<p> The term "underground economy" refers to output that is produced, and income that is generated, by agents who hide this fact from authorities. There has been a recent resurgence in interest in the underground economy and this interest has predominantly been stimulated by the perception that the underground economy is sizeable and growing. This dissertation is comprised of three essays, the goals of which are to provide empirical measures of underground activity.</p> <p> The first paper in this dissertation applies a modeling technique that treats the underground economy as an unobservable or latent variable and incorporates multiple indicator and multiple causal (MIMIC) variables to estimate a time-path of the size of broadly defined underground economy. Using macroeconomic Canadian data, the results indicate that the underground economy grew steadily over the sample period: from 7.5% of Gross Domestic Product (GDP) in 1976 to about 15.3% in 2001.</p> <p> The second paper uses microeconomic data and proposes a nonparametric expenditure-based approach to obtain estimates of income under-reporting by self- employed households. The approach is illustrated by estimating the effect of the Canadian Goods and Services Tax (GST) on income under-reporting. It is found that the difference between true and reported self-employment income is larger for households at the lower end of the self-employment income distribution and that there was no statistically significant change in under-reporting behaviour following the implementation of the GST.</p> <p>The third paper investigates the characteristics of businesses that engage in tax non-compliance using a survey of firms from around the world. Overall, small firms tend to be less compliant than larger firms. In addition, foreign owned firms, exporters, and firms that have audited financial statements are found to be more compliant but quite surprisingly, government ownership does not result in increased tax compliance. Finally, the existence of organized crime, high taxes, and government corruption all result in lower compliance.</p> / Thesis / Doctor of Philosophy (PhD)

While progress has been made in querying digital information contained in XML and HTML documents, success in retrieving information from the so called "hidden Web" (data behind Web forms) has been modest. There has been a nascent trend of developing autonomous tools for extracting information from the hidden Web. Automatic tools for ontology generation, wrapper generation, Weborm querying, response gathering, etc., have been reported in recent research. This thesis presents a system called Chameleon for automatic querying of and response gathering from the hidden Web. The approach to response gathering is based on automatic table structure identification, since most information repositories of the hidden Web are structured databases, and so the information returned in response to a query will have regularities. Information extraction from the identified record structures is performed based on domain knowledge corresponding to the domain specified in a query. So called "domain plug-ins" are used to make the dynamically generated wrappers domain-specific, rather than conventionally used document-specific.

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With the development of sequencing technologies, more and more sequence variants are available for investigation. Different types of variants in the human genome have been identified, including single nucleotide polymorphisms (SNPs), short insertions and deletions (indels), and large structural variations such as large duplications and deletions. Of great research interest is the functional effects of these variants. Although many programs have been developed to predict the effect of SNPs, few can be used to predict the effect of indels or multiple variants, such as multiple SNPs, multiple indels, or a combination of both. Moreover, fine grained prediction of the functional outcome of variants is not available. To address these limitations, we developed a prediction framework, HMMvar, to predict the functional effects of coding variants (SNPs or indels), using profile hidden Markov models (HMMs). Based on HMMvar, we proposed HMMvar-multi to explore the joint effects of multiple variants in the same gene. For fine grained functional outcome prediction, we developed HMMvar-func to computationally define and predict four types of functional outcome of a variant: gain, loss, switch, and conservation of function. / Ph. D.

This work studies the consequences of a human-initiated targeted attack on the electric power system by simulating the detonation of a bomb at one or more substations in and around Washington DC. An AC power flow based transient analysis on a realistic power grid model of Eastern Interconnection is considered to study the cascading events. A detailed model of control and protection system in the power grid is considered to ensure the accurate representation of cascading outages. Particularly, the problem of identifying a set of k critical nodes, whose failure/attack leads to the maximum adverse impact on the power system has been analyzed in detail. It is observed that a greedy approach yields node sets with higher criticality than a degree-based approach, which has been suggested in many prior works. Furthermore, it is seen that the impact of a targeted attack exhibits a nonmonotonic behavior as a function of the target set size k. The consideration of hidden failures in the protective relays has revealed that the outage of certain lines/buses in the course of cascading events can save the power grid from a system collapse. Finally, a comparison with the DC steady state analysis of cascading events shows that a transient stability assessment is necessary to obtain the complete picture of cascading events in the aftermath of a targeted attack on the power grid. / M.S. / The modern day power system has been identified as a critical infrastructure providing crucial support to the economy of a country. Prior experience has shown that a small failure of a component in the power grid can lead to widespread cascading events and eventually result in a blackout. Such failures can be triggered by devastating damage due to a natural calamity or because of a targeted adversarial attack on certain points in the power system. Given limited budget to avoid widespread cascading failures in the network, an important problem would be to identify critical components in the power system. In this research an attempt has been made to replicate the actual power system conditions as accurately as possible to study the impact of a targeted adversarial attack on different points in the network. Three heuristics have been proposed to identify critical nodes in the network and their performance has been discussed. The case studies of cascading events have been performed on a synthetic power system network of Washington DC to achieve the actual system conditions of an operating power grid.

