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Traditional federated learning faces challenges such as high communication costs, structural heterogeneity,and insufficient privacy protection. To address these issues, this paper proposes a federated learning evolutionary algorithm that applies sparse evolutionary training algorithm to reduce communication costs and integrates local differential privacy protection for participants’ privacy. Additionally, it utilizes the NSGA-Ⅲ algorithm to optimize the network structure and sparsity of the global federated learning model, adjusting the relationship between data availability and privacy protection. This achieves a balance between the effectiveness, communication costs, and privacy of the global federated learning model. Experimental results under unstable communication environments demonstrate that, on the MNIST and CIFAR-10 datasets, compared to the solution with the lowest error rate using the FNSGA-Ⅲ algorithm, the proposed algorithm improves communication efficiency by 57. 19% and 52. 17%, respectively. The participants also achieved（3. 46, 10-4） and（6. 52, 10-4）-local differential privacy. This algorithm can effectively reduce communication costs and protect participant privacy without significantly compromising the accuracy of the global model. [ABSTRACT FROM AUTHOR]

In view of the problems of data sparsity and the diversity in academic paper recom-mender systems,based on CONVNCF, this paper proposed an algorithm of convolution with word and heterogeneous information network for academic paper recommendation ( WN -APR) . Firstly, WN -APR algorithm learned user and paper' s diverse features from different semantics to alleviate the sparsity problem. Then it designed an outer product fusing way to seamlessly combine user features with paper features. Replacing of 2D CNN, this algorithm applied 3 D convolution to mine the influence of different features on the performance. Finally, it modified the BPR loss function to enhance diversity in recommendations. Experimental results on CiteULike-a and CiteULike-t datasets show that WN-APR improves the performance of accuracy and diversity over the baseline models. [ABSTRACT FROM AUTHOR]

In order to solve the problems of the current art circulation information is not transparent and the centralized trading authority is asymmetric, this paper proposed a art blockchain certificate traceability model based on three chains. First, in view of the large amount of data in a single piece of artwork information, this paper proposed a blockchain-based symmetric searchable encryption algorithm, and used the interplanetary file system to securely store artwork circulation records on-chain and off-chain. Secondly, for the characteristics of the increasing effective data of artwork information, this paper proposed a blockchain-based inverted fast query and traceability algorithm to help data users realize fast traceability and positioning. At the same time, this paper proposed for the first time that the false information alliance chain stores false art circulation records to realize the processing of false information. Finally, experiments verify the feasibility of the model for big data security storage and fast query traceability. Experiments show that the model is feasible and the query method is efficient. [ABSTRACT FROM AUTHOR]

In view of the problem of fuzziness of rating and tag in traditional collaborative filtering algorithms,this paper used trapezoidal fuzzy number to describe the mapping relationship between rating and satisfaction.The algorithm considered the impact of sparseness of the rating,constructed a new trapezoidal fuzzy rating model to determine the similarity based on fuzzy rating,analyzed the degree of membership between the tag and the item,and constructed a fuzzy item-tag matrix to measure the similarity based on the degree of tag membership.Finally,it used the improved scoring prediction strategy to estimate the score.The experimental results on the MovieLens dataset show that the proposed algorithm improves the prediction accuracy while suppressing the cold start of the project,alleviating the problems of fuzziness and sparseness,which indicates the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposed an electronic voting scheme based on one-time ring signature and blockchain to solve the problems of the current e-voting system in terms of anonymity, transparency and open vote-counting. This paper introduced one-time ring signature, blockchain and stealth address technology. The one-time ring signature technology solved the problem of voter anonymity, repeated voting and public verification. The blockchain technology could ensure the openness and transparency of the e-voting system. The one-time ring signature technology could ensure no one or institution could obtain the voting intermediate result before the voting ends. In order to improve the efficiency of vote-counting,it used the method of key sharing to improve the vote-counting algorithm. Through security and performance analysis, the scheme conforms to the basic security standards of an electronic voting system. [ABSTRACT FROM AUTHOR]

The digital trunking system has the characteristics that the calling mode is mainly group calling and the communication mode is mostly half duplex. For the problem that the traditional user classification method couldn't meet the classification requirements of digital cluster users, this paper proposed a digital cluster user classification method based on improved RFM model. Firstly, it introduced the average speech duration attribute to establish the recency vitality speak (RVS) model. Then, it used the analytic hierarchy process to determine the weight of each parameter in the model. Finally, it used the K-means + + clustering algorithm to classify digital cluster users. The simulation result shows that, by using the user classification method proposed in this paper, the accuracy of digital cluster user classification can reach more than 87. 9% . [ABSTRACT FROM AUTHOR]

For the security problem of the spread of digital image and the digital image encryption from plaintext and too depend on the chaotic systems, this paper proposed a digital image encryption and monitoring algorithm combined with plaintext-related chaotic mapping and SHA-256. The algorithm used the SHA-256 algorithm to calculate the hash value as record to monitor the spread of digital image. Lorenz chaotic mapping generated the corresponding cryptogram by using forward diffusion, associated plaintext scrambling and backward diffusion to encrypt the digital image. The results show that the algorithm can resist many attacks and achieve the security and concealment of image propagation. [ABSTRACT FROM AUTHOR]

