A Knowledge Graph Construction Approach for Legal Domain

Considering that the existing domain knowledge graphs have difficulty in updating data in a timely manner and cannot make use of knowledge sufficiently in the construction process, this paper proposes a legal domain knowledge graph construction approach based on \u27China Judgments Online\u27 in order to manage the cases\u27 knowledge contained in it. The construction process is divided into two steps. First, we extract the classification relationships of the cases from structured data. Then, we obtain attribute knowledge of cases from semi-structured data and unstructured data through a relationship extraction model based on an improved cross-entropy loss function. The triples describing knowledge of cases are stored through Neo4j. The accuracy of the proposed approach is verified through experiments and we construct a legal domain knowledge graph which contains more than 4K classification relationships and 12K attribute knowledge to prove its validity

Near-field beamforming performance analysis for acoustic emission source localization

This paper attempts to study the localization performance of a near-field acoustic emission (AE) beamforming by varying parameters such as array types, localization velocity, the maximum diameter of the array and the sensor spacing. To investigate how those parameters affect localization performance, an improved finite element method is established to obtain AE signals which take real propagation characteristics and have high signal to noise ratio. And AE signals of the finite element simulation under different parameters are obtained based on the presented method. Then AE beamforming is used to localize AE sources, and the influences of these parameters on the AE beamforming localization performing are analyzed. The results indicate that the parameters have impact on the localization accuracy clearly. This work can provide a reference for the selection of parameters when the beamforming is used to localize AE sources

BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation

Generating human action proposals in untrimmed videos is an important yet challenging task with wide applications. Current methods often suffer from the noisy boundary locations and the inferior quality of confidence scores used for proposal retrieving. In this paper, we present BSN++, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation. First, we propose a novel boundary regressor based on the complementary characteristics of both starting and ending boundary classifiers. Specifically, we utilize the U-shaped architecture with nested skip connections to capture rich contexts and introduce bi-directional boundary matching mechanism to improve boundary precision. Second, to account for the proposal-proposal relations ignored in previous methods, we devise a proposal relation block to which includes two self-attention modules from the aspects of position and channel. Furthermore, we find that there inevitably exists data imbalanced problems in the positive/negative proposals and temporal durations, which harm the model performance on tail distributions. To relieve this issue, we introduce the scale-balanced re-sampling strategy. Extensive experiments are conducted on two popular benchmarks: ActivityNet-1.3 and THUMOS14, which demonstrate that BSN++ achieves the state-of-the-art performance.Comment: Submitted to AAAI21. arXiv admin note: substantial text overlap with arXiv:2007.0988

Energy Storage - Driving towards a clean energy future

No abstract available