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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]

This paper proposed a collaborative topic regression model based on the preference for frequent topic sets to address the item-cold-start problem in academic paper recommendation. The algorithm took into account the user' s preference for research hotspots when selected academic papers, and used frequent topic sets to represent research hotspots. So, it expressed user' s preference for research hotspots as the user's preference for frequent topic sets. Firstly, it obtained the papers-topic probability distribution matrix through LDA algorithm and filter out the topics with higher probability. Then, the algorithm mined the frequently-occurring topic sets and gets the relationships between papers and frequent topic sets. Finally, it used the user's preference for frequent topic sets for the prediction of unknown scores. Experiments on CiteULike datasets show that the algorithm improves the recall, accuracy and RMSE over the matrix factorization model and the collaborative topic regression model. [ABSTRACT FROM AUTHOR]

There are many problems in the process of collecting paper data using single data source, such as insufficient data comprehensiveness and limited data collection speed due to website access frequency limitation. Aiming at these problems, this paper proposed a paper data crawling technology for multi-data sources. Firstly, it used the four Chinese document service websites-How Net, Wanfang Data, Weipu, and Chaoxing as data sources, completed the task of crawling and parsing list page data for the search keywords. Then it used the task scheduling strategy to remove repeated data and balance the tasks. Finally, it used multi-threads for each data source to crawl, parse and store the detail information of the papers, and built a website for search and display. Experiments show that under the same crawling and parsing speed, this technology can complete the paper information collection task more comprehensively and efficiently, which proves the effectiveness of this technology. [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]

Considering the unique users of scholars' social networks, this paper proposed a collaborative association model of paper and project data based on scholars' social networks. Firstly, the proposed model used the two-step feature selection method to preprocess the data, removed the irrelevant and redundant features and obtained the effective features that affected the association between the paper and the project. Then it would adopt TVSM to calculate the text similarity between the paper and the project, then formed recommendation sets for different papers/projects. Through the social network (SCHOLAT) data for researchers, the model was implemented and applied to SCHOLAT. The online application situation and user feedback show that the model has good accuracy and practicability. Furthermore, it can more fully explore the potential relationship between the paper and the project, provide users with better academic research management services, and propose a novel research method for analyzing the academic big data. [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]

Aiming at the low efficiency caused by massive academic paper data, this paper proposed a recommendation algorithm method based on the hierarchical mixed model named WSVD ++. According to the structural features of academic papers, the model constructed a weighted bipartite graph model. Firstly, this method extracted the features of each paper and constructed the composite relation graph according to the ratio of different features. Secondly, it used an improved PPR algorithm on the graph to calculate the importance weight of each paper, and then weighed the relation between the user and the paper. Finally, it recommend on the weighted bipartite graph by using SVD + + graph algorithm. The result shows that the proposed algorithm improves the recommended accuracy. The whole process implemented in distributed graph calculation system, that means the method has good expansibility and is suitable for big data processing. [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]

vIn the few-shot classification tasks, existing CNN models suffer from insufficient feature extraction, limited feature diversity and weak differentiation between classes in few-shot datasets, leading to low classification accuracy. To address these issues, this paper proposed a few-shot classification model called FMAF. Firstly, this method incorporated multi-granularity thought into the architecture of CNN feature extraction network to enhance feature diversity. Secondly, after the multi-granular feature extraction network, FMAF added a self-attention layer to extract key features from the multi-granular image features, based on the multi-granular attention features, FMAF employed a feature fusion method to combine the information from multiple-granularity attention features, highlighted the crucial features and improved feature representativeness. Finally, this paper utilized two classical few-shot datasets for experimental verification on miniImageNet and tieredImageNet. Experimental results show that FMAF method can effectively improve the accuracy and efficiency of classification. [ABSTRACT FROM AUTHOR]

Most images in real life have multi-label attributes. For multi-label images, ideally, the retrieved images should be ranked in descending order of similarity to the query image, namely their numbers of labels shared with the query image decrease sequentially. However, most hashing algorithms are mainly designed for the single label image retrieval, and the existing deep supervised hashing algorithms for multi-label image retrieval ignore the ranking performance of hash codes and do not fully utilize the label category information. To solve this problem, this paper proposed a deep supervised hashing with performanceaware ranking method(PRDH), which could effectively perceive and optimize the performance of the model and improve the effect of the multi-label image retrieval. In the hash learning part, this paper designed a ranking optimization loss function to improve the ranking performance of hash codes. At the same time, this paper adopted a spatial partition loss function to divide images with different numbers of shared labels into corresponding Hamming spaces. In order to fully utilize label information, this paper also explicitly proposed using predictive label for Hamming distance calculation in the retrieval stage, and designed a loss function for multi-label classification to achieve supervision and optimization of Hamming distance ranking. A large number of results of the retrieval experiments conducted in three multi-label benchmark datasets show that the evaluation metrics of PRDH outperform the state-of-the-art hashing approaches. [ABSTRACT FROM AUTHOR]

To solve the problem that the edge devices deployed by different operators have huge investment and the cached contents are isolated and have difficulty in sharing information, this paper improved the architecture of an edge cache system based on consortium chain, so that the edge devices deployed by different operators could break the content isolation and achieve a wider range of content sharing. In order to improve the operator cache revenue while ensuring the user transmission quality and reducing the user transmission delay, this paper firstly analyzed the impact of different content delivery methods on user transmission delay and operator caching benefits in terms of cache popularity, content size, and edge device collaboration capabilities. Then, it established an optimization problem with the goal of maximizing the cache benefit and minimizing the transmission delay. Finally, to solve the proposed high-dimensional large-scale caching decision problem, it used asynchronous advantage actor-critic (A3C) based content caching algorithm to determine optimal placement of content. Simulation results show that the proposed cache strategy can effectively enhance the revenue of edge caching, reduce the delay of content delivery and improve the user experience. [ABSTRACT FROM AUTHOR]

Backpropagation-based training methods for SNNs still face many problems and challenges, including that the spike firing process is non-differentiable and spike neurons have complex spatiotemporal dynamics processes. In addition, SNNs backpropagation training methods often do not consider the relationship of the error signal between adjacent spikes, greatly reducing the accuracy of the model. To this end, this paper proposed a cross-spike error backpropagation training method for deep spiking neural networks (CSBP), which divided the error backpropagation of neurons into two dependencies: the dependency of spike firing time with the postsynaptic membrane potential (DSFT) and the dependency between spike firing time (DBSFT). Among them, DSFT solved the problem of spike non-differentiability and DBSFT clarified the dependence between spikes, allowing error signals to propagate across spikes, improving biological rationality. In addition, this paper solved the problem of insufficient expressive ability in early spiking ResNet network architecture by modifying the structural order of the spike residual block. Experimental results show that the proposed method is significantly improved compared to the SOTA (state-of-the-art) training algorithms based on spike time. Under the same architecture, the improvement is 2.98% on the CIFAR10 dataset, and 2.26% on the DVS-CIFAR10 dataset. [ABSTRACT FROM AUTHOR]

SaaS is a cloud service model where users obtain software access rights by paying subscription fees. Due to the diversity of business operations, users exhibit significant variations in the online access rates for different software. Consequently, there are variations in the cloud computing resources consumed by different software applications. To avoid the risk of violating SLAs and incurring penalty payments, SaaS operators optimize the computational resource allocation for various software applications and impose subscription limits on each category of software. Considering SLA constraints, this paper formulated a resourceconstrained nonlinear integer programming model with the objective of maximizing revenue. Due to the computational complexity of the model, it cannot be solved in polynomial time, and this paper proposed a Q-learning-PSO hybrid algorithm for this NP-hand problem. This algorithm embedded Q-learning into PSO to dynamically adjust PSO parameters, thereby avoiding the issues of local optima and low computational efficiency associated with direct PSO application. Simulation experiments validate the effectiveness of the model and algorithm in different scenarios. The results indicate that the algorithm can achieve higher revenue for subscription limits and resource allocation with superior solving efficiency under the condition of limited cloud computing resources. Specifically, in scenarios with significant demand fluctuations, operators should aim to reduce the resource contention ratio of software. This can be achieved by provisioning an ample amount of virtual machine resources and enforcing strict subscription limits to ensure the quality of service, consequently reducing penalty payments. Conversely, in scenarios with minimal demand fluctuations, operators have the flexibility to increase the resource contention ratio of software. By relaxing subscription limits, they can seize a larger market share, thus realizing revenue maximization. [ABSTRACT FROM AUTHOR]

Predictive process monitoring can provide timely information during the operation of business processes, in order to take measures to address potential risks. How to improve the accuracy of process prediction has always been highly concerned. Most of the existing research methods focus on process prediction in static environments, with few combining digital twin technology for process prediction in dynamic environments. To this end, this paper proposed a method based on concept drift detection and constructed a digital twin process prediction model to predict the next activity. Firstly, this method used behavioral relationship between event streams and weight divergence to extract features from activities in the process and obtained the feature sets of data flows. Secondly, this method performed drift detection. It dynamically selected feature sets and input them into the artificial intelligence model for training and predicting the next activity. Then, it used advanced technologies such as the Internet of Things and cloud computing to create a digital twin virtual environment. Finally, this paper obtained a digital twin model based on concept drift. It carried out evaluation and analysis on publicly available datasets, and the experimental results show that the proposed method can improve the effectiveness of prediction. [ABSTRACT FROM AUTHOR]

In the airport area, the charging behavior of new energy special vehicles has great randomness, and the charging conditions of different types of special vehicles are different, which results in a large difference in the utilization rate of each charging pile in the airport area, affecting the healthy operation of electric distribution network of the airport. In response to the above phenomenon, this paper designed a two-stage collaborative charging scheduling strategy for eleven types of special vehicles. In the first stage, this paper analyzed the service process of different vehicles for flights, with the goal of minimizing the difference value in the start time of service for adjacent flights by the same vehicle, and generated a sequence of vehicles with charging needs. In the second stage, the strategy aimed to reduce the variance of time utilization of charging piles and vehicle charging queue time in each zone of the airport area. Based on sequence of vehicles in the previous stage, this paper used an improved adaptive mutation particle swarm optimization algorithm to solve this model, and validated the strategy by comparing it with actual vehicle charging data of a domestic hub airport. The experiment shows that this algorithm can achieve a 93.5% reduction in the waiting time for vehicle charging, and it reduces the overall variance of time utilization of charging piles in the airport area by 88.7%. Finally, this strategy achieves the goal of balanced use of charging piles. [ABSTRACT FROM AUTHOR]