The coverage tasks of mobile robots are evolving towards large-scale and intelligent di- rections demanding urgent requirements for the coverage efficiency and environmental adaptabil- ity of coverage path planning. To address the inadequate adaptability of the traditional boustro- phedon cellular decomposition in complex maps and to improve coverage efficiency, a complete coverage path planning method is proposed. Firstly, based on the boustrophedon cellular decom- position method. a strategy of area-descending traversal and monotone polygon judgment was proposed to merge cells, reducing approximately half of the cell quantity. Finally, by establis- hing the mapping relationship between the starting and ending positions of cells, a genetic algo- rithm was employed to optimize the selection of cells' starting positions and the global access or- der. The research results show that: 1) when processing maps with dimensions of 1 300 pixels. the algorithm in this paper can obtain calculation results within 10 s, and compared with the boustrophedon method, neural network method, and contour line method, the growth rate of calculation time is smaller with the increase of map area: 2) compared with the boustrophedon method, contour line method, neural network method, and energy-optimal method, the total op- eration time of robots in this paper is reduced by 5.4% to 47.0%, and the ineffective operation time is reduced by 5.8% to 29.2%; 3) the average coverage rate of this algorithm on 1 800 test maps reaches 99,91%; 4) the tests further confirm the significant coverage efficiency advantages of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper investigates a fast and efficient dynamic channel access method for distributed channel access scenarios with strong jamming and limited channel resources to achieve effective anti-jamming communication performance. The proposed problem is constructed as a correlated equilibrium (CE)-based game model with jamming signals designed as coordination signals. To effectively raise the convergence performance under different jamming patterns, the event counters (ECs) are designed, where the coordination signal is improved according to the characteristics of the jamming signal. Next, a distributed cooperative anti-jamming approach is constructed based on coordinated learning, which effectively accelerates the generation of CE points. Based on the above approach, this paper designs a distributed channel access anti-jamming algorithm based on coordination signal optimization, and the simulation results show that the proposed approach can effectively achieve cooperative anti-jamming and channel access. Compared with the existing work, the proposed approach can achieve better communication performance and fairness in the face of sweep jamming. In the face of random jamming, it can achieve faster and better communication performance without losing fairness. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the positioning accuracy of traditional SLAM algorithm decreases due to the influence of dynamic feature points in dynamic environment, this paper proposed SF-VINS which fused semantic information. Firstly, it incorporated the PP-LiteSeg semantic segmentation network into the front-end of system based on the VINS-Mono algorithm and removed the dynamic feature points according to the results of semantic segmentation. Then, it constructed the semantic probability error constraint with the pixel semantic probabilities and employed adaptive weights for feature points in the backend. Based on these modifications, this paper proposed a new BA cost function and an optimization strategy for camera extrinsic parameters to improve the accuracy of state estimation. Finally, this paper conducted experiments on the VIODE and NTU VIRAL datasets to validate its effectiveness. The experimental results show that the proposed algorithm has advantages in terms of positioning accuracy and robustness in both dynamic and static scenes compared with state-of-the-art visual-inertial SLAM algorithms. [ABSTRACT FROM AUTHOR]

Existing cost allocation methods for adaptive video steganography mainly focus on specific transform coefficients, resulting in lower capacity. Moreover, distortion drift is a significant challenge for steganography in HEVC videos. Therefore, this paper proposed a cost allocation method that combined the intra-frame and inter-frame processes of HEVC video coding to achieve high-capacity, low-distortion transmission in high-performance adaptive video steganography. Firstly, the method investigated the discrete sine transform features in HEVC video coding, analyzing the error propagation patterns of these coefficients under disturbance. During the embedding processed, it conducted a detailed analysis on the intra-block distortion, interblock distortion, and inter-frame distortion caused by modifying transform coefficients. The algorithm also took into account the differentiation in inter-block distortion resulting from steganography in different blocks, leading to the classification of blocks. This paper maximized the utilization of all non-zero transform coefficients, allocating distinct distortion costs for various carrier coefficients. The covert information was then embedded into frames that minimally impacted video quality. Experimental results indicate that, compared to existing HEVC video coefficient domain steganography methods, the proposed algorithm demonstrates advantages in terms of video bitrate, video quality, and embedding capacity. [ABSTRACT FROM AUTHOR]

Most of the existing MAPF methods based on communication learning have poor scalability or aggregate too much redundant information, resulting in inefficient communication. To solve these problems, this paper proposed disruptor identifiable communication (DIC), which learned concise communication excluding non-disruptors by judging whether the agent in the center of the field of view would change its decision-making due to the presence of neighbors, and successfully filtered out redundant information. At the same time, this paper further instantiated DIC and developed a new highly scalable distributed MAPF solver: disruptor identifiable communication based on reinforcement and imitation learning algorithm (DICRIA). Firstly, the disruptor discriminator and the policy output layer of DICRIA identified the disruptor. Secondly, the algorithm updated the information of the disruptor and the communication wish sender in two rounds of communication respectively. Finally, DICRIA output the final policy according to the coding results of each module. Experimental results show that DICRIA S performance is better than other similar solvers in almost all environment settings, and the algorithm increases the success rate by 5.2% on average compared to the baseline solver. Especially in dense problem instances with large-size maps, the algorithm even increases the success rate of DICRIA by 44.5% compared to the baseline solver. [ABSTRACT FROM AUTHOR]

To solve the flexible job shop dynamic scheduling problem with fuzzy quality control time, which is more realistic, this paper established two rescheduling models for machine breakdown and urgent order insertion with the objective of minimizing completion time. Two models were for three failure scenarios: emergency order insertion, machine failure during no-load operation, and machine failure during work-piece processing. To solve this model, this paper designed a novel genetic-neighborhood search algorithm that integrated cellular-automata-based neighborhood search and random restart hill-climbing. Aiming at the characteristics of dual sub-decisions involving order sequencing and machine selection, this paper designed a two-layer coding scheme including process code and machine code, and designed corresponding genetic operations such as crossover and mutation for them. After that, this paper applied the genetic operations into the framework of cellular-automata-based neighborhood search to enhance the algorithm's global search capability, and integrated the random restart hill-climbing algorithm based on key operations to improve the algorithms local development capability. Experiments tested 10 flexible job shop scheduling instances and one flexible job shop scheduling instance with fuzzy quality control time to verify the effectiveness of the proposed algorithm and models. In addition, the test results under different failure scenarios show the proposed method outperforms the backward-based scheduling strategy used in practice. [ABSTRACT FROM AUTHOR]

Maintaining population diversity and improving algorithm search efficiency are two major problems that need to be solved urgently in the multimodal multi-objective optimization. To address the above problems, this paper proposed a multimodal multi-objective brain storm optimization algorithm based on zoning search and reinforcement learning (MMBSO-ZSRL). In the MMBSO-ZSRL, the decision space was first decomposed into multiple subspaces to reduce the search difficulty and maintain the population diversity. Subsequently, the proposed algorithm used SARSA algorithm to balance the global exploration and local exploitation capabilities of the brain storm optimization algorithm. Additionally, the MMBSO-ZSRL utilized the special crowding distance to select individuals for guiding the population evolution. To verify the performance of the proposed algorithm, this paper selected six advanced multimodal multi-objective optimization algorithms and the IEEE CEC2019 multimodal multi-objective problem benchmark test suite for experiments. Experimental results demonstrate that the overall performance of the MMBSO-ZSRL is significantly better than that of compared algorithms. The proposed MMBSO-ZSRL can not only find the Pareto front with better diversity and approximation, but also find more Pareto optimal solutions in the decision space. [ABSTRACT FROM AUTHOR]

