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A central aim of cognitive science is to understand the fundamental mechanisms that enable humans to navigate and make sense of complex environments. In this letter, we argue that computational complexity theory, a foundational framework for evaluating computational resource requirements, holds significant potential in addressing this challenge. As humans possess limited cognitive resources for processing vast amounts of information, understanding how humans perform complex cognitive tasks requires comprehending the underlying factors that drive information processing demands. Computational complexity theory provides a comprehensive theoretical framework to achieve this goal. By adopting this framework, we can gain new insights into how cognitive systems work and develop a more nuanced understanding of the relation between task complexity and human behavior. We provide empirical evidence supporting our argument and identify several open research questions and challenges in applying computational complexity theory to human decision-making and cognitive science at large., (© 2023 Cognitive Science Society LLC.)

The implementation of turn restrictions represents a critical research challenge in chiplet-based systems, with the objective of achieving deadlock-free communication. Nevertheless, existing methodologies encounter difficulties in terms of computational complexity, which impedes the design process. Moreover, as the scale of the problem increases, the cost of addressing it becomes increasingly untenable. In this paper, we introduce the efficient turn restrictions setting (ETRS) method to reduce the computational cost of implementing turn restrictions for boundary routers in chiplet-based systems. In stage 1, we present a symmetry-based preprocessing algorithm (SBPA). SBPA exploits the symmetry inherent in chiplet topologies by generating multiple sets of identical modes, ensuring that scenarios with the same objective function value are calculated only once per iteration. In stage 2, a heuristic selection algorithm (HSA) for turn restrictions based on NSGA-II is proposed as a means of searching for approximately optimal solutions and of solving the problem quickly. In stage 3, different filtering criteria are introduced to evaluate the Pareto fronts of HSA for making decisions on the placement of boundary routers and their turn restrictions. Evaluation results reveal that the proposed ETRS method surpasses existing solutions in terms of computation efficiency. Moreover, it delivers satisfactory optimal objective values. [ABSTRACT FROM AUTHOR]

Possibilistic fuzzy c-means (PFCM) clustering is a kind of hybrid clustering method based on fuzzy c-means (FCM) and possibilistic c-means (PCM), which not only has the stability of FCM but also partly inherits the robustness of PCM. However, as an extension of FCM on the objective function, PFCM tends to find a suboptimal local minimum, which affects its performance. In this paper, we rederive PFCM using the majorization-minimization (MM) method, which is a new derivation approach not seen in other studies. In addition, we propose an effective optimization method to solve the above problem, called MMPFCM. Firstly, by eliminating the variable V ∈ R p × c , the original optimization problem is transformed into a simplified model with fewer variables but a proportional term. Therefore, we introduce a new intermediate variable s ∈ R c to convert the model with the proportional term into an easily solvable equivalent form. Subsequently, we design an iterative sub-problem using the MM method. The complexity analysis indicates that MMPFCM and PFCM share the same computational complexity. However, MMPFCM requires less memory per iteration. Extensive experiments, including objective function value comparison and clustering performance comparison, demonstrate that MMPFCM converges to a better local minimum compared to PFCM. [ABSTRACT FROM AUTHOR]

In order to solve the problem of missing various targets due to the limited memory and computing power of underwater equipment and also the complexity of the underwater environment, a lightweight and efficient underwater target detection algorithm YOLO_GN (YOLO with Ghost network) is proposed. Based on the basic framework of YOLOv5s, the algorithm designs a new backbone using GhostNetV2 and proposes Ghost_BottleneckV2 combined with dynamic sparse attention BiFormer to reduce computational costs and improve detection accuracy. The lightweight multi-scale convolutional LW-GSConv combined with VOV-GSCSP is used to capture the complex features of the input data more accurately and improve the network expression ability. In view of the imbalance of a large number of detection samples in underwater targets, the SlideLoss function is introduced and the optimizer of original model is updated to Sophia, so that the algorithm model has better generalization ability. Finally, the YOLO_GN algorithm is equipped with the Raspberry Pi 4B development board, and the camera is called up to realize real-time detection of underwater targets. Simulation results show that the proposed method can achieve 85.35% detection accuracy on the URPC dataset, which is 2.43% higher than the most common architecture in underwater scenarios, and computational complexity is reduced by 46.47%. Moreover, it can achieve better object detection effect in embedded terminals. [ABSTRACT FROM AUTHOR]

A broadband preconditioner based on a modified version of the sparsified nested dissection ordering (m-spaNDO) technique is proposed for the full wave discrete exterior calculus (DEC) A-Φformulation solver in electromagnetics. The matrix equation discretized by the DEC A-Φ solver is in general complex symmetric and indefinite. When conductive media and disparate mesh are involved, the DEC A-Φ matrix equation is ill-conditioned, and proper preconditioner must be utilized to accelerate iterative solver convergence. In this letter, an introduction to the DEC A-Φ solver is provided, followed by the implementation details of the m-spaNDO preconditioner. Numerical examples in this paper show that the proposed m-spaNDO preconditioner can effectively accelerate the convergence of iterative solvers in solving ill-conditioned problems. The m-spaNDO preconditioned DEC A-Φ solver has O(N logN) computational complexity and the efficiency of the preconditioner is independent of change in parameters such as frequency and conductivity in the problem, which indicates the broadband stable nature of the m-spaNDO preconditioner. [ABSTRACT FROM AUTHOR]

Image fusion is a pivotal image-processing technology designed to merge multiple images from various sensors or imaging modalities into a single composite image. This process enhances and extracts the information contained across the images, resulting in a final image that is more informative and of superior quality. This paper introduces a novel method for infrared and visible image fusion, utilizing nested connections and frequency-domain decomposition techniques to effectively solve the problem of lost image detail features. By incorporating depthwise separable convolution technology, the method reduces the computational complexity and model size, thereby increasing computational efficiency. A multi-scale residual fusion network, R2FN (Res2Net Fusion Network), has been designed to replace traditional manually designed fusion strategies, enabling the network to better preserve detail information in the image while improving the quality of the fused image. Moreover, a new loss function is proposed, which is aimed at enhancing important feature information while preserving more significant features. Experimental results on public datasets indicate that the method not only retains the detail information of visible-light images but also highlights the significant features of infrared images while maintaining a minimal number of parameters. [ABSTRACT FROM AUTHOR]

