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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]

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]

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]

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 federated learning framework that uses autoencoder model to detect malicious model updates is an excellent defense framework for poisoning attacks. However, the existing autoencoder-based models face several challenges such as training difficulties and limited anomaly detection capability. In view of the above problems, this paper proposed an algorithm for detecting malicious model updates of federated learning based on β-VAE: the central server stabilized the training dataset by suppressing random attributes of the training samples, and used the dataset to train β-VAE anomaly detection model in real time. The model computed the anomaly score of task model updates uploaded by the clients, and then to detect and exclude malicious model updates based on the dynamic threshold of anomaly score. This paper evaluated the performance of the proposed algorithm on three federated learning tasks. Specifically, these tasks include classification on the MNIST dataset using the logistic regression (LR) model, classification on the FEMNIST dataset using the convolutional neural network (CNN) model, and character prediction on the Shakespeare dataset using the recurrent neural network (RNN) model. The experimental results show that, the task model under this algorithm achieves higher accuracy compared to other defense algorithms. This indicates that in Non-IID scenarios, the algorithm exhibits strong robustness against poisoning attacks in federated learning. [ABSTRACT FROM AUTHOR]

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

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

To solve the problems of low recognition accuracy and poor generalization performance of existing face anti-spoofing models in different application scenarios, this paper adopted the idea of disentangled representation learning and proposed a face anti-spoofing method based on disentangled representation learning. This method adopted U-Net architecture and ResNet18 as the encoder-decoder. In the first stage of training, it inputted real samples so that the encoder only learned information related to real samples. In the second stage, this paper built an adversarial learning network, inputted samples without labels, feature fusion of the pre-trained encoder output and the new encoder output, reconstructed the image by the decoder, and performed adversarial training with the original image in the discriminator to achieve feature decoupling. Compared with some classic face anti-spoofing methods, the model paper achieved better detection performance. The lowest detection error rate in several experiments on the OULU-NPU data set is only 0.8%, which is better than classic detection methods such as STDN. The face anti-spoofing method used staged training to enable the model to obtain a more discriminative feature representation than the end-to-end model in adversarial training. It adopted a multi-classification strategy in the deception feature map output stage to reduce the impact of different image noises on classification results, and experiments on public data sets verified the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

LR serves as a generalized linear classifier for binary classification in supervised learning, exhibiting characteristics of simplicity in structure, strong interpretability, and effective fitting when dealing with linear data. However, its classification performance becomes limited when confronted with high-dimensional, uncertain, and linearly inseparable data. To address the inherent limitations of logistic regression, this paper introduced the theory of granular computing and proposed a novel logistic regression model called rotating granular logistic regression (RGLR). This paper introduced the theory of rotating granulation, where different angles of rotation were applied to pairs of features forming a plane coordinate system. This process constructed rotating granules by rotating pairs of features at various angles on the plane coordinate system, and granulated to form rotating granule vectors on multiple plane coordinate systems. This paper further defined the size, measurement, and operational rules of granules, and proposed a loss function for rotating granular logistic regression. The optimized solution of the rotating granular logistic regression was obtained by solving the value of the loss function. Finally, experiments are conducted using multiple UCI datasets, and the results compared across various evaluation metrics, indicate the effectiveness of the rotating granular logistic regression model. [ABSTRACT FROM AUTHOR]

To overcome AMR s limited generalization and low classification accuracy in non-cooperative communication contexts with low signal-to-noise ratio, this paper proposed a model named CGDNN, which integrated CNN, GRU and deep neural networks. To mitigate noise impact on modulation detection, this paper initially denoised I/Q sampled signal using wavelet thresholding. Subsequently, this paper utilized CNN and GRU for extracting spatial and temporal features from signals before proceeding to classification through fully connected layers. Besides enhancing AMR performance, the CGDNN model significantly reduced computational complexity compared to competitors. Experiment results demonstrate an average recognition accuracy of 64.32% on the RML2016. 10b dataset, with an enhancement in signal classification accuracy from 12 dB to 0 dB. Moreover, the model substantially decreased confusion between 16QAM and 64QAM, achieving a peak recognition accuracy of 93.9% at 18 dB. CGDNN model effectively improved AMR detection accuracy under low signal-to-noise ratio conditions and enhanced model training efficiency. [ABSTRACT FROM AUTHOR]

