Search

In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time. [ABSTRACT FROM AUTHOR]

In the field of secure communications, the robustness of cipher images transmitted in various channels is becoming increasingly important. In this paper, a robust image encryption algorithm combining a new chaotic system and discrete cosine transform is proposed, which is interlinked with plain information and is resistant to high-intensity noise attacks. First, a 5D continuous hyperchaotic system is proposed, leading to an interrelated sequence of five chaotic sequences. Second, the plain image is subjected to discrete cosine transform. Then the transform domain image is quantized, and some high-frequency components are removed, and then the high-frequency components are filled with chaotic sequences. Next, the transform domain image is scrambled, and inverse discrete cosine transform is performed, and its gray value is mapped to obtain a spatial domain image. Finally, the spatial image is scrambled by the spiral transformation, and then the diffusion operation is performed to obtain the encrypted image. Through the simulation experiment, the histogram, correlation, differential attack, and robustness are analyzed. The experimental results show that the proposed encryption algorithm can resist high-intensity noise attacks and has good encryption performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-dimensional multifractal sign retention detrending fluctuation analysis algorithm (2D MF-S-DFA), which takes the sign of the residual matrix into account when calculating the detrending fluctuation function in traditional 2D MF-DFA. We evaluate these two methods based on images constructed from p -model multiplicative cascades. The results indicate that the numerical solution of the images extracted by 2D MF-S-DFA is closer to the theoretical solution of the multiplicative cascade images. In addition, we also compare the performance of the two methods after transforming the important parameters of the multiplicative cascade images. The results show that the relative errors and overall distance between the generalized Hurst exponent H (q) and the scale exponent τ (q) extracted by 2D MF-S-DFA and the image feature values are smaller than those of 2D MF-DFA. We can conclude that the sign retention algorithm outperforms the traditional 2D MF-DFA. [ABSTRACT FROM AUTHOR]

Multi-furnace scheduling simultaneously is an important part to increase productivity and reduce the production cost in single crystal silicon enterprises. In the restrained power consumption requirements environment, the optimal sequencing of process operation start-time for single crystal furnaces is a challenging problem. To solve this problem, the scheduling model of multi-furnace scheduling is established in this paper to minimize the maximum completion time. Then, an improved DE algorithm called the multi-strategy individual adaptive mutation differential evolution algorithm (MSIADE) is presented to address the scheduling model. In the improved DE algorithm, the different dimensional and multi-strategy mutation operations are adopted to refrain the algorithm from the local optimal, then the different mutation factors are assigned to each individual through the rank of fitness function value to strengthen the exploration ability of the MSIADE algorithm. Simulation experiments results based on the standard test functions and the established scheduling model show the feasibility in the established model and the effectiveness in the proposed algorithm. [ABSTRACT FROM AUTHOR]

Occlusion area detection is a crucial step affecting the performance of the binocular stereo matching algorithm, but the traditional method of occlusion area detection has two major problems, including left–right consistency detection (LRC). First, these algorithms must obtain the left and right disparity maps with precision. Second, these algorithms cannot detect the occlusion region at the image's borders. We propose the single view occlusion area detective (SVOAD) algorithm to detect these occlusion areas and better deal with them. The SVOAD can detect the area of occlusion from a single image, thereby reducing the computational cost. Additionally, the algorithm can detect the occlusion region in all image regions. This paper also improves the guided filter so that it works better with the end-to-end neural network and makes the SVOAD algorithm work better. [ABSTRACT FROM AUTHOR]

For an optimization problem known to be NP-Hard, the dichotomy study investigates the reduction instances to determine the line separating polynomial-time solvable vs NP-Hard instances (easy vs hard instances). In this paper, we investigate the well-studied Hamiltonian cycle problem (HCYCLE), and present an interesting dichotomy result on split graphs. T. Akiyama et al. (1980) have shown that HCYCLE is NP-complete on planar bipartite graphs with maximum degree 3. We use this result to show that HCYCLE is NP-complete for K 1 , 5 -free split graphs. Further, we present polynomial-time algorithms for Hamiltonian cycle in K 1 , 3 -free and K 1 , 4 -free split graphs. We believe that the structural results presented in this paper can be used to show similar dichotomy result for Hamiltonian path problem and other variants of Hamiltonian cycle (path) problems. [ABSTRACT FROM AUTHOR]

Consistent tangent stiffness plays a crucial role in delivering a quadratic rate of convergence when using Newton's method in solving nonlinear equations of motion. In this paper, consistent tangent stiffness is derived for a three-dimensional (3D) wheel–rail interaction element (WRI element for short) originally developed by the authors and co-workers. The algorithm has been implemented in finite element (FE) software framework (OpenSees in this paper) and proven to be effective. Application examples of wheelset and light rail vehicle are provided to validate the consistent tangent stiffness. The quadratic convergence rate is verified. The speeds of calculation are compared between the use of consistent tangent stiffness and the tangent by perturbation method. The results demonstrate the improved computational efficiency of WRI element when consistent tangent stiffness is used. [ABSTRACT FROM AUTHOR]