In order to improve the delivery efficiency of logistics, this paper proposed a vehicle routing problem with drone and time window considered by the time window, UAV (unmanned aerial vehicle) power change and multi-point continuous delivery of UAV. Then, this paper proposed a hybrid particle swarm optimization algorithm with local search to solve it. Based on the hybrid particle swarm optimization algorithm, the proposed algorithm transformed the problem solution space into the algorithm search space by constructing an efficient encoding and decoding strategy. Further, the proposed algorithm combined a single point insertion strategy, a vehicle replacement strategy and a UAV replacement strategy to form a local search strategy to improve the optimization ability. The experimental results show that the proposed model is more efficient than the model of pure vehicle distribution and saves 31.51% of the cost. The proposed algorithm is better than the four comparison algorithms, and its highest optimization rate is 82.08%. [ABSTRACT FROM AUTHOR]

In order to address the limitations of the neighborhood rough set model caused by neighborhood parameters and inadequate sample information representation, this paper proposed a rough set model based on the maximum coalition theory. By normalizing the neighborhood information system and utilizing the maximum coalition set to describe neighborhood granular information, the proposed model enabled finer information division and significant reduction of uncertainty in the boundary region. Combining the model with three-way clustering, this paper designed a three-way clustering algorithm based on maximum coalition rough set. Comparative experiments on six UCI public data sets show that the proposed algorithm has better clustering quality than the comparison algorithms, and has higher comparison accuracy when dealing with boundary region samples. [ABSTRACT FROM AUTHOR]

Objectives: Curve simplification is of importance in automated map generalization; nevertheless, the Douglas-Peucker (DP) algorithm popularly used in map generalization is not automatic, because a key parameter called distance tolerance (ε) must be given by experienced cartographers and needs to be input before execution of the algorithm. Methods: To solve the problem, this paper proposed a method to automatically calculate ε and by which the automation of the DP algorithm is achieved. The method consists of the following steps: (1) A formula is constructed by the Hausdorff distance for calculating the similarity degree (Ssim) between a curve at a larger scale and its simplified counterpart at a smaller scale. (2) 15 linear rivers are selected, and each of them is manually simplified to get their counterparts at seven different scales. The Ssim of each original river and each of its simplified counterpart at a smaller scale can be obtained using the formula constructed by the Hausdorff distance, and 15×7=105 coordinate pairs consisting of (S,Ssim) can be got, and a function between Ssim and S are constructed by the curve fitting using the coordinates. (3) In the meanwhile, the 15 rivers are simplified using a number of ε, and the Ssim of each original river and each of its simplified counterpart at a smaller scale can be calculated using the formula constructed by the Hausdorff distance. In this way, a number of coordinate pairs (ε,Ssim) are got, and a function between ε and Ssim is constructed by the curve fitting. (4) By the function between Ssim and S and that between ε and Ssim, a formula between ε and S can be deducted. Using the formula ε can be calculated automatically, because in a map generalization task S is usually known. After this step, automation of the DP algorithm is achieved. Results: The experiment results show that (1) The proposed DP algorithm can automatically simplify the rivers in a specific geographical area to get the results at different scales; and (2) the resulting river curves generated by the proposed DP algorithm have a high degree of similarity with the ones made by experienced cartographers. Their average similarity degree is 0.927. Conclusion: The proposed DP algorithm can simplify curve features on maps automatically, and the results are highly intelligent and credible. Although only river data is tested in this paper, the principle of the proposed method can be extended to other linear features on maps. Our future work will be on improving the accuracy of the proposed DP algorithm using more river data so that the algorithm can be used in practical map generalization engineering. [ABSTRACT FROM AUTHOR]

Aiming at the path planning problem of mobile robot in dynamic environment, this paper proposes a fusion algorithm (IHHO-IDWA) of Improved Harris Hawk Algorithm (IHHO) and Improved Dynamic Window Algorithm (IDWA）. First of all, this paper proposed a fusion adaptive chaos and core population dynamic division strategy, a fusion golden sine strategy and a dynamic cloud optimal disturbance resolution strategy to improve the performance of and solve the lack of search performance in the late stage of the Harris Eagle algorithm. In addition, in view of the problems that the dynamic window algorithm has a long planned path and easy to fall into deadlock, it proposed three improvement strategies to solve them: Firstly, add sub-functions to ensure that the algorithm can plan a shorter path; secondly, propose an adaptive weight strategy, balance the local obstacle avoidance ability and global search performance of the algorithm; thirdly, set the initial heading angle to avoid path redundancy. Finally, through numerical experiments of test functions, CEC2014 functions, and static and dynamic path planning experiments, it is verified that the performance of IHHO and IDWA in this paper has been significantly improved; through the 50×50 large-scale dynamic map, it is verified that the path planned by the fusion algorithm is shorter than that planned by the control group algorithm. 11.51%, proving the superiority of the method in this paper. [ABSTRACT FROM AUTHOR]

In the era of e-commerce, accurately predicting user purchase intentions has become a crucial factor for enhancing sales efficiency and optimizing the customer experience. Addressing the limitations of traditional ensemble strategies, which often suffer from subjective biases during the model design phase, this paper introduced an adaptive evolutionary ensemble learning model to predict user purchase intentions. This model adaptively selected the optimal base learners and meta-learners, incorporating both the predictive information from the base learners and the differential information between features to expand the feature dimensions, enhancing prediction accuracy. Moreover, to further refine the predictive capabilities of the model, this paper designed a binary adaptive differential evolution algorithm for feature selection, aiming to identify features that significantly influence the prediction outcome. Research results show that the binary adaptive differential evolution algorithm outperforms traditional optimization algorithms in global searches and feature selection. Compared to six common ensemble models and the DeepForest model, the proposed evolutionary ensemble model achieves a 2.76% and 2.72% increase in AUC value, respectively, and effectively mitigates the impacts of data imbalance [ABSTRACT FROM AUTHOR]