To address the issues of existing misinformation detection approaches, which primarily focus on single-modal data analysis and ignore the correlation between information during detection, this paper proposed a multimodal misinformation detection model combined with the social network graph (MMD-SNG model). This model used the pre-trained Transformer model and the image caption model to extract the semantics of each modality from multiple perspectives. It incorporated the features of propagated information into the text and image data by fusing the social network graph of the information dissemination process. Finally, this model used a multimodal co-attention mechanism to allocate the weights of each modality for misinformation detection. This paper conducted comparative experiments on two real datasets including Twitter and Weibo, the proposed MMD-SNG model achieved a consistent detection accuracy of approximately 88%, which was higher than existing misinformation detection approaches such as EANN and PTCA. The experimental results demonstrate that the proposed model can fuse multimodal information effectively to improve the accuracy of misinformation detection. [ABSTRACT FROM AUTHOR]

Existing research on knowledge base question answering (KBQA) typically relies on comprehensive knowledge bases, but often overlooks the critical issue of knowledge graph sparsity in practical applications. To address this shortfall, this paper introduced a knowledge representation learning method that transforms knowledge bases into low-dimensional vectors. This transformation effectively eliminated the dependence on subgraph search spaces inherent in traditional models and achieved inference of implicit relationships, which previous research had not explored. Furthermore, to counter the propagation of errors in downstream question-answering inference caused by semantic understanding errors of questions in traditional KBQA information retrieval, this paper introduced an answer inference re-ranking mechanism based on knowledge representation learning. This mechanism utilized pseudo-twin networks to represent knowledge triplets and questions separately, and integrated features from the core entity attention evaluation stage of upstream tasks to effectively re-rank the answer inference result triplets. Finally, to validate the effectiveness of the proposed algorithm, this paper conducted comparative experiments on the China Mobile RPA knowledge graph question-answering system and an English open-source dataset. Experimental results demonstrate that, compared to existing models in the same field, the proposed method performs better in multiple key evaluation indicators such as hits@n, accuracy, and F1-scores, proving the superiority of the proposed KBQA answer inference reranking algorithm based on knowledge representation learning in handling implicit relationship inference in sparse knowledge graphs and KBQA answer inference. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the credibility of edge nodes in the Internet of Vehicles could not be guaranteed, this paper proposed a reputation-based task offloading and resource allocation model for the Internet of Vehicles, and used the reputation of edge nodes recorded on the blockchain to evaluate its credibility, so as to help the terminal devices select reliable edge nodes for task offloading. At the same time, this paper modeled the offloading strategy as the time delay and energy consumption minimization problem under the reputation constraint, and used the multi-agent deep deterministic policy gradient algorithm to solve the approximate optimal solution of the NP-hard problem. The edge server received rewards based on the completion of task offloading, and then updated the reputation recorded on the blockchain. Simulation experiments show that the proposed algorithm reduces in terms of time delay and energy consumption by 25.58% to 27.44% compared with the benchmark testing schemes. [ABSTRACT FROM AUTHOR]

Most traditional drone cross-domain authentication schemes rely on PKI, which are highly dependent on trusted third parties and prone to single points of failure and low authentication efficiency. Accordingly, this paper proposed a block-chain-based UAV network cross-domain identity authentication protocol scheme, which not only effectively realized the cross-domain authentication of UAVs, but also improved many security problems faced by the traditional PKI-based model. At the same time, to discover the privacy protection of UAV network cross-domain authentication, this paper proposed a method to anonymize the UAV cross-domain identity authentication identity. The results of security analysis and simulation experiments show that the scheme can not only resist common network attacks, but also significantly improve authentication efficiency and reduce time overhead. [ABSTRACT FROM AUTHOR]

The existing change detection methods are difficult to balance local features and global features, and the boundary between change objects and backgrounds is blurred, so this paper proposed a remote sensing image building change detection method based on local-global feature coupling and boundary guidance. In the encoding stage, the method adopted parallel convolutional neural network and Transformer to extract the local features and global representation of remote sensing images, respectively. At different scales, the local-global feature coupling module fused local features and global feature representation to enhance the expression ability of image features. In addition, it introduced the boundary guidance branch to obtain the prior boundary information of the change objects, so that its guide change map can highlight the structural characteristics of the building and promote the accurate boundary location. This paper conducted experiments on the LEVIR-CD and WHU datasets, resulting in F1-score of 91.25% and 91.27%, IoU of 83.90% and 83.95%, respectively. The experimental results show that the method has a great improvement in the detection accuracy and good generalization ability. [ABSTRACT FROM AUTHOR]

Aiming at the situation of imperfect spectrum sensing of cognitive radio in actual cognitive ultra-dense network sce- narios, this paper proposed a resource allocation scheme based on imperfect spectrum sensing. The target was to maximize the energy efficiency of secondary network in cognitive ultra-dense network with imperfect spectrum sensing while taking into ac- count cross-tier/co-tier interference constraints and ensuring users quality of service. And this paper constructed the energy efficiency optimization problem based on the network model, which was a mixed integer non-convex programming problem, then transformed it into an equivalent convex optimization problem by the time-sharing relaxation method and Dinkelbach me- thod, and finally used the Lagrangian dual method to get its optimal solution, thereby obtained the sub-channel and power allo- cation strategy for optimal energy efficiency. Based on this, this paper proposed an iterative sub-channel and power allocation algorithm. In order to trade off the computational complexity, this paper also proposed a practical sub-channel and power allo- cation algorithm. Simulation results show that the proposed algorithms effectively improve energy efficiency. [ABSTRACT FROM AUTHOR]