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model's execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach. [ABSTRACT FROM AUTHOR]

In the large-scale and ultra-dense Internet of Vehicles (IoVs), constructing the simplest backbone network is an urgent problem to be solved. In fact, constructing the simplest backbone network is an NP-hard problem, and at present, there is no effective solution. In this paper, we propose a graph-based clustering algorithm to solve this problem and construct the simplest backbone network in the large-scale and ultra-dense IoV. We establish a backbone network model for the large-scale and ultra-dense IoV and optimize the backbone network by employing a novel local search iterative algorithm. Simulation results show that with the increase in node density, the number of clusters selected by the proposed algorithm tends to be stable, while the number of optimized clusters decreases by 28.87% on an average. Thus, the proposed algorithm can effectively simplify the backbone network. [ABSTRACT FROM AUTHOR]

For a large class of closed orientable 3-manifolds, we present a polynomial algorithm to go from a triangulation to a framed link presenting the same manifold. The algorithm applies whenever we can get a resoluble gem from the triangulation. Most minimal gems have this property (about 95% of gems in our complete catalogue up to 28 vertices). From the gems which fail, nothing prevents another gem from inducing the same 3-manifold to be resoluble. We leave as an open problem presenting a 3-manifold which is not induced by a resoluble gem. This paper is an application of the theory of gems to 3-manifold topology. [ABSTRACT FROM AUTHOR]

In this paper, we study kernel ridge-less regression, including the case of interpolating solutions. We prove that maximizing the leave-one-out ( CV loo ) stability minimizes the expected error. Further, we also prove that the minimum norm solution — to which gradient algorithms are known to converge — is the most stable solution. More precisely, we show that the minimum norm interpolating solution minimizes a bound on CV loo stability, which in turn is controlled by the smallest singular value, hence the condition number, of the empirical kernel matrix. These quantities can be characterized in the asymptotic regime where both the dimension (d) and cardinality (n) of the data go to infinity (with n d → γ as d , n → ∞). Our results suggest that the property of CV loo stability of the learning algorithm with respect to perturbations of the training set may provide a more general framework than the classical theory of Empirical Risk Minimization (ERM). While ERM was developed to deal with the classical regime in which the architecture of the learning network is fixed and n → ∞ , the modern regime focuses on interpolating regressors and overparameterized models, when both d and n go to infinity. Since the stability framework is known to be equivalent to the classical theory in the classical regime, our results here suggest that it may be interesting to extend it beyond kernel regression to other overparameterized algorithms such as deep networks. [ABSTRACT FROM AUTHOR]

A graph G is a 3-degree 4-chordal graph if every cycle of length at least five has a chord and the degree of each vertex is at most three. In this paper, we investigate the structure of 3-degree 4-chordal graphs, which is a subclass of 3-degree graphs. We present a structural characterization based on minimal vertex separators and bi-connected components. Many classical problems are known to be NP-complete for 3-degree graphs, and 3-degree 4-chordal graphs are a nontrivial subclass of 3-degree graphs. Using our structural results, we present polynomial-time algorithms for many classical problems which are grouped into (i) subset problems (vertex cover and its variants, feedback vertex set, Steiner tree), (ii) Hamiltonicity and its variants, (iii) graph embedding problems (treewidth, minimum fill-in), and (iv) Coloring problems(rainbow coloring). [ABSTRACT FROM AUTHOR]

In temporal networks, PageRank-based methods are usually used to calculate the importance of nodes. However, almost all the methods focus on the first-order relationships between nodes while ignore higher-order interactions between nodes in the graph. Considering that temporal motifs are recurring, higher-order and significant network connectivity patterns, which can capture both temporal and higher-order structural features in dynamic networks, this paper proposes a novel Personalized Temporal Motif PageRank (PTMP) algorithm to measure the importance of nodes in temporal networks. Specifically, to capture temporal information and higher-order features, we develop a method extracting temporal motif instances from temporal networks, and design an algorithm to compute the weighted motif adjacency matrix and the diagonal motif out-degree matrix, then define a motif transition matrix, which contains the personalized feature and can be used to compute the importance score of nodes. Finally, we make the steady-state analysis for the PTMP algorithm and compare it with other state-of-the-art baselines on multiple real-world datasets. The experimental results demonstrate that the PTMP algorithm is capable of mining much richer important nodes information accurately and effectively. [ABSTRACT FROM AUTHOR]

In this paper, we provide a Monte Carlo method to calculate multiple stochastic integrals which is based on a.s. distributional convergence. [ABSTRACT FROM AUTHOR]

