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This study introduces a non-convex fractional-order hyper-Laplacian variational model for Gaussian noise removal. It employs first the primal-dual hybrid gradient algorithm to solve problems involving overlapping group sparse structures. Additionally, this paper designs a new algorithm leveraging the framework of the Chambolle-Pock algorithm with convergence and aims to recover high-quality images. The model, integrating the overlapping group sparse structure of the hyper-Laplacian prior with the non-convex fractional-order total variation, exhibits superior performance in reducing the staircase effect and maintaining sharp edge contours. To further improve the performance of the algorithm, a semi-adaptive p (x) non-convex penalty weight assignment mechanism is designed by introducing the structure tensor, which according to the characteristics of each region of the image and the noise level. The effectiveness and superiority of the proposed algorithm in image denoising with simulation experiments and comparative analyses are fully verified. • A new non-convex fractional-order hyper-Laplace variation model is proposed. • The PDHG algorithm is introduced to solve optimization problems involving OGS-HL for the first time. • The convergence and complexity of the new design algorithm are analyzed. • A semi-adaptive p (x) determined by the structure of the image and the noise level is designed. [ABSTRACT FROM AUTHOR]

This paper is concerned with a variant of the traveling salesman problem (TSP) called the maximum scatter traveling salesman problem (MSTSP). The goal of MSTSP is to find a Hamiltonian cycle that maximizes the minimum length edge of the cycle. This problem has real-world applications in domains such as manufacturing and medical imaging. Both symmetric and asymmetric versions of the problem are considered in this paper. To address this problem, we have developed two evolutionary approaches. Our first approach is based on a genetic algorithm, whereas the second approach is based on a differential evolution algorithm. Initial solution generation procedure, variation operators (crossover and mutation) used in our approaches are designed considering the characteristics of this problem. The solutions obtained through variation operators are improved further through a local search that is specially adapted for MSTSP. For benchmarking, we have compared the performance of our proposed approaches with the state-of-the-art approaches available in the literature. Our approaches obtained better quality solutions in a shorter time for both symmetric and asymmetric versions of the problem, thereby clearly demonstrating their effectiveness in solving MSTSP. • This paper is concerned with the maximum scatter traveling salesman problem (MSTSP). • Two evolutionary approaches are proposed for solving this problem. • First approach is a steady-state genetic algorithm, while the latter is a discrete differential evolution algorithm. • Various components of our approaches are designed considering the characteristics of MSTSP. • Computational results show the effectiveness of our proposed approaches. [ABSTRACT FROM AUTHOR]

This paper considers the offline assignment of fixed priorities in partitioned preemptive real-time systems where tasks have precedence constraints. This problem is crucial in this type of systems, as having a good fixed priority assignment allows for an efficient use of the processing resources while meeting all the deadlines. In the literature, we can find several proposals to solve this problem, which offer varying trade-offs between the quality of their results and their computational complexities. In this paper , we propose a new approach, leveraging existing algorithms that are widely exploited in the field of Machine Learning: Gradient Descent, the Adam Optimizer, and Gradient Noise. We show how to adapt these algorithms to the problem of fixed priority assignment in conjunction with existing worst-case response time analyses. We demonstrate the performance of our proposal on synthetic task-sets with different sizes. This evaluation shows that our proposal is able to find more schedulable solutions than previous heuristics, approximating optimal but intractable algorithms such as MILP or brute-force, while requiring reasonable execution times. [ABSTRACT FROM AUTHOR]

In this paper, a novel spectral method is presented and studied for the fourth order problem with mixed boundary in a cylindrical domain. The basic idea of our approach is to reduce the original problem into a series of decoupled two-dimensional fourth-order problems first, by using the cylindrical coordinate transformation and Fourier expansion, and then adopt the standard spectral method to solve the decoupled problems. A new essential pole condition is proposed to overcome the difficulty caused by the introduction of singularity and variable coefficients in cylindrical coordinate transformation. Existence and uniqueness of the weak solution and the discrete numerical solution are proved, and error estimates of the spectral method are derived. Furthermore, the efficient implementation of our algorithm is discussed, where a set of effective basis functions are constructed to ensure the sparsity of the mass matrix and stiffness matrix. Numerical examples are presented to validate the theoretical findings and the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

