Job Satisfaction and Its Relationships with Age, Gender and Educational Background in a Vietnamese Context : A thesis presented in partial fulfilment of the requirement for the degree of Master of Business Studies (Management) at Massey University, Manawatu New Zealand

The present study aims at examining the reliability and validity of a Vietnamese version of the Job Satisfaction Survey (JSS) which was developed by Spector (1997). It also reveals the current overall job satisfaction and investigates the relationship between job satisfaction and age, gender, and educational background among a specific community, the auditors and ex-auditors in Vietnam. With these goals, a quantitative cross-sectional design has been employed for the research. A pilot study with 68 Vietnamese respondents establishes a solid foundation for the final Vietnamese-translated version of the JSS. In the main study, a sample of 202 Vietnamese auditors and ex-auditors is recruited. The JSS in Vietnamese demonstrates a high internal consistency with the Cronbach’s alpha coefficient of α = .91. Moreover, an exploratory factor analysis reports an underlying construct of nine dimensions, which is similar to the original version of the JSS. The convergent and divergent validity of the scale are also analysed and return satisfactory results. The present research suggests that the auditors and ex-auditors in Vietnam are generally satisfied with their jobs and, surprisingly, the auditors are reported to be happier than their ex-colleagues in every job aspect. There is no relationship found between the overall job satisfaction and age or gender for this specific community, while a significant correlation between job satisfaction and educational background is confirmed. However, the women of this community are reported to be more likely to experience a lower level of job satisfaction when they get older or when they have a better educational background. The present study provides audit companies in Vietnam with recommendations for improving the job satisfaction of their employees. Its findings suggest that these firms should pay more attention to their older female employees as well as the ones with higher educational backgrounds due to their vulnerability to a lower level of job satisfaction than the opposite gender. Furthermore, directions and indications for future research are also offered in the present dissertation

A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.Comment: Technical Report. 34 pages, 5 figure

An inventory of Lattice Boltzmann models of multiphase flows

This document reports investigations of models of multiphase flows using Lattice Boltzmann methods. The emphasis is on deriving by Chapman-Enskog techniques the corresponding macroscopic equations. The singular interface (Young-Laplace-Gauss) model is described briefly, with a discussion of its limitations. The diffuse interface theory is discussed in more detail, and shown to lead to the singular interface model in the proper asymptotic limit. The Lattice Boltzmann method is presented in its simplest form appropriate for an ideal gas. Four different Lattice Boltzmann models for non-ideal (multi-phase) isothermal flows are then presented in detail, and the resulting macroscopic equations derived. Partly in contradiction with the published literature, it is found that only one of the models gives physically fully acceptable equations. The form of the equation of state for a multiphase system in the density interval above the coexistance line determines surface tension and interface thickness in the diffuse interface theory. The use of this relation for optimizing a numerical model is discussed. The extension of Lattice Boltzmann methods to the non-isothermal situation is discussed summarily.Comment: 59 pages, 5 figure

A consensus based network intrusion detection system

Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the network but analysis is often centralized or partly centralized. These systems are not scalable and suffer from the single point of failure, i.e. attackers only need to target the central node to compromise the whole system. This paper proposes an anomaly-based fully distributed network intrusion detection system where analysis is run at each data collecting point using a naive Bayes classifier. Probability values computed by each classifier are shared among nodes using an iterative average consensus protocol. The final analysis is performed redundantly and in parallel at the level of each data collecting point, thus avoiding the single point of failure issue. We run simulations focusing on DDoS attacks with several network configurations, comparing the accuracy of our fully distributed system with a hierarchical one. We also analyze communication costs and convergence speed during consensus phases.Comment: Presented at THE 5TH INTERNATIONAL CONFERENCE ON IT CONVERGENCE AND SECURITY 2015 IN KUALA LUMPUR, MALAYSI

A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning

This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured output space. Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches. In the case of the least square loss function, we provide a closed form solution, which is obtained by solving a system of linear equations. In the case of Support Vector Machine (SVM) classification, our formulation generalizes in particular both the binary Laplacian SVM to the multi-class, multi-view settings and the multi-class Simplex Cone SVM to the semi-supervised, multi-view settings. The solution is obtained by solving a single quadratic optimization problem, as in standard SVM, via the Sequential Minimal Optimization (SMO) approach. Empirical results obtained on the task of object recognition, using several challenging datasets, demonstrate the competitiveness of our algorithms compared with other state-of-the-art methods.Comment: 72 page