Attribute reduction is a part of the most basic and significant research contents in rough set theory. The so-called attribute reduction is the smallest independent subset that keeps certain properties of information table unchanged. In this paper, four interval-valued intuitionistic fuzzy probabilistic rough set models and their natures are given on the basis of the dominant-, inferior- and interval-valued intuitionistic fuzzy probabilistic rough set models. At the same time, the interval-valued intuitionistic fuzzy numbers are transformed by fuzzy degree, and the approximate accuracy and approximate classification quality under the dominance relation are used for reduction, at last, the feasibility is verified by an example. [ABSTRACT FROM AUTHOR]

The image copy–move forgery can achieve the realistic effects without leaving any obvious manipulation traces because the copied source and pasted target belong to the homogenous sources. Image copy–move forgery detection (CMFD) is still a serious challenge issue for researchers, e.g. computational efficiency, occlusive (smoothing) object detection, and partial object detection, under various post-processing and geometrical transformations. This paper proposes a novel CMFD scheme using the characteristics of local descriptors. First, the proposed method deeply analyzes the structure and mines the inherent characteristics of the local descriptor (SURF) for feature extraction in the coarse and smooth regions. Subsequently, the proposed method takes the kernel features as coarse feature matching to decrease matching costs. Then, the found candidate keypoints with a relatively small quantity are used with complete features to perform fine keypoint matching for getting suspicious candidate keypoint pairs. Furthermore, based on the deep mining of the inherent local descriptor characteristics, the proposed method can indicate the forgery region localizations through keypoint pairs, even the forgeries suffering from various geometrical transformations. Then, the proposed method proposes adaptive dual-filtering algorithms of K -means relying on keypoint characteristics, and spatial distance for hierarchical progress of feature filtering. Finally, Delaunay Triangulation relies on the true-positive keypoint pairs to perform the forgery region matting effectively and efficiently. The experimental results demonstrate that the proposed method can achieve the best F1 scores compared to the state-of-the-art methods in most cases, especially in anti-scaling transformations. [ABSTRACT FROM AUTHOR]

Digital microfluidic biochips (DMFBs) are emerging as an alternative to the cumbersome traditional laboratories for biochemical analysis. DMFBs come under micro-electro-mechanical systems and are a class of lab-on-a-chip devices. DMFBs provide automation, miniaturization and software programmability. The droplet routing algorithm determines concurrent routes for a set of droplets from their source cells to individual target cells on a DMFB. In this paper, a double deep Q-network (DDQN)-based droplet routing algorithm has been proposed. DDQN is a temporal difference-based deep reinforcement algorithm that combines Double Q-learning with a deep neural network algorithm. In the proposed work, routes for droplets are determined by DDQN, and later collisions are resolved using stalling and/or detouring. The latest arrival time of droplets arriving last at its target and cell utilization is taken as objectives for routing algorithm performance evaluation. The proposed method is evaluated on two standard benchmark suites. Simulation results show that the proposed DDQN-based droplet routing algorithm produces competitive results compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Image registration of multimodal remote sensing images plays a vital role in remote sensing image analysis. However, there are significant nonlinear intensity differences between multimodal remote sensing image pairs, making it difficult for most traditional image registration algorithms to meet the registration requirements. In this paper, we propose a novel edge descriptor utilizing edge information, which has not only affine invariance but is also insensitive to nonlinear intensity differences. Moreover, we utilize the proposed descriptor to design a multimodal image registration algorithm. We use several different multimodal image pairs to evaluate the proposed algorithm. The experimental results show that the proposed algorithm holds a stable performance and can still achieve accurate spatial alignment even with the huge nonlinear intensity differences. [ABSTRACT FROM AUTHOR]

In this paper, a new 4D chaos system is introduced based on the mathematical structure through composing and transferring. The behavior of the proposed chaotic system is studied using different tests, such as Lyapunov exponent, bifurcation diagram and histogram. In order to show the advantage of the new chaos system, the proposed system is used for image encryption. In the proposed algorithm, in addition to the new chaos system, cellular automata is also used to improve the security of the algorithm. The proposed algorithm has a separate structure for encryption key, which increases the security of the proposed algorithm. The security of the proposed environment which is evaluated using different types of security tests shows the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Given an undirected graph, a balanced node separator is a set of nodes whose removal splits the graph into connected components of limited size. Balanced node separators are used for graph partitioning, for the construction of graph data structures, and for measuring network reliability. It is NP-hard to decide whether a graph has a balanced node separator of size at most k. Therefore, practical algorithms typically try to find small separators in a heuristic fashion. In this paper, we present a branching algorithm that for a given value k either outputs a balanced node separator of size at most k or certifies that k is a valid lower bound. Using this algorithm iteratively for growing values of k allows us to find a minimum balanced node separator. To make this algorithm scalable to real-world (road) networks of considerable size, we first describe pruning rules to reduce the graph size without affecting the minimum balanced separator size. In addition, we prove several structural properties of minimum balanced node separators which are then used to reduce the branching factor and search depth of our algorithm. We experimentally demonstrate the applicability of our algorithm to graphs with thousands of nodes and edges. We showcase the usefulness of having minimum balanced separators for judging the quality of existing heuristics, for improving preprocessing-based route planning techniques on road networks, and for lower bounding important graph parameters. Finally, we discuss how our ideas and methods can also be leveraged to accelerate the heuristic computation of balanced node separators. [ABSTRACT FROM AUTHOR]