In recent years, the research of indoor positioning technology has mainly focused on WI-FI, Bluetooth, ultra-wideband, and pseudo-satellites. Each of these indoor positioning technologies has different advantages, but also has various limitations, especially in indoor areas such as corridors, tunnels, and stairs. Due to the limitations of application scenarios, navigation and positioning accuracy and efficiency, it is difficult to form a satisfactory navigation and positioning solution for indoor special environment. In order to solve the positioning problem of indoor long and narrow areas, a multi-source fusion navigation and positioning algorithm based on pseudo-satellite assistance is proposed in this paper. Under certain map information constraints, it can use the direct arrival signal of a single pseudo-satellite to correct the location information output by mobile phone PDR(Pedestrian Dead Reckoning), and improve the navigation and positioning performance of indoor passageway and corridor areas. To realize the system, it is necessary to ensure that a direct pseudo-satellite signal can be received in any area of the corridor. The position of the pseudo-satellite can be determined according to the captured pseudo-satellite signal, and the pseudo-range change rate can be estimated by calculating the carrier phase change rate. In this paper, we deduce the relationship between the pseudo-range change rate and the change of the mobile phone PDR position, and build an optimization model based on map information constraints, which improves the positioning accuracy of the system for indoor narrow and long areas. A large number of experimental results show that the system has high positioning accuracy in the real indoor long and narrow environment. The average positioning accuracy of the algorithm in indoor environments is better than 0.4 m, and there is no significant change in the performance after a long time of cumulative positioning. [ABSTRACT FROM AUTHOR]

• The current 3D human body reconstruction technology requires a large number of 3D scanning equipment and a complex dynamic capture process, which significantly limits its rapid adoption in these application domains. The purpose of this article is to simplify the process of 3D reconstruction. • By combining deep learning methods, an end-to-end 3D human body reconstruction framework is proposed to achieve the goal of reconstructing a high-precision 3D human body model based on RGB images. [Display omitted] In recent years, with the improvement of artificial intelligence technology, it has become possible to reconstruct high-precision 3D human body models based on ordinary RGB images. The current 3D human body reconstruction technology requires complex external equipment to scan all angles of the human body, which is complicated to be implemented and cannot be popularized. In order to solve this problem, this paper applies deep learning models on reconstructing 3D human body based on monocular images. First of all, this paper uses Stacked Hourglass network to perform convolution operations on monocular images collected from different views. Then Multi-Layer Perceptrons (MLPs) are used to decode the encoded high-level images. The feature codes in the two views(main and side) are fused, and the interior and exterior points are classified by the fusion features, so as to obtain the corresponding 3D occupancy field. At last, the Marching Cube algorithm is used for 3D reconstruction with a specific threshold and then we use Laplace smoothing algorithm to remove artifacts. This paper proposes a dense sampling strategy based on the important joint points of the human body, which has a certain optimization effect on the realization of high-precision 3D reconstruction. The performance of the proposed scheme has been validated on the open source datasets, MGN dataset and the THuman dataset, provided by Tsinghua University. The proposed scheme can reconstruct features such as clothing folds, color textures, and facial details,and has great potential to be applied in different applications. [ABSTRACT FROM AUTHOR]

The paper considers the disadvantages of existing approaches to testing software and hardware data security tools in order to confirm compliance of the implemented functionality with the declared characteristics. It demonstrates the necessity of ensuring completeness and optimality of testing. The paper describes some testing approaches based on the development of mathematical models using the automata theory and ensuring completeness of testing, but leaving the question of its optimality open. It describes the approach proposed earlier by the authors, which ensures both completeness and optimality of testing using the graph theory. In accordance with this approach, the software and hardware data security tool is represented as a directed graph without loops or multiple edges. The graph vertices correspond to the states of the software or hardware component, and the directed edges correspond to the transitions of the data security tool from one state to another when performing non-target functions or security functions. On the basis of this approach, the authors propose an algorithm for solving the problem of testing software and hardware data security tools using a number of well-known algorithms on graphs. In the paper, it is substantiated that a solution to the problem of ensuring completeness and optimality of testing exists if and only if any vertex of the derived graph obtained by removing all unused vertices and edges either belongs to a directed chain or lies in a strongly connected component. Application of the proposed algorithm for solving the problem of testing one of the software and hardware security tools is considered, and the possibility of its application in practice is confirmed. [ABSTRACT FROM AUTHOR]

The efficient processing of mass stream data has attracted wide attention in the database field. The skyline query on the sensor data stream can monitor multiple targets in real time, to avoid abnormal events such as fire and explosion, which is very useful in the practical application of sensor data monitoring. However, real-world stream data may often contain incomplete data attributes due to faulty sensing devices or imperfect data collection techniques. Skyline queries over incomplete data streams may lead to a lack of transitivity and loop domination issues. To solve the problem of the skyline query over incomplete data streams, firstly, this paper uses differential dependency rule (DD) to fill the missing attribute values of data in the incomplete data stream. Then, the hierarchical grid index (HGrid) is introduced into the field of skyline query to improve pruning efficiency. In the process of skyline calculation, this paper only keeps as few calculation results as possible for the data that may affect the result to avoid a large number of repeated calculations. Thus, S_IDS (Skyline query algorithm over Incomplete Data Stream) is proposed to query skyline results with high confidence from the incomplete data stream. Finally, by comparing with the most advanced skyline query algorithms over incomplete data streams, the correctness and efficiency of the proposed S_IDS algorithm are proved. [ABSTRACT FROM AUTHOR]

