges in each set meet some special requirements. Specific linear layouts include k -Stack Layout, k -Queue Layout, k -Arch Layout, Mixed s -Stack q -Queue Layout and others. In this paper, we present a unified approach for kernelization of these linear layout problems parameterized by the vertex cover number τ of the input graph. The key point underlying our approach is to partition each set of related vertices into two distinct subsets with respect to the specific layouts, which immediately leads to some efficient reduction rules. We first apply this approach to the Mixed s -Stack q -Queue Layout problem and show that it admits a kernel of size 2 (τ) , which results in an algorithm running in time (2 2 (τ) + τ ⋅ | V |) , where | V | denotes the size of the input graph. Our work does not only confirm the existence of a fixed-parameter tractable algorithm for this problem mentioned by Bhore et al. (J. Graph Algorithms Appl. 2022), but also derives new results for the k -Stack Layout problem and for the k -Queue Layout problem respectively. We also employ this approach to the Upward k -Stack Layout problem and obtain a new result improving that presented by Bhore et al. (GD 2021). Last but not least, we use this approach to the k -Arch Layout problem and obtain a similar result. [ABSTRACT FROM AUTHOR]

In this paper, aiming at the problems of slow estimation speed and low estimation precision of traditional fractional-order system (FOS) parameter estimation method, an improved Archimedes optimization algorithm (IAOA) is proposed to calculate the optimal value. By establishing the parameter estimation model and the cost function, the parameter estimation problem is formulated as an optimization problem. As opposed to the Archimedes optimization algorithm (AOA), the IAOA introduces three improvements: leadership behavior, levy flight behavior and a new adaptive strategy. This paper verifies the performance of the IAOA by selecting 10 classic test functions. IAOA is applied to the parameter estimation problem of fractional-order unified system to verify the accuracy and feasibility of the algorithm. The simulation results prove that the IAOA has better global optimization ability and estimation accuracy than the original algorithm. [ABSTRACT FROM AUTHOR]

Let = { T 1 , T 2 , ... , T k } be a set of k ≥ 2 spanning trees in a graph G. The k spanning trees are called completely independent spanning trees (CISTs for short) if the paths joining every pair of vertices x and y in any two trees have neither vertex nor edge in common except for x and y. Particularly, is called a dual-CIST provided k = 2. For data transmission applications in reliable networks, the existence of a dual-CIST can provide a configuration of fault-tolerant routing called protection routing. This paper investigates the problem of constructing a dual-CIST in the n -dimensional hierarchical folded cubic network H F Q n . The network is a two-level network using folded hypercube F Q n as clusters to reduce the diameter, hardware overhead and improve the fault tolerance ability. We propose a recursive algorithm to construct a dual-CIST of H F Q n in (2 2 n) time for n ≥ 2 , where the time required is the same scale as the number of vertices of H F Q n . Also, the diameter of each constructed CIST is 4 n + 1. [ABSTRACT FROM AUTHOR]

In this paper, we changed the methodology for pixel value differencing. The proposed method work on RGB color images improves the existing PVD technique in terms of embedding capacity and overcomes the issue of falling off boundaries in the traditional PVD technique, and provides security to the secret message from histogram quantization attack. Color images are composed of three different color channels (red, green, and blue), so we cannot apply the traditional pixel value differencing algorithm to them. Due to that, the proposed technique divides the RGB photograph in red, blue, and green channels. Following that the modified pixel value differencing algorithm is employed to all successive pixels of color channels. We get the total embedding capacity by adding the embedding capacities of each color component. After embedding the data, we concatenate the color channels to get the stegoimage. On a series of color images, we tested our pixel value differencing approach and found that the stego-picture's visual excellence and payload capacity were reasonable. The variation in histogram between the stego and cover photographs was minor, making it resistant to histogram quantization attacks, and the suggested approach also solves the issue of falling off the boundary. [ABSTRACT FROM AUTHOR]