For a connected graph G (V , E) , we use the notation d (a 1 , a 2) to represent the distance between two node points a 1 and a 2 and it is the minimum of the lengths of all paths between them. The eccentricity e (a) of a node point a ∈ V is considered as the maximum length of all shortest paths starts from a to the remaining nodes, i.e., e (a) = max { d (a , u 1) : u 1 ∈ V }. The diameter of a graph G , we denote it by d (G) and it is the length of the longest shortest path in G , i.e., d (G) = max { e (a) : a ∈ V }. Also, the radius of a graph G , we denote it by the symbol r (G) and it is the least eccentricity of all node points in V , i.e., r (G) = min { e (a) : a ∈ V }. The central vertex/node point x of a graph G is a node whose eccentricity is same as G 's radius, i.e., e (x) = r (G). The collection of all central nodes of a graph G is considered as the center of G and it is symbolized by c (G) , i.e., c (G) = { x ∈ V : e (x) = r (G) }. A graph may have one or more central vertices. This paper develops an optimal algorithm to compute the diameter, radius and central node (s) of the permutation graph having n node points in O (n) time. We have also established a tight relation between radius and diameter of permutation graphs. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm to solve vehicle–bridge interaction (VBI) problems. It describes the VBI system in a linear second-order differential-algebraic equation (DAE) manner, incorporating the motions of both the vehicle and the bridge subsystems, together with the contact forces. On account of the conjugation between the VBI system's motions and contact forces, the key feature of the proposed algorithm is to solve the DAE with the the algebraic method of elimination by substitution, avoiding the singularity of the differential operator. It enables a versatile and consensus solution scheme for different types of contact mechanisms, including the rigid contact model with constraints, the elastic contact model with the compliance method, and the creep tangential contact model, as well as different wheel–rail profiles. Combined with an appropriate numerical integration scheme, it allows an explicit expression of the contact forces, subsequently leading to the partitioned non-iterative analysis of the motions of both the vehicle and the bridge subsystems. In addition, by incorporating the dynamic condensation of wheels' degrees of freedom into those of upper components in a matrix form, i.e. a reduced system approach, the proposed algorithm can avoid the numerical drift associated with the kinematic constraints on the acceleration level. To demonstrate the accuracy of the proposed approach, this study first examines a benchmark example of a single degree of freedom sprung mass model traversing a simply supported bridge. Further, it considers a more realistic problem of one and eight train vehicles crossing three three-span continuous bridges. The comparisons and the agreements with existing algorithms prove the accuracy and computational efficiency of the proposed analysis scheme. [ABSTRACT FROM AUTHOR]

Computation and decision problems related to argumentation frameworks with higher-order attacks have not received a lot of attention so far. This paper is a step towards these issues. First, it provides a labelling counterpart for the structure semantics of Recursive Argumentation Frameworks (RAF). Second, it investigates the complexity of decision problems associated with RAF. This investigation shows that, for the higher expressiveness offered by these enriched systems, the complexity is the same as for classical argumentation frameworks. As a side contribution, a new semantics for RAF, the semi-stable semantics, and a new process for translating RAF into Argumentation Frameworks without higher-order attacks (AF), are introduced. Finally, new notions which are the counterparts of equivalent notions already existing for AF (among them, the Strongly Connected Components — SCC) are defined and investigated in order to involve them in the future development of algorithms for computing RAF labelling semantics. [ABSTRACT FROM AUTHOR]

With the explosively growing amount of online information, recommender system becomes an important tool to help users efficiently find their desired information. In this paper, we propose a Graph Neural Network Social Recommendation Based on Coupled Influence by analyzing the social influence of 2-level friends (CI-GNNSR). First, we mine the user's historical rating information and second-degree social information. Then, to learn the feature representation of users and movies, multiple Graph Attention Networks (GAT) are used to model the user-movie Graph and social network Graph. Our algorithm uses an attention-based memory network to learn the interest influence representation between users and their collaborative friends, which can distinguish the related factors among different users' friends. The experiment results show that CI-GNNSR enhances the accuracy of recommendation by considering users' social influence factors from multiple perspectives. [ABSTRACT FROM AUTHOR]

3D image is a kind of image closer to life, and 3D image has a stronger visual impact. In the past, researchers mostly protected flat images, but ignored the protection of 3D images. This paper combines the 2D chaotic system and uses random cross diffusion to propose a new encryption algorithm for 3D images which are STL format. Different from the traditional scrambling and diffusion operations, this paper uses only one iterative operation to achieve the effects of scrambling and diffusion, making the algorithm more secure. In the encryption algorithm, each value of the ciphertext is XORed with the value of the plaintext on the same coordinate axis and different from the coordinate where the ciphertext is located, the chaotic sequence, the ciphertext of different coordinate axes significantly reduces the correlation between the adjacent coordinate values. In addition, the proposed 3D image encryption algorithm is tested by statistical analysis. The experimental results show that this 3D image encryption algorithm has high security. [ABSTRACT FROM AUTHOR]

