Co-ordinated management of intelligent pervasive spaces

The content of this paper is a snapshot of a current project looking at producing a real-time sensor-based building assessment tool, and a system that personalises work-spaces using multi-agent technology. Both systems derive physical environment information from a wireless sensor network that allows clients to subscribe to real-time sensed data. The principal ideologies behind this project are energy efficiency and well-being of occupants; in the context of leveraging the current state-of-the-art in agent technology, wireless sensor networks and building assessment systems to enable the optimisation and assessment of buildings. Participants of this project are from both industry (construction and research) and academia.

Políticas para avaliação da qualidade do Ensino Superior no Brasil: um balanço crítico

Este trabalho tem como objetivo contribuir para o debate sobre a avaliação do Ensino Superior no Brasil, apresentando um estudo a respeito dos sistemas utilizados para as medidas de qualidade e produtividade. Busca-se analisar, por meio da revisão bibliográfica e da análise documental, as origens do processo de avaliação, a seqüência histórica dos debates políticos que definiram os programas de trabalho na área, as concepções metodológicas adotadas por esses programas, os instrumentos de mensuração e acompanhamento que foram desenvolvidos e os sistemas de indicadores criados para aferir a qualidade de ensino nas instituições, bem como o desempenho dos estudantes. A discussão contempla todos os sistemas de avaliação praticados no Brasil até o ano de 2005 e conclui que, desde os primeiros procedimentos estabelecidos, houve uma evolução contínua na definição de indicadores mais precisos e eficientes. Os resultados alcançados pela pesquisa que fundamenta o trabalho aplicam-se explicitamente à revisão dos instrumentos de avaliação praticados no Brasil. O trabalho sugere indicadores ainda não utilizados no processo histórico de avaliação, buscando o aperfeiçoamento do sistema atual.

Gestão municipal, qualidade de ensino e a avaliação do rendimento escolar: um estudo do município de Cosmorama

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

O Sistema de Avaliação de Rendimento Escolar do Estado de São Paulo (SARESP) e suas implicações no contexto de uma escola estadual no município de Osvaldo Cruz/SP

Pós-graduação em Educação - FCT

O olhar do professor e dos alunos do ensino médio sobre o currículo de educação física do estado de São Paulo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Corporalidades, desejos e políticas de subversão

This paper raises questions about the ways in which corporeality and processes desiring were built within binary systems, universalistic and subjected to imprisonment restricted to heteronormativity and phallocentrism, and most often, the only references that guide the schools, their curricula and assessment systems. Critically and expanded the questions between the schools, and their agents and their expressions of dissent corporeality, gender, sexuality, gender and other markers of social stigmas. Presents readings that show that even still conservative schools already produce programs that facilitate discussions on diversity and human and allow you to create pedagogies and educational policies that may be secular, democratic and inclusive

What's Working in Family-Based Services--or, What's Left to Believe in During a Time of Such Doubt?

This article is a qualitative, personal report from the field, designed to highlight current developments in family-based theory and practice that bring hopefulness to workers and clients. The author, an experienced human services consultant and family therapist, draws from his recent experience in a number of states to identify exemplars of practice in the following areas: integrative theory building, functional family assessment, systems change in regard to inter-agency coordination and foster care, community building in low income neighborhoods, developing humility as helpers, and addressing issues of hope and spirituality with clients and with co-workers. Given the turbulent and hostile political environment for family-based services, this article challenges us to remember that effectiveness in helping others is directly related to our feelings of hopefulness about ourselves and our world.

How can video analysis help laparoscopic surgeons?

Automatic analysis of minimally invasive surgical (MIS) video has the potential to drive new solutions that alleviate existing needs for safer surgeries: reproducible training programs, objective and transparent assessment systems and navigation tools to assist surgeons and improve patient safety. As an unobtrusive, always available source of information in the operating room (OR), this research proposes the use of surgical video for extracting useful information during surgical operations. Methodology proposed includes tools' tracking algorithm and 3D reconstruction of the surgical field. The motivation for these solutions is the augmentation of the laparoscopic view in order to provide orientation aids, optimal surgical path visualization, or preoperative virtual models overlay

Minimally invasive surgical video analysis: a powerful tool for surgical training and navigation

Analysis of minimally invasive surgical videos is a powerful tool to drive new solutions for achieving reproducible training programs, objective and transparent assessment systems and navigation tools to assist surgeons and improve patient safety. This paper presents how video analysis contributes to the development of new cognitive and motor training and assessment programs as well as new paradigms for image-guided surgery.