In this study, a tyre defect detection model is improved and optimized under the YOLOv5 framework, aiming at radial tyre defects with characteristics such as an elongated shape and various target sizes and defect types. The DySneakConv module is introduced to replace the first BotteneckCSP in the Backbone network. The deformation offset of the DySneakConv module is used to make the convolutional energy freely adapt to the structure to improve the recognition rate of tyre defects with elongated features; the AIFI module is introduced to replace the fourth BotteneckCSP, and the self-attention mechanism and the processing of large-scale features are used to improve the recognition rate of tyre defects with elongated features using the AIFI module. This latter module has a self-attention mechanism and the ability to handle large-scale features to solve the problems of diverse tyre defects and different sizes. Secondly, the CARAFE up-sampling operator is introduced to replace the up-sampling operator in the Neck network. The up-sampling kernel prediction module in the CARAFE operator is used to increase the receptive field and allow the feature reorganization module to capture more semantic information to overcome the information loss problem of the up-sampling operator. Finally, based on the improved YOLOv5 detection algorithm, the Channel-wise Knowledge Distillation algorithm lightens the model, reducing its computational requirements and size while ensuring detection accuracy. Experimental studies were conducted on a dataset containing four types of tyre defects. Experimental results for the training set show that the improved algorithm improves the mAP0.5 by 4.6 pp, reduces the model size by 25.6 MB, reduces the computational complexity of the model by 31.3 GFLOPs, and reduces the number of parameters by 12.7 × 106 compared to the original YOLOv5m algorithm. Experimental results for the test set show that the improved algorithm improves the mAP0.5 by 2.6 pp compared to the original YOLOv5m algorithm. This suggests that the improved algorithm is more suitable for tyre defect detection than the original YOLOv5. [ABSTRACT FROM AUTHOR]

It is difficult for lightweight neural networks to produce accurate 6DoF pose estimation effects due to their accuracy being affected by scale changes. To solve this problem, we propose a method with good performance and robustness based on previous research. The enhanced PVNet-based method uses depth-wise convolution to build a lightweight network. In addition, coordinate attention and atrous spatial pyramid pooling are used to ensure accuracy and robustness. This method effectively reduces the network size and computational complexity and is a lightweight 6DoF pose estimation method based on monocular RGB images. Experiments on public datasets and self-built datasets show that the average ADD(-S) estimation accuracy and 2D projection index of the improved method are improved. For datasets with large changes in object scale, the estimation accuracy of the average ADD(-S) is greatly improved. [ABSTRACT FROM AUTHOR]

The mathematical side of applied problems in multiple subject areas (biology, pattern recognition, etc.) is reduced to the problem of discrete optimization in the following mathematical method. We were provided a network and graphs in its leaves, for which we needed to find a rearrangement of graphs by non-leaf nodes, in which the given functional reached its minimum. Such a problem, even in the simplest case, is NP-hard, which means unavoidable restrictions on the network, on graphs, or on the functional. In this publication, this problem is addressed in the case of all graphs being so-called "structures", meaning directed-loaded graphs consisting of paths and cycles, and the functional as the sum (over all edges in the network) of distances between structures at the endpoints of every edge. The distance itself is equal to the minimal length of sequence from the fixed list of operations, the composition of which transforms the structure at one endpoint of the edge into the structure at its other endpoint. The list of operations (and their costs) on such a graph is fixed. Under these conditions, the given discrete optimization problem is called the reconstruction problem. This paper presents novel algorithms for solving the reconstruction problem, along with full proofs of their low error and low polynomial complexity. For example, for the network, the problem is solved with a zero error algorithm that has a linear polynomial computational complexity; and for the tree the problem is solved using an algorithm with a multiplicative error of at most two, which has a second order polynomial computational complexity. [ABSTRACT FROM AUTHOR]

This paper presents an efficient two-dimensional (2D) direction of arrival (DOA) estimation method, termed as decoupled projected atomic norm minimization (D-PANM), to solve the angle-ambiguity problem. It first introduces a novel atomic metric via projecting the original atom set onto a smoothing space, based on which we formulate an equivalent semi-definite programming (SDP) problem. Then, two relatively low-complexity decoupled Toeplitz matrices can be obtained to estimate the DOAs. We further exploit the structural information hidden in the newly constructed data to avoid pair matching for the azimuth and elevation angles when the number of sensors is odd, and then propose a fast and feasible decoupled alternating projections (D-AP) algorithm, reducing computational complexity to a great extent. Numerical simulations are performed to demonstrate that the proposed algorithm is no longer restricted by angle ambiguity scenarios, but instead provides a more stable estimation performance, even when multiple signals share the same angles in both azimuth and elevation dimensions. Additionally, it greatly improves the resolution, with control of the computation load compared with the existing atomic norm minimization (ANM) algorithm. [ABSTRACT FROM AUTHOR]

Bayesian optimization algorithms are widely used for solving problems with a high computational complexity in terms of objective function evaluation. The efficiency of Bayesian optimization is strongly dependent on the quality of the surrogate models of an objective function, which are built and refined at each iteration. The quality of surrogate models, and hence the performance of an optimization algorithm, can be greatly improved by selecting the appropriate hyperparameter values of the approximation algorithm. The common approach to finding good hyperparameter values for each iteration of Bayesian optimization is to build surrogate models with different hyperparameter values and choose the best one based on some estimation of the approximation error, for example, a cross-validation score. Building multiple surrogate models for each iteration of Bayesian optimization is computationally demanding and significantly increases the time required to solve an optimization problem. This paper suggests a new approach, called exploratory landscape validation, to find good hyperparameter values with less computational effort. Exploratory landscape validation metrics can be used to predict the best hyperparameter values, which can improve both the quality of the solutions found by Bayesian optimization and the time needed to solve problems. [ABSTRACT FROM AUTHOR]

