A evolução das relações sociais, o acelerado avanço tecnológico e a dinâmica das relações comerciais fizeram com que o conhecimento e a informação se tornassem bens de inegável valor em um mundo globalizado e em constante transformação. Como conseqüência, a informação constitui instrumento essencial para o desenvolvimento das relações humanas, e sua transmissão pauta-se, em regra, pela confiança depositada nos diversos interlocutores. No entanto a troca de informações pode acarretar a quebra dessa confiança, causando danos à pessoa ou ao patrimônio e dando origem à obrigação de repará-los. O presente trabalho, desenvolvido a partir de compilação doutrinária e jurisprudencial, objetiva proceder à análise e discussão da responsabilidade daqueles que dão informações, conselhos ou recomendações a outrem, em especial no que tange às relações entre particulares. O exercício potencialmente danoso do dever de informar, a negligente transmissão de um parecer ou de um simples esclarecimento e os prejuízos daí decorrentes justificam o estudo mais detalhado deste tema. Assim, pretende-se discorrer sobre os fundamentos do dever de informar, de dar conselhos ou recomendações, tomando-se por base a ordem de valores de nosso ordenamento, alicerce necessário para o desenvolvimento das relações pessoais e fonte de um dever ético de conduta, a fim de demonstrar a existência de uma responsabilidade pela quebra da confiança depositada no sistema jurídico brasileiro, cuja violação gera a obrigação de reparação dos danos que dela decorram. Sobre essa base valorativa, nosso trabalho objetiva proceder à análise da responsabilidade pelos danos causados nas relações entre particulares, em virtude de conselhos, recomendações ou informações, seja na fase que antecede a formação do contrato, seja na fase de sua conclusão e execução ou até mesmo posteriormente à sua extinção. / The development of social relationships, the swift technological advancements, and the dynamics of business relations have turned knowledge and information into invaluable assets in an ever changing, globalized world. As a result, information has become a fundamental tool in the development of human relations. Disclosure of information is currently subject to the mutual reliability exercised by the several actors involved in the process. However, exchanging information may lead to a failure in complying with such reliability, which is likely to cause ultimate damage to persons or assets and therefore result in the obligation to repair. This paper was prepared on the basis of data gathered from case law and legal rules, and aims to review and discuss the liability assigned to those who provide information, counseling and recommendations to third parties, particularly within the framework of private parties. The reasons for a more detailed approach to this issue lie on the potentially damaging nature of the obligation to inform and the damage that is likely to result from the reckless rendering of either an opinion or more specific information. This paper will approach the fundamentals of the obligation to render information, counseling or recommendations on the basis of the Brazilian legal system, which is not only a pillar of personal relations development, but also a source for ethical conduct obligation. To a larger extent, it aims to provide evidence that liability stems from relying on the Brazilian legal system, and that failure to comply with said system will lead into the obligation of repairing the damage incurred. Based on these values, this paper will further review the liability derived from providing counseling, recommendations or information within the scope of private parties relationships. This applies to the initial stages of an agreement as well as the termination and settlement thereof and the procedures following thereafter.

Submitted by JÚLIO HEBER SILVA (julioheber@yahoo.com.br) on 2018-02-19T11:16:14Z No. of bitstreams: 2 Dissertação - Fernando Henrique da Silva - 2018.pdf: 7574243 bytes, checksum: 54853391be8cd76fc74017985311ad09 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-02-19T11:31:02Z (GMT) No. of bitstreams: 2 Dissertação - Fernando Henrique da Silva - 2018.pdf: 7574243 bytes, checksum: 54853391be8cd76fc74017985311ad09 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-02-19T11:31:02Z (GMT). No. of bitstreams: 2 Dissertação - Fernando Henrique da Silva - 2018.pdf: 7574243 bytes, checksum: 54853391be8cd76fc74017985311ad09 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-16 / Hemotherapy units has difficulties to optimize the search for blood donors in emergency situations, as well as to keep their blood stocks at adequate levels. On the other hand, the use of computational techniques for prediction has obtained promissing results in several areas of knowledge, and can be seen as a fundamental tool in obtaining blood donations, however, are little explored in this sector. Given this gap, this research aimed to analyze and develop prediction techniques to optimize the search for donors with higher conversion rate to the donation, focusing on data mining techniques. For this, we first analyzed the performance of traditional literature classifiers applied to a real database, which produced unsatisfactory prediction results. Seeking for higher quality results we propose a top-k recommendation approach of blood donors, which uses heuristics to estimate a confidence degree in donation. Computational experiments show that the top-k recommendation approach achieves good results for all three developed heuristics. The support vector-based heuristic achieving 94.09% of precision among the top-10 recommended, and 99.90% of precision for top-1, for the same data set that the classifiers were not successful. It is expected that the results of this research will contribute to the academic community due to the variety of classifiers analyzed and especially due to the proposed top-k recommendations approach. In the future, this approach can be better analyzed with other databases and even improved by the development of new heuristics. In addition, it is believed that the developed top-k approach can be used in health prediction systems, with a focus on predicting blood donors, especially in emergency situations. / Unidades Hemoterápicas encontram dificuldades para otimizar a busca por doadores de sangue em situações de emergência, assim como para manter seus estoques sanguíneos em níveis adequados. Por outro lado, a utilização de técnicas computacionais de predição tem obtido ótimos resultados em várias áreas do conhecimento, podendo ser vista como uma ferramenta fundamental na obtenção de doações de sangue, contudo, são pouco exploradas neste setor. Dado esta lacuna, este trabalho objetivou em analisar e desenvolver técnicas de predição para otimizar a busca por doadores com maior taxa de conversão à doação, com foco em técnicas de mineração de dados. Para isto, primeiramente analisou-se o desempenho de classificadores tradicionais da literatura aplicados a uma base de dados real, o que produziu resultados de predição insatisfatórios. Na busca de resultados de maior qualidade foi proposta uma abordagem de recomendação dos top-k, que utiliza heurísticas para a estimar a confiança em doação. Experimentos computacionais demonstram que a abordagem de recomendação top-k alcança bons resultados para todas as três heurísticas desenvolvidas. A heurística baseada em vetores de suporte obteve taxas de precisão de 94,09% entre os top-10 recomendados, chegando a 99,90% de precisão para o top-1, para a mesma base em que não se obteve sucesso com o uso de classificadores. É esperado que os resultados deste trabalho contribuam para a comunidade acadêmica devido a variedade de classificadores analisados e principalmente pela abordagem de recomendações top-k proposta. Futuramente esta abordagem poderá ser melhor analisada com outras bases de dados e até mesmo aprimorada pelo desenvolvimento de novas heurísticas. Além disso, acredita-se que a abordagem top-k desenvolvida possa ser utilizada em sistemas predição na área da saúde, com foco na predição de doadores de sangue principalmente em situações de emergência.

