分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-27 合作期刊: 《数据智能(英文)》
摘要: There is a growing interest in developing human-computer dialogue systems which is an important branch in the field of artificial intelligence (AI). However, the evaluation of large-scale Chinese human-computer dialogues is still a challenging task. To attract more attention to dialogue evaluation work, we held the fourth Evaluation of Chinese Human-Computer Dialogue Technology (ECDT). It consists of few-shot learning in spoken language understanding (SLU) (Task 1) and knowledge-driven multi-turn dialogue competition (Task 2), the data sets of which are provided by Harbin Institute of Technology and Tsinghua University. In this paper, we will introduce the evaluation tasks and data sets in detail. Meanwhile, we will also analyze the evaluation results and the existing problems in the evaluation.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: In this work, we present a catalog of cataclysmic variables (CVs) identified from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 H$\alpha$-emission spectra, and obtain a classification accuracy of the algorithm of over 99.6$\%$ from the cross-validation set. We then apply the template matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which helps us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further discuss the new CV candidates in subtypes based on their spectral features, including five DN subtype during outbursts, five NL subtype and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry, and confirm their previous classification by the LAMOST spectra.