At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images.

Knowledge graphs as external information has become one of the mainstream directions of current recommendation systems. Various knowledge-graph-representation methods have been proposed to promote the development of knowledge graphs in related fields. Knowledge-graph-embedding methods can learn entity information and complex relationships between the entities in knowledge graphs. Furthermore, recently proposed graph neural networks can learn higher-order representations of entities and relationships in knowledge graphs. Therefore, the complete presentation in the knowledge graph enriches the item information and alleviates the cold start of the recommendation process and too-sparse data. However, the knowledge graph’s entire entity and relation representation in personalized recommendation tasks will introduce unnecessary noise information for different users. To learn the entity-relationship presentation in the knowledge graph while effectively removing noise information, we innovatively propose a model named $knowledge$—$enhanced$ $hierarchical$ $graph$ $capsule$ $network$ (KHGCN), which can extract node embeddings in graphs while learning the hierarchical structure of graphs. Our model eliminates noisy entities and relationship representations in the knowledge graph by the entity disentangling for the recommendation and introduces the attentive mechanism to strengthen the knowledge-graph aggregation. Our model learns the presentation of entity relationships by an original graph capsule network. The capsule neural networks represent the structured information between the entities more completely. We validate the proposed model on real-world datasets, and the validation results demonstrate the model’s effectiveness.

As FPGA design complexity increases, the correctness and reliability of logic synthesis tools are critical to ensuring correct hardware implementation. These tools translate hardware description languages (e.g., Verilog) into gate-level netlists, where latent faults may introduce functional errors or performance degradation during synthesis. Existing approaches rely on automatically generated Verilog test cases to find these latent faults. However, their effectiveness depends heavily on generator configurations and is typically guided by input diversity, which fails to accurately capture differences in synthesis behavior. Moreover, the high-dimensional configuration space of generator further hinders efficient exploration. To address these challenges, we propose SynaSpace, a behavior-driven configuration optimization framework for fault detection in logic synthesis tools. SynaSpace focuses on synthesis behavior coverage to guide configuration search, by constructing behavioral representations through joint analysis of synthesis logs and gate-level netlists. The framework comprises four components: (1) configuration space modeling for unified parameter representation; (2) Bayesian optimization–based configuration search for efficient exploration; (3) synthesis behavior characterization and coverage evaluation for capturing and quantifying behavioral differences; and (4) fault detection and utility modeling for extracting effective feedback via differential testing and deduplication. These components are integrated into a unified optimization framework to enable efficient configuration exploration and improved testing effectiveness. We evaluate SynaSpace on two established logic synthesis tools (i.e., Vivado and Yosys). SynaSpace identifies 18 faults across four categories, all of which have been confirmed and fixed by vendors and the open-source community.