Self-esteem,empathy and their impacts on cyberbullying among young adults

This paper investigated the impacts of self-esteem and empathy on cyber bullies, victims and bystanders. Additionally, it also examined their impacts on emotional responses experienced, and actions taken by the perpetrators, victims and bystanders. Self-administered surveys were used to gather data from a large sample of 1263 young adults, mostly university students in Malaysia (M_age?=?20.9?years; SD?=?1.22). The Rosenberg Self-Esteem Scale and Toronto Empathy Scale were used to measure self-esteem and empathy, respectively. Binary logistic regressions revealed no significant impacts of self-esteem and empathy on the participants, regardless of their roles. However, self-esteem was found to have significant relationships with victims’ feeling angry and reporting a cyberbullying incident. As for bystanders, self-esteem also had significant relationships with feeling angry, sad, victim-pity and defending the victims. Empathy had no significant relationships with any of the actions and emotional responses for bullies, victims and bystanders.     

Actions,emotional reactions and cyberbullying – From the lens of bullies,victims, bully-victims and bystanders among Malaysian young adults

This paper examined the emotional reactions and actions involving cyberbullying, focusing on the cyber bullies, victims, bully-victims and bystanders. Gender analysis was conducted to examine if males and females behave and react differently. Self-administered surveys were used to gather data from a large sample of 1158 young adults, mostly university students in Malaysia (M_age?=?21.0?years; SD?=?2.16). Findings indicate the presence of cyberbullying perpetration after the schooling years, with 8% (N?=?93) bullying, 18.6% (N?=?216) victimization, 15.2% (N?=?174) bullying and victimization, and 53.4% (N?=?675) witnessing a cyberbullying incident in the past one year. Most of the bullies reported to be remorseful; however the majority did nothing after a perpetration. Most of the victims on the other hand, experienced anger, sadness and depression after a victimization with the majority claiming to have defended themselves (75%). The majority of the bully-victims regretted their actions, pitied the victims and felt angry after a cyberbullying perpetration/victimization. Bystanders mostly reported feeling pity for the victim and angry at the bullies, with the majority (61.5%) claiming to have defended the victims. However, 40% of them behaved indifferently out of fear retaliation. Finally, gender analysis revealed females to have significantly experienced more emotions than males whereas more males did nothing after a cyberbullying incident, both as victims and bystanders.     

Cyberbullying: Effect of emergency perception on the helping tendencies of bystanders

The effect of the emergency perception of bystanders of cyberbullying victims on helping behaviors is often neglected in research on cyberbullying. In this study, we explored the influence of this cognitive factor on cyber-bystanders’ helping tendencies as well as elucidated possible underlying processes. The results of two studies were reported. In Study 1, 150 undergraduates read a true case of a girl experiencing cyberbullying. The results indicated that when the participants perceived the victim’s situation to be more critical (i.e., higher emergency perception), their helping tendencies were stronger, partly through increased state empathy followed by feelings of responsibility to help. In Study 2, we randomly assigned 300 undergraduates to two groups. The low emergency group read the same cyberbullying case as Study 1, whereas the cyberbullying case read by the high emergency group contained additional emergency information of the victim. The results indicated that the high emergency group expressed stronger helping tendencies than did the low emergency group. This effect was caused by a stronger perception that the victim was in an emergency situation, which not only strengthened the participants’ helping tendencies directly but also indirectly through increasing their state empathy and feelings of responsibility to help.     

基于决策树的网络客户分类方法研究

传统的网络购物只是对商品进行一个简单的分类和陈列,对于电子商务的商家并没有对网络消费者的购物数据进行深入研究探讨.针对网络购物过程中消费者选择商品的趋向性的不同,引入了基于决策树分类方法对网络客户购买商品的行为进行分析,并从决策树中挖掘出影响网络购物的主要因素以及各因素对网络购买行为的强弱影响程度.实验结果表明,此方法可以有效的对网络客户进行分类,有利于决策分析.     

Engaging in Violence on American Television: A Comparison of Child, Teen, and Adult Perpetrators

This study examines the perpetrators of violence on American television in terms of their chronological age. In particular, the content analysis compares the amount and nature of violence committed by child and teen characters to that committed by adult characters. The results suggest that younger perpetrators are depicted in several ways that pose risks for the child viewer. Compared to adult perpetrators, child perpetrators are more often portrayed as attractive, are less likely to be punished for aggression, and engage in violence that results in fewer negative consequences to their victims. In addition, these younger characters are disproportionately featured on the very programs and channels that are targeted to the child audience. The findings are discussed in terms of children's attention to, and social learning from different types of characters on television.     

Cyberbullying perpetration and victimization among children and adolescents: A systematic review of longitudinal studies

In this systematic review of exclusively longitudinal studies on cyberbullying perpetration and victimization among adolescents, we identified 76 original longitudinal studies published between 2007 and 2017. The majority of them approached middle school students in two waves at 6 or 12 months apart. Prevalence rates for cyberbullying perpetration varied between 5.3 and 66.2 percent, and for cyberbullying victimization between 1.9 and 84.0 percent. Person-related factors (e.g., traditional bullying, internalizing problems) were among the most studied concepts, primarily examined as significant risk factors. Evidence on the causal relationships with media-related factors (e.g., (problematic) Internet use), and environmental factors (e.g., parent and peer relations) was scarce. This review identified gaps for future longitudinal research on cyberbullying perpetration and victimization in childhood and adolescence.     

Activity classification using realistic data from wearable sensors

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82% for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.     

电力潜在敏感客户预测的大数据方法应用

随着 95598 业务的不断发展延伸,人工话务强度增大。为了进一步加深对客户隐性特征以及诉求的认识和理解,提升 95598 人工精细化客户服务水平,对投诉倾向等客户服务中的典型应用场景进行了需求细化。基于电力服务工单数据,选取建模关键指标,通过熵权法、主成分分析和决策树等数据挖掘算法,对潜在投诉倾向客户和计划停电敏感客户进行识别,以便有针对性地进行服务资源调度,充分做好应对措施,有效减少投诉压力,提升服务精度。     

Identification of key cyberbullies: A text mining and social network analysis approach

Cyberbullying is a major problem in society, and the damage it causes is becoming increasingly significant. Previous studies on cyberbullying focused on detecting and classifying malicious comments. However, our study focuses on a substantive alternative to block malicious comments via identifying key offenders through the application of methods of text mining and social network analysis (SNA). Thus, we propose a practical method of identifying social network users who make high rates of insulting comments and analyzing their resultant influence on the community. We select the Korean online community of Daum Agora to validate our proposed method. We collect over 650,000 posts and comments via web crawling. By applying a text mining method, we calculate the Losada ratio, a ratio of positive-to-negative comments. We then propose a cyberbullying index and calculate it based on text mining. By applying the SNA method, we analyze relationships among users so as to ascertain the influence that the core users have on the community. We validate the proposed method of identifying key cyberbullies through a real-world application and evaluations. The proposed method has implications for managing online communities and reducing cyberbullying.     

An enhanced feature selection filter for classification of microarray cancer data

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well‐known classifiers, including the naive Bayes, radial basis function network, instance‐based classifier, decision‐based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information–based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.     

基于改进的ID3算法生成决策树方法(英文)

The ID3 algorithm is a classical learning algorithm of decision tree in data mining.The algorithm trends to choosing the attribute with more values,affect the efficiency of classification and prediction for building a decision tree.This article proposes a new approach based on an improved ID3 algorithm.The new algorithm introduces the importance factor λ when calculating the information entropy.It can strengthen the label of important attributes of a tree and reduce the label of non-important attributes.The...     

基于手机信令的城市道路交通状态实时预测

为准确、实时预测道路交通状态,通过分析影响交通的因素,利用决策树算法对速度和环境因素等数据进行建模,确定交通拥堵发生的规则,在此基础上结合实时的移动用户和环境因素数据对交通状态进行预测。以中国河北保定城区为例进行实验,验证了该方法的有效性。同时,研究发现,基于决策树算法进行道路交通状态预测的方法具有较好的扩展性。     

基于数据挖掘的电信客户流失预测分析

针对电信客户日益严重地流失问题,通过某电信运营商的历史资料,对电信PAS流失客户的自然属性和行为属性进行研究,利用决策树算法建立了客户流失预测模型。通过对模型进行评估分析,得到预测效果较好的模型,最后加入成本因素,进一步优化了模型。     

一种加权聚类划分决策树算法

针对斜划分决策树算法普遍存在时间效率低、部分算法仅能应用于二分类问题，提出了一种基于加权距离的聚类决策树算法。通过Relief-F算法为预测属性计算权重，并将权重用于树结点中数据的聚类过程，使用分簇结果对结点进行多路划分，得到可直接用于多分类问题的决策树。理论分析和实验结果表明，该算法与经典轴平行决策树相比，拥有更好的泛化能力以及相近的算法时间复杂度，与大部分斜决策树相比，在付出更少计算代价的前提下，获得了近似的正确率以及模型简洁度。     

