513 research outputs found
Quantitative analysis of distributed systems
PhD ThesisComputing Science addresses the security of real-life systems by using
various security-oriented technologies (e.g., access control solutions
and resource allocation strategies). These security technologies
signficantly increase the operational costs of the organizations in
which systems are deployed, due to the highly dynamic, mobile and
resource-constrained environments. As a result, the problem of designing
user-friendly, secure and high efficiency information systems
in such complex environment has become a major challenge for the
developers.
In this thesis, firstly, new formal models are proposed to analyse the
secure information
flow in cloud computing systems. Then, the opacity of work
flows in cloud computing systems is investigated, a threat
model is built for cloud computing systems, and the information leakage
in such system is analysed. This study can help cloud service
providers and cloud subscribers to analyse the risks they take with
the security of their assets and to make security related decision.
Secondly, a procedure is established to quantitatively evaluate the
costs and benefits of implementing information security technologies.
In this study, a formal system model for data resources in a dynamic
environment is proposed, which focuses on the location of different
classes of data resources as well as the users. Using such a model, the
concurrent and probabilistic behaviour of the system can be analysed.
Furthermore, efficient solutions are provided for the implementation of
information security system based on queueing theory and stochastic
Petri nets. This part of research can help information security officers
to make well judged information security investment decisions
A new paradigm for uncertain knowledge representation by Plausible Petri nets
This paper presents a new model for Petri nets (PNs) which combines PN principles with the foundations of information theory for uncertain knowledge representation. The resulting framework has been named Plausible Petri nets (PPNs). The main feature of PPNs resides in their efficiency to jointly consider the evolution of a discrete event system together with uncertain information about the system state using states of information. The paper overviews relevant concepts of information theory and uncertainty representation, and presents an algebraic method to formally consider the evolution of uncertain state variables within the PN dynamics. To illustrate some of the real-world challenges relating to uncertainty that can be handled using a PPN, an example of an expert system is provided, demonstrating how condition monitoring data and expert opinion can be modelled
Modeling and Evaluation of Single Machine Flexibility Using Fuzzy Entropy and Genetic Algorithm Based Approach
International audienceFlexibility has long been recognized as a manufacturing capability that has the potential to impact mainly the competitive position of an organization. The entropy approach, which was extended from information theory, fell in handling problems with incomplete and uncertain data, because it depicts only the stochastic aspects included with measured observations. In order to get a global view, this work proposes a new approach based on fuzzy entropy concept. The development of the fuzzy model results in a set of nonlinear constrained problems to be solved using a metaheuristics method. The applicability of our approach is illustrated through a flexible manufacturing cell. By adopting such framework, both dimensions of uncertainty in system modeling, expressed by stochastic variability and imprecision, can be taken into consideration
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Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction
The analysis of sequences is important for extracting in- formation from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for learning melodic sequences. The model is similar to a previous suc- cessful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch se- quence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. Results show that this RBM-based prediction model performs better than previ- ously evaluated n-gram models and also outperforms them in certain cases. It is able to make use of information present in longer sequences more effectively than n-gram models, while scaling linearly in the number of free pa- rameters required
A GUIDED SIMULATION METHODOLOGY FOR DYNAMIC PROBABILISTIC RISK ASSESSMENT OF COMPLEX SYSTEMS
Probabilistic risk assessment (PRA) is a systematic process of examining how engineered systems work to ensure safety. With the growth of the size of the dynamic systems and the complexity of the interactions between hardware, software, and humans, it is extremely difficult to enumerate the risky scenarios by the traditional PRA methods. Over the past 15 years, a host of DPRA methods have been proposed to serve as supplemental tools to traditional PRA to deal with complex dynamic systems. A new dynamic probabilistic risk assessment framework is proposed in this dissertation. In this framework a new exploration strategy is employed. The engineering knowledge of the system is explicitly used to guide the simulation to achieve higher efficiency and accuracy. The engineering knowledge is reflected in the "Planner" which is responsible for generating plans as a high level map to guide the simulation. A scheduler is responsible for guiding the simulation by controlling the timing and occurrence of the random events. During the simulation the possible random events are proposed to the scheduler at branch points. The scheduler decides which events are to be simulated. Scheduler would favor the events with higher values. The value of a proposed event depends on the information gain from exploring that scenario, and the importance factor of the scenario. The information gain is measured by the information entropy, and the importance factor is based on the engineering judgment. The simulation results are recorded and grouped for later studies. The planner may "learn" from the simulation results, and update the plan to guide further simulation.
SIMPRA is the software package which implements the new methodology. It provides the users with a friendly interface and a rich DPRA library to aid in the construction of the simulation model. The engineering knowledge can be input into the Planner, which would generate a plan automatically. The scheduler would guide the simulation according to the plan. The simulation generates many accident event sequences and estimates of the end state probabilities
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