Augmented context-based recommendation service framework using knowledge over the Linked Open Data cloud

This research proposes ACARDS (Augmented-Context bAsed RecommenDation Service) framework that is able to utilize knowledge over the Linked Open Data (LOD) cloud to recommend context-based services to users. To improve the level of user satisfaction with the result of the recommendation, the ACARDS framework implements a novel recommendation algorithm that can utilize the knowledge over the LOD cloud. In addition, the noble algorithm is able to use new concepts like the enriched tags and the augmented tags that originate from the hashtags on the SNSs materials. These tags are utilized to recommend the most appropriate services in the user’s context, which can change dynamically. Last but not least, the ACARDS framework implements the context-based reshaping algorithm on the augmented tag cloud. In the reshaping process, the ACARDS framework can recommend the highly receptive services in the users’ context and their preferences. To evaluate the performance of the ACARDS framework, we conduct four kinds of experiments using the Instagram materials and the LOD cloud. As a result, we proved that the ACARDS framework contributes to increasing the query efficiency by reducing the search space and improving the user satisfaction on the recommended services.     

计及Spark和属性权重的售电套餐推荐方法

针对电力市场用户群庞大，交易过程中售电套餐选择困难的问题，在Spark环境下设计了一种售电套餐推荐方法，同时也解决了售电套餐推荐过程中在大数据环境下的可扩展性及实时性问题。首先，计算出每个套餐属性的权重值，从而计算得到售电套餐综合相似度。然后，计及用户和套餐两方面提出一种售电套餐推荐方法，实现售电套餐的精准推荐。实验表明，提出的推荐方法能够明显提高推荐的准确度，并且在分布式环境下具有良好的推荐效率和可扩展性。     

基于通信的港口管理系统的研究

讨论了如何利用Access7.0设计包含有通信的管理信息系统，并结合生产管理系统的实例。利用微软公司的Visual Basic中包含的通信控件，实现Windows95环境下通信程序的设计方法，介绍了通信控件的属性以及使用方法，给出了基本的通信程序。     

Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.     

基于窗口的面向对象的GUI设计

文中讨论了一种基于窗口的对象用户界面设计思想及方法，详细描述了各种类的定义。在ＤＯＳ环境下运用ＷａｔｃｏｍＣ＋＋语言在微机上开发运行。它可以使用户方便地进行再开发工作，界面运用事件处理方式由窗口、弹出菜单、图标、按钮等组成。效果良好。     

基于聚类分析的新书推荐

针对大多数图书馆管理系统中的个性化服务中的新书推荐只是定时将新到馆的图书罗列出来让读者浏览进行推荐,推荐效果差的问题。本文在比较三大常用的推荐技术的优缺点后,采用基于聚类分析的推荐算法推荐新书,并具体给出实现的方法。     

基于知识图谱的互补项目推荐

在缺乏用户交互互补项目方面数据的情况下,将用户对项目的偏好融合到只考虑项目关系的互补项目推荐中,提高推荐模型的性能。提出一种基于知识图谱的互补项目推荐方法,在用户历史交互项目集中推测用户交互的互补项目,基于知识图谱提取用户对互补项目的偏好,利用图像与文本学习项目之间的互补关系,最后基于神经网络实现二者的共同学习。提出的方法在Amazon数据集上与次优的基线方法相比,ACC提升了7%,precision提升了3%,这说明提出的方法性能优异。该算法共同学习用户对项目的偏好与项目之间的互补关系,提升了推荐性能。     

卷积融合文本和异质信息网络的学术论文推荐算法

针对交互数据稀疏和推荐多样性问题,基于卷积协同过滤推荐框架提出卷积融合文本和异质信息网络的学术论文推荐算法（WN-APR）。首先学习不同语义下用户和论文的多样化特征,缓解数据稀疏问题;然后基于外积设计不同语义特征相互增强的方式融合它们,并使用三维卷积神经网络代替二维卷积神经网络充分挖掘不同特征对性能的影响;最后改进贝叶斯个性化排序损失函数增强推荐多样性。在CiteuLike-a、CiteuLike-t数据集上的实验结果表明,相比于基线模型,WN-APR在准确率和多样性的四个指标上都有所提升。     

Using CANARY event detection software for water quality analysis in the Milwaukee River

Urban water sources are susceptible to various contamination events as a result of natural, accidental, and human-induced occurrences. An early warning monitoring system provides timely information on changes in urban water quality. In this study, an analysis was made with CANARY event detection software (EDS) to monitor water quality parameters in river water and to identify the onset of anomalous water quality periods. Water quality signals including pH, conductivity, and turbidity from the Milwaukee River over specified periods during the summer season of 2018–2020 were employed as inputs to event detection algorithms in CANARY. The data analysis results show that CANARY can be useful as an early warning system for monitoring contamination in urban water sources and help to identify abnormal conditions quickly. The sensibility of the model relies on optimizing the configuration parameters, which involves selecting the ideal set of parameters for the event detection algorithm and adjusting the BED parameters to increase or decrease the probability of generating an alarm. The number of events reported between the Linear Prediction Correction Filter (LPCF) and Multivariate Nearest Neighbor (MVNN) algorithms varied as a result of different residual calculation mechanisms. Climate factors that contributed to the abnormal water quality events in the river were examined. The analysis of rainfall on water quality was carried out using a statistical method by determining whether there is a significant difference (p-value) between the seasonal mean water quality data and the mean value of water parameters during the sampling duration. Regression analysis was also performed to estimate the best model that describes the relationship between each of the water quality parameters and temperature.