In order to solve the drawbacks of existing brain extraction network, i. e., complex network structure, large amounts of parameters and low inference speed, this paper proposed a novel network 3D UNeXt for fast and effective brain extraction. 3D UNeXt greatly reduced parameters and the number of floating point operators(FLOPs), and achieved promising results with the combination of 3D convolution, 3D MLP and multi-scale feature fusion. 3D UNeXt used U-Net as the basic architecture and employed 3D convolutional modules to obtain local features in encoding stage. Specifically, the proposed 3D MLP module at the bottleneck stage enhanced the extraction of global features and long-range dependencies among them. In decoding stage, this paper designed a lightweight multiscale feature fusion module to effectively fuse multiscale low-level features and high-level counterparts. In detail, the 3D MLP module performed linear shift operations in three different axes to obtain global receptive fields from different dimension features and establish long-range dependencies among them. This paper compared 3D UNeXt with other counterparts on three datasets: IBSR, NFBS, and HTU-BrainMask. Experimental results show that the 3D UNeXt is superior over other baselines in terms of network parameters, FLOPs, inference accuracy, and inference speed. [ABSTRACT FROM AUTHOR]

Cooperative encryption and decryption is an important research direction in secure multi-party computation. It can achieve data protection and privacy protection safely and efficiently. To solve the problem of high computational complexity in the offline calculation phase of existing SM4 collaborative encryption and decryption schemes, this paper proposed a new SM4 collaborative encryption and decryption scheme based on oblivious polynomial evaluation. The scheme utilized pre-calculated polynomial sets and sets of polynomial values to complete S-box collaborative computation in the online stage, thereby improving the performance of the online calculation stage. This paper proved the correctness and security of the proposed scheme, and compared with four different schemes, the results show that the computational efficiency of the proposed scheme is significantly higher than that of other schemes. This shows that the proposed scheme can complete SM4 cooperative encryption and decryption safely and efficiently. [ABSTRACT FROM AUTHOR]

Aimin at the copyright problem surrounding 3D models of neural radiation fields focused on implicit representation, this paper tackled this issue by considering the embedding and extraction of neural radiation field watermarks as inverse prob-lems of image transformations, and proposed a scheme for protecting the copyright of neural radiation fields using invertible neural network watermarking. This scheme utilized 2D image watermarking technology to safeguard 3D scenes. In the forward process of the invertible network, the watermark was embedded in the training image of the neural radiation field. In the re- verse process, the watermark was extracted from the image rendered by the neural radiation field. This ensured copyright protection for both the neural radiation field and the 3D scene. However, the rendering process of the neural radiation field may result in the loss of watermark information. To address this, the paper introduced an image quality enhancement module. This module utilized a neural network to recover the rendered image and subsequently extract the watermark. Simultaneously, the watermark was embedded in each training image to train the neural radiation field. This enabled the extraction of watermark in- formation from multiple viewpoints. Experimental results demonstrate that the watermarking scheme outlined in this paper effectively achieves copyright protection and highlights the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

Federated learning is a kind of advanced distributed machine learning algorithm, which realizes multi-party cooperative training while ensuring the user's control over the data. However, the existing federated learning algorithms have many problems in dealing with Non-IID data, gradient information leakage and dynamic user offline. To solve these problems, this paper proposed a gradient hidden safe clustering and privacy-protecting federated learning based on quaternion, zero sharing and secret sharing techniques. Firstly, it used quaternion rotation technology to hide the first-round model gradient and achieve secure clustering stratification without altering the gradient feature distribution, so as to solve the performance degradation issue caused by Non-IID data. Secondly, this paper designed a chain zero sharing algorithm, using single strategy to protect the user model gradient mask. Then, it used the threshold secret sharing to improve the robustness against offline users. Multi-dimensional comparison with other existing algorithms shows that the accuracy of SCFL is improved by about 3.13%~16.03% under the Non-IID data distribution, and the overall running time is improved by about 3~6 times. Mean while, the security of information transmission is guaranteed at any stage, satisfying the design goals of accuracy, security and efficiency. [ABSTRACT FROM AUTHOR]

Multi-source energy supply is an effective way to increase the proportion of clean energy and assist manufacturing enterprises in green and low-carbon transformation. However, due to factors such as season and weather, renewable energy output fluctuates, affecting the stability of energy system supply. To address this problem, this paper constructed an uncertain integer programming model for the coupling optimization of enterprise production operation and energy planning, using interval numbers to describe the uncertain information of energy output. At the same time, it proposed a multi-objective hybrid whale swarm algorithm by designing multiple group fusion strategies and external file update strategies, which effectively utilized the optimization characteristics of each algorithm to improve overall performance and obtain a better Pareto solution set. Finally, it demonstrated the feasibility and effectiveness of the proposed model and solution method through comparative experiments on algorithm performance and energy strategies. At the same time, the advantages and competitiveness of the algorithm designed in this paper to solve uncertain optimization models and the multi-source energy supply mode can effectively help enterprises achieve energy conservation, emission reduction and sustainable production goals have been verified. [ABSTRACT FROM AUTHOR]