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

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

In order to solve the problem that the daily rehabilitation training of cardiovascular patients depends on directing by health care professions in rehabilitation centre, during assessing and correcting the movements system about cardiovascular patients independent rehabilitation training at home, this paper proposed an action assessment method for cardiovascular patients' rehabilitation training based on key points of the human skeleton (ASRT-PHS). Firstly, this paper constructed a dataset for rehabilitation training actions using a camera and data augmentation in accordance with the specified rehabilitation training specification for cardiovascular patients. Secondly, this paper employed a deep learning-based detector and pose estimator to capture human body positions and extract key points of the human skeleton, respectively, and then input the results into a convolutional neural network for action recognition. Thirdly, by calculating joint angle thresholds, joint distance ratio and assessing standard motions, this paper constructed a motion segmentation model based on joint distance ratios and an action assessment model based on action joint angle thresholds. This paper investigated the optimal combination of ASRT-PHS by assessing its performance with various joint angle thresholds and action recognition approaches. The results show that ASRT-PHS achieves an average action recognition, segmentation and assessment accuracy of 92.78%, 77.6% and 87%, respectively. Furthermore, case tests about the true cardiovascular patients show that the average accuracy of the prototype system is 71.3%, which provides a feasible intelligent auxiliary system for patients autonomous rehabilitation training at home. [ABSTRACT FROM AUTHOR]

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

The combination processing of molds and electronic product joint inspection introduce the mandatory concurrent operations on the same machine (MCDSM), which breaks the constraint that the same machine cannot process different jobs at the same time in the job shop scheduling (JSP) To solve this type of job shop scheduling problem, this paper proposed an adaptive hybrid initialization genetic algorithm (AHIGA). Firstly, this paper defined the problem as a generalized JSP considering mandatory concurrent operations on the same machine (GJSPMCO) problem, and established an optimization model using mixed integer programming with the objective of minimizing the maximum completion time. Next, it designed corresponding encoding and decoding techniques to support the representation and resolution of feasible scheduling plans within the constraints of MCOSM. Meanwhile, it established a population hybrid initialization method to generate high-quality feasible solutions. This paper also designed novel crossover and mutation operation methods to ensure the feasibility of newly generated solutions under the constraint of MCOSM. Additionally, it constructed an adaptive crossover and mutation operators to enable the adaptive updating of offspring, thereby enhancing the algorithms global search capability. Finally, it constructed 40 test cases based on JSP benchmark, and used these test cases and an example of joint inspection of electronic products for experiments. The results show that the proposed model and algorithm can effectively solve GJSPMCO problem, and the improvement strategies can enhance the quality of solutions, demonstrating the feasibility of the model and the superiority of AHIGA. [ABSTRACT FROM AUTHOR]

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

Periodic high utility sequential pattern mining (PHUSPM) has attracted much attention because it can find more practical regular patterns in time series. However, existing PHUSPM algorithms struggle to effectively handle incremental updates and overlook the downward closure property and complexity of the algorithm in large-scale data. To solve this problem, this paper proposed an IncPUS-Miner algorithm, which effectively realized the incremental mining of periodic high-utility sequential patterns (PHUSPs). IncPUS-Miner introduced a novel data structure called pu-tree. Each tree node corresponded to an updated utility list(UUL) to store the auxiliary information of the corresponding sequence. When incremental data was added, this structure allowed flexible updates to project information, thereby enhancing the dynamic adaptability and scalability of the algorithm. In addition, this paper proposed two new upper bounds of sequence utility, PUB and EUB, and two corresponding pruning strategies, which effectively reduced the computational burden. The experimental results show that the IncPUS-Miner algorithm effectively realizes the incremental mining of PHUSPs on real data, and shows superior performance compared with other algorithms. [ABSTRACT FROM AUTHOR]

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

In recent years, blockchain has attracted increasing attention in both academic and industrial circles, and the privacy of blockchain remains a controversial issue when deploying blockchain in different applications. This paper systematically reviewed the current privacy protection schemes and mechanisms in blockchain, and gave insights into the security and privacy of blockchain. Firstly, this paper briefly introduced the main functions, types and privacy definitions of blockchain technology, and analyzed the privacy issues it faced. Then, from a technical perspective, this paper divided blockchain privacy protection technology into four categories: decentralized identity authentication, privacy protocol, encryption technology and obfuscation technology, as well as eight sub-categories: decentralized identity authentication, secure multi-party computation, differential privacy, homomorphic encryption, zero-knowledge proof, coin mixing technology, ring signature and anonymous communication. This paper studied them separately, and summarized the mechanisms of different privacy protection technologies on permissioned and permissionless blockchains. Finally, this paper conducted qualitative research on the main characteristics and attributes of the eight privacy protection technologies, and discussed future research directions of privacy protection technologies. The research provided reference for blockchain developers to choose appropriate privacy protection schemes and for researchers to choose research directions for blockchain privacy protection. [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]

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]

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]

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 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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]