In the online facility assignment problem OFA (k , { c i } i = 1 k) , there exist k servers s 1 , ... , s k on a metric space where each s i has an integer capacity c i and a request arrives one-by-one. The task of an online algorithm is to irrevocably match a current request with one of the servers with vacancies before the next request arrives. As special cases for OFA (k , { c i } i = 1 k) , we consider OFA (k , { c i } i = 1 k) on a line , which is denoted by OFAL (k , { c i } i = 1 k) and OFAL eq (k , { c i } i = 1 k) , where the latter is the case of OFAL (k , { c i } i = 1 k) with equidistant servers. In this paper, we perform the competitive analysis for the above problems. As a natural generalization of the greedy algorithm grdy, we introduce a class of algorithms called MPFS (Most Preferred Free Servers) and show that any MPFS algorithm has the capacity-insensitive property, i.e., for any MPFS algorithm alg for OFA (k , { c i } i = 1 k) , if alg is c -competitive when c 1 = ⋯ = c k = 1 , then alg is c -competitive for general { c i } i = 1 k . By applying the capacity-insensitive property of the greedy algorithm grdy, we derive the matching upper and lower bounds 4 k − 5 on the competitive ratio of grdy for OFAL eq (k , { c i } i = 1 k). To investigate the capability of MPFS algorithms, we show that the competitive ratio of any MPFS algorithm alg for OFAL eq (k , { c i } i = 1 k) is at least 2 k − 1. Then, we propose a new MPFS algorithm idas (Interior Division for Adjacent Servers) for OFAL (k , { c i } i = 1 k) and show that the competitive ratio of idas for OFAL eq (k , { c i } i = 1 k) is at most 2 k − 1 , i.e., idas for OFAL eq (k , { c i } i = 1 k) is best possible in all the MPFS algorithms. We also give numerical experiments to investigate the performance of idas and grdy and show that idas performs better than grdy for distribution of request sequences with locality. [ABSTRACT FROM AUTHOR]

The sine cosine algorithm (SCA) and multi-verse optimizer (MVO) are the recognized optimization strategies frequently employed in numerous scientific areas. However, both SCA and MVO grapple with optimizing the transition between the exploitation and exploration mechanisms. Furthermore, MVO exhibits constraints in its exploitation capabilities. To tackle these limitations, this paper introduces a hybrid model termed SMVO, combining the advantages of both SCA and MVO. This hybrid approach seeks to harmonize exploitation and exploration stages by leveraging the unique advantages of each parent algorithm. The efficacy of SMVO was assessed using 23 benchmark test functions, revealing its competitive performance against not only SCA and MVO but also the ant lion optimization (ALO) and the dragonfly algorithm (DA). Additionally, SMVO's applicability was further validated by successfully addressing three distinct engineering optimization challenges, underscoring its stability and promise as a global optimization tool. [ABSTRACT FROM AUTHOR]

This paper is concerned with the ℓ 1 , ∞ -norm ball constrained multi-task learning problem, which has received extensive attention in many research areas such as machine learning, cognitive neuroscience, and signal processing. To address the challenges of solving large-scale multi-task Lasso problems, this paper develops an inexact semismooth Newton-based augmented Lagrangian (Ssnal) algorithm. When solving the inner problems in the Ssnal algorithm, the semismooth Newton (Ssn) algorithm with superlinear or even quadratic convergence is applied. Theoretically, this paper presents the global and asymptotically superlinear local convergence of the Ssnal algorithm under standard conditions. Computationally, we derive an efficient procedure to construct the generalized Jacobian of the projector onto ℓ 1 , ∞ -norm ball, which is an important component of the Ssnal algorithm, making the computational cost in the Ssn algorithm very cheap. Comprehensive numerical experiments on the multi-task Lasso problems demonstrate that the Ssnal algorithm is more efficient and robust than several existing state-of-the-art first-order algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel nonconvex and nonsmooth optimization model for low-light or inhomogeneous image segmentation which is a hybrid of Mumford–Shah energy functional and Retinex theory. The given image is decomposed into the reflectance component and the illumination component by solving Retinex-based Mumford–Shah model with L 1 − L 2 regularizer. Indeed, the existence of the L 1 − L 2 regularizer means the nonsmooth term in the model is nonconvex. Thus, it is difficult to solve the proposed model directly. An alternating direction method of multipliers (ADMM) algorithm is developed to solve the proposed nonconvex and nonsmooth model. We apply a novel splitting technique in our algorithm to ensure that all subproblems admit closed-form solutions. Theoretically, we prove that the sequence generated by our proposed algorithm converges to a stationary point under mild conditions. Next, once the reflectance is obtained, the K -means clustering method is utilized to complete the segmentation. We compare the proposed Retinex-based method with other state-of-the-art segmentation methods under special lighting conditions. Experimental results show that the proposed method has better performance for both gray-scale images and color images efficiently in terms of the quantitative and qualitative results. [ABSTRACT FROM AUTHOR]

Noise radar waveform, as an important waveform in the field of low-probability-of-intercept radars, has been widely studied. However, the general noise waveform has a high peak average power ratio (PAPR), which leads to a reduction of signal-to-noise ratio. Thus, it is necessary to control the noise waveform PAPR to a suitable range. In this paper, the PAPR value reduction problem of low sidelobe noise waveform is studied. First, the proposed new algorithm is given a general overview. Then, the Combined Spectral Shaping and Peak-to-Average Power Ratio Reduction (COSPAR) algorithm is adjusted according to the alternate projection method and phase retrieval algorithm, and an improved COSPAR (ICOSPAR) algorithm with a faster execution speed and higher waveform generation efficiency is derived. The specific method performs waveform projection between the spectral constraint domain and the PAPR constraint domain and then evaluates the computational load of the ICOSPAR algorithm. Simulation results show that the ICOSPAR algorithm is reliable and efficient in generating low PAPRs and low sidelobe noise waveforms. [ABSTRACT FROM AUTHOR]