In the context of zk-SNARK, MSM emerges as the predominant computational bottleneck. To address this problem, this paper proposed a GPU-based parallel MSM computation approach. Firstly, the method performed fine-grained task decomposition of MSM to enhance algorithmic computational parallelism, fully leveraging the extensive parallel computing capabilities of GPU. Additionally, it reduced data transfer overhead by employing shared memory for the parallel reduction of sub-MSM tasks within the same window. Secondly, the method introduced a window partitioning strategy based on thread-level task load analysis of the underlying computational modules to search for the optimal scalar window, thereby minimizing the computational cost of MSM subtasks. Lastly, the method optimized the data storage structure used for scalar form transformation and mitigated latency issues in the large-scale scalar form conversion process by employing data overlap transfer and hidden communication time. This paper implemented the MSM parallel computation method based on CUDA on NVIDIA GPU and established a comprehensive zero-knowledge proof heterogeneous computing system. Experimental results show that the proposed method achieves an acceleration ratio of 1.38 times compared to the current state-of-the-art MSM calculation module of cuZK. The overall system based on the improved MSM is 186 times better than the industry-popular Bellman, and 1.96 times better than cutting edge heterogeneous version Bellperson, validating the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

To effectively address the real-time updates of passenger demand orders on online carpooling platforms, this paper proposed an algorithm for continuously assigning orders to drivers with unfinished orders. The proposed algorithm ensured an increase in driver earnings while enhancing the dispatch efficiency of the carpooling platform. Specifically, this paper developed a double-layer scheduling model which maximized the total driver revenue on the basis of the service quality and operational costs of the carpooling system, and proposed a double-layer algorithm for this model. The bottom layer involved the construction of a model for the carpooling routing problem, which was solved by using an improved genetic algorithm. The upper layer determined the order of task assignments, which adopted the greedy algorithm to call the model in the botton layer and compared revenue changes to obtain the final scheduling result. The model demonstrated its effectiveness and feasibility through a specific example, validating the prompt determination of order matching results and travel routes. The calculated results faithfully reflect real-world scenarios. Comparative experiments shows that the model not only achieves the goal of increasing driver earnings, but also effectively reduces delay time and travel distances. This has positive reference significance for related studies on carpooling scheduling problems in the context of real-time order updates. [ABSTRACT FROM AUTHOR]

With the widespread application of machine learning models, researchers have gradually recognized the limitations of such methods. Most of these models are black-box models, resulting in poor interpretability. To address this issue, this paper proposed a rule-based interpretable model and rule reduction method based on ensemble learning models, which included generating optimized random forest models, discovering and reducing redundant rules, and other steps. Firstly, this paper proposed an evaluation method for random forest models, and optimized the key parameters of random forest models based on the idea of reinforcement learning, resulting in a more interpretable random forest model. Secondly, the rule sets extracted from the random forest model were subjected to redundancy elimination, resulting in a more concise rule set. Experimental results on public datasets show that the generated rule sets perform well in terms of prediction accuracy and interpretability. [ABSTRACT FROM AUTHOR]

Dynamic 3D mazes present more challenging environments for reinforcement learning due to their uncertainty and incomplete information. Conventional reward functions can lead to slow and ineffective task training. This paper proposed an event-triggered integrated rewards scheme to solve the problem of finding multiple targets in a dynamic maze using reinforcement learning. The scheme expressed the various behavioral states in the 3D maze as events, which in turn derived the rewards. This paper divided rewards into environmental rewards and internal rewards. Environmental rewards directly related to the 3D maze mission and included node rewards reflecting the mission objectives and constraint rewards reflecting the mission constraints. Internal rewards linked to the agent's emotional state during the learning process and encompassed both judgement and mood rewards. The average performance of the integrated reward shows a 54.66% improvement compared to the upgraded reward. The results suggest that the integrated reward scheme offers benefits by increasing satisfaction with task completion, promoting exploration, and boosting training efficiency. [ABSTRACT FROM AUTHOR]

This paper proposed a particle swarm algorithm based on pyramid layer topology to optimize energy consumption trajectories of industrial robots. The algorithm introduced a pyramid layer topology to sort and stratify the particles thus improving the competitive strategy of the algorithm and increasing the population diversity. The algorithm introduced a new cooperation strategy to update the speed and position of the particles. It introduced a victory percentage to adaptively adjust the weight coefficients of the particle swarm algorithm and improved the search efficiency of the particles. In order to verify the effectiveness of the algorithm, this paper tested it on the set of test functions and compared it with other eight variants of particle swarm algorithms. The results show that the performance of the proposed algorithm has significant advantages. Finally, it applied the algorithm to industrial robot trajectory planning. Simulation experiments show that the algorithm can effectively solve the optimal trajectory of the robot's energy consumption, and the energy consumption of the robot is significantly reduced, which meets the kinematics and dynamics constraints of industrial robots. [ABSTRACT FROM AUTHOR]

This paper aimed to optimize the node selection problem in existing oracle machine schemes in the blockchain environment in order to improve the accuracy and reliability of oracle machine node selection. This paper introduced ORLM, a deep reinforcement learning based middleware for blockchain prophecy machine node selection. This middleware considered the consumption of multiple nodes under different service demands and modeled the reputation value of the prophecy machine nodes to sick the reputation value of the oracle machine data-providing nodes, thus avoiding the selection of nodes with malicious history as much as possible. With the deep reinforcement learning DQN algorithm, the middleware is able to optimize the process of selecting nodes for better node selection with security. The experimental results show that the oracle middleware is able to better satisfy the service requests of users. And it has high scalability and availability. It proves that the introduction of deep reinforcement learning to optimize the oracle node selection is a feasible direction. [ABSTRACT FROM AUTHOR]

