Concept management for large-scale text data is critical in domains such as healthcare informatics, digital libraries, and news classification. However, the variability in concept structures and the diversity of application requirements pose challenges for existing automated methods, which often lack the flexibility to accommodate customized needs. Meanwhile, manual classification remains resource-intensive and inefficient. To address this issue, we propose CateSift, an interactive approach that integrates public knowledge to streamline the classification process and incorporates expert knowledge to formulate classification models. The main contributions of this work are as follows: (1) a visualization interface, called CateSift , that facilitates users in constructing and refining classification models for large-scale data, and (2) A prompt-based model that can integrate expert knowledge to iteratively refine hierarchical classification structures. Specifically, CateSift provides users with a hierarchical concept tree that highlights concepts with uncertain classifications and invites users to optimize the classification models by injecting knowledge. To address the issue of large-scale data, CateSift allows users to steer the classification model by adjusting the classification tree or annotating classifications. Case studies indicate that the proposed approach effectively and efficiently supports classification for large-scale data. • This study presents an interactive classification framework, CateSift , which employs multi-level prompt templates and integrates prompt-based large language models with expert knowledge to perform hierarchical classification on large-scale datasets. This method effectively overcomes the limitations of existing automated approaches regarding accuracy and hierarchical structure flexibility, as well as the inefficiencies associated with manual classification. • An interactive prototype system is introduced to support users in interpreting and guiding model classification outcomes, enabling efficient detection of potential errors and unstable hierarchical components. The system facilitates iterative model refinement through user-provided corrections and annotations, accommodating diverse domain-specific needs and further decreasing user effort.

Composite federated learning provides a comprehensive framework for addressing machine learning tasks that incorporate regularization terms. Nevertheless, numerous established methods within this framework face challenges stemming from data heterogeneity and the high variance induced by stochastic gradients mapping. These issues cause significant deviations in clients' local models, resulting in unstable and slow convergence of the global model and increased communication costs. To address this, we propose a novel algorithm named FedSAGA, designed to solve composite federated optimization problems that include a nonsmooth global regularization term. In FedSAGA, we construct a combination of the latest and historical local model updates without incurring additional communication overhead. This estimate serves as a control variate on the client side to reduce the variance introduced by stochastic gradient mapping and client sampling. To further accelerate convergence, we leverage the inertial extrapolation step locally at the clients. Theoretical analysis demonstrates that FedSAGA attains a sublinear convergence rate under general convex settings and a linear convergence rate under strongly convex settings. Numerical experiments based on both real and synthetic datasets effectively demonstrate the convergence of the proposed algorithm under partial client participation.

Momentum methods based on the inertial technique have been widely adopted in federated learning (FL). However, a unified framework for FL acceleration methods needs further exploration. The further, gradient descent is the main training algorithms in FL, which, although powerful, is not universally feasible or best choice. Motivated by this, we provide a unified framework to bridge the gap between practice and theory by the distributed version of general inertial Krasnosel’ski ${ }^{\text {c }}$ i-Mann (DG-IKM) iteration and consider operator splitting methods applicable to nonsmooth optimization. By formulating the federated optimization problem as a monotone inclusion problem and instantiating the general framework, we propose a novel accelerated algorithm, FedIDR, which achieves fast convergence by using inertial technique in the parallel Douglas-Rachford splitting method. And then, the inertial Krasnosel’ski ${ }^{\text {-}}$ i-Mann (IKM) theory allows us to reuse the general convergence results by formulating FedIDR as an application of the nonexpansive operator. Finally, we derive the convergence rate and numerical experiments based on real and synthetic datasets are considered to evaluate the proposed algorithm.