TANGRAM block cipher algorithm employs a bit-slice approach and is compatible with multiple software and hardware platforms. In response to TANGRAM-128/128 algorithm, this paper proposed a design plan which used Verilog HDL for FPGA implementation. This paper firstly provided an introduction to the characteristics and processed of TANGRAM algorithm and presented a scheme for reducing resource consumption using a finite-state machine for 44 rounds of encryption and decryption computation. Secondly, the engineering implementation of the FPGA algorithm was completed by the domestic Gaoyun platform and subjected to functional simulation and data correctness validation. Furthermore, relevant tests were performed on Quartus II 13.1.0 platform for comparison. Test results show that, based on Cyclone IV E EP4CE40F29C6 chip from Altera, TANGRAM block cipher algorithm has a maximum clock frequency of 138. 64 MHz and an encryption/decryption speed of 403.30 Mbps. While based on the GW2A-55 chip from Gaoyun, the maximum clock frequency is 96.537 MHz and the encryption/decryption speed is 280. 80 Mbps. [ABSTRACT FROM AUTHOR]

In order to better cope with large-scale high-dimensional sparse data sets in today’s era, this paper combines the advantages of BB method, mini-batch algorithm and stochastic variance reduced gradient（SVRG）, and proposes a mini-batch sparse stochastic variance reduced gradient method（MSSVRG-R2BB） with randomly improved BarzilaiBorwein step size. First, on the basis of calculating the total gradient in the outer loop of SVRG, this article adds the subgradient of L1 norm, designs a sparse approximate gradient for the inner loop of SVRG, and obtains a sparse SVRG algorithm（SSVRG）. On this basis, this paper propose to use the improved BB method of random selection to automatically calculate and update the step size in the mini-batch sparse stochastic variance reduced gradient method, which solves the step size selection problem of the mini-batch algorithm, and expands the MSSVRG-R2BB algorithm. Numerical experiments show that MSSVRG-R2BB algorithm can not only reduce the operation cost and reach the convergence upper bound faster, but also achieve the same optimization level of other advanced small-batch algorithms, and perform stably and well for different initial parameter selection. [ABSTRACT FROM AUTHOR]

Aiming at the specific target search and controllable angle guidance of unmanned surface ships in uncertain waters, this paper explores the target detection method based on YOLO and the guidance control method based on side angle constraint. On the basis of analyzing the uncertain conditions such as sea fog, the paper designs the correlation filter with im- proved YOLOv5 algorithm to extract the target features, combines depth information to realize the detection and positioning of water surface targets, sliding mode variable structure guidance control to achieve the high maneuverability target side angle constraint guidance. TCP/IP combined with ROS is used to create a comer constrained terminal guidance system for information processing. Two-dimensional simulation experiment is completed with Autolabor platform, and the result of target guidance error is 1.54%, which proves the effectiveness of the method proposed. [ABSTRACT FROM AUTHOR]

This paper introduced a novel approach for image retrieval, the scene-aware object saliency ranking algorithm（SASR）,which addressed the issue of traditional image retrieval techniques relying on semantic similarity and neglecting the crucial importance of object relationships within a scene. SASR consisted of two stages. In the first stage, this paper proposed a viewpoint data-based method called the “combined threshold” to annotate true value labels for object-level saliency ranking, simplifying the annotation of ranking labels. In the second stage, this paper presented an object-level saliency ranking network based on graph convolutional networks that resolved several specific difficulties encountered in sorting objects. The proposed algorithm improved on the current saliency ranking label generation methods and was tested via a large number of comparative experiments. The experimental results on the SALICON dataset show that the SASR algorithm enhances saliency ranking perfor-mance significantly. Moreover, the results from the NUS-WIDE dataset indicate that, when supported by the proposed algorithm, image retrieval performance increases by an average of 2%,which solidifies the efficacy of the proposed algorithm [ABSTRACT FROM AUTHOR]

Regular path query is a technique of using regular expressions to query graph data, usually uses finite state automata to achieve query matching. The matching result of the existing regular path query method is a sequence of vertex pairs, and the structure of the graph is not fully preserved. In order to solve this problem, this paper proposed a method of structured regular path query for graph data. By structuring constraints between different sequences, the query results could be transformed from paths to subgraphs. For this purpose, the method firstly defined a structured regular path query language, and then designed a structured query parsing and a matching algorithm based on this structure. Experimental results on simulated and real datasets verify the influence of network size on query speed. This paper compared the proposed method with the control group. Experimental results show that the proposed method can realize structured query under the premise of satisfying regular expression constraints. [ABSTRACT FROM AUTHOR]

This paper proposed a robust peer-to-peer optimization method in the context of uncertain environments to address the uncertainty in closed-loop supply chain networks while considering demand uncertainty and facility disruption risk. This paper formulated a robust optimization model to minimize the total cost of the closed-loop supply chain network. Additionally, this paper proposed a Prim-DMGA that firstly identified high-quality initial solutions using the Prim algorithm and then optimized the routing and facility selection solutions in a two-layer adaptive GA feedback loop. The experimental results demonstrate that the Prim-DMGA generates better objective function values than the single-layer Prim-MGA and traditional GA with less computational time. Further, the results obtained from the algorithm are superior to those obtained by using the CPLEX software in solving large-scale scenarios. It concludes that the proposed robust optimization model effectively reduces the adverse effects caused by uncertainty and improves the robustness of closed-loop supply chain networks. Moreover, the Prim-DMGA generates better solutions with less computational time. [ABSTRACT FROM AUTHOR]