At present, it is a general trend to introduce intelligent detection and control into traffic signal control system, especially reinforcement learning and deep reinforcement learning methods show great technical advantages in scalability, stability and extensibility, and have become a research hotspot in this field. This paper studied traffic signal control tasks based on reinforcement learning, systematically sorted out the classification and application of reinforcement learning and deep reinforcement learning in the field of intelligent traffic signal control on the basis of extensive research results on traffic signal control methods, and summarized feasible solutions to large-scale traffic signal control problems by using multi-agent cooperation. This paper classified and summarized the factors affecting the traffic scene of large-scale traffic signal control, put forward the current challenges and potential research directions in this field from the perspective of improving the performance of traffic signal controllers. [ABSTRACT FROM AUTHOR]

Semantic segmentation plays a crucial role in various fields such as medical image analysis and battlefield situatio- nal awareness. However, a single client often cannot provide a sufficient quantity and diversity of training data for the model. Therefore, it is necessary to train semantic segmentation models from distributed data, which exhibits complex and diverse characteristics. To prevent data privacy breaches and safeguard data security, the application of federated learning in the col- laborative training of semantic segmentation models across multiple clients has become a hot research topic in the field. Buil- ding upon the definition of federated semantic segmentation, this paper conducted a comprehensive analysis around the key characteristics of data heterogeneity and label deficiency in distributed complex data samples. The study encompassed a review of issues, methods, and exemplary model instances in federated semantic segmentation, evaluating the applicability and cha- racteristics of different methods, summarizing current application outcomes. The paper also proposed potential research oppor- tunities to address the issues of data heterogeneity and label deficiency. The research provides insights and references for the development of federated semantic segmentation methods and related studies tailored for distributed complex data samples. [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]

In recent years, the number of papers in scientific social network had grown at an explosive rate. It was difficult for users to find papers related to their requirement. And the paper recommendation is one of the key methods to solve this problem. However, most of the existing methods only focus on rating prediction, ignoring the ranking problem between papers. In addition, a lot of social information in scientific social network were not fully considered in the traditional recommendation method. Therefore, this paper proposed an improved List-wise method for scientific paper recommendation. It systematically analyzed The friendship information between users , the titles , abstracts and tags of scientific papers and incorporated theses information into the improved List-wise method. In order to verify the validity of the proposed method , this paper crawled data from a scientific social network , i. e. , CiteULike , to conduct experiments. Experimental results show that the proposed method gets the best recommendation results and performs well in scalability compared to the other traditional recommendation methods. [ABSTRACT FROM AUTHOR]

Paper folding is a common phenomenon. This paper could be referred to as the shell, and the model was the key for efficient simulation of thin shell. Based on the high cost and more complex of physical modeling and based on the accuracy of deformation was not enough of geometric modeling. Therefore, in order to realize the simulation of paper folding, this paper proposed a paper folding simulation based on physical model and geometric model. First of all, based on the hinge model and the mass spring model of paper thin shell model. Second, updating the position of the particle by the implicit integration method, it determined the initial deformation of the paper,and increased the deformation constraint. Lastly, according to Poisson deformation determining the final deformation state. The experimental results show that the hybrid model has the advantages of high speed and high stability. [ABSTRACT FROM AUTHOR]

Aiming at the problems of inaccurate semantic segmentation results and rough saliency maps of indoor scene images, this paper proposed a network architecture (feature regulator and dual-path guidance, FG-Net) based on multi-modal feature optimization extraction and dual-path guided decoding. Specifically, the feature regulator sequentially performed noise filtering, re-weighted representation, differential complementation and interactive fusion on the multi-modal features at each stage, and optimized multi-modal feature representation in the feature extraction process by strengthening RGB and depth feature aggregation. Then, the dual-path guidance component introduced rich cross-modal cues after feature interactive fusion in the decoding stage to further take advantage of multi-modal features. The dual-path cooperative guidance structure outputted a more detailed saliency map by integrating multi-scale and multi-level feature information in the decoding stage. This paper conducted experiments on the public datasets NYUD-v2 and SUN RGB-D, and achieved 48.5% in the main evaluation metric mIoU, which is better than other state-of-the-art algorithms. The results show that the algorithm achieves more refined semantic segmentation of indoor scene images, and has good generalization and robustness. [ABSTRACT FROM AUTHOR]

Emerging network architectures and technical service requirements for B5G and 6G will enable MEC with CF-mMIMO, helping to handle compute-intensive and latency-sensitive tasks in distributed IoT. For CF-mMIMO-assisted MEC systems, this paper aimed to minimize the delay in completing computational tasks of different task types under energy constraint. In order to solve the above goals, this paper designed a task offloading strategy based on UEs, multiple APs and CPU (central processing unit) for cloud-edge-end collaboration. Specifically, according to the different data types of each UE and AP service, this paper firstly used the convex optimization and graph matching methods to alternately iterate to optimize the offload association and task ratio. Then, under the limitation of the backhaul link, this paper used an improved binary whale optimization algorithm to further offload the tasks of unallocated terminals and associated access points to the cloud with efficient processing. Compared with other meta-heuristics such as ant colony optimization algorithm and hybrid gray wolf optimization algorithm, the proposed algorithm has better performance on discrete offload optimization problems, which can provide a good offload optimization strategy for distributed systems and greatly reduce the average delay of the whole network. [ABSTRACT FROM AUTHOR]

