Controle de acesso é um tópico de pesquisa importante tanto para a academia quanto para a indústria. Controle de Acesso Baseado em Papéis (CABP) foi desenvolvido no início dos anos 1990, tornando-se um padrão generalizado para controle de acesso em vários produtos e soluções computacionais. Embora modelos CABP sejam largamente aceitos e adotados, ainda existem questões para responder. Um dos principais desafios de pesquisa em segurança baseada em papéis é determinar se uma política de controle de acesso é consistente em um ambiente altamente dinâmico. Nossa pesquisa visa preencher essa lacuna fornecendo um método para analisar políticas CABP com respeito a dois aspectos significativos: segurança e dinamismo envolvendo papéis e objetos. Para este propósito, desenvolvemos um modelo de descrição e simulação de política usando rede de Petri colorida e CPN Tools. O modelo descreve e é capaz de simular vários estados CABP em um contexto de educação a distância típico. Usando este modelo, foi possível analisar o espaço de estados produzido pela rede de Petri colorida em um cenário dinâmico envolvendo a criação de novos papéis e objetos. O resultado da análise de alcançabilidade da rede de Petri da política demonstrou que é possível verificar a consistência de políticas de controle de acesso considerando a dinamicidade de papéis e objetos, e apontou vantagens de aplicabilidade da modelagem de políticas de segurança em ambientes distribuídos utilizando rede de Petri colorida. / Access control is an important research topic both for academia and industry. Role Based Access Control (RBAC) was developed in the early 1990s, becoming a generalized standard of access control for many products and computing solutions. Although RBAC models have been widely accepted and adopted, there are issues to answer. One of the key challenges for role-based security research is to characterize whether an access control policy is consistent in a highly dynamic environment. Our research aims filling this gap providing a method to analyze RBAC policies with respect to two significant aspects: security and dynamics involving roles and objects. For this purpose, we developed a policy description and simulation model using colored Petri net and the CPN Tools. The model describes and is capable to simulate many RBAC states in a typical distance education context. Using this model it was possible to analyze the state space provided by colored Petri net that simulates a dynamic environment and the creation of new roles and objects. The result of the reachability analysis of Petri net policy showed that it is possible to check the consistency of access control policies considering dynamic of roles and objects, and point out the advantages and applicability of modeling security policies in distributed environments using colored Petri net.

La gestion d’une plate-forme selon une stratégie de cross dock est un processus logistique efficace et dynamique qui vise à transférer directement les produits d'un fournisseur à un client. Cette thèse aborde les problèmes d'affectation aux portes et de gestion des ressources sous contraintes de fenêtres du temps dans un cross dock spécifique. Les problèmes sont formulés comme des modèles de programmation mathématique mixte (MIP). L’objectif est de minimiser la somme de la distance parcourue dans l’entrepôt et du coût qui contient les coûts liés aux ressources et un coût de pénalité. Une heuristique basée sur les algorithmes génétiques est proposée pour résoudre ce problème. Deux méthodes de réparation des gènes sont décrites pour rendre les solutions irréalisables réalisables. Les résultats montrent que l’algorithme génétique surpasse la résolution du modèle MIP à l’aide du solveur CPLEX en un temps donné, pour des instances de taille moyenne et de grande taille. Afin de décrire le comportement et de recueillir des informations pertinentes sur la gestion en cross docks, nous proposons un modèle basé sur réseau de Pétri. Un modèle par réseau de Pétri est construit et la simulation est réalisée avec le logiciel Tina. Par simulation, avec différents nombres de ressources, nous obtenons des temps pertinents pour améliorer les fenêtres de temps originales, le makespan de chacun des postes de travail, l'intervalle de temps libre dans le cross dock et la quantité de ressources disponible à chaque période de temps, ce qui peut fournir des conseils utiles à la gestion des ressources. A partir des résultats de la simulation, la formulation MIP est améliorée. / Cross docking is an efficient and dynamic logistic process that directly transfers goods from a supplier to a customer. This thesis addresses the door assignment and resource management problem with truck time windows constraints for a specific cross dock. The problems are formulated as mixed integer programming (MIP) models. The objective is to minimize the weighted sum of the total travel distance and cost which includes labor cost and penalty cost. A heuristic based on genetic algorithm (GA) is developed to solve the problems. Two gene repair methods are proposed to repair infeasible solutions. The computational results show that genetic algorithms outperforms the solution of MIP model with CPLEX in a given CPU time, for medium and large size instances, and that the second gene repair method outperforms the first one. In order to describe the behavior and gather information on the cross dock, a model based on Petri net is built to study the cross docks and simulations are carried out with Tina. The simulation results for different resource number lead us to obtain the relevant times to improve the original time windows, the makespan at each work station, the free time interval in the cross dock and the free resource number at each time period, which provide relevant information on the resource management. Besides, according to the simulation results, the original MIP formulations are improved. Then we propose a new MIP formulation, which determine not only door assignment, but also resources at each time period at each work station. Computational results reveal that the new MIP model control resources in cross dock more efficiently and outperforms the first model.