The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. In this domain, many real-life decision problems need to be solved repeatedly with changing data and parameters. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, to deal with such problems, a new evolutionary framework with multiple novel mechanisms is proposed. The new mechanisms are for (1) dealing with both linear and non-linear components in the constraint functions, (2) identifying the rate of change in the coefficients of the variables and (3) updating the population efficiently after every change occurs in the problem. To evaluate the performance of the proposed algorithm, we designed a new set of 13 dynamic benchmark problems, each of which consists of 20 dynamic changes and 3 different scenarios. The results demonstrate that the proposed algorithm significantly contributes in achieving good quality solutions, high feasibility rates and fast convergence in rapidly changing environments. In addition, the framework shows its capability of using different meta-heuristics to solve dynamic problems. [ABSTRACT FROM AUTHOR]

Quantum computing has entered its mature life thanks to the availability of cloud-based Noisy Intermediate-Scale Quantum (NISQ) technologies. These devices allow quantum researchers and practitioners to design, develop and test quantum algorithms on actual hardware, paving the way toward new approaches in solving problems intractable by classical computers. However, in spite of these achievements in quantum technologies, the size of the problems that can be actually solved by quantum algorithms is still limited by the small number of qubits and the considerable noise in computation that still characterizes NISQ devices. As a consequence, there is a strong need of introducing innovative computing architectures able to interconnect a set of NISQ devices to increase the capabilities of current quantum computers of solving hard problems in a reliable way. In this paper, the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) is introduced as a reference computational model by which designing innovative frameworks where quantum devices interact to solve a complex problem by working cooperatively. In detail, the D-NISQ model consists of a hybrid and hierarchical architecture where classical and quantum processors interact to iteratively split up a problem into a collection of sub-problems, solve each one of them by means of a proper quantum algorithm, and fuse output quantum information so as to compute the final solution to the problem posed. As demonstrated by two case studies based on the well-known Grover's algorithm, a multi-threaded implementation of the D-NISQ reference model allows for greater reliability in solving problems by quantum computation. • Quantum computing has entered its mature life thanks to NISQ technologies. • NISQ devices are still characterized by critical issues in computation. • Distributed quantum architectures can improve the capabilities of NISQ devices. • D-NISQ is a reference model for distributed quantum computation. [ABSTRACT FROM AUTHOR]

A tutorable algorithm for solving text-diagram function (TDF) problems is an essential technology for building the humanoid tutorial service since "function" is a core portion of mathematics. Solving TDF problems encounters challenges in understanding diagrams, representing the functions of being compound objects, and solving the mixture group of functions and universal relations. To address these challenges, this paper proposes a relation-centric algorithm, leveraging on making breakthrough in handling function, understanding diagram, and generating tutorable solution. The proposed algorithm comprises two phases: problem understanding and symbolic solver. In the problem understanding, it proposes a S 2 P (Syntax Semantics with Period) model method of acquiring relations from text and a L 2 (Line Segment with Labels Pattern) model method of acquiring relations from diagrams. To get the problem fully understood, this phase acquires both universal relations and period relations. In the symbolic solver, the function is first built from the acquired relations. Then an equation-function interaction method is created to solve a mixture system of relations and functions. The developed algorithm is the first one for solving text-diagram function problems. Experimental results show that the proposed algorithm not only has high accuracies of 74.5% on Math23KtoF and 80.8% on TnD1K datasets, but also can produce the tutorable solutions of TDF problems. [ABSTRACT FROM AUTHOR]