To address the issues of low efficiency and frequent security vulnerabilities in smart contract development, this paper proposed a smart contract auto-generation method based on code comment optimization. Firstly, it constructed a smart contract clustering analysis model based on the semantic information of smart contract code associated annotations to achieve fast and accurate clustering of functions similar to smart contracts. Then, it divided the smart contract knowledge base into different levels such as the contract layer, function layer, and interface layer associated with the annotation. And based on the clustered code and annotation information, it constructed a diversified Prompt feature prompt statement dataset. Finally, it achieved automatic generation of smart contracts with specific needs based on the large language model ChatGLM2-6B and with the help of P-Tuning v2 fine-tuning technology. In order to test the effectiveness of this method, this paper conducted comparative experiments with existing methods using the bilingual translation quality assessment assistant tool BLEU and the code security detection tools Mythril and VaaS. Experimental results show that the average BLEU of the code generated by this method is increased by about 13%, and the code security is improved by about 6%. This method integrates code annotation information into the automatic generation of smart contracts, effectively improves the quality of smart contracts, and provides a new method for the efficient development of safe and reliable smart contracts. [ABSTRACT FROM AUTHOR]

Currently, researchers are focusing on efficient applications and resource scheduling strategies in vehicular edge computing (VEC) environments, however, the real-world validation of these applications and strategies is often limited by cost and time and cannot be performed quickly and efficiently. The emergence of edge/fog computing simulators such as iFogSim2 reduces the cost of experiments. However, the connection switching and resource allocation requirements of high-speed moving vehicles pose challenges to the application of edge/fog computing simulators under VEC. Therefore, this paper improved iFogSim2 by designing VECSim, a VEC environment simulator that supported high-speed mobility. It integrated open-source base station data and constructed vehicle trajectory datasets so that researchers can focus on resource allocation strategies. Firstly, to simplify the experimental steps, this paper improved the mobile trajectory data parsing module and adapted the vehicle trajectory data generated by Simulation of Urban Mobility (SUMO), a microscopic transportation simulation software. Secondly, this paper modeled distributed applications under VEC based on the distributed data flow model and provided a service migration benchmark policy algorithm. In addition, VECSim introduced a time-performance optimization method to accelerate the simulation event processing by parallelizing the operation, which improved the time performance of the simulation tool. Experiments show that compared with similar service migration algorithms in iFogSim2, the proposed service migration algorithm exhibits good stability under the validation of large-scale motor vehicle trajectory dataset, and the time-performance optimization method achieves a 5.3% performance improvement in execution time. [ABSTRACT FROM AUTHOR]

Knowledge tracing aims to model the state of knowledge and ultimately predict the future performance of learners by describing exercises through the representation of concepts. However, in terms of the representation of concepts, the current research doesn’t model the influence of historical knowledge concepts on the temporal relationship of the current concepts, nor does it describe the role of the spatial relationship between various concepts in the exercise. In order to solve these problems, this paper proposed a knowledge tracing model characterized by temporal and spatial correlation fusion. First of all, based on the degree of temporal correlation between concepts, it modelled the temporal effect of historical concepts from current concepts. Secondly, it modelled the spatial interaction between several concepts contained in the exercise to obtain the representation of knowledge points containing temporal and spatial information through the graph attention network. Finally, it used the above representation of concepts to derive the representation of the exercises, and generated the current state of knowledge through the self-attention mechanism. In the experimental stage, this paper compared the performance of the proposed model with the five relevant knowledge tracing models on four real datasets. The results show that the proposed model has better performance. In particular, compared to the five comparative models on the ASSISTments2017 dataset, the AUC and ACC are improved by 1.7%～7.7% and 7.3%～12.1%, respectively. At the same time, the ablation experiment proves the effectiveness of modeling the temporal and spatial correlation between concepts, and the training process experiment shows that the proposed model has certain advantages in the representation of concepts and the modeling of their interaction relationships. The application examples can also show that the model has better practical results than other knowledge tracing models. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining aims to discover the association between spatial features and has been an important research direction in spatial data mining. Spatial co-location pattern mining method based on column calculation (CPM-Col algorithm) avoids the most time-consuming operation of generating table instances and directly searches for participating instances. This method has become one of the most efficient approaches. However, backtracking search for participating instances remains a bottleneck, especially in dense datasets and long pattern mining. To accelerate the search for participating instances, this paper proposed two improvements to the CPM-Col algorithm with less extra computations. Firstly, the row instances found by CPM-Col algorithm were stored as partial table instances, for avoiding backtracking calculations of many instances. Secondly, after successfully finding a row instance, some instances of the first feature were obtained by the sub-clique reaction of the row instance. Based on these improvements, this paper proposed a co-location pattern mining method based on improved column calculation (CPM-iCol algorithm) and discussed complexity, correctness, and completeness. Experiments were conducted on synthetic and real datasets. Comparing to a classical algorithm join-less and CPM-Col, the CPM-iCol algorithm significantly reduces mining time and backtracking times. The results show that the proposed algorithm has better performance and scalability than CPM-Col algorithm, especially in dense datasets. [ABSTRACT FROM AUTHOR]