本篇論文以裴氏網(Petri-Net)描述系統，提出一啟發式的演算法則 ， 將此裴氏網分割為數個子系統，以便建構為分散式系統中獨立運作的 伺服器。吾人設計一系列的模擬實驗，以測試此演算法的績效。而後，本 篇論文列出影響分割的變數及各變數間的關係，並分析本演算法之特性。 而根據模擬實驗的結果分析，吾人對分散式環境之應用系統發展提出建議 。 / In this thesis, we use the Petri-Net to model a system, and we propose a heuristic algorithm to partition the Petri-Net into several autonomous servers. We then conduct a series of simula- tion experiments to test the performance of the algorithm and to identify the variables which influence the partition of the Petri-Net. Some factors influencing the properties of a partition are identified and their inter-relationships are shown. Based upon the simulation, we provide some suggestions for the develop- ment of the application in the distributed system environment.

雲端運算是近來全世界最熱門的資訊議題，任何設備只要能夠連上網路，就可以享有各種不同的網路服務，這些服務可能建置在不同的機器設備上，甚至可能在相隔遙遠的雲端裡，因此也稱之為雲端服務，在操作過程中，使用者不需要擔心服務安裝在哪裡，亦不用擔心服務如何達成，可想而知網路服務的背後，隱藏著很複雜的技術與架構。 網路服務的開發方式相當地簡單且快速，為系統帶來更大的便利與彈性，不過在管理的部份，卻變得越來越複雜，例如不易瞭解網路的結構與狀態、潛在的無窮迴圈及冗餘的流程問題，甚至是在資源共享的情況下，因等待或其他因素而導致死結的情況產生。這些問題將使得網路服務組合在執行時期，可能發生無法預期的錯誤。情況嚴重時，系統可能會完全鎖死或停止，對公司將造成重大的財務及商譽上的損失。這些流程的問題，需要在網路服務組合執行前先進行驗證，本研究透過流程轉換成派翠網路的分析，以確保流程設計的正確性與穩定性。 因此，本論文運用網路服務標準，將雲端服務轉換成派翠網路模型，再以派翠網路中的針織法為分析基礎，檢驗模型的狀態，避免死結發生，並提供網路特徵報告書，以降低管理的複雜度，進而提升服務的穩定性。 / Cloud computing is regarded as the most popular recent ICT topic. Any equipment which can be connected to Internet can provide its user have the access to the various services, which may be built on different machines, or even may exist in the distant 'clouds' far away. However, in the operating process, the user has no need to worry where the service locates, needless to say how the service operates, which means that a considerably amount of data and techniques hide behind the Web service. Web service is an artifical piece of art, the user and the manager can concatenate several Web services online into a bigger one according to the conditions they need, which is a considerably simple and fast developing method bringing more convenience and flexibility for the system. Nonetheless, the managerial part also becomes more complex in problems like potential infinite loops and abundant procedures, for instance. What is worse is under the circumstance of resource sharing, the deadlocks happen while pending or other factors occur. These issues will lead to severe errors while the Web service compound operates. If the system completely locks and stops, serious financial damage and loss of commercial reputation will be caused to the company. Hence the design of procedures needs to be validated and transformed as Petri Net analysis before the Web service compound operates in order to assure the accuracy and stability of the procedure design. Therefore, this paper uses Web service standards, turning Web services into Petri Net models with knitting technique as analytical base, to validate the accuracy and stability of the model to avoid deadlock to happen in order to enhance the reliability of the service.

