Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

A single-phase algorithm for mining high utility itemsets using compressed tree structures

Mining high utility itemsets (HUIs) from transaction databases considers such factors as the unit profit and quantity of purchased items. Two-phase tree-based algorithms transform a database into compressed tree structures and generate candidate patterns through a recursive pattern-growth procedure. This procedure requires a lot of memory and time to construct conditional pattern trees. To address this issue, this study employs two compressed tree structures, namely, Utility Count Tree and String Utility Tree, to enumerate valid patterns and thus promote fast utility computation. Furthermore, the study presents an algorithm called single-phase utility computation (SPUC) that leverages these two tree structures to mine HUIs in a single phase by incorporating novel pruning strategies. Experiments conducted on both real and synthetic datasets demonstrate the superior performance of SPUC compared with IHUP, UP-Growth, and UP-Growth+ algorithms.     

Comparing open data benchmarks: Which metrics and methodologies determine countries’ positions in the ranking lists?

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.     

Profiling of knowledge organisation systems for the annotation of Linked Data cultural resources

The importance of cultural heritage for supporting the knowledge economy has promoted its digitisation and online publication. Many cultural heritage repositories have published millions of digitised items using semantic web technologies and Linked Data approaches. These repositories frequently use knowledge organisation systems to classify the resources, but the domain heterogeneity makes it difficult to know if they are the most suitable ones. This paper describes the process used to discover and profile the knowledge organisation systems used in the cultural heritage domain. Additionally, for those knowledge organisation systems with a thesaurus-like structure, a detailed quality analysis is performed. The results of this analysis can be used as a key factor for the selection of knowledge organisation systems in classification tasks.     

基于线状特性的大数据地铁用户识别与定位方法

从MR原始数据特性出发研究数据结构与字段,采用基于线状特性分析MR大数据的地铁用户识别过滤方法和位置定位方法,该方法经过验证能够有效输出地铁用户数据并定位在地图上,解决人工地铁测试效率低且测试片面的问题,实现基于大数据的自动化地铁网络评估,提供海量的有效定位数据用于地铁评估与分析。     

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

Note on nonparametric spectral analysis of wideband spectrum with missing data via sample-and-hold interpolation and deconvolution

In [1] a procedure for bias-free estimation of the autocorrelation function is introduced for equidistantly sampled data with randomly occurring samples being invalid. The method incorporates sample-and-hold interpolation of the missing data points. The occurring dynamic error of the primary estimate of the correlation function is treated by a deconvolution procedure with two parameters $c_0$ and $c_1$ with $c_0+2c_1=1$, which are the on-diagonal and the aside-diagonal parameters of a specific correction matrix (at all lag times except zero). The parameters $c_0$ and $c_1$ were obtained as a function of the probability α of a sample to be valid by numerical simulation. However, explicit expressions for the parameters $c_0(\alpha )=1-\frac{2}{\alpha }+\frac{2}{{\alpha }^2}$ and $c_1(\alpha )=\frac{1}{\alpha }-\frac{1}{{\alpha }^2}$ can be derived, which might improve the usability of the deconvolution procedure in [1].     

基于概要数据结构的全网络持续流检测方法

持续流是隐蔽的网络攻击过程中显现的一种重要特征，它不产生大量流量且在较长周期内有规律地发生，给传统的检测方法带来极大挑战。针对网络攻击的隐蔽性、单监测点的重负荷和信息有限的问题，提出全网络持续流检测方法。首先，设计一种概要数据结构，并将其部署在每个监测点；其次，当网络流到达监测点时，提取流的概要信息并更新概要数据结构的一位；然后，在测量周期结束时，主监测点将来自其他监测点的概要信息进行综合；最后，提出流持续性的近似估计，通过一些简单计算为每个流构建一个位向量，利用概率统计方法估计流持续性，使用修正后的持续性估计检测持续流。通过真实的网络流量进行实验，结果表明，与长持续时间流检测算法（TLF）相比，所提方法的准确性提高了50%，误报率和漏报率分别降低了22%和20%，说明全网络持续流检测方法能够有效监测高速网络流量。     

A Method of Applying Flight Data to Evaluate Landing Operation Performance

Abstract

Pilots’ operation has an important effect on flight safety and performance, particularly in the final landing stage when pilots need to deal with complicated operations. This study aims to determine the potential value of flight data and develop a method of evaluating a pilot’s performance during landing phase based on flight quick access recorder (QAR) data from the perspective of risk assessment. First, a Landing Operation Performance Evaluation Model was developed based on risk evaluation principles. Three landing parameters, which are touchdown distance, touchdown vertical acceleration and touchdown pitch angle, were selected as indicators to evaluate the pilots’ landing operation performance in this model. Second, the flight landing operation performance evaluation system (FLOPES) was set up based on the evaluation model. Test results showed that FLOPES can accomplish all calculation flow of operation performance evaluation. Finally, it concluded that this method is a more accurate and effective way for evaluating the landing operation performance of a flight. It could be as a practical tool for airlines to manage landing risk quantitatively and to provide a more practical support for improving training and design in aviation.Practitioner summary: This study aims to determine the potential value of flight data and to develop a method of evaluating pilot’s landing operation performance from the risk evaluation perspective. Test results showed that this method is effective and could be as a practical tool for airlines to manage landing risk and improve training.Abbreviations:QAR: Quick Access Recorder; FLOPES: Flight Landing Operation Performance Evaluation System; ICAO: International Civil Aviation Organization; IATA: International Air Transport Association; SMS: Safety Management System; CAAC: Civil Aviation Administration of China; FOQA: Flight Operations Quality Assurance; VBA: Visual Basic for Applications     

金沙江水电工程智能建造技术体系研究与实践

运用"全面感知、真实分析、实时控制"的智能闭环控制理论,提出了以工程建设资源要素动态精准管理、业务流程数字化管理、工艺过程智能控制、实物成本精确分析、结构安全与进度耦合分析及联动调控的数字化技术和智能化技术为核心,以水电工程数据模型如大坝全景信息模型DIM为基础,以智能建造管理平台iDam为主体的智能建造技术体系,并对工程数据结构分解与编码体系、工程数据感知传输共享技术、iDam平台的系统架构与业务架构、智能建造工程绩效等关键技术进行了分析。通过统一规范的工程数据结构与编码体系,iDam平台可集成工程建设全过程的基础数据、环境数据、过程数据和监测数据,为各业务模块服务;建立的基于工程技术和管理大数据的机理分析功能,可为工程建设的业主、施工、监理、设计、科研及运行等单位服务。智能建造初步实践表明,本文构建的技术体系是科学可行的,对基础设施智能建造技术与管理体系的研究与实践具有参考价值。