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abstract: Access control is one of the most fundamental security mechanisms used in the design and management of modern information systems. However, there still exists an open question on how formal access control models can be automatically analyzed and fully realized in secure system development. Furthermore, specifying and managing access control policies are often error-prone due to the lack of effective analysis mechanisms and tools. In this dissertation, I present an Assurance Management Framework (AMF) that is designed to cope with various assurance management requirements from both access control system development and policy-based computing. On one hand, the AMF framework facilitates comprehensive analysis and thorough realization of formal access control models in secure system development. I demonstrate how this method can be applied to build role-based access control systems by adopting the NIST/ANSI RBAC standard as an underlying security model. On the other hand, the AMF framework ensures the correctness of access control policies in policy-based computing through automated reasoning techniques and anomaly management mechanisms. A systematic method is presented to formulate XACML in Answer Set Programming (ASP) that allows users to leverage off-the-shelf ASP solvers for a variety of analysis services. In addition, I introduce a novel anomaly management mechanism, along with a grid-based visualization approach, which enables systematic and effective detection and resolution of policy anomalies. I further evaluate the AMF framework through modeling and analyzing multiparty access control in Online Social Networks (OSNs). A MultiParty Access Control (MPAC) model is formulated to capture the essence of multiparty authorization requirements in OSNs. In particular, I show how AMF can be applied to OSNs for identifying and resolving privacy conflicts, and representing and reasoning about MPAC model and policy. To demonstrate the feasibility of the proposed methodology, a suite of proof-of-concept prototype systems is implemented as well. / Dissertation/Thesis / Ph.D. Computer Science 2012

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Increasing freight transportation volumes continue to increase problems related to human health, congestion on the transport infrastructure, noise, climate changes, etc. Governments often want to minimize these negative effects, and this wish is expressed in societal goals, e.g., to reach environmental targets. An important instrument for reaching societal goals is governmental control policies, e.g., regulations, taxes and fees, which can influence the behaviour of the actors in a transport chain. Before implementing such control policies, it is crucial to predict their effects in order to make probable that it is a good measure. A review of models that consider the effects of governmental control policies on transportation has been performed which shows that macro-level models are mainly used for this purpose. However, the behaviour of the individual transport chain actors can hardly be captured in such models since the decision making actors are not modelled explicitly. Consequently, the negative effects caused by the decisions taken by the individual transport chain actors are not fully captured in these models. We believe that micro-level models have the potential to bridge the gap between governmental policy-making and the behaviour of transport chain actors. A micro-level model based on agent-technology has been developed which captures the environmental, quality and economical performance in transport chains, given different governmental control policies. The transport chain actors are represented by decision-making agents in the model. Logistical factors for characterizing transport chains have been identified and described according to degree of influence. To illustrate the usage of the micro-level model, simulation experiments based on a real world case have been performed where different levels of governmental control policies are introduced. The simulation results so far have showed that the model seems to simulate the behaviour of the transport chain actors correctly in the studied scenarios. The simulation tool can then be used as a decision support for policy-makers and serve as a complement to existing tools based on macro-level models.