Minimally invasive surgical video analysis: a powerful tool for surgical training and navigation

Analysis of minimally invasive surgical videos is a powerful tool to drive new solutions for achieving reproducible training programs, objective and transparent assessment systems and navigation tools to assist surgeons and improve patient safety. This paper presents how video analysis contributes to the development of new cognitive and motor training and assessment programs as well as new paradigms for image-guided surgery.

Machine Learning in Scientometrics

El aprendizaje automático y la cienciometría son las disciplinas científicas que se tratan en esta tesis. El aprendizaje automático trata sobre la construcción y el estudio de algoritmos que puedan aprender a partir de datos, mientras que la cienciometría se ocupa principalmente del análisis de la ciencia desde una perspectiva cuantitativa. Hoy en día, los avances en el aprendizaje automático proporcionan las herramientas matemáticas y estadísticas para trabajar correctamente con la gran cantidad de datos cienciométricos almacenados en bases de datos bibliográficas. En este contexto, el uso de nuevos métodos de aprendizaje automático en aplicaciones de cienciometría es el foco de atención de esta tesis doctoral. Esta tesis propone nuevas contribuciones en el aprendizaje automático que podrían arrojar luz sobre el área de la cienciometría. Estas contribuciones están divididas en tres partes: Varios modelos supervisados (in)sensibles al coste son aprendidos para predecir el éxito científico de los artículos y los investigadores. Los modelos sensibles al coste no están interesados en maximizar la precisión de clasificación, sino en la minimización del coste total esperado derivado de los errores ocasionados. En este contexto, los editores de revistas científicas podrían disponer de una herramienta capaz de predecir el número de citas de un artículo en el fututo antes de ser publicado, mientras que los comités de promoción podrían predecir el incremento anual del índice h de los investigadores en los primeros años. Estos modelos predictivos podrían allanar el camino hacia nuevos sistemas de evaluación. Varios modelos gráficos probabilísticos son aprendidos para explotar y descubrir nuevas relaciones entre el gran número de índices bibliométricos existentes. En este contexto, la comunidad científica podría medir cómo algunos índices influyen en otros en términos probabilísticos y realizar propagación de la evidencia e inferencia abductiva para responder a preguntas bibliométricas. Además, la comunidad científica podría descubrir qué índices bibliométricos tienen mayor poder predictivo. Este es un problema de regresión multi-respuesta en el que el papel de cada variable, predictiva o respuesta, es desconocido de antemano. Los índices resultantes podrían ser muy útiles para la predicción, es decir, cuando se conocen sus valores, el conocimiento de cualquier valor no proporciona información sobre la predicción de otros índices bibliométricos. Un estudio bibliométrico sobre la investigación española en informática ha sido realizado bajo la cultura de publicar o morir. Este estudio se basa en una metodología de análisis de clusters que caracteriza la actividad en la investigación en términos de productividad, visibilidad, calidad, prestigio y colaboración internacional. Este estudio también analiza los efectos de la colaboración en la productividad y la visibilidad bajo diferentes circunstancias. ABSTRACT Machine learning and scientometrics are the scientific disciplines which are covered in this dissertation. Machine learning deals with the construction and study of algorithms that can learn from data, whereas scientometrics is mainly concerned with the analysis of science from a quantitative perspective. Nowadays, advances in machine learning provide the mathematical and statistical tools for properly working with the vast amount of scientometrics data stored in bibliographic databases. In this context, the use of novel machine learning methods in scientometrics applications is the focus of attention of this dissertation. This dissertation proposes new machine learning contributions which would shed light on the scientometrics area. These contributions are divided in three parts: Several supervised cost-(in)sensitive models are learned to predict the scientific success of articles and researchers. Cost-sensitive models are not interested in maximizing classification accuracy, but in minimizing the expected total cost of the error derived from mistakes in the classification process. In this context, publishers of scientific journals could have a tool capable of predicting the citation count of an article in the future before it is published, whereas promotion committees could predict the annual increase of the h-index of researchers within the first few years. These predictive models would pave the way for new assessment systems. Several probabilistic graphical models are learned to exploit and discover new relationships among the vast number of existing bibliometric indices. In this context, scientific community could measure how some indices influence others in probabilistic terms and perform evidence propagation and abduction inference for answering bibliometric questions. Also, scientific community could uncover which bibliometric indices have a higher predictive power. This is a multi-output regression problem where the role of each variable, predictive or response, is unknown beforehand. The resulting indices could be very useful for prediction purposes, that is, when their index values are known, knowledge of any index value provides no information on the prediction of other bibliometric indices. A scientometric study of the Spanish computer science research is performed under the publish-or-perish culture. This study is based on a cluster analysis methodology which characterizes the research activity in terms of productivity, visibility, quality, prestige and international collaboration. This study also analyzes the effects of collaboration on productivity and visibility under different circumstances.