The ability to learn is essential for robots to function and perform services within a dynamic human environment. Robot programming by demonstration facilitates learning through a human teacher without the need to develop new code for each task that the robot performs. In order for learning to be generalizable, the robot needs to be able to grasp the underlying structure of the task being learned. This requires appropriate knowledge abstraction and representation. The goal of this thesis is to develop a learning by imitation system that abstracts knowledge of human demonstrations of a task and represents the abstracted knowledge in a hierarchical framework. The learning by imitation system is capable of performing both action and object recognition based on video stream data at the lower level of the hierarchy, while the sequence of actions and object states observed is reconstructed at the higher level of the hierarchy in order to form a coherent representation of the task. Furthermore, error recovery capabilities are included in the learning by imitation system to improve robustness to unexpected situations during task execution. The first part of the thesis focuses on motion learning to allow the robot to both recognize the actions for task representation at the higher level of the hierarchy and to perform the actions to imitate the task. In order to efficiently learn actions, the actions are segmented into meaningful atomic units called motion primitives. These motion primitives are then modeled using dynamic movement primitives (DMPs), a dynamical system model that can robustly generate motion trajectories to arbitrary goal positions while maintaining the overall shape of the demonstrated motion trajectory. The DMPs also contain weight parameters that are reflective of the shape of the motion trajectory. These weight parameters are clustered using affinity propagation (AP), an efficient exemplar clustering algorithm, in order to determine groups of similar motion primitives and thus, performing motion recognition. The approach of DMPs combined with APs was experimentally verified on two separate motion data sets for its ability to recognize and generate motion primitives. The second part of the thesis outlines how the task representation is created and used for imitating observed tasks. This includes object and object state recognition using simple computer vision techniques as well as the automatic construction of a Petri net (PN) model to describe an observed task. Tasks are composed of a sequence of actions that have specific pre-conditions, i.e. object states required before the action can be performed, and post-conditions, i.e. object states that result from the action. The PNs inherently encode pre-conditions and post-conditions of a particular event, i.e. action, and can model tasks as a coherent sequence of actions and object states. In addition, PNs are very flexible in modeling a variety of tasks including tasks that involve both sequential and parallel components. The automatic PN creation process has been tested on both a sequential two block stacking task and a three block stacking task involving both sequential and parallel components. The PN provides a meaningful representation of the observed tasks that can be used by a robot to imitate the tasks. Lastly, error recovery capabilities are added to the learning by imitation system in order to allow the robot to readjust the sequence of actions needed during task execution. The error recovery component is able to deal with two types of errors: unexpected, but known situations and unexpected, unknown situations. In the case of unexpected, but known situations, the learning system is able to search through the PN to identify the known situation and the actions needed to complete the task. This ability is useful not only for error recovery from known situations, but also for human robot collaboration, where the human unexpectedly helps to complete part of the task. In the case of situations that are both unexpected and unknown, the robot will prompt the human demonstrator to teach how to recover from the error to a known state. By observing the error recovery procedure and automatically extending the PN with the error recovery information, the situation encountered becomes part of the known situations and the robot is able to autonomously recover from the error in the future. This error recovery approach was tested successfully on errors encountered during the three block stacking task.

This dissertation addresses the development of integrative modeling strategies capable of combining deterministic and stochastic, discrete and continuous, as well as multi-scale features. The first set of studies combines the purely deterministic modeling methodology of Biochemical Systems Theory (BST) with a hybrid approach, using Functional Petri Nets, which permits the account of discrete features or events, stochasticity, and different types of delays. The efficiency and significance of this combination is demonstrated with several examples, including generic biochemical networks with feedback controls, gene regulatory modules, and dopamine based neuronal signal transduction. A study expanding the use of stochasticity toward systems with small numbers of molecules proposes a rather general strategy for converting a deterministic process model into a corresponding stochastic model. The strategy characterizes the mathematical connection between a stochastic framework and the deterministic analog. The deterministic framework is assumed to be a generalized mass action system and the stochastic analogue is in the format of the chemical master equation. The analysis identifies situations where internal noise affecting the system needs to be taken into account for a valid conversion from a deterministic to a stochastic model. The conversion procedure is illustrated with several representative examples, including elemental reactions, Michaelis-Menten enzyme kinetics, a genetic regulatory motif, and stochastic focusing. The last study establishes two novel, particle-based methods to simulate biochemical diffusion-reaction systems within crowded environments. These simulation methods effectively simulate and quantify crowding effects, including reduced reaction volumes, reduced diffusion rates, and reduced accessibility between potentially reacting particles. The proposed methods account for fractal-like kinetics, where the reaction rate depends on the local concentrations of the molecules undergoing the reaction. Rooted in an agent based modeling framework, this aspect of the methods offers the capacity to address sophisticated intracellular spatial effects, such as macromolecular crowding, active transport along cytoskeleton structures, and reactions on heterogeneous surfaces, as well as in porous media. Taken together, the work in this dissertation successfully developed theories and simulation methods which extend the deterministic, continuous framework of Biochemical Systems Theory to allow the account of delays, stochasticity, discrete features or events, and spatial effects for the modeling of biological systems, which are hybrid and multiscale by nature.