In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research methods, such as Bayesian attack graphs, lack evidence correlation and real-time reflection of the network attack events, and high computational complexity for attack analysis. To solve these problems, this study proposes a Dynamic Uncertain Causal Attack Graph (DUCAG) model and a Causal Chain-based Risk Probability Calculation (CCRP) algorithm. The DUCAG model is constructed to represent the uncertain underlying causalities among network attack events, and the CCRP algorithm aims at dynamically updating the causality weights among different network attack events and attacker hypotheses based on alarm information and causal chain reasoning process. By causality simplification and causality reasoning methods, the CCRP efficiently predicts the attacker behaviors and potential attack likelihood under uncertain time-varying attack situations, and is robust to the incompleteness and redundancy in alarm information. Four experiments under different attack scenarios demonstrate that, the DUCAG model can effectively characterize and predict the complex and uncertain attack causalities, in a manner of high time efficiency. The proposed method has application significance on cloud computing platforms by dynamically evaluating network security status, predicting the future behaviors of attackers, and assisting in adjusting network defense strategies. [ABSTRACT FROM AUTHOR]

To overcome the problems of the high sidelobe levels and low computational efficiency of traditional Capon-based beamformers in optimizing the two-dimensional (elevation–azimuth) beampatterns of conformal arrays, in this paper, we propose a robust Capon beamforming method with sparse group constraints that is solved using the alternating-direction method of multipliers (ADMM). A robustness constraint based on worst-case performance optimization (WCPO) is imposed on the standard Capon beamformer (SCB) and then the sparse group constraints are applied to reduce the sidelobe level. The constraints are two sparsity constraints: the group one and the individual one. The former was developed to exploit the sparsity between groups based on the fact that the sidelobe can be divided into several different groups according to spatial regions in two-dimensional beampatterns, rather than different individual points in one-dimensional (azimuth-only) beampatterns. The latter is considered to emphasize the sparsity within groups. To solve the optimization problem, we introduce the ADMM to obtain the closed-form solution iteratively, which requires less computational complexity than the existing methods, such as second-order cone programming (SOCP). Numerical examples show that the proposed method can achieve flexible sidelobe-level control, and it is still effective in the case of steering vector mismatch. [ABSTRACT FROM AUTHOR]

Although some existing sparse representation (SR) methods are robust for target detection in passive bistatic radar (PBR), they still face the challenges of high computational complexity and poor detection performance for extremely low-signal-to-clutter ratio (SCR) target. So, an average effective subcarrier (AES)-domain sparse representation approach is investigated in this paper. Firstly, the AES-based SR model is proposed to solve the problem of high computational complexity, which is established by utilizing the sparseness of the orthogonal frequency-division multiplexing (OFDM) with cyclic prefix (CP) signals in each effective subcarrier domain. Then, considering the difficulty of detecting extremely low-SCR targets, clutter cancellation is implemented by the SR-based optimization model. Two AES-S algorithms, namely AES-S-based clutter cancellation in the time domain (AES-S-T) and AES-S-based clutter cancellation in the subcarrier domain (AES-S-C), are proposed, and the computational complexity is further reduced. Finally, extensive simulation and experimental results illustrate that the proposed algorithms have good detection performance and low computational complexity in PBR detection scene. [ABSTRACT FROM AUTHOR]

Fractal antennas are mainly used in multiband applications. However, these types of arrays suffer from numerous disadvantages, such as high sidelobe levels, low directivity, poor taper efficiency, and high design computational complexity. In this paper, the conventional fractal procedures are redesigned and efficient clustered subarrays are deployed, such that their multiband properties are maintained while simultaneously achieving significant improvements in radiation characteristics. A genetic optimization algorithm is used to find the optimal clustered fractal shapes and their associated amplitude distributions, such that the sidelobe levels are minimized at the narrower beam width, i.e. maximum feasible directivity. Since the optimization process is carried out at the clustered level, it can be represented by merely a few variables, which solves the problem of time intensity. Simulation results confirm the superiority of the proposed clustered fractal array, where the sidelobe level has been reduced to more than -10 dB over a wide range of frequencies. Directivity and taper efficiency have been improved by more than 6 dB and 50%, respectively, in comparison to the parameters of conventional, original fractal arrays. Moreover, the proposed fractal array pattern offers an additional advantage, as it is capable of wide sidelobe nulling at some undesired directions. [ABSTRACT FROM AUTHOR]

Traditional human pose estimation methods typically rely on complex models and algorithms. Lite-HRNet can achieve an excellent performance while reducing model complexity. However, its feature extraction scale is relatively single, which can lead to lower keypoints' localization accuracy in crowded and complex scenes. To address this issue, we propose a lightweight human pose estimation model based on a joint channel coordinate attention mechanism. This model provides a powerful information interaction channel, enabling features of different resolutions to interact more effectively. This interaction can solve the problem of human pose estimation in complex scenes and improve the robustness and accuracy of the pose estimation model. The introduction of the joint channel coordinate attention mechanism enables the model to more effectively retain key information, thereby enhancing keypoints' localization accuracy. We also redesign the lightweight basic module using the shuffle module and the joint channel coordinate attention mechanism to replace the spatial weight calculation module in the original Lite-HRNet model. By introducing this new module, we not only improve the network calculation speed and reduce the number of parameters of the entire model, but also ensure the accuracy of the model, thereby achieving a balance between performance and efficiency. We compare this model with current mainstream methods on the COCO and MPII dataset. The experimental results show that this model can effectively reduce the number of parameters and computational complexity while ensuring high model accuracy. [ABSTRACT FROM AUTHOR]

