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Negative selection algorithms play an important role in anomaly detection. Interface detectors are a special negative selection algorithm that completely eliminates outer holes, but there are detection blind areas. This paper proposes a negative selection algorithm with hypercube interface detectors for anomaly detection. It uses self-sample clusters to construct self-space, and boundary self-sample clusters to describe the interface detectors. It eliminates the detection blind area and improves the detection rate. To validate the performance of the proposed method, experiments were conducted using the iris dataset, the skin segmentation dataset, the Breast Cancer of Wisconsin (BCW) dataset, and the Waveform Database Generator (Version 2) dataset. Experimental results show that the proposed method in this paper has a higher detection rate, lower false alarm rate, and fewer detectors than other anomaly detection methods for the same parameters. • HI-detector breaks away from the hyperspherical detector used by traditional negative selection algorithms. • HI-detector consists of a cluster of multiple hypercube self-samples. • HI-detector completely eliminates duplicate coverage of holes and detectors. • HI-detector has only one parameter that needs to be set. [ABSTRACT FROM AUTHOR]

Identifying the abnormal samples from a data set and determining their type are two key tasks of anomaly detection. However, the existing anomaly detection algorithms are generally faced with the defects of weak generalization ability and insufficient interpretation, the core reason of which is that they cannot mine specific features for different abnormal types. In this paper, a new anomaly detection algorithm aiming at feature selection for different abnormal types is developed. Inspired by self-supervised learning, we take the stationarity of variance changes of abnormal score similarity as a pretext task and combine it with wrapped search method. Then, the features and the corresponding parameters for different abnormal types can be screened to apply to the downstream task of anomaly integration detection. To verify the efficiency of the new algorithm, we conduct two sets of experiments to compare the new algorithm with 11 classical anomaly detection and 3 clustering anomaly detection algorithms on the data sets WDBC, WPBC and Wilt from DAMI database with the evaluation measures P@n , Adj- P@n , AP, Adj-AP and AUC. The experiment results show that, both in the identification and classification on abnormal samples, all performance measures of the new algorithm are explicitly better than that of the contrastive algorithms. Also, we apply the new algorithm to the Chinese auto insurance market, and find that the results can help managers to identify the main patterns of fraudulent claims and to summarize the feature combinations of fraud behaviors. In general, the new algorithm developed in this paper has the following advantages compared with traditional algorithms: 1) It can directly capture abnormal features and realize effective recognition of abnormal types, which effectively bridge the gap between abnormal judgement and feature screening. 2) Automatic screening of abnormal features can be completed under the condition of self-updating learning optimal strategy. 3) Only a few features are extracted from all features to reveal the abnormal characteristics, which significantly improves the interpretability and generalization ability of the algorithm and its results. In a word, through the novel self-supervised design method, feature screening is skillfully integrated into the anomaly detection task, which may provide a new way for anomaly detection research. [ABSTRACT FROM AUTHOR]

In this paper, to investigate the problem of radio wave propagation in complex environments, a new algorithm is proposed based on the equivalence principle algorithm and the domain decomposition method to rapidly and accurately predict electromagnetic scattering and radio wave propagation in half-space. First, equivalent surfaces are introduced to decompose the obstacle region and replace the scattering effect of obstacles within the equivalent surfaces with equivalent input and output electromagnetic currents. Then, the operator S, representing the relationship between the input and output equivalent electromagnetic currents on the equivalent surfaces, and the operator T, representing the interactions between the equivalent electromagnetic currents on each equivalent surface, are derived; this allows the calculation process of the overall spatial field in a complex environment to be simplified to the iterative calculation process of operators S and T. To reduce the complexity of calculating S, a neural network embedded with physical information is introduced to replace the calculation process. Finally, the training results are applied to an example and compared with the theoretical numerical results yielded by the method of moments in different cases with obstacles. The results reveal that the new method proposed in this paper is highly accurate and achieves a substantial improvement in calculation speed. Therefore, our proposed algorithm provides a new and reliable method for calculating the spatial field in complex environments. • The EPA is introduced to use equivalent surfaces to replace the scattering inside it. • The operators S and T are introduced into the process of solving the field. • A neural network with physical information embedded is applied to calculate the S. • Random undulating terrains are generated by Karhunen–Loève expansion. • MoM providing a good foundation for training is used to generate dataset. [ABSTRACT FROM AUTHOR]

Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research. [Display omitted] • Application of sensors and artificial intelligence to analyse ankle biomechanics. • Lab sensors predict parameters, paving the way for post-modern rehabilitation. • Field devices show potential for ankle injury prediction. • Focus on streamlined smart sensors and effective data management in the future. [ABSTRACT FROM AUTHOR]

With the advent of the post-pandemic era, the global economic situation has tightened, and the economic benefits of container ports have also been affected. This paper attempts to build a novel berth-tugboat-quay crane joint scheduling method for container ports to reduce the economic cost of container ports. Firstly, from the perspective of the economic cost of container ports, considering random economic factors, this paper proposes a new berth-tugboat-quay crane joint dispatch optimization model, namely E-BUQC, by taking the minimum time cost of vessels in port, the cost of additional freight distance for trucks, the cost of tugboat operation, and the minimum operation cost of quay cranes as the objective function. Then, in order to obtain a relatively high-quality solution, based on chaotic mapping and quantum computing, this paper constructs a new chaotic quantum adaptive satin bower bird optimizer algorithm, namely CQASBO, formulates exclusive coding rules for the E-BUQC model, develops a feasible integer algorithm, and proposes a new joint scheduling solution method of berth-tugboat-quay crane, named E-BUQC_CQASBO. Subsequently, based on the simulation examples of two ports in northern China, the new solution method is tested. The simulation test results show that the E-BUQC model obtains an appropriate solution; The CQASBO constructed in this paper has better performance than other traditional comparison algorithms selected in this paper and can provide a better solution when solving the E-BUQC model. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a distributed machine learning method to protect users' privacy and security. It currently faces the following two problems (McMahan et al., 2017): (1) The performance of the global model degrades when dealing with Non-Independent Identically Distributed (Non-IID) data. Other existing classical methods do not have a rigorous theory based on error processing ; (2) In the process of global model aggregation, the classical FL algorithm either directly averages local models or solely considers the proportion of local data to assign weights to the models, without accounting for the discrepancies between the local models. In this paper, a federated aggregation algorithm Federated Maximum Mean Discrepancy (FedMMD) is proposed to address the deviation between the local models and Non-IID. First of all, this paper utilizes the Dilated Convolution Meet Transformer (DCMT) model for local model feature extraction. This approach aims to capture more feature information and minimize the impact of Non-IID scenarios. Secondly, the learning stability of the local model data participating in the aggregation is compared with Maximum Mean Discrepancy (MMD). The weights of the local models involved in the aggregation are determined using the SKNQ (Student–Keuls–Newman-Q) method and the entropy weight method. The SKNQ method calculates and compares the MMD across multiple clients, while the entropy weight method assigns different weights to clients based on their deviations. On the standard dataset, the FedAvg algorithm (McMahan et al., 2017), the FedProx algorithm (Li et al., 2020) and the FedMMD algorithm are compared. The experimental results demonstrate that the FedMMD algorithm, used in training the global model, not only enhances the accuracy of learning IID and Non-IID data but also improves the generalization capability of the global model. • An DCMT model is proposed to alleviate the model fluctuations caused by NonIID data. • An MMD method is proposed to compared horizontally before the model is aggregated. • Entropy weight method and the SKNQ method are combined to allocate local models' weight when they are fused into global model. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