In recent years there has been an exponential growth in Internet traffic resulting in increased network bandwidth requirements which, in turn, has led to stringent processing requirements on network layer devices like routers. Present backbone routers on OC 48 links (2.5Gbps) have to process four million minimum-sized packets per second. Further, the functionality supported in the network devices is also on the increase leading to programmable processors, such as Intel's IXP, Motorola's C5 and IBM's.NP. These processors support multiple processors and multiple threads to exploit packet-level-parallelism inherent in network workloads. This thesis studies the performance of network processors. We develop a Petri Net model for a commercial network processors (Intel IXP 2400,2850) for three different applications viz., IPv4 forwarding, Network Address Translation and IP security protocols. A salient feature of the Petri net model is its ability to model the application, architecture and their interaction in great detail. The model is validated using the intel proprietary tool (SDK 3.51 for IXP architecture) over a range of configurations. Our Performance evaluation results indicate that 1. The IXP processor is able to support a throughput of 2.5 Gbps for all modeled applications. 2. Packet buffer memory (DRAM) is the bottleneck resource in a network proces sor and even multithreading is ineffective beyond a total of 16 threads in case of header processing applications and beyond 32 threads for payload processing applications. Since DRAM is the bottleneck resource we explore the benefits of increasing the DRAM banks and other software schemes like offloading the packet header to SRAM. The second part of the thesis studies the impact of parallel processing in network processor on packet reordering and retransmission. Our results indicate that the concurrent processing of packets in a network processor and buffer allocation schemes in TFIFO leads to a significant packet reordering, (61%), on a 10-hop network (with packet sizes of 64 B) which in turn leads to a 76% retransmission under the TCP fast-restransmission algorithm. We explore different transmit buffer allocation schemes namely, contiguous, strided, local, and global for transmit buffer which reduces the packet retransmission to 24%. Our performance results also indicate that limiting the number of microengines can reduce the extent of packet reordering while providing the same throughput. We propose an alternative scheme, Packetsort, which guarantees complete packet ordering while achieving a throughput of 2.5 Gbps. Further, we observe that Packetsort outperforms, by up to 35%, the in-built schemes in the IXP processor namely, Inter Thread Signaling (ITS) and Asynchronous Insert and Synchronous Remove (AISR). The final part of this thesis investigates the performance of the network processor in a bursty traffic scenario. We model bursty traffic using a Pareto distribution. We consider a parallel and pipelined buffering schemes and their impact on packet drop under bursty traffic. Our results indicate that the pipelined buffering scheme outperforms the parallel scheme.

The ability to learn is essential for robots to function and perform services within a dynamic human environment. Robot programming by demonstration facilitates learning through a human teacher without the need to develop new code for each task that the robot performs. In order for learning to be generalizable, the robot needs to be able to grasp the underlying structure of the task being learned. This requires appropriate knowledge abstraction and representation. The goal of this thesis is to develop a learning by imitation system that abstracts knowledge of human demonstrations of a task and represents the abstracted knowledge in a hierarchical framework. The learning by imitation system is capable of performing both action and object recognition based on video stream data at the lower level of the hierarchy, while the sequence of actions and object states observed is reconstructed at the higher level of the hierarchy in order to form a coherent representation of the task. Furthermore, error recovery capabilities are included in the learning by imitation system to improve robustness to unexpected situations during task execution. The first part of the thesis focuses on motion learning to allow the robot to both recognize the actions for task representation at the higher level of the hierarchy and to perform the actions to imitate the task. In order to efficiently learn actions, the actions are segmented into meaningful atomic units called motion primitives. These motion primitives are then modeled using dynamic movement primitives (DMPs), a dynamical system model that can robustly generate motion trajectories to arbitrary goal positions while maintaining the overall shape of the demonstrated motion trajectory. The DMPs also contain weight parameters that are reflective of the shape of the motion trajectory. These weight parameters are clustered using affinity propagation (AP), an efficient exemplar clustering algorithm, in order to determine groups of similar motion primitives and thus, performing motion recognition. The approach of DMPs combined with APs was experimentally verified on two separate motion data sets for its ability to recognize and generate motion primitives. The second part of the thesis outlines how the task representation is created and used for imitating observed tasks. This includes object and object state recognition using simple computer vision techniques as well as the automatic construction of a Petri net (PN) model to describe an observed task. Tasks are composed of a sequence of actions that have specific pre-conditions, i.e. object states required before the action can be performed, and post-conditions, i.e. object states that result from the action. The PNs inherently encode pre-conditions and post-conditions of a particular event, i.e. action, and can model tasks as a coherent sequence of actions and object states. In addition, PNs are very flexible in modeling a variety of tasks including tasks that involve both sequential and parallel components. The automatic PN creation process has been tested on both a sequential two block stacking task and a three block stacking task involving both sequential and parallel components. The PN provides a meaningful representation of the observed tasks that can be used by a robot to imitate the tasks. Lastly, error recovery capabilities are added to the learning by imitation system in order to allow the robot to readjust the sequence of actions needed during task execution. The error recovery component is able to deal with two types of errors: unexpected, but known situations and unexpected, unknown situations. In the case of unexpected, but known situations, the learning system is able to search through the PN to identify the known situation and the actions needed to complete the task. This ability is useful not only for error recovery from known situations, but also for human robot collaboration, where the human unexpectedly helps to complete part of the task. In the case of situations that are both unexpected and unknown, the robot will prompt the human demonstrator to teach how to recover from the error to a known state. By observing the error recovery procedure and automatically extending the PN with the error recovery information, the situation encountered becomes part of the known situations and the robot is able to autonomously recover from the error in the future. This error recovery approach was tested successfully on errors encountered during the three block stacking task.

