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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]

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]

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]

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]

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]

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]

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]

In unmanned aerial vehicle Ad hoc network (UANET), increasing the network size will increase the average number of hops between nodes, making network management and routing protocol operation more difficult. Clustering structure can be used to optimize network management and improve network scalability. This paper proposed a fast and stable weighted clustering algorithm to address the instability and poor robustness of cluster structures caused by the high mobility of drones. Compared with existing weighted clustering algorithms, this algorithm improved the selection of four indicators: link retention rate, node degree and relative speed. It proposed an efficient cluster maintenance mechanism to address the cluster oscillation caused by cluster head node disconnection in battlefield and emergency scenarios. Finally, it verified the performance of this algorithm through simulation. The results show that compared with existing improved weighted clustering algorithms, this algorithm can effectively reduce clustering time, and quickly recover in the event of cluster head nodes dropping, making it more suitable for network deployment in complex environments. [ABSTRACT FROM AUTHOR]

This paper proposed a parallel deep forest algorithm based on Spark and NRSCA strategy (PDF-SNRSCA), aiming to address several issues encountered by parallel deep forest algorithms in big data environments, such as excessive redundancy and irrelevant features, low utilization rate of features at both ends, slow model convergence speed, and low parallel efficiency of cascading forests. Firstly, the algorithm proposes a feature selection strategy (FSNRS) based on neighborhood rough sets and fisher score, which measures the correlation and redundancy of features to effectively reduce the number of redundant and irrelevant features. Secondly, it proposed a scanning strategy based on random selection and equidistant extraction (S-RSEE) to ensure that all features are utilized with the same probability and solve the problem of low utilization rate of two ends in multi-scanning. Finally, combining with the Spark framework, the algorithm realized the parallel training of cascading forests, and it proposed a feature filtering mechanism based on the importance index (FFM-II) to balance the dimensions of enhanced class vectors and original class vectors, thereby accelerating the model convergence speed. Meanwhile, the algorithm designed a task scheduling mechanism based on SCA (TSM-SCA) to redistribute tasks and ensure load balancing in the cluster, which solves the problem of low parallel efficiency of cascading forests. Experiments show that the PDF-SNRSCA algorithm can effectively improve the classification performance of deep forests and greatly enhance the efficiency of parallel training of deep forests. [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]

Current bitmap collaboration models suffer from issues such as large volume of data transmission, poor flexibility, overlapping conflicts and storage pressure. To solve these problems, this paper proposes a collaborative editing model for complex bitmaps. Collaborative operation that sends only the coordinates and color change, featuring low data volume, multi-dimensional granularity, and overlap able. Adds a four-dimensional bitmap collaborative architecture with spatiotemporal relationship, and only saves the edited pixel data, which can save more storage space; A replica state control algorithm based on rollback that ensures operations are executed on correct replica. An algorithm that can dynamically generate branch versions according to the current state and policy configuration, solving the problem of too many branch versions. Finally, inferring model correctness based on the CCI model, develop a prototype collaborative editor “Co-Pixel”, execute experiments based on randomly generated operation sets, simulation of actual editing behavior against Adobe Lightroom. Results show that this model can avoid conflicts between independent operations to a greater extent, can reduce the algorithmic overhead in the consistency maintenance process, can flexibly adjust the intent retention policy, and can generate a version of the result that reduces the user’s intent maintenance effort. The model in this paper provides a good solution for collaborative bitmap editing. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient feature extraction ability of deep clustering algorithm on multivariate time series data( MTS), This paper proposes a new deep clustering structure model (MDTC. In order to extract the key features of MTS and realize dimensionality reduction, one-dimensional convolution learns the attribute and temporal dimension feature representation of MTS and the autoencoder structure composed of network layers such as recurrent neural network; To improve the model’s ability to represent temporal features, this paper proposes MCBAM temporal attention module, which is used to enhance the representation features of different time periods in the MTS sequence. This paper conducts experiments on nine publicly available UEA multivariate time series datasets, compared with other autoencoders, the autoencoder structure of the model improves by 2%-9% on 7 data sets; Compared with other attention modules, the MCBAM module of the model improves by 0.3%-2% on the six datasets. Experiments show the effectiveness of the MDTC model structure and MCBAM module, and the model has excellent performance compared with other clustering algorithms. [ABSTRACT FROM AUTHOR]