In recent years, the reduced biquaternion algebras have been widely used in color image processing problems and in the field of electromagnetism. This paper studies eigen-problems of reduced biquaternion matrices by means of a complex representation of a reduced biquaternion matrix and derives new algebraic algorithms to find the eigenvalues and eigenvectors of reduced biquaternion matrices. This paper also concludes that the number of eigenvalues of an n × n reduced biquaternion matrix is infinite. In addition, the proposed algebraic algorithms are shown to be effective in application to a color face recognition problem. • The eigen-problems of reduced biquaternion matrices are further studied based on the complex representation form. • Propose new algebraic algorithms for finding the eigenvalues and the eigenvectors of a reduced biquaternion matrix. • An n × n reduced biquaternion matrix has infinite eigenvalues. • There are multiple eigenvalues corresponding to the same eigenvector of a reduced biquaternion matrix. • The proposed method is more comprehensive and can find more eigenvalues of a reduced biquaternion matrix. [ABSTRACT FROM AUTHOR]

In numerous domains, there is ample evidence indicating that a significant portion of real-world data exhibits temporal variations, such as medical research and meteorological studies. Particularly in the era of big data, not only does the size of data change dynamically but also its dimensions at an unprecedented pace. Consequently, employing traditional methods to handle such dynamic data becomes highly impractical. To address this limitation, this paper proposes an incremental feature selection algorithm tailored for dynamic feature variations. For scenarios involving an increase in features, the novel algorithm efficiently identifies a target subset with effective features. In order to showcase the efficacy of the proposed algorithm, this paper conduct experiments using four commonly used classifiers and five UCI data sets. The experimental results further validate both the feasibility and efficiency of the new approach. [ABSTRACT FROM AUTHOR]

The drift caused by temperature variation is a crucial factor limiting the bias stability of hemispherical resonator gyroscopes. In this paper, mode reversal is applied to calibrate for the temperature drift of the hemispherical resonator gyroscope in the case of drastic change of ambient temperature. The control algorithm is proposed and the self-calibration by mode reversal is theoretically analyzed. The influence of mode reversal period on calibration is investigated experimentally. The experiment is conducted in a temperature chamber with a temperature range from −10 ℃ to 40 ℃. The effect of self-calibration by mode reversal is compared under different reversal periods and different change rates of temperature. Results suggested that the bias stability after calibration can be improved by shortening the mode reversal period. When the temperature changes sharply with a change rate of 10 ℃/min, the bias stability is improved by about 30 times. The research offers a new way for hemispherical resonator gyroscope to maintain excellent performance in the environment of drastic temperature changes. In this paper, the self-calibration by mode reversal is theoretically analyzed and the control algorithm is proposed. The influence of mode reversal period on calibration is investigated experimentally. The effect of self-calibration by mode reversal is compared under different reversal periods and different change rates of temperature. Results suggested that the bias stability after calibration can be improved by shortening the mode reversal period. When the temperature changes sharply with a change rate of 10 ℃/min, the mode reversal after shortening the reversal period can improve the bias stability is improved by about 30 times. The research offers a new way for hemispherical resonator gyroscope to maintain excellent performance in the environment of drastic temperature changes. [ABSTRACT FROM AUTHOR]

The Peaceman–Rachford splitting method (PRSM) has been widely studied in recent years because of its excellent numerical performance, especially with inertial acceleration. In this paper, we propose a multi-step inertial strictly contractive PRSM (shortly, MISCPRSM) for solving convex optimization problems, which has not been studied before. This paper aims to give an exhaustive analysis of the value relationship between multiple inertial parameters. The second subproblem utilizes an additional customized indefinite proximal term in order to obtain a closed-form solution. The global convergence and complexity result of the introduced method are analyzed by using variational inequality and basic inequalities. Finally, through simulation and computed tomography experiments, the effectiveness and robustness of the MISCPRSM are proved, and the detailed values of inertial parameters are given. [ABSTRACT FROM AUTHOR]

In this paper, we first summarize the algorithm presented in Blasco and Pérez-Díaz (2014) for computing the generalized asymptotes of algebraic curves implicitly defined. This algorithm is based on the computation of Puiseux series. The main and very important contribution of this paper is a new and efficient method that allows to easily compute all the generalized asymptotes of an algebraic plane curve implicitly defined by just solving a triangular system of equations. The method can be easily generalized to the case of algebraic curves implicitly defined in the n -dimensional space. [ABSTRACT FROM AUTHOR]

Aiming at the problems of long reconstruction time, high sampling rate and poor imaging effect caused by traditional illumination speckle in ghost imaging reconstruction, this paper proposes to use ordered orthogonal Hadamard derived speckle as the illumination speckle series of ghost imaging to reduce the measurement times and improve the quality of reconstructed images. In addition, the alternate direction multiplier method is introduced into the image reconstruction algorithm to further improve the imaging performance of ghost imaging. Finally, the method used in this paper is evaluated objectively, and the evaluation results show that the combination of Hadamard derived speckle and alternating direction multiplication has better imaging effect, and can obtain high-quality reconstructed images under low sampling conditions. It is expected that this method can be applied to target detection and recognition, improve detection and recognition ability, and help users get more useful information. • ADMM algorithm is used to solve the underdetermined equations in ghost imaging. • Reconstruction Hadamard combined with ADMM improves the reconstruction effect. • The proposed method has better reconstruction results at low sampling rates. [ABSTRACT FROM AUTHOR]

