The Internet has been designed as a best-effort network, which does not provide any additional services to applications using the network. Overlay networks, which form an application layer network on top of the underlying Internet, have emerged as popular means to provide specific services and greater control to applications. Overlay networks offer a wide range of services, including content distribution, multicast and multimedia streaming. In my thesis, I focus on overlay networks for content distribution, used by applications such as bulk data transfer, file sharing and web retrieval. I first investigate the construction of such overlay networks by studying the bootstrapping functionality in an example network (the Gnutella peer-to-peer system). This study comprises the analysis and performance measurements of Gnutella servents and measurement of the GWebCache system that helps new peers find existing peers on the Gnutella network. Next, I look at fairness issues due to the retrieval of data at a client in the form of multipoint-to-point sessions, formed due to the use of content distribution networks. A multipoint-to-point session comprises multiple connections from multiple servers to a single client over multiple paths, initiated to retrieve a single application-level object. I investigate fairness of rate allocation from a session point of view, and propose fairness definitions and algorithms to achieve these definitions. Finally, I consider the problem of designing an overlay network for content distribution, which is fair to competing overlay networks, while maximizing the total end-to-end throughput of the data it carries. As a first step, I investigate this design problem for a single path in an Overlay-TCP network. I propose two schemes that dynamically provision the number of TCP connections on each hop of an Overlay-TCP path to maximize the end-to-end throughput using few extraneous connections. Next, I design an Overlay-TCP network, with the secondary goal of intra-overlay network fairness. I propose four schemes for deciding the number of TCP connections to be used on each overlay hop. I show that one can vary the proportion of sharing between competing overlay networks by varying the maximum number of connections allowed on overlay hops in each competing network.

This is a case study on a water sports facility named Liquid Leisure which is owned by Stuart Marston. The aim of this case study is to get a deeper understanding of Liquid Leisure’s capital and business structure and how it affects Liquid Leisure when trying to raise capital. It will also look at potential knowledge gaps within Liquid Leisure and how these potential gaps have formed Liquid Leisure as a company. There are a significant number of research that point out that small/family businesses operates in different ways compared to large businesses. Because of these deviations it is important to know how small/family businesses think to get an understanding of their advantages and disadvantages. This research will provide a deeper understanding of Liquid Leisure as a small/family businesses and how they address corporate governance, bootstrapping and the agency problem. It will also give an understanding of the advantages and disadvantages facing Liquid Leisure when raising debt and equity. These aspects have been compared to the complexity in the characteristics of small/family businesses. This will later be compared to Stuart Marston’s views and how Liquid Leisure is being operated. The results shows that Liquid Leisure has some characteristics in common with other small/family businesses but that Stuart’s behaviour create some complications for Liquid Leisure.

Geotechnical engineering design has relied upon deterministic methods of analysis whereby values for analysis parameters and conditions are selected subjectively based on judgment with the intent of providing acceptable margins of safety. The objective of this research was to improve the use of probabilistic slope stability analysis in practice so that the design of slopes can be made on a consistent and probabilistic basis. The current research involved the development of a methodology for the measurement and modeling of the anisotropic autocorrelation distance of cohesive soils, which was demonstrated at Dyke 17 West of the McArthur Falls Generating Station. In-situ testing using the piezocone and laboratory testing was conducted to characterize the spatial variability of the effective-shear strength envelope. Vertical (down-hole) and horizontal (cross-hole) geostatistical analysis was conducted to assess the anisotropy of the semivariogram. The investigation identified that heterogeneous inclusions had significant impacts on the results, but that simplistic (visual) identification and filtering procedures were adequate. The effective-stress shear strength envelope was statistically characterized as a random field, which was simulated as a first-order Markov process using customized add-in functions in a limit-equilibrium slope stability analysis. The analysis accounts for the spatial variability of shear strength and is capable of simulating both isotropic and anisotropic autocorrelation functions. The study showed that the critical slip surface geometry and the probability of failure can be significantly different when the anisotropy of spatial correlation is accounted for. The study also showed that neglecting spatial correlation may over-estimate the probability of failure, however this finding was noted to be likely case-specific. The primary conclusion of the study was that appropriate representation of spatial correlation is essential to calculating the probability of failure. Finally, convergence of the probabilistic simulation was evaluated using bootstrapping of the simulated factor of safety distribution to assess the standard error in the mean factor of safety, standard deviation of factor of safety and the probability of failure. A convergence criterion based on the percentage standard error in the probability of failure was proposed and used to define the number of Monte-Carlo iterations required.

Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (\textit{happiness, anger, sadness, disgust, surprise, fear}). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose ``final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs.

abstract: Though the likelihood is a useful tool for obtaining estimates of regression parameters, it is not readily available in the fit of hierarchical binary data models. The correlated observations negate the opportunity to have a joint likelihood when fitting hierarchical logistic regression models. Through conditional likelihood, inferences for the regression and covariance parameters as well as the intraclass correlation coefficients are usually obtained. In those cases, I have resorted to use of Laplace approximation and large sample theory approach for point and interval estimates such as Wald-type confidence intervals and profile likelihood confidence intervals. These methods rely on distributional assumptions and large sample theory. However, when dealing with small hierarchical datasets they often result in severe bias or non-convergence. I present a generalized quasi-likelihood approach and a generalized method of moments approach; both do not rely on any distributional assumptions but only moments of response. As an alternative to the typical large sample theory approach, I present bootstrapping hierarchical logistic regression models which provides more accurate interval estimates for small binary hierarchical data. These models substitute computations as an alternative to the traditional Wald-type and profile likelihood confidence intervals. I use a latent variable approach with a new split bootstrap method for estimating intraclass correlation coefficients when analyzing binary data obtained from a three-level hierarchical structure. It is especially useful with small sample size and easily expanded to multilevel. Comparisons are made to existing approaches through both theoretical justification and simulation studies. Further, I demonstrate my findings through an analysis of three numerical examples, one based on cancer in remission data, one related to the China’s antibiotic abuse study, and a third related to teacher effectiveness in schools from a state of southwest US. / Dissertation/Thesis / Doctoral Dissertation Statistics 2017