ID3算法在成绩分析中的应用研究

利用决策树分类算法对课程信息、教师信息、成绩信息等教学信息库中的数据进行分析,从而生成决策树并从决策树中挖掘出影响成绩高低的主要因素以及各因素对成绩影响的强弱程度。将数据挖掘技术应用到数据的多维分析中,可以更好地为教学管理人员提供决策支持。     

Determining Significant Classification Factors for Senior Learning: A Case Study of Thai Seniors and Social Media Skill Learning

The senior learns in order to have a better quality of life. The challenge of seniors in learning is their learning ability that deteriorates because of age. Suitable management for different types of seniors, so called personalized learning is required. Therefore, this study focuses on determining significant classification factors for classification of seniors which is an important component of personalized learning. In this study, the assumption of personal background and health issue can be used for classifying types of seniors. The decision tree is used for determining significant classification factors and constructing the model. The study is conducted with 75 seniors for social network skill learning. The classification results show that the significant classification factors affecting the classification model of senior learning are age, daily internet time spending, number of applications, memory problem, and education background. The model constructed by decision tree provides 93.33% classification accuracy. Also, the obtained factors are verified by testing with two machine learning methods including artificial neural network (ANN) and K-nearest neighbors (K-NN). The comparison results show that 5 factors provide high classification accuracy for both classifiers, which are 93.33% and 92.00% for ANN, and K-NN, respectively.

     

On-line hazard aversion: a Bayes approach

A Bayes theorem and fault tree (BFT) method is developed for online hazard aversion in process systems. BFT requires as input a prediction of the measurement patterns resulting from system operating nodes, equipment malfunctions and disturbances, and the prior rates of equipment malfunctions and disturbances. The Bayes theorem is used to estimate the posterior probability of each fault candidate, given the online measurements, producing a list of fault candidates ranked by their posterior probabilities. A fault tree is then used to estimate the current top event probability, given the online measurements in which basic event probabilities are the posterior probabilities of the faults. The future safety of the process is measured by safety meters which compare the estimated top event probability at given times over a time horizon. The estimated current top event probability and the safety meters indicate process safety, providing the basis for an unplanned shutdown, maintenance, or corrective action decision. A negative feedback process control loop illustrates the accuracy of BFT and lays the groundwork for applying BFT to more complicated process systems     

基于相关分析的多目标优化Pareto优劣性预测

昂贵多目标进化算法中,目标向量评估所需计算时间或实验成本高昂,大量昂贵评估必然导致成本灾难.本文根据多目标优化Pareto优劣性取决于各目标分量的序关系这一关键性质,提出一种序拟合方法进行Pareto优劣性预测.在分析样本数据决策空间与目标空间序相关性的基础上,通过线性相关的假设条件,建立低成本的序关系预测方程,并用预测的序关系确定Pareto优劣性.然后对典型多目标优化问题进行Pareto优劣性预测对比实验,结果表明所提方法显著提高了Pareto优劣性的预测精度.最后,将该预测方法集成到NSGA-II算法中,可以避免进化过程中的模型重构,有效减少昂贵目标向量的评估次数.     

Automatic construction of online catalog topologies

A good online catalog is crucial to the success of an e-commerce web site. Traditionally, an online catalog is mainly built by hand. To what extent this can be automated is a challenging problem. Recently, there have been investigations on how to reorganize an existing online catalog based on some criteria, but none of them has addressed the problem of organizing an online catalog automatically from scratch. This paper attempts to tackle this problem. We model an online catalog organization as a decision tree structure and propose a metric, based on the popularity of products and the relative importance of product attribute values, to evaluate the quality of a catalog organization. The problem is then formulated as a decision tree construction problem. Although traditional decision tree algorithms, such as C4.5, can be used to generate online catalog organization, the catalog constructed is generally not good based on our metric. An efficient greedy algorithm (GENCAT) is thus developed, and the experimental results show that GENCAT produces better catalog organizations based on our metric.     

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

The healthcare domain is basically “data rich”, yet tragically not every one of the information are dug which is required for finding concealed examples and successful basic leadership used to find learning in database and for restorative research, especially in heart malady forecast. This article has examined forecast frameworks for heart disease utilizing more number of info attributes. In this article, we proposed an altered calculation for classification with decision trees which furnishes precise outcomes when contrasted and others calculations. The proposed work is planned to show the data mining method in disease forecast frameworks in medicinal space by utilizing avaricious way to deal with select the best attributes. Our investigation demonstrates that among various prediction models neural networks and Gini index prediction models results with most noteworthy precision for heart attack prediction. A portion of the discretization strategies like voting technique are known to deliver more precise decision trees. To improve execution in coronary illness finding, this research work examines the outcomes in the wake of applying a scope of procedures to various sorts of decision trees and accuracy and sensitivity are attained by the execution of elective decision tree methods.