Detecting and characterising shocks is challenging because they do not occur in isolation but are instead superimposed onto underlying vehicle vibrations which themselves are a result of the interaction with uneven road surfaces. Consequently, shocks are buried within vehicle vibration response measurements (usually acceleration). This paper presents the development and validation of an automated algorithm to detect shocks from vertical acceleration signals measured from road vehicles. To avoid inherent difficulties with experimentation, this initial paper is confined to numerical simulation whereby the response of two typical quarter-car truck models (one with air ride and the other with steel suspension) when travelling on artificially-generated random road elevation profiles laced with Hanning-shaped surface aberrations of known amplitude, lengths and location along the elevation profile. The shock detection algorithm was developed as a dual mode classifier to accommodate the two natural frequencies of each 2DoF quarter car models. Detection was implemented by first passing the vibration response signal through a band-pass filter around the two resonant modes in turn then calculating the filtered signals' instantaneous frequency (envelope) by means of the Hilbert transform. Shock detection was based on the local peak-to-mean ratio (LPTMR) of the instantaneous magnitude where 'local' was defined by the duration of the filtered signal's impulse response function. Sensitivity analysis and validation were undertaken on artificially-generated roads of varying roughness onto which aberrations of known shape were superimposed. The effectiveness and limitations (detection threshold) of the algorithm were evaluated by creating a range of aberrations with a broad range of lengths (effective frequencies) and diminishing amplitudes. Results show that shocks of 'significant' magnitudes are always detected with no detection of false positives. As the road roughness increases relative to the aberration amplitudes, the resulting shocks become increasingly drowned-out by the vibration response due to the underlying road roughness, especially for the quarter-car model with steel suspension. The main conclusion is that the signal analysis approach taken is ultimately effective and needs to be further validated using experimental response data. [ABSTRACT FROM AUTHOR]

The consortium blockchain has been widely applied in various fields such as agricultural product traceability, supply chain management, and logistics transportation. As an indispensable component of a consortium blockchain, the consensus algorithm ensures the consistency and trustworthiness of each node in the network. However, existing consensus algorithms in large-scale consortium blockchain scenarios suffer from low system throughput and high latency due to the complexity of communication processes, rendering them impractical for real-world use. To address these issues, this paper proposes a novel consensus algorithm called credit evaluation-based practical Byzantine fault tolerance (CE-PBFT). This algorithm designs a new node credit evaluation model that considers node completion rate, consensus decay, and node behavior. It effectively measures and reflects the specific reliability status of nodes during system operation, thereby enhancing system reliability and security. Additionally, the paper introduces the innovative use of decision tree algorithms to analyze network node behavior and simplifies the existing consensus protocol. Nodes are categorized as excellent, good, ordinary, or poor based on the classification results, and non-Byzantine nodes are dynamically selected accordingly. This greatly improves the overall efficiency of the system. The performance of CE-PBFT is validated through experiments and compared with PBFT, G-PBFT, RBFT, WBFT and PPoR. Experimental results demonstrate that in large-scale consortium scenarios, CE-PBFT significantly improves system throughput, effectively reduces transaction latency and communication overhead, and outperforms the compared protocols. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new multichannel fast normalized least mean square (MC-FNLMS) algorithm for audio teleconferencing systems. The main idea behind the proposed MC-FNLMS algorithm is based on the introduction of a first order prediction process on the input noisy signals before the adaptive filtering process of each channel. The proposed algorithm is considered as an alternative solution for the existing algorithm in multi-channel applications. A full stability analysis of the proposed algorithm is derived along this paper. Simulation results of a comparison between the proposed MC-FNLMS algorithm and the two classical Multichannel Normalized Least Mean Square (MC-NLMS) and the Multichannel Affine Projection (MC-APA) algorithms, in terms of convergence speed, noise reduction and computational complexity, show the superiority of the proposed algorithm on multichannel adaptive noise reduction. [ABSTRACT FROM AUTHOR]

With the enhancement of people's security awareness, transmitting secret information securely has gradually become a demand for the public. Steganography is a technology of representing secret information within another carrier, aiming at transmitting secret information without causing suspicion. Most of the traditional steganographic algorithms hide secret information by modifying the statistical characteristics, which will leave traces to the carriers. Although these modifications are too tiny to be distinguished by human eyes, they can be detected by steganalysis algorithms. Differently, coverless steganography, also written as steganography without embedding, accomplish the process of information hiding by constructing the relationship between secret information and carriers. Due to no modification to the carriers, all of the steganalysis algorithms are expired. In this paper, more than 90 papers are included to provide a review in coverless steganographic algorithms, covering the major development process of coverless image and video steganographic algorithms. The main contribution of the existing methods is summarized. Besides, the current general issues of capacity, robustness, and security are discussed adequately for both image and video algorithms. Especially, the security of coverless steganography is discussed for the first time from theoretical analysis to actual investigation in this review. [ABSTRACT FROM AUTHOR]

• Novel Methodology Integration: Introduction of a novel methodology combining Geographic Information System-based fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and filtration algorithms. • Extensive Evaluation of Solar Energy Sites (SES): Evaluation of 19,237 coordinated data points for SES potential based on 12 diverse criteria, including climatic conditions, technical feasibility, accessibility, environmental considerations, and social factors. • Double Filtration Process: Implementation of a meticulous double filtration process, systematically eliminating unsuitable locations and resulting in a refined list of 1,862 potential SES. • Fuzzy TOPSIS Algorithm Selection: Utilization of the fuzzy TOPSIS algorithm to further narrow down the SES selection to the top 100, with closeness coefficients ranging from 0.7 to 0.85, highlighting optimal solutions. • Spatial Distribution Analysis and Model Validation: Presentation of an interesting spatial distribution of the top 100 SES, clustered in three major categories, and validation of the proposed model through a solar suitability map in ArcGIS Pro, demonstrating a 69.01 % average relative percentage consistency with the model's results, similarly, the code used is adaptable to various polygon layers and raster maps, serving as a valuable resource for researchers conducting large-scale solar energy site identification in future studies. Solar energy stands as an increasingly attractive and environmentally responsible energy source, possessing the potential to meet the burgeoning power needs of the Sarawak region. This paper proposes a novel methodology that combines Geographic Information System-based fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and filtration algorithms to identify the top 100 optimal Solar Energy Sites (SES). The research encompasses the extra-large scale potential SES comprising 19,237 coordinated data points. It evaluates SES based on 12 diverse criteria spanning climatic conditions, technical feasibility, accessibility, environmental considerations, and social factors. This research employs a meticulous double-layer filtration process to systematically eliminate unsuitable locations, resulting in a refined list of 1,862 potential SES. Subsequently, the fuzzy TOPSIS algorithm has further narrowed down the selection to the top 100 SES, constituting the core results of this research. The results reveal an interesting distribution of the identified SES, which mainly clustered in 3 huge categories (Kuching-Samarahan, Sibu-Kapit-Mukah and Bintulu-Miri). The identified 100 SES possess high closeness coefficients, which range from 0.7 to 0.85, where 1 indicates the best solution of the fuzzy TOPSIS algorithm. Additionally, the paper presents a spatial distribution analysis of the top-ranking sites across various divisions and districts. To validate the results obtained from the proposed model, a solar suitability map has been developed in ArcGIS Pro. When comparing the results from the proposed model to those obtained from the solar suitability map, most SES have consistent relative percentages, with only one SES falling below 50 %. To provide an overview of the similarity between the two models, the average relative percentage is computed at 69.01 %, signifying the proximity of SES to the optimal value of the solar suitability map. Furthermore, the developed source code in this research exhibits flexibility, featuring filtration algorithms that can accommodate any raster maps. It serves as an asset for researchers interested in conducting comprehensive large-scale SES identification in future case studies. [ABSTRACT FROM AUTHOR]

