Project (B.S.)--James Madison University, 2009. / Includes bibliographical references.

A part of the Swedish TV-show Levande livet that aired between 1983 and 1984 was devoted to wine. This was the first time a wine tasting was being broadcasted in Sweden. Terms as ”sweaty horse” and ”moulded pile of leaves” – that the wine connoisseurs Carl Jan Granqvist and Knut-Christian Gröntoft used to describe the wines – became objects of both appreciation and ridicule. Their way of talking about wine reminds of Robert Parker’s wine language, which grew of importance from the 1970s and onwards.                       The purpose of this thesis is to try to write a history of taste. By researching how the TV-show was received by the daily press in Sweden, it is possible to come to terms with what kind of opinions and attitudes a wine tasting challanged. This thesis shows how the viewers, by tasting wine and trying to articulate their taste experiences in the language provided by Granqvist and Gröntoft, became members of a taste community. This taste community was not only being sustained by a shared language for taste experience, it also affected the viewers own taste of the wine.                       By doing this it is possible to describe in what way everyone was urged to practice their own taste and become a connoisseur.

This thesis examines philosophical controversies surrounding the evaluation of medical treatments, with a focus on the evidential roles of randomised trials and mechanisms in Evidence-Based Medicine. Current 'best practice' usually involves excluding non-randomised trial evidence from systematic reviews in cases where randomised trials are available for inclusion in the reviews. The first paper challenges this practice and evaluates whether adding of evidence from non-randomised trials might improve the quality and precision of some systematic reviews. The second paper compares the alleged methodological benefits of randomised trials over observational studies for investigating treatment benefits. It suggests that claims about the superiority of well-conducted randomised controlled trials over well-conducted observational studies are justified, especially when results from the two methods are contradictory. The third paper argues that postulating the unpredictability paradox in systematic reviews when no detectable empirical differences can be found requires further justification. The fourth paper examines the problem of absence causation in the context of explaining causal mechanisms and argues that a recent solution (Barros 2013) is incomplete and requires further justification. Solving the problem by describing absences as causes of 'mechanism failure' fails to take into account the effects of absences that lead to vacillating levels of mechanism functionality (i.e. differences in effectiveness or efficiency). The fifth paper criticises literature that has emphasised functioning versus 'broken' or 'non-functioning' mechanisms emphasising that many diseases result from increased or decreased mechanism function, rather than complete loss of function. Mechanistic explanations must account for differences in the effectiveness of performed functions, yet current philosophical mechanistic explanations do not achieve this. The last paper argues that the standard of evidence embodied in the ICE theory of technological function (i.e. testimonial evidence and evidence of mechanisms) is too permissive for evaluating whether the proposed functions of medical technologies have been adequately assessed and correctly ascribed. It argues that high-quality evidence from clinical studies is necessary to justify functional ascriptions to health care technologies. / <p>QC 20150312</p>

The script, Millennium Bridge, is an investigation into the passions and fears that are shaping contemporary Australia today. Charting the political climate of the past decade, at the play's centre a man is building a bridge from Australia to Asia. The central dramatic question being asked is &quotIn an environment where the emphasis on economic prosperity overrides that of human rights and freedom of speech--what will be the consequences for the Australian people?" The accompanying analysis of the ten-year period it took to write Millennium Bridge illuminates the significance of institutional issues on a play and playwright's development. Written from the perspective of a mid-career playwright, the paper argues that the professional and personal circumstances within which a work of art is created (and their effect on the playwright's confidence and financial capacities) are a significant determinant of the productivity of playwrights.