Task scheduling algorithms are the core method for resource allocation and deployment in cloud computing. To meet the current task demand of cloud computing while the amount of data grows exponentially, the paper focused on systematically sorting and summarizing of task scheduling algorithms. Categorized by cloud environment, it studied and analyzed four types of scheduling algorithms, namely single cloud, federated cloud, hybrid cloud, and multi-cloud. For single cloud environment, the paper elaborated from the perspectives of traditional heuristics, meta heuristics, and hybrid task scheduling algorithms. For federated cloud, hybrid cloud, and multi-cloud environments, the paper elaborate from the perspectives of workflow and independent task scheduling algorithms. Through comparisons, this paper summarized the advantages, disadvantages, and optimization performance of existing algorithms, and formed conclusive opinions and open questions, laying the foundation for future research on container clouds, data clouds, and algorithms that balanced resource allocation and task scheduling. [ABSTRACT FROM AUTHOR]

In reinforcement learning, the policy gradient algorithm often needs to model the continuous-time process as a discrete-time process through sampling. To model the problem more accurately, it improves the sampling frequency. However, the excessive sampling frequency may reduce the training efficiency. To solve this problem, this paper proposed action stable updating algorithm. This method calculated the probability of action repetition using the change of the output of the policy function, and randomly repeated or changed the action based on this probability. This paper theoretically analyzed the performance of this method. This paper evaluated the performance of this method in nine different environments and compared it with the existing methods. This method surpassed existing methods in six of these environments. The experimental results show that this method can improve the training efficiency of the policy gradient algorithm in continuous-time problems. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of extracting tree breast diameter from backpack laser scanning（BLS） data, this paper filtered out the noise points whose BLS scan data was more than 10 cm away from the nearest point, and used the irregular triangulation algorithm to extract the ground point, and subtracted the elevation value of the point cloud from the corresponding ground point elevation value to normalize the elevation of the point cloud data. At an elevation of 0.8 to 1.8 m, slice with a step of 0.1 m, and filter out different point clouds to extract DBH. The smallest diameter of the fitting circle of all slices in each tree was selected as the diameter at breast height of the tree, and the results of the test results, measured data and the results of extracting DBH from point cloud only after preprocessed were compared. The results showed that by filtering out low intensity point clouds, the root mean square error（RMSE） of the three plots were reduced from 2.63 cm to 0.99 cm, 5.43 cm to 4.72 cm, and 4.01 m to 2.15 cm, the average error decreased from 1.90 cm to 0.79 cm, 5.24 cm to 4.16 cm, and 3.36 cm to 1.58 cm. In the forest data scanned by BLS, the point cloud with low intensity at breast diameter was a point cloud with large stitching error, which would affect the accuracy of the breast diameter extraction result. Filtering out this part of the point cloud can improve the accuracy. The proportion of large stitching error point clouds was related to the terrain, and the proportion of low quality point clouds on slopes was larger than that on flat ground. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient representation capability for details and frequency information in traditional infrared and visible image fusion methods, this paper proposed a fusion algorithm based on spectral feature hybrid Transformer. The algorithm utilized the Fourier transform to map image domain features to the frequency domain. Then the novel complex Transformer extracted deep-frequency information from the source images and mixed them with the features of the image domain to enhance the representation capability for edges and details. Additionally, the algorithm used a token replacement module to evaluate the saliency of Transformer tokens and eliminate the tokens with lower scores to prevent the presence of artifacts in the fused image. Qualitative and quantitative experiments conducted on the MSRS dataset demonstrate that the proposed algorithm exhibits superior fusion performance compared to nine state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Addressing the issues of redundant exploration and inapplicability in dynamic environments inherent in fast marching tree algorithm (FMT), this paper proposed the APF-Dynamic FMT algorithm, which integrated artificial potential field method. This algorithm designed a sampling point guidance function based on artificial potential field method, which could dynamically adjust the sampling point generation range according to the environment information to reduce redundant exploration. Additionally, this algorithm designed a dynamic path tree adjustment mechanism, when the existing path was affected by the environment change, it could re-plan a new excellent path on the basis of the remaining path tree that is not affected, which was suitable for solving the path planning problem in dynamic environment. The results verify that the APF-Dynamic FMT algorithm can significantly improve the success rate and path quality of path planning while consuming the same computational resources, and when the existing path is affected by the dynamic environment, it can efficiently re-plan the passable excellent path. [ABSTRACT FROM AUTHOR]

The conventional cloud-end MCS system currently faces problems of excessive load, leading to a significant increase in delay and energy consumption during the data aggregation process, inevitably causing a decrease in data aggregation efficiency. To tackle this issue, this paper proposed a cloud-edge-end MCS computation offloading algorithm based on APDQN. Firstly, it established a utility function considering the balanced optimization of delay and energy consumption, with the maximization of system utility as an optimized goal. Secondly, improving the P-DQN algorithm, it proposed a computational offloading algorithm AP-DQN for combining resource allocation. This algorithm, leveraging the advantages of MCS, designated idle users as one of the offloading devices. Finally, the problem was solved using the proposed method. Experimental results show that, compared to existing algorithms, the proposed method significantly improves data aggregation efficiency and maintains excellent system stability. [ABSTRACT FROM AUTHOR]

In the deep reinforcement learning method, the intrinsic curiosity model (ICM) guides the agent to obtain the opportunity to learn unknown strategies in the sparse reward environment, but the curiosity reward is a state difference value, which will make the agent pay too much attention to the exploration of new states, then could be the problem of blind exploration arises. To solve the above problem, this paper proposed an intrinsic curiosity model algorithm based on knowledge distillation (KD-ICM). Firstly, it introduced the method of knowledge distillation to make the agent acquire more abundant environmental information and strategy knowledge in a short time and accelerate the learning process. Secondly, by pre-training teachers' neural network model to guide the forward network to obtain a forward network model with higher accuracy and performance, reduced the blind exploration of agents. It designed two different simulation experiments on the Unity simulation platform for comparison. The experiments show that in the complex simulation task environment, the average reward of KDICM algorithm is 136% higher than that of ICM, and the optimal action probability is 13.47% higher than that of ICM. Both the exploration performance of the agent and the exploration quality can be improved, and it verifies the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