Path planning is a key factor for the unmanned surface vehicle (USV) to achieve efficient navigation. In this paper, to solve the global path planning and obstacle avoidance problems for the USV, an improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed. Three main improvement parts are composed of the proposed algorithm. Firstly, the radial basis function (RBF) neural network is combined with the Q-Learning algorithm to approximate the action value function Q, which improves the convergence speed of the Q-Learning algorithm. Secondly, to ensure that the planned path conforms to the maneuvering characteristics of the USV, the heading angle, motion characteristics, ship length, and safety of the USV are taken into account by the proposed algorithm. Based on these factors, the action space and reward function are optimized, the state space is reconstructed, and the safety threshold is proposed. Finally, a third-order Bezier curve is used to smooth the initial path, so that the USV can maintain its heading stability during navigation. Based on simulation results, the proposed NSFQ algorithm outperforms the A* and RRT algorithms in terms of evaluation indicators such as heading angle, angular velocity, path length, sailing time, and path smoothness. • An improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed in this paper. • The heading angle, angular velocity, and other indicators of the USV are taken into account by this proposed algorithm. • A safety threshold is proposed to ensure the safety of the USV. • A third-order Bezier curve is used to smooth the initial path. [ABSTRACT FROM AUTHOR]

The objective of image hiding is to conceal a secret image in a cover image inconspicuously. The challenges for image hiding focus on the embedding capacity, image quality and security. To contend with these challenges, this paper presents a novel image hiding method using Invertible Rescaling Net (IRN) and Similarity of Bits Pairs (SBP). Because of the downscaling ability of IRN, our method is able to hide a secret image into a cover image with the same dimensions. Moreover, the superior rescaling capabilities of IRN enable us to produce a recovered image of high-quality. We improve the SBP embedding technique and combine it with differential coding to boost the quality of the stego image. In the experiments, the quality of the recovered secret image and stego image is assessed using various quality evaluation metrics, and for the security, the anti-steganalysis performance is also evaluated in this paper. Extensive experimental results reveal that the quality of both the recovered secret image and the stego-image in our method outperforms several advanced methods and provides a high degree of security. • We apply invertible rescaling net (IRN) to image hiding task for the first time. • We improve the SBP encoding algorithm to prevent the degradation of the quality of the stego image. • Our method considers the robustness against the classical adversary's steganalysis. [ABSTRACT FROM AUTHOR]

Accurate prediction of traffic flow is crucial to building a smart city. Given the nonlinearity of traffic flow, this paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence combined with the DROP strategy. The algorithm is referred to as TRELM-DROP. The Tent chaos strategy and residual correction method reduce the impact of randomness in traffic flow. On this basis, the Tent and residual correction strategy avoids the weight optimization of the ELM algorithm using the iterative method. A DROP strategy is proposed in the proposed algorithm to improve its ability to predict traffic flow under varying conditions. A comprehensive comparison of 36 real-world datasets is presented in this paper, comparing TRELM-DROP with other benchmark models. The results show that the proposed algorithm can produce the best prediction performance regarding various prediction error metrics under various traffic conditions without iterative optimization. • An improved non-iterative traffic flow forecasting model is proposed. • Tent chaos strategy and residual correction strategy are developed. • A DROP strategy combining Dropout and Dropconnect is proposed. [ABSTRACT FROM AUTHOR]

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

• An ISSS positioning algorithm based on Bluetooth AOA technology is proposed, and the Angle estimation effect of this algorithm for uniform linear array (ULA) and uniform rectangular array (URA) antennas is experiment. • To account for two common sources of indoor interference. We reduce the coherent interference with signals using the spatial smoothing technique and the interference of transient occlusion with signals using the DBSCON technique, respectively. The layout of positioning device in different indoor Spaces and the algorithm of location calculation under different layout are discussed. The positioning accuracy of these algorithms is evaluated by the experimental data. This paper investigates the Bluetooth direction finding technique and combines it with the improved signal subtraction subspace (ISSS) algorithm for precise indoor positioning. First, the phase offset caused by the electronic switch is solved by the phase compensation algorithm. Then, the ISSS algorithm is proposed by combining the spatial smoothing techniques algorithm, the density-based spatial clustering of applications with noise (DBSCON) techniques and the signal subtraction subspace (SSS), which reduces the interference of coherent signals and caused by moving between people in the room. Finally, different placement methods for receivers in different spatial shapes are proposed. In this paper, the positioning accuracy of the algorithm is tested using Silicon Labs' Bluetooth device. The results show that the average positioning errors are 0.92 m for single receivers and 0.30 m for dual receivers in a room size of 3 m * 3 m. Therefore, the method has a promising application in achieving accurate indoor positioning. [ABSTRACT FROM AUTHOR]