In order to further optimize the overlapping community detection algorithm, this paper proposed a new definition of node importance based on degree and node clustering coefficient, and the nodes were updated in descending order of node importance, and the node update strategy was fixed to improve the stability of community detection. On this basis, this paper proposed an OCD-GEMPA. The algorithm combined the node2vec model to represent the nodes in a low-dimensional vector, constructed a matrix of weight values between nodes, calculated the label attribution coefficient according to the weight values, and selected labels accordingly, avoiding the problem of random selection. Experimental verification of the algorithm on real data sets and synthetic data sets shows that compared to other overlapping community detection algorithms, the OCD-GEMPA algorithm has significant improvements in both EQ and NMI indicators, with better accuracy and stability. [ABSTRACT FROM AUTHOR]

In order to solve the problem that it is difficult for the model to perceive implicit information due to the lack of external knowledge in rumor detection, which limits the ability of the model to mine deep information, this paper proposed knowledge graph based multi-feature fusion rumor detection (KGMRD) method. Firstly, for each event, it constructed posts and comments together into a text sequence and used a classifier to extract the emotional features. This paper constructed a knowledge graph based on text using ConceptNet and aggregated the entity representation in the knowledge graph with the semantic features of text using the attention mechanism, so as to obtain the enhanced semantic feature representation. Secondly, in terms of communication structure, for each event, this paper built its communication structure diagram based on the propagation and forwarding relationship of the post, and used DropEdge to prune the communication structure diagram, so as to obtain more effective communication structure characteristics. Finally, it fused the obtained features to get a new representation and compared seven models including SVM-RBF on three real datasets of Weibo, Twitter15 and Twitter16. The experimental results show that compared with the current baseline with the best effect, the KGMRD method has the best ACC on the Weibo dataset and improves the ACC by 1.1%, and there is a 2.2% improvement on Twitter15 and Twitter16 dataset in ACC. The experiment proves that the KGMRD method is reasonable and effective. [ABSTRACT FROM AUTHOR]

This paper investigated the issue of covert network attacks on video surveillance systems in light of their networking and intelligent development, which brought about new risks. The primary objective of this research was to explore numerous cases of covert network attacks and summarize the specific characteristics of such attacks targeting video surveillance systems. Additionally, this paper examined the unique advantages of honeypot technology in detecting network attack behaviors and identifying attack clues, and outlined the honeypot defense methods against covert attacks on video surveillance systems. Furthermore, considering the shortcomings in the visual scene deployment of video surveillance honeypots, this paper introduced a deep scene fabrication defense framework that combined generative Al models with video surveillance honeypots. Finally, it proposed the development direction of honeypot defense technology for video surveillance systems. [ABSTRACT FROM AUTHOR]

Floating car GPS data serves as the foundation for processing traffic information, with the rapid increase in the number of monitored vehicles, a massive amount of GPS data is generated, posing great challenges to map matching. To address the shortcomings of traditional matching methods in terms of matching efficiency and accuracy, this paper proposed a real time parallel map matching algorithm for massive GPS data that ensured both high matching accuracy and computational efficiency. This paper firstly reconstructed an efficient and accurate real-time map matching algorithm for streaming data by introducing a comprehensive weight factor that considered velocity and direction to enhance the offline map matching algorithm that relied on historical trajectories. Then, it introduced the Spark Streaming distributed computing framework to achieve real-time and parallel computation of the map matching algorithm, significantly improving the efficiency of real-time map matching. Experimental results demonstrate that the proposed algorithm achieves more than a 10% increase in matching accuracy compared to conventional topological matching algorithms on complex road sections, with an overall matching accuracy of over 95%. In terms of matching efficiency, it achieves approximately a fourfold improvement compared to an equivalent number of standalone servers. The experimental results show that the proposed algorithm achieves real-time map matching of 80 million GPS data points on a computing cluster composed of 11 machines, proving that the proposed algorithm can achieve real-time vehicle matching in urban areas. [ABSTRACT FROM AUTHOR]

Blockchain technology has great potentiality in the secure sharing of electronic health records. However, current solutions face issues such as large storage space and low consensus efficiency. This paper proposed a novel scheme based on blockchain and friend and relative nodes. Firstly, this paper adopted a distributed file system to store users’ complete electronic health records on offline servers, and constructed a set of user’s friend and relative nodes. Each user’s data was stored redundantly by its friend and relative nodes, and only the block’s header information was stored on the chain, reducing storage space. Then, this paper designed a data access mechanism based on multi-signature technology, using Shamir secret sharing mechanism to distribute user’s private keys to their relatives and friends. More than half of the relatives and friends could use their own private keys to reconstruct users’ private keys, solving data access problems in emergency situations. Finally, this paper proposed a credit based Byzantine fault-tolerant consensus mechanism, which used the top 10% nodes with credit values as the leader node set and utilized a random algorithm to generate the master node in the leader node set. And it improved consensus security. The experimental results show that compared with existing methods, the proposed scheme reduces the on-chain storage space by 1/7, improves data access efficiency by 15 times, and has higher consensus security while ensuring good latency and throughput. The proposed scheme can achieve secure and efficient sharing of electronic health records, providing a case for the application of blockchain in the medical field. [ABSTRACT FROM AUTHOR]