In order to further solve the optimization problem of reconnaissance constellation and strengthen the construction of space-based reconnaissance system, the current optimization methods of reconnaissance constellation are studied and summarized. The design and optimization of reconnaissance constellation has the characteristics of multi-parameter, multi-objective, nonlinear and discontinuous, which is a typical multi-objective optimization problem. Multi-objective evolutionary algorithm is a kind of biological intelligence algorithm based on population, which carries out probabilistic search in decision space according to predetermined heuristic rules. It does not need continuity, differentiability and other conditions of the problem, and can get a group of solution sets close to Pareto frontier within a limited number of search times. It can effectively solve the multi-objective optimization problem and has been widely used in the multi-objective optimization of reconnaissance constellation. This paper discusses the construction of the reconnaissance constellation optimization model and the classification, advantages and disadvantages of the multi-objective evolutionary algorithm, discusses the application of the multi-objective evolutionary algorithm in the reconnaissance constellation optimization, and points out the development direction of the reconnaissance constellation optimization based on the multi-objective evolutionary algorithm. [ABSTRACT FROM AUTHOR]

The existing ship object detection algorithms mostly rely on optimized improvements based on traditional object detection algorithms, without considering the scale and aspect ratio characteristics of ships, leading to issues such as missed detections and false detections in multi-scale object detection. To address this, the paper proposed a scale-adaptive receptive field ship detection method (SAF-YOLOX) based on YOLOXs. Firstly, it extracted different feature layers by the backbone network, which were fused by constructing a bidirectional feature pyramid, improving feature representation at various scales. Simultaneously, it designed an adaptive feature enhancement module to suppress redundant information introduced by the fusion of features at different scales, thereby attenuating background information. During the prediction phase, it employed a multi-branch parallel receptive field detection head, utilizing receptive fields adapted to target sizes and proportions for extracting scale-adaptive feature information. Additionally, it implemented a convergence-aware strategy, dynamically selecting and allocating samples based on the network s convergence state. This strategy ensured improved detection accuracy while maintaining detection speed. Experimental results demonstrate that the proposed method achieves an average detection accuracy of 93. 21% on the SeaShips dataset and 92.34% on the MCShips dataset. When compared to traditional YOLOXs, the method exhibits an improvement of 1.01% and 1.09%, respectively. The experimental results confirm that the proposed method, utilizing scale-adaptive receptive fields, can achieve high-precision detection of multi-scale ship targets. [ABSTRACT FROM AUTHOR]

In consideration of the superior performance of the wolf pack algorithm on single-objective optimization problems, combining with the biological habits of wolves to apply it to multi-objective optimization problems, this paper proposed a multiobjective wolf pack algorithm with elite guidance and information interaction (MOWPA-EGII). Firstly, MOWPA-EGII proposed an elite guidance strategy, using the elite wolf in the external file and the head wolf of the current sub-population to jointly guide the population movement, so that the artificial wolves uniformly distributed in the whole search space, and the strategy enhanced the global search ability of the algorithm; Secondly, it designed the information interaction mechanism to simulate the information transfer in the wolf hunting process, so that individuals with different advantages could transfer information to each other to ensure the hunting efficiency of the wolves and improved the ability of the algorithm to explore the optimal solution of the Pareto; Finally, it added the mutation operator to perturb the moving direction of the artificial wolves, so as to let the algorithm jump out of the local optimum and enhance the local search ability of the algorithm. In order to verify the effectiveness of MOWPA-EGII, comparing it with 5 classical algorithms and 10 recent algorithms, and the results show that MOWPA-EGII possesses good convergence and diversity, which proves that the present algorithm has a better optimization performance. [ABSTRACT FROM AUTHOR]

Existing studies based on convolutional neural networks mainly focus on the accuracy of image reconstruction, ignoring problems such as excessive parameters, insufficient feature extraction, and resource waste. In response to the above, this paper proposed multi-frequency feature extraction network (MFEN), which designed a lightweight lattice information interaction structure and used channel segmentation with multi-mode convolution combination to reduce the number of parameters. By separating the low-frequency, mid-frequency, and high-frequency information of the image and extracting the feature heterogeneity, it improved the expressiveness and feature differentiation of the network, made the network pay more attention to the restoration of texture detail features, and reasonably allocated the computational resources. In addition, it integrated the local binary pattern (LBP) algorithm into the network to enhance texture sensitivity, which further improved the network s ability to extract details. It experimentally verifies that the proposed method balances complexity and performance well. In the 2X zooming experiments on the Set5 dataset, compared to the conventional image super-resolution algorithm (SRCNN) based on convolutional neural network and the newer algorithm (MADNet), the peak signal-to-noise ratio (PSNR) of the proposed method is improved by 1.31 dB and 0. 12 dB respectively, and the number of parameters is reduced by 55% compared to MADNet. [ABSTRACT FROM AUTHOR]

Path-coverage oriented testing is a crucial method in software testing, and the rapid generation of high-quality test data to satisfy path coverage requirements has been a persistent research challenge. To address issues such as long running times, unstable exploration processes, and the generation of redundant test cases in existing intelligent optimization methods, this paper proposed a selection strategy based on the reinforcement learning paradigm applied to test data generation with path coverage as the criterion. By defining executable paths as the state of the intelligent agent, it defined the data selection after each iteration update as the agent's action. It associated the reward function with state changes, and employed a greedy strategy during the state update process to guide input data towards continuous variations in unexplored states. This iterative selection process aimed to continuously choose data that covered new executable paths, thereby achieving the goal of covering all execution paths of the target program. Experimental results demonstrate that compared to other algorithms, the proposed strategy significantly reduces running times and iteration counts while achieving notable improvements in coverage. Theoretical analysis supports the conclusion that the proposed strategy effectively realizes path coverage and enhances the efficiency of test data generation in practical applications. [ABSTRACT FROM AUTHOR]

In the current research on multi-view clustering, the majority of methods have not adequately considered the diversity of multiple views nor focused on the high-order neighborhood information of the data, which leds to clustering results that lack accuracy and struggle to uncover the underlying information in datasets. To address these issues, this paper proposed a multi-view clustering method based on diversity constraints and high-order information mining(MVCDCHO). Firstly, it designed a method for measuring diversity between views, utilizing diversity constraints to preserve the intersection features of the data while eliminating differing features across multiple views. Subsequently, it introduced a method for mining high-order information in views, requiring the intersection features of multiple views to approximate a mixed similarity graph, thereby extracting high-order information in data correlations that has been overlooked. Finally, it fused the intersection features of multiple views into a consensus graph and employ spectral clustering to obtain the clustering target graph. Additionally, it designed an alternating iterative method, iteratively learning to optimize the objective function. The experimental results show that MVCDCHO has excellent performance on the normalized mutual information (NMI), the adjusted Rand index (ARI), and the clustering accuracy (ACC). Theoretical analysis and experimental study underscore the crucial role of multi-view diversity and high-order information in the MVCDCHO algorithm, providing evidence for its superiority. [ABSTRACT FROM AUTHOR]

