The named entity recognition (NER) in the field of public interest litigation can assist prosecutors in handling cases and provide them with specific entities in making legal documents. Previously, the context-free deep learning model is used to catch the semantic comprehension, in which the static word vector is obtained without considering the context. Moreover, this kind of method relies on word segmentation technology and cannot solve the error transmission caused by word segmentation inaccuracy, which brings great challenges to the Chinese NER task. To tackle the above issues, an entity recognition method based on pretraining is proposed. First, based on the basic entities, three legal ontologies, NERP, NERCGP, and NERFPP are developed to expand the named entity recognition corpus in the judicial field. Second, a variant of the pretrained model BERT (Bidirectional Encoder Representations from Transformer) called BERT-WWM (whole-word mask)-EXT(extra) is introduced to catch the text character-level word vector hierarchical and the context bidirectional features, which effectively solve the problem of task boundary division of named entities. Then, to further improve the model recognition effect, the general knowledge learned from the pretrained model is used to fit the downstream neural network BiLSTM (bi-long short-term memory), and at the end of the architecture, CRF (conditional random fields) is introduced to restrict the label relationship. Finally, the experimental results show that the proposed method is more effective than the existing methods, which reach 96 and 90 in the F1 index of NER and NERP entities, respectively.

Visual Question Answering (VQA) is a challenging task that bridges the computer vision and natural language processing communities. It provide natural language answers to questions related to an associated image. Most existing VQA methods focus on the fusion and inference of visual features with the textual question. However, visual features often lack the necessary semantic information required to answer the questions accurately. To address this limitation, we propose a novel approach called Question-Guided Parallel Attention (QGPA), which effectively leverages the semantic information provided by an embedded image captioning model to answer related questions. First, we introduce an Attention-Aware (AA) mechanism that extends the traditional attention mechanism, helping to filter out incorrect or irrelevant information during answer prediction. Second, QGPA incorporates AA, which simultaneously utilizes visual features and semantic information from the embedded image captioning model to answer questions. Experiments results demonstrate that the accuracy of “Overall” of our proposed model delivers 72.57% and 72.55% on the test-dev and test-std split set of VQA-v2.0 dataset, respectively, which outperforms most existing VQA methods. The experiment results and ablation studies demonstrate that the proposed method has good performance.

This article investigates an energy supply method for rechargeable wireless sensor networks (WRSNs) utilizing a single uncrewed aerial vehicle (UAV). An UAV-assisted wireless sensor network operating in a one-to-many charging mode is considered, where sensors are powered wirelessly by the UAV. Based on node energy levels, an anchor point selection strategy named Node Energy-Aware Strategy (NEAS) is proposed to minimize the task cycle through energy-weighted clustering. Furthermore, an Enhanced Genetic Algorithm with Ant Colony Optimization-inspired Operators (EGA-ACO) is developed to improve path optimization through pheromone-guided selection, 2-opt local search, and anchor position refinement. Simulation results with 50 to 400 sensor nodes demonstrate that NEAS reduces flight distance by 43.3% and task time by 27.2% compared to the baseline GA algorithm. Building upon NEAS, EGA-ACO achieves an additional 3.0% reduction in flight distance and 11.8% reduction in task time. Collectively, the proposed EGA-ACO achieves 44.9% flight distance reduction and 35.8% task time reduction compared to GA, with advantages becoming more pronounced as network scale increases.