A charge and discharge management scheme is proposed. The stored electric energy in distributed storage devices will converge to the consistent. The consistency of the stored electric energy of the devices helps to maintain the maximum load capacity and maximum consumption capacity of distributed storage devices. The charging and discharging process is constructed as a time-varying optimization problem, and the proposed algorithm can respond to the time-varying parameters of the distributed storage devices in real time. The time-varying neurodynamic algorithms can obtain time-varying optimal solution trajectories to give the optimal charging and discharging strategy in real time. In addition, the proposed approach in this paper focuses on the privacy protection of device data. Each device can calculate the power of discharging or charging by communicating with the partially connected nodes. Numerical simulations of the proposed scheme in the paper are given to verify the effectiveness of the scheme. Numerical simulations show that our scheme can make the electric energy stored in each storage device converge and maintain the maximum load capacity or maximum consumption capacity of the whole distributed storage device. [ABSTRACT FROM AUTHOR]

The rapid growth of contents on the Web in different languages increases the demand of Cross-Lingual Information Retrieval (CLIR). The accuracy of result suffers due to many problems such as ambiguity and drift issue in query. Query Expansion (QE) offers reliable solution for obtaining suitable documents for user queries. In this paper, we proposed an architecture for Hindi–English CLIR system using QE for improving the relevancy of retrieved results. In this architecture, for the addition of term(s) at appropriate position(s), we proposed a location-based algorithm to resolve the drift query issue in QE. User queries in Hindi language have been translated into document language (i.e. English) and the accuracy of translation is improved using Back-Translation. Google search has been performed and the retrieved documents are ranked using Okapi BM25 to arrange the documents in the order of decreasing relevancy to select the most suitable terms for QE. We used term selection value (TSV) for QE and for retrieving the terms, we created three test collections namely the (i) description and narration of the Forum for Information Retrieval Evaluation (FIRE) dataset, (ii) Snippets of retrieved documents against each query and (iii) Nearest-Neighborhood (NN) words against each query word among the ranked documents. To evaluate the system, 50 queries of Hindi language are selected from the FIRE-2012 dataset. In this paper, we performed two experiments: (i) impact of the proposed location-based algorithm on the proposed architecture of CLIR; and (ii) analysis of QE using three datasets, i.e. FIRE, NN and Snippets. In the first case, result shows that the relevancy of Hindi–English CLIR is improved by performing QE using the location-based algorithm and a 12% of improvement is achieved as compared to the results of QE obtained without applying the location-based algorithm. In the second case, the location-based algorithm is applied on three datasets. The Mean Average Precision (MAP) values of retrieved documents after QE are 0.5379 (NN), 0.6018 (FIRE) and 0.6406 (Snippets) for the three test collections, whereas the MAP before QE is 0.37102. This clearly shows the significant improvement of retrieved results for all three test collections. Among the three test collections, QE has been found most effective along with Snippets as indicated by the results with the improvements of 6.48% and 19.12% over FIRE and NN test collections, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we study mean–variance–Conditional Value-at-Risk (CVaR) portfolio optimization problem with short selling, cardinality constraint and transaction costs. To tackle its mixed-integer quadratic optimization model for large number of scenarios, we take advantage of the penalty decomposition method (PDM). It needs solving a quadratic optimization problem and a mixed-integer linear program at each iteration, where the later one has explicit optimal solution. The convergence of PDM to a partial minimum of original problem is proved. Finally, numerical experiments using the S&P index for 2020 are conducted to evaluate efficiency of the proposed algorithm in terms of return, variance and CVaR gaps and CPU times. [ABSTRACT FROM AUTHOR]

In this paper, a modified eigensystem realization algorithm for modal parameter identification and relaxation parameter is extended from a viscous damping system to a nonviscous damping system. The equation of motion with nonviscous damping in second-order form is expressed in the state space formulation. The eigenvalues of the second-order form equation are proved equal to the state space formulation. The extended method is based on the proposed nonlinear relationship between the eigenvalues and the damping parameters of a nonviscous damping system. The identified procedure of the proposed method is given in detail. Finally, the method is validated using three numerical examples for assessing its accuracy. [ABSTRACT FROM AUTHOR]