The sequential stabilizing spline (SSS) algorithm is a remarkable algorithm for identifying linear dynamical systems. It can guarantee the stability of an estimated model by polishing the maximum modulus roots of an equation. Therefore, the root structure of an equation plays an important role in the SSS algorithm. Although the traditional power iterative method can be applied to approximate the extremal eigenvalues which have the largest modulus, it has two limitations: (1) the extremal eigenvalues must be real numbers, and (2) the structures of the extremal eigenvalues should be known a priori. In this paper, a novel power iterative method is proposed to approximate the extremal eigenvalues. Compared with the traditional power iterative method, the method in this paper can (1) determine the types of the extremal eigenvalues without prior knowledge of the matrix; (2) approximate the true values of the extremal eigenvalues regardless of their type; (3) become a worthy addition to SSS algorithm and gradient descent algorithm. Simulation examples demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The widespread development of monitoring devices in power system has indeed led to the generation of large amounts of power consumption data. Storing and transmitting this enormous volume of data has become a significant challenge for power system operators and researchers. Although high data rates are available for transmission, data compression is still necessary for power system applications to lessen the load of data transmission and storage. The wavelet transform plays an important role in data compression for power system applications. This paper presents an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. In general, some wavelet coefficients are neglected to produce DWT-based data compression by using hard or soft thresholds. But the volume of compressed data differs for various threshold values. In this context, selecting an optimal threshold is a challenging task in power system data compression. This paper proposes a multi-objective particle swarm optimisation (MO-PSO) for optimal threshold selection. The proposed approach is tested using IEEE power quality wave data sets and Dutch Residential Energy Datasets (DRED). The proposed MO-PSO algorithm finds the global optimum threshold, and it outperforms the other existing algorithms. The proposed algorithm efficiently compresses the test dataset with a maximum compression ratio (CR) of 2.07. • The development of an adaptive algorithm for data compression is applied in power system data. • MO-PSO algorithm is proposed to find the optimal threshold in WT based data compression. • Results are compared with recent research works. • The efficiency of data compression algorithm is evaluated by using compression ratio, mean square error and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) has received considerable interest in the field of energy management and optimization. The problem aims to determine the most cost-effective power allocation strategy that satisfies the power demand and all physical constraints of the power system. To solve this problem, we propose an algorithm based on differential evolution and adopt a hybrid mutation strategy, a linear population size reduction mechanism, and an improved single-unit repair mechanism. Experimental results confirmed that these mechanisms are useful for performance improvement. The proposed algorithm (L -HMDE) showed good performance when compared with more than 90 algorithms in solving 22 test cases. It could provide high-quality solutions stably and efficiently. In addition to designing a good algorithm, we present a review of over 100 papers and highlight their algorithm features. We also provide a comprehensive collection of test cases in the literature. Through careful examination and verification, data coefficients of these test cases and solutions to them are included in this paper as a useful reference for researchers who are interested in this problem. • A differential evolution-based algorithm (L-HMDE) is proposed to address the economic dispatch problem. • The L-HMDE integrates a hybrid mutation strategy, a population size reduction mechanism, and an improved repair procedure. • It shows good solution quality and high efficiency when compared with more than 90 existing algorithms on 22 test cases. • A comprehensive collection of test cases and solutions is also provided. [ABSTRACT FROM AUTHOR]

[Display omitted] • A machine learning-assisted Impact Localization (IL) architecture is proposed. • Adaptive Sparse Noise Reduction Algorithm is studied to extract signal features. • RIME-optimized Dual-layer Support Vector Regression (RDSVR) is developed. • RDSVR enables to select optimal parameters and accurately predict impact location. In this paper, a machine-learning architecture with the integration of two strategies including data enhancement and adaptive generation scheme for Impact Localization (IL) are developed to address the aforementioned issues for location identification of impacts on composite laminates. Two main contributions are included in this research: First, response signals collected from low-speed impact experiments under various working conditions are denoised using Adaptive Sparse Noise Reduction Algorithm (ASNRA), which aims at maximizing the preservation of the original signal amplitude, thereby avoiding the underestimation of pulse features during denoising. Then a RIME-optimized Dual-layer Support Vector Regression (RDSVR) method for the real-time update of hyperparameters is implemented in the machine-learning architecture to realize IL. The superior performances of the IL architecture over different IL models are validated throughout the numerical examples in terms of stability and efficiency. Results demonstrate that proposed architecture has the ability to realize the accurate and robust IL of composite laminates. [ABSTRACT FROM AUTHOR]

Feature extraction techniques are widely used in fields such as machine learning, pattern recognition, and image processing. The quality of feature extraction is crucial to the generalization ability of a model. This paper proposes a feature extraction technique based on the Shapley value method and an improved Minimum Redundancy Maximum Relevance (mRMR) analysis method. The improved mRMR algorithm enhances the recognition ability for fitting effects of multi-variable feature subsets by traversing all possible combinations of input variable subsets. That is, the result of feature subset selection with fewer input variables does not affect the feature subset selection process when there are more input variables, thus avoiding the limitations of the original algorithm. The research results indicate that using the Shapley value method in conjunction with the improved mRMR algorithm proposed in this study can select the optimal feature subset containing fewer feature variables and achieve a lower MSE value. This contributes to achieving lower computational complexity and higher data fitting accuracy. This technique is applied to the feature extraction scenarios of power battery output power signals and instantaneous fuel consumption signals in hybrid electric vehicles, and constructs the optimal feature subsets for the aforementioned signals. • Exhibits superior global search capabilities, achieving improved data fitting results. • Improves the construction efficiency of the optimal feature subset. • Helps to improve the data fitting accuracy of the plant model. [ABSTRACT FROM AUTHOR]

The problem of two-sided matching markets is well-studied in computer science and economics, owing to its diverse applications across numerous domains. Since market participants are usually uncertain about their preferences in various online matching platforms, an emerging line of research is dedicated to the online setting where one-side participants (players) learn their unknown preferences through multiple rounds of interactions with the other side (arms). Sankararaman et al. [23] provide an Ω (N log ⁡ (T) Δ 2 + K log ⁡ (T) Δ) regret lower bound for this problem under serial dictatorship assumption, where N is the number of players, K (≥ N) is the number of arms, Δ is the minimum reward gap across players and arms, and T is the time horizon. Serial dictatorship assumes arms have the same preferences, which is common in reality when one side participants have a unified evaluation standard. Recently, the work of Kong and Li [10] proposes the ET-GS algorithm and achieves an O (K log ⁡ (T) Δ 2 ) regret upper bound, which is the best upper bound attained so far. Nonetheless, a gap between the lower and upper bounds, ranging from N to K , persists. It remains unclear whether the lower bound or the upper bound needs to be improved. In this paper, we propose a multi-level successive selection algorithm that obtains an O (N log ⁡ (T) Δ 2 + K log ⁡ (T) Δ) regret bound when the market satisfies serial dictatorship. To the best of our knowledge, we are the first to propose an algorithm that matches the lower bound in the problem of matching markets with bandits. [ABSTRACT FROM AUTHOR]

Given an edge-weighted metric complete graph with n vertices, the maximum weight metric triangle packing problem is to find a set of n / 3 vertex-disjoint triangles with the total weight of all triangles in the packing maximized. Several simple methods can lead to a 2/3-approximation ratio. However, this barrier is not easy to break. Chen et al. proposed a randomized approximation algorithm with an expected ratio of (0.66768 − ε) for any constant ε > 0. In this paper, we improve the approximation ratio to (0.66835 − ε). Furthermore, we can derandomize our algorithm. [ABSTRACT FROM AUTHOR]

