FDI spill-overs, absorptive capacity and domestic firms' technical efficiency in Vietnamese wearing apparel industry

This study empirically examines relationship between FDI spill-overs and technical efficiency of domestic firms and role of the absorptive capacity of domestic firms. Data on Vietnamese Annual Enterprises Survey are exploited to build a firm-level panel data on the Vietnamese wearing apparel industry from 2009 to 2013. By applying stochastic production frontier model, this paper shows that there are positive vertical spill-over effects but no horizontal effects. Moreover, this study finds the negative impact of the absorptive capacity of domestic firms on benefits reaped from FDI externalities

Stabilization for equal-order polygonal finite element method for high fluid velocity and pressure gradient

This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system. This technique is constructed by a local pressure projection which is extremely simple, yet effective, to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique. In this research, some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method

High Quality P2P Service Provisioning via Decentralized Trust Management

Trust management is essential to fostering cooperation and high quality service provisioning in several peer-to-peer (P2P) applications. Among those applications are customer-to-customer (C2C) trading sites and markets of services implemented on top of centralized infrastructures, P2P systems, or online social networks. Under these application contexts, existing work does not adequately address the heterogeneity of the problem settings in practice. This heterogeneity includes the different approaches employed by the participants to evaluate trustworthiness of their partners, the diversity in contextual factors that influence service provisioning quality, as well as the variety of possible behavioral patterns of the participants. This thesis presents the design and usage of appropriate computational trust models to enforce cooperation and ensure high quality P2P service provisioning, considering the above heterogeneity issues. In this thesis, first I will propose a graphical probabilistic framework for peers to model and evaluate trustworthiness of the others in a highly heterogeneous setting. The framework targets many important issues in trust research literature: the multi-dimensionality of trust, the reliability of different rating sources, and the personalized modeling and computation of trust in a participant based on the quality of services it provides. Next, an analysis on the effective usage of computational trust models in environments where participants exhibit various behaviors, e.g., honest, rational, and malicious, will be presented. I provide theoretical results showing the conditions under which cooperation emerges when using trust learning models with a given detecting accuracy and how cooperation can still be sustained while reducing the cost and accuracy of those models. As another contribution, I also design and implement a general prototyping and simulation framework for reputation-based trust systems. The developed simulator can be used for many purposes, such as to discover new trust-related phenomena or to evaluate performance of a trust learning algorithm in complex settings. Two potential applications of computational trust models are then discussed: (1) the selection and ranking of (Web) services based on quality ratings from reputable users, and (2) the use of a trust model to choose reliable delegates in a key recovery scenario in a distributed online social network. Finally, I will identify a number of various issues in building next-generation, open reputation-based trust management systems as well as propose several future research directions starting from the work in this thesis

Probabilistic Estimation Quality Ratings of Online Services

Accurate estimation of quality of online services is both an important and difficult problem, since a service has many interdependent quality attributes influenced by several contextual factors. It is even more challenging as quality ratings come from sources with unknown reliability, each source may rate a service on different quality aspects. Although several solutions have been proposed, there is little work addressing all these issues thoroughly. In this paper, we show that domain knowledge on service structure and related constraints, such as causal dependencies among quality attributes and contextual factors, while widely available, can be exploited to effectively address the above issues in a theoretically-sound framework. Theoretical analysis shows that computational cost of the approach is acceptable, and accurate evaluation of service quality requires a reasonable number of user feedback, provided services have a small number of quality attributes and contextual factors

Probabilistic Estimation of Peers’ Quality and Behaviors for Subjective Trust Evaluation

The management of trust and quality in decentralized systems has been recognized as a key research area over recent years. In this paper, we propose a probabilistic computational approach to enable a peer in the system to model and estimate the quality and behaviors of the others subjectively according to its own preferences. Our solution is based on the use of graphical models to represent the dependencies among different QoS parameters of a service provided by a peer, the associated contextual factors, the innate behaviors of the reporters and their feedback on quality of the peer being evaluated. We apply the EM algorithm to learn the conditional probabilities of the introduced variables and perform necessary probabilistic inferences on the constructed model to estimate peer's quality and behaviors. Interestingly, our proposed framework can be shown as the generalization of many existing trust computational approaches in the literature with several additional advantages: first, it works well given few and sparse feedback data from the reporting peers; second, it also considers the dependencies among the QoS attributes of a peer, related contextual factors, and underlying behavioral models of reporters to produce more reliable estimations; third, the model gives outputs with well-defined semantics and useful meanings which can be used for many purposes, for example, it computes the probability that a peer is trustworthy in sharing its experiences or in providing a service with high quality level under certain environmental conditions