The secure computation of the graph structure is an important element in the field of secure calculation of graphs, which is important in querying data in graphs, since there are no algorithms for the graph edit distance problem that can resist attacks by malicious adversaries. In this paper, for the problem of secure computation of similarity edit distance of graphs, firstly, the encoding method applicable to the Paillier encryption algorithm is proposed, and the XOR operation scheme is proposed according to the Paillier homomorphic encryption algorithm. Then, the security algorithm under the semi-honest model is designed, which adopts the new encoding method and the XOR operation scheme. Finally, for the malicious behaviors that may be implemented by malicious participants in the semi-honest algorithm, using the hash function, a algorithm for secure computation of graph editing distance under the malicious model is designed, and the security of the algorithm is proved, and the computational complexity and the communication complexity of the algorithm are analyzed, which is more efficient compared with the existing schemes, and has practical value. The algorithm designed in this paper fills the research gap in the existing literature on the problem of graph edit distance and contributes to solving the problem. [ABSTRACT FROM AUTHOR]

Learning effective graph representations in an unsupervised manner is a popular research topic in graph data analysis. Recently, contrastive learning has shown its success in unsupervised graph representation learning. However, how to avoid collapsing solutions for contrastive learning methods remains a critical challenge. In this paper, a simple method is proposed to solve this problem for graph representation learning, which is different from existing commonly used techniques (such as negative samples or predictor network). The proposed model mainly relies on an asymmetric design that consists of two graph neural networks (GNNs) with unequal depth layers to learn node representations from two augmented views and defines contrastive loss only based on positive sample pairs. The simple method has lower computational and memory complexity than existing methods. Furthermore, a theoretical analysis proves that the asymmetric design avoids collapsing solutions when training together with a stop-gradient operation. Our method is compared to nine state-of-the-art methods on six real-world datasets to demonstrate its validity and superiority. The ablation experiments further validated the essential role of the asymmetric architecture. [ABSTRACT FROM AUTHOR]

The fast and accurate detection of steel surface defects has become an important goal of research in various fields. As one of the most important and effective methods of detecting steel surface defects, the successive generations of YOLO algorithms have been widely used in these areas; however, for the detection of tiny targets, it still encounters difficulties. To solve this problem, the first modified PP-YOLOE algorithm for small targets is proposed. By introducing Coordinate Attention into the Backbone structure, we encode channel relationships and long-range dependencies using accurate positional information. This improves the performance and overall accuracy of small target detection while maintaining the model parameters. Additionally, simplifying the traditional PAN+FPN components into an optimized FPN feature pyramid structure allows the model to skip computationally expensive but less relevant processes for the steel surface defect dataset, effectively reducing the computational complexity of the model. The experimental results show that the overall average accuracy (mAP) of the improved PP-YOLOE algorithm is increased by 4.1%, the detection speed is increased by 2.06 FPS, and the accuracy of smaller targets (with a pixel area less than 322) that are more difficult to detect is significantly improved by 13.3% on average, as compared to the original algorithm. The detection performance is also higher than that of the mainstream target detection algorithms, such as SSD, YOLOv3, YOLOv4, and YOLOv5, and has a high application value in industrial detection. [ABSTRACT FROM AUTHOR]

During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter. [ABSTRACT FROM AUTHOR]

Research on fire detection based on deep learning has been increasing lately, and current models differ in accuracy and computational complexity. To reduce the loss of semantic information and improve prediction accuracy, this paper combines dynamic threshold grayscale segmentation and residual network transfer learning. An effective training data extension strategy integrates the experimental data of a real fire platform and a forest-image-based fire database, where the experimental data of the real fire platform are shot based on different lighting and backgrounds. It has been proven that this is a good solution that can effectively solve the problem of training robust deep models relying on datasets with large diversity, which can improve the overall performance of the training model. Then, we introduce a network architecture based on dynamic threshold grayscale segmentation and residual net transfer learning, which reaches residual net level accuracy on image-based fire detection tasks with less computation. We train the model with a self-built image dataset, test it on a general fire image dataset, and evaluate it on a self-built fire video dataset. In numerous experiments, our method produced a training and testing accuracy of 98.26% and 99.90%, respectively, with corresponding losses of 0.036 and 0.04. The evaluation accuracy was 90% under the condition of a complex sample environment. Compared with other models, our method has advantages in accuracy, training loss, and cost time. Comprehensive comparisons show effectiveness and improved overall performance. [ABSTRACT FROM AUTHOR]

In this paper, the problem of single-channel blind source separation (SCBSS) is addressed using a novel approach that combines the adaptive mode separation-based wavelet transform (AMSWT) and the density-based clustering with sparse reconstruction. The proposed method is performed in the time–frequency domain and in a reverberant environment. First, using the Fourier transform, the amplitude spectrum of the observed mixture signal is obtained. Then, using variational scaling and wavelet functions, the AMSWT is used to adaptively extract spectral intrinsic components (SICs). To obtain a better time–frequency resolution, the AMSWT is applied to each mode. Thus, the SCBSS problem is transformed into a non-underdetermined. Then, for each frequency bin, the density-based clustering, reformulated to the eigenvector clustering problem, is performed to estimate the mixing matrix. Finally, sparse reconstruction is used to reconstruct the sources. The proposed method is evaluated using objective measures of separation quality. A computational complexity evaluation based on time consumption is also performed. Simulation results show that the proposed method is very effective for solving the SCBSS problem and provides better separation performances than the reference methods. However, the proposed method is computationally expensive. [ABSTRACT FROM AUTHOR]

Case-based planning is an approach to planning where previous planning experience provides guidance to solving new problems. Such a guidance can be extremely useful, or even necessary, when the new problem is very hard to solve, or the stored previous experience is highly valuable, because, e.g., it was provided or validated by human experts, and the system should try to reuse it as much as possible. To do so, a case-based planning system stores in a library previous planning experience in the form of already encountered problems and their solutions. The quality of such a plan library critically inuences the performance of the planner, and therefore it needs to be carefully designed and created. For this reason, it is also important to update the library during the lifetime of the system, as the type of problems being addressed may evolve or differ from the ones the library was originally designed for. Moreover, like in general case-based reasoning, the library needs to be maintained at a manageable size, otherwise the computational cost of querying it grows excessively, making the entire approach ineffective. In this paper, we formally define the problem of maintaining a library of cases, discuss which criteria should drive the maintenance, study the computational complexity of the maintenance problem, and propose offline techniques to reduce an oversized library that optimize different criteria. Moreover, we introduce a complementary online approach that attempts to limit the growth of the library, and we consider the combination of offline and online techniques to ensure the best performance of the case-based planner. Finally, we experimentally show the practical effectiveness of the offline and online methods for reducing the library. [ABSTRACT FROM AUTHOR]