This thesis presents novel methods for creating and improving hierarchical hidden Markov models. The work centers around transforming a traditional tree structured hierarchical hidden Markov model (HHMM) into an equivalent model that reuses repeated sub-trees. This process temporarily breaks the tree structure constraint in order to leverage the benefits of combining repeated sub-trees. These benefits include lowered cost of testing and an increased accuracy of the final model-thus providing the model with greater performance. The result is called a merged and simplified hierarchical hidden Markov model (MSHHMM). The thesis goes on to detail four techniques for improving the performance of MSHHMMs when applied to information extraction tasks, in terms of accuracy and computational cost. Briefly, these techniques are: a new formula for calculating the approximate probability of previously unseen events; pattern generalisation to transform observations, thus increasing testing speed and prediction accuracy; restructuring states to focus on state transitions; and an automated flattening technique for reducing the complexity of HHMMs. The basic model and four improvements are evaluated by applying them to the well-known information extraction tasks of Reference Tagging and Text Chunking. In both tasks, MSHHMMs show consistently good performance across varying sizes of training data. In the case of Reference Tagging, the accuracy of the MSHHMM is comparable to other methods. However, when the volume of training data is limited, MSHHMMs maintain high accuracy whereas other methods show a significant decrease. These accuracy gains were achieved without any significant increase in processing time. For the Text Chunking task the accuracy of the MSHHMM was again comparable to other methods. However, the other methods incurred much higher processing delays compared to the MSHHMM. The results of these practical experiments demonstrate the benefits of the new method-increased accuracy, lower computation costs, and better performance.

An ergodic hidden Markov model (EHMM) can be useful in extracting underlying structure embedded in connected speech without the need for a time-aligned transcribed corpus. In this research, we present a query-by-example (QbE) spoken term detection system based on an ergodic hidden Markov model of speech. An EHMM-based representation of speech is not invariant to speaker-dependent variations due to the unsupervised nature of the training. Consequently, a single phoneme may be mapped to a number of EHMM states. The effects of speaker-dependent and context-induced variation in speech on its EHMM-based representation have been studied and used to devise schemes to minimize these variations. Speaker-invariance can be introduced into the system by identifying states with similar perceptual characteristics. In this research, two unsupervised clustering schemes have been proposed to identify perceptually similar states in an EHMM. A search framework, consisting of a graphical keyword modeling scheme and a modified Viterbi algorithm, has also been implemented. An EHMM-based QbE system has been compared to the state-of-the-art and has been demonstrated to have higher precisions than those based on static clustering schemes.

ABSTRACT OF THE DISSERTATION OF Yonglian Wang, for Doctor of Philosophy degree in Electrical and Computer Engineering, presented on May 19, 2009, at Southern Illinois University- Carbondale. TITLE: SPEECH RECOGNITION UNDER STRESS MAJOR PROFESSOR: Dr. Nazeih M. Botros In this dissertation, three techniques, Dynamic Time Warping (DTW), Hidden Markov Models (HMM), and Hidden Control Neural Network (HCNN) are utilized to realize talker-independent isolated word recognition. DTW is a technique utilized to measure the distance between two input patterns or vectors; HMM is a tool utilized to model speech signals using stochastic process in five states to compare the similarity between signals; and HCNN calculates the errors between actual output and target output and it is mainly built for the stress compensated speech recognition. When stress (Angry, Question and Soft) is induced into the normal talking speech, speech recognition performance degrades greatly. Therefore hypothesis driven approach, a stress compensation technique is introduced to cancel the distortion caused by stress. The database for this research is SUSAS (Speech under Simulated and Actual Stress) which includes five domains encompassing a wide variety of stress, 16,000 isolated-word speech signal samples available from 44 speakers. Another database, called TIMIT (10 speakers and 6300 sentences in total) is used as a minor in DTW algorithm. The words used for speech recognition are speaker-independent. The characteristic feature analysis has been carried out in three domains: pitch, intensity， and glottal spectrum. The results showed that speech spoken under angry and question stress indicates extremely wide fluctuations with average higher pitch, higher RMS intensity, and more energy compared to neutral. In contrast, the soft talking style has lower pitch, lower RMS intensity, and less energy compared to neutral. The Linear Predictive Coding (LPC) cepstral feature analysis is used to obtain the observation vector and the input vector for DTW, HMM, and stress compensation. Both HMM and HCNN consist of training and recognition stages. Training stage is to form references, while recognition stage is to compare an unknown word against all the reference models. The unknown word is recognized by the model with highest similarity. Our results showed that HMM technique can achieve 91% recognition rate for Normal speech; however, the recognition rate dropped to 60% for Angry stress condition, 65% for Question stress condition, and 76% for Soft stress condition. After compensation was applied for the cepstral tilts, the recognition rate increased by 10% for Angry stress condition, 8% for Question stress condition, and 4% for Soft stress condition. Finally, HCNN technique increased the recognition rate to 90% for Angry stress condition and it also differentiated the Angry stress from other stress group.