Ensemble unsupervised autoencoders and Gaussian mixture model for cyberattack detection

Previous studies have adopted unsupervised machine learning with dimension reduction functions for cyberattack detection, which are limited to performing robust anomaly detection with high-dimensional and sparse data. Most of them usually assume homogeneous parameters with a specific Gaussian distribution for each domain, ignoring the robust testing of data skewness. This paper proposes to use unsupervised ensemble autoencoders connected to the Gaussian mixture model (GMM) to adapt to multiple domains regardless of the skewness of each domain. In the hidden space of the ensemble autoencoder, the attention-based latent representation and reconstructed features of the minimum error are utilized. The expectation maximization (EM) algorithm is used to estimate the sample density in the GMM. When the estimated sample density exceeds the learning threshold obtained in the training phase, the sample is identified as an outlier related to an attack anomaly. Finally, the ensemble autoencoder and the GMM are jointly optimized, which transforms the optimization of objective function into a Lagrangian dual problem. Experiments conducted on three public data sets validate that the performance of the proposed model is significantly competitive with the selected anomaly detection baselines.     

Demand forecasting model using hotel clustering findings for hospitality industry

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.     

瞬变电磁测深在小秦岭大湖金钼矿床深部找矿中的应用

大湖金矿位于小秦岭金矿田北矿带的东部,是小秦岭地区一个大型石英脉型金、钼矿床,目前主要勘探中深部矿产资源。本文结合矿区地质、地球物理特征,在其所判定的成矿远景区,开展了瞬变电磁测深法找矿工作,圈定电性异常体,指导钻探工程的布设,取得了较好的找矿效果,对小秦岭其它地区深部勘探工作具有一定的参考价值。     

Predicting citation counts based on deep neural network learning techniques

With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.     

论浅阅读时代图书馆的书评工作

本文通过对时下流行的浅阅读现象产生的社会背景以及负面影响的解读,探讨图书馆在浅阅读时代,如何利用书评的信息功能,影响读者的阅读倾向,激发读者的阅读和求知兴趣,做好指导读者正确阅读书籍内容,深入理解作品的思想内涵,提高阅读鉴别能力和欣赏水平的工作,更好地实现让读者读到有思想力度和精神厚度的图书这一目标.     

基于CNKI数据库的中国深潜技术文献研究

基于1980-2018年中国深潜技术811篇文献数据,运用文献计量、SVM聚类以及可视化分析等方法,对研究热点、热点年度变化、研究机构、作者及合作网络等情况进行综合分析。研究显示:(1)中国深潜技术研究总体上受到持续关注,2010年以来研究文献呈现暴发性增长态势;(2)研究热点集中在“导航与通信”“结构”“控制”技术,“材料”与“生命支持”技术研究不足;(3)军队系统与高校为研究主力,合作研究未形成规模效应。     

基于霍克-布朗准则的深埋隧道塌方机制泛函突变分析

研究目的：分析深埋隧道的塌方机制并推导塌方体形状曲线的解析表达式。创新要点：基于霍克-布朗准则,采用泛函突变理论推导得到了深埋隧道塌方体形状曲线的解析表达式,并据此分析了围岩参数变化对塌方体形状的影响规律。研究方法：通过理论分析建立深埋圆形隧道的解析模型（图1）,采用泛函突变理论推导基于霍克-布朗准则的隧道塌方体形状曲线解析表达式,并研究围岩参数变化对塌方体形状的影响规律（图3和4,表2）。通过与自然拱理论和模型试验结果的对比（图5–7）验证本文解析解的正确性。重要结论：采用泛函突变理论推导了基于霍克-布朗准则的深埋隧道塌方体形状曲线解析表达式。该解析表达式简洁直观,不仅可以预测无支护条件下隧道的塌落体尺寸,还可以估算塌落围岩作用于衬砌上的荷载。     

"扎根"课堂:翻转课堂的扎根式教学创新

新冠肺炎疫情影响下混合式教学在中国大学校园得到广泛应用,越来越多大学教师采用翻转课堂来开展教学活动,但在如何实现以学习者为中心方面仍面临着诸多挑战,进而影响翻转课堂的教学过程与质量。为改善翻转课堂在教学活动等方面存在的不足之处,运用扎根理论来改进翻转课堂的教学模式设计,基于扎根理论研究程序开发扎根式课堂学习规范和标准,构建以个体问题导向学习、小组合作翻转研讨、集体翻转研讨等为特征的翻转学习机制,并在课程教学中进行了实践应用,检验其必要性和可行性。研究表明,对翻转课堂进行扎根式教学设计,可以更有效地实现以学习者为中心的翻转教学,为大学生从浅层学习走向深度学习提供有益帮助。     

混合学习环境下的深度学习活动研究:设计、实施与评价的三重奏

在教育高质量发展理念的指引下,如何结合混合学习环境开展深度学习活动,目前有效的方法和路径尚不明确。研究依据并拓展尼尔森·莱尔德的深度学习理论,设计了契约性、高阶性、整合性和反思性四类学习活动,构建了“四阶三环”干预实施模型,并以“教育文献检索与分析”课程为例开展实证研究。结果表明:深度学习活动能够促进学科核心知识的掌握,有助于协作、沟通表达、学习毅力等关键学习能力的提升和思维结构的发展。但是,学生认为深度学习活动具有一定难度,负担较重,对各类活动的价值认知亦存在较大差异。最后,研究从判断标准、条件创设、成果产出和科学评价等方面提出了混合学习环境下有效开展深度学习活动的具体建议。     

教师在线交流与深度互动的能力评估研究——以海盐教师博客群体的互动深度分析为例

针对专家提出的教师博客发展瓶颈,本研究探讨了教师博客交互程度与深度学习之间的关系,采用社会网络分析和内容分析法,量化分析了教师博客交互的状况和深度,发现当前教师博客的交互深度仅限于浅层互动,活跃的参与者之间也难以达到三层循环的有效交流,回复内容只是对观点的浅层认识,无法实现网络环境下的深度学习,据此提出了促进教师博客互动由浅入深的建议,对教师在线交流深度互动的评价研究具有重要意义。