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For the adaptation of rapid environment variation and human affairs cost reduction, the tendency toward introducing flexible workaround and the atypical employee for the government departments already becomes quite obviously, but in the enforcement process should try to avoid causing the policy value conflicts, the opposition between workers and capitalists, and social conflicts. Therefore, it needs to have the complete promoting policy and suitable methods to the transformation of the flexible rules of choosing talent on order to strive for society approval. The research object focuses on the official staff and the atypical employee who deal with environment cleaning affairs in New Taipei City, Taichung City, and Kaohsiung City. For the purpose of understanding the thoughts of the official staff and the atypical employee, the discussion for the individual attribute, transformation leadership behavior, affective organizational commitment and organizational citizenship behavior is regarding as the reference of flexible human resource usage of government departments. The following are the discoveries in this research results. 1.The ¡§affective organizational commitment¡¨ for the official staff and atypical employee has obviously influences on ¡§altruism behavior¡¨ and the influences on the atypical employee are greater than the official staff. 2.The ¡§inspirational motivation and intellectual stimulation¡¨ for the official staff and atypical employee have obviously influences on ¡§altruism behavior¡¨ and the influences on the atypical employee are greater than the official staff. 3.The ¡§affective organizational commitment¡¨ has remarkable influence on the ¡§seriousness and responsibility¡¨ of the atypical employee, but not for the official staff. These results show that the employment identity plays an important role exactly in the employment relations. Different employment identity will affect employee not only how they treat the relationship between themselves and employers but also the responses on their behaviors. This research focuses on the discussion on the research results, and also brings up some advices for government departments on physical future researches of flexible human resource usage.

Feature selection is a pre-processing step in data-mining and machine learning, and plays an important role for analyzing high-dimensional data. Appropriately selected features can not only reduce the complexity of the mining or learning process, but also improve the accuracy of results. In the past, the concept of performing the task of feature selection by attribute clustering was proposed. If similar attributes could be clustered into groups, attributes could be easily replaced by others in the same group when some attribute values were missed. Hong et al. also proposed several genetic algorithms for finding appropriate attribute clusters. Their approaches, however, suffered from the weakness that multiple chromosomes would represent the same attribute clustering result (feasible solution) due to the combinatorial property, thus causing a larger search space than needed. In this thesis, we thus attempt to improve the performance of the GA-based attribute-clustering process based on the grouping genetic algorithm (GGA). Two GGA-based attribute clustering approaches are proposed. In the first approach, the general GGA representation and operators are used to reduce the redundancy of chromosome representation for attribute clustering. In the second approach, a new encoding scheme with corresponding crossover and mutation operators are designed, and an improved fitness function is proposed to achieve better convergence speed and provide more flexible alternatives than the first one. At last, experiments are made to compare the efficiency and the accuracy of the proposed approaches and the previous ones.

Cloud computing has been developed rapidly in recent years, and offers novel concepts and innovations in computer use. One application of cloud computing is that people can designate a proxy to help them to execute a number of tasks in certain situations instead of undertaking all tasks themselves. With this application, people can benefit from the proxy; however, some information is revealed to the proxy, such as their activities, and private data. That is, the proxy is aware of the actions of people through delegation processes, and proxy re-encryption which is a cryptographic primitive has been proposed to solve this problem. In the proxy re-encryption system, when a user (e.g., Alice) wants to send a ciphertext that is encrypted by her secret key and stored in the cloud to another user (e.g., Bob), she can designate a proxy to transform the ciphertext into a different ciphertext that can be decrypted by Bob¡¦s private key. Based on attribute-based encryption and proxy re-encryption, we propose attribute-based proxy re-encryption with bilinear pairing. Furthermore, in the proposed scheme, third paries cannot decrypt the ciphertext if they do no have matching attributes, regardless of being helped by proxy. Finally, we offer security proofs to demonstrate that the proposed scheme satisfies the essential requirements of attribute-based encryption schemes and proxy re-encryption schemes.

One of the petroleum industry?s goals is to reduce the environmental impact of oil and gas operations in environmentally sensitive areas. To achieve this, a number of Environmentally Friendly Drilling (EFD) technologies have been developed to varying degrees. For example, the use of an elevated platform as an alternative to the gravel pad is less intrusive and leads to a more environmentally friendly approach to drilling operations. Elevated drilling platforms will require the use of piles. Another alternative to the gravel pad is the use of composite mats. Since the demand of low impact technologies for drill site construction has rapidly increased, the parametric study for the feasibility of using pile foundations and composite mats is conducted in this research. Even though a number of EFD technologies have already been developed to varying degrees, few have been integrated into a field demonstrable drilling system (i.e., combination of technologies) compatible with ecologically sensitive areas. In general, it is difficult to select the best combination of EFD technologies for a given site because there are many possible combinations and many different evaluation criteria. The proposed technology evaluation method is based on a systems analysis that can be used for integrating current and new EFD technologies into an optimal EFD system. An optimization scheme is suggested based on a combination of multi-attribute utility theory and exhaustively enumerating all possible technology combinations to provide a quantitative rationale and suggest the best set of systems according to a set of criteria, with the relative importance of the different criteria defined by the decision-maker. In this research, the sensitivity of the optimal solution to the weight factors and the effects of the uncertainty of input scores are also discussed using a case study. An application of the proposed approach is described by conducting a case study in Green Lake at McFaddin, TX. The main purpose of this case study is to test the proposed technology evaluation protocol in a real site and then to refine the protocol. This research describes the results of the case study which provided a more logical and comprehensive approach that maximized the economic and environmental goals of both the landowner and the oil company leaseholder.

