Predicting temporal Quality of Service (QoS) data is critical for optimizing network services and rationalizing resource allocation in cloud computing and service-oriented systems. Existing mainstream methods have achieved promising predictive performance. However, their purely data-driven manner limits their ability to capture non-stationary temporal patterns, thereby leading to accuracy degradation when temporal QoS data exhibits fluctuations. To tackle this limitation, we propose a novelExtendedKalman Filter-EnhancedLatent Feature Analysis (EKL) model to perform efficient and accurate temporal QoS prediction from the perspective of bidirectional model-data-driven. Its main idea is three-fold: a) designing a model-driven feature producer to obtain the temporal latent features to capture the intricate temporal pattern following the principle of an Extended Kalman Filter; b) building a data-driven feature producer based on the alternating least squares algorithm to identify time-invariant latent features describing intrinsic user-service characteristics; c) exploiting a density-oriented parallel strategy that achieves workload balancing by sorting users in accordance with their service invocation density, which effectively elevates computational efficiency. In addition, we provide a rigorous theoretical analysis to formally prove the convergence of the proposed EKL. Experimental evaluations conducted on real-world temporal QoS datasets reveal that our proposed EKL surpasses existing state-of-the-art models with respect to both computational efficiency and prediction accuracy for missing temporal QoS data.

A dynamic graph (DG) is frequently encountered in numerous real-world scenarios. Consequently, A dynamic graph convolutional network (DGCN) has been successfully applied to perform precise representation learning on a DG. However, conventional DGCNs typically consist of a static GCN coupled with a sequence neural network (SNN) to model spatial and temporal patterns separately. This decoupled modeling mechanism inherently disrupts the intricate spatio-temporal dependencies. To address the issue, this study proposes a novel Tensorized Lightweight Graph Convolutional Network (TLGCN) for accurate dynamic graph learning. It mainly contains the following two key concepts: a) designing a novel spatio-temporal information propagation method for joint propagation of spatio-temporal information based on the tensor M-product framework; b) proposing a tensorized lightweight graph convolutional network based on the above method, which significantly reduces the memory occupation of the model by omitting complex feature transformation and nonlinear activation. Numerical experiments on four real-world datasets demonstrate that the proposed TLGCN outperforms the state-of-the-art models in the weight estimation task on DGs.

Dynamic graphs offer more precise modeling of real-world applications compared to static graphs. Therefore, learning on dynamic graphs has garnered significant research attention in recent years. Unfortunately, the current approaches for learning dynamic graphs remain inadequate in capturing global spatial-temporal dependency. This issue arises from capturing biased spatial and temporal dependencies, thereby weakening the coupling between spatial and temporal dimensions. To overcome it, we propose a Self-Reliant Global Dependency Apprehending Framework on Dynamic Graphs, namely SGD-DyG. Specifically, we first design a frequency self-enhanced learning module that examines the global inherent interactions of the node features hidden in the frequency domain. Furthermore, we propose a global-local-mixed self-supervised learning module that maximizes spatial-temporal mutual information between local node and global graph embeddings. Extensive experiments on seven real-world dynamic graph datasets validate that the proposed SGD-DyG consistently exceeds state-of-the-art models.

A dynamic graphs (DG) is frequently adopted to describe the evolving interactions between nodes in real-world applications such as device communication networks. Temporal patterns are the natural characteristics of DG and are also the key to representation learning. However, most of the existing dynamic GCN models consist of static GCN and sequence modules, resulting in the separation of spatiotemporal information and the inability to effectively capture the complex temporal patterns in DG. To solve this problem, this study proposes a Diverse Transformation-Augmented Graph Tensor Convolutional Network (DTGTCN) with three-fold ideas: a) leveraging the tensor M-product to formulate the unified graph tensor convolution network (GTCN) without separate representation of spatiotemporal information; b) introducing three transformation schemes into GTCN to model complex temporal patterns for aggregating temporal information; c) building the ensemble of diverse transformation schemes to obtain high representation capacity. Empirical studies on four DGs emerging from communication networks demonstrate that owing to diverse transformation, the proposed DTGTCN significantly outperforms state-of-the-art models in addressing the task of link weight estimation.

Utilizing traditional object detectors to build a few-shot object detection (FSOD) model ignores the differences between classification and regression tasks and causes task conflict and class confusion, resulting in a decline in classification performance. In contrast, this paper focuses on the above shortcomings and utilizes the strategies of Back Propagation and Dynamic Learning to construct a model for addressing FSOD, named BPDL. Our BPDL has a two-fold main idea: a) it uses the optimized localization boxes to alleviate the task conflict and refine classification features by a correction loss, and b) it develops a dynamic learning strategy to filter the confusing features and mine more realistic prototype representations of the categories to calibrate classification. Extensive experiments on multiple benchmarks show that our BPDL model outperforms existing methods and advances the FSOD task’s state-of-the-art.

Skeleton-based action recognition has achieved promising performance recently, but there are still many challenges, e.g., the structural relation between joints and the different attention of frames, due to the complex spatial-temporal evolution of skeletal joints. In this paper, we propose a spatial reasoning and context-aware attention network for skeleton-based action recognition, which consists of a spatial reasoning module and a context-aware attention module. The spatial reasoning module can exploit the structural relation between joints to obtain the spatial features within each skeleton frame, followed by the context-aware attention module learning the different attention of frames. We perform experiments on two datasets and verify the effectiveness of each module in the proposed network. The comparison results demonstrate that our method achieves state-of-the-art performance.