Deep image clustering networks have the capability to categorize unlabeled images, thereby effectively utilizing them. This paper synthesizes recent researches about deep image clustering network and summarizes them within a general framework. Image Preprocessing part transforms collected images into a dataset accepted by the network, then Image Embedding part embeds images from the dataset into vectors which represents image features. After Feature Processing part further reduces the dimensionality or enhances these features, the following Feature Clustering part divides the images into several categories. Cluster Result Processing part treats the clustering outcomes to implement weighted, multi-view, subspace, multimodal, or self-supervised methods. Downstream Applications part employ the clustering results to address various real-world problems. The performance of common baseline clustering methods and several feature extraction architectures are also compared and analyzed. The results indicate that within deep image clustering networks, the choice of clustering algorithm has a relatively minor impact on clustering performance, whereas the selection of the feature extraction network architecture is decisive for clustering metrics. The choice of architecture should be customized based on the characteristics of the dataset. Finally, we provide suggestions for potential research directions in deep image clustering networks. • Analyzes deep image clustering theory, its performance benefits, and limitations. • Summarizes recent deep image clustering improvements with experimental data analysis. • Introduces deep image clustering applications and future improvement prospects. [ABSTRACT FROM AUTHOR]

The recent multi-objective optimization problems are very complex which require an effective and robust evolutionary method for obtaining a global Pareto-optimal solutions. Most of these multi-objective evolutionary techniques are population-based methods which usually work on random search approaches and often trapped into local minima during their execution. A new fuzzy tuned mayfly algorithm (FTMA) is presented in this paper, to obtain the Pareto-optimal solutions for any complex multi-objective power system problem. It has a self-adapting global exploration capability to get the best Pareto-optimal solutions by varying two crucial parameters i.e., crossover (P c) and mutation P m probabilities. Moreover, a model of Pareto-dominance is employed to rank non-dominating solutions to maintain the diversity within the populations. The proposed evolutionary approach is examined on a standard ZDT benchmark test suite and its algorithmic performance is statistically compared with a few of the recent optimization techniques such as NSGA-II, NSGA-III, MMODE, MOAVOA and MMA algorithm. It is further applied to minimize two distinct objective functions (i.e., total transmission loss and voltage stability index) for IEEE-118 bus system and IEEE-24 bus RTS. A comprehensive statistical analysis is presented for the obtained numerical results, which proves that proposed FTMA has better convergence with better solution diversity in comparison to the other competing algorithms. A comprehensive analysis based on statistical results reveal that the proposed FTMA is superior for obtaining better non-dominating pareto-fronts as compared to other competing methods. [ABSTRACT FROM AUTHOR]

During past several decades, extensive interdisciplinary collaboration has greatly promoted the scientific innovation. Especially, collaborations among top scholars have become major driving forces in advancing scientific progress. Therefore, this article focusses on the mechanism and modes underlying collaboration of scholars. The advantage of this paper resides in its capacity to elucidate the hierarchical architecture and dynamics of top scholars' collaboration, discern the hidden collaborative association probabilities, and provide recommendation for fostering intellectual exchange and information dissemination. Results show that: (1) In terms of collaboration features, it exhibits a significant clustering effect. The largest three communities concentrate in Asia, Europe, and North America. Meanwhile, we further introduce dominant flow analysis, an algorithm originated from the geography, to excavate hierarchy architecture of collaborations. We have identified three kinds of scholars who perform distinct roles in the dissemination of knowledge: leading scholars tend to play a guiding role, senior scholars tend to act as a bridge to enlarge the collaboration range, and general scholars predominantly contribute to the collaborations. (2) In terms of formation mechanism of collaborations, we found the collaboration of top scholars are grounded in their publications' quality instead of quantity. Furthermore, the closed rather than opened triangular configuration potently catalyzes knowledge transmission. (3) Meanwhile, hidden collaborative partnerships are extrapolated based on the model proposed in this article. In these hidden patterns, scholars who are geographically close and have similar research background potentially develop further research collaborations, and at least one scholar in collaborations has high citations. [ABSTRACT FROM AUTHOR]

• Hub capacities are considered for the single-allocation Hub Maximal Covering problem. • A new Mixed-Integer Linear-Programming model is proposed and solved for the problem. • A Comprehensive fitness landscape analysis shows the problem space is rugged plain. • A new GARVND algorithm with original Crossover and Mutation operators is proposed. • Experiments showed that GARVND outdid GA, SA, VNS, TS, & GWO+SA methods in objective value. In this paper, a new variation to the Hub Maximal Covering problem called the Capacitated Single-Allocation Hub Maximal Covering Problem (CSAHMCP) is addressed for the first time and a novel binary-integer mathematical model has been proposed. To analyze the features of the problem's solution space and to determine the most effective heuristic approach to solve it, a comprehensive Fitness Landscape Analysis is performed which reveals that the problem is of 'rugged plain' type and can best be solved using an algorithm with good exploration and exploitation capabilities at the same time. Accordingly, we proposed a new algorithm named GARVND, which is a hybrid of the Genetic Algorithm acting as the main framework for diversification, and a variant of the Variable Neighborhood Descent algorithm called 'Reduced VND' acting as a means to improve individual solutions (intensification) by searching in the spaces generated by four types of neighborhood operators. We also introduced two original crossover and mutation operators that can be used for any hub location solution method. The parameters of GARVND are tuned toward less computational times and higher solution quality by using the Taguchi experiment design technique. We solved 114 CSAHMCP instances (several times each) from the Australian Post (AP) dataset using the proposed GARVND, as well as by the Genetic Algorithm, Variable Neighborhood Search, Simulated Annealing, Tabu Search algorithms, and a hybrid algorithm consisting of Grey Wolf Optimizer and Simulated Annealing, in addition to the Gurobi exact solver. Extensive computational results and statistical analyses showed that GARVND yields favorable solutions, outperforming all the compared algorithms by producing smaller gaps with global optimal objective values. In addition, to illustrate the practical value of the CSAHMCP and the GARVND method, we performed a cost-flow sensitivity analysis for the AP Central Sydney network and came up with the best expansion scenarios to be adopted by the management. [ABSTRACT FROM AUTHOR]

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

The paper provides a new wave spectrum reconstruction algorithm for short-crested sea state conditions in the frequency-domain, based on the wave buoy analogy and the Spearman rank correlation coefficient. After a brief review about the main wave spectrum assessment methods, the new algorithm is outlined and a stepwise procedure is proposed for its employment in a numerical code developed in Matlab. A wide benchmark study is carried out to systematically investigate the effectiveness of the proposed method, assuming the S175 containership as reference vessel. The simulation of ship motions in the time-domain is performed by a dedicated code, developed in Matlab and based on the solution of Cummins equations. The main wave spectrum parameters are randomly varied at various heading angles, from following to head sea conditions, to systematically investigate the effectiveness of the proposed method. Another set of random simulations is provided by randomly varying the ship speed and heading angle to investigate the statistics of errors and test the new algorithm in almost all possible scenarios, representative of unimodal short-crested sea state conditions the ship may encounter during its lifetime. The main outcomes of the research are discussed and some possible improvements are outlined for future research. • Wave buoy analogy for sea state assessment. • Development of a wave spectrum assessment procedure based on Spearman rank correlation algorithm. • Benchmark study devoted to investigating the effectiveness of the proposed procedure. • Investigation about the statistics of errors to select the minimum time duration of ship motion measurements. • Matlab codes for time-domain simulation of ship motions and wave spectrum assessment in the frequency-domain. [ABSTRACT FROM AUTHOR]