Promotion evaluation procedures for a centralized referral system /

"OPRD-83-3."

Management effectiveness: Assessing management of protected areas?

To maximise the potential of protected areas, we need to understand the strengths and weaknesses in their management and the threats and stresses that they face. There is increasing pressure on governments and other bodies responsible for protected areas to monitor their effectiveness. The reasons for assessing management effectiveness include the desire by managers to adapt and improve their management strategies, improve planning and priority setting and the increasing demands for reporting and accountability being placed on managers, both nationally and internationally. Despite these differing purposes for assessment, some common themes and information needs can be identified, allowing assessment systems to meet multiple uses. Protected-area management evaluation has a relatively short history. Over the past 20 years a number of systems have been proposed but few have been adopted by management agencies. In response to a recognition of the need for a globally applicable approach to this issue, the IUCN World Commission on Protected Areas developed a framework for assessing management effectiveness of both protected areas and protected area systems. This framework was launched at the World Conservation Congress in Jordan in 2000. The framework provides guidance to managers to develop locally relevant assessment systems while helping to harmonise assessment approaches around the world. The framework is strongly linked to the protected area management process and is adaptable to different types and circumstances of protected areas around the world. Examples from Fraser Island in Australia and the Congo Basin illustrate the use of the framework.

Options for guaranteeing the effective management of the world's protected areas

A rapid increase in the number and size of protected areas has prompted interest in their effectiveness and calls for guarantees that they are providing a good return on investment by maintaining their values. Research reviewed here suggests that many remain under threat and a significant number are already suffering deterioration. One suggestion for encouraging good management is to develop a protected-area certification system: however this idea remains controversial and has created intense debate. We list a typology of options for guaranteeing good protected-area management, and give examples, including: danger lists; self-reporting systems against individual or standardised criteria; and independent assessment including standardised third-party reporting, use of existing certification systems such as those for forestry and farming and certification tailored specifically to protected areas. We review the arguments for and against certification and identify some options, such as: development of an accreditation scheme to ensure that assessment systems meet minimum standards; building up experience from projects that are experimenting with certification in protected areas; and initiating certification schemes for specific users such as private protected areas or institutions like the World Heritage Convention.

Application and evaluation of classification trees for screening unwanted plants

Risk assessment systems for introduced species are being developed and applied globally, but methods for rigorously evaluating them are still in their infancy. We explore classification and regression tree models as an alternative to the current Australian Weed Risk Assessment system, and demonstrate how the performance of screening tests for unwanted alien species may be quantitatively compared using receiver operating characteristic (ROC) curve analysis. The optimal classification tree model for predicting weediness included just four out of a possible 44 attributes of introduced plants examined, namely: (i) intentional human dispersal of propagules; (ii) evidence of naturalization beyond native range; (iii) evidence of being a weed elsewhere; and (iv) a high level of domestication. Intentional human dispersal of propagules in combination with evidence of naturalization beyond a plants native range led to the strongest prediction of weediness. A high level of domestication in combination with no evidence of naturalization mitigated the likelihood of an introduced plant becoming a weed resulting from intentional human dispersal of propagules. Unlikely intentional human dispersal of propagules combined with no evidence of being a weed elsewhere led to the lowest predicted probability of weediness. The failure to include intrinsic plant attributes in the model suggests that either these attributes are not useful general predictors of weediness, or data and analysis were inadequate to elucidate the underlying relationship(s). This concurs with the historical pessimism that we will ever be able to accurately predict invasive plants. Given the apparent importance of propagule pressure (the number of individuals of an species released), future attempts at evaluating screening model performance for identifying unwanted plants need to account for propagule pressure when collating and/or analysing datasets. The classification tree had a cross-validated sensitivity of 93.6% and specificity of 36.7%. Based on the area under the ROC curve, the performance of the classification tree in correctly classifying plants as weeds or non-weeds was slightly inferior (Area under ROC curve = 0.83 +/- 0.021 (+/- SE)) to that of the current risk assessment system in use (Area under ROC curve = 0.89 +/- 0.018 (+/- SE)), although requires many fewer questions to be answered.