LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

Determining robust reaction kinetics from limited data

Accurate chemical kinetics are essential for reactor design and operation. However, despite recent advances in “big data” approaches, availability of kinetic data is often limited in industrial practice. Herein, we present a comparative proof-of-concept study for kinetic parameter estimation from limited data. Cross-validation (CV) is implemented to nonlinear least-squares (LS) fitting and evaluated against Markov chain Monte Carlo (MCMC) and genetic algorithm (GA) routines using synthetic data generated from a simple model reaction. As expected, conventional LS is fastest but least accurate in predicting true kinetics. MCMC and GA are effective for larger data sets but tend to overfit to noise for limited data. LS-CV strongly outperforms these methods at much reduced computational cost, especially for significant noise. Our findings suggest that implementation of CV with conventional regression provides an efficient approach to kinetic parameter estimation with high accuracy, robustness against noise, and only minimal increase in complexity.     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.     

Structural and operational management of Turkish airports: a bootstrap data envelopment analysis of efficiency

This paper explores the structural and operational dimensions of the efficiencies of airports. The two-stage procedure is suggested to assess the efficiencies of airports in this study. In the first-stage, Classification and Regression Tree, which is one of the machine-learning approaches used to divide the airports into homogeneous and thus comparable sub-groups. In the second stage, the bootstrap data envelopment analysis approach obtains more precise structural and operational efficiency scores. To illustrate the proposed framework use, we applied it to a real case associated with Turkish airports. The results demonstrate that this framework presents a more comprehensive assessment of airport performance rather than conventional data envelopment analysis models. Moreover, it provides to show the deficiencies of the structural and operational management of airports. The findings can help anywhere airport authorities as well as Turkish airport authorities.     

Evaluating the profile of myofibrillar proteins and its relationship with tenderness among five styles of dry-cured hams

In order to investigate the relationship between profile of myofibrillar proteins and tenderness among 2 kinds of Chinese hams (Jinhua and Xuanwei) and 3 kinds of European hams (Iberian, Serrano and Parma), shear force, myofibril fragmentation index (MFI), SDS-PAGE, carbonyls content and Raman spectroscopy were investigated. The shear force and salt content of Chinese hams were significantly higher than that of European hams, while moisture content was lower than that of European hams (p < 0.05). MFI values and SDS-PAGE profile revealed that the degradation of myofibrillar proteins in Chinese hams was lower than in European hams. In addition, Chinese hams showed significantly higher carbonyls content and β-sheet content compared with European hams, indicated that proteins aggregation intensively inhibited the degradation of myofibrillar proteins in Chinese hams. These results indicated that the higher shear force in Chinese style hams could be attributed to the lower moisture content and limited proteolysis.     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

基于间隔理论的过采样集成算法

针对传统集成算法不适用于不平衡数据分类的问题，提出基于间隔理论的AdaBoost算法（MOSBoost）。首先通过预训练得到原始样本的间隔；然后依据间隔排序对少类样本进行启发式复制，从而形成新的平衡样本集；最后将平衡样本集输入AdaBoost算法进行训练以得到最终集成分类器。在UCI数据集上进行测试实验，利用F-measure和G-mean两个准则对MOSBoost、AdaBoost、随机过采样AdaBoost（ROSBoost）和随机降采样AdaBoost（RDSBoost）四种算法进行评价。实验结果表明，MOSBoost算法分类性能优于其他三种算法，其中，相对于AdaBoost算法，MOSBoost算法在F-measure和G-mean准则下分别提升了8.4%和6.2%。     

基于哈希链的软件定义网络路径安全

针对软件定义网络中，控制器无法保证下发的网络策略能够在转发设备上得到正确执行的安全问题，提出一种新的转发路径监控安全方案。首先以控制器的全局视图能力为基础，设计了基于OpenFlow协议的路径凭据交互处理机制；然后采用哈希链和消息验证码作为生成和处理转发路径凭据信息的关键技术；最后在此基础上，对Ryu控制器和Open vSwitch开源交换机进行深度优化，添加相应处理流程，建立轻量级的路径安全机制。测试结果表明，该机制能够有效保证数据转发路径安全，吞吐量消耗比SDN数据层可信转发方案（SDNsec）降低20%以上，更适用于路径复杂的网络环境，但时延和CPU使用率的浮动超过15%，有待进一步优化。     

Quantitative Analysis of Crime Incidents in Chicago Using Data Analytics Techniques

In this paper we aim to identify certain social factors that influence, and thus can be used to predict, the occurrence of crimes. The factors under consideration for this analytic are social demographics such as age, sex, poverty, etc., train ridership, traffic density and the number of business licenses per community area in Chicago, IL. A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area.     

基于DSP的多元数据同步采集与存储系统的设计与实现

从硬件和软件两个角度出发,介绍基于DSP的多元数据同步采集与存储系统的组成、工作模式以及功能的测试。系统主要由上位机和数字采集与存储单元组成,其中数字采集与存储单元的硬件部分包括电源模块,值班电路模块,数据采集模块,数据存储模块,时钟同步模块。系统采用DSP作为中央处理芯片,利用经过同步后的秒脉冲作为触发信号,实现同步数据采集。以CF卡作为存储介质,实现数据自容式存储。软件部分实现自检、同步、数据采集存储功能。经过测试,系统工作稳定,功能正常,同步精度在100ns以内。