After the outbreak of the epidemic, the distribution of living supplies in residential areas under closed management become one of the focal issues to be solved urgently, and this problem can be abstracted as the the location problem of distribution points for living supplies during the epidemic, whose essence is the NP-Hard problem in combinational optimization. This paper studied the exact algorithm of the location problem of distribution points for living supplies during the lockdown period. Firstly, the paper researched some mathematical properties and corresponding proof of the problem, and these properties could be used to reduce the size of the given problem, thereby reducing the difficulty of the problem solving. Then the paper designed allocation sub-algorithm, upper and lower bound sub-algorithms and the reduced order sub-algorithm. Based on these sub-algorithms, the paper proposed a backtracking algorithm with reduction which can reduce the size of the problem and get the optimal solution. Finally,the paper analyzed and solved several examples to further illustrate the principle and execution process of the proposed algorithm. The results show that the proposed algorithm can reduce the difficulty of solving the problem by reducing the size of the problem. [ABSTRACT FROM AUTHOR]

The multiple knapsack problem（MKP） is a special knapsack problem with great difficulty. In order to solve MKP by differential evolution（DE）, this paper first established the integer programming model of MKP. Based on a simple and efficient new transfer function based on modulo operation, it proposed a novel discrete differential evolution algorithm MODDE. Then, the method used an efficient algorithm GROA to eliminate the unfeasible solution of MKP by greedy strategy. Therefrom, this paper proposed a new method for solving MKP based on MODDE. Finally, it used MODDE to solve 30 international instances of MKP. Comparing with 4 representative evolution algorithms shows that MODDE not only has better calculation results; but also has stronger stability. It is indeed an efficient algorithm for solving MKP [ABSTRACT FROM AUTHOR]

In order to achieve a fair distribution of benefits in federated learning, an indicator that quantifies the contribution of each data provider to the global model is critical. Aiming at the problems of privacy leakage, opacity and dependence on central server in existing contribution evaluation schemes, this paper proposed a transparent federated learning contribution evaluation scheme based on blockchain. Firstly, this paper proposed an improved Paillier secure aggregation algorithm, which avoided the inference of user local data in the model aggregation stage by joint decryption. Secondly, this paper proposed a method to approximate the contribution based on the gradient of user cumulative submission, which solved the problem of privacy leakage in the existing contribution evaluation scheme. In addition, it integrated the evaluation of the contribution into the consensus process of the blockchain, making the evaluation results auditable. Finally, experiments based on MNIST dataset show that the proposed method can effectively evaluate the contribution. [ABSTRACT FROM AUTHOR]

Fuzzy Job-Shop scheduling problem is a classic embodiment of complex scheduling, and an excellent scheduling scheme designed for this problem can improve production efficiency. At present, the research on fuzzy Job-Shop scheduling problem mainly focused on single objective, this paper proposed an improved grey wolf optimization（IGWO） algorithm to solve the bi-objective fuzzy flexible Job-Shop scheduling problem to minimize the fuzzy completion time and the total load of fuzzy machines. This algorithm proposed a two-layer coding method to make IGWO discretization, designed a strategy based on HV contribution degree to improve population diversity. Then it used reinforcement learning method to determine global and local search parameters, improved two crossover operators to help individuals evolve in different update modes, and used two-level variable neighborhood and four replacement strategies to improve local search capability. Finally, this paper carried out several groups of experiments on several examples to verify the effectiveness of the improved strategy. In most test cases, the performance of IGWO algorithm is better than the comparison algorithm, with good convergence and distribution. [ABSTRACT FROM AUTHOR]