Situation prediction, as a critical component of the situation awareness framework, plays a vital role in decisionmaking systems such as traffic management and cybersecurity. However, most existing studies still focus primarily on univariate time series, lacking effective modeling and optimization mechanisms for multivariate situation data with exogenous variables.In view of the above research status, this paper proposes a novel prediction method called Improved Grey Wolf Optimizer -Neural Basis Expanded Analysis of Time Series with Exogenous Variables (CWGWO-NBEATSx), specifically designed for time series situation prediction tasks involving multivariate exogenous variables. The proposed approach integrates the powerful modeling capability of N-BEATSx with an improved grey wolf optimization algorithm (CWGWO), aiming to achieve joint optimization of model architecture, critical hyperparameters, and exogenous variable selection. Within the CWGWO-N-BEATSx framework, N-BEATSx is, for the first time, applied to situation prediction. Key hyperparameters influencing the performance of NBEATSx are identified and quantified, and a CWGWO algorithm—enhanced through the incorporation of chaotic mapping and adaptive weighting mechanisms—is introduced to optimize these hyperparameters. A multi-objective fitness function is constructed by jointly considering prediction accuracy and model complexity, and extensive empirical evaluations are conducted on real-world situation and time series datasets.Experimental results demonstrate that CWGWO outperforms eight mainstream metaheuristic algorithms. Compared with five state-of-the-art (SOTA) methods, CWGWO-N-BEATSx reduces the average MAE on situation datasets and time series datasets by 17.614% and 17.55%, respectively; the average SMAPE by 16.27% and 8.932%; and the MSE by 23.488% and 43.87%, respectively. In addition, CWGWO-N-BEATSx maintains relatively low model complexity, validating its superior performance and strong potential for practical application.

Privacy concerns related to location-based services are becoming increasingly critical. To address the limitations of previous research, which has often neglected the temporal attributes of trajectories and relied solely on average noise addition methods, a trajectory data publishing scheme is proposed based on an R-tree storage structure. Initially, trajectory data is dimensionalized using a spatiotemporal optimal trajectory segmentation algorithm, and the preprocessed key trajectory data is organized into a PSR-Tree. Subsequently, an incremental privacy budget allocation strategy is applied to add noise to the trajectory data. Finally, a Hidden Markov Model is employed to constrain noise addition at the PSR-Tree nodes, fulfilling the requirements for trajectory data publishing. Experimental comparisons with existing methods demonstrate that this approach effectively addresses the issue of insufficient data usability and exhibits robust scalability.

Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion.

Location-based service (LBS) devices have enhanced daily life convenience but raised privacy concerns regarding user location data. For this, this research presents a novel approach to safeguard trajectory privacy through Similarity-based Trajectory Partitioning (STSM). The process begins by dividing trajectory equivalence classes based on distinct timestamps. Trajectory similarity is then assessed within each class using metrics such as Frechet distance, trajectory direction, and speed. Subsequently, a trajectory graph is constructed for each class, and the Dijkstra algorithm is applied to partition the graph using the k-subgraph method, effectively converting trajectory partitioning into graph partitioning. To meet publication requirements, Laplace noise is added to sensitive data linked to nodes and edges within the subgraph. Comparative experiments on real-world data confirm the efficacy, rationality, data availability, and privacy protection capabilities of the proposed trajectory partitioning algorithm, underscoring its superiority over alternative methods.

Abstract With the increasingly extensive applications of the network, the security of internal network of enterprises is facing more and more threats from the outside world, which implies the importance to master the network risk assessment skills. To improve the accuracy is an importent issue. In the big data era, there are various security protection techniques and different types of group data. Meanwhile, Online Social Networks (OSNs) and Social Internet of Things (SIoT) are becoming popular patterns of meeting people and keeping in touch with friends (Jiang et al. ACM Comput Surv 49:10:1–10:35 2016; Shen et al. 2017). Risk assessment, as a bridge between security experts and network administrators, whose accuracy can influence the judgment of administrators to the entire network state. In order to solve this problem, this essay uses the Baum Welch algorithm to optimize the risk assessment process by establishing the HMM model, which can improve the accuracy of the evaluation value. Firstly, behavior of the attacker is described in-depth by the attack graph generated through MulVAL framework. Then, the nodes on the attack path can will be evaluated and the value will be further evaluated by the Bayesian model. Finally, by establishing the hidden Markov model, the corresponding parameters can be defined and the most likely probabilistic state transition sequence can be calculated by using the Viterbi algorithm and Baum Welch algorithm to deduce the attack intent with the highest possibility.

Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks.

Most of the risk assessments of the attack graph are static and have a fixed assessment scenario, which limit the real-time nature of the situation assessment. This paper presents an activity theory model to analyse the contradictions in the attack behaviour. In order to assess the maximum probability path of an attacker and dynamically remain in control for the overall situation, a definition of attacker's benefit (loss/gain) value calculated by contradictory vector is proposed. The attacker's budget is applied as an unbiased amount in the least square genetic algorithm, optimises the fitness function of the genetic algorithm. Experimental results reveal that the improved least square genetic algorithm with unbiased estimator effectuate higher gains owing to the high fit degree of fitness function. With the coming evidence, the maximum probability attack paths get a more accurate and dynamic risk assessment of the situation.

The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.