To meet the high-performance requirements of fifth-generation (5G) and sixth-generation (6G) wireless networks, in particular, ultra-reliable and low-latency communication (URLLC) is considered to be one of the most important communication scenarios in a wireless network. In this paper, we consider the effects of the Rician fading channel on the performance of cooperative device-to-device (D2D) communication with URLLC. For better performance, we maximize and examine the system's minimal rate of D2D communication. Due to the interference in D2D communication, the problem of maximizing the minimum rate becomes non-convex and difficult to solve. To solve this problem, a learning-to-optimize-based algorithm is proposed to find the optimal power allocation. The conventional branch and bound (BB) algorithm are used to learn the optimal pruning policy with supervised learning. Ensemble learning is used to train themultiple classifiers. To address the imbalanced problem, we used the supervised undersampling technique. Comparisons are made with the conventional BB algorithm and the heuristic algorithm. The outcome of the simulation demonstrates a notable performance improvement in power consumption. The proposed algorithm has significantly low computational complexity and runs faster as compared to the conventional BB algorithm and a heuristic algorithm. [ABSTRACT FROM AUTHOR]

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Human pose estimation, as the basis of advanced computer vision, has a wide application perspective. In existing studies, the high-capacity model based on the heatmap method can achieve accurate recognition results, but it encounters many difficulties when used in real-world scenarios. To solve this problem, we propose a lightweight pose regression algorithm (RepNet) that introduces a multi-parameter network structure, fuses multi-level features, and combines the idea of residual likelihood estimation. A well-designed convolutional architecture is used for training. By reconstructing the parameters of each level, the network model is simplified, and the computation time and efficiency of the detection task are optimized. The prediction performance is also improved by the output of the maximum likelihood model and the reversible transformation of the underlying distribution learned by the flow generation model. RepNet achieves a recognition accuracy of 66.1 AP on the COCO dataset, at a computational speed of 15 ms on GPU and 40 ms on CPU. This resolves the contradiction between prediction accuracy and computational complexity and contributes to research in lightweight pose estimation. [ABSTRACT FROM AUTHOR]

With unlabeled and high-dimensional data explosion, unsupervised feature selection has become an essential step in many machine learning and data mining tasks. Many dictionary learning based models have been successfully developed for unsupervised feature selection in recent years. These models learn an over-complete dictionary to investigate more data distribution information. However, over-complete dictionary learning will generate redundancy in the latent representations for data. Moreover, if data contain noise, dictionary learning will also yield noise in the latent representations. In this paper, we propose a novel unsupervised feature selection framework, named dictionary learning for unsupervised feature selection via dual sparse regression. In this model, dictionary learning is first embedded into a sparse regression to learn an over-complete dictionary with sparse representations for data, in which the redundancy and noise are eliminated. The data are then projected to the representations to evaluate the significance of features using the other sparse regression. We also offer an efficient algorithm to solve this problem and theoretically analyze its convergence and computational complexity, which is proportional to the data dimensionality. Finally, the evaluation results with the k-means task utilizing the selected features on 9 benchmark datasets demonstrate the superiority of our approach in terms of effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

The satisfiability problem (SAT) in propositional logic determines if there is an assignment of values that makes a given propositional formula true. Recently, fast SAT solvers have been developed, and SAT encoding research has gained attention. This enables various real-world problems to be transformed into SAT and solved, realizing a solution to the original problems. We propose a new encoding method, Fractional Encoding, which focuses on the At-Most-K constraints—a bottleneck of computational complexity—and reduces the scale of logical expressions by dividing target variables. Furthermore, we confirm that Fractional Encoding outperforms existing methods in terms of the number of generated clauses and required auxiliary variables. Hence, it enables the efficient solving of real-world problems like planning and hardware verification. [ABSTRACT FROM AUTHOR]

Although the discriminative correlation filter- (DCF)-based tracker improves tracking performance, some object representation issues can still be further optimized. On the one hand, the DCF tracker's deep convolutional features contain many noisy channels, and assigning the same weights to multiple channels cannot distinguish the importance of different channels. On the other hand, a simple weighted fusion approach cannot fully utilize the benefits of different feature types. We propose a visual object tracking algorithm based on adaptive channel weighting and feature game fusion to solve these problems. In this study, an adaptive channel weighting strategy is designed to assign suitable weights to each channel based on the average energy ratio of the target and background regions in the feature channels and prune the channels with low weights to improve feature robustness and reduce computational complexity. Simultaneously, the game theory concept is introduced in the multifeature fusion. The handcrafted features are combined with shallow and deep convolutional features according to feature complementarity. Then, the two combined features are seen as two sides of the game, continuously gamed during the tracking process to generate a feature model with a higher representation capacity. Extensive experiments are conducted on four mainstream visual tracking benchmark datasets, including OTB2015, VOT2018, LaSOT, and UAV123. The experimental results show that the proposed algorithm performs outstandingly compared to the state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

The cryptographic system is the foundation of blockchain security mechanisms. This paper constructs a blockchain security mechanism based on the Chinese national cryptographic SM2 algorithm, involving the generation of public–private key pairs and the signing of block information using key pairs. However, the original SM2 algorithm has a high computational complexity, which cannot meet the requirements of low on-chain delay in blockchain applications. To solve this problem, this paper optimizes the SM2 digital signature algorithm. The scalar multiplication operation in the digital signature algorithm has a large computational complexity, which affects the efficiency of a digital signature. To reduce its computational complexity, this paper improves the traditional window non-adjacent form (w-NAF) algorithm for the scalar multiplication operation and proposes a low computational complexity Fw-NAF algorithm based on the prime number precomputation window, which greatly reduces its computational complexity. [ABSTRACT FROM AUTHOR]