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-07-20T13:33:02Z No. of bitstreams: 1 soniamariadacosta.pdf: 997637 bytes, checksum: aa42c0476e55556a62f413f620912986 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-22T15:18:58Z (GMT) No. of bitstreams: 1 soniamariadacosta.pdf: 997637 bytes, checksum: aa42c0476e55556a62f413f620912986 (MD5) / Made available in DSpace on 2016-07-22T15:18:58Z (GMT). No. of bitstreams: 1 soniamariadacosta.pdf: 997637 bytes, checksum: aa42c0476e55556a62f413f620912986 (MD5) Previous issue date: 2011-09-01 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste estudo, de base experimental, focaliza-se a delimitação da categoria adjetivo por crianças adquirindo o Português Brasileiro (PB) como língua materna. Mais especificamente, investiga-se se traços formais de afixos flexionais de gênero de adjetivos e nomes são representados por crianças na faixa etária de 2-3 anos, de modo a permitir que elas os identifiquem na interface fônica e os interpretem na interface semântica para que, a partir da concordância, possam estabelecer a referência, à semelhança do que fazem os falantes adultos da língua. A pesquisa se fundamenta em uma teoria linguística que contemple o problema da aquisição da linguagem – particularmente a teoria de Princípios e Parâmetros, nos termos do Programa Minimalista (Chomsky, 1995; 1999 e obras posteriores) – e abordagens psicolinguísticas que considerem, como meios de desencadear a aquisição de uma língua: (i) o tratamento do sinal acústico da fala (bootstrapping fonológico: Morgan & Demuth, 1996; Christophe et al., 1997); (ii) a análise do material linguístico pela criança na aquisição de significado lexical (bootstrapping sintático: Gleitman, 1990). Para o desenvolvimento desta pesquisa, foram realizados três experimentos: no primeiro, conduzido com adultos falantes nativos do PB e aprendizes do PB como L2, buscou-se verificar sua preferência ao associar o adjetivo ao nome em sentenças com duas estruturas sintáticas possíveis - referência ao Nome Sujeito (NS) ou ao Nome Objeto (NO): (1a) O pai abraçou o filho triste e (1b) A atriz beijou a cantora emocionada. No segundo, conduzido com crianças de 2-3 anos, investigou-se a sua capacidade de identificar a marca morfofonológica de gênero do adjetivo para associálo ao nome ao qual se refere em sentenças como: (2a) O papai abraçou a filhinha cansado e (2b) O filhinho olhou a mamãe nervosa. Em um terceiro experimento, conduzido com crianças da mesma faixa etária das do segundo, procurou-se verificar a preferência dessas crianças ao associar o adjetivo ao nome, em sentenças com duas estruturas sintáticas possíveis  referência ao Nome Sujeito (NS) ou ao Nome Objeto 6 (NO): (3a) A mamãe olhou a filhinha assustada e (3b) O filhinho olhou o papai cansado. No primeiro experimento, assim como no terceiro, as sentenças, por apresentarem ambiguidade na adjunção do adjetivo ao NP objeto (NP2) ou ao NP sujeito (NP1), permitiam que esse atributo pudesse ser aposto localmente, ligando-se ao NP2, ou não-localmente, referindo-se ao NP1. Os resultados indicam que, com base nos princípios de Late Closure e Minimal Attachment (FRAZIER, 1979), a aposição local é a escolha de aposição default para essa estrutura, tanto por parte dos falantes adultos quanto por parte das crianças. Os resultados do segundo experimento indicam que crianças capazes de produzir enunciados com mais de duas palavras conseguem identificar, na interface fônica, informação relativa ao gênero de adjetivos, interpretando-a na interface semântica, para atribuir a propriedade expressa pelo adjetivo a um nome, estabelecendo, assim, a referência. / This dissertation, as an experimental study, focuses on the delimitation of adjective category by infants acquiring Brazilian Portuguese (BP) as their first language. It investigates if formal properties of gender inflection affixes of adjectives and nouns are represented by infants at the age of 2-3, enabling them to identify such features in the phonetic interface in order to interpret them in the semantic one, so that, starting from noun agreement, they can establish reference as well as adult speakers do. The research is based on a linguistic theory that considers the language acquisition problem – especially the Principles and Parameters Theory, in terms of the Minimalist Program (Chomsky, 1995-2001) – and psycholinguistic approaches that consider, as forms of bootstrap language acquisition: (i) the treatment of the acoustic signal of speech (phonological bootstrapping: Morgan & Demuth, 1996; Christophe et al., 1997); (ii) the analysis of linguistic material by infants in lexical meaning acquisition (syntactic bootstrapping: Gleitman, 1990). Three experimental tasks were carried out: the first one, involved native BP speakers and foreign adult speakers learning BP as a second language. It aimed to verify their preference in associating adjective to noun in sentences with two possible syntactic structures – reference to Subject Noun (SN) or to Object Noun (ON): (1a) O pai abraçou o filho triste and (1b) A atriz beijou a cantora emocionada. The second one, involving infants between 2-3 years of life, aimed to investigate their ability to identify the adjective‟s morphophonological feature of gender in order to associate it with the Noun to which it is referring in sentences such as: (2a) O papai abraçou a filhinha cansado and (2b) O filhinho olhou a mamãe nervosa. The third experiment involved infants at the same age of the second one and it aimed to verify their preference in associating the adjective with the noun in sentences with two possible syntactic structures – reference to Subject Noun (SN) or to Object Noun (ON): (3a) A mamãe olhou a filhinha assustada and (3b) O filhinho olhou o papai cansado. In the first and in the third experiments, sentences were ambiguous regarding the 8 adjunction of adjective to the NP-object (NP2) or to the NP-subject (NP1), allowing the attribute apposition to be local, associated with NP2, or non-local, associated with NP1. Results suggest that, based on the principles of Late closure and Minimal Attachment (FRAZIER, 1979), local apposition is the default choice to such structure, both for adult speakers and for infants. Second experiment results indicate that infants producing sentences with more than two words are able to identify, in phonetic interface, the information related to adjective gender and they are also able to interpret it in semantic interface in order to attribute the property expressed by adjective to a noun, therefore, establishing reference.