The traditional differential evolution（DE） algorithm balanced global exploration and local exploitation inadequately, and had problems with easily falling into local optimal solutions, low solution accuracy and slow convergence speed. Therefore, this paper proposed a differential evolution algorithm based on random neighborhood mutation and optimal opposition-based learning（RNODE） and analyzed for its complexity. Firstly, the algorithm generated a random neighborhood for each individual in the current population, and used the global best individual to guide the neighborhood best individual to generate a composite basis vector, combined with an adaptive update mechanism of the control parameters to constitute a random neighborhood mutation strategy, which enabled the algorithm maintained its exploration ability and guided the population towards the optimal direction. Secondly, to further help the algorithm jump out of the local optimum, the algorithm performed the optimal opposition-based learning strategy on the poorer individuals to expand the search area. Finally, this paper compared RNODE with 9 algorithms to verify the effectiveness and advancement of RNODE. The experimental results on 23 benchmark functions and 2 real-world engineering optimization problems show that the RNODE algorithm has a higher convergence accuracy, faster speed and a greater stability. [ABSTRACT FROM AUTHOR]

[Objectives]Aiming at the difficulty of identifying pests and diseases in the real environment of bergamot in the field, this paper proposed a method for detecting pests and diseases of bergamot based on improved SSD(single shot MultiBox detector)algorithm—SSD-Res50-3C algorithm. [Methods]The backbone network partially replaced the original VGG16 network with the ResNet50 network, and enhanced the model's ability to extract the characteristics of pests and diseases of bergamot in the real background of the field. A lightweight and efficient feature fusion module was added before predicting the feature layer to improve the multi-scale feature fusion capability of the SSD algorithm, and further improved the anti-interference ability of the SSD algorithm in the real background of the field. [Results]The mean average precision of SSD-Res50-3C algorithm was 92.86%, which was 6.61% higher than that of the original SSD algorithm, and the FPS(frames per second)reached 64.1. Compared with YOLO v3, YOLO v4, YOLO v5x6, Faster R-CNN, and EfficientDet-D3 models, the mean average precision of SSD-Res50-3C algorithm was 6.41%, 2.01%, 0.79%, 0.58%, and 5.10%, respectively, and FPS was 16.20, 40.28, 24.40, 36.20, and 54.84, respectively. [Conclusions]The pests and diseases detection method of bergamot based on the improved SSD algorithm proposed in this paper could weaken the interference information of the real environment in the field, accurately identify the target of pests and diseases of bergamot in the real environment of the field, and provide a new idea for the detection of pests and diseases of bergamot in the real environment of the field. [ABSTRACT FROM AUTHOR]

In the evolution of the Internet from Web 1.0 to Web 3.0, algorithms have played an irreplaceable role, enabling information technology to functionally realize the transmission of one-way content to decentralized interaction. Algorithms have a subtle impact on people's lives, work, and learning, and this impact is so great that it has produced the idea of algorithmic society and algorithmic era. Algorithms belong to the basic category of digital law and are an important tool for digital rule of law. In existing research, scholars are more concerned about how to regulate algorithms, and less concerned about the legal nature of algorithms. There has been a brief discussion on this issue. However, how the legal nature of algorithms should be defined is still inconclusive. This article aims to answer this question. To this end, this paper sorts out the existing theoretical schemes of the legal nature of algorithms and their interrelationships, including speech, due process, patents, and trade secrets, and analyzes the sources and arguments of these theoretical schemes. There are internal and external reasons why the legal nature of algorithms has formed many disputes. These reasons are also difficult to define the legal nature of algorithms, including: the algorithm ontology and carrier are easy to confuse, the algorithm and the program are indiscriminate, the algorithm and data, the result mix, and the limitation of governance rules. When seeking justification for various theoretical claims, one logical premise is easily overlooked, that is, the criteria for determining the legal nature of algorithms. This paper puts forward these standards, namely the principle of legality, the principle of respecting the fruits of labor, the principle of maintaining public safety, the principle of promoting innovation, and the principle of maximizing social benefits. On this basis, this paper examines various theoretical schemes, and finally proves that the legal nature of the algorithm is a trade secret, not something else. In terms of methodology, this paper uses the method of identification analysis to discuss various scenarios one by one. Compared with previous literature, this paper makes contributions in the following aspects: first, this paper sorts out the existing debates on the legal nature of algorithms in detail, grasps the arguments of these theoretical schemes, and specifically examines them. Second, this paper analyzes the reasons for many disputes over the legal nature of algorithms. The widespread use of algorithms has also caused heated debates about algorithms. Algorithms have been in the field of legal science for a long time, but there are frequent misunderstandings about how their legal nature should be defined, and the reasons behind them deserve careful analysis. Third, this paper clarifies the criteria for defining the legal nature of algorithms, and argues that algorithms are trade secrets on this basis. Algorithms cannot be called speech, nor are they due process. The argument that algorithms are patents is also untenable, and although algorithms have the possibility of patentability in the current legal system, algorithm patenting is not a good solution. [ABSTRACT FROM AUTHOR]