Versatile Video Coding (VVC) introduces many new coding technologies, such as quadtree with nested multi-type tree (QTMT), which greatly improves the efficiency of VVC coding. However, its computational complexity is higher, which affects the application of VVC in real-time scenarios. Aiming to solve the problem of the high complexity of VVC intra coding, we propose a low-complexity partition algorithm based on edge features. Firstly, the Laplacian of Gaussian (LOG) operator was used to extract the edges in the coding frame, and the edges were divided into vertical and horizontal edges. Then, the coding unit (CU) was equally divided into four sub-blocks in the horizontal and vertical directions to calculate the feature values of the horizontal and vertical edges, respectively. Based on the feature values, we skipped unnecessary partition patterns in advance. Finally, for the CUs without edges, we decided to terminate the partition process according to the depth information of neighboring CUs. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 54.08% of the encoding time on average, and the BDBR (Bjøntegaard delta bit rate) only increases by 1.61%. [ABSTRACT FROM AUTHOR]

In order to overcome the shortcomings of existing electrowetting display defect detection models in terms of computational complexity, structural complexity, detection speed, and detection accuracy, this article proposes an improved YOLOv7-based electrowetting display defect detection model. The model effectively optimizes the detection performance of display defects, especially small target defects, by integrating GhostNetV2 modules, Acmix attention mechanisms, and NGWD (Normalized Gaussian Wasserstein Distance) Loss. At the same time, it reduces the parameter size of the network model and improves the inference efficiency of the network. This article evaluates the performance of an improved model using a self-constructed electrowetting display defect dataset. The experimental results show that the proposed improved model achieves an average detection rate (mAP) of 89.5% and an average inference time of 35.9 ms. Compared to the original network, the number of parameters and computational costs are reduced by 19.2% and 64.3%, respectively. Compared with current state-of-the-art detection network models, the proposed EW-YOLOv7 exhibits superior performance in detecting electrowetting display defects. This model helps to solve the problem of defect detection in industrial production of electrowetting display and assists the research team in quickly identifying the causes and locations of defects. [ABSTRACT FROM AUTHOR]

Cell-like P systems with channel states, which are a variant of tissue P systems in membrane computing, can be viewed as highly parallel computing devices based on the nested structure of cells, where communication rules are classified as symport rules and antiport rules. In this work, we remove the antiport rules and construct a novel variant, namely, cell-like P systems with channel states and symport rules, where one rule is only allowed to be nondeterministically applied once per channel. To explore the computational efficiency of the variant, we solve the S A T problem and obtain a uniform solution in polynomial time with the maximal length of rules 1. The results of our work are reflected in the following two aspects: first, communication rules are restricted to only one type, namely, symport rules; second, the maximal length of rules is decreased from 2 to 1. Our work indicates that the constructed variant with fewer rule types can still solve the S A T problem and obtain better results in terms of computational complexity. Hence, in terms of computational efficiency, our work is a notable improvement. [ABSTRACT FROM AUTHOR]

Evidence theory is widely used to deal with the fusion of uncertain information, but the fusion of conflicting evidence remains an open question. To solve the problem of conflicting evidence fusion in single target recognition, we proposed a novel evidence combination method based on an improved pignistic probability function. Firstly, the improved pignistic probability function could redistribute the probability of multi-subset proposition according to the weight of single subset propositions in a basic probability assignment (BPA), which reduces the computational complexity and information loss in the conversion process. The combination of the Manhattan distance and evidence angle measurements is proposed to extract evidence certainty and obtain mutual support information between each piece of evidence; then, entropy is used to calculate the uncertainty of the evidence and the weighted average method is used to correct and update the original evidence. Finally, the Dempster combination rule is used to fuse the updated evidence. Verified by the analysis results of single-subset proposition and multi-subset proposition highly conflicting evidence examples, compared to the Jousselme distance method, the Lance distance and reliability entropy combination method, and the Jousselme distance and uncertainty measure combination method, our approach achieved better convergence and the average accuracy was improved by 0.51% and 2.43%. [ABSTRACT FROM AUTHOR]

The ultra-reliable and low-latency communication (URLLC) systems are expected to support the stringent quality of service (QoS) demands in the Internet of Things (IoT) networks. In order to support the strict latency and reliability constraints, it is preferable to deploy a reconfigurable intelligent surface (RIS) in the URLLC systems to improve the link quality. In this paper, we focus on the uplink of an RIS-assisted URLLC system, and we propose to minimize the transmission latency under the reliability constraints. To solve the non-convex problem, a low-complexity algorithm is proposed by using the Alternating Direction Method of Multipliers (ADMM) technique. The RIS phase shifts optimization, which is typically non-convex, is efficiently solved by formulating as a Quadratically Constrained Quadratic Programming (QCQP) problem. Simulation results verify that our proposed ADMM-based method is able to achieve better performance than the conventional semi-definite relaxation (SDR)-based method with lower computational complexity. Our proposed RIS-assisted URLLC system is able to significantly reduce the transmission latency, which highlights the great potential in deploying RIS in the IoT networks with strict reliability requirements. [ABSTRACT FROM AUTHOR]

We consider in this paper a class of linear multiplicative programming problems that arise from numerous applications such as network flows and financial optimization. The problem is first transformed into an equivalent nonlinear optimization problem to provide a novel convex quadratic relaxation. A simplicial branch-and-bound algorithm is then designed to globally solve the problem, based on the proposed relaxation and simplicial branching process. The convergence and computational complexity of the algorithm are also analyzed. The results of numerical experiments confirm the efficiency of the proposed algorithm for tested instances. [ABSTRACT FROM AUTHOR]

