In this dissertation, we describe the portable, open-source distributed simulation framework (WINDS) targeting simulations of wireless network infrastructures that we have developed. We present the simulation framework which uses modular architecture and apply the framework to studies of mobility pattern effects, routing and intrusion detection mechanisms in simulations of large-scale wireless ad hoc, infrastructure, and totally mobile networks. The distributed simulations within the framework execute seamlessly and transparently to the user on a symmetric multiprocessor cluster computer or a network of computers with no modifications to the code or user objects. A visual graphical interface precisely depicts simulation object states and interactions throughout the simulation execution, giving the user full control over the simulation in real time. The network configuration is detected by the framework, and communication latency is taken into consideration when dynamically adjusting the simulation clock, allowing the simulation to run on a heterogeneous computing system. The simulation framework is easily extensible to multi-cluster systems and computing grids. An entire simulation system can be constructed in a short time, utilizing user-created and supplied simulation components, including mobile nodes, base stations, routing algorithms, traffic patterns and other objects. These objects are automatically compiled and loaded by the simulation system, and are available for dynamic simulation injection at runtime. Using our distributed simulation framework, we have studied modern intrusion detection systems (IDS) and assessed applicability of existing intrusion detection techniques to wireless networks. We have developed a mobile agent-based IDS targeting mobile wireless networks, and introduced load-balancing optimizations aimed at limited-resource systems to improve intrusion detection performance. Packet-based monitoring agents of our IDS employ a CASE-based reasoner engine that performs fast lookups of network packets in the existing SNORT-based intrusion rule-set. Experiments were performed using the intrusion data from MIT Lincoln Laboratories studies, and executed on a cluster computer utilizing our distributed simulation system.

No description available.

No description available.

Third Generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio technology can bring significantcant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogeneous communication systems. In this work, an approach for applying cognition in wireless networks is presented. Also, two machine learning techniques are used to create a hybrid cognitive engine. Furthermore, the concept of cognitive radio resource management along with some of the network applications are discussed. To evaluate the proposed approach cognition is applied to three typical wireless network problems: improving coverage, handover management and determining recurring policy events. A cognitive engine, that uses case-based reasoning and a decision tree algorithm is developed. The engine learns the coverage of a cell solely from observations, predicts when a handover is necessary and determines policy patterns, solely from environment observations. / Ph. D.

Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e eficazes para: modelagem dos dados que suportarão a recomendação; recuperação da informação que descrevem os dados; combinação dessas informações dentro de métricas de similaridade, popularidade ou adequabilidade; criação de modelos descritivos dos itens sob recomendação; e evolução da inteligência do sistema de forma que ele seja capaz de aprender a partir da interação com o usuário. A tomada de decisão por um sistema de recomendação é uma tarefa complexa que pode ser implementada a partir da visão de áreas como inteligência artificial e mineração de dados. Dentro da área de inteligência artificial há estudos referentes ao método de raciocínio baseado em casos e da recomendação baseada em casos. No que diz respeito à área de mineração de dados, os SR podem ser construídos a partir de modelos descritivos e realizar tratamento de dados textuais, constituindo formas de criar elementos para compor uma recomendação. Uma forma de minimizar os pontos fracos de uma abordagem, é a adoção de aspectos baseados em uma abordagem híbrida, que neste trabalho considera-se: tirar proveito dos diferentes tipos de SR; usar técnicas de resolução de problemas; e combinar recursos provenientes das diferentes fontes para compor uma métrica unificada a ser usada para ranquear a recomendação por relevância. Dentre as áreas de aplicação dos SR, destaca-se a recomendação de notícias, sendo utilizada por um público heterogêneo, amplo e exigente por relevância. Neste contexto, a presente pesquisa apresenta uma abordagem híbrida para recomendação de notícias construída por meio de uma arquitetura implementada para provar os conceitos de um sistema de recomendação. Esta arquitetura foi validada por meio da utilização de um corpus de notícias e pela realização de um experimento online. Por meio do experimento foi possível observar a capacidade da arquitetura em relação aos requisitos de um sistema de recomendação de notícias e também confirmar a hipótese no que se refere à privilegiar recomendações com base em similaridade, popularidade, diversidade, novidade e serendipidade. Foi observado também uma evolução nos indicadores de leitura, curtida, aceite e serendipidade conforme o sistema foi acumulando histórico de preferências e soluções. Por meio da análise da métrica unificada para ranqueamento foi possível confirmar sua eficácia ao verificar que as notícias melhores colocadas no ranqueamento foram as mais aceitas pelos usuários / Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user\'s expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user\'s interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a ``case\'\' model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users

Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e eficazes para: modelagem dos dados que suportarão a recomendação; recuperação da informação que descrevem os dados; combinação dessas informações dentro de métricas de similaridade, popularidade ou adequabilidade; criação de modelos descritivos dos itens sob recomendação; e evolução da inteligência do sistema de forma que ele seja capaz de aprender a partir da interação com o usuário. A tomada de decisão por um sistema de recomendação é uma tarefa complexa que pode ser implementada a partir da visão de áreas como inteligência artificial e mineração de dados. Dentro da área de inteligência artificial há estudos referentes ao método de raciocínio baseado em casos e da recomendação baseada em casos. No que diz respeito à área de mineração de dados, os SR podem ser construídos a partir de modelos descritivos e realizar tratamento de dados textuais, constituindo formas de criar elementos para compor uma recomendação. Uma forma de minimizar os pontos fracos de uma abordagem, é a adoção de aspectos baseados em uma abordagem híbrida, que neste trabalho considera-se: tirar proveito dos diferentes tipos de SR; usar técnicas de resolução de problemas; e combinar recursos provenientes das diferentes fontes para compor uma métrica unificada a ser usada para ranquear a recomendação por relevância. Dentre as áreas de aplicação dos SR, destaca-se a recomendação de notícias, sendo utilizada por um público heterogêneo, amplo e exigente por relevância. Neste contexto, a presente pesquisa apresenta uma abordagem híbrida para recomendação de notícias construída por meio de uma arquitetura implementada para provar os conceitos de um sistema de recomendação. Esta arquitetura foi validada por meio da utilização de um corpus de notícias e pela realização de um experimento online. Por meio do experimento foi possível observar a capacidade da arquitetura em relação aos requisitos de um sistema de recomendação de notícias e também confirmar a hipótese no que se refere à privilegiar recomendações com base em similaridade, popularidade, diversidade, novidade e serendipidade. Foi observado também uma evolução nos indicadores de leitura, curtida, aceite e serendipidade conforme o sistema foi acumulando histórico de preferências e soluções. Por meio da análise da métrica unificada para ranqueamento foi possível confirmar sua eficácia ao verificar que as notícias melhores colocadas no ranqueamento foram as mais aceitas pelos usuários / Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user\'s expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user\'s interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a ``case\'\' model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users

It is no secret that 'managerial' solutions are not, on average, nearly as reliable as 'technical' solutions. The focus of this work is to clarify the reasons why this is so, and to seek ways to increase the reliability of managerial solutions. The causes of this situation are both subjective (human factor failure), which can be influenced, and objective (complexity of the problem, the specifics of human behaviour, etc.) that can be only minimally influenced. Significant subjective causes at work were identified as: a. cognitive distortions at the mental level of thinking of the problem solvers; b. deficiencies in making inference and drawing conclusions; c. incorrect argumentation. There are two ways to reduce these causes: 1. cultivation of managerial thinking of the problem solvers; 2. the use of reserves in the implementation of approaches and tools of theoretical disciplines that already operate successfully elsewhere and are beneficial for managerial solutions. The first way deals with procedures for managerial solutions formulated in the language of the relevant discipline (the language of management), expressed by natural language and the chain of formulas (calculations) and visual (graphic) tools in the form of managerial decision trees, diagrams and charts with the rules of 'managerial logic'. This is generally defined as a set of approaches, tools, methods and skills needed for credible justification when solving managerial problems. Specifically it deals with: - the 'case-based reasoning' approach, which aims at finding the best point of view on a given problem and analysing all considered aspects within its context step-by-step in detail; - translating the tools and methods of modern logic (especially its intuitionistic version) from the language of logic into the language of management taking into account the factual content of expressive means of the language of management including the ability of their effective application; - respecting the principles of rational and ethical argumentation within managerial solutions. The second way circumvents managerial solution procedures by recasting the managerial task to the task of a scientific discipline (logic, game theory, etc.) and derives the correct result therein. In this context we talk about the use of knowledge of theoretical disciplines in management. Both of these ways are demonstrated in the work in a number of illustrative examples and the annexed case studies addressing the specific tasks of managerial practice.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal.

