Student’s perceptions towards using e-learning via Facebook

Research on utilising social networks for teaching and learning is relatively scarce in the context of information systems. There is far more emphasis on studying the usage of social networks towards fulfilling individuals’ basic social needs. This study uses the unified theory of acceptance and use of technology (UTAUT2) to analyse students’ intention to use and use of e-learning via Facebook. It incorporates playfulness into the UTAUT2 model and categorises the determinants of intention to use e-learning via Facebook into three categories, namely, hedonic values, utilitarian values, and communication values. The data were collected in a two-stage survey from 170 undergraduate students, and the model was tested using structural equation modelling. We found that hedonic motivation, perceived playfulness, and performance expectancy were strong determinants of students’ intention to use e-learning, while habit and facilitating conditions all positively affected students’ use of e-learning via Facebook. The results of this study report new knowledge that academic institutions can utilise to create appropriate e-learning environments for teaching and learning. A number of theoretical and managerial implications for universities’ implementation technologies were also identified.     

带厚尾噪声的鲁棒Student’s t容积滤波器

为了解决带厚尾过程和量测噪声的非线性状态估计问题, 本文提出了新的鲁棒非线性滤波器. 首先, 对带厚尾噪声的非线性系统进行了数学建模, 并推导出了基于Student’s t 近似的鲁棒非线性滤波器(Robust Student’s t based nonlinear filter, RSTNF)的一般结构. 其次, 针对RSTNF 中涉及到的含有关于Student’s t 分布的多维非线性函数积分求解问题, 提出了基于Student’s t 分布的三次幂球径容积准则(Student’s t spherical-radial cubature rule, STSRCR), 并在此基础上, 设计了新的鲁棒Student’s t 容积滤波器(Robust Student’s t based cubature filter, RSTCF). 最后, 利用目标跟踪仿真验证了本文提出的带厚尾噪声的RSTCF 的有效性以及与现有方法相比的优越性.     

基于随机矩阵建模的Student’s t逆Wishart滤波器

针对复杂的异常噪声条件下扩展目标跟踪问题,本文基于随机矩阵模型(RMM)提出了一种Student’s t逆Wishart滤波算法.首先,运用Student’s t分布对异常的过程噪声和量测噪声进行建模,运用逆Wishart分布对目标扩展状态进行建模,以更加合理的数学模型表征异常噪声条件下基于稀疏量测的目标基本轮廓特征.进一步的,本文详细推导了能稳健估计目标椭圆形状特征的Student’s t逆Wishart滤波算法,能在形状和方向动态演变过程中有效估计扩展目标的多重特征.最后,通过构造异常噪声条件下椭圆扩展目标跟踪的仿真实验验证了所提算法的有效性.     

Solution to Euler’s elastic problem

Euler’s problem on stationary configurations of elastic rod with fixed endpoints and tangents at the endpoints is considered. The corresponding optimal control problem is reduced to several systems of algebraic equations in Jacobi’s functions. An algorithm and software for solving the optimal control problem are constructed.     

Turing’s Responses to Two Objections

In this paper I argue that Turing’s responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace’s objection, we can better understand Turing’s response to Lady Lovelace’s objection. Finally, I argue that by understanding Turing’s responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis in his philosophical thinking about the nature of mind.

Introduction to the Geometer’s Angle

Geometer Rachel Fletcher introduces the Geometer’s Angle, a column in the Nexus Network Journal dedicated to geometrical constructions, the procedures for drawing them, their use, significance and symbolism.     

ETSI’s human factors contribution to eEurope

This article describes the most recent development in the European telecommunications industry from the end-user perspective, considering generic user interface elements for interaction with mobile communication systems, as described in a series of European Telecommunication Standards Institute (ETSI) deliverables. It also reports the results of work from recent and ongoing activities in the ETSI Technical Committee Human Factors (TCHF), performed by major industry representatives under the European Commissions eEurope Initiative. The availability of a standardised set of universal, generic interaction elements increases the transfer of knowledge to use mobile devices and services, improves the overall usability of the entire interactive mobile environment and improves access for all. Such a transfer becomes even more important in a world of ubiquitous devices and services to the young, elderly and disabled.     

Speech-based solution to Parkinson’s disease management

Parkinson’s disease (PD) is a neurological disorder marked by decreased dopamine levels in the brain. Persons suffering from PD, exhibits vocal symptoms such as dysphonia and dysarthria. Speech impairments in PD are grouped together and called as hypokinetic dysarthria. Traditional PD management is based on a patient’s clinical history and through physical examination as there are currently no known biomarkers for its diagnosis. Automatic analysis techniques aid clinicians in diagnosis and monitoring patients using speech and provide frequent, cost effective and objective assessment. This paper presents pilot experiment to detect presence of dysarthria in speech and detect level of severity based on deep learning approach. Automated feature extraction and classification using convolutional neural network shows 77.48% accuracy on test samples of TORGO database with five fold validation. Using transfer learning, system performance is further analyzed for gender specific performance as well as in detection of severity of disease.

     

Linearity and iterator types for Gödel’s System

System Open image in new window is a linear λ-calculus with numbers and an iterator, which, although imposing linearity restrictions on terms, has all the computational power of Gödel’s System  Open image in new window . System Open image in new window owes its power to two features: the use of a closed reduction strategy (which permits the construction of an iterator on an open function, but only iterates the function after it becomes closed), and the use of a liberal typing rule for iterators based on iterative types. In this paper, we study these new types, and show how they relate to intersection types. We also give a sound and complete type reconstruction algorithm for System  Open image in new window .     