Multi-Objective Travelling Salesman Problem (MOTSP) is one of the most crucial problems in realistic scenarios, and it is difficult to solve by classical methods. However, it can be solved by evolutionary methods. This paper investigates the Constrained Multi-Objective Travelling Salesman Problem (CMOTSP) and the Constrained Multi-Objective Solid Travelling Salesman Problem (CMOSTSP) under an uncertain environment with zigzag uncertain variables. To solve CMOTSP and CMOSTSP models under uncertain environment, the expected value and optimistic value models are developed using two different ranking criteria of uncertainty theory. The models are transformed to their deterministic forms using the fundamentals of uncertainty. The Models are solved using two solution methodologies Aspiration level-based Multi-Objective Quasi Oppositional Jaya Algorithm (AL-based MOQO Jaya) and Fuzzy Programming Technique (FPT) with linear membership function. Further, the numerical illustration is solved using both methodologies to demonstrate its application. The sensitivity of the OVM model's objective functions regarding confidence levels is also investigated to look at the variation in the objective function. The paper concludes that the developed approach has solved CMOTSP and CMOSTSP efficiently with an effective output and provides alternative solutions for decision-making to DM. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive robust control scheme for accuracy enhancement of eye-to-hand photogrammetry-based industrial robots subject to uncertainties. The proposed method uses two control loops: internal and external loops. The former is the dynamic controller designed for controlling the robot's joints. The external loop is the kinematic controller to minimize the error of the end-effector detected by the photogrammetry sensor. An adaptive integral terminal fractional-order super-twisting algorithm (AITFOSTA) is developed and employed for both control loops. AITFOSTA is an integral sliding-mode controller (ISMC) whose nominal control law is terminal. Its switching part is replaced with a fractional-order super-twisting algorithm (FOSTA), reducing the chattering to a great extent while rejecting the uncertainties. Additionally, an adaptive uncertainty and disturbance estimator based on radial basis function neural networks (RBFNNs) is designed and employed to reduce the uncertainty bounds, contributing to further chattering reduction. The stability analysis of the proposed controller is also presented. Simulation and experimental results show the superiority of the proposed method over other well-known approaches by reaching an unprecedented tracking accuracy, i.e. 0.06 mm and 0.18 ∘ for position and orientation, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we define the adjacency matrix of a semigraph. We give the conditions for a matrix to be semigraphical and give an algorithm to construct a semigraph from the semigraphical matrices. We derive lower and upper bounds for largest eigenvalues. We study the eigenvalues of adjacency matrix of two types of star semigraphs. [ABSTRACT FROM AUTHOR]

In this paper, we present an efficient algorithm, namely Orienting and Final Circles (OFC)-Delaunay Triangulation, for constructing the Delaunay triangulation of a finite planar point set using the idea of the Method of Orienting Curves (introduced by Phu in Zur Lösung einer regulären Aufgabenklasse der optimalen Steuerung im Groen mittels Orientierungskurven, Optimization 18(1) (1987) 65–81 and Ein konstruktives Lösungsverfahren für das Problem des Inpolygons kleinsten Umfangs von J. Steiner, Optimization 18(3) (1987) 349–359). The concepts of orienting and final circles are introduced. The Delaunay edges are determined by orienting circles and a final circle. Our algorithm has O (n log n) worst-case running time. The algorithm is implemented in Python and some numerical examples are presented. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a set of algorithms for defect identification in any crystal system based on structural data from molecular dynamics simulations. The set, named FEDIS, consists of two algorithms: the extended centrosymmetric parameter (E-CSP) method and the fast neighbor distance analysis (F-NDA) method. The E-CSP extends the Central Symmetric Parameter (CSP) method for centrally symmetric materials by introducing a compensation term for asymmetric crystal that adapts to all crystal systems. The F-NDA modifies the Nearest Neighbor Analysis (NDA) method by replacing vector computation with scalar computation. The developed algorithms are validated through several cases that demonstrate their effectiveness and efficiency in detecting various types of defects. The algorithms are implemented in C++ and integrated into 3D interactive interface software that can be downloaded on GitHub. [ABSTRACT FROM AUTHOR]

[M. Ma, The connectivity of exchanged hypercubes, Discrete Math., Algorithms Appl. 2(2) (2010) 213–220.] proved that the vertex connectivity and the edge connectivity of the exchanged hypercube are each equal to the minimum degree of the graph. This paper presents a proof of that result, which is substantially simpler and more intuitive. [ABSTRACT FROM AUTHOR]