This research aims at exploring the interaction and differences of the relations among knowledge protection, relationship capital, strategic attribute of knowledge, and alliance performance. Through a mail survey, 95 effective samples of cross-border learning alliances of Taiwanese hi-tech firms were collected and analyzed. Using regression and simultaneous equations models, the proposed model was tested. The main conclusions are presented as follows: 1.Communication, trust, and commitment construct the determinants of relationship capital. 2.Knowledge which has more tacit, complexity, and special attributes is a more significant in strategic asset. 3.It is significant that the strategic attribute of knowledge will guarantee a influence on the relationship capital 4.It follows that the strategic attribute of knowledge that is transferred by alliances maintain a positive influence on knowledge protection by the target focus firm. 5.Relationship capital will guarantee a positive influence apparently on knowledge protection by the focus firm. 6.The higher the degree of relationship capital between alliance partners, the greater the alliance performance between the partners 7.It follows that the higher the degree of individual knowledge protection between alliance partners, the poorer the alliance performance between the partners.

Capability Maturity Model Integrated (CMMI) developed by Software Engineering Institute of Carnegie Mellon University is aimed to improve the productivity of software development. From CMM to CMMI, they have been adopted worldwide. But the large numbers of unsuccessful and/or painful implementations have proved these implementations easier said than done. The software industry in Taiwan faces the challenge of global competition. Continuous improvement of productivity becomes inevitable. To achieve this goal, recently, government and non-government organizations together promote the CMMI. This thesis focuses on the Decision Analysis and Resolution of CMMI Level 3. Through the design of related methods and the development of software tools, the decision process can be more structured and more effective. Potential applications include evaluation and selection of software and hardware, outsourcing partner, tools, risk resolution alternatives or people. The typical Go/No-Go decision can also fit into it. The decision analysis methods included in this research are Multi-attribute Decision Making and Analytic Hierarchy Process(AHP). In addition, alternative screening and group decision making can be integrated into the above methods.

There is a growing interest in electronic auctions in the literature. Many researchers work on the single attribute version of the problem. Multi-attribute version of the problem is more realistic. However, this brings a substantial difficulty in solving the problem. In order to overcome the computational difficulties, we develop an Evolutionary Algorithm (EA) for the case of multi-attribute multi-item reverse auctions. We generate the whole Pareto front using the EA. We also develop heuristic procedures to find several good initial solutions and insert those in the initial population of the EA. We test the EA on a number of randomly generated problems and compare the results with the true Pareto optimal front obtained by solving a series of integer programs. We also develop an exact interactive approach that provides aid both to the buyer and the sellers for a multi-attribute single item multi round reverse auction. The buyer decides on the provisional winner at each round. Then the approach provides support in terms of all attributes to each seller to be competitive in the next round of the auction.

This thesis analyzes how parallel processing with Graphics Processing Unit (GPU) could be used for massive crowd simulation, not only in terms of rendering but also the computational power that is required for realistic simulation. The extreme population in massive crowd simulation introduces an extra computational load, which is quite difficult to meet by using Central Processing Unit (CPU) resources only. The thesis shows the specific methods and approaches that maximize the throughput of GPU parallel computing, while using GPU as the main processor for massive crowd simulation. The methodology introduced in this thesis makes it possible to simulate and visualize hundreds of thousands of virtual characters in real-time. In order to achieve two orders of magnitude speedups by using GPU parallel processing, various stream compaction and effective memory access approaches were employed. To simulate crowd behavior, fuzzy logic functionality on the GPU has been implemented from scratch. This implementation is capable of computing more than half billion fuzzy inferences per second.

In this study, we address the long-term project pricing problem for a company that operates in the defense industry. The pricing problem is a bid project pricing problem which includes various technical and financial uncertainties, such as estimations of workhour content of the project and exchange &amp / inflation rates. We propose a Robust Optimization (RO) approach that can deal with the uncertainties during the project lifecycle through the identification of several discrete scenarios. The bid project&rsquo / s performance measures, other than the monetary measures, for R&amp / D projects are identified and the problem is formulated as a multi-attribute utility project pricing problem. In our RO approach, the bid pricing problem is decomposed into two parts which are v solved sequentially: the Penalty-Model, and the RO model. In the Penalty-Model, penalty costs for the possible violations in the company&rsquo / s workforce level due to the bid project&rsquo / s workhour requirements are determined. Then the RO model searches for the optimum bid price by considering the penalty cost from the Penalty-Model, the bid project&rsquo / s performance measures, the probability of winning the bid for a given bid price and the deviations in the bid project&rsquo / s cost. Especially for the R&amp / D type projects, the model tends to place lower bid prices in the expected value solutions in order to win the bid. Thus, due to the possible deviations in the project cost, R&amp / D projects have a high probability of suffering from a financial loss in the expected value solutions. However, the robust solutions provide results which are more aware of the deviations in the bid project&rsquo / s cost and thus eliminate the financial risks by making a tradeoff between the bid project&rsquo / s benefits, probability of winning the bid and the financial loss risk. Results for the probability of winning in the robust solutions are observed to be lower than the expected value solutions, whereas expected value solutions have higher probabilities of suffering from a financial loss.