Multimodal information extraction (IE) tasks have attracted increasing attention because many studies have shown that multimodal information benefits text information extraction. However, existing multimodal IE datasets mainly focus on sentence-level image-facilitated IE in English text, and pay little attention to video-based multimodal IE and fine-grained visual grounding. Therefore, in order to promote the development of multimodal IE, we constructed a multimodal multilingual multitask dataset, named M$^{3}$3D, which has the following features: (1) It contains paired document-level text and video to enrich multimodal information; (2) It supports two widely-used languages, namely English and Chinese; (3) It includes more multimodal IE tasks such as entity recognition, entity chain extraction, relation extraction and visual grounding. In addition, our dataset introduces an unexplored theme, i.e., biography, enriching the domains of multimodal IE resources. To establish a benchmark for our dataset, we propose an innovative hierarchical multimodal IE model. This model effectively leverages and integrates multimodal information through a Denoised Feature Fusion Module (DFFM). Furthermore, in non-ideal scenarios, modal information is often incomplete. Thus, we designed a Missing Modality Construction Module (MMCM) to alleviate the issues caused by missing modalities. Our model achieved an average performance of 53.80% and 53.77% on four tasks in English and Chinese datasets, respectively, which set a reasonable standard for subsequent research. In addition, we conducted more analytical experiments to verify the effectiveness of our proposed module. We believe that our work can promote the development of the field of multimodal IE.

An improved artificial potential field (IAPF) algorithm is proposed in this paper to promote the path planning characteristics of autonomous underwater vehicle (AUV). There are two critical problems restricting the application of obstacle avoidance path planning for the artificial potential field (APF) algorithm, one is the local minima and the other is the target unreachability problem. Therefore, the distance between the target and the AUV in attractive field is introduced into the repulsive potential field to modify the repulsive force so as to solve the problem of target unreachability. Moreover, an additional force is added to the resultant force to break the equilibrium when the local minima situation occurs. Ultimately, the simulation experiment validates that the improved artificial potential field method is an efficient way to realize the path planning for AUV in 3D space.