The Averaged One-Dependence Estimators (AODE) algorithm is an improvement of the naive Bayes algorithm, which allows all the attributes dependent on one common attribute, called parent attribute, thus forming One-Dependence Estimators (ODE). The classification probability is estimated by averaging the conditional probability of the ODE. When there is a dependency relationship between attributes, the AODE algorithm can better capture these relationships, thus improving classification performance. The AODE algorithm treats the parent and child attribute values equally in different ODEs. However, the parent attribute value and the child attribute value in different ODEs have different importance for classification. In this paper, two attribute value weighted AODE based on Kullback–Leibler divergence are proposed, one is parent attribute value weighted AODE, and the other is child attribute value weighted AODE. Comparative experiments were carried out with 30 datasets in the UCI database, and the experiments indicate that the performance of the parent attribute value weighted AODE algorithm with Kullback–Leibler divergence is significantly better than the original AODE algorithm, and its performance is also better than the mutual information weighted AODE algorithm. [ABSTRACT FROM AUTHOR]

This paper tackles the intricate challenge of autonomous navigation for Unmanned Aerial Vehicles (UAVs) through dynamically changing environments. We focus on a sophisticated Deep Reinforcement Learning (DRL) approach using the Soft Actor-Critic (SAC) algorithm, optimized for UAV path planning within a continuous action space. This methodology leverages environmental image data to enhance the precision of flight maneuvers and effective obstacle avoidance. Our approach, validated through extensive simulations in Gazebo and field tests, demonstrates the algorithm’s efficacy in enabling UAVs to adeptly navigate obstacles using depth maps. The study further explores the robustness of the SAC algorithm by comparing it with traditional DRL methods, emphasizing its superior performance in real-world applications. This research contributes significantly to advancing UAV technology, particularly in autonomous motion planning, by integrating cutting-edge machine learning techniques. The video link is: https://www.youtube.com/watch?v=Nd_aMzejNXY. [ABSTRACT FROM AUTHOR]

Underwater bubble plume images contain a wealth of information on wave field and flow characteristics, which can provide valuable research data for marine development, environmental protection, and underwater surveys. However, based on fusing image features and wave field environment features, identifying accurately the underwater bubble plume is still very difficult. In order to improve the accuracy and robustness of bubble plume identification in complex underwater environments, an underwater bubble plume recognition algorithm based on multi-feature fusion understanding is proposed. In this paper, a weight-independent dual-channel residual convolutional neural network (CNN) for feature extraction of the original optical images and the nonsubsampled contourlet transform (NSCT) low-frequency images, and the multi-scale composite feature map groups are generated. Then adaptive fusion is performed based on the feature contribution of the target in different types of images. Next, logical region of interest (ROI) masks are generated by the attention mechanism and superimposed on the fused image to further highlight the target features. Finally, the multi-scale dual-channel fused feature maps containing ROI masks are used for underwater bubble plume target recognition. The experimental results show that the designed recognition network can effectively fuse the features of the original optical images and the NSCT low-frequency imagers, improve the depth of information fusion, and retain the target texture features and the morphological features while reducing the interference of the background information, and have good recognition accuracy and robustness for multi-scale bubble targets in the underwater environment. [ABSTRACT FROM AUTHOR]

Printed circuit boards (PCBs) play a critical role in electronic products. Ensuring these products' long-term reliability and consistent performance requires effective PCB defect detection. Although existing deep learning models for PCB defect detection are not highly accurate, they often neglect capability considerations. This paper introduces a precise, fast, and lightweight defect detection model, CCG-YOLO, based on an enhanced YOLOv5 model to address this issue. The enhancements in CCG-YOLO can be summarized as follows: (1) Improved Backbone network: The feature extraction ability of the Backbone network is enhanced by introducing a C3HB module, which fosters spatial interaction capabilities. (2) Lightweight feature fusion network: A lightweight convolution structure called Ghost-Shuffle Convolution is incorporated in the feature fusion network, remarkably reducing model parameters while maintaining performance. (3) Efficient residual networking: To enhance model performance further, a CNeB module is introduced based on the ConvNeXt network, which replaces the C3 module in the Neck. CNeB improves model detection accuracy and reduces the number of model parameters. The combination of these enhancements results in impressive performance. CCG-YOLO achieves mean average precision (mAP@0.5) of 99.5% and 88.75% in mAP@0.5:0.95 on the TDD-Net public dataset. Compared with the original YOLOv5s algorithm, CCG-YOLO offers a 4.24% improvement in mAP@0.5:0.95, a 1 MB reduction in model size, a 0.472 M decrease in the number of parameters, a 0.6G floating point operation reduction in computational complexity, and a 120 frames per second real-time inference speed. These experimental results underscore that the proposed model excels in accuracy and speed and has a compact size for PCB defect detection. Moreover, CCG-YOLO is easily deployable on low-end devices, making it well-suited for meeting the real-time requirements of industrial defect detection. [ABSTRACT FROM AUTHOR]