Efficient panoramic video compression is very important for immersive and low-latency visual experience. The panoramic video on the server is projected onto the 2D plane, encoded and transmitted to the client and then back-projected the 2D image to the spherical surface for users to watch. However, The panoramic video projection will produce stretching deformation in the two poles and pixel redundancy, and destroy the original information entropy distribution. Aiming at this problem, this paper proposed an improved entropy balance algorithm. To achieve entropy balance, this paper used the R-λ model in rate control to redistribute the target bit according to the projection area change ratio, and then adjusted the coding parameters to optimize the coding. The experimental results show that the proposed algorithm can achieve 1.87% bit rate saving and 10.07% coding time reduction compared with the benchmark VTM14.0,and indicate that the algorithm can achieve higher compression rate and shorter coding time in panoramic video coding. [ABSTRACT FROM AUTHOR]

Current point cloud-based 3D object detection methods largely rely on large-scale, high-quality 3D annotations. In order to reduce the required amount of labels, this paper proposed a new 3D object detection method based on the SESS network, trust region pseudo-supervised strategy for semi-supervised 3D object detection. Firstly, this paper designed a trust region pseudo-supervised strategy, which divided the output of the student network into labeled and unlabeled parts. The labeled part used ground truth for supervised learning, and based on the class and object prediction confidence score of the teacher network, the unlabeled part used an effective filtering mechanism to filter out high-quality teacher predictions and converted them into corresponding pseudo-labels for supervising the unlabeled part of the student network. Secondly, this paper designed a Trans Vote module, which used the Transformer mechanism to enhance the mutual attention between each point cloud and its neighboring points and aggregated the local features of the point cloud.With 10% labeled data and mAP@0.25,this method surpasses the baseline by 8.63% and 6.75% on the ScanNetV2 and SUN RGB-D datasets, which significantly improves the detection accuracy of semi-supervised 3D object detection algorithm. [ABSTRACT FROM AUTHOR]

In order to address the shortcomings of the facial landmarks detection models, which cannot model the relations between long-distance landmarks, this paper proposed a parallel multi-branch architecture combining with convolution and Transformer for facial landmarks tasks, called MCTN, it utilized the dynamic attention mechanism to model the long-distance relations between facial landmarks. The multi-branch parallel structure designing allowed MCTN to include shared weights, global information fusion and other merits. What’s more, this paper proposed the novel Transformer structure, Deformer, which could make the MCTN focused attention weights faster on sparse and meaningful locations and solved the problem of slow convergence of Transformer. MCTN reached 4.33%,3.12% and 3.15% normalized average error respectively on the WFLW,300W and COFW datasets, the results show that MCTN utilizes Transformer with CNN multi-branch parallel structure and Deformer structure to dramatically outperform other facial landmarks localization algorithms based on convolution network. [ABSTRACT FROM AUTHOR]