The Resource-Constrained Project Scheduling Problem (RCPSP) is a significant and important issue in the field of project management. It arises during project planning when resources must be allocated among tasks with specific time constraints. Solving this problem enables the optimization of project execution time, minimization of resource costs, and increased efficiency of the entire team's work. Due to the increasing complexity of projects, the development of new methods and algorithms to solve RCPSP is relevant nowadays. The existing methods for obtaining approximate solutions with guaranteed accuracy are characterized by high computational complexity and are often ineffective in considering the specific constraints of the problem. Fast heuristic approaches also have several drawbacks related to fine-tuning algorithm parameters and strong dependence on the quality of the initial solution. This paper investigates the features of the linear integer programming (LIP) model to solve RCPSP. The proposed LIP model is universal and scalable, enabling it to fully consider all specific aspects of the problem. The paper provides a construction algorithm of a functional space of the model and discusses the estimation of complexity. From the estimation of the mentioned algorithm's complexity, it is observed that the general complexity of the proposed approach is proportional to a controlled parameter of the LIP. Increasing this controlled parameter can significantly reduce the dimensionality of the initial problem, thus leading to the effectiveness of the LIP model-based approach in terms of computational resources. An upper bound for the value of this parameter is obtained for a special case of the RCPSP. Using the obtained balanced value, a numerical experiment was carried out on real-world samples. [ABSTRACT FROM AUTHOR]

In this paper, we propose a three-dimensional autonomous drone exploration system (ADES) with a lightweight and low-latency saliency prediction model to explore unknown environments. Several studies have applied saliency prediction in drone exploration. However, these studies are not sufficiently mature. For example, the computational complexity and the size of the developed prediction models have not been considered. In addition, some studies have only proposed saliency prediction models without actually applying them to drones. The ADES system proposed in this paper has a small and fast saliency prediction model and uses a novel drone exploration approach based on visual-inertial odometry to solve the practical problems encountered during drone exploration, such as collisions with and the repeated exploration of salient objects. The proposed ADES system performs comparably to the state-of-the-art, multiple-discontinuous-image saliency prediction network TA-MSNet and enables drones to explore unknown environments with high efficiency. [ABSTRACT FROM AUTHOR]

The article presents an approach based on machine learning with symbolic regression for the synthesis of a stabilization system for a mobile robot. This approach is universal and allows you to numerically solve the synthesis problem in a general setting without the need to form a training sample, instead relying only on the value of the functional. The synthesis is implemented to stabilize the mobile robot Rosbot in the Gazebo simulation environment. The feedback stabilization system is received by the network operator method. The advantage of the method is that it can be applied to a control object of any complexity and linearity. [ABSTRACT FROM AUTHOR]

The dichromatic number of a digraph D is the minimum number of colors of a vertex coloring of D such that D has no monochromatic cycles, the dichromatic number is an extension of the chromatic number to the class of digraphs. The Hajós join is a tool to obtain r -chromatic graphs, using the dichromatic number, J. Bang-Jensen et. al. extended the Hajós join to digraphs, and thus obtained a tool to obtain r-dichromatic digraphs. J. Bang-Jensen et. al. posed in 2020 the problem of how to obtain the symmetric cycle of length 5 from symmetric cycles of length 3. We recently solved this problem by applying a genetic algorithm. In this article, we generalize the construction of the symmetric cycle of length 5 and determine the computational complexity as Θ(n ln(n)). [ABSTRACT FROM AUTHOR]

For incomplete datasets with mixed numerical and symbolic features, feature selection based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly. However, its evaluation function only considers the information contained in the lower approximation of the neighborhood decision, which easily leads to the loss of some information. To solve this problem, we construct a novel NMRS-based uncertain measure for feature selection, named neighborhood multi-granulation self-information-based pessimistic neighborhood multi-granulation tolerance joint entropy (PTSIJE), which can be used to incomplete neighborhood decision systems. First, from the algebra view, four kinds of neighborhood multi-granulation self-information measures of decision variables are proposed by using the upper and lower approximations of NMRS. We discuss the related properties, and find the fourth measure-lenient neighborhood multi-granulation self-information measure (NMSI) has better classification performance. Then, inspired by the algebra and information views simultaneously, a feature selection method based on PTSIJE is proposed. Finally, the Fisher score method is used to delete uncorrelated features to reduce the computational complexity for high-dimensional gene datasets, and a heuristic feature selection algorithm is raised to improve classification performance for mixed and incomplete datasets. Experimental results on 11 datasets show that our method selects fewer features and has higher classification accuracy than related methods. [ABSTRACT FROM AUTHOR]

Semantic segmentation of underground mine roads is very important to efficiently obtain road information from images. The boundary of underground mine roads is not obvious, the environment is complex, and road identification is difficult. In order to effectively realize the accurate identification of underground mine roads, a network identification model using a deep learning technique is proposed. Choosing BiSeNet as the basic framework, adopting a unified attention fusion module, and using channel and spatial attention to enrich the fusion feature representation can effectively obtain feature information and reduce the loss of feature information. In addition, the lightweight network STDC is integrated into the backbone network to reduce computational complexity. Finally, experiments were carried out on underground mine roads. The experimental results show that the mean intersection over union and pixel accuracy of the proposed method reached 89.34% and 98.34%, respectively, and the recognition speed reached 23 f/s when identifying underground mine roads. In this study, the underground mine road recognition model trained by deep learning technology can solve the problem of underground mine road recognition with high accuracy. [ABSTRACT FROM AUTHOR]

Due to the ever-changing shape and scale of ships, as well as the complex sea background, accurately detecting multi-scale ships on the sea while considering real-time requirements remains a challenge. To address this problem, we propose a model called S-DETR based on the DETR framework for end-to-end detection of ships on the sea. A scale attention module is designed to effectively learn the weights of different scale information by utilizing the global information brought by global average pooling. We analyzed the potential reasons for the performance degradation of the end-to-end detector and proposed a decoder based on Dense Query. Although the computational complexity and convergence of the entire S-DETR model have not been rigorously proven mathematically, Dense Query can reduce the computational complexity of multi-head self-attention from O (N q 2) into O (N q) . To evaluate the performance of S-DETR, we conducted experiments on the Singapore Maritime Dataset and Marine Image Dataset. The experimental results show that the proposed method can effectively solve the problem of multi-scale ship detection in complex marine environments and achieve state-of-the-art performance. The model inference speed of S-DETR is comparable to that of single-stage target detection models and meets the real-time requirements of shoreside ship detection. [ABSTRACT FROM AUTHOR]

