Session-based recommendation systems focus on capturing users’ evolving intents from short interaction sequences, yet they persistently face three key challenges: the difficulty in dynamically discriminating between short-term and long-term interests, the inherent tradeoff between sequential modeling and relational dependency learning, and the pervasive noise and sparsity in real-world session data. To tackle these issues, we propose Multivariate Relationship Graph Embedding (MRGE), a novel framework that synergizes enhanced recurrent modeling with graph-structured representations. Specifically, MRGE leverages a self-attention–enhanced RNN to concurrently model short-term intents and long-term preferences within sessions, while constructing a heterogeneous session graph that captures multi-relational item dependencies without compromising temporal fidelity. In addition, we introduce an auxiliary edge augmentation mechanism based on neighbor similarity to mitigate data sparsity and noise, thereby facilitating more robust information propagation. Extensive experiments on three public benchmarks— Delicious , Gowalla , and Foursquare —show that MRGE consistently surpasses state-of-the-art baselines and achieves significant improvements in top- \(K\) recommendation accuracy. Our implementation is available at: https://github.com/July-jz/MRGEcode .

Spatiotemporal fusion strategies are a crucial direction in traffic flow prediction. However, studies often emphasize the learning of local dynamic spatiotemporal dependencies from historical data while neglecting the potential impacts of label sequence autocorrelation, nonstationary signals, and temporal pattern changes on spatiotemporal dependency modeling. For example, the delayed propagation of abnormal traffic conditions, such as sudden traffic congestion, and abnormal weather between nodes and within sequences may trigger signal shifts, which in turn lead to changes in local flow patterns. Such changes can produce locally dependent misleading learning, making it difficult for spatiotemporal fusion strategies to accurately reflect the true relationships between signals. We propose a framework for traffic flow prediction, which first enhances the original signals in a targeted manner using knowledge of the autocorrelation of sequences through a multiform feature enhancement module, to obtain a more representative and enriched feature representation for model training. The framework processes features by decoupling multi-granularity in temporal patterns, comprehensively identifying complex traffic patterns, and eliminating the impact of nonstationary noise. A dual-channel spatiotemporal fusion network models local spatiotemporal dependencies and global seasonal dependencies to reasonably predict traffic. Experimental results on four real-world datasets show that the original method improves the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics by an average of 5.47%, 4.27%, and 7.05%, respectively, compared to all the metrics of the baseline model over the last two years. We also evaluated the performance of each module through ablation studies.

The first major contribution of the paper is the proposal of using an improved DEtection Transformer network (named R2N-DETR) and Kinect-V2 camera for detecting multiple-size peaches under orchards with varied illumination and fruit occlusion. R2N-DETR model first employed Res2Net-50 to extract a fused low-high level feature map containing fine spatial features and precise semantic information of multi-size peaches from Red-Green-Blue-Depth (RGB-D) images. Second, the encoder-decoder was performed on the feature map to obtain the global context. Finally, all detected objects were detected according to each object’s global context. For the detection of 1101 RGB-D images (imaged from two orchards over three years), the R2N-DETR model achieves an average precision of 0.944 and an average detecting time of 53 ms for each image. The developed system could provide precise visual guidance for robotic picking and contribute to improving yield prediction by providing accurate fruit counting.

Centrality is a family of metrics for characterizing the importance of a vertex in a graph. Although a large number of centrality metrics have been proposed, a majority of them ignores uncertainty in graph data. In this article, we formulate closeness centrality on uncertain graphs and define the batch closeness centrality evaluation problem that computes the closeness centrality of a subset of vertices in an uncertain graph. We develop three algorithms, MS-BCC , MG-BCC, and MGMS-BCC , based on sampling to approximate the closeness centrality of the specified vertices. All these algorithms require to perform breadth-first searches (BFS) starting from the specified vertices on a large number of sampled possible worlds of the uncertain graph. To improve the efficiency of the algorithms, we exploit operation-level parallelism of the BFS traversals and simultaneously execute the shared sequences of operations in the breadth-first searches. Parallelization is realized at different levels in these algorithms. The experimental results show that the proposed algorithms can efficiently and accurately approximate the closeness centrality of the given vertices. MGMS-BCC is faster than both MS-BCC and MG-BCC because it avoids more repeated executions of the shared operation sequences in the BFS traversals.