U-ATM: An Autonomous Trust Model for Exploring Ubiquitous Collective

Ubiquitous e-service is one of the most recent links in the chain of evolution that has characterized the different eras of the internetworking environment. In order to leap the trust barrier for the user to embracing these ubiquitous e-services, we propose an Autonomous Trust Model for exploring collective wisdom in the ubiquitous environment (hereafter termed “U-ATM”) as an instance of ASEM. ASEM (Ambient e-Service Embracing Model) addresses the core elements (of relevance to the integrated concern of trust, reputation and privacy) required for assuring such desired features as convenience, safety, fairness and collaboration for mobile users when they engage with ambient e-services. The U-ATM highlights the distributed peer-to-peer interactions under an ad-hoc network composition. It especially accommodates the dynamic short-lived identity characteristics and lightweight computational capacity of mobile devices. The U-ATM we have developed is based on the ZigBee architecture as a collaborative application in the upper layer of the ubiquitous environment. U-ATM design concepts are elaborated and evaluated. A simulation is conducted. Simulation outcomes for trust decision quality enhancement show significant improvement over traditional designs. U-ATM makes it possible for users to collaborate with the nearby user groups for establishing a reliable and trustworthy interaction environment. It also facilitates and empowers the potential benefits of various ubiquitous e-service applications

The relationship of teachers\u27 job satisfaction and their perceptions of principals\u27 leadership styles in private vocational high schools in a selected metropolitan area of Taiwan

The primary purpose of this study was to determine the relationship between teachers\u27 job satisfaction and their perceptions of principals\u27 leadership styles (transformational, transactional, and laissez-faire leadership styles) in the Kaohsiung Metropolitan Area of Taiwan. A secondary purpose was to examine the difference between teachers\u27 gender, educational level, and length of service in terms of their perceptions of the three different leadership styles and job satisfaction. The sample consisted of 629 full-time private vocational high school teachers. The translated Multifactor Leadership Questionnaire (MLQ) was used to measure teachers\u27 perceptions of their principals\u27 leadership styles. The translated Minnesota Satisfaction Questionnaire (MSQ) Short Form was used to measure teachers\u27 general job satisfaction. The demographic sheet designed by the researcher was used to request subjects to provide information about gender, educational level, and length of service. Pearson product-moment correlations were computed between each leadership scale and general job satisfaction. Stepwise multiple regression analysis was used to predict which subset of MLQ leadership scales would most influence job satisfaction. The t test was used to compare the difference on teachers\u27 gender, educational level, and length of service with both the perceptions concerning the leadership styles of their principals and their job satisfaction. The major conclusions drawn from the study were: (a) Teachers perceived their principals\u27 leadership styles to be predominantly laissez-faire, (b) overall transformational leadership and its subscales were positively correlated with general job satisfaction, (c) female teachers perceived their principals as more transformational leaders and less laissez-faire leaders than male teachers, but there was no significant difference between male and female teachers\u27 perceptions of their principals\u27 transactional leadership style, (d) there was no significant difference between teachers with bachelor degree and less than bachelor degree nor between those who had served more and less than 10 years, in terms of their perceptions of the three different leadership styles, and (e) higher levels of job satisfaction were found among female teachers, those with bachelor degree, and those with less than 10 years of service

Online Regularized Learning Algorithm for Functional Data

In recent years, functional linear models have attracted growing attention in statistics and machine learning, with the aim of recovering the slope function or its functional predictor. This paper considers online regularized learning algorithm for functional linear models in reproducing kernel Hilbert spaces. Convergence analysis of excess prediction error and estimation error are provided with polynomially decaying step-size and constant step-size, respectively. Fast convergence rates can be derived via a capacity dependent analysis. By introducing an explicit regularization term, we uplift the saturation boundary of unregularized online learning algorithms when the step-size decays polynomially, and establish fast convergence rates of estimation error without capacity assumption. However, it remains an open problem to obtain capacity independent convergence rates for the estimation error of the unregularized online learning algorithm with decaying step-size. It also shows that convergence rates of both prediction error and estimation error with constant step-size are competitive with those in the literature.Comment: 32 page