In this paper, the distributed Nash equilibrium seeking problem for multi-agent finite games is considered. Considering the locally-informed communication structure, previous centralized game-theoretic learning dynamic methods are not feasible. To this end, we propose a distributed log-linear algorithm by introducing a timestamp-based communication mechanism that enables agents to acquire estimates of the true action profile from their neighborhoods. It is shown that the proposed distributed log-linear algorithm significantly outperforms the existing best response dynamics regarding both effectiveness and reliability. For illustration, a locally-informed multi-player Sudoku puzzle is introduced, where each cell is taken as a player, and each player fills numbers only based on the local information of its neighboring players. The simulation results validate the effectiveness of our algorithm in solving the locally-informed multi-player Sudoku puzzle. Our methodology provides a viable and benchmark approach to address the fully distributed locally-informed multi-agent joint decision-making problem. • A distributed log-linear algorithm is proposed for Nash seeking in finite games. • A locally-informed multiplayer Sudoku puzzle is initialized to evaluate algorithm. • The proposed algorithm is superior in terms of both effectiveness and reliability. [ABSTRACT FROM AUTHOR]

Determining the path for an evolutionary algorithm is crucial for its performance. Current methods for sampling reference points guiding evolutionary algorithms are inadequate for dealing with convex and concave Pareto fronts,and the uniformity of sampling results decreases significantly in high-dimensional spaces. In this paper,we propose a reference point sampling method based on angular relationships to tackle these issue. And we propose the concept of optimally distributed individuals based on the IGD indicator to ensure the distribution of the evolutionary process and prevent the algorithm from converging to local optima. Additionally,we introduce a novel method for calculating individuals' fitness within the population,ensuring convergence,uniformity,and distribution of the evolutionary algorithm,thereby enhancing selection pressure among non-dominated individuals. Experimental results on diverse benchmark test problems demonstrate that the proposed algorithm competes favorably with six advanced evolutionary algorithms for many-objective optimization. • A new reference point sampling method based on angular relationship is proposed. • The concept of optimal distributive solution is proposed to expand search space. • A new method for calculating individual fitness is proposed to select offspring. • The algorithm is extremely competitive with five advanced algorithms for MaOPs. [ABSTRACT FROM AUTHOR]

Surrogate-assisted multi-objective optimization algorithms have attracted widespread attention due to their outstanding performance in computationally expensive real-world problems. However, there is relatively little research about multi-objective optimization with complex and expensive constraints. Hence, a data-driven co-evolutionary exploration (DDCEE) algorithm is presented in this paper for the above-mentioned problems, where Radial Basis Functions are utilized to train dynamically updated surrogate models for each objective and constraint. Specifically, a data-driven co-evolutionary exploration framework is proposed to fully utilize and mine the potential available information of RBF models, and RBF models are constantly updated to guide co-evolutionary in discovering valuable feasible regions and achieving global optimization. In co-evolutionary exploration, one population focuses on exploring the entire space without considering constraints, while the other population focuses on exploring feasible regions and collaborating by sharing their respective offspring. Reference vectors are introduced in co-evolutionary exploration to divide the objective space into several sub-regions for further selection. Furthermore, an adaptive selection of promising samples strategy is presented to reasonably utilize the information of solutions with good convergence and enhance the convergence and diversity of the Pareto front. After comprehensive experiments on constrained multi/many-objective benchmark cases and an engineering application problem, DDCEE shows more stable and impressive performance when compared with five state-of-art algorithms. ● A notably effective data-driven co-evolutionary exploration algorithm is proposed to solve expensive CMOPs. ● The data-driven co-evolutionary exploration framework effectively handles complex constrained multi-objective problems. ● An adaptive selection strategy for promising samples is presented to enhance the convergence and diversity of the PF. [ABSTRACT FROM AUTHOR]

The key to solving constrained multiobjective optimization problems (CMOPs) lies in maintaining the feasibility, convergence, and diversity of the population. In recent years, various constraint handling techniques (CHTs) and strategies have been proposed to enhance the performance of constrained multiobjective evolutionary algorithms (CMOEAs). However, most of these algorithms face difficulties in dealing with problems that have large infeasible regions and discontinuous small feasible regions, as they have trouble crossing large infeasible regions while simultaneously maintaining the convergence and diversity of the population. To tackle this issue, this paper proposes a dual-population auxiliary coevolutionary algorithm with an enhanced operator, denoted as DAEAEO. Auxiliary population 1 employs an improved ϵ -constraint handling technique to provide high-quality feasible solutions for the main population. Auxiliary population 2 adopts the non-dominated sorting method to provide favorable objective information for the main population to help it cross the infeasible region. In addition, to further improve diversity, each population adopts an enhanced operator and a genetic operator to generate offspring, respectively. Finally, knowledge transfer between offspring is realized. Compared to six state-of-the-art CMOEAs on DASCMOPs, LIR-CMOPs, DOC test suites, and two real-world problems, the proposed DAEAEO achieved superior performance, especially for CMOPs with large infeasible regions and discontinuous small feasible regions. • A constrained multiobjective coevolutionary algorithm is proposed, termed DAEAEO. • A dual-population auxiliary evolutionary strategy is proposed. • The use of knowledge transfer promotes information interaction among offspring. • An enhanced search operator is developed to balance convergence and diversity. • The effectiveness of DAEAEO is verified on test suites and two real-world problems. [ABSTRACT FROM AUTHOR]

In recent years, in the field of Multi-Agent Systems (MAS), significant progress has been made in the research of algorithms that combine Reinforcement Learning (RL) with Attention Mechanism (AM). However, there is a lack of comprehensive reviews in this field. Based on this, this paper does the following work. Firstly, it reviews the classical algorithms of RL and AM; Secondly, it systematically introduces the combination of RL and AM; Thirdly, it sorts out their application in the field of single-agent and multi-agent, and pays attention to and looks forward to the challenges and future research prospects in this field; The last part offers a comprehensive analysis of the challenges encountered by research in this field and anticipates future research paths. The research offers a conceptual understanding and theoretical foundation for future applications of RL using AM and facilitates further in-depth study in this field for researchers. [ABSTRACT FROM AUTHOR]