This paper developed a progressive hierarchical algorithm combining image texture complexity and wavelet coefficient characteristics to meet the requirements of remote desktop environment. Firstly, the algorithm allocated bits for each image block according to texture complexity in order to improve the utilization of target bit budget. Then, it optimized the hierarchical structure of high-frequency detail components in the progressive hierarchical algorithm by analyzing the order of the importance of different subbands features in the image block. And it also designed a progressive hierarchical algorithm that added subbands transmission layer by layer based on the wavelet coefficient characteristics. Finally, according to the allocated bits and the selected layering method, the paper implemented a codec system for progressive transmission of each image block. Simulation experiments show that the proposed progressive transmission algorithm can reconstruct better images in different image transmission environments, and is more in line with the visual perception characteristics of the human eye. The algorithm is more adaptive to weak networks and performs better in the field of desktop remote video image transmission and processing. [ABSTRACT FROM AUTHOR]

In recent years, various human action recognition algorithms have achieved excellent recognition performance based on a large number of labeled samples. However, in practical applications, acquiring training samples and their corresponding labels is time-consuming and laborious, which limits the actual implementation of the algorithm. This paper summarized the deep learning algorithms for action recognition under weak supervision and few-shot learning. Firstly, in the case of weak supervision, it classified and summarized the semi-supervised action recognition methods and unsupervised domain adaptation video action recognition methods separately. Then, it reviewed video action recognition algorithms based on few-shot learning in detail. Further, it summarized the relevant human behavior datasets and analyzed and compared the performance of various relevant video action recognition algorithms on these datasets. Finally, the paper summarized the full text and discusses the future development trend of human action recognition. [ABSTRACT FROM AUTHOR]

The naive Bayes classification algorithm is a simple and practical method for classification. There were a lot of studieson the assumption of conditional independence between attributes. The researchwas committed to eliminate redundant attributes and to reduce the correlation between attributes, with the aim to obtain some new attributes beingmore independentof and adapted to naive Bayes algorithm. However, the independence between new attributes was not easy to measure. Therefore, the improvement of the naive Bayes algorithm was not supported sufficiently by theory but was more supported by data experiments. This paper definedGauss distributed data and proposedan improved naive Bayes algorithm using the Gram-Schmidt orthogonalization method, making it convenient for classification with Gaussian distribution data. The improved method was different from the previous method. Unlike previous methods that explicitly construct new attributes set or attributes transformation matrix, the approach in the current paper directly orthogonalized the sample data of attributes. It was also provedthat the abstract new attributes corresponding to the orthogonalized attributes datawere independence. This showedthat the assumption of conditional independence in the original naive Bayes algorithm will not cause obstacles to the use of the algorithm for the classification of Gauss distributed data, as this constraint can be satisfied after Gram-Schmidt orthogonalization. [ABSTRACT FROM AUTHOR]

In view of the problems that the existing 3D reconstruction methods based on multi views do not fully consider the visibility of pixels in other views, which leads to insufficient reconstruction integrity and difficult reconstruction in weak texture and occlusion areas, this paper proposed an efficient cascaded 3 D reconstruction network. Firstly, this paper proposed an adaptive cost aggregation method with visibility awareness for cost aggregation. Then this paper obtained the visibility of the pixels in the view through the network, which could improve the integrity of the occlusion area reconstruction. It predicted the disparity range of each pixel based on variance, and constructed the spatially varying depth hypothesis surface for staged reconstruction. In the last stage, this paper proposed a depth map optimization module based on convolution spatial propagation network to obtain the optimized depth map. Finally, this paper used an improved depth map fusion algorithm to check the consistency of the reprojection errors of the pixels and 3D points in all views, and obtained a dense point cloud. The results of quantitative and qualitative comparison with other methods on DTU data sets show that the proposed method can reconstruct scenes with better performance in detail. [ABSTRACT FROM AUTHOR]

