Wentao Xiao

Dawulie Jinensibieke, Mieradilijiang Maimaiti, Wentao Xiao et al.

Relation Extraction (RE) serves as a crucial technology for transforming unstructured text into structured information, especially within the framework of Knowledge Graph development. Its importance is emphasized by its essential role in various downstream tasks. Besides the conventional RE methods which are based on neural networks and pre-trained language models, large language models (LLMs) are also utilized in the research field of RE. However, on low-resource languages (LRLs), both conventional RE methods and LLM-based methods perform poorly on RE due to the data scarcity issues. To this end, this paper constructs low-resource relation extraction datasets in 10 LRLs in three regions (Central Asia, Southeast Asia and Middle East). The corpora are constructed by translating the original publicly available English RE datasets (NYT10, FewRel and CrossRE) using an effective multilingual machine translation. Then, we use the language perplexity (PPL) to filter out the low-quality data from the translated datasets. Finally, we conduct an empirical study and validate the performance of several open-source LLMs on these generated LRL RE datasets.

Kuangen Zhang, Wen Zhang, Wentao Xiao et al.

Powered prostheses are effective for helping amputees walk on level ground, but these devices are inconvenient to use in complex environments. Prostheses need to understand the motion intent of amputees to help them walk in complex environments. Recently, researchers have found that they can use vision sensors to classify environments and predict the motion intent of amputees. Previous researchers can classify environments accurately in the offline analysis, but they neglect to decrease the corresponding time delay. To increase the accuracy and decrease the time delay of environmental classification, we propose a new decision fusion method in this paper. We fuse sequential decisions of environmental classification by constructing a hidden Markov model and designing a transition probability matrix. We evaluate our method by inviting able-bodied subjects and amputees to implement indoor and outdoor experiments. Experimental results indicate that our method can classify environments more accurately and with less time delay than previous methods. Besides classifying environments, the proposed decision fusion method may also optimize sequential predictions of the human motion intent in the future.