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2017-02-22T11:54:05Z No. of bitstreams: 1 cristianopereiradealmeida.pdf: 1470118 bytes, checksum: 522a21d105ff4f78a62d83e79c30931c (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-02-22T14:52:08Z (GMT) No. of bitstreams: 1 cristianopereiradealmeida.pdf: 1470118 bytes, checksum: 522a21d105ff4f78a62d83e79c30931c (MD5) / Made available in DSpace on 2017-02-22T14:52:08Z (GMT). No. of bitstreams: 1 cristianopereiradealmeida.pdf: 1470118 bytes, checksum: 522a21d105ff4f78a62d83e79c30931c (MD5) Previous issue date: 2007-08-29 / Esta dissertação diz respeito ao processo de identificação dos elementos das categorias lexicais Nome e Adjetivo no Português do Brasil, buscando dar conta de especificidades do PB, como a variação possível de ordem dos elementos constituintes do DP complexo e a ausência de marcas morfofonológicas que distingam adjetivos de nomes A hipótese de trabalho assumida é a de que, em um primeiro momento da aquisição da linguagem, a informação estrutural disponibilizada pela ordem preferencial de ocorrência destes elementos no PB atua como uma pista robusta da qual a criança faz uso. A abordagem teórica adotada busca conciliar um modelo de língua que trate do fenômeno de aquisição (Programa Minimalista: Chomsky, 1995), com modelos de processamento voltados para a aquisição da linguagem (modelos de bootstrapping). Foram desenvolvidas atividades experimentais cujos resultados apontam para a importância da informação estrutural, em um primeiro momento, e da informação semântica em etapas posteriores do processo de identificação de nomes e adjetivos. / This dissertation aims at the identification process of elements in the lexical categories of Name and Adjectives in Brazilian Portuguese trying to treat the specificities of the BP such as the possible variation of the complex DP constituent elements order and the absence of morphophonological marks that distinguish adjectives and names. The working hypothesis assumed is that in a first moment of the language acquisition the structural information made available by the preferential order of occurrence of that elements in BP acts as a powerful clue of which the child makes use of. The theoretical approach adopted tries to conciliate a language model that treats the phenomenon of acquisition (Minimalist Program: Chomsky, 1995) and processing models for the language acquisition (bootstrapping models). Experimental activities were developed and the results point to the importance of the structural information in a first moment and the semantic information in posterior stages of the identification process of names and adjectives.

Logic regression (LR) is a methodology to identify logic combinations of binary predictors in the form of intersections (and), unions (or) and negations (not) that are linearly associated with an outcome variable. Logic regression uses the predictors as inputs and enables us to identify important logic combinations of independent variables using a computationally efficient tree-based stochastic search algorithm, unlike the classical regression models, which only consider pre-determined conventional interactions (the “and” rules). In the thesis, we focused on LR with a binary outcome in a logistic regression framework. Simulation studies were conducted to examine the performance of LR under the assumption of independent and correlated observations, respectively, for various characteristics of the data sets and LR search parameters. We found that the proportion of times that LR selected the correct logic rule was usually low when the signal and/or prevalence of the true logic rule were relatively low. The method performed satisfactorily under easy learning conditions such as high signal, simple logic rules and/or small numbers of predictors. Given the simulation characteristics and correlation structures tested, we found some but not significant difference in performance when LR was applied to dependent observations compared to the independent case. In addition to simulation studies, an advanced application method was proposed to integrate LR and resampling methods in order to enhance LR performance. The proposed method was illustrated using two simulated data sets as well as a data set from a real-life situation. The proposed method showed some evidence of being effective in discerning the correct logic rule, even for unfavorable learning conditions.

Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (\textit{happiness, anger, sadness, disgust, surprise, fear}). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose ``final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs.

This diploma thesis analyses various approaches to calculate risk-free interest rate. In the beginning it deals with the term risk-free asset a various types of bases we could start calculating from. The paper suggests using spot rates and searches for alternative interest rates on the market. These are subsequently applied to real data coming from Czech market. Specifically they are the bootstrapping method and also the method of deriving risk-free interest rate from interest swap rate. Closing thoughts are dealing with various problems an appraiser may encounter while calculating risk-free rate. For example the problem of using nominal/real rates, nonexistent long-term government bonds or the presence of a default risk for particular government. This diploma thesis closes with a decision tree that could serve as a lead for appraiser in the process of estimating risk-free interest rate.