深网信息资源采集初探

深网信息资源采集是当前业界普遍关注的热点之一。本文首先介绍了深网概念，然后详细分析了影响深网信息资源采集的因素，并在总结国内外相关研究与实践的基础上，提出深网信息资源采集策略与思考。     

美国深网实践工作研究

论文介绍了深网的原理，分析了美国BrightPlanet公司和Yahoo公司在深网实践工作中所取得的成果。     

西部大开发与非公有制经济领域的统战工作

西部大开发离不开国家的支持 ,但也不能全靠国家投资。非公有制经济作为我国经济成分的一个重要组成部分 ,在整个国民经济中起着越来越明显的作用。如何启动民间资本 ,发展各种形式的非公有制经济 ,是西部大开发的一个重要问题 ,也是统战部门利用时机 ,进一步做好非公有制经济代表人士工作的又一个任务     

浅谈土钉墙的内部整体稳定性

本文对《建筑基坑支护技术规程》(JGJ120-99)和《基坑土钉支护技术规程》(CECS96:97)中有关土钉支护设计的计算方法,从土钉墙的内部整体稳定性分析这一个方面进行分析和比较,并给出了这两种规程的相对安全水平。     

I explain,therefore I learn: Improving students’ assessment literacy and deep learning by teaching

Partnership in higher education emphasises an active role for students in both teaching and learning. This pedagogical culture is likely to make students assessment literate and engage them in deep learning. In this study, Iranian students experiencing learning-by-teaching (LbT) in private language institutes were interviewed to compare their perceptions toward assessment and learning with their counterparts without this experience. Findings show that LbT fosters students’ assessment literacy and deep learning. Results also reveal that by teaching other students, quasi-teachers promote a broader understanding of assessment and grade practices in comparison to other students. Unlike their counterparts, quasi-teachers de-emphasised grades and showed a greater focus on learning. Moreover, explaining the materials to other students provided them with a deeper cognitive process resulting in deeper learning. These results underscore the perceived importance of partnership in higher education.     

Social media opinion summarization using emotion cognition and convolutional neural networks

Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data.     

NASA深空网的改造计划及进展

详细介绍了美国的深空网增强计划及进展。     

转化学习理论视域下虚拟学习共同体中深度学习发生机制设计

深度学习指要求学习者能够批判性地进行学习,反思自身原有的认知结构并在此基础上建构新知识的一种学习方式,它有利于培养学习者的高级思维能力。在终身学习的大背景下,学习者要不断提升自己的学习层次,适应新情况,探索新问题,拥有较强的学习能力,因而深度学习的教学价值潜力不言而喻。在转化学习理论的指导下,借助学习共同体环境,对深度学习的发生机制进行了设计,期望引导学习者在解决问题的过程中提升学习能力。     

Key phrase aware transformer for abstractive summarization

Abstractive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucial step for encoding input documents. Input documents usually contain some key phrases conveying salient information, and it is important to encode these phrases completely. However, existing transformer-based summarization works did not consider key phrases in input when determining attention weights. Consequently, some of the tokens within key phrases only receive small attention weights, which is not conducive to encoding the semantic information of input documents. In this paper, we introduce some prior knowledge of key phrases into the transformer-based summarization model and guide the model to encode key phrases. For the contextual representation of each token in the key phrase, we assume the tokens within the same key phrase make larger contributions compared with other tokens in the input sequence. Based on this assumption, we propose the Key Phrase Aware Transformer (KPAT), a model with the highlighting mechanism in the encoder to assign greater attention weights for tokens within key phrases. Specifically, we first extract key phrases from the input document and score the phrases’ importance. Then we build the block diagonal highlighting matrix to indicate these phrases’ importance scores and positions. To combine self-attention weights with key phrases’ importance scores, we design two structures of highlighting attention for each head and the multi-head highlighting attention. Experimental results on two datasets (Multi-News and PubMed) from different summarization tasks and domains show that our KPAT model significantly outperforms advanced summarization baselines. We conduct more experiments to analyze the impact of each part of our model on the summarization performance and verify the effectiveness of our proposed highlighting mechanism.     

Understanding information diffusion with psychological field dynamic

Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified.