A formação de redes colaborativas é uma importante forma das organizações estimularem a inovação, reduzirem custos e transferir conhecimentos. Uma forma particular de rede colaborativa, as redes auto-organizadas, demonstram grande capacidade de promover o aprendizado dos envolvidos, sistematizar o conhecimento e promover a inovação. Já os programas Seis Sigma, adotados por diversas organizações, possuem como objetivo principal a redução da variabilidade de um processo, resultando na redução de custos e melhoria da qualidade. A literatura sobre redes auto-organizadas pouco apresenta sobre a formação destas redes e a literatura sobre Seis Sigma é voltada a métodos estatísticos. Desta forma, o objetivo desta pesquisa é identificar os elementos para um modelo de referência baseado no programa Seis Sigma para a formação e gerência de redes de inovação auto-organizadas. Foram realizados cinco estudos de caso em diferentes organizações. Os programas Seis Sigma destas organizações foram modelos com o uso da metodologia Enterprise Knowledge Development (EKD). Com base na literatura estudada foram identificas as necessidades de mudanças que viabilizariam a formação de redes de inovação auto-organizadas. Como resultado tem-se os elementos para o modelo de referência para formação e gerência de redes de inovação auto-organizadas baseado no programa Seis Sigma. / The formation of collaborative networks is an important way for organizations to stimulate innovation, reduce costs and transfer knowledge. A particular form of collaborative network, the self-organized networks have shown great capability to promote the learning of those involved, systematize knowledge and lead to innovation. The Six Sigma programs, adopted by several organizations, have as main objective the process variability reduction, resulting in lower costs and quality improvement. The literature on self-organized networks presents little about the formation of these kind of networks and literature on Six Sigma is focused on statistical methods. Thus, the objective of this research is to identify the elements for a reference model based on the Six Sigma program for setting up and management of innovation selforganized networks. Five case studies were conducted in different organizations. The Six Sigma programs of these organizations were modeled using the Enterprise Knowledge Development (EKD) methodology. Based on the studied literature the needs for changes that enables the formation of innovation self-organized networks were identified. As a result there are the elements of the reference model for the formation and management of innovation self-organized networks based on the Six Sigma program.

A formação de redes colaborativas é uma importante forma das organizações estimularem a inovação, reduzirem custos e transferir conhecimentos. Uma forma particular de rede colaborativa, as redes auto-organizadas, demonstram grande capacidade de promover o aprendizado dos envolvidos, sistematizar o conhecimento e promover a inovação. Já os programas Seis Sigma, adotados por diversas organizações, possuem como objetivo principal a redução da variabilidade de um processo, resultando na redução de custos e melhoria da qualidade. A literatura sobre redes auto-organizadas pouco apresenta sobre a formação destas redes e a literatura sobre Seis Sigma é voltada a métodos estatísticos. Desta forma, o objetivo desta pesquisa é identificar os elementos para um modelo de referência baseado no programa Seis Sigma para a formação e gerência de redes de inovação auto-organizadas. Foram realizados cinco estudos de caso em diferentes organizações. Os programas Seis Sigma destas organizações foram modelos com o uso da metodologia Enterprise Knowledge Development (EKD). Com base na literatura estudada foram identificas as necessidades de mudanças que viabilizariam a formação de redes de inovação auto-organizadas. Como resultado tem-se os elementos para o modelo de referência para formação e gerência de redes de inovação auto-organizadas baseado no programa Seis Sigma. / The formation of collaborative networks is an important way for organizations to stimulate innovation, reduce costs and transfer knowledge. A particular form of collaborative network, the self-organized networks have shown great capability to promote the learning of those involved, systematize knowledge and lead to innovation. The Six Sigma programs, adopted by several organizations, have as main objective the process variability reduction, resulting in lower costs and quality improvement. The literature on self-organized networks presents little about the formation of these kind of networks and literature on Six Sigma is focused on statistical methods. Thus, the objective of this research is to identify the elements for a reference model based on the Six Sigma program for setting up and management of innovation selforganized networks. Five case studies were conducted in different organizations. The Six Sigma programs of these organizations were modeled using the Enterprise Knowledge Development (EKD) methodology. Based on the studied literature the needs for changes that enables the formation of innovation self-organized networks were identified. As a result there are the elements of the reference model for the formation and management of innovation self-organized networks based on the Six Sigma program.