Adversarial sample generation problem is a hot issue in the security field of deep learning. Evolutionary algorithm has been widely used to solve this problem in recent years because of its good global search ability. However, existing methods still suffer from the "curse of dimensionality" when attacking high-resolution images. In this paper, a two-stage frequency domain generation algorithm of black-box adversarial samples based on differential evolution is proposed. In the first stage, a representative image-guided differential evolution method is proposed to quickly generate a universal adversarial perturbation with a high attack success rate in the frequency-domain. In the second stage, a space reduction strategy based on frequency-domain pixel blocks is designed to reduce the search space and alleviate the problem of "curse of dimensionality". In addition, a new space–frequency interaction sensitivity measure is introduced to evaluate the similarity between the adversarial samples and the original images. The adversarial perturbations obtained by the measure are more in line with the subjective perception of the human eye. Finally, compared with several typical black-box adversarial sample generation algorithms, experimental results show that the proposed algorithm can achieve higher attack success rate with less prediction times. • Proposing a two-stage evolutionary generation framework of adversarial samples. • Developing a space–frequency interaction sensitivity measure. • Presenting a representative image-guided differential evolutionary algorithm. • Designing an individual adversarial perturbation generation algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the two-dimensional strip-packing (2DSP) problem of placing a set of rectangular pieces onto a fixed-width rectangular sheet to minimize the total length used. We propose a Block-Based Heuristic Search Algorithm (BBHSA) to solve 2DSP problems with guillotine cut constraints. Initially, it converts the 2DSP problem into a series of 2D rectangular packing problems (2DRP), where the size of the sheet is fixed. In the BBHSA, rectangular pieces are aggregated into blocks, which are partial solutions without residual space. These blocks provide the ingredients for a good layout and are basic components in a tree-based constructive search process. Two basic operations, called placing & splitting and approximate binary search are used in the search process. Furthermore, several block-based placement rules are explored to speed up the search process and improve solution quality. To verify the performance of our proposed algorithm, we conducted extensive experiments using the zero-waste benchmark and non-zero-waste benchmark instances. The results show that BBHSA demonstrates computational effectiveness, particularly in zero-waste cases, achieving optimal solutions for almost all zero-waste benchmark instances reported in the existing literature. [ABSTRACT FROM AUTHOR]

The feature subset obtained by traditional feature selection algorithms usually contains many irrelevant features and redundant features, which increases the size of the feature set and reduces classification accuracy. More importantly, these irrelevant features and redundant features may lead to local optimum. How to efficiently select the global optimal feature subset from high-dimensional data is a challenging task. To solve the problem, an adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection (ADSCO) is proposed in this paper. The contributions of this algorithm are as follows. Firstly, an adaptive dual strategy constrained optimization mechanism (ADSC) is developed. In the ADSC, the adaptive linear constraint strategy and the random constraint strategy are designed to search for the feature subsets that satisfy the constraints. This can reduce the search space of candidate solutions. Secondly, the dual population-constrained coevolutionary optimizer (CO) is developed to help the algorithm jump out of the local optimum and search for better optimal solutions to further improve the classification accuracy of ADSCO. Finally, the ablation experiments are executed to validate the effectiveness of the proposed ADSC and CO. The ADSCO algorithm is tested on 8 high-dimensional datasets and 8 low-dimensional datasets, and compared with seven typical population feature selection algorithms. Experimental results show that the proposed ADSCO algorithm is superior to the 7 comparison algorithms in terms of classification accuracy and feature subset size, demonstrating its strong competitiveness in tackling high-dimensional data feature selection. [ABSTRACT FROM AUTHOR]

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively. [ABSTRACT FROM AUTHOR]

We address in this paper the two-machine job shop scheduling problem with a single server that sets up the jobs before they get processed on the machines. The server is only needed during the set-up and becomes free at the end of this phase. Moreover, the set-ups are non-anticipatory and the set-up times are sequence-independent. We seek a schedule that minimizes the overall completion time, also called the makespan. We propose several lower bounds to the problem and prove the N P -hardness in the strong sense of two restricted cases. In addition, we present a linear time algorithm for a special case. In order to solve the general problem, we develop a genetic and simulated annealing algorithms that use feasibility guaranteed procedures. An experimental study is carried out to analyze the performance of these meta-heuristic methods. [ABSTRACT FROM AUTHOR]

The Multi-Objective Discrete Network Design Problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin–destination pair instead of shared arterials. [ABSTRACT FROM AUTHOR]

Industrial scheduling problems (ISPs), especially industrial production workshop scheduling problems (IPWSPs) in various sectors like manufacturing, and power require allocating resources to tasks within multiple constraints. Addressing these complex challenges involves computationally solving problems with numerous variables and constraints. Lagrangian relaxation-based algorithms (LRAs) effectively tackle ISPs (particularly IPWSPs) by relaxing constraints through multipliers. While LRAs may violate original constraints, heuristic algorithms rectify this by crafting feasible solutions. LRAs offer efficient problem-solving and yield high-quality results with manageable computational effort. This review presents the cutting-edge LRAs for ISPs, based on 200 papers from the fields of supply chain, manufacture, power, etc. It exemplifies LRAs' principles through practical cases, and provides a classification of problems in detail, along with LRA's problem-solving process. We discuss strengths, weaknesses, and future research prospects, offering comprehensive guidance. Summarizing these findings, the review enhances understanding of LRAs' application potential in solving ISPs for practitioners and researchers. • Propose tutorials & examples for mastering ISP solving with LRAs. • Demonstrate an algorithm framework and diverse solution designs. • List pros & cons of using LRAs and analyze applicable scenarios. • Organize LRA development stages and future research directions. [ABSTRACT FROM AUTHOR]

