In this paper, I will explore the hidden costs and risks of IT offshoring and how to manage those costs so that realistic expectations can be set. Establishing a baseline of such expectations will help companies understand and follow best practices in planning, executing, and managing the full offshore lifecycle. While details will vary with each project, offshore vendor, and company, these costs are seen across most offshore ventures and, if understood, will help explain why so many offshoring attempts fail, help IT managers and decision makers anticipate potential problems, and help CIOs more accurately predict cost savings. / text

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This paper investigates optimal portfolio strategies in a market with partial information on the drift. The drift is modelled as a function of a continuous-time Markov chain with finitely many states which is not directly observable. Information on the drift is obtained from the observation of stock prices. Moreover, expert opinions in the form of signals at random discrete time points are included in the analysis. We derive the filtering equation for the return process and incorporate the filter into the state variables of the optimization problem. This problem is studied with dynamic programming methods. In particular, we propose a policy improvement method to obtain computable approximations of the optimal strategy. Numerical results are presented at the end. (author's abstract)

This thesis presents a method to investigate the extent to which articulatory based acoustic features can be exploited to reduce ambiguity in automatic speech recognition search. The method proposed is based on a lattice re-scoring paradigm implemented to integrate articulatory based features into automatic speech recognition systems. Time delay neural networks are trained as feature detectors to generate feature streams over which hidden Markov models (HMMs) are defined. These articulatory based HMMs are combined with HMMs defined over spectral energy based Mel frequency cepstrum coefficient (MFCC) acoustic features through a sequential lattice re-scoring procedure. The optimum phone strings are found by maximizing the log-linear combination of acoustic and language models likelihoods during recognition. The associated log-linear weights are estimated using a discriminative model combination approach. All the experiments are performed using the DARPA TIMIT speech database and the results are presented in terms of phone accuracies.

In this work, we mostly concentrate on the hidden shift problem for non-injective functions. It is worthwhile to know that the query complexity of the non-injective hidden shift problem is exponential in the worst case by the well known bounds on the unstructured search problem. Hence, we can make this problem more tractable by imposing additional constraints on the problem. Perhaps the first constraint that comes to mind is to address the average case problem. In this work, we show that the average case non-injective hidden shift problem can be reduced to the injective hidden shift problem by giving one such reduction. The reduction is based on a tool we developed called injectivization. The result is strong in the sense that the underlying group can be any finite group and that the non-injective functions for which we have defined the hidden shift problem can have range in an arbitrary finite set. Using this tool, we simplify the main result of a recent paper by about the hidden shift problem for Boolean-valued functions by reducing that problem to Simon's problem. They also posed an open question which is subject to personal interpretation. We answer the seemingly most general interpretation of the question. However, we use our own techniques in doing so (the authors ask if their techniques can be used for addressing that problem). Another constraint that one can consider is to have a promise on the structure of the functions. In this work we consider the hidden shift problem for c-almost generalized bent functions. A class of functions which we defined that includes the generalized bent functions. Then we turn our attention toward the generalized hidden shift problem which is easier than injective hidden shift problem and hence more tractable. We state some of our observations about this problem. Finally we show that the average classical query complexity of the non-injective hidden shift problem over groups of form (Z/mZ)^n when m is a constant is exponential, which also immediately implies that the classical average query complexity of the non-injective hidden shift problem is exponential. We also show that the worst-case classical query complexity of the generalized injective hidden shift problem over the same group is high, which implies that the classical query complexity of the hidden shift problem is high.

Thesis (Ph. D.)--University of Wyoming, 2005. / Title from PDF title page (viewed on March 10, 2008). Includes bibliographical references (p. 107-111).

Thesis (doctoral)--Johannes-Gutenberg-Universität, Mainz, 2007. / Includes bibliographical references (p. [395]-423) and register.

Thesis (M.Eng.(Mechanical Engineering))--University of Pretoria, 2004. / Summaries in Afrikaans and English. Includes bibliographical references (leaves 80-82).

Thesis (M.A.)--Trinity Evangelical Divinity School, 1992. / Abstract. Includes bibliographical references (leaves 104-115).

Thesis (Th. M.)--Dallas Theological Seminary, 2003. / Includes bibliographical references (leaves 102-113).