NeRF is a deep learning model aimed at modeling three-dimensional implicit spaces, and it holds significant value in the representation and rendering of 3D scenes. However, due to the complex training process, substantial computational resources, and time requirements, the usability and practicality of the NeRF algorithm are somewhat limited. Addressing the pain points of NeRF optimization has become a hot topic in the field of computer vision. This paper aimed to provide a comprehensive review of the optimization and application of NeRF. Firstly, it delved into the basic principles of NeRF and outlined the current optimization status from the perspectives of rendering quality, computational complexity, and pose. Secondly, it enumerated the application scenarios of NeRF to provide references for future, more efficient and practical algorithmic optimizations. Finally, it summarized the strengths and limitations of NeRF and proposed potential future directions tailored to harness the tremendous potential of NeRF in 3D rendering, scene synthesis, and beyond. [ABSTRACT FROM AUTHOR]

The compression strategy plays an important role in the performance of IoT time series data storage system. However, the current compression strategies can not adapt to the characteristics of NVM and IoT time series data. This paper proposes a polymorphic cooperative compression strategy for IoT time-series data based on NVM. Firstly, the overall structure of IoT time series data is given. Then, to address the consistent patterns in IoT time series data and the different granularity between user-space and kernel-space operations on NVM, a dual-compression strategy is devised. Initially, a lightweight compression method is applied directly as IoT time series data is received in user-space. This method efficiently reduces the volume of data for storage, while minimizing the impact on the timeliness of data storage. Moreover, a deep compression algorithm is designed for the kernel-space, primarily focusing on querying and analyzing anomalous time series data. Additionally, to address the competition for NVM bandwidth between deep compression and data storage, a dynamic adjustment algorithm that guarantees write bandwidth is proposed. Finally, a prototype of the polymorphic cooperative compression strategy is implemented and YCSB-TS is used to evaluate the results. The results show that the proposed method can effectively improve the write throughput of IoT time-series data by up to 161.3% and reduce the storage space by up to 14.6%, compared with InfluxDB, OpenTSDB, KairosDB and TVStore. [ABSTRACT FROM AUTHOR]

Aiming at the problem that feature extraction is inaccurate and the reconstruction image is not natural enough in blind super-resolution reconstruction, this paper proposed an image blind super-resolution reconstruction based on degradation aware and sequence residuals. This paper proposed a mini-residual group combined degeneration aware and sequence residuals as the backbone network. Then the method constructed a symmetrical enhanced multi-scale residual block. In the image reconstruction part, this paper used the bottleneck attention module and the sub-pixel convolutional module to emphasize the multi-dimensional elements of the image. Finally, the method made a global residual connection. Compared with the current representative algorithm DASR, experiments show that the PSNR and SSIM of the proposed algorithm are improved 0.145dB and 0.0014 on Set14×2, and the PSNR of the proposed algorithm is improved 1.898dB and 0.252dB on Set14×3/4, respectively. The proposed algorithm achieves better performance than several current image super-resolution algorithms on five standard test sets. [ABSTRACT FROM AUTHOR]

The existing one-class support vector machine algorithms are based on the idea of "minimum margin". They consider only the noise close to the space origin and are sensitive to noise. The purpose of this paper is to improve the anti-noise ability of the one-class support vector machine through the idea of "optimal margin distribution", which considers both of the noise close to the space origin and that far from the space origin. This paper proposes a novel One-Class Optimal Margin Distribution Machine (OCODM) method. This method maximizes the margin and minimizes the margin variance to optimize the margin distribution. The experimental results have shown that compared to the existing one-class support vector machine algorithms, this method has better robustness. The conclusion is that this method is a supplement of the existing one-class support vector machine algorithms and can enhance the anti-noise ability of existing methods. [ABSTRACT FROM AUTHOR]

In order to alleviate the problems of frequent I/O of storage and space waste caused by dense graph data generated by hot topics in social networks, it propose a cache replacement algorithm based on topic heat evolution acceleration (THEA-CR) according to the evolution and update law of topic generation and death. Firstly, this algorithm divided the social network data into some topic clusters to identify anchor targets. Secondly, this paper calculated the topic heat evolution acceleration to evaluate and judge the priority of hot data. Finally, this paper designed a dual queue cache replacement strategy to replace and update the cache space according to topic concern and access frequency. Massive comparative experiments verify the feasibility and effectiveness of the proposed algorithm in Sina Weibo dataset with the baselines cache replacement algorithms. The results show that the proposed THEA-CR can improve the cache hit rate by about 31.4% on average and shorten the query response time by about 27.1% in different query operations of social network dense graph data. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of whale optimization algorithm (WOA) when solving problems such as high dimension, multi-peak and non-origin of optimal value, such as low convergence accuracy and easy to be captured by local optimum, this paper proposed a whale optimization algorithm based on multi-dimensional variation learning and distributed optimization (MLDOWOA) . Firstly, this algorithm introduced adaptive weights and dominant individual interference to dynamically adjust the direction of individual spiral surround, which improved the global search ability and convergence accuracy of the algorithm. Then, in order to further expand the search range of the algorithm, the improved algorithm used a multi-dimensional variation learning mechanism to adaptively plan the direction of population variation. Finally, it put forward the distributed optimization mechanism to coordinate the step size of search, so as to help the population break through the limitation of search stagnation in the middle and late stage. The test results on 8 high dimensional benchmark functions and 4 fixed dimensional benchmark functions show that, compared with the basic WOA, SSA and the improved WOA algorithms ACWOA, AWOA, MSIWOA, ADWOA, the proposed algorithm has significant advantages in convergence accuracy and ability to cope with high dimensional functions. This paper used the algorithm to tune the parameters of the FOPID controller, and compared the control results with the research results of this engineering problem in recent years, which prove the excellent performance of the proposed algorithm in FOPID parameter tuning problems. [ABSTRACT FROM AUTHOR]