Due to the haze weather, the outdoor image quality is degraded, which reduces the image contrast, thereby reducing the efficiency of computer vision systems such as target recognition. There are two aspects of the traditional algorithm based on the principle of dark channel to be improved. First, the restored images obviously contain color distortion in the sky region. Second, the white regions in the scene easily affect the atmospheric light estimated. To solve the above problems, this paper proposes a single-image dehazing and image segmentation method via dark channel prior (DCP) and adaptive threshold. The sky region of hazing image is relatively bright, so sky region does not meet the DCP. The sky part is separated by the adaptive threshold, then the scenery and the sky area are dehazed, respectively. In order to avoid the interference caused by white objects to the estimation of atmospheric light, we estimate the value of atmospheric light using the separated area of the sky. The algorithm in this paper makes up for the shortcoming that the algorithm based on the DCP cannot effectively process the hazing image with sky region, avoiding the effect of white objects on estimating atmospheric light. Experimental results show the feasibility and effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a high-frequency AC-link DC–AC converter is presented with detailed analysis. This converter is used as an interface between a dc power source and a grid. The studied converter is a universal power converter which consists of two bridges separated by an AC-link; each bridge is connected to a source or it feeds a load. The link of this converter contains a parallel pair inductance-capacitor. The inductance is used to stock/supply energy and for the link capacitor, it allows to perform soft switching during turning on/turning off of the switches. The studied converter has considerable advantages compared to the other topologies. Its fundamental properties are, especially, the compactness, reliability and efficiency which it ensures. Also, it guarantees longer lifetime and the possibility to transfer power in the two power flow directions. In this talk, the principles of the ac-link inverter operation are clearly explained in this paper. Simulation results, under MATLAB/SIMULINK, are shown to validate the correct operation and the efficiency of the proposed converter topology. The control algorithm is, also, experimentally implemented using a dSPACE 1104 control board. [ABSTRACT FROM AUTHOR]

In this paper, the Benettin–Wolf algorithm for determining all Lyapunov exponents of noncommensurate fractional-order systems modeled by Caputo's derivative and the corresponding Matlab code are presented. The paper continues the work started in [Danca & Kuznetsov, 2018], where the Matlab code of commensurate fractional-order systems is given. To integrate the extended systems, the Adams–Bashforth–Moulton scheme for fractional differential equations is utilized. Like the Matlab program for commensurate-order systems, the program presented in this paper prints and plots all Lyapunov exponents as function of time. The program can be simply adapted to plot the evolution of the Lyapunov exponents as a function of orders, or a function of a bifurcation parameter. Special attention is paid to the periodicity of fractional-order systems and its influences. The case of noncommensurate Lorenz system is demonstrated. [ABSTRACT FROM AUTHOR]

This paper is concerned with a numerical spectral solution to a one-dimensional linear telegraph type equation with constant coefficients. An efficient Galerkin algorithm is implemented and analyzed for treating this type of equations. The philosophy of utilization of the Galerkin method is built on picking basis functions that are consistent with the corresponding boundary conditions of the telegraph type equation. A suitable combination of the orthogonal shifted Gegenbauer polynomials is utilized. The proposed method produces systems of especially inverted matrices. Furthermore, the convergence and error analysis of the proposed expansion are investigated. This study was built on assuming that the solution to the problem is separable. The paper ends by checking the applicability and effectiveness of the proposed algorithm by solving some numerical examples. [ABSTRACT FROM AUTHOR]

In order to restore the faulty path in network more effectively, we propose the maintaining constrained path problem. Give a directed acyclic graph (DAG) G = (V (G) , E (G)) with some faulty edges, where | V (G) | = n , | E (G) | = m. For any positive number D , we give effective maintain algorithm for finding and maintaining the path between source vertex s ∈ V (G) and destination t ∈ V (G) with length at most D. In this paper, we consider the parameters ∥ δ ∥ and | δ | which are used to measure the numbers of edges and vertices which are influenced by faulty edges, respectively. The main technique of this paper is to define and solve a subproblem called the one to set constrained path problem (OSCPP) which has not been addressed before. On the DAG, compared with the dynamic shortest path algorithm with time complexity O (∥ δ ∥ + | δ | log | δ |) [16] and the shortest path algorithm with time complexity O (m + n) [18], based on the algorithm for OSCPP, we develop a maintaining constrained path algorithm and improve the time complexity to O (∥ δ ∥ + | δ |) in the case that all shortest paths from each vertex u ∈ V (G) to t have been given. [ABSTRACT FROM AUTHOR]

Within the broad ambit of algorithm design, the study of dynamic graph algorithms continues to be a thriving area of research. Commensurate with this interest is an extensive literature on the topic. Not surprisingly, dynamic algorithms for all varieties of shortest path problems, in view of their practical importance, occupy a preeminent position. Relevant to this paper are fully dynamic algorithms for chordal graphs. Surprisingly, to the best of our knowledge, there seems to be no reported results for the problem of dynamic algorithms for strongly chordal graphs. To redress this gap, in this paper, we propose a semi-dynamic algorithm for edge-deletions and a semi-dynamic algorithm for edge-insertions in a strongly chordal graph, G. The query complexity of an edge-deletion is O (d u 2 d v 2) , where d u and d v are the degrees of the vertices u and v of the candidate edge { u , v } , while the query complexity of an edge-insertion is O (n 2) , where n is the number of vertices of G. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) is the hottest network paradigm, which permits the Secondary Users (SUs) like wireless devices/users for intelligent accessing of unallocated radio spectrum. Such accessing happens by enabling interference-free transmission of Primary Users (PUs), who are allotted with some deserved radio spectrum portions. This radio communication paradigm has effective usage in vehicular networks, where communication should be established from vehicles to static stations (vehicle-to-infrastructure) or within vehicles (vehicle-to-vehicle), without allotting dedicated frequencies. Nevertheless, the major issue in designing CR is that it must be built to aid in efficient transmitting and sensing of data through the available radio spectrum channels. This paper proposes a Model Predictive Control (MPC)-based prediction model, via a Deep Learning approach. Here, a Deep Belief Network (DBN) allows in predicting the PU transmission state as idle or busy. Moreover, this paper comes out with a new optimization concept that achieves more accurate and precise prediction. The weight of DBN is optimally selected to pave way for effective performance. Further, a new hybrid algorithm named as Cuckoo Search-Grasshopper Optimization Algorithm (CS-GOA) is proposed. The performance of the proposed model is compared over the other conventional models, in terms of channel utilization and back off, and proved for supremacy. The throughput of the proposed model, even at 50 SUs is better, when compared to other methods. CS-GOA achieved better channel utilization and backoff rate, as compared to ProMAC and NN, when the numbers of SUs and PUs in the architecture are varied with time. [ABSTRACT FROM AUTHOR]

With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance. [ABSTRACT FROM AUTHOR]

In haptic interaction technology, texture haptic display is an important part. In order to perceive color image texture better, the haptic display methods of color texture based on force feedback and tactile feedback are proposed in this work. On the one hand, through the study of the physiological and psychological perception characteristics of color information, a new force rendering method of color image texture based on force feedback device is presented. The experimental results of color texture force perception show that the color texture force rendering algorithm in this paper works well. On the other hand, a color texture vibration tactile model is established based on the designed vibrotactile device. Its effectiveness is verified by vibration tactile perception experiment. Finally, for the color texture image of the real object surface, the force and vibrotactile display methods of color texture in this paper are used to conduct perception experiments and compared. [ABSTRACT FROM AUTHOR]

The new coronavirus spreads widely through droplets, aerosols and other carriers. Wearing a mask can effectively reduce the probability of being infected by the virus. Therefore, it is necessary to monitor whether people wear masks in public to prevent the virus from spreading further. However, there is no mature general mask wearing detection algorithm. Based on tiny YOLOv3 algorithm, this paper realizes the detection of face with mask and face without mask, and proposes an improvement to the algorithm. First, the loss function of the bounding box regression is optimized, and the original loss function is optimized as the Generalized Intersection over Union (GIoU) loss. Second, the network structure is improved, the residual unit is introduced into the backbone to increase the depth of the network and the detection of two scales is expanded to three. Finally, the size of anchor boxes is clustered based on k -means algorithm. The experimental results on the constructed dataset show that, compared with the tiny YOLOv3 algorithm, the algorithm proposed in this paper improves the detection accuracy while maintaining high-speed inference ability. [ABSTRACT FROM AUTHOR]

Metric learning is a critical problem in classification. Most classifiers are based on a metric, the simplest one is the KNN classifier, whose outcome is directly decided by the given metric. This paper will discuss semi-supervised metric learning. Most traditional semi-supervised metric learning algorithms preserve the local structure of all the samples (including labeled and unlabeled) in the input space, when making the same labeled samples together and separating different labeled samples. In most existing methods, the local structure is calculated by the Euclidean distance which uses all the features. As we all know, high dimensional data lies on a low dimension manifold, and not all the features are discriminative. Thus, in this paper, we try to explore the latent structure of the samples and use the more discriminative features to calculate the local structure. The latent structure is learned by clustering random forest and cast into similarity between samples. Based on the hierarchical structure of the trees and the split function, the similarity is obtained from discriminant features. Experimental results on public data sets show our algorithm outperforms the traditional similar related algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness. [ABSTRACT FROM AUTHOR]