The present prevailing loop closure detection algorithm is mainly applicable for simultaneous localization and mapping (SLAM). Its effectiveness is contingent upon environmental conditions, which can fluctuate due to variations in lighting or the surrounding scenario. Vision-based algorithms, while adept during daylight hours, may falter in nocturnal settings. Conversely, lidar methods hinge on the sparsity of the given scenario. This paper proposes an algorithm that comprehensively utilizes lidar and image features to assign weighted factors for loop closure detection based on multi-modal sensor fusion. First, we use k -means clustering to produce a point cloud spatial global bag of words. Second, an improved deep learning method is used to train feature descriptors of images while scan context is also used to detect candidate point cloud features. After that, different feature-weighted factors are assigned for homologous feature descriptors. Finally, the detection result related to the maximum weight factor is designated to the optimal loop closure. The adaptive weighted loop closure (AWLC) algorithm we proposed inherits the advantages of different loop closure detection algorithms and hence it is accurate and robust. The AWLC method is compared with popular loop detection algorithms in different datasets. Experiments show that the AWLC can maintain the effectiveness and robustness of detection even at night or in highly dynamic complex environment. [ABSTRACT FROM AUTHOR]

In addition to Power Quality (PQ) problems caused by solid state devices in the distribution side of grid connected Photo Voltaic (PV) system, quality of power also suffers in the source side by the usage of converters. Generally, PQ problems mitigation strategy has turned into a critical issue in grid-connected PV systems. This paper proposes a control topology for PQ issues mitigation scheme in grid connected PV systems. The primary goal of this study is to enhance the PQ in power distribution system through eradicating the voltage interruption, voltage swell and sag as well as voltage fluctuation. To accomplish these changes, Unified Power Quality Conditioners (UPQCs) with controllers were utilized. Then the faults are mitigated by the utilization of UPQC with Tree Seeds Algorithm (TSA) — Adaptive Neuro-Fuzzy Interference System (ANFIS). At first, the normal properties of the grid-connected PV systems are dissected. From that point forward, a few interruptions are made in the system and the abnormal characteristics are analyzed. Then the abnormal characteristics of PQ problems are diminished in the utilization of UPQC and TSA–ANFIS controllers. UPQC is one of the best filters topologies which can mitigate and control all PQ problems. The combined algorithms of TSA-ANFIS are used to keep the dc-link voltage in UPQC at consistent level. The proposed hybrid approach is executed in MATLAB/Simulink platform and the performance is studied. Further, the proposed method is compared with existing Elephant Herding Optimization (EHO) technique and also with hybrid EHO-ANFIS techniques. The comparison results showed that the proposed technique is superior to the EHO and EHO-ANFIS techniques. [ABSTRACT FROM AUTHOR]

This paper focuses on developing and analyzing a new AR-iterative scheme designed for estimating fixed points of generalized non-expansive mappings of the Suzuki kind. The scheme’s stability and convergence properties are rigorously proven, highlighting its superior convergence rate for non-expansive mappings when compared to existing schemes in the literature. Additionally, an application of the AR-iterative scheme to differential equations is presented. These findings underscore the effectiveness and versatility of the proposed scheme in numerical analysis and computational mathematics. [ABSTRACT FROM AUTHOR]

Being inspired by the biological eye, event camera is a novel asynchronous technology that poses a paradigm shift in acquisition of visual information. This paradigm enables event cameras to capture pixel-size fast motions much more naturally compared to classical cameras.In this paper, we present a new asynchronous event-driven algorithm for detection of high-frequency pixel-size periodic signals using an event camera. Development of such new algorithms to efficiently process the asynchronous information of event cameras is essential to utilize its special properties and potential, and being a main challenge in the research community.It turns out that this algorithm, which was developed in order to satisfy the new paradigm, is related to an untreated theoretical problem in probability: Let 0 ≤ τ1 ≤ τ2 ≤⋯ ≤ τm ≤ 1 originated from an unknown distribution. Let also 휖,δ ∈ ℝ, and d ∈ ℕ. What can be said about the probability Φ(m,d) of having more than d adjacent τi-s pairs that the distance between them is δ, up to an error 휖? This problem, which reminds the area of order statistic, shows how the new visualization paradigm is also an opportunity to develop new areas and problems in mathematics. [ABSTRACT FROM AUTHOR]

In this paper, we present a new encoding and decoding method based on the recently introduced Minesweeper Model. Here, the main idea is to use Fibonacci–Lucas tree diagrams, and this proposed method uses a public key and a private key. To test this method, a new algorithm is constructed to enable a faster and more accurate control. [ABSTRACT FROM AUTHOR]

