Exploring the relationship between Internet ethics in university students and the big five model of personality

The widespread use of the Internet and the convenient mechanism it provides, such as easy access, easy downloads, and easy copy and paste functions have made many types of unethical behaviors easier, particularly those involving students in academic settings. Among the issues in ethics within the academic environment that can be triggered by the Internet are fraudulence, plagiarism, falsification, delinquency, unauthorized help, and facility misuse. Given these issues, the study seeks to investigate the extent to which students at a public university in Malaysia engage in such unethical behavior and their relationship with the big five personality model. This study was conducted using a survey method of 252 students in three different academic faculties. The results of factor analyses confirm and refine the reliability of the scales for both big five personality variables and unethical Internet behaviors as conceptualized through Internet triggered academic dishonesty (ITADS). The findings indicate that personality traits such as (1) agreeableness, (2) conscientiousness and (3) emotional stability are significantly and negatively correlated with unethical Internet behavior in university students. Significant differences in facility misuse are also observed between the three academic faculties investigated. This research should provide significant contributions to educators in designing the computer ethics curriculum and in allowing for educational institutions as well as other organizations in developing relevant policies and guidelines on matters pertaining to academic conduct, utilization of computers and Internet, and recruitment exercises.     

Characterising and exploiting workloads of highly interactive video-on-demand

This paper presents a detailed characterisation of user behaviour for a series of interactive video experiments over a 12 month period, in which we served popular sporting and musical content. In addition to generic VCR-like features, our custom-built video-on-demand application provides advanced interactivity features such as bookmarking. The dramatic impact of such functionality on how users consume content is studied and analysed. We discuss in detail how this user behaviour can be exploited by content distributors to improve user experience. Specifically, we study how simple dynamic bookmark placement and interactivity-aware content pre-fetching and replication can reduce the impact of highly interactive media on CDN performance.     

GENOTOXICITY IN CHIRONOMUS KIIENSIS (CHIRONOMIDAE: DIPTERA) AFTER EXPOSURE TO POLLUTED SEDIMENTS FROM RIVERS OF NORTH PENINSULAR MALAYSIA: IMPLICATION FOR ECOTOXICOLOGICAL MONITORING

Rapid industrialization and urbanization has led to increasing input of chemical contaminants into the aquatic environment of Malaysia. Despite the threat civilization poses to the biota, there are still very few relevant studies on ecotoxicological testing of river ecosystems. To overcome this knowledge gap, we examined lethal and genotoxic effects of sediments from different rivers of the northern Malaysia against Chironomus kiiensis, a group well represented in the aquatic fauna of this region. We exposed the larvae to sediments from Selama River (SR), Permatang Rawa River (PRR) and Kilang Ubi River (KUR) at various durations (0, 6, 12, 24 and 48 h). The larval mortality was monitored, whereas DNA damage in survivors' cells was determined using the comet assay. Pollution level indexed by the amounts of heavy metals and other organic contaminants in the sediment showed progressive increases from SR to PRR to KUR. Highly polluted sediments (PRR to KUR) were detrimental to C. kiiensis larvae, most of which did not survive following exposure for long periods. DNA analyses revealed greater damages in nuclei derived from larvae maintained on polluted sediments, in particular, those from KUR. The effects on the genomic material of C. kiiensis larvae occurred in a time‐dependent manner, with damage level increasing as exposure time progressed. Our results highlight the genotoxic properties of polluted sediments. More importantly, this study showed that C. kiiensis larvae could respond to different levels of pollution with respect to exposure time. It is concluded that C. kiiensis larvae is a potential candidate for river ecotoxicological monitoring. Copyright © 2012 John Wiley & Sons, Ltd.     

基于动态规划的水电站机组间负荷分配程序开发

水电站机组间负荷分配计算中的阶段变量与状态变量的个数越多,计算量就越大,且计算程序也越复杂,因而仅用手工计算很难做到。为此,利用动态规划法建立了水电站厂内经济运行流量分配数学模型。使用编程软件Visual Basic开发了水电站机组间负荷分配程序;该程序界面友好,使用方便,且能大大提高计算速度。应用该程序,对水电站机组间的负荷分配进行了实际应用计算,得出了经济效益最优的负荷分配方案。     

地理视角下深圳市房价及其影响因素研究——基于随机森林模型

随着国民生活水平的提高,人们对房价的关注度也持续升高。传统的房价研究主要按时间序列法进行预测,然而房价是多指标影响因子,除与历史房价、房屋自身属性有关外,还与所处区位的地理环境、配套设施以及城市的规划分区等多方面因素相关。本文以2019年深圳市二手住宅小区单价为研究对象,从地理视角下,选取教育资源、医疗设施、交通便捷度、环境因素、规划因素的相关指标作为房价影响因子,通过随机森林的方法进行样本训练得到深圳市房价研究模型,对影响深圳市房价的上述指标进行分析研究。根据随机森林模型输出的变量重要性得知,医疗设施对深圳市房价影响最为显著。将测试数据输入预测模型,当实际的房价在均值附近时,此模型预测效果较好。     

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages: (1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) pre-processing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.     

Design of Nonlinear Servo Systems by the Fuzzy Method

This paper presents the design of nonlinear servo systems using the fuzzy method. The idea behind this control method is to divide the operating region of a nonlinear system into a collection of local linear servo control systems and apply the fuzzy method for calculation of the control input. The control input of each local linear servo system is calculated using the Davison–Smith method and the Hikita pole assignment method. We can prove that all states of the fuzzy servo system are bounded and that the output errors converge to zero by the Lyapunov method. Simulations on a nonlinear mass‐spring‐damper system illustrate the effectiveness of the proposed method.