Les travaux présentés dans cette thèse s'inscrivent dans une problématique d'aide à la conception de systèmes, à la planification de leur projet de développement et à leur couplage. L'aide à la conception et à la planification repose sur la formalisation de deux grands types de connaissances : les connaissances méthodologiques utilisables quel que soit le projet de conception et, les connaissances métier spécifiques à un type de conception et/ou de planification donné. Le premier chapitre de la thèse propose un état de l'art concernant les travaux sur le couplage des processus de conception de systèmes et de planification des projets associés et expose la problématique de nos travaux. Deux partie traitent ensuite, d'une part, des connaissances méthodologiques et, d'autre part, des connaissances métier. La première partie expose trois types de couplages méthodologiques. Le couplage structurel propose de formaliser les entités de conception et de planification puis permet leur création et leur association. Le couplage informationnel définit les attributs de faisabilité et de vérification pour ces entités et synchronise les états de ces dernières vis-à-vis de ces attributs. Enfin, le couplage décisionnel consiste à proposer, dans un même espace et sous forme de tableau de bord, les informations nécessaires et suffisantes à la prise de décision par les acteurs du projet de conception. La seconde partie propose de formaliser, d'exploiter et de capitaliser la connaissance métier. Après avoir formalisé ces connaissances sous forme d'une ontologie de concepts, deux mécanismes sont exploités : un mécanisme de réutilisation de cas permettant de réutiliser, en les adaptant, les projets de conception passés et un mécanisme de propagation de contraintes permettant de propager des décisions de la conception vers la planification et réciproquement. / The work presented in this thesis deals with aiding system design, development project planning and its coupling. Aiding design and planning is based on the formalization of two kind of knowledge: methodological knowledge that can be used in all kind of design projects and business knowledge that are dedicated to a particular kind of design and/or planning. The first chapter presents a state of the art about coupling system design process and project planning process and gives the problem of our work. Then, two parts deal with design and planning coupling thanks to, on one hand, methodological knowledge, and on the other hand, business knowledge. The first part presents three types of methodological coupling. The structural coupling defines design and planning entities and permits its simultaneous creation of and its association. The informational coupling defines feasibility and verification attributes for these entities and synchronizes its attribute states. Finally, the decisional coupling consists in proposing, in a single dashboard, the necessary and sufficient information to make a decision by the design project actors. The second part proposes to formalize, to exploit and to capitalize business knowledge. This knowledge is formalized with ontology of concepts. Then, two mechanisms are exploited: a case reuse mechanism that permits to reuse and adapt former design projects and a constraint propagation mechanism that allows propagating decisions from design to planning and reciprocally.

[pt] Durante as etapas iniciais de design, o designer forma um entendimento inicial sobre o problema que ele deve resolver e desenvolve suas primeiras ideias, geralmente influenciadas por conhecimentos de design passados. Com o objetivo de auxiliar o design de IHC (interação humano-computador) neste contexto, nós investigamos como podemos usar o raciocínio baseado em casos (CBR) para ajudar designers a acessar e reutilizar conhecimentos de design para resolver novos problemas de IHC. Nós conduzimos entrevistas com designers de IHC profissionais para coletar dados sobre como eles lidam com problemas de design e suas motivações e expectativas sobre o uso de conhecimentos de design auxiliado por uma ferramenta de CBR. Usando estes dados, construímos uma ferramenta, chamada CHIDeK, que contém uma biblioteca contendo casos de design de IHC e fornece acesso aos casos através de navegação facetada, links diretos entre casos e busca. Para investigar como o CHIDeK influencia a atividade de design, conduzimos um estudo que simulava a etapa inicial de design de IHC de um sistema online de reserva de bicicletas. Alguns participantes podiam resolver o problema enquanto tinham acesso ao CHIDeK e outros deviam resolver sem o CHIDeK. Descobrimos que os casos no CHIDeK ajudaram o design motivando o processo de reflexão dos designers, ativando memórias de experiências com sistemas similares aos descritos nos casos e ajudando a gerar novas ideias. Também identificamos algumas limitações na representação dos casos, o que oferece oportunidade para novas pesquisas. Comparando ambos os tipos de atividade de design, percebemos que os designers sem a biblioteca de casos usaram a mesma solução para um dos itens descrito no cenário do estudo, enquanto os designers com os casos variaram entre duas soluções. Concluímos dizendo que uma ferramenta de CBR tem muito potencial para ajudar na atividade de design, porém existem problemas que devem ser endereçados por pesquisas futuras. / [en] During the early stages of design, the designer forms an initial understanding about the problem and some ideas on how to solve it, often influenced by previous design knowledge. In order to support HCI design in this context, we investigated ways to use case-based reasoning (CBR) to help designers access and reuse design knowledge to solve new HCI design problems. We conducted interviews with professional HCI designers to collect data about how they deal with design problems, and their motivations and expectations regarding the use of design knowledge aided by a CBR tool. Using this data, we designed and developed a tool called CHIDeK, which has a library containing HCI design cases and provides access to them through faceted navigation, direct links between cases, and search. To investigate the way CHIDeK influences the design activity, we conducted a study that simulated the early stage of HCI design of an online bike reservation system. Some participants could solve the problem while having access to CHIDeK and others had to solve it without CHIDeK. We discovered that the cases from CHIDeK supported the design by motivating the designers reflective process, triggering their memories of experiences with systems similar to the ones in cases, and helping generate new ideas. We also identified some limitations in the case representation, which offers an opportunity for further research. When comparing both kinds of design activities, we noticed that designers without the case library used the same solution for one of the issues described in the study scenario, while the designers with the cases varied between two solutions. We concluded that a CBR tool has much potential to aid the design activity, but there are still issues that need to be addressed by further research.