Within Open Relation Extraction (ORE) tasks, the Zero-shot ORE method is to generalize undefined relations from predefined relations, while the Unsupervised ORE method is to extract undefined relations without the need for annotations. However, despite the possibility of overlap between predefined and undefined relations in the training data, a unified framework for both Zero-shot and Unsupervised ORE has yet to be established. To address this gap, we propose U-CORE: A Unified Deep Cluster-wise Contrastive Framework for both Zero-shot and Unsupervised ORE, by leveraging techniques from Contrastive Learning (CL) and Clustering.1 U-CORE overcomes the limitations of CL-based Zero-shot ORE methods by employing Cluster-wise CL that preserves both local smoothness as well as global semantics. Additionally, we employ a deep-cluster-based updater that optimizes the cluster center, thus enhancing the accuracy and efficiency of the model. To increase the stability of the model, we adopt Adaptive Self-paced Learning that effectively addresses the data-shifting problems. Experimental results on three well-known datasets demonstrate that U-CORE significantly improves upon existing methods by showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI on Unsupervised ORE tasks.

大数据时代的到来,为在线教育带来了前所未有的机遇.如何有效利用机遇实现在线教育变革,是当前大数据应用重点关注的问题.数据分析技术为军事职业教育学习平台提供了技术支持与服务保障,围绕学生、教师、管理员的在线教育服务不断变革、创新,数据分析应用逐渐在线学习平台中发挥重要作用.本文以在线教育数据应用现状研究分析为基础,展开军事职业教育数据分析平台应用研究,并对军事职业教育数据分析平台的应用发展方向提出思考.

随着信息技术的发展和在线教育的展开,人们逐渐从信息匮乏的时代走入了信息过载的时代,如何将海量课程资源与用户需求进行匹配成为一大难题.面向职业教育学习平台,研究基于协同过滤算法的个性化课程推荐模型,分别提出基于用户的推荐模型UserCF和基于对象的推荐模型ItemCF,并引入多进程思想优化模型训练效率.其研究成果应用在某职业教育学习平台,取得较好的效果.

鉴于疫情期间线下考试开展的困难性,以及高校课程的考试需求,设计并开发基于共享题库的云考试系统,用于组织远程在线考试.本文分析了该系统的功能设计和系统框架,介绍了组卷策略、数据库设计等关键技术的实现方式,并进行在线部署应用实践,上线结果表明该系统一定程度上解决了用户在线考试的需求,改善了用户的学习体验.

In the field of meteorology, weather forecasting requires meteorological radar echo data as support. However, the lack of radar data due to malfunctions or lack of radar deployment has greatly affected the stability of weather forecasting. In order to solve this problem, this paper proposes a meteorological radar echo data reconstruction technology based on satellite data realized by deep learning neural network technology, so as to supplement the missing meteorological radar echo data. In order to realize this technology, we constructed a new dataset, designed a neural network structure suitable for it, and obtained a model with great effect after training. After the final test, it is found that this model can reconstruct radar data well and has practical value.

Modelling normal data is one of the major challenges in outlier detection. Deep learning has been proven to be effective in modelling underlying distributions of input training data. However, the existing deep learning-based methods normally focus on how to alleviate the negative impact from the presence of outliers when they model the full dataset. Besides, insufficient size of training data also leads to unsatisfactory training of deep networks. This paper proposes a WGAN-empowered deep Autoencoder-based Outlier detection approach (GAEO for short) which presents a novel way to directly and effectively model reliable normal data. GAEO first obtains initial normal data by a proposed initial distance-based outlier scoring function, then constructs a WGAN network to perform normal data augmentation to obtain adequate training samples for subsequent normal data modelling. The deep autoencoder is then trained to discover and yield distributions and patterns of normal data, and the reconstruction error is defined as outlierness of each data object. In our experiments, we investigate the effectiveness of our method GAEO compared with three state-of-the-art outlier detectors on ten real-world datasets, and discuss the impact of the parameters in GAEO. We show that our method GAEO significantly outperforms its contenders by 4% to 20% AUC improvement.

Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.