A segmentação dos nomes nas suas partes constitutivas é uma etapa fundamental no processo de integração de bases de dados por meio das técnicas de vinculação de registros. Esta separação dos nomes pode ser realizada de diferentes maneiras. Este estudo teve como objetivo avaliar a utilização do Modelo Escondido de Markov (HMM) na segmentação nomes e endereços de pessoas e a eficiência desta segmentação no processo de vinculação de registros. Foram utilizadas as bases do Sistema de Informações sobre Mortalidade (SIM) e do Subsistema de Informação de Procedimentos de Alta Complexidade (APAC) do estado do Rio de Janeiro no período entre 1999 a 2004. Uma metodologia foi proposta para a segmentação de nome e endereço sendo composta por oito fases, utilizando rotinas implementadas em PL/SQL e a biblioteca JAHMM, implementação na linguagem Java de algoritmos de HMM. Uma amostra aleatória de 100 registros de cada base foi utilizada para verificar a correção do processo de segmentação por meio do modelo HMM.Para verificar o efeito da segmentação do nome por meio do HMM, três processos de vinculação foram aplicados sobre uma amostra das duas bases citadas acima, cada um deles utilizando diferentes estratégias de segmentação, a saber: 1) divisão dos nomes pela primeira parte, última parte e iniciais do nome do meio; 2) divisão do nome em cinco partes; (3) segmentação segundo o HMM. A aplicação do modelo HMM como mecanismo de segmentação obteve boa concordância quando comparado com o observador humano. As diferentes estratégias de segmentação geraram resultados bastante similares na vinculação de registros, tendo a estratégia 1 obtido um desempenho pouco melhor que as demais. Este estudo sugere que a segmentação de nomes brasileiros por meio do modelo escondido de Markov não é mais eficaz do que métodos tradicionais de segmentação. / The segmentation of names into its constituent parts is a fundamental step in the integration of databases by means of record linkage techniques. This segmentation can be accomplished in different ways. This study aimed to evaluate the use of Hidden Markov Models (HMM) in the segmentation names and addresses of people and the efficiency of the segmentation on the record linkage process. Databases of the Information System on Mortality (SIM in portuguese) and Information Subsystem for High Complexity Procedures (APAC in portuguese) of the state of Rio de Janeiro between 1999 and 2004 were used. A method composed of eight stages has been proposed for segmenting the names and addresses using routines implemented in PL/SQL and a library called JAHMM, a Java implementation of HMM algorithms. A random sample of 100 records in each database was used to verify the correctness of the segmentation process using the hidden Markov model. In order to verify the effect of segmenting the names through the HMM, three record linkage process were applied on a sample of the aforementioned databases, each of them using a different segmentation strategy, namely: 1) dividing the name into first name , last name, and middle initials; 2) division of the name into five parts; 3) segmentation by HMM. The HMM segmentation mechanism was in good agreement when compared to a human observer. The three linkage processes produced very similar results, with the first strategy performing a little better than the others. This study suggests that the segmentation of Brazilian names by means of HMM is not more efficient than the traditional segmentation methods.

Dans cette thèse, nous nous intéressons à une classe particulière de modèles stochastiques de croissance de plantes structure-fonction à laquelle appartient le modèle GreenLab. L’objectif est double. En premier lieu, il s’agit d’étudier les processus stochastiques sous-jacents à l’organogenèse. Un nouveau cadre de travail combinatoire reposant sur l’utilisation de grammaires formelles a été établi dans le but d’étudier la distribution des nombres d’organes ou plus généralement des motifs dans la structure des plantes. Ce travail a abouti `a la mise en place d’une méthode symbolique permettant le calcul de distributions associées `a l’occurrence de mots dans des textes générés aléatoirement par des L-systèmes stochastiques. La deuxième partie de la thèse se concentre sur l’estimation des paramètres liés au processus de création de biomasse par photosynthèse et de son allocation. Le modèle de plante est alors écrit sous la forme d’un modèle de Markov caché et des méthodes d’inférence bayésienne sont utilisées pour résoudre le problème. / This PhD focuses on a particular class of stochastic models of functional-structural plant growth to which the GreenLab model belongs. First, the stochastic processes underlying the organogenesis phenomenon were studied. A new combinatorial framework based on formal grammars was built to study the distributions of the number of organs or more generally patterns in plant structures. This work led to the creation of a symbolic method which allows the computation of the distributions associated to word occurrences in random texts generated by stochastic L-systems. The second part of the PhD tackles the estimation of the parameters of the functional submodel (linked to the creation of biomass by photosynthesis and its allocation). For this purpose, the plant model was described by a hidden Markov model and Bayesian inference methods were used to solve the problem.