Dans cette thèse, nous abordons le problème de l'analyse des sentiments. Plus précisément, nous sommes intéressés à analyser le sentiment exprimé dans les textes de médias sociaux.Nous allons nous concentrer sur deux tâches principales: la détection de polarité de sentiment dans laquelle nous cherchons à déterminer la polarité (positive, négative ou neutre) d'un texte donné et l'extraction de cibles d’opinion et le sentiment exprimé vers ces cibles (par exemple, pour le restaurant nous allons extraire des cibles comme la nourriture, pizza, service). Notre principal objectif est de construire des systèmes à la pointe de la technologie qui pourrait faire les deux tâches. Par conséquent, nous avons proposé des systèmes supervisés différents suivants trois axes de recherche: l'amélioration de la performance du système par la pondération de termes, en enrichissant de la représentation de documents et en proposant un nouveau modèle pour la classification de sentiment.Pour l'évaluation, nous avons participé à un atelier international sur l'évaluation sémantique (Sem Eval), nous avons choisi deux tâches: l'analyse du sentiment sur Twitter dans laquelle nous déterminer la polarité d'un tweet et l'analyse des sentiments basée sur l’aspect dans laquelle nous extrayons les cibles d'opinion dans les critiques de restaurants, puis nous déterminons la polarité de chaque cible, nos systèmes ont été classés parmi les premiers trois meilleurs systèmes dans toutes les sous-tâches. Nous avons également appliqué nos systèmes sur un corpus des critiques de livres français construit par l'équipe Open Edition pour extraire les cibles d'opinion et leurs polarités. / In this thesis, we address the problem of sentiment analysis. More specifically, we are interested in analyzing the sentiment expressed in social media texts such as tweets or customer reviews about restaurant, laptop, hotel or the scholarly book reviews written by experts. We focus on two main tasks: sentiment polarity detection in which we aim to determine the polarity (positive, negative or neutral) of a given text and the opinion target extraction in which we aim to extract the targets that the people tend to express their opinions towards them (e.g. for restaurant we may extract targets as food, pizza, service).Our main objective is constructing state-of-the-art systems which could do the two tasks. Therefore, we have proposed different supervised systems following three research directions: improving the system performance by term weighting, by enriching the document representation and by proposing a new model for sentiment classification. For evaluation purpose, we have participated at an International Workshop on Semantic Evaluation (SemEval), we have chosen two tasks: Sentiment analysis in twitter in which we determine the polarity of a tweet and Aspect-Based sentiment analysis in which we extract the opinion targets in restaurant reviews, then we determine the polarity of each target. Our systems have been among the first three best systems in all subtasks. We also applied our systems on a French book reviews corpus constructed by OpenEdition team for extracting the opinion targets and their polarities.

Made available in DSpace on 2015-07-22T20:50:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-01-01 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Contexto: consideramos as revisões sistemáticas como melhor nível de evidência para a tomada de decisão nos cuidados com a saúde, cujo rigor metodológico oferece uma diversidade de implicações para a prática clínica e para a pesquisa científica. Objetivo: avaliar a proporção de revisões sistemáticas completas da Colaboração Cochrane que permitem a aplicação prática dos resultados e implicações para a pesquisa científica. Tipo de Estudo: estudo transversal de revisões sistemáticas da Cochrane Library issue 4, 2004. Método: Análise da amostra aleatória de revisões sistemáticas dos 50 grupos colaborativos especializados da Cochrane. A extração de dados foi baseada nas conclusões dos autores, interpretações de metanálises e no contexto de cada revisão sistemática. As implicações para a prática foram classificadas em três categorias: A) “evidências que apóiam a utilização da intervenção testada”; B) ”evidências que contra-indicam a utilização da intervenção”; C) “ausência de evidências para recomendar ou desestimular a intervenção”. As implicações para a pesquisa científica foram categorizadas em: 1) “recomendação para mais pesquisas” e 2) “sem necessidade para recomendar novos estudos”. Número de estudos incluídos e metanálises foram também quantificados. Resultados: 1016 revisões sistemáticas foram analisadas, o que correspondeu a 46,60% da totalidade disponível na Cochrane Library, issue 4, 2004. As proporções e intervalo de confiança (IC) de 95% das implicações para a prática clínica foram: A) 44,39 (95% IC, 42,16 – 46,62) %; B) 6,79 (95% IC, 5,66 – 7,92)%; C) 48,81 (95% IC, 46,57 – 51,07)%. O total de revisões sistemáticas que recomendam a realização de mais estudos foi de 95,96% (95% IC, 95,08 – 97,04). O número de estudos incluídos foi de 13.830 (mediana 8 e moda 2) e o total de metanálises incluídas nas revisões sistemáticas avaliadas, de 6.461 (mediana 2 e moda 0). Conclusão: A grande maioria das revisões sistemáticas não traz orientações específicas com relação ao benefício ou malefício de uma intervenção, comparativamente ao grupo controle para determinada situação clínica. Há uma proporção significativa de revisões sistemáticas que sugerem recomendações de novos estudos para responderem à questão clínica da revisão. Há poucos estudos primários que respondem ao critério de inclusão da revisão sistemática, sugerindo uma qualidade metodológica pobre. Há pouca quantidade de metanálise por revisão sistemática para os desfechos clínicos de interesse. / Context: we consider systematic reviews the best level of evidence for the decision making in the health care, which methodological severity offers a diversity of implications to clinical practice and to scientific research. Objective: to assess the proportion of the complete systematic reviews of Cochrane Colaboration that allow practice application of results and implication to scientific research. Design and Setting: Cross-sectional study of systematic reviews of Cochrane Library issue 4, 2004. Main Outcomes Measures: 1016 systematic reviews published throughout 50 Cochrane Collaborative Review Groups were analysed randomly. Data extraction was based on the authors’ conclusions, meta-analysis interpretations and on the context of each systematic review. The implications to practice had been classified in three categories: A) evidences that support the use of the tested intervention. B) evidences that contraindicate the intervention use. C) absence of evidences to recommend or discourage the intervention. The implications to scientific research had been categorized in: 1) recommendation to further research and 2) no necessity to recommend new studies. Number of included studies and meta-analysis were also quantified. Results: 1016 systematic reviews were analyzed, which corresponded to 46,60% of the available totality in the Cochrane Library, issue 4, 2004. The proportions and confidence interval (CI) of 95% of the implications to clinical practice were: A) 44,39 (95% IC, 42,16 – 46,62) %; B) 6,79 (95% IC, 5,66 – 7,92)%; C) 48,81 (95% IC, 46,57 – 51,07)%. The totality of systematic reviews that recommend the accomplishment of further studies was 13.830 (medium 8 and mode 2) and the totality of included meta-analysis of the evaluated systematic reviews, 6.641 (medium 2 and mode 0). Conclusion: the great majority of systematic reviews do not bring specific orientations with relations to the benefit or curse of an intervention, comparatively to control group for certain clinical situation. There are a significant proportion of systematic reviews that suggest recommendations of new studies to answer to the clinical question of the review. There are few primary studies that answer the inclusion criterion of the systematic review and suggest a poor methodological quality. There is a little amount of meta-analysis by systematic review for the clinical outcomes of interest. / TEDE / BV UNIFESP: Teses e dissertações