Aiming at the problems of mobile ad hoc networks, such as limited network coverage, unstable connection, and vulnerability to malicious attacks when nodes cooperate, this paper combined with blockchain technology to increase the security and integrity of data and proposed a blockchain model for mobile ad hoc networks based on Hashgraph. Firstly, this paper designed a clustering algorithm, which divides the nodes into different clusters, selects cluster heads to count the number of nodes in the cluster, and writes them to events for propagation to ensure the smooth progress of consensus; Secondly, it optimized the Gossip protocol, and proposed the FS-Gossip（Fast Spreading Gossip）protocol, which reduces the blindness of neighbor node selection, improves the propagation efficiency, and increases the detection speed of new cluster nodes; Finally, it improved the complex consensus calculation in Hashgraph, and proposed a propagation mechanism based on cluster head priority. The lightweight consensus and propagation mechanism is applied to nodes in the cluster to speed up event confirmation, reduce delay, and improve throughput. The simulation results verify the advantages of the model in terms of delay, throughput and propagation efficiency. [ABSTRACT FROM AUTHOR]

To address the limitations of existing asynchronous consensus algorithms, such as high communication overhead during multiple rounds and random selections due to the lack of reputation mechanism in random sampling, this paper proposed an efficient asynchronous Byzantine fault-tolerant algorithm called PenguinBFT. Firstly, this paper broadcasted transactions directly during transaction broadcasting to significantly reduce consensus communication overhead. Secondly, this paper introduced a node reputation evaluation mechanism to select a block generator from a relatively stable node set, which reduces the number of random selections required. Finally, this paper partitioned network nodes and different nodes access different partitions for transaction recovery when necessary, reducing communication overhead and improving transaction recovery efficiency. Experimental results demonstrate that when the number of nodes exceeds 64, the PenguinBFT algorithm has shown more than a 50% improvement in communication overhead, throughput, and transaction confirmation delay when compared to the HoneyBadgerBFT, DumboBFT, and DispersedLedger algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem of tracking skip-glide targets using radar, this paper proposes an Unscented Kalman Filter (UKF) tracking algorithm based on the improved Jerk model. Aiming at the estimation error introduced by the artificial preset acceleration variance and Jerk frequency of the conventional Jerk algorithm, this method calculates the acceleration variance adaptively through the current position estimation value and the current position one-step prediction value, and correlates the jerk frequency with the acceleration variance, realizing the adaptive adjustment of the model parameters while estimating the target motion state. At the same time, the improved Jerk model is combined with UKF algorithm, and the overall algorithm flow is given, and simulation experiments are carried out. The simulation results show that compared with the conventional Jerk model algorithm, the method proposed in this paper realizes the adaptive adjustment of model parameters, making the tracking process more adaptable to the maneuvering characteristics of the target. [ABSTRACT FROM AUTHOR]

Aiming at the problems of inaccurate large target segmentation and loss of small target information in current real-time semantic segmentation methods, this paper proposed a real-time semantic segmentation algorithm based on multi-branch networks. First of all, this paper optimized the bilateral segmentation network, and designed pyramid branches to expand the receptive field to cover large objects in the field of view and fully combine context information; secondly, designed a bilateral guidance fusion module to map deep and shallow features Provide guidance information to make up for the loss of small target information. Finally, this paper verified the proposed method on the Cityscapes dataset. The experimental results show that the proposed model achieves an average intersection ratio of 77.8% at an inference speed of 51.3 FPS, and the accuracy is increased by 2.5 percentage points compared with the baseline. The proposed method adopts the pyramid branch to obtain the characteristics of semantic edge regions at different scales while expanding the receptive field, and enhance the modeling ability of semantic boundaries, and the proposed bilateral guidance fusion module can more effectively integrate features of different levels, Compensating for the information loss caused by downsampling can better guide model learning. [ABSTRACT FROM AUTHOR]

Most of the Encryption-then-Compression (ETC) methods can only obtain several limited fixed compression ratios. However, arbitrary encryption compression ratios instead of limited fixed compression ratios are more suitable for practical requirements. To this end, this paper proposed a lossy compression algorithm for encrypted color images with arbitrary compression ratios. It combines the uniform and random downsampling to compress the encrypted images, obtaining arbitrary compression ratios of an encrypted image. The receiver receives the compressed sequence of the encrypted image and obtains the decrypted image by decompression and decryption. The proposed scheme then characterizes the lossy reconstruction of the original image from the decrypted image as an optimization problem with the downsampling compression-based constraint. This scheme designs a convolutional neural network-based image reconstruction model for lossy ETC to resolve this problem, which is denoted the ETC-oriented Reconstruction Network (ETRN). ETRN consists of shallow feature extraction (SFE), residual in residual (RIR), residual content supplementation (RCS), and down-sampling constraint (DC). The experimental simulation results show that the encrypted color image lossy compression algorithm proposed in this paper can obtain excellent compression and reconstruction performance, which fully demonstrates the feasibility and effectiveness of this method. [ABSTRACT FROM AUTHOR]

How to enhance system consensus is an important problem in the research of multi agent systems. The conventional consensus protocols usually does not consider the Hub Nodes in the topology, and the topology weight was single, which made the system easier to split. Based on the idea that key figures in the interpersonal network could promote information exchange in different communities, this paper proposed an influence network model（INM）. First, this paper proposed a Distributed Hub Node Identify algorithm（ DHNI） to distinguish between Hub Nodes and Non Hub Nodes. Distributed multi-agent system could apply DHNI. Secondly, this paper proposed a distributed Hub Node based topology reweighting algorithm （DHNTR） to quantify the influence of different nodes on their neighbors. Finally, this paper proposed an influence network-based multi-agent consensus protocol. this paper designed a common Lyapunov function and analyzed the global stability of the system. this paper proved the stability of the system in the sense of Lyapunov. Simulation results show that the protocol can enhance system consensus. [ABSTRACT FROM AUTHOR]