State of Tennessee, The Budget, Fiscal Year 2010-2011

https://digitalcommons.memphis.edu/govpubs-tn-finance-administration-annual-budget-publications/1034/thumbnail.jp

CPA\u27s handbook of fraud and commercial crime prevention

https://egrove.olemiss.edu/aicpa_guides/1819/thumbnail.jp

Term Association Modelling in Information Retrieval

Many traditional Information Retrieval (IR) models assume that query terms are independent of each other. For those models, a document is normally represented as a bag of words/terms and their frequencies. Although traditional retrieval models can achieve reasonably good performance in many applications, the corresponding independence assumption has limitations. There are some recent studies that investigate how to model term associations/dependencies by proximity measures. However, the modeling of term associations theoretically under the probabilistic retrieval framework is still largely unexplored. In this thesis, I propose a new concept named Cross Term, to model term proximity, with the aim of boosting retrieval performance. With Cross Terms, the association of multiple query terms can be modeled in the same way as a simple unigram term. In particular, an occurrence of a query term is assumed to have an impact on its neighboring text. The degree of the query term impact gradually weakens with increasing distance from the place of occurrence. Shape functions are used to characterize such impacts. Based on this assumption, I first propose a bigram CRoss TErm Retrieval (CRTER2) model for probabilistic IR and a Language model based model CRTER2LM. Specifically, a bigram Cross Term occurs when the corresponding query terms appear close to each other, and its impact can be modeled by the intersection of the respective shape functions of the query terms. Second, I propose a generalized n-gram CRoss TErm Retrieval (CRTERn) model recursively for n query terms where n>2. For n-gram Cross Term, I develop several distance metrics with different properties and employ them in the proposed models for ranking. Third, an enhanced context-sensitive proximity model is proposed to boost the CRTER models, where the contextual relevance of term proximity is studied. The models are validated on several large standard data sets, and show improved performance over other state-of-art approaches. I also discusse the practical impact of the proposed models. The approaches in this thesis can also provide helpful benefit for term association modeling in other domains

CPA\u27s handbook of fraud and commercial crime prevention

https://egrove.olemiss.edu/aicpa_guides/1820/thumbnail.jp

CPA\u27s handbook of fraud and commercial crime prevention

https://egrove.olemiss.edu/aicpa_guides/1823/thumbnail.jp

New hampshire general court, journal of the house of representatives, containing the 2001 session December 6, 2000 through December 12, 2001.

Titles and imprints vary; Some volumes include miscellaneous state documents and reports; Rules of the House of Representative

Senate journal, 6 December 2000.

Titles and imprints vary; Some volumes include miscellaneous state documents and reports; Rules of the Senat

Extension of Quantitative Risk Assessment to the Analysis of External Hazard Factors in the Chemical and Process Industry

The PhD research project is aimed at developing and applying an innovative framework toward Risk Assessment of cascading events within the chemical and process industry, addressing both domino and security-based events. Cascading events are catastrophic accidents, triggered by external hazard factors, including safety-based (i.e., domino) and security-based events. In the chemical industry domain, barriers provide a crucial role for the prevention, control and mitigation of cascading events. Therefore, it is necessary to apply innovative techniques, aimed at the evaluation of barriers technical performance and at their optimal economic allocation, to be inserted within Quantitative Risk Assessment (i.e., QRA). Concerning barriers technical performance, the research activity is aimed at applying Bayesian Networks to safety barriers performance assessment, regarding domino events. Starting from a conventional approach, preliminary applications have been aimed at implementing a Bayesian approach to barriers performance assessment concerning major accidents. Therefore, the approach has been extended to domino accident analysis, in purpose to evaluate the effect of barriers introduction within modelling. The case studies demonstrated that the application of a Bayesian approach provides a very accurate barriers performance assessment within QRA, with reference to external hazard factors driven accidents (i.e., domino events), offering a realistic risk picture. Concerning barriers optimal economic allocation, the research activity is aimed at developing and applying an original economic model for the prevention of security-based cascading events. The model includes security upgrades performance and costs assessment, evaluation of benefits and definition of threat and vulnerability probabilities. The application of economic techniques, by means of cost-benefit and cost-effectiveness analyses, enables barriers optimal allocation within budgets constraints. Validation of the model is provided by application to relevant case studies. Therefore, the model enables defining rational criteria for barriers optimal selection and allocation and its outputs support the inclusion of security hazards within QRA, and related decision-making