To address the susceptibility of existing MTS(multivariate time series)anomaly detection models to training set contamination and their limited ability to capture complex spatial-temporal correlations in MTS,a novel anomaly detection model based on spatial-temporal normalizing flow was proposed.This model employed the conditional normalizing flow to estimate the density of patterns in MTS,enabling robust anomaly detection even in the presence of contaminated training data.Additionally,a patched long short-term memory module was introduced to effectively learn long-term temporal dependencies within MTS,and a dynamic graph learning module based on attention mechanisms was devised to model the evolving correlations among different dimensions of MTS.Experimental results on three real-world cyber-physical system datasets demonstrate that the proposed model significantly outperforms state-of-the-art baselines in both detection accuracy and robustness.

Federated learning facilitates big data analysis and applications while safeguarding user privacy, thus emerging as a pivotal paradigm in distributed machine learning. Building on this foundation, multi-objective federated learning (MOFL) — which focuses on the collaborative optimization of multiple objectives such as communication cost and computational efficiency — has become one of the current research hotspots. However, MOFL still suffers from performance degradation in scenarios with heterogeneous data and dynamic network topologies, limiting its practical applicability. To address these challenges, this paper proposes a multi-objective hierarchical aggregation optimization method tailored for dynamic network structures. Specifically, a hierarchical aggregation mechanism is adopted to tackle the dynamic variations in client-side neural network models, which optimizes the training process of MOFL and significantly enhances computational efficiency under dynamic network and heterogeneous data environments. Experimental results verify that the proposed method achieves remarkable performance improvements across different data distributions: it attains an average performance enhancement of 50.73% compared with the NSGA-III algorithm. Furthermore, comprehensive comparative experiments with other state-of-the-art multi-objective optimization algorithms demonstrate its overall superior performance, confirming the scalability of the proposed method in practical scenarios.

Addressing edge-level data heterogeneity in hierarchical federated learning remains a major research challenge, as it often induces global model bias, slower convergence, and client unfairness that are difficult to reconcile. In this paper, we propose G-M3CS, a gradient-based multi-metric multi-objective client selection algorithm, along with its enhanced version G-M3CS+. By integrating the NSGA-II algorithm, we formulate optimization objectives such as gradient diversity and gradient history consistency to identify high-value clients. Furthermore, we introduce a dynamic weighting mechanism that harmonizes the dual aggregation processes of cloud-edge and edge-client layers, enabling adaptive optimization for heterogeneous edge data distributions and achieving a better tradeoff between performance and fairness. Extensive experiments conducted under diverse heterogeneous scenarios and datasets demonstrate that the proposed method improves global model accuracy by an average of 12.9% and fairness by 26.6% compared with existing algorithms. Particularly, under strong heterogeneity conditions such as the CIFAR-10 dataset, G-M3CS + achieves a 23.3% performance improvement over baseline methods, effectively mitigating the training challenges induced by edge-level data heterogeneity.

Time series anomaly detection (TSAD) has long been a hot research topic in data mining due to its various applications. Recent studies challenge the effectiveness of popular deep learning methods for TSAD, suggesting their failure in detecting subtle and prolonged anomalies. Outlier Exposure (OE) and Masked Autoencoder (MAE) emerge as two promising paradigms (classification and reconstruction) for solving the above problems. However, OE-based methods are constrained by poor generalization, while MAE-based methods are limited by masking misalignment issues. To address these limitations, this paper proposes a novel framework, CoAD, which unifies the two paradigms to leverage their complementary strengths while mitigating their respective weaknesses. In this framework, the classification module generates probability-informed soft masks for the reconstruction module, which in turn alleviates the generalization problem of the classification module. This cooperative design enables CoAD to effectively detect subtle and complex anomalies that are often overlooked by existing methods. Additionally, the classification module is carefully designed to resolve issues related to improper classification granularity and the neglect of frequency information. Extensive experiments on high-quality benchmark datasets, conducted under rigorous evaluation protocols, demonstrate that CoAD significantly outperforms both state-of-the-art deep learning and traditional data mining methods, highlighting the potential of deep learning in TSAD. Moreover, CoAD is lightweight and substantially faster than existing SOTA methods, demonstrating its practical value for large-scale, real-time applications.