In recent years, network science has received significant attention and application. As an important mesoscale structure of networks, community structure reveals the inherent modular structure and potential functionality of networks. Therefore, maintaining the community structure during attacks is crucial. Existing studies on community robustness primarily focus on two attack behaviors: malicious attacks and random failures. However, little is known on the community robustness in the more practical situations where the attackers have limited ability to access precise network information, leading to gray information attack behaviors. Hence, in this paper, we investigate the robustness of network communities under gray information attacks and the enhancing algorithms. Following the community robustness measure for the sequential attacks, we establish a unified evaluation framework for attacks with gray information by introducing a gray attack coefficient, which treats the usual random failures and malicious attacks as the two special situations in the proposed framework. Several enhancing algorithms with local search strategy are exquisitely devised. The efficacy of our framework and algorithms which significantly improve the community robustness against gray attacks is demonstrated by the experimental results on a variety of real-world networks. Our findings have important implications not only in enhancing the community robustness of existing networks but also in designing robust ones from the scratch, which paves the way to further understand and seek strategies and solutions for network community robustness with gray information. [ABSTRACT FROM AUTHOR]

Many real-world applications arising from social networks, personalized recommendations, and others, require extracting a relatively small but broadly representative portion of massive data sets. Such problems can often be formulated as maximizing a monotone set function with cardinality constraints. In this paper, we consider a streaming model where elements arrive quickly over time, and create an effective, and low-memory algorithm. First, we provide the first single-pass linear-time algorithm, which is a a deterministic algorithm, achieves an approximation ratio of [ γ 4 a (1 + γ + γ 2 + γ 3) − ] for any ≥ 0 with a query complexity of ⌈ n / a ⌉ + a and a memory complexity of O (a k log (k) log (1 /)) , where a is a positive integer and γ is the submodularity ratio. However, the algorithm may produce less-than-ideal results. Our next result is to describe a multi-streaming algorithm, which is the first deterministic algorithm to attain an approximation ratio of 1 − e − γ − with linear query complexity. [ABSTRACT FROM AUTHOR]

This paper examines the stability of the à trous algorithm under arbitrary iteration in the context of a more general study of shift-invariant filter banks. The main results describe sufficient conditions on the associated filters under which an infinitely iterated shift-invariant filter bank is stable. Moreover, it is shown that the stability of an infinitely iterated shift-invariant filter bank guarantees that of any associated finitely iterated shift-invariant filter bank, with uniform bounds. The reverse implication is shown to hold under an additional assumption on the low-pass filter. Finally, it is also shown that the separable product of stable one-dimensional shift-invariant filter banks produces a stable two-dimensional shift-invariant filter bank. [ABSTRACT FROM AUTHOR]

In this paper, we consider integrating the scalar auxiliary variable time discretization with the virtual element method spatial discretization to obtain energy-stable schemes for Allen–Cahn-type gradient flow problems. In order to optimize CPU time during calculations, we propose two step-by-step solving SAV algorithms by introducing a novel auxiliary variable to replace the original one. Then, linear, decoupled, and unconditionally energy-stable numerical schemes are constructed. However, due to truncation errors, the auxiliary variable is not equivalent to the continuous case in the original definition. Therefore, we propose a novel relaxation technique to preserve the original energy dissipation rule. It not only retains all the advantages of the above algorithms but also improves accuracy and consistency. Finally, a series of numerical experiments are conducted to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

For the purposes of our analysis, we shall work with the Steenrod algebra 풜 over the field of two elements, 픽2. Our paper focuses on expanding the outcomes from our preceding study, which serves as Part I. Precisely, we will initially compute the dimension of the “cohit” space 픽2 ⊗풜Ps, where Ps is defined as the graded polynomial ring 픽2[x1,x2,…,xs :,deg(xi) = 1,i = 1, 2,…,s], for the specific case of s = 5. This computation will be carried out explicitly for the generic degree 21 ⋅ 2t − 5, where t is a positive integer. We have developed a novel algorithm implemented in SageMath to compute the case where t = 2. We subsequently study the dimension of 픽2 ⊗풜Ps in degrees s + 5 for 8 ≤ s ≤ 9. One of our findings is the correction of some erroneous results in the previous work by Moetele and Mothebe. We, additionally, give an explicit formula for the dimension of 픽2 ⊗풜Ps in degree fourteen for all s > 0 and in degree fifteen for all s > 0,s≠10. We also describe a computational method implemented in SageMath to explicitly account for the number of spikes of degree 2s−1 − s in the 풜-module Ps, applicable for all positive integers s > 1. In application, we examine W. Singer’s conjecture on the algebraic transfer of rank 5 in degrees 21 ⋅ 2t − 5 for all t ≥ 0 and of ranks s > 0 in internal degrees d ranging from 13 to 15. Considering higher homological degrees, we analyze the behavior of the algebraic transfer of rank 7 in the generic degrees 23 ⋅ 2t − 7 for t = 0 and ℓ ⋅ 2t − 7 for ℓ ∈{9, 16},t ≤ 3. We then demonstrate that the (in)decomposable elements Pc0 ∈Ext풜7,23⋅20(픽2, 픽2), k0 = k ∈Ext풜7,9⋅22(픽2, 픽2), h6D2 ∈Ext풜7,27(픽2, 픽2), Q2(0) ∈Ext풜7,26(픽2, 픽2) and Q2(1) ∈Ext풜7,27(픽2, 픽2) are not in the image of the Singer transfer. [ABSTRACT FROM AUTHOR]