The existing methodologies used for multilevel thresholding are not efficient in terms of both accuracy and computation time. Two-dimensional histogram-based techniques are better in terms of accuracy while they are computation intensive. The slime mould algorithm used for optimization mainly depends on the best leader and two randomly pooled slime moulds from the population, which leads to poor exploitation with more iteration to converge. These problems are solved in this paper by introducing a novel normalized square difference (NSD) based multilevel thresholding technique using a leader slime mould algorithm (LSMA). The contributions are many fold – i) a NSD based multilevel thresholding method is proposed using the gray level and normalized square difference (GLNSD) 2-D histogram with reduced computation; ii) LSMA is suggested; iii) 23 classical and 6 modern composition test functions from the IEEE CEC 2014 test suite are considered for evaluation of LSMA; iv) experiments on multispectral images are presented. The benefits are – i) reduces computations, ii) improves accuracy. The qualitative metrics used for analysis include – search history, trajectory, and average fitness history. Scalability analysis and statistical analysis (using Friedman's mean rank test) are presented. The proposal is compared with state-of-the-art techniques and found better. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we consider the problems for covering multiple intervals on a line. Given a set B of m line segments (called "barriers") on a horizontal line L and another set S of n horizontal line segments of the same length in the plane, we want to move all segments of S to L so that their union covers all barriers and the maximum movement of all segments of S is minimized. Previously, an O (n 3 log ⁡ n) -time algorithm was given for the case m = 1. In this paper, we propose an O (n 2 log ⁡ n log ⁡ log ⁡ n + n m log ⁡ m) -time algorithm for a more general setting with any m ≥ 1 , which also improves the previous work when m = 1. We then consider a line-constrained version of the problem in which the segments of S are all initially on the line L. Previously, an O (n log ⁡ n) -time algorithm was known for the case m = 1. We present an algorithm of O (m log ⁡ m + n log ⁡ m log ⁡ n) time for any m ≥ 1. These problems may have applications in mobile sensor barrier coverage in wireless sensor networks. [ABSTRACT FROM AUTHOR]

This paper characterizes a class of repeated moral hazard problem where the agent's replacement action is necessary for assuring the performance of important materials and equipment. However, due to the principal's costly monitoring, the continuously decreasing performance caused by the agent's successive shirking behaviors will significantly harm the principal's profits or welfare. For sake of performance assurance and monitoring efficiency, this paper adopts two reinforcement learning algorithms to find and test the strategies on the final path satisfying the conditions of (Restricted) Experience Based Equilibrium. Experimental results in a case of quality assurance of grain demonstrate the effectiveness of the model and the solution methods. Practically, this framework especially fits a kind of government-enterprise relation, such as in the field of emergency materials management and safety production management. Given the increasing need of developing systems for the government's management, this paper aims to provide applicable strategies and guidelines from a theoretical perspective. [ABSTRACT FROM AUTHOR]

The diffusion method is one of the main methods of community detection in complex networks. In this method, the use of the concept that diffusion within the nodes that are members of a community is faster than the diffusion of nodes that are not in the same community. In this way, the dense subgraph will detect the graph in the middle layer. The LPA algorithm, which mimics epidemic contagion by spreading labels, has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of diffusion methods. This algorithm is one of the detection algorithms of most popular communities in recent years because of possessing some advantages including linear time order, the use of local information, and non-dependence on any parameter; however, due to the random behavior in LPA, there are some problems such as unstable and low quality resulting from larger monster communities. This algorithm is easily adaptable to attributed network. In this paper, it is supposed to propose a new version of the LPA algorithm for attributed graphs so that the detected communities solve the problems related to unstable and low quality in addition to possessing structural cohesiveness and attribute homogeneity. For this purpose, a weighted graph of the combination of node attributes and topological structure is produced from an attributed graph for nodes which have edges with each other. Also, the centrality of each node will be calculated equal to the influence of each node using Laplacian centrality, and the steps of selecting the node are being enhanced for updating as well as the mechanism of updating based on the influence of nodes. The proposed method has been compared to other primary and new attributed graph clustering algorithms for real and artificial datasets. In accordance with the results of the experiments on the proposed algorithm without parameter adjusting for different networks of density and entropy criteria, the normalized mutual information indicates that the proposed method is more efficient and precise than other state-of-the-art attributed graph clustering methods. [ABSTRACT FROM AUTHOR]