With the growing phenomenon of an aging population, an increasing numberof older people are living alone for domestic and social reasons. Based on thisfact, falling accidents become one of the most important factors in threateningthe lives of the elderly. Therefore, it is necessary to set up an application to de-tect the daily activities of the elderly. However, falling detection is difficult to recognize because the "falling" motion is an instantaneous motion and easy to confuse with others.In this thesis, three data mining methods were employed on wearable sensors' value; first which contains the continuous data set concerning eleven activities of daily living, and then an analysis of the different results was performed. Not only could the fall be detected, but other activities could also be classified. In detail, three methods including Back Propagation Neural Network, Support Vector Machine and Hidden Markov Model are applied separately to train the data set.What highlights the project is that a new  idea is put forward, the aim of which is to design a methodology of accurate classification in the time-series data set. The proposed approach, which includes obtaining of classifier parts and the application parts allows the generalization of classification. The preliminary results indicate that the new method achieves the high accuracy of classification,and significantly performs better than other data mining methods in this experiment.

Development of Cybersecurity processes and strategies should take two main approaches. One is to develop an efficient and effective set of methodologies to identify software vulnerabilities and patch them before being exploited. Second is to develop a set of methodologies to predict the behavior of attackers and execute defending techniques based on attacking behavior. Managing of Vulnerabilities and analyzing them is directly related to the first approach. Developing of methodologies and models to predict the behavior of attackers is related to the second approach. Both these approaches are inseparably interconnected. Our effort in this study mainly focuses on developing useful statistical models that can give us signals about the behavior of cyber attackers. Analytically understanding of vulnerabilities in statistical point of view helps to develop a set of statistical models that works as a bridge between Cybersecurity and Abstract Statistical and Mathematical knowledge. Any such effort should begin with properly understanding the nature of Vulnerabilities in a computer network system. We start this study with analyzing "Vulnerability" based on inferences that can be taken from National Vulnerability Database (NVD). In Cybersecurity context, we apply Markov approach to develop suitable predictive models to successfully estimate the minimum number of steps to compromise a security goal that an attacker would take using the concept of Expected Path Length (EPL). We have further developed Non-Homogeneous Stochastic model by improving EPL estimates in to a time dependent variable. This approach analytically applied in a simple model of computer network with discovered vulnerabilities resulted in several useful observations exemplifying the applicability in real world computer systems. The methodology indicated a measure of the "Risk" associated with the model network as a function of time indicating defending professionals on the threats they are facing and should anticipate to face. Furthermore, using a similar approach taken in well-known Google page rank algorithm, a new ranking algorithm of vulnerability ranks with respect to time for computer network system is also presented in this study. With better IT resources analytical models and methodologies presented in this study can be developed into more generalized versions and apply in real world computer network environments.

Analysis on Vulnerabilities and Vulnerability Life Cycle is at the core of Cybersecurity related studies. Vulnerability Life Cycle discussed by S. Frei and studies by several other scholars have noted the importance of this approach. Application of Statistical Methodologies in Cybersecurity related studies call for a greater deal of new information. Using currently available data from National Vulnerability Database this study develops and presents a set of useful Statistical tools to be applied in Cybersecurity related decision making processes. In the present study, the concept of Vulnerability Space is defined as a probability space. Relevant theoretical analyses are conducted and observations in the vulnerability space in aspects of events and states are discussed. Transforming IT related cybersecurity issues into analytical formation so that abstract and conceptual knowledge from Mathematics and Statistics can be applied is a challenge. However, to overcome rising threats from Cyber-attacks such an integration of analytical foundation to understand the issues and develop strategies is essential. In the present study we apply well known Markov approach in a new approach of Vulnerability Life Cycle to develop useful analytical methods to assess the Risk associated with a vulnerability. We also presents, a new Risk Index integrating the results obtained and details from the Common Vulnerability Scoring System (CVSS). In addition, a comprehensive study on the Vulnerability Space is presented discussing the likelihood of probable events in the probability sub-spaces of vulnerabilities. Finally, an Extended Vulnerability Life Cycle model is presented and discussed in relation to States and Events in the Vulnerability Space that lays down a strong foundation for any future vulnerability related analytical research efforts.

Master of Science / Department of Computing and Information Sciences / David A. Gustafson / The use of hidden Markov models in autism recognition and analysis is investigated. More specifically, we would like to be able to determine a person's level of autism (AS, HFA, MFA, LFA) using hidden Markov models trained on observations taken from a subject's behavior in an experiment. A preliminary model is described that includes the three mental states self-absorbed, attentive, and join-attentive. Futhermore, observations are included that are more or less indicative of each of these states. Two experiments are described, the first on a single subject and the second on two subjects. Data was collected from one individual in the second experiment and observations were prepared for input to hidden Markov models and the resulting hidden Markov models were studied. Several questions subsequently arose and tests, written in Java using the JaHMM hidden Markov model tool- kit, were conducted to learn more about the hidden Markov models being used as autism recognizers and the training algorithms being used to train them. The tests are described along with the corresponding results and implications. Finally, suggestions are made for future work. It turns out that we aren't yet able to produce hidden Markov models that are indicative of a persons level of autism and the problems encountered are discussed and the suggested future work is intended to further investigate the use of hidden Markov models in autism recognition.