Time series anomaly detection remains one of the most active research areas in data mining due to its wide range of real-world applications. In recent years, numerous deep learning-based methods have been proposed for this task. However, deep learning-based methods fail to detect subsequence anomalies with long durations, lack explainability, and are vulnerable to training set contamination. This paper addresses these issues by proposing a novel deep learning framework for effective, explainable, and robust time series anomaly detection. Our framework, MMA, incorporates the MLP-Mixer backbone with a Masked Autoencoder-based anomaly detection approach to allow for a significantly larger input window size (10 to 20 times larger than the input window sizes of current models). This larger input window enables our model to detect challenging subsequence anomalies. Meanwhile, a contrast learning module is proposed to aid in detecting subtle anomalies that fail to be identified by residual errors. Furthermore, a dynamic anomaly filtering method is introduced to mitigate the impact of subsequence anomalies on the reconstruction of surrounding normal regions to reduce false alarms. Extensive experiments on univariate and multivariate time series datasets demonstrate that our proposed framework significantly outperforms state-of-the-art methods across rigorous evaluation metrics. Additionally, MMA has a strong ability to reconstruct potential normal patterns in anomalous regions, providing high levels of explainability. Moreover, MMA demonstrates high robustness to various types of training set pollution.

Mobile Edge Computing (MEC) improves how well devices work and saves energy by moving computing tasks to nearby servers, while Non-Orthogonal Multiple Access (NOMA) makes better use of available bandwidth, making NOMA-MEC networks a very interesting area for research. However, most current research uses fixed channel assumptions and single-agent reinforcement learning methods, which do not account for the changing nature of real network channels and overlook the benefits of using multiple agents. To solve these problems, we suggest a new multi-agent reinforcement learning (MARL) system that can adjust offloading choices and resource distribution in NOMA-MEC networks as network conditions change over time. The proposed method employs quadratic transformation and convex approximation for efficient resource allocation and adopts a multi-agent MAPPO algorithm to address high-dimensional and dynamic environments. Many tests show that our new combined method uses much less energy than single-agent algorithms, and the theory behind it is supported by the results we got from experiments. Additionally, the strategy shows great flexibility and reliability in changing situations, highlighting its promise for smart resource management in future 6G-enabled MEC systems.

Generalized Category Discovery (GCD) aims to leverage labeled old categories data and abundant unlabeled data to discover novel categories unseen during training. However, prior studies have revealed significant limitations in parametric classifiers for novel class recognition. This paper rethinks the role of self-labeling in parametric classification for GCD and identifies that while self-labeling introduces training biases, it holds untapped potential for boosting model performance. We conduct an in-depth analysis of the reason why self-labeling fails in parametric classification and propose a Decoupled Self-labeling (DSL) approach that separates pseudo-labels generation from the predictive classification head. This design minimizes the learning bias introduced by unreliable pseudo-labels while preserving their utility for feature learning. Additionally, we introduce a label encoding risk minimization strategy that provides supervised guidance for unlabeled further extract valuable information from pseudo-labels. Comprehensive evaluations on GCD benchmarks demonstrate that DSL achieves state-of-the-art performance, outperforming parametric and non-parametric baselines, especially in the novel categories.

