Este trabalho apresenta a proposta de desenvolvimento de componentes de software orientados a serviço para prover informações financeiras e fitossanitárias, aplicadas ao mercado brasileiro de laranjas. O setor citrícola brasileiro produz metade do suco de laranja no mundo e é o responsável por 80% da comercialização internacional, gerando 420 mil empregos diretos e movimentando anualmente US$ 5,0 bilhões em negócios. A cada ano a produção de laranja aumenta, fazendo com que os produtores rurais necessitem de informações confiáveis para o apoio à decisão. O desenvolvimento dos componentes foi elaborado através de uma arquitetura de referência para sistemas de informação orientados a serviço. Esta arquitetura está dividida em camadas lógicas que definem a apresentação, integração, troca de informações e serviços de software. Os componentes desenvolvidos integram a camada de serviço fornecendo os custos operacionais, a previsão de ocupação de mão de obra, a série mensal de preços e o calendário fitossanitário. Como prova de conceito foi desenvolvido um software para apoio à decisão que utiliza os componentes financeiro e fitossanitário desenvolvidos. O objetivo deste software é apoiar o produtor a obter uma melhor utilização de seus recursos, e com isso aumentar a produtividade. A principal contribuição deste trabalho é disseminar a arquitetura de componentes abertos para a agricultura, a proposição e elaboração de dois novos componentes financeiro e fitossanitário e a implementação de uma ferramenta de auxílio à tomada de decisão no processo de produção de laranjas. / This work presents a service-oriented software component development proposal to provide financial and phytosanitary information applied to the Brazilian orange market. The Brazilian citric sector produces half of the world\'s orange juice and is responsible for 80% of the international commercialization, creating 420 thousand direct jobs and moving annually US$ 5,0 billion in business. Each year the orange production increases, making the rural producers need reliable information for decision support. The development of the components was elaborated on a reference architecture for service-oriented information systems. This architecture is divided in logical layers that define the presentation, integration, information exchange and software services. The components developed integrate the service layer, providing the operational costs, the occupation forecast, the monthly price series and the phytosanitary plant timetable. As proof of concept, a software was developed for decision support that uses the financial and phytosanitary components that were developed. The objective of this software is support the producer to obtain a better use of his resources and as a result increase his productivity. The main contribution of this work is disseminating the architecture of open components for agriculture, the proposition and elaboration of two new components for the architecture and the implementation of a tool to aid in decision-making in the process of orange production.

O processo conhecido como assinatura digital possui as características necessárias para prover autenticação, integridade e não repúdio de geração de documentos eletrônicos. No entanto, os requisitos que determinam a validade de uma assinatura digital podem variar de acordo com o contexto de negócio envolvido. Estes requisitos podem ser formalizados em um documento de políticas chamado de política de assinatura. Uma política de assinatura consiste em um conjunto de regras que determinam a validade de uma assinatura digital em um contexto legal ou de negócio. O Instituto Europeu de Padrões de Telecomunicações (ETSI) propôs um modelo de política de assinatura associado à validação de uma assinatura digital independente das outras assinaturas digitais existentes no documento. Porém, documentos com mais de uma assinatura podem apresentar requisitos de relacionamento entre si como, por exemplo, ordem de geração das assinaturas. Além disso, o emissor de uma política de assinatura deve assegurar sua integridade e autenticidade. A proposta do trabalho consiste na avaliação do modelo de política de assinatura proposto pelo ETSI e apresentar uma extensão deste modelo para contemplar o relacionamento entre as assinaturas de um documento e a autenticação da política de assinatura antes de ser utilizada para gerar ou verificar uma assinatura digital. / The process known as digital signature has the characteristics necessary to provide authentication, integrity and non repudiation of electronic documents. However, the requirements to determine the validity of a digital signature can vary the context of involved business in accordance with. These requirements can be written in a document of policies called signature policy. A signature policy consists of a set of rules to determine the validity of a digital signature in a legal or business context. The European Telecommunications Standards Institute (ETSI) presented the model of policies of signature associated with the validation of an individual signature. Documents with more than one signature can present requirements of relationship between the signatures as, for example, the order to generation of the signatures. Moreover, the issuer of the signature policies must assure its integrity and authenticity. The proposal of the work consists of the evaluation of the model of signature policy considered for the ETSI and to present an extension of this model to contemplate the relationship between the signatures of a document and the authentication of the signature policy.