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2010年前，台灣經濟蓬勃發展而帶動了房地產業興盛，台灣房地產在市場上的價格不斷攀升，導致社會普遍關注，為了防止房地產業泡沫化、抑制高房價，政府單位採取了一連串的打房政策，期望這些措施能夠平穩市場上的價格、緩和房價上漲趨勢。政府相關單位在2011年後積極推出因應政策，例如實施課徵奢侈稅、房價實價登錄等等，願房地產業能夠朝著明朗、有條不紊的方向前進。實施這些政策必定會嚴重衝擊到房地產業，房地產業正在經歷嚴格考驗，面對重重困境，建設公司應該如何進行轉型？不動產開發商業同業公會又應該扮演什麼樣的角色？ 　　本研究欲透過文獻分析與市場資料，探討在政府打房政策下，建設公司要如何因應環境而做出轉型。根據研究結果顯示：市場經濟趨勢的改變、避免房地產業所帶來的風險，建設公司能以漸進的方式來做轉型，像是可以併購飯店、投資新建飯店，避開市場瓶頸以創造現金流。不動產開發商業同業公會陪伴建設公司歷經景氣的高峰與谷底，竭盡幫助建設公司面對困境。面對環境的趨勢、法令的變動，不動產開發商業同業公會必須隨時掌握即時資訊，加強監測與分析預警，做好前瞻性的工作，且扮演著與政府協調溝通的角色，地位顯的更相形重要，另外，適時地輔佐各建設公司，凝聚同業力量，亦能使不動產開發商業同業公會更加成長與茁壯。 / Taiwan's booming economy led to the prosperity of the real estate market before 2010. Prices for real estate in Taiwan's market continue to rise, leading to widespread social concern. In order to prevent the burst of real estate bubbles and restrain the effect of high prices, the government adopted a series of housing policies, hoping these measures could stabilize market prices and ease the upward trend of housing prices. 　　Relevant government units then actively introduced responsive policies after 2011, such as the imposition of the luxury tax, and the implementation of registering the actual selling price of real estate, etc., in the hopes that the real estate industry would be clear and methodical in the future. The implementation of these policies will certainly be a serious assault on the real estate industry. While the real estate industry is undergoing severe challenges and facing many difficulties, how the construction companies should transform, and what role the Real Estate Development Association should play are of particular concern. 　　Through citation analysis and market data, this study discusses how the construction companies should transform in response to a changing environment. According to the research result, in the face of changing market economic trends and the risks posed by the real estate industry, construction companies could transform in a progressive way. By merging with hotels and investing in newly-built hotels, they could avoid the bottleneck of the market and generate cash flow. The Real Estate Development Association accompanied the construction companies through both the peak and bottom of prosperity, doing its best to help construction companies face difficulties. 　　Faced with the trends of this environment, as well as changes in the laws and decrees, the Real Estate Development Association must keep abreast of real-time information, reinforce monitoring and pre-warning analysis. It must perform proactively and play a coordinative and communicative role with the government, as it seems relatively more important. In addition, it must assist each construction company duly, not only to build cohesion within this industry, but also to enable the Real Estate Development Association to grow and prosper even more.

A Web-server farm is a specialized facility designed specifically for housing Web servers catering to one or more Internet facing Web sites. In this dissertation, stochastic dynamic programming technique is used to obtain the optimal admission control policy with different classes of customers, and stochastic uid- ow models are used to compute the performance measures in the network. The two types of network traffic considered in this research are streaming (guaranteed bandwidth per connection) and elastic (shares available bandwidth equally among connections). We first obtain the optimal admission control policy using stochastic dynamic programming, in which, based on the number of requests of each type being served, a decision is made whether to allow or deny service to an incoming request. In this subproblem, we consider a xed bandwidth capacity server, which allocates the requested bandwidth to the streaming requests and divides all of the remaining bandwidth equally among all of the elastic requests. The performance metric of interest in this case will be the blocking probability of streaming traffic, which will be computed in order to be able to provide Quality of Service (QoS) guarantees. Next, we obtain bounds on the expected waiting time in the system for elastic requests that enter the system. This will be done at the server level in such a way that the total available bandwidth for the requests is constant. Trace data will be converted to an ON-OFF source and fluid- flow models will be used for this analysis. The results are compared with both the mean waiting time obtained by simulating real data, and the expected waiting time obtained using traditional queueing models. Finally, we consider the network of servers and routers within the Web farm where data from servers flows and merges before getting transmitted to the requesting users via the Internet. We compute the waiting time of the elastic requests at intermediate and edge nodes by obtaining the distribution of the out ow of the upstream node. This out ow distribution is obtained by using a methodology based on minimizing the deviations from the constituent in flows. This analysis also helps us to compute waiting times at different bandwidth capacities, and hence obtain a suitable bandwidth to promise or satisfy the QoS guarantees. This research helps in obtaining performance measures for different traffic classes at a Web-server farm so as to be able to promise or provide QoS guarantees; while at the same time helping in utilizing the resources of the server farms efficiently, thereby reducing the operational costs and increasing energy savings.