The thesis is focused on the description of the company, its organizational structure and finance companies. There are also described each of the posts leading employees, their competencies and powers. The paper also analyzed the various processes taking place in the hotel facilities and there is value out of these processes.

Submitted by Milena Rubi (milenarubi@ufscar.br) on 2017-10-09T17:30:32Z No. of bitstreams: 1 CARDOSO_Emerson_2017.pdf: 3299853 bytes, checksum: bda5605a1fb8e64f503215e839d2a9a6 (MD5) / Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-10-09T17:30:45Z (GMT) No. of bitstreams: 1 CARDOSO_Emerson_2017.pdf: 3299853 bytes, checksum: bda5605a1fb8e64f503215e839d2a9a6 (MD5) / Approved for entry into archive by Milena Rubi (milenarubi@ufscar.br) on 2017-10-09T17:32:37Z (GMT) No. of bitstreams: 1 CARDOSO_Emerson_2017.pdf: 3299853 bytes, checksum: bda5605a1fb8e64f503215e839d2a9a6 (MD5) / Made available in DSpace on 2017-10-09T17:32:49Z (GMT). No. of bitstreams: 1 CARDOSO_Emerson_2017.pdf: 3299853 bytes, checksum: bda5605a1fb8e64f503215e839d2a9a6 (MD5) Previous issue date: 2017-08-04 / Não recebi financiamento / Before buying a product or choosing for a trip destination, people often seek other people’s opinions to obtain a vision of the quality of what they want to acquire. Given that, opinions always had great influence on the purchase decision. Following the enhancements of the Internet and a huge increase in the volume of data traffic, social networks were created to help users post and view all kinds of information, and this caused people to also search for opinions on the Web. Sites like TripAdvisor and Yelp make it easier to share online reviews, since they help users to post their opinions from anywhere via smartphones and enable product manufacturers to gain relevant feedback quickly in a centralized way. As a result, most people nowadays trust personal recommendations as much as online reviews. However, competition between service providers and product manufacturers have also increased in social media, leading to the first cases of spam reviews: deceptive opinions published by hired people that try to promote or defame products or businesses. These reviews are carefully written in order to look like authentic ones, making it difficult to be detected by humans or automatic methods. Thus, they are used, in a misleading way, in attempt to control the general opinion, causing financial harm to business owners and users. Several approaches have been proposed for spam review detection and most of them use techniques involving machine learning and natural language processing. However, despite all progress made, there are still relevant questions that remain open, which require a criterious analysis in order to be properly answered. For instance, there is no consensus whether the performance of traditional classification methods can be affected by incremental learning or changes in reviews’ features over time; also, there is no consensus whether there is statistical difference between performances of content-based classification methods. In this scenario, this work offers a comprehensive comparison between traditional machine learning methods applied in spam review detection. This comparison is made in multiple setups, employing different types of learning and data sets. The experiments performed along with statistical analysis of the results corroborate offering appropriate answers to the existing questions. In addition, all results obtained can be used as baseline for future comparisons. / Antes de comprar um produto ou escolher um destino de viagem, muitas pessoas costumam buscar por opiniões alheias para obter uma visão da qualidade daquilo que se deseja adquirir. Assim, as opiniões sempre exerceram grande influência na decisão de compra. Com o avanço da Internet e aumento no volume de informações trafegadas, surgiram redes sociais que possibilitam compartilhar e visualizar informações de todo o tipo, fazendo com que pessoas passassem a buscar também por opiniões na Web. Atualmente, sites especializados, como TripAdvisor e Yelp, oferecem um sistema de compartilhamento de opiniões online (reviews) de maneira fácil, pois possibilitam que usuários publiquem suas opiniões de qualquer lugar através de smartphones, assim como também permitem que fabricantes de produtos e prestadores de serviços obtenham feedbacks relevantes de maneira centralizada e rápida. Em virtude disso, estudos indicam que atualmente a maioria dos usuários confia tanto em recomendações pessoais quanto em reviews online. No entanto, a competição entre prestadores de serviços e fabricantes de produtos também aumentou nas redes sociais, o que levou aos primeiros casos de spam reviews: opiniões enganosas publicadas por pessoas contratadas que tentam promover ou difamar produtos ou serviços. Esses reviews são escritos cuidadosamente para parecerem autênticos, o que dificulta sua detecção por humanos ou por métodos automáticos. Assim, eles são usados para tentar, de maneira enganosa, controlar a opinião geral, podendo causar prejuízos para empresas e usuários. Diversas abordagens para a detecção de spam reviews vêm sendo propostas, sendo que a grande maioria emprega técnicas de aprendizado de máquina e processamento de linguagem natural. No entanto, apesar dos avanços já realizados, ainda há questionamentos relevantes que permanecem em aberto e demandam uma análise criteriosa para serem respondidos. Por exemplo, não há um consenso se o desempenho de métodos tradicionais de classificação pode ser afetado em cenários que demandam aprendizado incremental ou por mudanças nas características dos reviews devido ao fator cronológico, assim como também não há um consenso se existe diferença estatística entre os desempenhos dos métodos baseados no conteúdo das mensagens. Neste cenário, esta dissertação oferece uma análise e comparação compreensiva dos métodos tradicionais de aprendizado de máquina, aplicados na detecção de spam reviews. A comparação é realizada em múltiplos cenários, empregando-se diferentes tipos de aprendizado e bases de dados. Os experimentos realizados, juntamente com análise estatística dos resultados, corroboram a oferecer respostas adequadas para os questionamentos existentes. Além disso, os resultados obtidos podem ser usados como baseline para comparações futuras.

Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from mobile devices. The value and impact of social media makes it however an attractive attack target. In this thesis, we focus on the following social media vulnerabilities. First, review centered social networks such as Yelp and Google Play have been shown to be the targets of significant search rank and malware proliferation attacks. Detecting fraudulent behaviors is thus paramount to prevent not only public opinion bias, but also to curb the distribution of malware. Second, the increasing use of mobile visual data in news networks, authentication and banking applications, raises questions of its integrity and credibility. Third, through proof-of- concept implementations, we show that data reported from wearable personal trackers is vulnerable to a wide range of security and privacy attacks, while off-the-shelves security solutions do not port gracefully to the constraints introduced by trackers. In this thesis we propose novel solutions to address these problems. First, we introduce Marco, a system that leverages the wealth of spatial, temporal and network information gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Second, we propose FairPlay, a system that correlates review activities, linguistic and behavioral signals gleaned from longitudinal app data, to identify not only search rank fraud but also malware in Google Play, the most popular Android app market. Third, we describe Movee, a motion sensor based video liveness verification system, that analyzes the consistency between the motion inferred from the simultaneously and independently captured camera and inertial sensor streams. Finally, we devise SensCrypt, an efficient and secure data storage and communication protocol for affordable and lightweight personal trackers. We provide the correctness and efficacy of our solutions through a detailed theoretic and experimental analysis.

The internet is the home for massive volumes of valuable data constantly being created, making it difficult for users to find information relevant to them. In recent times, online users have been relying on the recommendations made by websites to narrow down the options. Online reviews have also become an increasingly important factor in the final choice of a customer. Unfortunately, attackers have found ways to manipulate both reviews and recommendations to mislead users. A Recommendation System is a special type of information filtering system adapted by online vendors to provide suggestions to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. On the other hand, many spammers write deceptive reviews to change the credibility of a product/service. This work aims to address these issues by treating the review manipulation and shilling attack scenarios independently. For the shilling attacks, we build an efficient Reinforcement Learning-based shilling attack method. This method reduces the uncertainty associated with the item selection process and finds the most optimal items to enhance attack reach while treating the recommender system as a black box. Such practical online attacks open new avenues for research in building more robust recommender systems. When it comes to review manipulations, we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about 92\% of all spam reviewers using an unsupervised learning approach.<br>