This thesis undertakes an empirical study of software evolution by analyzing open source software (OSS) systems. The main purpose is to aid in understanding OSS evolution. The work centers on collecting large quantities of structural data cost-effectively and analyzing such data to understand software evolution <em>dynamics</em> (the mechanisms and causes of change or growth). <br /><br /> We propose a multipurpose systematic approach to extracting program facts (<em>e. g. </em>, function calls). This approach is supported by a suite of C and C++ program extractors, which cover different steps in the program build process and handle both source and binary code. We present several heuristics to link facts extracted from individual files into a combined system model of reasonable accuracy. We extract historical sequences of system models to aid software evolution analysis. <br /><br /> We propose that software evolution can be viewed as <em>Punctuated Equilibrium</em> (<em>i. e. </em>, long periods of small changes interrupted occasionally by large avalanche changes). We develop two approaches to study such dynamical behavior. One approach uses the evolution spectrograph to visualize file level changes to the implemented system structure. The other approach relies on automated software clustering techniques to recover system design changes. We discuss lessons learned from using these approaches. <br /><br /> We present a new perspective on software evolution dynamics. From this perspective, an evolving software system responds to external events (<em>e. g. </em>, new functional requirements) according to <em>Self-Organized Criticality</em> (SOC). The SOC dynamics is characterized by the following: (1) the probability distribution of change sizes is a power law; and (2) the time series of change exhibits long range correlations with power law behavior. We present empirical evidence that SOC occurs in open source software systems.

This thesis investigates an informally self-organized street market, Porta Portese, in Rome, Italy. As a response to the contemporary phenomenon of migration, of people and goods, Porta Portese reflects the city’s evolving urban, cultural and social dynamics under the impact of global forces. Based on fieldwork executed from 2007-2009, this thesis builds on the idea of scoring in an attempt to establish a framework of tangible notations, using mediums ranging from time-lapse photography to pattern mapping. The language of architecture is adapted to render visible the spatial dynamics in the fabrication of the market. Despite its lack of representation, Porta Portese leaves its mark as a layer of the invisible city of Rome. One can trace its terrain through palpable memories of a collective urban and cultural experience, for it transports ideas, images and values between different worlds based on universal understandings. Ultimately, this thesis advocates for an interpretive representation of places like Porta Portese as valuable urban spaces that celebrate and satisfy the needs of direct human experience. This is achieved through enabling the neglected voice of a place that can strike a resonating chord of dialogue amongst differences - and it all begins from a story about Porta Portese.

This thesis undertakes an empirical study of software evolution by analyzing open source software (OSS) systems. The main purpose is to aid in understanding OSS evolution. The work centers on collecting large quantities of structural data cost-effectively and analyzing such data to understand software evolution <em>dynamics</em> (the mechanisms and causes of change or growth). <br /><br /> We propose a multipurpose systematic approach to extracting program facts (<em>e. g. </em>, function calls). This approach is supported by a suite of C and C++ program extractors, which cover different steps in the program build process and handle both source and binary code. We present several heuristics to link facts extracted from individual files into a combined system model of reasonable accuracy. We extract historical sequences of system models to aid software evolution analysis. <br /><br /> We propose that software evolution can be viewed as <em>Punctuated Equilibrium</em> (<em>i. e. </em>, long periods of small changes interrupted occasionally by large avalanche changes). We develop two approaches to study such dynamical behavior. One approach uses the evolution spectrograph to visualize file level changes to the implemented system structure. The other approach relies on automated software clustering techniques to recover system design changes. We discuss lessons learned from using these approaches. <br /><br /> We present a new perspective on software evolution dynamics. From this perspective, an evolving software system responds to external events (<em>e. g. </em>, new functional requirements) according to <em>Self-Organized Criticality</em> (SOC). The SOC dynamics is characterized by the following: (1) the probability distribution of change sizes is a power law; and (2) the time series of change exhibits long range correlations with power law behavior. We present empirical evidence that SOC occurs in open source software systems.