A novel approach to exploring company’s financial soundness: Investor’s perspective

Prediction of company’s life cycle stage change; creation of an ordered 2D map allowing to explore company’s financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique “t-distributed stochastic neighbor embedding”.Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company’s financial soundness from a rating agency perspective. The proposed use of information not only from companies’ financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody’s rating agency.Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company’s management team. It was also found that company’s life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging.     

Semi-supervised variational Bayesian Student’s t mixture regression and robust inferential sensor application

Data-driven inferential sensor has been widely adopted to estimate key quality relevant variables. However, industrial dataset usually presents many characteristics such as nonlinearity, non-Gaussianity, insufficiency of labeled samples, contamination of outliers, etc. These intractable characteristics have rendered significant difficulties in developing high-performance inferential sensor. This paper deals with these issues in the probabilistic way by proposing a robust semi-supervised variational Bayesian Student’s t mixture regression (referred to as the ‘SSVBSMR’). Specifically, in the SSVBSMR, the nonlinear and non-Gaussian characteristics are handled by Bayesian finite mixture models (FMM), and the Student’s t distribution is employed to constitute the components of FMM, which makes the SSVBSMR robust against outliers. In addition, the SSVBSMR exploits unlabeled samples to remedy the insufficiency of labeled samples. Furthermore, the SSVBSMR treats all model parameters as stochastic rather than deterministic such that the model selection can be automatically and efficiently completed and some limitations of the maximum likelihood method (such as overfitting and singular covariance) can be alleviated. A variational Bayesian expectation–maximization-based learning algorithm is also developed to train the SSVBSMR. Two cases are carried out to investigate the performance of the SSVBSMR, and the results demonstrate its effectiveness and feasibility compared to several state-of-the-art methods.     

You Are How You Behave-Spatiotemporal Representation Learning for College Student Academic Achievement

Scholarships are a reflection of academic achievement for college students.The traditional scholarship assignment is strictly based on final grades and cannot recognize students whose performance trend improves or declines during the semester.This paper develops the Trajectory Mining on Clustering for Scholarship Assignment and Academic Warning(TMS)approach to identify the factors that affect the academic achievement of college students and to provide decision support to help low-performing students attain better performance.Specifically,we first conduct feature engineering to generate a set of features to characterize the lifestyles patterns,learning patterns,and Internet usage patterns of students.We then apply the objective and subjective combined weighted k-means(Wosk-means)algorithm to perform clustering analysis to identify the characteristics of different student groups.Considering the difficulty in obtaining the real global positioning system(GPS)records of students,we apply manually generated spatiotemporal trajectories data to quantify the direction of trajectory deviation with the assistance of the PrefixSpan algorithm to identify low-performing students.The experimental results show that the silhouette coefficient and Calinski-Harabasz index of the Wosk-means algorithm are both approximately 1.5 times to that of the best baseline algorithm,and the sum of the squared error of the Wosk-means algorithm is only the half of the best baseline algorithm.     

Performance Study of Distributed Multi-Agent Intrusion Detection System

Traditional Intrusion Detection System (IDS) based on hosts or networks no longer meets the security requirements in today’s network environment due to the increasing complexity and distributivity. A multi-agent distributed IDS model, enhanced with a method of computing its statistical values of performance is presented. This model can accomplish not only distributed information collection, but also distributed intrusion detection and real-time reaction. Owing to prompt reaction and openness, it can detect intrusion behavior of both known and unknown sources. According to preliminary tests, the accuracy ratio of intrusion detection is higher than 92% on the average.     

Exploring Explanation Effects on Consumers’ Trust in Online Recommender Agents

This paper explores how explanations within an online recommender agent (RA) affect consumers’ trust in the RA and their willingness to adopt its recommendations. The paper proposes a theoretical research model which employs a multidimensional notion of trust and considers the effects of varying two features of the RA: explanation availability, i.e., providing, or not, an explanation of the underlying reasoning process of an RA; and explanation mode, i.e., using graphs or text as the means of providing explanations. In addition, we investigate the impact of a consumer’s perceived level of personalization of the RA as a measured variable. Two dimensions of trust—trust in integrity and trust in competence—along with intention to accept the recommendations serve as the dependent variables within the model. A within-subject experiment is conducted with four RAs. Empirical data provide evidence that explanation availability, explanation mode, and perceived personalization all have significant influence on consumers’ trust beliefs in an RA and willingness to adopt its recommendations. Effects of both the availability and mode of explanations on consumers’ trust beliefs are found to be mediated by consumers’ perceived personalization of the RA that, in turn, mediate the effects on intention to use.     

From Dubins’ car to Reeds and Shepp’s mobile robot

In the paper, a control system with intermediate dynamics between Dubins’ car and Reeds and Shepp’s mobile robot is investigated. Time-limited reachable sets and reachable sets at given time are computed. Families of semipermeable curves that are useful for the detection of jumps of the value function of time-optimal control problem are constructed. Research is partly supported by the Russian Foundation for Fundamental Research under Grants 06-01-00414 and 07-01-96085.     

Japan plans to issue smart driver’s licence

The Japanese National Police Agency (NPA) has announced plans to begin issuing contactless smart card driver’s licences to more than 75 million licence holders. One of the aims of the card is to cut down on card forgeries, which have become a particular problem in recent years.This is a short news story only. Visit www.compseconline.com for the latest computer security industry news.