The accuracy of existing face recognition models cannot improve due to the influence of masks and other occlusion factors. The current mainstream research methods integrate and apply the occluded and unoccluded scenes to multiple scenes after separate training. Aiming at the limitation of occluded face recognition model, this paper proposed an improved face feature rectification network (FFR-N et) model. This model could be used for face recognition with or without occlusion, and be applied to mask and glasses occlusion recognition scenes. FFR-Net proposed a face feature rectification module. In order to make full use of the feature information of the unocclusion area, the spatial branch of the module introduced involution operator to expand the image information interaction area and enhance the face feature information in the spatial range. The channel branch introduced coordinate attention to capture cross channel information to enhance the feature representation, which was conducive for the model to locate and identify the target area more accurately. Using Meta-ACON as a new dynamic activation function, it improved model generalization and calculation accuracy by dynamically adjusting the degree of linearity or nonlinearity. Finally, this paper trained the improved FFR-Net on the CASIA-Webface processed face dataset with or without mask occlusion. The accuracy of the test results on the LFW processed face dataset with or without mask occlusion and Meglass dataset are 82. 50% and 89. 7 5% respectively, which is superior to the existing algorithm, and verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to maximize the quality of experience ( QoE) of users, this paper designed a cellular network model using cache-enabled unmanned aerial vehicle ( UA V) for assisted communication. This model used a combination of U AV communication and edge caching to offload traffic, aiming to maximize QoE by jointly optimizing UAV deployment, cache placement and user association, and this paper used mean opinion score (MOS) to evaluate QoE. Based on the problem definition, this paper proposed a joint optimization algorithm for UAV deployment, cache placement and user association to maximize the MOS. This algorithm used K-means algorithm to create clusters for users to deploy UAV based on user locations, used penalty function-based convex optimization algorithm to obtain a cache placement strategy and greedy algorithm to obtain an association strategy for users. Several simulation experiments verify the effectiveness and feasibility of the proposed algorithm. The experimental results show that the proposed joint optimization algorithm has better performance in terms of MOS, backhaul traffic offloading and content access delay compared with the three benchmark algorithms. [ABSTRACT FROM AUTHOR]

A variety of factors will affect the combination and distribution of the molds used in the production of prefabricated components on the mold table, and will produce many combination and distribution results. In order to solve such problems and quickly find the best distribution results of the molds on the mold table, this paper proposed a mold combination method based on the combination of backtracking method and improved Eclat algorithm. Firstly, it built a binary tree model based on the mold combination relationship in the historical data, searched the binary tree through the backtracking method, and obtained all theoretically feasible mold combination results. Then it pre-placed these combination results by using the improved BL positioning algorithm on the mold table with a fixed area, and screened out the mold combination results that met the actual placement conditions. It used Eclat algorithm to obtain the optimal combination set of mold adaptation from the selected combination results, determined the association rules between different types of molds,and used the rules to guide the allocation process of molds on the mold table in the new component production task. At the same time, this paper improved the Eclat algorithm by introducing the partition idea and the priori constraint idea based on probability, so as to improve its operation efficiency when dealing with large-scale data. Finally, through the simulation test of the case data in the prefabricated component manufacturing enterprise, it verified the effectiveness of the method to quickly solve the problem of mold table combination allocation. The results show that the mold combination allocation based on backtracking and DEclat algorithm method has a good effect on the utilization of the mold area, the reduction of the mold changing time and the reduction of the total completion time. [ABSTRACT FROM AUTHOR]

To address the problems that the basic APO algorithm tends to fall into local optimum and poor distribution, this paper proposed a PEMOAPO algorithm. Firstly, this paper used the strategy of tent mapping and reverse learning to initialize the population and enhance the population diversity. Secondly, it introduced a mechanism of partition-guided individuals for evolution, and it adopted different mass functions and virtual force calculation rules to iteratively update the individuals in the feasible and infeasible domains to enhance the convergence performance of the algorithm. Choosing MW series and C_DTLZ series as the benchmark test functions in simulation experiments, and the comprehensive performance evaluation index comparison analysis, statistical analysis, convergence analysis and time complexity analysis show that the improved algorithm has good diversity and convergence, and can converge to the Pareto front quickly. [ABSTRACT FROM AUTHOR]