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.

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.

This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically.

Using an autoethnographic methodology in tandem with a social constructivist lens, the purpose of this study is to critically inquire into the implicit lessons learned by competitive Latin dancers from their participation in the hidden curriculum of DanceSport culture. Additionally, this research looks to outline the intrapersonal and interpersonal development experienced by DanceSport dancers. Doing so, I focus on the voice of the dancers in the exploration of International DanceSport studios and competition floors as sites of informal and non-formal education, particularly in relation to the hidden curriculum of DanceSport. My role as an autoethnographic researcher and participant in this study was to collect data through multimodal expression, as well as ongoing dialogue with participants. As researcher and participant, I completed the same hands-on task and interview questions as participants, and journaled to explore thoughts concerning my DanceSport experience. Information obtained from interview transcripts and journal entries has been organized, coded, and analyzed creating themes. A description and interpretation of the findings uncovers the unifying theme(s) of six narratives. Narrative inquiry reveals that the hidden curriculum of DanceSport is the awakening and development of dormant confidence as dancers acquire implicit lessons pertaining to the development of intra and interpersonal knowledge. A discussion exploring the unique ways this dissertation contributes to the DanceSport community and body of literature concludes this study. This is alongside an assessment as to the culture’s ability to meet current goals of formal education, and the potential Latin DanceSport has to be used as an educational tool in the development of student well-being.

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.

Orientador: Fábio Violaro / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-22T07:58:43Z (GMT). No. of bitstreams: 1 Carvalho_SarahNegreirosde_M.pdf: 2350561 bytes, checksum: 950d33430acbd816700ef5de4c78fa5d (MD5) Previous issue date: 2013 / Resumo: Com o contínuo desenvolvimento da tecnologia, há uma demanda crescente por sistemas de síntese de fala que sejam capazes de falar como humanos, para integrá-los nas mais diversas aplicações, seja no âmbito da automação robótica, sejam para acessibilidade de pessoas com deficiências, seja em aplicativos destinados a cultura e lazer. A síntese de fala baseada em modelos ocultos de Markov (HMM) mostra-se promissora em suprir esta necessidade tecnológica. A sua natureza estatística e paramétrica a tornam um sistema flexível, capaz de adaptar vozes artificiais, inserir emoções no discurso e obter fala sintética de boa qualidade usando uma base de treinamento limitada. Esta dissertação apresenta o estudo realizado sobre o sistema de síntese de fala baseado em HMM (HTS), descrevendo as etapas que envolvem o treinamento dos modelos HMMs e a geração do sinal de fala. São apresentados os modelos espectrais, de pitch e de duração que constituem estes modelos HMM dos fonemas dependentes de contexto, considerando as diversas técnicas de estruturação deles. Alguns dos problemas encontrados no HTS, tais como a característica abafada e monótona da fala artificial, são analisados juntamente com algumas técnicas propostas para aprimorar a qualidade final do sinal de fala sintetizado / Abstract: With the continuous development of technology, there is a growing demand for text-to-speech systems that are able to speak like humans, in order to integrate them in the most diverse applications whether in the field of automation and robotics, or for accessibility of people with disabilities, as for culture and leisure activities. Speech synthesis based on hidden Markov models (HMM) shows to be promising in addressing this need. Their statistical and parametric nature make it a flexible system capable of adapting artificial voices, insert emotions in speech and get artificial speech of good quality using a limited amount of speech data for HMM training. This thesis presents the study realized on HMM-based speech synthesis system (HTS), describing the steps that involve the training of HMM models and the artificial speech generation. Spectral, pitch and duration models are presented, which form context-dependent HMM models, and also are considered the various techniques for structuring them. Some of the problems encountered in the HTS, such as the characteristic muffled and monotone of artificial speech, are analyzed along with some of the proposed techniques to improve the final quality of the synthesized speech signal / Mestrado / Telecomunicações e Telemática / Mestra em Engenharia Elétrica

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.

Speech is intuitive, fast and easy to generate, but it is hard to index and easy to forget. What is more, listening to speech is slow. Text is easier to store, process and consume, both for computers and for humans, but writing text is slow and requires some intention. In this thesis, we study speech recognition which allows converting speech into text, making it easier both to create and to use information. Our tool of study is Hidden Markov Models which is one of the most important machine learning models in speech and language processing. The aim of this thesis is to do a rst study in Hidden Markov Models and understand their importance, particularly in speech recognition. We will go through three fundamental problems that come up naturally with Hidden Markov Models: to compute a likelihood of an observation sequence, to nd an optimal state sequence given an observation sequence and the model, and to adjust the model parameters. A solution to each problem will be given together with an example and the corresponding simulations using MatLab. The main importance lies in the last example, in which a rst approach to speech recognition will be done.

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