A Recursive tree-structured neural network with goal forgetting and information aggregation for solving math word problems

Most existing state-of-the-art neural network models for math word problems use the Goal-driven Tree-Structured decoder (GTS) to generate expression trees. However, we found that GTS does not provide good predictions for longer expressions, mainly because it does not capture the relationships among the goal vectors of each node in the expression tree and ignores the position order of the nodes before and after the operator. In this paper, we propose a novel Recursive tree-structured neural network with Goal Forgetting and information aggregation (RGFNet) to address these limits. The goal forgetting and information aggregation module is based on ordinary differential equations (ODEs) and we use it to build a sub-goal information feedback neural network (SGIFNet). Unlike GTS, which uses two-layer gated-feedforward networks to generate goal vectors, we introduce a novel sub-goal generation module. The sub-goal generation module could capture the relationship among the related nodes (e.g. parent nodes, sibling nodes) using attention mechanism. Experimental results on two large public datasets i.e. Math23K and Ape-clean show that our tree-structured model outperforms the state-of-the-art models and obtains answer accuracy over 86%. Furthermore, the performance on long-expression problems is promising.¹     

RoSAS: Deep semi-supervised anomaly detection with contamination-resilient continuous supervision

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%–30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies.     

A new deep graph attention approach with influence and preference relationship reconstruction for rate prediction recommendation

Graph neural networks have been frequently applied in recommender systems due to their powerful representation abilities for irregular data. However, these methods still suffer from the difficulties such as the inflexible graph structure, sparse and highly imbalanced data, and relatively shallow networks, limiting rate prediction ability for recommendations. This paper presents a novel deep dynamic graph attention framework based on influence and preference relationship reconstruction (DGA-IPR) for recommender systems to learn optimal latent representations of users and items. The entire framework involves a user branch and an item branch. An influence-based dynamic graph attention (IDGA) module, a preference-based dynamic graph attention (PDGA) module, and an adaptive fine feature extraction (AFFE) module are respectively constructed for each branch. Concretely, the first two attention modules concentrate on reconstructing influence and preference relationship graphs, breaking imbalanced and fixed constraints of graph structures. Then a deep feature aggregation block and an adaptive feature fusion operation are built, improving the network depth and capturing potential high-order information expressions. Besides, AFFE is designed to acquire finer latent features for users and items. The DGA-IPR architecture is formed by integrating IDGA, PDGA, and AFFE for users and items, respectively. Experiments reveal the superiority of DGA-IPR over existing recommendation models.     

高校图书馆应着力培养大学生的“三种习惯”

教育就是要培养良好的习惯。高校图书馆作为大学生素质教育的主阵地,应着力培养学生的三种习惯：“深阅读”习惯、“利用图书馆”习惯、“低碳生活”习惯。参考文献4。     

对大型深水库水温分层和滞温效应原因的分析

大型深水库会形成水温分层现象,部分深水库还会产生滞温效应,对下游生产及生态环境造成一定的不利影响。文章从物理原理上探讨大型深水库水温分层和滞温效应的原因,对当前水库水温的计算公式理论提供依据。     

Digitally Inspired Thinking: Can Social Media Lead to Deep Learning in Higher Education?

In this study, students from a variety of disciplines, who were enrolled in six courses that incorporate the use of social media, were surveyed to evaluate their perception of how the integration of social-media tools supports deep approaches to learning. Students reported that social media supports deep learning both directly and indirectly, makes learning easier, promotes long-term retention of content, and fosters a more engaging and enjoyable learning environment. These findings suggest that integration of social media into college courses can support deep approaches to learning.     

Slow thinking and deep learning: Tversky and Kahneman's taxi cabs

This article is based on classroom application of a problem story constructed by Amos Tversky in the 1970s. His intention was to evaluate human beings' intuitions about statistical inference. The problem was revisited by his colleague, the Nobel Prize winner Daniel Kahneman. The aim of this article is to show how popular science textbooks can serve as a source for rich classroom activity, with a little care in the implementation by teachers. Kahneman describes the problem as ‘standard’ and answers using a fixed point number. I describe how I have encouraged my students to challenge the certainty of this assertion by identifying ambiguities that are left unexplained in the story. This way, I claim to stimulate individuals to indeed move towards Thinking, Fast and Slow, the title of Kahneman's book.     

学习科学视域下的e-Learning深度学习研究

深度学习是学习科学的主旨,也是现阶段所要破解的e-Learning难题,因此,对学习科学视域下的e-Learning深度学习进行研究,追溯起源,把握热点对推进e-Learning学习意义深远。在梳理e-Learning的现状及主要问题的基础之上,剖析了学习科学领域深度学习的重要性,以深度学习的内涵与特征、有意义学习是深度学习的主旨、高水平思维是深度学习的核心等理念为依据构建了e-Learning环境下深度学习分析模型,继而又以此为据,对e-Learning深度学习的研究现状进行述评。研究发现,为了进一步推动学习科学的发展,深度学习的研究必须在学习过程的评估与分析、情感体验等方面进一步加强,在研究方法与研究视角方面进行多角度创新,研究应该更精细深入,多进行相关的量化研究,以充分利用学习评估技术对深度学习的现状进行把脉。     

引文网络中文献深度聚合方法与实证研究——以WOS数据库中XML研究论文为例

科学文献之间通过引用关系构成了特定研究主题的知识网络,其单向无回路的特征揭示了学科主题的知识结构和发展过程.本文以WOS数据库中XML研究论文所构成的引文网络为例,利用引文关系权重与文献节点权重确定核心文献,并在此基础上从阈值和权值“高地”两个角度对核心文献进行聚合.研究发现:文献核心程度的确定过程充分考虑了不同引用实质上的重要程度区别,据此计算得到的引文和文献节点权重能够准确反映文献的质量;阈值聚合能够迅速发现整个学科发展过程中最核心的文献和引文;权值“高地”聚合分析结果更为多样,并能弥补阈值聚合在揭示次重要子结构方面的不足,发现整个知识体系发展过程中丰富的研究维度.     

滑坡深部位移曲线特征及稳定性判识——以崇尊高速公路龙井滑坡为例

结合龙井滑坡深部位移监测,总结归纳了龙井滑坡深部累积位移曲线“V”型、“B”型、“r”型、“钟摆”型及“复合”型等几种,提出了依据滑坡深部位移监测资料判别滑坡稳定性的判识方法．