Let G = (V,E) be a simple graph with no isolated vertices. A dominating set S of G is said to be a cosecure dominating set of G, if for every vertex v ∈ S there exists a vertex u ∈ V ∖S such that uv ∈ E and (S∖{v}) ∪{u} is a dominating set of G. The Minimum Cosecure Domination problem is to find a minimum cardinality cosecure dominating set of G. In this paper, we show that the decision version of the problem is NP-complete for split graphs, undirected path graphs (subclasses of chordal graphs), and circle graphs. We also present a linear-time algorithm to compute the cosecure domination number of cographs (subclass of circle graphs). In addition, we present a few results on the approximation aspects of the problem. [ABSTRACT FROM AUTHOR]

As an important research topic in social network analysis, the influence maximization problem is aimed at identifying a set of influential nodes from a social network that can produce the maximum propagation cascade effect. Developing flattering policies that can balance both the solution quality and computational efficiency is one of the key concerns on this problem. To address the deficiency of the classical static memetic algorithms that only consider the quality of neighboring nodes but lack the adaptability to different network topologies, this paper proposes a hybrid dynamic memetic algorithm (HDMA) for the influence maximization problem. According to the proposed framework, each node in the network is first assigned a label to indicate its importance. Based on the local influence, the potential influential nodes are added to the candidate set by ranking the importance, which is adopted to improve the quality of the initial population and prune the search space. An adaptive roulette taboo strategy by combining the stochastic local searching method, greedy mechanism with the centrality metrics is constructed to enhance the adaptability of the algorithm as well as its accuracy. The results prove that the HDMA can adapt well to different network structures. Higher accuracy solutions are obtained in faster running time, which verifies the satisfying performance of the algorithm in balancing global search and local exploitation. [ABSTRACT FROM AUTHOR]

In recent years, there has been significant research directed toward the maximization of k-submodular functions, owing to their wide-ranging applications in fields such as social networks, sensor placement, and various other domains. The utilization of the greedy method and randomized techniques is prevalent for the design of algorithms that offer favorable performance guarantees for such problems. However, these approaches adopt a single-item selection strategy at each iteration, leading to high query complexity. The Threshold-Decreasing Algorithm is conceptualized to address this issue by choosing multiple items using a predefined threshold value, and in case no items remain, the threshold is decremented by a specified factor. This approach effectively reduces complexity. In this paper, we merge the principles of the threshold-decreasing and greedy methods to enhance the complexity of constrained k-submodular maximization problems, thereby achieving near-optimal approximation ratios. [ABSTRACT FROM AUTHOR]

In this paper, we have applied a new method called Laplace Residual Power Series Method (LRPSM) introduced by Eriqate et al. for solving the fuzzy non-homogeneous wave equation. The algorithm gains powerful results for this kind of problem. Initially, we analyze in general the Laplace residual power series technique, and then we expand it and use it to solve the fuzzy non-homogeneous wave equation with the extended Hukuhara differentiability. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable image confusion scheme, which uses Linear Congruential Generators (LCGs)-based Pseudorandom Bits Generator (PRBG). The PRBG is based on the variable input-coupled LCG with a reconfigurable clock divider. The proposed algorithm encrypts the input image up to four times successively using different random sequences in every attempt. This new scheme aims to efficiently extract statistically strong pseudorandom sequences from a proposed PRBG with a large keyspace and simultaneously increase the security level of the encrypted image. This PRBG was initially designed on Virtex-5 (XC5VLX110T), Virtex-7 (XC7VX330T) and Artix-7 (XC7A100T) Field Programmable Gate Arrays (FPGAs). The statistical properties of the proposed PRBG for four different configurations are verified by the National Institute of Standards and Technology (NIST) tests. Thereafter, a reconfigurable encryption/decryption algorithm that uses the proposed PRBG is developed for secure image encryption. The encryption process was accomplished using the MATLAB tool after obtaining the PRBG keys from the FPGA. To show the quality and strength of the encryption process, security analysis [correlations and Number of Pixels Change Rate (NPCR)] is performed. Security analysis results are compared with the conventional encryption algorithm to show that the developed reconfigurable encryption scheme provides better results in security tests. [ABSTRACT FROM AUTHOR]