Effective Usage of Computational Trust Models in Rational Environments

Computational reputation-based trust models using statistical learning have been intensively studied for distributed systems where peers behave maliciously. However practical applications of such models in environments with both malicious and rational behaviors are still very little understood. In this paper, we study the relation between their accuracy measures and their ability to enforce cooperation among participants and discourage selfish behaviors. We provide theoretical results that show the conditions under which cooperation emerges when using computational trust models with a given accuracy and how cooperation can be still sustained while reducing the cost and accuracy of those models. Specifically, we propose a peer selection protocol that uses a computational trust model as a dishonesty detector to filter out unfair ratings. We prove that such a model with reasonable misclassification error bound in identifying malicious ratings can effectively build trust and cooperation in the system, considering rationality of participants. These results reveal two interesting observations. First, the key to the success of a reputation system in a rational environment is not a sophisticated trust learning mechanism, but an effective identity management scheme to prevent whitewashing behaviors. Second, given an appropriate identity management mechanism, a reputation-based trust model with a moderate accuracy bound can be used to enforce cooperation effectively in systems with both rational and malicious participants. As a result, in heterogeneous environments where peers use different algorithms to detect misbehavior of potential partners, cooperation may still emerge. We verify and extend these theoretical results to a variety of settings involving honest, malicious and strategic players through extensive simulation. These results will enable a much more targeted, cost-effective and realistic design for decentralized trust management systems, such as needed for peer-to-peer, electronic commerce or community systems

Towards Probabilistic Estimation of Quality of Online Services

Accurate estimation of quality of online services is both an important and difficult problem, since a service has many interdependent quality attributes influenced by several contextual factors. It is even more challenging as quality ratings come from sources with unknown reliability, each source may rate a service on different quality aspects. Although several solutions have been proposed, there is little work addressing all these issues thoroughly. In this paper, we show that domain knowledge on service structure and related constraints, such as causal dependencies among quality attributes and contextual factors, while widely available, can be exploited to effectively address the above issues in a theoretically-sound framework. Theoretical analysis shows that computational cost of the approach is acceptable, and accurate evaluation of service quality requires a reasonable number of user feedback, provided services have a small number of quality attributes and contextual factors

QoS-Aware Utility-Based Resource Allocation in Mixed-Traffic Multi-User OFDM Systems

This paper deals with the joint subcarrier and power allocation problem in a downlink multi-user orthogonal frequency division multiplexing system subject to user delay and minimum rate quality-of-service (QoS) requirements over a frequency-selective multi-carrier fading channel. We aim to maximize the utility-pricing function, formulated as the difference between the achieved spectral efficiency and the associated linear cost function of transmit power scaled by a system-dependent parameter. For a homogeneous system, we show that the joint resource allocation can be broken down into sequential problems while retaining the optimality. Specifically, the optimal solution is obtained by first assigning each subcarrier to the user with the best channel gain. Subsequently, the transmit power for each subcarrier is adapted according to water-filling policy if the global optimum is feasible, else it is given by a nonwater-filling power adaptation. For a heterogeneous system, an optimal solution needs exhaustive search and hence, we resort to two reduced-complexity sub-optimal algorithms. Algorithm-I is a simple extension of the aforementioned optimal algorithm developed for a homogeneous system, while Algorithm-II further takes into consideration the heterogeneity in user QoS requirements for performance enhancement. Simulation results reveal the impacts of user QoS requirements, number of subcarriers and number of users on the system transmit power

Survey on Vietnamese teachers’ perspectives and perceived support during COVID-19

The COVID-19 pandemic has caused unprecedented damage to the educational system worldwide. Besides the measurable economic impacts in the short-term and long-term, there is intangible destruction within educational institutions. In particular, teachers – the most critical intellectual resources of any schools – have to face various types of financial, physical, and mental struggles due to COVID-19. To capture the current context of more than one million Vietnamese teachers during COVID-19, we distributed an e- survey to more than 2,500 randomly selected teachers from two major teacher communities on Facebook from 6th to 11th April 2020. From over 373 responses, we excluded the observations which violated our cross-check questions and retained 294 observations for further analysis. This dataset includes: (i) Demographics of participants; (ii) Teachers' perspectives regarding the operation of teaching activities during the pandemic; (iii) Teachers' received support from their schools, government bodies, other stakeholders such as teacher unions, and parents' associations; and (iv) teachers' evaluation of school readiness toward digital transformation. Further, the dataset was supplemented with an additional question on the teachers' primary source of professional development activities during the pandemic