As one of the social relations, "distrust" also performs an important role in online Web sites. We also observe that distrust information can also be incorporated to improve recommendation quality. Hence, the last part of this thesis studies the problem on how to improve recommender system by considering explicit distrust information among users. We make the assumption that users' distrust relations can be interpreted as the "dissimilar" relations since user ui distrusts user ud indicates that user ui disagrees with most of the opinions issued by user ud. Based on this intuition, the distrust relations between users can be easily modeled by adding the regularization term into the objective functions of the user-item matrix factorization. The experiments on the Epinions dataset indicate that distrust information is at least as important as trust information. / However, the data sparsity problem of the involved user-item matrix seriously affects the recommendation quality. Many existing approaches to recommender systems cannot easily deal with users who have made very few ratings. The objective of this thesis is to study how to build effective and efficient approaches to improve the recommendation performance. / In this thesis, we first propose two collaborative filtering methods which only utilize the user-item matrix for recommendations. The first method is a neighborhood-based collaborative filtering method which designs an effective missing data prediction algorithm to improve recommendation quality, while the second one is a model-based collaborative filtering method which employs matrix factorization technique to make the recommendation more accurate. / In view of the exponential growth of information generated by online users, social contextual information analysis is becoming important for many Web applications. Hence, based on the assumption that users can be easily influenced by the friends they trust and prefer their friends' recommendations, we propose two recommendation algorithms by incorporating users' social trust information. These two methods are based on probabilistic matrix factorization. The complexity analysis indicates that our approaches can be applied to very large datasets since they scale linearly with the number of observations, while the experimental results show that our methods perform better than the state-of-the-art approaches. / Recommender Systems are becoming increasingly indispensable nowadays since they focus on solving the information overload problem, by providing users with more proactive and personalized information services. Typically, recommender systems are based on Collaborative Filtering, which is a technique that automatically predicts the interest of an active user by collecting rating information from other similar users or items. Due to their potential commercial values and the associated great research challenges, Recommender systems have been extensively studied by both academia and industry recently. / Ma, Hao. / "December 2009." / Advisers: Irwin King; Michael R. Lyu. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 136-154). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Mobile devices, such as smartphones and wearable devices, have become a popular platform to deliver recommendations and interact with users. To learn the decision rule of assigning recommendations, i.e. policy, neither one homogeneous policy for all users nor completely heterogeneous policy for each user is appropriate. Many attempts have been made to learn a policy for making recommendations using observational mobile health (mHealth) data. The majority of them focuses on a homogeneous policy, that is a one-fit-to-all policy for all users. It is a fair starting point for mHealth study, but it ignores the underlying user heterogeneity. Users with similar behavior pattern may have unobservable underlying heterogeneity. To solve this problem, we develop a personalized learning framework that models both population and personalized effect simultaneously. In the first part of this dissertation, we address the personalized policy learning problem using longitudinal mHealth application usage data. Personalized policy represents a paradigm shift from developing a single policy that may prescribe personalized decisions by tailoring. Specifically, we aim to develop the best policy, one per user, based on estimating random effects under generalized linear mixed model. With many random effects, we consider new estimation method and penalized objective to circumvent high-dimensional integrals for marginal likelihood approximation. We establish consistency and optimality of our method with endogenous application usage. We apply our method to develop personalized prompt schedules in 294 application users, with a goal to maximize the prompt response rate given past application usage and other contextual factors. We found the best push schedule given the same covariates varied among the users, thus calling for personalized policies. Using the estimated personalized policies would have achieved a mean prompt response rate of 23% in these users at 16 weeks or later: this is a remarkable improvement on the observed rate (11%), while the literature suggests 3%-15% user engagement at 3 months after download. The proposed method compares favorably to existing estimation methods including using the R function glmer in a simulation study. In the second part of this dissertation, we aim to solve a practical problem in the mHealth area. Low response rate has been a major issue that blocks researchers from collecting high quality mHealth data. Therefore, developing a prompting system is important to keep user engagement and increase response rate. We aim to learn personalized prompting time for users in order to gain a high response rate. An extension of the personalized learning algorithm is applied on the Intellicare data that incorporates penalties of the population effect parameters and personalized effect parameters into learning the personalized decision rule of sending prompts. The number of personalized policy parameters increases with sample size. Since there is a large number of users in the Intellicare data, it is challenging to estimate such high dimensional parameters. To solve the computational issue, we employ a bagging method that first bootstraps subsamples and then ensembles parameters learned from each subsample. The analysis of Intellicare data shows that sending prompts at a personalized hour helps achieve a higher response rate compared to a one-fit-to-all prompting hour.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 155-162).

