Microservice deployment should consider dependency constraints and resource requirements while minimizing deployment costs. There are various dependencies among microservices, and such intricate dependencies bring great difficulties to efficient deployment. Despite significant progress, existing deep learning-based methods are not expressive enough to capture the complexity of microservice dependencies. Meanwhile, metaheuristic methods encode such dependencies into problem-specific heuristics through explicit formulae, yet such formulations remain static and inherently limited. This paper addresses these issues by formulating the microservice deployment problem (MSDP) as a variable-sized bin packing problem. We first introduce a heterogeneous graph neural network to effectively represent the complex dependencies of MSDP. Based on this representation, we propose a novel reinforcement learning policy to design heuristics without the need to derive explicit formulae. Guided by the learned heuristics, we further devise a parallel ant colony optimization algorithm to accelerate solution construction. Experimental results from three representative microservice applications demonstrate that our method obtains near-optimal solutions while significantly outperforming the state-of-the-art methods in computational efficiency.

In microseismic monitoring systems (MMS), efficiently and accurately recognizing fracture waveforms and picking their arrival times of P-wave and S-wave are two crucial signal processing tasks for timely rockburst warnings. Previous studies have overlooked the potential relationship between these two tasks and have processed them independently, leading to redundant computations and performance bottlenecks. An intuitive solution is to integrate the two tasks into a multitask learning (MTL) framework. However, due to their competing optimization objectives, where recognition primarily focuses on global shape features and picking relies on local detail features, conventional parallel MTL structures often suffer from conflicting gradients. This conflict leads to a severe seesaw phenomenon that results in significant performance degeneration in picking task. To address this issue, we proposeMSNet, a cascade MTL framework forMicroseismicSignal processing. In this framework, a self-supervised fracture-perception module and a dual-scale fusion module are proposed to model the hierarchical dependence between recognition and picking tasks. Specifically, these modules enable a forward cascaded feature refinement from recognition to picking, as well as an information supplement from picking to recognition. Thus, MSNet transforms the two tasks from parallel competing learning into a mutually cooperative process that effectively mitigates the seesaw phenomenon. Finally, MSNet is validated on two datasets collected from different tunnel boring machine excavation projects. The results demonstrate its superiority over state-of-the-art methods in terms of accuracy that achieves an approximate 2% improvement under laboratory conditions. Furthermore, the practical applicability of MSNet is further validated through its deployment in a real-world tunnelling project, where it successfully detects rockburst events and provides real-time warnings.

Social Internet of Things (SIoT) has emerged as a novel paradigm that enhances IoT service capabilities by leveraging device-level social relationships. However, the explosive growth of heterogeneous devices, the dynamic mobile device trajectories, and complex spatiotemporal interaction patterns severely hinder SIoT network navigability. Particularly, the device mobility and contextual diversity pose significant challenges to social relation classification, a critical task for efficient routing and service discovery. Existing approaches, primarily designed for static or homogeneous networks, fail to adequately capture the evolving contextual dependencies and the spatiotemporal heterogeneity in SIoT. To address these challenges, we propose Trajectory-Aware Graph LLM (TAGLLM), a novel framework that enhances SIoT navigability through context-aware relation classification. TAGLLM introduces a multi-feature fusion trajectory evolutionary graph encoder to jointly model complex device attributes, social relations, and dynamic trajectories. Furthermore, a structural graph-text token alignment strategy is designed to exploit the generalization ability and contextual understanding capabilities of Large Language Models (LLMs), enabling more effective modeling of heterogeneous and dynamic SIoT scenarios. Extensive experiments on real-world SIoT datasets demonstrate that TAGLLM outperforms state-of-the-art baselines across multiple evaluation metrics, highlighting its potential to push the frontier of graph learning and LLM integration in SIoT applications.