Knowledge intensive applications require an intelligent environment, which can perform deductions in response to user queries or events that occur inside or outside of the applications. For that, we propose a Fuzzy Petri Net (FPN) model to represent the knowledge and the behavior in an intelligent object-oriented database environment, which integrates fuzzy, active and deductive rules with database objects. By gaining intelligent behaviour, the system maintains objects to perceive dynamic occurences and user queries. Thus, objects can produce new knowledge or keep themselves in a consistent, stable, and upto-date state. The behavior of a system can be unpredictable due to the rules triggering or untriggering each other (non-termination). Intermediate and final database states may also differ according to the order of rule executions (non-confluence). In order to foresee and solve problematic behavior patterns, we employ static rule analysis on the FPN structure that provides easy checking of the termination property without requiring any extra construct. In addition, with our proposed inference algorithm, we guarantee confluent rule executions. The techniques and solutions provided in this study can be utilized in various complex systems, such as weather forecasting applications, environmental information systems, defense applications, video database applications, etc. We implement a prototype of the model for the weather forecasting of the Central Anatolia Region

Games on net are in prominence due to the high level of interaction between pairs, situation that promotes a high number of youth spending hours in front of the computer. What guides this paper is to use this motivation for scientific learning, also focusing the education socio-constructivist view (VYGOTSKY, 1996). This paper's goal is to develop a computational model for the Scientific Reasoning teaching through the use of collaborative games, using the Coloured Petri Net formalism. This project relevance is in the belief that the shaped educational game functions as a way of learning, through principles of Scientific Reasoning, with a playful focus. It was proposed a frame that defines since the conception of collaborative game, including the interface to be applied, to the game execution. Then it was made the game interaction process using the formalism based on Coloured Petri Net. In order to evaluate this study, it was developed a game archetype on net that was used with high-school students, what made possible evidence, through questionnaires, this model viability, which uses collaborative learning through Scientific Reasoning / Jogos em rede estão em destaque graças ao alto nível de interação entre pares, situação promotora do alto índice de jovens que passam horas em frente ao computador. O que norteia este trabalho é usar esta motivação para aprendizagem de forma científica, contemplando também a visão sócio-construcionista (VYGOTSKY, 1996) de educação. Esta estudo tem como objetivo o desenvolvimento de um modelo computacional para o ensino do Raciocínio Científico por meio da utilização de jogos colaborativos, utilizando o formalismo Rede de Petri Colorida. A relevância do projeto reside na crença de que o jogo educacional modelado funcione como um meio de aprendizagem, pelos princípios do Raciocínio Científico, com uma abordagem lúdica. Foi proposta uma arquitetura que define desde a concepção do jogo colaborativo, passando pela interface a ser aplicada, chegando à execução do jogo. Foi então modelado o processo de interação do jogo utilizando o formalismo baseado em Rede de Petri Colorida. Para analisar esse estudo, desenvolveu-se um protótipo de jogo em rede que foi aplicado a uma turma de alunos do segundo ano do ensino médio, quando se constatou, através de questionários, a viabilidade deste modelo, que utiliza aprendizagem colaborativa usando Raciocínio Científico