This thesis investigates an informally self-organized street market, Porta Portese, in Rome, Italy. As a response to the contemporary phenomenon of migration, of people and goods, Porta Portese reflects the city’s evolving urban, cultural and social dynamics under the impact of global forces. Based on fieldwork executed from 2007-2009, this thesis builds on the idea of scoring in an attempt to establish a framework of tangible notations, using mediums ranging from time-lapse photography to pattern mapping. The language of architecture is adapted to render visible the spatial dynamics in the fabrication of the market. Despite its lack of representation, Porta Portese leaves its mark as a layer of the invisible city of Rome. One can trace its terrain through palpable memories of a collective urban and cultural experience, for it transports ideas, images and values between different worlds based on universal understandings. Ultimately, this thesis advocates for an interpretive representation of places like Porta Portese as valuable urban spaces that celebrate and satisfy the needs of direct human experience. This is achieved through enabling the neglected voice of a place that can strike a resonating chord of dialogue amongst differences - and it all begins from a story about Porta Portese.

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This thesis describes a set of software applications for real-time gesturally con- trolled interactions with music and sound. The applications for each system are varied but related, addressing unsolved problems in the field of audio and music technology. The three systems presented in this work capture 3D human motion with spatial sensors and map position data from the sensors onto sonic parameters. Two different spatial sensors are used interchangeably to perform motion capture: the radiodrum and the Xbox Kinect. The first two systems are aimed at creating immersive virtually-augmented environments. The first application uses human ges- ture to move sounds spatially in a 3D surround sound by physically modelling the movement of sound in a space. The second application is a gesturally controlled self- organized music browser in which songs are clustered based on auditory similarity. The third application is specifically aimed at extending musical performance through the development of a digitally augmented vibraphone. Each of these applications is presented with related work, theoretical and technical details for implementation, and discussions of future work. / Graduate

