基于Bert预训练模型的虚假新闻文本检测

虚假新闻剥夺了人们获取真相的权利,也给社会和国家带来了许多危害。文中以虚假新闻文本为例,分析和验证了多种预训练语言模型在虚假新闻文本分类上的分类效果。经实验证明,相较于其他语言模型,Bert预训练语言模型取得了较好的结果,预测准确率为86.97%,召回率为88.12%,F1值为87.54%。     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Removal of imidacloprid and acetamiprid from tea infusions by microfiltration membrane

Removal of imidacloprid and acetamiprid in tea infusions by microfiltration membrane using dead‐end model was investigated in the present study. The results showed that microfiltration significantly promoted the removal of both pesticides (P < 0.05) in tea infusions. Furthermore, the extent of removal was strongly influenced by the pore size of membrane, operational pressure and the concentrations of tea infusions. The initial concentration of imidacloprid and acetamiprid showed no significant effect on their removal rates. The maximum removal rates were 79.7% for imidacloprid and 81.9% acetamiprid. The changes in major chemical components of tea infusions after microfiltration were evaluated. The results indicated that microfiltration caused no considerable changes in total polyphenols and total free amino acids, and small but statistically significant losses (6.3–18.0%) of eight catechins and three methylxanthines when filtration volume reached to 200 mL. The present study validated the application of microfiltration as a potentially feasible and promising method for the removal of imidacloprid and acetamiprid residues from tea infusions.     

基于微博用户兴趣话题的相似用户挖掘

相似用户挖掘是提高社交网络服务质量的重要途径，在面向大数据的社交网络时代，准确的相似用户挖掘对于用户和互联网企业等都有重要的意义，而根据用户自己的兴趣话题挖掘的相似用户更符合相似用户的要求。提出了一种基于用户兴趣话题进行相似用户挖掘的方法。该方法首先使用TextRank话题提取方法对用户进行兴趣话题提取，再对用户发表内容进行训练，计算出所有词之间的相似度。提出CP（Corresponding Position similarity）、CPW（Corresponding Position Weighted similarity）、AP（All Position similarity）、APW（All Position Weighted similarity）四种用户兴趣话题词相似度计算方法，通过用户和相似用户间关注、粉丝重合率验证相似用户挖掘效果，APW similarity的相似用户的关注/粉丝重合百分比为1.687%，优于提出的其他三种算法，分别提高了26.3%、2.8%、12.4%，并且比传统的文本相似度方法Jaccard相似度、编辑距离算法、余弦相似度分别提高了20.4%、21.2%、45.0%。因此APW方法可以更加有效地挖掘出用户的相似用户。     

基于查询的新闻多文档自动摘要技术研究

针对新闻文本领域,该文提出一种基于查询的自动文本摘要技术,更加有针对性地满足用户信息需求。根据句子的TF-IDF、与查询句的相似度等要素,计算句子权重,并根据句子指示的时间给定不同的时序权重系数,使得最近发生的新闻内容具有更高的权重,最后使用最大边界相关的方法选择摘要句。通过与基于TF-IDF、Text-Rank、LDA等六种方法的对比,该摘要方法ROUGE评测指标上优于其他方法。从结合评测结果及摘要示例可以看出,该文提出的方法可以有效地从新闻文档集中摘取核心信息,满足用户查询内容的信息需求。     

液压支架快速撤除技术研究应用

新汶矿业集团经过探索和实践,研究运用了差异互补式全掩护支护技术、掩护支架自牵引联动装置工艺、矿车运输、滑移运输多用轨道、自动找正装车平台、液控调整装置等新技术、新工艺,做到了液压支架综合流程快速回撤,实现了高效化、连贯化安全回撤,取得了良好的经济效益。     

基于门控联合池化自编码器的通用性文本表征

为了学习文本的语义表征,以往的研究者主要依赖于复杂的循环神经网络(recurrent neural networks, RNNs)和监督式学习方法。该文提出了一种门控联合池化自编码器(gated mean-max AAE)用于学习中英文的文本语义表征。该文的自编码器完全通过多头自注意力机制(multi-head self-attention mechanism)来构建编码器和解码器网络。在编码阶段,提出了均值—最大化(mean-max)联合表征策略,即同时运用平均池化(mean pooling)和最大池化(max pooling)操作来捕获输入文本中多样性的语义信息。为促使联合池化表征可以全面地指导重构过程,解码器采用门控操作进行动态关注。通过在大规模中英文未标注语料上训练模型,获得了高质量的句子编码器。在重构文本段落的实验中,该文模型在实验效果和计算效率上均超越了传统的RNNs模型。将公开训练好的文本编码器,使其可以方便地运用于后续的研究。     

Al2O3/Yttria‐Stabilized Zirconia Hollow‐Fiber Membrane Incorporated with Iron Oxide for Pb(II) Removal

Removal by absorptive ceramic membranes can simultaneously absorb and separate metal ions from water. Alumina/yttria‐stabilized zirconia (Al₂O₃/YSZ) hollow‐fiber membranes, fabricated using phase inversion and sintering process, were deposited with iron oxide by an in‐situ hydrothermal process. The results showed that α‐Fe₂O₃ was produced and incorporated across the membranes. A reduction in flux was recorded with the deposition of α‐Fe₂O_3. However, it improved the adsorption capacity for heavy metal adsorption. The adsorption‐separation test demonstrated that the optimized membrane is able to completely remove Pb(II) ions after two hours.     

Kernel-based fourth-order diffusion for image noise removal

The fourth-order partial differential equations have good performance on noise smoothing and edge preservation without creating blocky effects on smooth regions. However, for low signal-to-noise ratio images, the discrimination between edges and noise is a challenging problem. A novel kernel-based fourth-order diffusion is proposed in this paper. It introduces a kernelized gradient operator in the fourth-order diffusion process, which leads to more effective noise removal capability. Experiment results show that this method outperforms several previous anisotropic diffusion methods for noise removal and edge preservation.