Since its inception, control of data congestion on the Internet has been based on stochastic models. One of the first such models was Random Early Detection. Later, this model was reformulated as a dynamical system, with the average queue sizes at a router's buffer being the states. Recently, the dynamical model has been generalized to improve global stability. In this paper we review the original stochastic model and both nonlinear models of Random Early Detection with a two-fold objective: (i) illustrate how a random model can be "smoothed out" to a deterministic one through data aggregation and (ii) how this translation can shed light into complex processes such as the Internet data traffic. Furthermore, this paper contains new materials concerning the occurrence of chaos, bifurcation diagrams, Lyapunov exponents and global stability robustness with respect to control parameters. The results reviewed and reported here are expected to help design an active queue management algorithm in real conditions, that is, when system parameters such as the number of users and the round-trip time of the data packets change over time. The topic also illustrates the much-needed synergy of a theoretical approach, practical intuition and numerical simulations in engineering. [ABSTRACT FROM AUTHOR]

In this paper, based on multi-arbitrators and the product states, a new quantum signature scheme (QSS) is proposed. In our scheme, two arbitrators are used to share private keys with the signer. The signer encodes the bit message into orthogonal product states, which are locally indistinguishable. Then, to produce the signature, the signer signs the product states with the key-controlled Hadamard operator and Y operator. Our scheme has the good virtues as follows. First, our signature not only has the security property of unforgeability, but also has the property of strong non-repudiation. Second, when signing a message and verifying a signature, the partners do not need to prepare or transmit any entangled particle. Third, the partners do not need to perform the state comparison algorithm. Fourth, the signer does not need to share any private key with the signature verifier. For our scheme, the signer only shares two private keys with the arbitrators. At last, in our scheme, the repudiation can be flexibly solved. Compared with the other schemes with semi-trusted arbitrator, ours has better performances in security, practicability and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a quantum decomposition algorithm (QDA) that decomposes the problem ∂ ρ ∂ t = ℒ ρ = λ ρ into a summation of eigenvalues times phase–space variables. One interesting feature of QDA stems from its ability to simulate damped spin systems by means of pure quantum harmonic oscillators adjusted with the eigenvalues of the original eigenvalue problem. We test the proposed algorithm in the case of undriven qubit with spontaneous emission and dephasing. [ABSTRACT FROM AUTHOR]

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hyperchaotic circuit system that is extremely sensitive to circuit parameters and system initial values. The typical dynamical behaviors of the hyperchaotic system such as one-parameter bifurcation diagrams, two-parameter bifurcation diagrams and Lyapunov exponent spectrum are discussed in detail. Then an image encryption algorithm is designed by using the hyperchaotic sequences. In this algorithm, Zigzag algorithm is used to scramble pixel positions, and backward diffusion and forward diffusion algorithms are used to diffuse pixel values. The results indicate that the proposed algorithm can improve the security of the image encryption algorithm. In addition, some circuit simulation experiments are performed to verify the sensitivity of the hyperchaotic circuit to parameters by Multisim. Finally, the hyperchaotic circuit is implemented by using physical elements. The simulation results are consistent with the experimental results of hardware circuit, which effectively verifies the existence of hyperchaotic attractors. [ABSTRACT FROM AUTHOR]

The main intention of this paper is to develop a new intelligent framework for web page classification and re-ranking. The two main phases of the proposed model are (a) classification, and (b) re-ranking-based retrieval. In the classification phase, pre-processing is initially performed, which follows the steps like HTML (Hyper Text Markup Language) tag removal, punctuation marks removal, stop words removal, and stemming. After pre-processing, word to vector formation is done and then, feature extraction is performed by Principle Component Analysis (PCA). From this, optimal feature selection is accomplished, which is the important process for the accurate classification of web pages. Web pages contain several features, which reduces the classification accuracy. Here, the adoption of a new meta-heuristic algorithm termed Opposition based-Tunicate Swarm Algorithm (O-TSA) is employed to perform the optimal feature selection. Finally, the selected features are subjected to the Enhanced Convolutional-Recurrent Neural Network (E-CRNN) for accurate web page classification with enhancement based on O-TSA. The outcome of this phase is the categorization of different web page classes. In the second phase, the re-ranking is involved utilizing the O-TSA, which derives the objective function based on similarity function (correlation) for URL matching, which results in optimal re-ranking of web pages for retrieval. Thus, the proposed method yields better classification and re-ranking performance and reduce space requirements and search time in the web documents compared with the existing methods. [ABSTRACT FROM AUTHOR]

Clustering techniques are used to split data into clusters where each cluster contains elements that look more similar to elements in the same cluster than elements in other clusters. Some of these techniques are capable of handling clustering process uncertainty, while other techniques may have stability issues. In this paper, a novel method, called Minimum Information Gain Roughness (MIGR), is proposed to select the clustering attribute based on information entropy with rough set theory. To evaluate its performance, three benchmark UCI datasets are chosen to be clustered by using MIGR. Then, the resulting clusters are compared to those which are resulted from applying Min-Min-Rough (MMR) and information-theoretic dependency roughness (ITDR) algorithms. Both last-mentioned techniques were already compared with a variety of clustering algorithms like k-modes, fuzzy centroids, and fuzzy k-modes. The Global purity, the overall purity, and F-measure are considered here as performance measures to compare the quality of the resulting clusters. The experimental results show that the MIGR algorithm outperforms both MMR and ITDR algorithms for clustering categorical data. [ABSTRACT FROM AUTHOR]