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

In order to solve the problems in the traditional ensemble learning model, selective ensemble algorithms are often used to optimize the ensemble learning model. To settle the shortcomings of the current selective ensemble algorithm existing in the selection efficiency and predict outcomes, this paper proposes selective ensemble algorithm basing on stacking ensemble framework. The algorithm mainly uses the agglomerated hierarchical clustering (AHC) algorithm and the metropolis criterion of simulated annealing to select the type and number of base learners. In terms of empirical analysis, Lending-Club data is used to build a multi-classification model. The experimental results show that compared with the single learner model, the AHC-Metropolis selective ensemble algorithm has better performance and stability in predicting multi-classification problems, and can provide a basis for improving the financial risk control system and ensuring national security. [ABSTRACT FROM AUTHOR]

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. [ABSTRACT FROM AUTHOR]

A key component to the recent success of reinforcement learning is the introduction of neural networks for representation learning. Doing so allows for solving challenging problems in several domains, one of which is robotics. However, a major criticism of deep reinforcement learning (DRL) algorithms is their lack of explainability and interpretability. This problem is even exacerbated in robotics as they oftentimes cohabitate space with humans, making it imperative to be able to reason about their behavior. In this paper, we propose to analyze the learned representation in a robotic setting by utilizing Graph Networks (GNs). Using the GN and Layer-wise Relevance Propagation (LRP), we represent the observations as an entity-relationship to allow us to interpret the learned policy. We evaluate our approach in two environments in MuJoCo. These two environments were delicately designed to effectively measure the value of knowledge gained by our approach to analyzing learned representations. This approach allows us to analyze not only how different parts of the observation space contribute to the decision-making process but also differentiate between policies and their differences in performance. This difference in performance also allows for reasoning about the agent's recovery from faults. These insights are key contributions to explainable deep reinforcement learning in robotic settings. [ABSTRACT FROM AUTHOR]

Wafer Map Pattern Recognition (WMPR) is a critical aspect of semiconductor manufacturing. It indicates how to improve the manufacturing yields as we probe into the failure issues of the processes. In literature works, researchers often use balanced datasets with ample datapoints to address WMPR tasks, however, novel defects often emerge with few previous observations in real-world manufacturing. Unfortunately, efforts to solve WMPR problems in few-shot scenarios remain scanty. To bridge this gap, we define a new task, Few Shot Wafer Map Pattern Recognition(FSWMPR), which attempts to learning a classifier to distinguish unseen classes with only a few labeled instances available. In such a task, expeditiously learning transferable feature embeddings is extremely challenging. In this paper, we propose an innovative two-stage strategy to wrestle with the problem of FSWMPR. In the first stage, we leverage a masked autoencoder to obtain efficacious representations of defect wafer map images through reconstructing pixel values of masked patches based on smooth-l1 loss. In the second stage, we create a novel finetuning mechanism, "Dynamic Multi-Loss Adaptation Mechanism", which utilize three cooperative losses to accelerate fast feature transfer for few-shot scenarios. Surprisingly, if three losses are reduced to one comparative loss, we still achieve more competitive accuracy than those meta-learning or finetuning methods, which is worth noting that our two stages involve no label information at all. Extensive experiments and analyses are conducted on WM811K datasets. Compared with other algorithms, our methods offer fresh solutions by creatively integrating self-supervised masked autoencoder with a novel finetune mechanism which is efficacious for FSWMPR. [ABSTRACT FROM AUTHOR]

Unconstrained quadratic optimization problems are a common mathematical challenge encountered in various domains. These problems involve optimizing quadratic functions without explicit constraints. In a distributed computing environment, solving these optimization problems collectively among multiple computational nodes is a complex and crucial task. This paper introduces a distributed algorithm within a multi-agent framework that aims to find the global minimizer for such problems. The proposed algorithm demonstrates exponential convergence, assuming a static and connected communication network. Additionally, numerical simulations are conducted to support the theoretical findings. • A distributed algorithm is devised to solve the convex quadratic optimization problem. • The algorithm leverages the concept of strong duality in its development. • The technique solves QP over undirected multi-agent networks without exchanging minimizer estimates. • The algorithm achieves rapid, exponential convergence towards the global optimizer. • Simulation studies confirm the theoretical claims. [ABSTRACT FROM AUTHOR]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