Reducing automobile-based gasoline consumption has been a major U.S. public policy issue recently. A key driving force behind policymakers' desire is the concern of environmental externalities and national security. Currently, there are three public policies towards reducing automobile gasoline consumption: raising federal gasoline tax, raising the Corporate Average Fuel Economy (CAFE) Standards and vehicle scrappage subsidies of government to retirement of old vehicles. My research studies the effectiveness of these policies in the United States. Among all polices, economists often argue that higher gasoline tax would be more effective in improving fuel economy efficiency. In my first chapter, I ask how gasoline prices influence households' automobile replacement decisions and thus market fuel economy efficiency, which is measured by average mileage per gallon in a city. I specify and estimate a structural dynamic model of consumer preference for new and used vehicles following the methodology proposed by Gowrisankaran and Rysman (2009). Since gasoline costs accounts for 65% of total operating costs, the current and future gasoline price must need to be taken into consideration for rational forward-looking consumers when they are making vehicle choices. Besides, the replacement decision for vehicles is dynamic as well: facing depreciation as the automobile ages and the improving features for new products, consumers need to decide whether to replace the vehicle in the current period or later. Therefore, a dynamic model of consumer choice would be crucial to correct policy evaluation of fuel economy efficiency, while previous literature fails to consider the dynamics. By taking dynamics into consideration, I am able to capture the inherent dynamic nature of a forward-looking consumer's decision, with rational expectation on the evolution of vehicle attributes and retail gasoline prices. I estimate the model using a rich dataset combing vehicle registration data on different cities, vehicle characteristic data, average gasoline price, etc. Although a high gasoline tax is never put in practice in the U.S. and may not be political feasible, I further conduct an experiment of raising gasoline tax to test how fuel economy efficiency is affected based on my model estimates. Experiments suggest that keeping a $4 gasoline price would result in a steady trend for a city's fleet fuel efficiency increase, while doubling current rate will only increase fuel efficiency in the first several years, but experience drops over time. The Corporate Average Fuel Economy (CAFE) are regulations in the United States that intended to improve the average fuel economy of cars and light trucks sold in the US. However, it is long been realized that with a more fuel efficiency car, consumers may be induced to drive more which partially offsets the original energy saving by the policy. Therefore, to assess the effectiveness of CAFE standards, it is crucial to ask: how fuel economy efficiency, which is measured by mileage per gallon (MPG), affects households' vehicle mileage traveled and its distribution. In my second chapter, I answer the question by estimating a structural model for joint determination of vehicle fuel efficiency choice and vehicle mileage traveled each year with a detailed micro-level data of National Household Travel Survey 2001. I further study the distributional effects on vehicle miles of fuel efficiency using instrumental quantile regression. Comparison on results and tests of weak instruments between my method and literature suggest that my model and choice of instruments provide consistent estimates, while using choice probabilities as instruments is not valid. My results support some earlier findings of rebound effects with a more precise quantitative estimation. In addition, I find new evidence that costs associated with raising CAFE standards vary across different quantiles of annual mileage driven and are especially high for those with below-average vehicle mileage driven. These findings also provide rationale in support of a tax on mileage, which is more effective in reducing gasoline consumptions, comparing to the costs of CAFE standards. My third chapter focus on 2009 CARS Program (Cash-for-Clunker). The 2009 CARS program attempted to boost the sale of new fuel efficient vehicles to replace old gas guzzlers. The program established a two-tier incentive system depending on whether buyers purchased a passenger vehicle or an SUV. The result is that many of the new purchased vehicles are indeed SUVs. The CARS program collected information about the old scrapped vehicles and linked it to the actual purchase of the new vehicles. It is thus possible to analyze the effect of preference inertia in choices by comparing the characteristics of old and new vehicles. The fact that effective prices that consumers face are determined by the mileage class of the old car also allows us to evaluate the distribution of valuation trade-offs between mileage and other characteristics such as size, performance, and vehicle class. My findings suggest that the 2009 Cash-for-Clunker is not very effective in terms of affecting consumers' choice of SUVs and big cars. For transactions under the program, consumers still prefer SUVs and large cars. The extra $1000 rebates actually increase consumers' tastes towards SUVs. / text

Thesis (Ph.D.)--University of Melbourne, Dept. of Criminology, 2007. / Typescript. Includes bibliographical references (leaves 276-305).

The concept of “Environmental authoritarian” becomes the focus of discussion in the academic field of environmental politics since it has limited observations. In the recent years, the severe air pollute issue is considered as a threat by both citizens and authorities in China. Along with the more stricter environmental protection laws being issued, even the measures of the factory closings and coal ban seems to become acceptable as the population  have suffered in the heavy smog for years. Does this led to the rise of “environmental authoritarian” in China?  Taking this as a heuristic point, I observe the positions of various stakeholders in the environmental governance as well as present the findings from research on the implementation and enforcement of air pollution controls measures in Jing-Jin-Ji region. I offer a critical examination of “environmental authoritarian”, especially the levels of public participation at the policy process. After to do the research, the key funding is that the concept of “Environmental authoritarian” is too simplified, it cannot cover the complex and detailed environmental governance at local empirical cases.