Federated Learning (FL) is a promising distributed machine learning framework that emphasizes privacy protection. However, inconsistencies between local optimization objectives and the global objective, commonly referred to as client drift, primarily arise due to non-independently and identically distributed (Non-IID) data, multiple local training steps, and partial client participation in training. The majority of current research tackling this challenge is mainly based on the empirical risk minimization (ERM) principle, while giving little consideration to the connection between the global loss landscape and generalization capability. This study proposes FedGAM, an innovative FL algorithm that incorporates Gradient Norm-Aware Minimization (GAM) to efficiently search for a local flat landscape. FedGAM specifically modifies the client model training objective to simultaneously minimize the loss value and first-order flatness, thereby seeking flat minima. To directly smooth the global flatness, we propose the more significant FedGAM-CV, which employs control variables to correct local updates, guiding each client to train models in a globally flat direction. Experiments on three datasets (CIFAR-10, MNIST, and FashionMNIST) demonstrate that our proposed algorithms outperform existing FL baselines, effectively finding flat minima and addressing the client drift problem.

Smart contracts are decentralized applications de-ployed extensively on blockchain. Due to their economic nature, vulnerabilities in smart contracts can lead to potential significant economic and property losses, disrupting the stable ecosystem of Ethereum. Therefore, the detection of smart contract vul-nerabilities is of paramount importance. Current mainstream methods for smart contract vulnerability detection rely on heuris-tic algorithms based on manual design, which lack reusability across different application scenarios, are time-consuming, and exhibit suboptimal accuracy. To enhance vulnerability detection effectiveness, a method tailored for timestamp vulnerabilities in smart contracts is proposed, named SESCD, based on self-ensembling pretraining. The proposed approach first identifies potential data propagation paths for timestamp vulnerabilities, prunes them, and leverages self-ensembling pretrained models to learn about these propagation paths. Furthermore, the training process is optimized through knowledge distillation to improve the model's ability to detect whether smart contracts contain timestamp vulnerabilities. SESCD demonstrates superior vulner-ability detection and generalization capabilities, alleviating performance instability issues caused by insufficient training data. To validate the effectiveness of SESCD, comparative experiments are conducted on a real-world dataset of smart contracts against 13 mainstream smart contract vulnerability detection methods. Experimental results show that SESCD achieves precision, recall, and F1 scores of 0.91, 0.93, and 0.92 respectively in detecting timestamp vulnerabilities. Compared to the 13 mainstream methods, SESCD exhibits an average relative improvement of 28%, 30%, and 30%, significantly enhancing the detection capabilities of timestamp vulnerabilities.

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks.

Entity resolution (ER), which aims to identify whether data records from various sources refer to the same real-world entity, is a crucial part of data integration systems. Traditional ER solutions assumes that data records are stored in relational tables with an aligned schema. However, in practical applications, it is common that data records to be matched may have different formats (e.g., relational, semi-structured, or textual types). In order to support ER for data records with varying formats, Generalized Entity Resolution has been proposed and has recently gained much attention. In this paper, we propose PromptER, a model based on pre-trained language models that offers an efficient and effective approach to accomplish Generalized Entity Resolution tasks. PromptER starts with a supervised contrastive learning process to train a Transformer encoder, which is afterward used for blocking and fine-tuned for matching. Specially, in the record embedding process, PromptER uses the proposed prompt embedding technique to better utilized the pre-trained language model layers and avoid embedding bias. Morever, we design a novel data augmentation method and an evaluation method to enhance the performance of the proposed model. We conduct experiments on the Generalized Entity Resolution dataset Machamp and the results show that PromptER significantly outperforms other state-of-art methods in the blocking and matching tasks.

Entity Matching (EM) aims to determine whether records in two datasets refer to the same real-world entity. Existing work often uses Pre-trained Language Models (PLMs) for feature representation, converting EM to a binary classification task. However, due to the dependence of PLMs on large labeled datasets and the overlap between train and test sets in current EM benchmarks, these methods often underperform in real-world scenarios (e.g., small data size, hard negative samples, and unseen entities). To address the limitations of existing methods, we propose SETEM, a self-ensemble training method leveraging the stability and strong generalization of ensemble models to tackle these challenges in real-world scenarios. Additionally, we develop a faster training method for low-resource applications. Experiments on benchmark datasets show that SETEM outperforms Ditto and HierGAT on the F1 score. In particular, SETEM shows the greatest advantage with small datasets and a high proportion of unseen entities in the test set, achieving up to a 9.61% F1 score increment over baselines on the WDC dataset.

面向大规模物联网系统高动态、运行环境复杂等特征,构建了基于机会网络的存储-携带-转发信息传输模式,进而提出了带有传输不确定性的信息传输性能评估模型.在此基础上,综合考虑信息传输过程的能量消耗与传输性能等目标,提出了基于庞特李雅金极大值定理的最优传输策略.进一步,通过理论分析证明了最优传输策略服从阈值形式.仿真实验表明,模型的误差低于4.0 8％.同时,实验也表明所提出的最优传输策略明显优于传统的静态策略,并随着信息有效期的递增,性能表现更好.

Federated learning technology solves the problems of data islands and privacy leakage to a certain extent.However it has shortcomings such as high communication cost, unstable communication, and uneven distribution of participant performance.In order to overcome these shortcomings and achieve a balance between model effectiveness, fairness, and communication costs, an improved NSGA-Ⅲ algorithm for multi-objective optimization of federated learning is proposed.First, a federated learning multi-objective optimization model is constructed to maximize the accuracy of the global model, minimize the variance of the global mo-del accuracy distribution and minimize the communication cost of participant, and an improved NSGA-Ⅲ algorithm based on fast greedy initialization is proposed, which improves the efficiency of NSGA-Ⅲ for multi-objective optimization of federated learning.Experimental results show that the proposed optimization method can obtain a better Pareto solution than the classical multi-objective evolutionary algorithm.Compared with the standard model of federated learning, the optimized model can effectively lower the communication cost and the variance of the global model accuracy distribution while ensuring the accuracy of the global model.

In mobile crowdsensing (MCS), the battery of participants is often limited. When participants perform too many sensing tasks resulting in insufficient remaining energy, they will exit the MCS system. This article mainly addresses the energy balancing problem to prolong the system lifespan. By this means, it can ensure adequate participants and promote the completion of tasks. First, it formulates a discrete time optimization model, which transforms abovementioned problem into the online control of task admission and allocation. In addition, this model uses remaining energy variance of the participants to measure the degree of balance. Next, an online energy balancing strategy (OEBS) is proposed based on the Lyapunov optimization, which can realize energy balance without utility loss. Finally, an approximate optimal policy is presented based on the linear programming and genetic algorithm to solve abovementioned optimization problem. Experiments show that OEBS effectively maintains adequate participants, prolongs the lifespan of MCS system and maximizes the system utility even when there are few participants with multiple tasks. Specifically, the lifespan in OEBS is longer than that in utility optimization algorithm (UOA) and LP-relaxation algorithm significantly. The total utility in OEBS is more than that in UOA. OEBS can maximizes average utility of system by adjusting ${\bm{V}}$ . In addition, the energy balancing ability of OEBS is always effective as ${\bm{V}}$ changes.

Federated learning (FL) is a new distributed learning framework that is different from traditional distributed machine learning: (1) differences in communication, computing, and storage performance among devices (device heterogeneity), (2) differences in data distribution and data volume (data heterogeneity), and (3) high communication consumption. Under heterogeneous conditions, the data distribution of clients varies greatly, which leads to the problem that the convergence speed of the training model decreases and the training model cannot converge to the global optimal solution. In this work, an FL algorithm based on stratified sampling and regularization (FedSSAR) is proposed. In FedSSAR, a density-based clustering method is used to divide the overall client into different clusters, then, some available clients are proportionally extracted from different clusters to participate in training which realizes unbiased sampling for the overall client and reduces the aggregation weight variance of the client. At the same time, when calculating the model local loss function, we limit the update direction of the model by a regular term, so that heterogeneous clients are optimized in the globally optimal direction. We prove the convergence of FedSSAR theoretically and experimentally, and demonstrate the superiority of FedSSAR by comparing it with other FL algorithms on public datasets.

Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. Each client node uploads the trained model parameter information to the central server based on the local training data, and the central server aggregates the parameter information to achieve the purpose of common training. In the real environment, the distribution of data among nodes is often inconsistent. By analyzing the influence of independent identically distributed data (non-IID) on the accuracy of FL, it is shown that the accuracy of the model obtained by the traditional FL method is low. Therefore, we proposed the diversified sampling strategies to simulate the non-IID data situation and came up with the OPTICS (ordering points to identify the clustering structure)-based clustering optimization federated learning method (OCFL), which solves the problem that the learning accuracy is reduced when the data of different nodes are non-IID in FL. Experiments indicate that OCFL greatly improves the model accuracy and training speed compared with the traditional FL algorithm.

Federated learning (FL) can tackle the problem of data silos of asymmetric information and privacy leakage; however, it still has shortcomings, such as data heterogeneity, high communication cost and uneven distribution of performance. To overcome these issues and achieve parameter optimization of FL on non-Independent Identically Distributed (non-IID) data, a multi-objective FL parameter optimization method based on hierarchical clustering and the third-generation non-dominated sorted genetic algorithm III (NSGA-III) algorithm is proposed, which aims to simultaneously minimize the global model error rate, global model accuracy distribution variance and communication cost. The introduction of a hierarchical clustering algorithm on non-IID data can accelerate convergence so that FL can employ an evolutionary algorithm with a low FL client participation ratio, reducing the overall communication cost of the NSGA-III algorithm. Meanwhile, the NSGA-III algorithm, with fast greedy initialization and a strategy of discarding low-quality individuals (named NSGA-III-FD), is proposed to improve the convergence efficiency and the quality of Pareto-optimal solutions. Under two non-IID data settings, the CNN experiments on both MNIST and CIFAR-10 datasets show that our approach can obtain better Pareto-optimal solutions than classical evolutionary algorithms, and the selected solutions with an optimized model can achieve better multi-objective equilibrium than the standard federated averaging (FedAvg) algorithm and the Clustering-based FedAvg algorithm.

Federated learning(FL) is a new distributed learning framework for privacy protection,which is different from traditional distributed machine learning:1)differences in communication,computing,and storage performance among devices(device heterogeneity),2)differences in data distribution and data volume(data heterogeneity),and 3)high communication consumption.Under heterogeneous conditions,the data distribution of clients varies greatly,which leads to the decrease of model convergence speed.Especially in the case of highly heterogeneous condition,the traditional FL algorithm cannot converge and the training loss curve will fluctuate greatly with the increase of local iterations.In this work,a FL algorithm based on stratified sampling optimization(FedSSO) is proposed.In FedSSO,a density-based clustering method is used to divide the overall client into different clusters.Then,some available clients are proportionally extracted from different clusters to participate in training.Therefore,various data are involved in each training round to ensure that FL can accelerate convergence to the optimal solution.The strategy of learning rate decay and the choice of local iterations is set to ensure the convergence.The convergence of FedSSO algorithm is proved theoretically and experimentally,andthe superiority of FedSSO is demonstrated by comparing it with other FL algorithms on public MNIST,Cifar-10,and Sentiment140 datasets.

2020年由于重大卫生事件的影响,大量教育工作者从线下教育转到了线上.这对支持在线教育的计算机与软件科学技术的发展提出了更高的要求,尤其是针对云计算的基础设施建设发展,大规模的基于共用云计算的在线教育会对云计算的基础设施从传统的烟囱式建设转变到共用云架构建设上来.文章从现代化共用云计算的优势与特点出发,重点分析了共用云计算对在线教育模式的影响以及共用云计算支撑下的在线教育特征.为研究如何更好地利用共用云计算进行在线教育的教育工作者提供参考.