Question Answering (QA) System is very useful as most of the deep learning related problems can be modeled as a question answering problem. Consequently, the field is one of the most researched fields in computer science today. The last few years have seen considerable developments and improvement in the state of the art, much of which can be credited to upcoming of Deep Learning. In this paper, a discussion about various approaches starting from the basic NLP and algorithms based approach has been done and the paper eventually builds towards the recently proposed methods of Deep Learning. Implementation details and various tweaks in the algorithms that produced better results have also been discussed. The evaluation of the proposed models was done on twenty tasks of babI dataset of Facebook. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex dataset. To address these problems, a novel data adaptive Bregman clustering algorithm (DBGSA) is proposed in this paper, which combines the universal gravitational algorithm to bring similar points closer in the dataset. In addition, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8% compared to the contrasted algorithms, making it applicable to diverse datasets. [ABSTRACT FROM AUTHOR]

In 2001, Károlyi, Pach and Tóth introduced a family of point sets to solve an Erdős–Szekeres type problem, which have been used to solve several other Erdős–Szekeres type problems. In this paper we refer to these sets as nested almost convex sets. A nested almost convex set X has the property that the interior of every triangle determined by three points in the same convex layer of X , contains exactly one point of X . In this paper, we introduce a characterization of nested almost convex sets. Our characterization implies that there exists at most one (up to equivalence of order type) nested almost convex set of n points, for any integer n . We use our characterization to obtain a linear time algorithm to construct nested almost convex sets of n points, with integer coordinates of absolute values at most O ( n log 2 ⁡ 5 ) . Finally, we use our characterization to obtain an O ( n log ⁡ n ) -time algorithm to determine whether a set of points is a nested almost convex set. [ABSTRACT FROM AUTHOR]

This paper proposes validates an Iterative Feedback Tuning (IFT) algorithm, which is a classical and also popular data-driven algorithm, on tower crane systems. The IFT algorithm is applied to improve the performances of three proportional-integral (PI) controllers by solving an optimization problem. The IFT algorithm is validated using experiments to control the cart position, the arm angular position and the payload position. The experiments are conducted on a tower crane system in authors' laboratory, and the experimental setup is laboratory equipment that has strong nonlinearities. Ten iterations are performed to highlight the control system performance using IFT controllers. [ABSTRACT FROM AUTHOR]