Vários problemas do mundo real podem ser analisados como problemas de otimização. Na bioinformática, em especial, como exemplos podem ser citados o alinhamento múltiplo de sequências, a filogenia, a predição de estruturas de proteínas e RNA, entre outros. As Meta-heurísticas Populacionais (MhP) são técnicas baseadas em interações de conjuntos de soluções candidatas, como elementos de uma população, utilizadas na otimização de funções. Seu uso é especialmente interessante na otimização de problemas onde há conhecimento parcial ou nenhum do espaço de busca. O objetivo deste trabalho é investigar o uso de auto-organização da população de um sistema imunológico artificial (AIS) a fim de aplicá-lo no problema de docking, que pode ser visto como um problema de otimização multimodal complexo. O AIS é um tipo de MhP inspirado na microevolução do sistema imunológico adaptativo de organismos complexos. Neste, as soluções candidatas representam células do sistema imunológico que busca se adaptar para a eliminação de um patógeno. O desenvolvimento do algoritmo foi baseado no opt-aiNet, que utiliza dos princípios das teorias de seleção clonal e maturação de afinidade para realizar a otimização de funções. Adicionalmente, o opt-aiNet, inspirado na teoria de redes imunológicas, realiza uma etapa de supressão, que busca eliminar soluções semelhantes, aumentando assim a diversidade populacional. Esta etapa é computacionalmente custosa, dado que é feito o cálculo da distância entre todos os possíveis pares de células (soluções) afim de eliminar aquelas próximas de acordo com um dado critério. A proposta deste trabalho é o desenvolvimento de um algoritmo de supressão auto-organizável, inspirado no fenômeno da criticalidade auto-organizada, buscando diminuir a influência da seleção de parâmetros e a complexidade da etapa de supressão. O algoritmo proposto foi testado em um conjunto de funções contínuas conhecidas e comumente utilizadas pela comunidade de computação evolutiva. Os resultados obtidos foram comparados com aqueles de uma implementação do opt-aiNet. Em adição, foi proposta a utilização de operadores de mutação com distribuição q-gaussiana nos AISs desenvolvidos. O algoritmo foi também aplicado no problema de docking rígido baseado em complementaridade de superfícies e minimização de colisões, especificamente no docking de proteínas. Os resultados foram comparados com aqueles de um algoritmo genético, resultando em um melhor desempenho obtido pelo algoritmo proposto. / Many real world problems can be described as optimization problems. In bioinformatics in special, there is multiple sequence alignment, filogeny and RNA and Protein structure prediction, among others. Population based metaheuristics are techniques based in the interaction of a set of candidate solutions as elements of a population. Its use is specially interesting in optimization problems where there is little or no knowledge of the search space. The objective of this work is to study the use of self-organization of population in an artificial imune system for use in the docking problem, considered a complex multimodal optimization problem. The artificial imunme system is a type of population based methaheuristics inspired in the microevolution of the adaptive immune system of complex organisms. Candidate solutions represent cells of the immune system adapting its antibodies to eliminate a pathogen. The development of the algorithm was based in the opt-aiNet, based in the principles of clonal selection and affinity maturation for function optimization. Additionally, the opt-aiNet, inspired in theories of immune network, makes a suppression stage to eliminate similiar solutions and control diversity. This stage is computationally expensive as it calculates the distance between every possible pair of cells (solutions) eliminating those closer than a threshold. This work proposes a self-organized suppression algorithm inspired in the self-organized criticality, looking to minimize the influence of parameter selection and complexity of the suppression stage in opt-aiNet. The proposed algorithm was tested in a set of well-known functions in the evolutionary computation community. The results were compared to those of an implementation of the opt-aiNet. In addition, we proposed a mutation operator with q-Gaussian distribution for the artificial immune systems. The algorithm was then applied in the rigid protein docking problem based in surface complementarity and colision avoidance. The results were compared with a genetic algorithm and achieved a better performance.

Real-time coherent Grazing-Incidence Small-Angle X-ray Scattering was used to investigate the average kinetics and the fluctuation dynamics during self-organized ion beam nano-patterning of two semiconductor surfaces: silicon at room temperature and germanium heated above its recrystallation temperature. For silicon nano-patterning, initially flat samples at room temperature were bombarded by a broad collimated beam of 1keV Ar+ and Kr+ ions at 65° polar angle, leading to the amorphization of the ion-irradiated surfaces and the spontaneous formation of nanoscale ripples. The temporal evolution of the average X-ray scattering intensity shows the evolution of average kinetics, while the fluctuation dynamics can be investigated by correlation of X-ray speckles. The surface behavior at early times can be explained within a linear theory framework. The transition away from the linear theory behavior is observed in the dynamics since the intensity correlation function quickly evolves into a compressed exponential decay on length scales corresponding to the peak wavelength and a stretched exponential decay on shorter length scales. The correlation times for silicon nano-patterning are maximum at the ripple wavelengths while they are smaller at other wavelengths. This has notable similarities and differences with the phenomenon of de Gennes narrowing. Overall, this dynamics behavior is found to be consistent with the simulations of a nonlinear growth model by Harrison et al. Following the formation of self-organized nano-ripples, they move across the surface. Homodyne X-ray alone cannot detect the motion, but because of the gradient of ion flux across the sample, we were able to measure in-situ the corresponding ripple velocity gradient by cross-correlating speckles and tracking their movements. For germanium nano-patterning at an elevated temperature, flat germanium samples kept at 300°C were bombarded by 1keV Ar+ ions at normal incidence. Unlike the case when surfaces are amorphizated during room temperature bombardment, the crystalline nano-pattern formation occurs mainly due to a surface instability caused by the Ehrlich-Schwoebel barrier. By using a linear theory analysis on the X-ray scattering intensities in the early times, we measured the contribution of the Ehrlich-Schwoebel barrier to the crystalline nano-patterning kinetics.