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Intelligence analysts today are faced with many challenges, chief among them being the need to fuse disparate streams of data and rapidly arrive at analytical decisions and quantitative predictions for use by policy makers. A forecasting tool to anticipate key events of interest is an invaluable aid in helping analysts cut through the chatter. We present the design of user interfaces for the EMBERS system, an anticipatory intelligence system that ingests myriad open source data streams (e.g., news, blogs, tweets, economic and financial indicators, search trends) to generate forecasts of significant societal-level events such as disease outbreaks, protests, and elections. A key research issue in EMBERS is not just to generate high-quality forecasts but provide interfaces for analysts so they can understand the rationale behind these forecasts and pose why, what-if, and other exploratory questions. This thesis presents the design and implementation of three visualization interfaces for EMBERS. First, we illustrate how the rationale behind forecasts can be presented to users through the use of an audit trail and its associated visualization. The audit trail enables an analyst to drill-down from a final forecast down to the raw (and processed) data sources that contributed to the forecast. Second, we present a forensics tool called Reverse OSI that enables analysts to investigate if there was additional information either in existing or new data sources that can be used to improve forecasting. Unlike the audit trail which captures the transduction of data from raw feeds into alerts, Reverse OSI enables us to posit connections from (missed) forecasts back to raw feeds. Finally, we present an interactive machine learning approach for analysts to steer the construction of machine learning mod-els. This provides fine-grained control into tuning tradeoffs underlying EMBERS. Together, these three interfaces support a range of functionality in EMBERS, from visualization of algorithm output to a complete framework for user feedback via a tight human-algorithm loop. They are currently being utilized by a range of user groups in EMBERS: analysts, social scientists, and machine learning developers, respectively. / Master of Science

Introdução: As Instituições Federais de Ensino Superior (IFES) são organizações complexas, sendo este o motivo do surgimento de inúmeras metodologias de controle e avaliação em seu âmbito. Objetivo: Avaliar os resultados dos indicadores de gestão e de qualidade das IFES, período 2009-2016, identificando quais variáveis mais impactam no indicador de qualidade. Métodos: Os dados foram coletados junto ao site das IFES, Ministério da Educação, Sistema Integrado de Monitoramento Execução e Controle do Ministério da Educação. As variáveis de gestão foram os nove indicadores propostos pelo Tribunal de Contas da União (TCU) e os Indicadores de qualidade foram os empregados pelo Instituto Nacional de Estudos e Pesquisas Educacionais (INEP). Foram empregadas estatísticas descritivas, análise de correlação entre as variáveis, e para identificar a capacidade de explicação do indicador de qualidade, através dos indicadores de gestão, regressão linear múltipla. Resultados: Quanto à capacidade de explicação do Índice Geral de Cursos (IGC) se obteve: para as IFES de pequeno porte, um R² ajustado de 0,458, que teve como previsores o Índice de Qualidade do Corpo Docente (IQCD)=0,520; Conceito CAPES (CO.CAPES)= 0,271, Taxa de Sucesso na Graduação (TSG)=0,138. Para o médio porte, um R² ajustado de 0,761, teve como previsores, IQCD=0,520; CO.CAPES=0,456; TSG=0,250; Aluno Temo Integral por Professor Equivalente (ALIxPROF)= -0,098. Para as instituições de grande porte, o R² ajustado foi de 0,858, tendo como previsores CO.CAPES=0,692; TSG=-0,213, Grau de Envolvimento na Pós-Graduação (GEPG)= 0,218, ALIxPROF=0,124. Conclusão: A instituição de maior porte vem obtendo melhores resultados que as de menor porte. As instituições da região Norte, mesmo classificadas como de médio ou grande porte, apresentam resultados muito abaixo das outras IFES de mesmo porte das outras regiões; em contrapartida, as IFES da região Sul e Sudeste se destacam com os melhores resultados. / Introduction: The Brazilian Federal Institutions of Higher Education (IFES) are complex organizations, reason for the appearance of numerous control and evaluation methodologies in their scope. Objective: To evaluate the results of the IFES management and quality indicators, identifying which variables have most impact in the quality indicator during the period 2009-2016. Methods: Data were collected from the website of the IFES, the Ministry of Education and the Integrated Monitoring and Enforcement System of the Ministry of Education. The management variables were the nine indicators proposed by the Federal Audit Court (TCU) and the quality Indicators were those used by the National Institute of Educational Studies and Research (INEP). Were used descriptive statistics, correlation analysis between variables and, to identify the ability to explain the quality indicator through management indicators, multiple linear regression. Results: As for the explanatory capacity of the General Course Index (IGC), a R² of 0.458 was obtained for the small IFES, which had as predictors the Quality Index of the Faculty (IQCD) = 0.520; Concept CAPES (CO.CAPES) = 0.271, Graduation Success Rate (TSG) = 0.138. For the medium-sized company, an adjusted R² of 0.761 had as predictors, IQCD = 0.520; CO.CAPES = 0.456; TSG = 0.250; Full-time Student by Equivalent Teacher (ALIxPROF) = -0.098. For large institutions, adjusted R² was 0.858, with CO.CAPES = 0.692; TSG = -0.213, Degree of Involvement in Postgraduate Studies (GEPG) = 0.218, ALIxPROF = 0.124. Conclusion: The larger institution has obtained better results than the smaller ones. The institutions of the North region, even classified as medium or large, have results well below the other IFES of the same size in the other regions. In contrast, the South and Southeast IFES stand out with the best results.

by Lo Wai-chu, Lo Yiu-hee. / Abstract also in Chinese / Bibliography: leaves 138-139 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1983

Thesis (M. P. A.)--Texas State University-San Marcos, 2009. / "Fall 2009." Includes bibliographical references (leaves 79-82).

Thesis (M. Soc. Sc.)--University of Hong Kong, 1984.

The Envision Central Texas (ECT) scenario-based regional planning process formulated its preferred vision for future growth in Central Texas in 2004. While the process was successful in developing a unified vision for future development for a region undergoing rapid growth, it is questionable whether any implementation has been seen on the ground since. Utilizing the performance indicators originally used to assess the alternative growth scenarios during the ECT planning process, this study attempts to monitor the region’s growth trajectory since that time. In other words, is the region realizing its preferred vision for new population growth and urban development? An analysis using remote sensing of satellite imagery, GIS and available data was undertaken to calculate eleven performance measures for the current regional context. Results of the indicator analysis helped to form a better understanding of the region’s growth since the ECT vision was adopted. For the most part, the region is continuing to grow in its “baseline” or “business as usual” pattern of development, characterized by low-density, separate use development on the urban fringe. Although the ECT vision is still in its early stages of implementation, the region faces challenges in realizing its fulfillment. The findings of this study are the product of the only quantifiable monitoring efforts of ECT implementation to date. They should be used in conjunction with qualitative monitoring already undertaken by ECT to better understand the region’s growth trajectory, and be used as a model for monitoring ECT implementation in the future. Additionally, the analysis offers a discussion of how the use of performance indicators can be improved in future scenario-based planning processes. / text

Thesis (Dr. P.H.)--University of Texas Health Science Center at Houston, School of Public Health, 2007. / Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6622. Adviser: Jan Risser. Includes bibliographical references.

Thesis (Ph. D.)--Victoria University (Melbourne, Vic.), 2007. / Includes bibliographical references.

Thesis (Ph.D.)--Australian National University, 2003.