Massive amounts of information are generated in various social media and spread across multi-social networks through individual forwarding and sharing, which greatly enhance the speed and scope of transmission, but also bring great challenges to the control and governance of misinformation. The characteristics of the spread of misinformation across multi-social networks are considered, this article investigates the novel problem of misinformation influence minimization by entity protection on multi-social networks, and systematically tackling this problem. We analyse the hardness and the approximation property of the problem. We construct a multi-social networks coupled method and devise a pruning and filtering rule. We develop a two-stage discrete gradient descent (TD-D) algorithm to solve NP-Hard problems. We also construct a two-stage greedy (TG) algorithm with the approximate guarantee to verify the algorithm we developed. Finally, the effectiveness of our proposed methods is analysed in synthetic and real multi-network datasets (contains up to 202K nodes and 2.5M edges). The results show that the ability of the TD-D and TG algorithms to suppress the spread of misinformation is basically the same, but the running time of the TG algorithm is much higher than (far more than 10 times) that of the TD-D algorithm. [ABSTRACT FROM AUTHOR]

Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universality, a heavy workload, and poor robustness. Most deep learning-based methods make it difficult to effectively balance accuracy and efficiency. To improve the comprehensive perception ability of lane line geographic information in a natural traffic environment, a lane line detection algorithm based on a mixed-attention mechanism residual network (ResNet) and row anchor classification is proposed. A mixed-attention mechanism is added after the backbone network convolution, normalization and activation layers, respectively, so that the model can focus more on important lane line features to improve the pertinence and efficiency of feature extraction. In addition, to achieve faster detection speed and solve the problem of no vision, the method of lane line location selection and classification based on the row direction is used to detect whether there are lane lines in each candidate point according to the row anchor, reducing the high computational complexity caused by segmentation on a pixel-by-pixel basis of traditional semantic segmentation. Based on TuSimple and CurveLane datasets, multi-scene, multi-environment, multi-linear road image datasets and video sequences are integrated and self-built, and several experiments are designed and tested to verify the effectiveness of the proposed method. The test accuracy of the mixed-attention mechanism network model reached 95.96%, and the average time efficiency is nearly 180 FPS, which can achieve a high level of accuracy and real-time detection process. Therefore, the proposed method can meet the safety perception effect of lane line geographic information in natural traffic environments, and achieve an effective balance between the accuracy and efficiency of actual road application scenarios. [ABSTRACT FROM AUTHOR]

For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system. [ABSTRACT FROM AUTHOR]

In recent years, sparse direction-of-arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar has attracted extensive attention and been extensively studied, especially the method based on the classic least absolute shrinkage and selection operator (LASSO) estimator. The alternating-direction method of multipliers (ADMM) is an effective method for solving this problem at the cost of introducing an additional user parameter. To avoid introducing an additional user parameter, this paper adopts an equivalent transformation in the form of the generalized SParse Iterative Covariance-based Estimation (qSPICE) cost function to obtain a mean squared minimized form of the cost function. Then, the problem is transformed into a sparse optimization problem in the form of a weighted LASSO. Next, this unconstrained optimization problem is decomposed into three subproblems, which are solved separately to reduce the dimension of each problem and thus reduce the overall computational complexity based on ADMM. Simulation results and measured data indicate that the proposed method significantly outperforms the traditional super-resolution DOA estimation method and ADMM-LASSO method and slightly outperforms qSPICE in terms of resolution and sidelobe suppression capability. In addition, the proposed method has a much lower computational complexity and substantially fewer iterations than qSPICE. [ABSTRACT FROM AUTHOR]

Originally, tangles were invented as an abstract tool in mathematical graph theory to prove the famous graph minor theorem. In this paper, we showcase the practical potential of tangles in machine learning applications. Given a collection of cuts of any dataset, tangles aggregate these cuts to point in the direction of a dense structure. As a result, a cluster is softly characterized by a set of consistent pointers. This highly flexible approach can solve clustering problems in various setups, ranging from questionnaires over community detection in graphs to clustering points in metric spaces. The output of our proposed framework is hierarchical and induces the notion of a soft dendrogram, which can help explore the cluster structure of a dataset. The computational complexity of aggregating the cuts is linear in the number of data points. Thus the bottleneck of the tangle approach is to generate the cuts, for which simple and fast algorithms form a sufficient basis. In our paper we construct the algorithmic framework for clustering with tangles, prove theoretical guarantees in various settings, and provide extensive simulations and use cases. Python code is available on github. [ABSTRACT FROM AUTHOR]

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Skeleton-based action recognition can achieve a relatively high performance by transforming the human skeleton structure in an image into a graph and applying action recognition based on structural changes in the body. Among the many graph convolutional network (GCN) approaches used in skeleton-based action recognition, semantic-guided neural networks (SGNs) are fast action recognition algorithms that hierarchically learn spatial and temporal features by applying a GCN. However, because an SGN focuses on global feature learning rather than local feature learning owing to the structural characteristics, there is a limit to an action recognition in which the dependency between neighbouring nodes is important. To solve these problems and simultaneously achieve a real-time action recognition in low-end devices, in this study, a single head attention (SHA) that can overcome the limitations of an SGN is proposed, and a new SGN-SHA model that combines SHA with an SGN is presented. In experiments on various action recognition benchmark datasets, the proposed SGN-SHA model significantly reduced the computational complexity while exhibiting a performance similar to that of an existing SGN and other state-of-the-art methods. [ABSTRACT FROM AUTHOR]