In multivariate time series prediction tasks, the inter- and intra-variable relations have significant influence on prediction outcomes. In many engineering and industrial scenarios, the multivariate time series also contain a large number of subjective influencing factors, such as settings and behaviors of users. Existing learning methods neglect the interactions of these subjective factors among variables. This leads to the learning of incorrect inter-variable influences, consequently yielding inaccurate prediction results. To address this challenge, we propose a Decoupled Casal Attention Network (DECA) for multivariate time series prediction from a spatiotemporal learning perspective. multivariate time series prediction. The causality decoupling module, based on the captured causal relations among variables, disentangles the subjective factors from the objective factors. Then the objective learning module utilizes an objective causal attention to capture objective cross-variable dependencies; while the subjective learning module utilizes a subjective causal graph attention to capture subjective influences. Finally, the prediction module fuses the multi-scale features of subjective and objective factors to produce predictions. The performance is evaluated using three benchmark datasets. Results indicate that, compared to state-of-the-art methods, DECA exhibits superior accuracy in multivariate time series prediction and can be effectively used for recommendations.

Surface cracks in infrastructure are a key indicator of structural safety and degradation. Visual-based crack detection is a critical task for the enormous application demands of infrastructure industries. Convolution operations have been widely deployed due to the strong feature learning abilities. However, global feature dependencies of multi-scale cracks are ignored due to the limited receptive field.In addition, the detection of cracks with low contrast suffers a serious performance loss.Therefore, to address the scale-adaptive crack detection problem, we propose a context-aware correlation convolutional network for scale-adaptive crack detection named CASA-Net. CASA-Net is capable of extracting multi-scale crack features for distinguishing between cracks and surface backgrounds, and evaluating feature correlations to capture global contexts. CASA-Net is composed of the multi-scale distinguishing feature extraction (MDFE) module and the context-aware feature correlation (CAFC) module. Specifically, the MDFE module consists of multiple cascaded convolutional layers and distinguishing feature extraction layers (DFLayers). The CAFC module consists of a mapping block and cascaded correlators to capture the context-aware features for long-range interactions. The performance of CASA-Net is evaluated on a benchmark crack dataset. The experimental results indicate that CASA-Net outperforms rival methods by achieving an F1-Score of 0.65 and an AP50 of 63.9%.

As a variant task of time-series segmentation, trajectory segmentation is a key task in the applications of transportation pattern recognition and traffic analysis. However, segmenting trajectory is faced with challenges of implicit patterns and sparse results. Although deep neural networks have tremendous advantages in terms of high-level feature learning performance, deploying as a blackbox seriously limits the real-world applications. Providing explainable segmentations has significance for result evaluation and decision making. Thus, in this article, we address trajectory segmentation by proposing a Bayesian Encoder-Decoder Network (BED-Net) to provide accurate detection with explainability and references for the following active-learning procedures. BED-Net consists of a segmentation module based on Monte Carlo dropout and an explanation module based on uncertainty learning that provides results evaluation and visualization. Experimental results on both benchmark and real-world datasets indicate that BED-Net outperforms the rival methods and offers excellent explainability in the applications of trajectory segmentation.

Ventricular fibrillation is the most lethal arrhythmia. At present, the treatment of ventricular fibrillation is commonly received after the onset of the disease, which mainly depends on external defibrillation and drug-assisted therapy. Although activity of heartbeats can be described and analyzed using the most popular technique ECG (electrocardiogram), there is still no widely recognized prediction methods for ventricular fibrillation. Therefore, in this paper, in order to realize warning of ventricular fibrillation, we focus on the detection of atrial fibrillation and ventricular flutter, which are the arrhythmias often occurring before ventricular fibrillation. We propose a frequency-domain LSTM (Long Short-Term Memory), which uses heartbeat waves transformed from the original time domain into the frequency domain as input. Furthermore, to address the problem of big ECG data training efficiency and scalability, we also provide an implementation of our method under the distributed computing framework MapReduce in the Spark cluster. Experimental results indicate that our method achieves excellent classification performance compared with rival methods.