Thesis (M.B.Sc.)--Victoria University of Wellington, 2007. / Includes bibliographical references.

Thesis (Ph.D.)--University of Western Sydney, 2009. / A thesis presented to the University of Western Sydney, College of Health and Science, School of Computing and Mathematics, in fulfilment of the requirements for the degree of Doctor of Philosophy. Includes bibliographies.

Thesis (M.S.)--Florida State University, 2002. / Advisor: Dr. Kathleen Burnett, Florida State University, School of Information Studies. Title and description from dissertation home page (viewed Oct. 3, 2003). Includes bibliographical references.

Business Processes (BP) and Information Technology (IT) are two areas that work very closely in helping organisations to keep or retain competitive advantage. Therefore, design in these areas should consider the advantages provided by, and the limitations that each of these domains imposes on each other. BP design tries to ensure that IT specifications are considered during the design of BP. Similarly, Information Systems (IS) design attempts to capture organisational needs, known as IS functional and Non-Functional Requirements (NFR), in order to meet the organisational goals. Despite this, BP and IT modelling techniques barely depict the way IT may affect BP performance or vice versa. For example, Business Process Simulation (BPS) is one of the modelling techniques that has been increasingly used to support process design. The performance measurements obtained from BPS models, though, are obtained considering only organisational issues, and thus cannot be used to assess the impact that IT may have on process performance. Similarly, IT modelling techniques do not provide IS performance measurements, and hence cannot depict the way IS may improve BP performance. The relationship between BP and IT can be alternatively described in terms of the relationships between BP, IS and Computer Networks (CN). By looking at the parameters that govern these relationships a simulation framework was developed, namely ASSESS-IT, that develops simulation models that provide performance measurements of BP, IS and CN, and thus can reflect the impact that IT (IS and CN) may have on BP performance. This research uses a case study to test the proposed framework (theory testing), to understand the way BP, IS, and CN domains interact (discovery), and to propose alternative theories to solve the problems found (theory building). The experimentation with the ASSESS-IT framework suggests that in order to portray the impact that IT may have on BP, analysts in these domains should first identify those performance specifications that describe how well the IS delivers its functionality (also known as non-functional requirements). It was found that when the IS does not depend on determined response time, the relationships between BP, IS and CN can be assessed using only the relationship between BP and IS. An alternative simulation framework, namely BPISS, is proposed to produce BPS models that provide performance measurements of BP and IS. Thus, BP and IT analysts can investigate the impact that a given IS design may have on BP performance, and identify a better BP and IS solution.

The primary aim of this research is to investigate methods of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives. A foundational objective is to introduce a novel bidirectional, symmetrical fuzzy logic theory which may prove valuable to information retrieval, including internet searches of distributed data objects. A further objective is to design, implement and apply the novel theory to an experimental information retrieval system called ANACALYPSE, which automatically computes the relevance of a large number of unseen documents from expert relevance feedback on a small number of documents read. A further objective is to define a methodology used in this work as an experimental information retrieval framework consisting of multiple tables including various formulae which anow a plethora of syntheses of similarity functions, ternl weights, relative term frequencies, document weights, bidirectional relevance feedback and history adjusted term weights. The evaluation of bidirectional relevance feedback reveals a better correspondence between system ranking of documents and users' preferences than feedback free system ranking. The assessment of similarity functions reveals that the Cosine and Jaccard functions perform significantly better than the DotProduct and Overlap functions. The evaluation of history tracking of the documents visited from a root page reveals better system ranking of documents than tracking free information retrieval. The assessment of stemming reveals that system information retrieval performance remains unaffected, while stop word removal does not appear to be beneficial and can sometimes be harmful. The overall evaluation of the experimental information retrieval system in comparison to a leading edge commercial information retrieval system and also in comparison to the expert's golden standard of judged relevance according to established statistical correlation methods reveal enhanced system information retrieval effectiveness.