循证决策视角下的患者健康咨询主题分析

[目的/意义]为了将大数据更好地用于循证决策,文章构建了循证决策视角下的患者健康主题分析模型。[方法/过程]首先使用LDA主题模型对高血压问答数据进行主题探测,并结合文献提炼出高血压八大主题;再次使用问卷调查法和层次分析法对主题探测结果进行评估;最后对得到的证据进行实践讨论。[结果/结论]文章对大数据和循证决策的结合做了尝试性探索,结果表明循证决策视角下的患者健康主题分析模型,具有一定的实用性和创新性。     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

基于改进的K-means算法的异常检测

聚类算法通常用于数据的聚类。除此,它还可以用于异常数据的检测。首先介绍了基于划分的聚类算法K-means,然后给出改进算法I-K-means的算法描述,最后通过实例进行异常分析。     

Towards sentiment and Temporal Aided Stance Detection of climate change tweets

Climate change has become one of the most significant crises of our time. Public opinion on climate change is influenced by social media platforms such as Twitter, often divided into believers and deniers. In this paper, we propose a framework to classify a tweet’s stance on climate change (denier/believer). Existing approaches to stance detection and classification of climate change tweets either have paid little attention to the characteristics of deniers’ tweets or often lack an appropriate architecture. However, the relevant literature reveals that the sentimental aspects and time perspective of climate change conversations on Twitter have a major impact on public attitudes and environmental orientation. Therefore, in our study, we focus on exploring the role of temporal orientation and sentiment analysis (auxiliary tasks) in detecting the attitude of tweets on climate change (main task). Our proposed framework STASY integrates word- and sentence-based feature encoders with the intra-task and shared-private attention frameworks to better encode the interactions between task-specific and shared features. We conducted our experiments on our novel curated climate change CLiCS dataset (2465 denier and 7235 believer tweets), two publicly available climate change datasets (ClimateICWSM-2022 and ClimateStance-2022), and two benchmark stance detection datasets (SemEval-2016 and COVID-19-Stance). Experiments show that our proposed approach improves stance detection performance (with an average improvement of 12.14% on our climate change dataset, 15.18% on ClimateICWSM-2022, 12.94% on ClimateStance-2022, 19.38% on SemEval-2016, and 35.01% on COVID-19-Stance in terms of average F1 scores) by benefiting from the auxiliary tasks compared to the baseline methods.     

基于抗震防灾的住宅室内设计研究

由于人们居住环境的多样化和地震灾害的随机性,除了在建筑本身抗震结构上加强之外,也应当适当辅以必要的抗震室内设计来作为防范。本文就是探讨通过住宅室内功能区域的划分、家具与陈设的设计、设计材料的选用等方面在住宅室内中的设计方法,来尽可能多地防范并消除建筑室内的抗震薄弱环节,从而可以在一定程度上更好地保障人民在应对地震灾难时的生命与财产安全。     

经验模式分解去斑和顶帽变换在舰船检测预处理中的应用

提出了一种基于经验模式分解去斑和顶帽变换背景不均匀的预处理方法. 经验模式分解去斑算法先对图像每一列进行经验模式分解得到IMF函数,然后将原信号与第一、二模态相减得到初步处理图像,再对该图像每一行重复该操作从而得到去斑图像,该算法有效地去除斑点噪声;顶帽变换则有效地补偿了海浪带来的局部不均匀的背景亮度,提高了图像的信杂比,有利于目标的检测. 仿真结果证明了算法的有效性.     

基于自适应缺陷定位的机床故障点安全监测

通过对机床故障点的缺陷自适应定位实现对机床故障的安全监测和故障诊断,保障机床的高效运转。传统方法中,对机床故障点的缺陷定位采用基于图像分割的主动红外故障检测方法,当主动热激励源分布不均时,对机床故障点的监测准确性不高。提出一种改进的基于自适应缺陷定位的机床故障点安全监测方法,采用图像信号处理和信息论分析的方法,研究机床故障点安全监测技术,设计基于图像分割的机床故障点损伤检测算法,将缺陷点图像分割成一系列的小区域的过程是伴随着每次将采集系统进行一周的旋转前进,每转动一定的角度后内机床缺陷点开始出现一次成像。实现对机床故障点的安全监测。仿真实验表明,该算法能准确实现对机床故障点的定位,机床故障点能有效通过三维图像显示,监测的实时性和精准性较高,展示了该算法的优越性。     

基于文本内容的科学前沿探测方法研究进展

[目的/意义] 本文从科学前沿概念出发,对当前已有的基于文本内容的科学前沿探测方法进行梳理总结,为科学前沿探测方法的发展提供基本参考和启发。[方法/过程] 对科学前沿相关概念、基于文本内容的科学前沿探测方法进行了系统的总结,分析了现有探测方法的优缺点。[结果/结论] 对科学前沿极其相近概念做出进一步辨析,总结目前方法存在的问题,并提出未来发展的建议。     

一种大储量罐车紧急切断阀失灵检测方法研究

为了提高大储量液化石油气罐车紧急切断阀失灵检测的精度和效率,提出了基于增强模糊支持向量机的罐车紧急切断阀失灵检测方法。首先通过对大储量液化石油气罐车紧急切断阀的常见失灵类型进行分析,将其样本数据进行训练集和测试集的训练。其次利用模糊支持向量机算法进行罐车紧急切断阀失灵检测模型建立。然后利用粒子群进化算法进行该模型的最优参数、惩罚系数和隶属度的确定,最后利用优化的参数进行支持向量机的分类。通过训练集和测试集对该模型进行仿真实验,仿真实验结果表明,该方法的失灵检测准确率均在95%以上,具有较高的鲁棒性和可靠性。     

建筑材料检测在建筑行业中的作用

随着建筑行业的快速发展,建筑材料的种类不断增多,而且性能各异。建筑材料是建筑的基础,建筑材料质量的好坏就决定着建筑工程质量的好坏,因此对建筑材料进行检测就显得尤为重要。加强对建筑材料的检测才能够保证建筑工程的安全以及人们的生命安全,对于提高建筑工程的质量具有重要的作用。