From the perspective of service computation, the current service-oriented architecture of industrial chain networks faces issues such as the lack of multiple computing services and insufficient detection capabilities, especially in the context of insider trading detection services for multiplex networked industrial chains. Existing service computation methods are often limited to single-market or simplified network structures, making it difficult to fully capture the dynamic changes and cross-chain propagation characteristics of insider trading within complex multi-layered industrial chain networks. Therefore, developing an efficient and accurate insider trading detection service computation method in the environment of multiplex networked industrial chains remains a critical research challenge. To address this, this paper proposes an Insider Trading Detection Service Computation Method for Multiplex Networked Industrial Chains based on Hybrid Temporal Granularity Scaling (ITDSC-MNICHTGS). This method combines insider trading feature modeling in multiplex networks, an adaptive time granularity adjustment mechanism, and unsupervised learning techniques to accurately identify insider trading behaviors in multiplex networked industrial chains without relying on data labeling. Experimental results show that, compared to traditional detection methods, the proposed method outperforms in metrics such as precision, recall, and$\mathbf{F 1}$-score, effectively improving the reliability and applicability of insider trading detection.

The emergence of diversified computing paradigms and the proliferation of interconnected devices have transformed modern service computing systems into multiplex service networks. In these networks, services are provisioned across multiple interconnected layers, such as infrastructure layer, platform layer, and application layer. A distinctive characteristic of these systems is the presence of hybrid dynamics, characterized by the complex interplay of service request dynamics (random service request arrivals), service network dynamics (topology changes due to agent joins and leaves), and service resource dynamics (fluctuating service provisioning capabilities). These interrelated hybrid dynamics frequently propagate bidirectionally across system layers, creating intricate emergent behaviors and potentially triggering cascading load imbalances that can significantly degrade system performance and reliability. To comprehensively address these multidimensional challenges, we propose a novel and adaptive group-based task migration methodology. By systematically migrating tasks in cohesive groups rather than as isolated individual entities, this innovative approach effectively buffers the destabilizing impact of hybrid dynamics while simultaneously reducing computational and communication overhead associated with frequent decision-making processes. Through rigorous theoretical analysis and extensive experiments, our proposed method shows remarkable advantages in service computing scenarios with hybrid dynamics. The results validate the effectiveness of our group-based migration paradigm in addressing the challenging requirements of modern multiplex service computing systems.

The increasing complexity of industrial information service systems presents significant challenges for anomaly detection, particularly in ensuring service reliability across multilayered service networks. This paper proposes TS-GNN, a novel Temporal-Spatial Graph Neural Network framework that effectively integrates temporal pattern recognition with spatial dependency modeling for anomaly detection in industrial service environments. The framework employs a multi-scale temporal feature extraction mechanism that combines frequency-domain transformation with hierarchical decomposition to capture temporal patterns at different granularities. Subsequently, a graph neural network with attention-based message passing models spatial correlations and anomaly propagation patterns among service nodes. Comprehensive experiments on three benchmark datasets from service computing domains demonstrate that TSGNN achieves superior performance, with F1-scores of 94.74% on SWaT, 85.14% on SMD, and 96.36% on PSM datasets. Compared to the best baseline methods, TS-GNN shows consistent improvements with an average$\mathbf{F 1}$-score enhancement of$\mathbf{0. 7 9 \%}$points, providing an effective solution for enhancing the reliability and robustness of service computing systems.

To better identify user service needs and preferences, proactive conversational interactions are essential, a concept encapsulated by Conversational Recommender Systems (CRS) in service and recommendation domains. A key challenge faced by CRS lies in accurately capturing user preferences from dialogue contexts, particularly in non-stationary environments where traditional methods are hindered by cold-start problems and shifting service demands. Motivated by the strong generalization abilities of In-Context Learning (ICL) in dynamic and unfamiliar scenarios, this paper proposes ICD4CR, a causal decision-making framework grounded in in-context decision-making principles. Leveraging large Language Models (LMs), ICD4CR adopts a data-driven pretraining paradigm, enabling it to infer optimal recommendation strategies from historical dialogue trajectories in analogous service contexts, circumventing the need for explicit user modeling. We introduce a recommendation network that integrates seamlessly with the foundational LM, allowing ICD4CR to function as a fully end-to-end recommendation system. To enhance efficiency and adaptability, adapter-based techniques are employed for knowledge transfer and fine-tuning.

With the rapid development and deep integration of artificial intelligence and automation technologies, autonomous unmanned swarms dynamically organize into multiplex network structures based on diverse task requirements in adversarial environments. Frequent task variations lead to load imbalances among agents and between network layers, significantly increasing the risk of enemy detection and destruction. Existing approaches typically simplify multiplex networks into single-layer structures for task scheduling, failing to address these load imbalance issues. Moreover, the coupling between task dynamics and network multiplexity dramatically increases the complexity of designing task migration strategies, and it is proven NP-hard to achieve such load balancing. To address these challenges, this paper proposes a risk-aware task migration method that achieves dynamic load balancing by matching task requirements with both intra-layer agent capabilities and inter-layer swarm capabilities. Simulation results demonstrate that our approach significantly outperforms benchmark algorithms in task completion cost, task completion proportion, and system robustness. In particular, the algorithm achieves solutions statistically indistinguishable from the optimal solutions computed by the CPLEX solver, while exhibiting significantly reduced computational overhead.

Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. The proposed end-to-end framework for cross-subject emotion recognition aids biomedical researchers in effectively assessing individual emotional states, thereby enabling efficient treatment and interventions.