In this paper, we present an approximation algorithm for the maximum independent set (MIS) problem over the class of B 1 -VPG graphs when the input is specified by a B 1 -VPG representation. We obtain a 6 (log n) 2 -approximation algorithm running in O (n (log n) 3) time. This is an improvement over the previously best n -approximation algorithm [J. Fox and J. Pach, Computing the independence number of intersection graphs, in Proc. Twenty-Second Annual ACM-SIAM Symp. Discrete Algorithms (SODA 2011), 2011, pp. 1161–1165, doi:10.1137/1.9781611973082.87] (for some fixed > 0) designed for some subclasses of string graphs, on B 1 -VPG graphs. [ABSTRACT FROM AUTHOR]

In order to solve the key problems of secondary surveillance radar signal debugging on special devices such as DSP and FPGA, a parallel processing scheme of secondary surveillance radar response signal is proposed based on CPU–GPU architecture. It can effectively reduce the difficulty of code development and improve the portability of program. The parallel optimization design of each processing model of response signal is made through the characteristics of shared memory in CPU–GPU architecture to improve the processing speed of the algorithm. The proposed scheme is tested and analyzed on different graphics cards by the secondary surveillance radar Mode-5 response signal in this paper. The experimental results showed that it takes 8390.52960 us to implement the signal processing algorithm based on NVIDIA Tesla K40c graphics card, which can save 51.98% of time than NVIDIA Quadro K4200 graphics card, and makes it possible to do the real-time processing of the secondary surveillance radar signal. [ABSTRACT FROM AUTHOR]

Functional Distributed Constraint Optimization Problems (F-DCOPs) are a constraint processing framework for continuous variables in the multi-agent system. Particle swarm optimization-based F-DCOP (PFD) is a population-based algorithm to solve F-DCOP collaboratively. Although it can significantly reduce the computational overhead and memory requirements, its solution depends on the decision of root agent in the Breadth First Search (BFS) pseudo-tree and it is easy to fall into local optimum. To solve the above problems, this paper designed an improved PFD algorithm with Local Decision named PFD-LD, which effectively reduces the dependence on root agent through local decision. In addition, a mutation operator is used to avoid falling into local optimum. It is proved that PFD-LD is an anytime algorithm and local decision can expand the search of the solution space. Finally, the extensive experiments based on four types of benchmark problems show that the proposed algorithm outperforms state-of-the-art F-DCOP solving algorithms. [ABSTRACT FROM AUTHOR]

The existing helmet detection algorithms have disadvantages such as difficulty in detecting occluded targets, small targets, etc. To address those problems, a YOLO V4-based helmet detection improvement algorithm has been proposed. Firstly, the model's backbone structure is improved, and the backbone's multi-scale feature extraction capability is enhanced by using MCM modules with different sized convolutional kernels, the FSM channel attention module is used to guide the model to dynamically focus on the channel features of extracted small targets and obscured target information. Secondly, in order to optimize the model training, the latest loss function Eiou is used to replace Ciou for anchor frame regression prediction to improve the convergence speed and regression accuracy of the model. Finally, a helmet dataset is constructed from this paper, and a K-means clustering algorithm is used to cluster the helmet dataset and select the appropriate a priori candidate frames. The experimental results show that the improved algorithm has a significant improvement in detection accuracy compared with the original YOLO V4 algorithm, and can have a positive detection effect on small targets and obscured targets. [ABSTRACT FROM AUTHOR]

The prefix sums problem for an array X = (x 0 , x 1 , ... , x n − 1) is to compute all sums x 0 + x 1 ⋯ + x i , ∀ 0 ≤ i < n. In this paper, we introduce an improvement for the best previous algorithm that runs in O (log ∗ n) time using O (n / log ∗ n) processors on a Sum Concurrent Read Concurrent Write, Parallel Random Access Machine (Sum-CRCW PRAM). The improvements include (1) reducing the total number of operations and (2) reducing the amount of storage required by the algorithm. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, mobile wireless network as an important network communication mode is more and more widely used, but its security is worth paying attention to. According to different environment possible switch mobile wireless network security problem, the paper introduced secure localization algorithm, through combing the existing security vertical switching process, the corresponding vertical safety switch security index system was constructed, and the simulation experiment, from safety factor to switch from the mobile wireless network security monitoring the status of the transformation, and use the safety parameter to carry on the quantified computation expression. The simulation results prove that the visualization of the 5G network clearly shows the status of WLAN1. The simulation results show that the secure location algorithm is effective and can support secure vertical handoff in mobile wireless network. [ABSTRACT FROM AUTHOR]