l A non-convex fractional-order variational model is proposed to restore the image blurred by an unknown blur kernel. l Quaternion FTV with l p quasinorm constrained image and L 1 -norm constrained kernel are unified to a regularization framework. l The fractional-order total variation is extended to four-directional FTV including diagonal and back diagonal gradients. l An efficient algorithm based on the alternating direction method is proposed to address the non-convex optimization problem. This paper considers a variational model for single image blind restoration. By exploiting the fractional-order total variation (FTV) and L p quasinorm relaxation, a non-convex fractional-order variational model is proposed to restore the image blurred by an unknown blur kernel. A quaternion FTV model is first put forward to exploring more directional information of an image. The new model utilizes non-convex and non-smooth quaternion FTV with L p quasinorm to constrain the image and L 1 -norm to constrain the blur kernel, which are unified to a unified regularization framework. Further, an efficient algorithm is proposed to solve the non-convex problem via using the alternating direction minimization. The extensive experiments demonstrate the efficiency and viability of the proposed method and reveal superior performances in comparison with several existing methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Applying the IBBA for solving STNEP and DTNEP problems considering different OFs. • Proposing two modifications in the BBA to enhance the solution quality. • Applying the ANFIS to the WDN for finding the LTLF up to 2039. • Assessing the ANFIS based LTLF using standard evaluation tests. • Using standard and realistic networks to prove the robustness of the proposed IBBA. This paper proposes an improved binary bat algorithm (IBBA) for solving static and dynamic transmission network expansion planning (TNEP) problems for standard and realistic networks considering different objective functions (OFs). The proposed IBBA has two modifications to enhance the solution quality based on multi V-shaped transfer function and adaptive search space (ASS). The IBBA is applied to solve the static TNEP problem. A two-stage procedure is employed to solve the dynamic TNEP problem. In stage-1, the adaptive neuro-fuzzy inference system (ANFIS) is utilized to find the long-term load forecasting (LTLF) up to 2039. In stage-2, the IBBA is used to solve the dynamic TNEP problem. Two OFs are considered for solving TNEP problems. The first OF achieves the investment cost reduction. The second OF aims to minimize the total costs, which include the investment cost and the total costs of energy losses and reactive power compensation (RPC). The proposed procedure is applied to Garver's 6-bus system and the West Delta Network (WDN) as a part of the Unified Egyptian Transmission Network (UETN) to solve TNEP problems. The obtained results are compared with other methods to show the robustness of the proposed procedure for solving TNEP problems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. However, due to the relatively long distance between remote cloud and end nodes, cloud computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this paper, we propose a novel trust evaluation model based on the trust transitivity on a chain assisted by mobile edge nodes, which is used to ensure the reliability of nodes in the Internet of Things and prevent malicious attacks. The mobile edge nodes offer a new solution to solve the above problems with relatively strong computing and storage abilities. Firstly, we design calculation approaches to different trust chains to measure their trust degrees. Secondly, we propose an improved Dijkstra's algorithm for collecting trust information of sensor nodes by mobile edge nodes. Finally, the experimental results show that our trust model based on mobile edge nodes can evaluate sensor nodes more precisely and enhance the security on the Internet of Things. • An efficient trust chain evaluation model assisted by mobile edge nodes is proposed. • The model provides calculation methods of the value of the different trust chains. • Trust information of sensor nodes are collected by an improved Dijkstra algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust supervised subspace learning (RSSL) method for output-relevant prediction and detection against outliers. RSSL learns the robust subspaces by optimizing a joint problem over both the prediction of output and the reconstruction of input. To this end, the learned subspaces/data representations are informative, i.e., they are encapsulated with the critic information related to both the input and output, and thus can benefit the following tasks of output-related modeling and detection. Besides, we separate sparse items from the raw measurements to suppress the effects of outliers. An efficient optimization algorithm is designed to solve the optimization problem of RSSL. We further conduct post orthogonal decomposition upon the subspaces provided by RSSL so that the trimmed subspaces are more suitable for output-related detection. The efficacy of the proposed method is extensively verified by synthesis data and benchmark data. • We learn robust latent subspaces by a joint optimization of input and output, where the negative effects of outliers are suppressed by separating sparse items from the original data. • We develop an efficient algorithm to solve the proposed formulation of RSSL. The theoretical and empirical analysis demonstrate the effectiveness of the designed optimization algorithm. • The proposed RSSL is competent to the tasks of output prediction and output-related detection, where, for the latter task, post-decomposition is performed to further trim the subspaces generated by RSSL. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to achieve a simultaneous treatment of the even and odd truncated matricial Stieltjes moment problems in the most general case. These results are generalizations of results of Chen and Hu [5,19] which considered the particular case α = 0 . Our approach is based on Schur analysis methods. More precisely, we use two interrelated versions of Schur-type algorithms, namely an algebraic one and a function-theoretic one. The algebraic version was worked out in a former paper of the authors. It is an algorithm which is applied to finite or infinite sequences of complex matrices. The construction and investigation of the function-theoretic version of our Schur-type algorithm is a central theme of this paper. This algorithm will be applied to relevant subclasses of holomorphic matrix-valued functions of the Stieltjes class. Using recent results on the holomorphicity of the Moore–Penrose inverse of matrix-valued Stieltjes functions, we obtain a complete description of the solution set of the moment problem under consideration in the most general situation. [ABSTRACT FROM AUTHOR]

In this paper, the Periodic Capacitated Arc Routing Problem (PCARP) is investigated. PCARP is an extension of the well-known CARP from a single period to a multi-period horizon. In PCARP, two objectives are to be minimized. One is the number of required vehicles ( nv ), and the other is the total cost ( tc ). Due to the multi-period nature, given the same graph or road network, PCARP can have a much larger solution space than the single-period CARP counterpart. Furthermore, PCARP consists of an additional allocation sub-problem (of the days to serve the arcs), which is interdependent with the routing sub-problem. Although some attempts have been made for solving PCARP, more investigations are yet to be done to further improve their performance especially on large-scale problem instances. It has been shown that optimizing nv and tc separately (hierarchically) is a good way of dealing with the two objectives. In this paper, we further improve this strategy and propose a new Route Decomposition (RD) operator thereby. Then, the RD operator is integrated into a Memetic Algorithm (MA) framework for PCARP, in which novel crossover and local search operators are designed accordingly. In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals. The MA with RD (MARD) was evaluated and compared with the state-of-the-art approaches on two benchmark sets of PCARP instances and a large data set which is based on a real-world road network. The experimental results suggest that MARD outperforms the compared state-of-the-art algorithms, and improves most of the best-known solutions. The advantage of MARD becomes more obvious when the problem size increases. Thus, MARD is particularly effective in solving large-scale PCARP instances. Moreover, the efficacy of the proposed RD operator in MARD has been empirically verified. [ABSTRACT FROM AUTHOR]