Sodium ion (Na+) channels play an important role in excitable cells, as they are responsible for the initiation of action potentials. Understanding the electrical characteristics of sodium channels is essential in predicting their behavior under different physiological conditions. We investigated several Markov models for the human cardiac sodium channel (NaV1.5) to derive a minimal mathematical model that can describe the reported experimental data obtained using major voltage-clamp protocols. We obtained simulation results for current-voltage relationships, steady-state inactivation, the voltage dependence of normalized ion channel conductance; activation and deactivation, fast and slow inactivation and recovery from inactivation kinetics. Good agreement with the experimental data provides us with the mechanisms of the fast and slow inactivation of the human sodium channel and the coupling of its inactivation states to the closed and open states in the activation pathway.

Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload's contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates.

A demanda por abordagens eficientes para o problema de reconhecer a estrutura de cada gene numa sequência genômica motivou a implementação de um grande número de programas preditores de genes. Fizemos uma análise dos programas de sucesso com abordagem probabilística e reconhecemos semelhanças na implementação dos mesmos. A maior parte desses programas utiliza a cadeia oculta generalizada de Markov (GHMM - generalized hiddenMarkov model) como um modelo de gene. Percebemos que muitos preditores têm a arquitetura da GHMM fixada no código-fonte, dificultando a investigação de novas abordagens. Devido a essa dificuldade e pelas semelhanças entre os programas atuais, implementamos o sistema MYOP (Make Your Own Predictor) que tem como objetivo fornecer um ambiente flexível o qual permite avaliar rapidamente cada modelo de gene. Mostramos a utilidade da ferramenta através da implementação e avaliação de 96 modelos de genes em que cada modelo é formado por um conjunto de estados e cada estado tem uma distribuição de duração e um outro modelo probabilístico. Verificamos que nem sempre um modelo probabilísticomais sofisticado fornece um preditor melhor, mostrando a relevância das experimentações e a importância de um sistema como o MYOP. / The demand for efficient approaches for the gene structure prediction has motivated the implementation of different programs. In this work, we have analyzed successful programs that apply the probabilistic approach. We have observed similarities between different implementations, the same mathematical framework called generalized hidden Markov chain (GHMM) is applied. One problem with these implementations is that they maintain fixed GHMM architectures that are hard-coded. Due to this problem and similarities between the programs, we have implemented the MYOP framework (Make Your Own Predictor) with the objective of providing a flexible environment that allows the rapid evaluation of each gene model. We have demonstrated the utility of this tool through the implementation and evaluation of 96 gene models in which each model has a set of states and each state has a duration distribution and a probabilistic model. We have shown that a sophisticated probabilisticmodel is not sufficient to obtain better predictor, showing the experimentation relevance and the importance of a system as MYOP.

Available Bandwidth Estimation Techniques and Tools (ABETTs) have recently been envisioned as a supporting mechanism in areas such as compliance of service level agreements, network management, traffic engineering and real-time resource provisioning, flow and congestion control, construction of overlay networks, fast detection of failures and network attacks, and admission control. However, it is unknown whether current ABETTs can run efficiently in any type of network, under different network conditions, and whether they can provide accurate available bandwidth estimates at the timescales needed by these applications. This dissertation investigates techniques and tools able to provide accurate, low overhead, reliable, and fast available bandwidth estimations. First, it shows how it is that the network can be sampled to get information about the available bandwidth. All current estimation tools use either the probe gap model or the probe rate model sampling techniques. Since the last technique introduces high additional traffic to the network, the probe gap model is the sampling method used in this work. Then, both an analytical and experimental approach are used to perform an extensive performance evaluation of current available bandwidth estimation tools over a flexible and controlled testbed. The results of the evaluation highlight accuracy, overhead, convergence time, and reliability performance issues of current tools that limit their use by some of the envisioned applications. Single estimations are affected by the bursty nature of the cross traffic and by errors generated by the network infrastructure. A hidden Markov model approach to end-to-end available bandwidth estimation and monitoring is investigated to address these issues. This approach builds a model that incorporates the dynamics of the available bandwidth. Every sample that generates an estimation is adjusted by the model. This adjustment makes it possible to obtain acceptable estimation accuracy with a small number of samples and in a short period of time. Finally, the new approach is implemented in a tool called Traceband. The tool, written in ANSI C, is evaluated and compared with Pathload and Spruce, the best estimation tools belonging to the probe rate model and the probe gap model, respectively. The evaluation is performed using Poisson, bursty, and self-similar synthetic cross traffic and real traffic from a network path at University of South Florida. Results show that Traceband provides more estimations per unit time with comparable accuracy to Pathload and Spruce and introduces minimum probing traffic. Traceband also includes an optional moving average technique that smooths out the estimations and improves its accuracy even further.