Ordinal regression (OR) methods are designed for a type of classification problems where data labels have natural orders. In practice, data may be corrupted by label noise, which affects the training process thus degrading the generalization performance of OR methods. In OR, data are usually assumed to have latent variables underlying the ordinal labels, and label noise exhibits a special characteristic that it usually causes large latent variable value deviations. However, there are few existing works on OR considering label noise, and the existing works do not utilize the above-mentioned characteristic. Besides, most of the existing OR methods are centralized, which are inapplicable in some realistic distributed applications. In this paper, we utilize the characteristic of label noise in OR to develop a distributed robust support vector ordinal regression method (dRSVOR) under label noise. Specifically, after analyzing the characteristic of label noise in OR, we take the form of SVOR with explicit constraints to achieve robustness to one type of mislabeled samples. Then, we adopt correntropy, an information-theoretic measure, to achieve robustness to the other type of mislabeled samples. Theoretically, we analyze the consensus and convergence of dRSVOR. Experimentally, we conduct experiments on both synthetic data and real OR datasets to illustrate the effectiveness of the proposed method. The results show that the centralized version of dRSVOR outperforms several state-of-the-art OR methods considering label noise in centralized circumstances with label noise, and dRSVOR could approach the performance of the centralized version despite additional constraints in distributed scenarios. [ABSTRACT FROM AUTHOR]

This paper concentrates on the bipartite finite-time consensus (BFTC) problem of nonlinear multi-agent systems (MASs) under intermittent communication via event-triggered impulsive control. First, a finite-time consensus (FTC) protocol with an event-triggered mechanism is developed for the MASs with nonlinear dynamics to save limited resources. Then, an impulsive control scheme is introduced to the designed algorithm to further improve the control performance. Based on the structurally balanced graph theory, both antagonistic and cooperative interactions are discussed in this article. Regarding whether the MASs have encountered intermittent communication, sufficient conditions are provided for achieving consensus of the MASs by using the Lyapunov theory and inductive methods. Moreover, the Zeno behavior is excluded. Finally, numerical simulations are given to validate the effectiveness of the algorithm. The result shows that the designed event-triggered BFTC control protocol with an impulsive strategy has a faster convergence rate than that without an impulsive strategy. [ABSTRACT FROM AUTHOR]

[Display omitted] • 11 ecological indicators were abstracted using satellite remote sensing technology. • Escape routes planned by the improved algorithm are safer and more reliable. • Multiple scientific escape options for escapees were provided. The forest fire environment is complex and volatile, posing a serious threat to the lives of firefighters at any time. Aiming at how people choose escape routes when facing these dangers, this paper proposed an escape route planning method based on the improved A* algorithm. Taking the forest fire that occurred in Xintian County, Yongzhou City, Hunan Province on October 17, 2022, as the research object, this study collected data from multi-temporal remote sensing imagery including GF-4, and Sentinel-2 to obtain 11 factors affecting the escape route planning. We used the FAHP-CRITIC combination weighting method to analyze the weights of the escape impact factors. Forest fire scenes were categorized into five classes based on escape risk coefficient from high to low. The heuristic function and weight coefficients of the traditional A* algorithm were reconstructed to obtain the improved A* algorithm. The computing time of both A* algorithm is similar in the same fire scene. But the percentage of the escape route length, located inside the high-risk zones, in the total escape route planned by the improved algorithm decreased by 53.63% than that planned by the traditional A* algorithm. The escape risk coefficient was reduced by 24.22%, and the escape safety was significantly improved. On this basis, the study combined the improved A* algorithm with the dynamic escape window method to search for the nearest safe area to the escapees and compute the corresponding escape routes. The real-time safety of the algorithm was verified using GF-1 remote sensing images obtained 7 and 35 min after the previous fire burning moment. Results revealed that the escape paths planned by the improved A* algorithm remained in a safe state and were able to be updated in real time according to the trend of the fire. This demonstrates the ability of the improved A* algorithm to adapt to real-time changes in forest fire scenes. It can provide multiple reliable escape options for escapees, thus scientifically and effectively reducing human casualties. [ABSTRACT FROM AUTHOR]

The Domain Name System (DNS) is a prime target for cyber attacks, necessitating the monitoring and analysis of DNS activities to detect malicious behaviors. This paper presents an innovative DNS behavioral profiling approach that addresses challenges posed by the dynamic landscape of cyber threats, encompassing issues like evasion tactics, content variability, discerning malicious intent, navigating URL obfuscation, low and slow tactics, and maintaining accuracy in the face of diverse normal behaviors, contributing to the advancement of robust threat detection. The framework leverages unique feature behaviors and correlations, incorporating a novel feature selection algorithm, pattern extraction methodology, and a robust neural network architecture for accurate profile construction. The research also includes the development of ALFlowLyzer, a custom application layer network flow analyzer, and introduces the BCCC-CIC-Bell-DNS-2024 dataset, addressing limitations in widely used public DNS datasets. Experimental results demonstrate the effectiveness of the proposed model in profiling various DNS activities. [Display omitted] • Introduction of a new graph-based feature selection algorithm. • Introduction of a behavior similarity calculation algorithm. • Introduction of DNS Flow as a vital component in DNS analysis. • Creation of a new and sophisticated behavioral profiling system through pattern extraction and neural network architecture. • Implementation of ALFlowLyzer, an application layer network flow analyzer with more than 120 DNS features. • Introduction of a new malicious DNS dataset, BCCC-CIC-Bell-DNS-2024, with a DNS flow-based approach. [ABSTRACT FROM AUTHOR]

In the field of machine learning, the issue of class imbalance is a common problem. It refers to an imbalance in the quantity of data collected, where one class has a significantly larger number of data compared to another class, which can negatively affect the classification efficiency of algorithms. Under-sampling methods address class imbalance by reducing the quantity of data in the majority class, thereby achieving a balanced dataset and mitigating the class imbalance problem. Traditional under-sampling methods based on k-means clustering either set the unified value of k (number of clusters) or determine it directly based on the quantity of data in the minority or majority class. This paper proposes an adaptive k-means clustering under-sampling algorithm that calculates an appropriate k for each dataset. After clustering the majority class dataset into k clusters, our algorithm calculates the distances between the data within each cluster and the cluster centroids from two perspectives and selects data based on these distances. Subsequently, the subset of the majority class dataset are combined with the minority class dataset to generate a new balanced dataset, which is then used for classification algorithms. The performance of our algorithm is evaluated on 45 datasets. Experimental results demonstrate that our algorithm can dynamically determine appropriate k for different datasets and output a balanced dataset, thus enhancing the classification efficiency of machine learning algorithms. This work can provide new algorithmic ensemble strategies for addressing class imbalance problem. [ABSTRACT FROM AUTHOR]