With the growing importance of marine resources and the increasing demand for exploration of underwater environments, underwater target detection technology has become one of the key technologies in the fields of ocean engineering, underwater archaeology, and intelligent agriculture. However, due to the complexity and uncertainty of underwater environments, such as light attenuation, water turbidity, and dynamic changes of water currents, current target detection methods often perform poorly in underwater scenes. To solve the corresponding problems, this paper proposes the YOLOv5_OD_Conv model, which aims to improve the accuracy and generalisation ability of the model by introducing the OD_Conv full-dimensional dynamic convolution in the YOLOv5 Neck part. Simulation and experimental results show that the proposed method increases the detection accuracy P by 1.05%, the precision mAP0.5 by 1.5%, and the recall R by 0.43% compared to YOLOv5s. The improvement of detection effect is obvious, which proves the effectiveness of the method.

Abstract Event detection plays an essential role in the task of event extraction. It aims at identifying event trigger words in a sentence and classifying event types. Generally, multiple event types are usually well‐organized with a hierarchical structure in real‐world scenarios, and hierarchical correlations between event types can be used to enhance event detection performance. However, such kind of hierarchical information has received insufficient attention which can lead to misclassification between multiple event types. In addition, the most existing methods perform event detection in Euclidean space, which cannot adequately represent hierarchical relationships. To address these issues, we propose a novel event detection network HyperED which embeds the event context and types in Poincaré ball of hyperbolic geometry to help learn hierarchical features between events. Specifically, for the event detection context, we first leverage the pre‐trained BERT or BiLSTM in Euclidean space to learn the semantic features of ED sentences. Meanwhile, to make full use of the dependency knowledge, a GNN‐based model is applied when encoding event types to learn the correlations between events. Then we use a simple neural‐based transformation to project the embeddings into the Poincaré ball to capture hierarchical features, and a distance score in hyperbolic space is computed for prediction. The experiments on MAVEN and ACE 2005 datasets indicate the effectiveness of the HyperED model and prove the natural advantages of hyperbolic spaces in expressing hierarchies in an intuitive way.

Complex knowledge graph question answering (KGQA) aims at answering natural language questions by entities retrieving from a knowledge graph (KG). Recently, the relation path-based models have shown the unique advantage for complex KGQA. However, these existing models ignore the dependency between different relation paths, which leads to aimless reasoning over the KG. To resolve this issue, we propose the question-directed reasoning with relation-aware graph attention network (QRGAT) that encodes the reasoning process as a reasoning graph. The relation-aware GAT can recognize neighbor entities along with the corresponding relations for each entity. With the relation-aware GAT stacked in multiple layers, it can collaboratively capture the dependency of different relation paths for each entity. The question-directed reasoning utilizes the information learned by the relation-aware GAT to solve the aimless reasoning on the KG by constructing a reasoning graph. Extensive experiments demonstrate that our QRGAT outperforms the baseline models on both popular datasets WebQuestionsSP and ComplexWebQuestions. Compared with the strong GNN-based baseline NSM$_{+h}$, our QRGAT achieves the performance improvements of 2.3% on WebQuestionsSP and 3.6% on ComplexWebQuestions by the metric Hits@1.

3-D object detection from mobile phones in Device-to-Device (D2D) system provides a new smart payment tool for the next generation of fintech, which is more flexible and efficient than the traditional barcode. In this article, we propose a monocular 3-D object detection method based on depth-guided local convolution. The method combines the information of RGB image mode and depth mode by using a convolution kernel through depth image and works on a single RGB image locally. According to the multiscale input information, the convolution kernel is adaptively adjusted to capture the target objects of different scales, so as to improve the performance of 3-D object detection. In addition, we use the soft-non-maximum suppression algorithm instead of traditional non-maximum suppression to select the best prediction box. In order to further improve the accuracy of 3-D object detection, the depth estimation network and 3-D object detection network are jointly trained in this method to make the two networks constrain each other and achieve the best performance.

the existing software engineering techniques for software synthesis from requirements model to source code have many limitations. The synthesis approach shows that these limitations focused on the refinement relationship between the requirements specification and the desired system. We have to propose a new approach for code generation to overcome such limitations, i.e. refining the software behaviors in requirements model to code, distinguishing function information and architecture information from requirements model, among others. Hence, in this thesis we aim at the problems that how to modeling based on software behaviors, how to delimitate the system architecture and so on. And we also will show a sample, ultimately, to demonstrate our approach. Meanwhile, some additional techniques for synthesis to ensure the correctness of the source code will be recommended.