Context. Bug tracking systems play an important role in software maintenance. They allow users to submit bug reports. However, it has been observed that often a bug report submitted is a duplicate (when several users submit bug reports for the same problem, these reports are called duplicated issue reports) which results in considerable duplicate bug reports in a bug tracking system. Solutions for automating the process of duplicate bug reports detection can increase the productivity of software maintenance activities, as new incoming bug reports are directly compared with the existing bug reports to identify their similar bug reports, which is no need for the human to spend time reading, understanding, and searching. Although recently there has been considerable research on such solutions, there is still much room for improvement regarding accuracy and recall rate during the duplicate detection process. Besides, very few tools were evaluated in an industrial setting. Objectives. In this study, firstly, we aim to characterize automated duplicate bug report detection methods by exploring categories of all those methods, identifying proposed evaluation methods, specifying performance difference between the categories of methods. Then we propose a method leveraging recent advances on using semantic model – Doc2vec and present an overall framework - preprocessing, training a semantic model, calculating and ranking similarity, and retrieving duplicate bug reports of the proposed method. Finally, we apply an experiment to evaluate the performance of the proposed method and compare it with the selected best methods for the task of duplicate bug report detection Methods. To classify and analyze all existing research on automated duplicate bug report detection, we conducted a systematic mapping study. To evaluate our proposed method, we conducted an experiment with an identified number of bug reports on the internal bug report database of Axis Communication AB. Results. We classified automated duplicate bug report detection techniques into three categories - TOP N recommendation and ranking approach, binary classification approach, and decision-making approach. We found that recall-rate@k is the most common evaluation metric, and found that TOP N recommendation and ranking approach has the best performance among the identified approaches. The experimental results showed that the recall rate of our proposed approach is significantly higher than the combination of TF-IDF with Word2vec and the combination of TF-IDF with LSI. Our combination of Doc2vec and TF-IDF approach, has a recall rate@1-10 of 18.66%-42.88% in the TROUBLE data, which is an improvement of 1.63%-9.42% to the state-of-art. Conclusions. In this thesis, we identified and classified 44 automated duplicate bug report detection research papers by conducting a systematic mapping study. We provide an overview of the state-of-art, identifying evaluation metrics, investigating the scientific evidence in the reported results, and identifying needs for future research. We implemented a bug tracking system with a duplicate bug report detection module where a list of Top-N related bug reports (along with a numerical value representing a similar score) is created. After conducting the experiment, we found that our proposed approach - the combination of Doc2vec and TF-IDF approach produces the best recall rate.Keywords: Similar

In the modern world, service oriented applications are becoming more and more popular from year to year. To remain competitive, these Web services must provide the high level of quality. From another perspective, the end user is interested in getting the service, which fits the user's requirements the best: for limited resources get the service with the best available quality. In this work, the model for automated service selection was presented to solve this problem. The main focus of this work was to provide high accuracy of this model during the prediction of Web service’s response time. Therefore, several machine learning algorithms were selected and used in the model as well as several experiments were conducted and their results were evaluated and analysed to select one machine learning algorithm, which coped best with the defined task. This machine learning algorithm was used in final version of the model. As a result, the selection model was implemented, whose accuracy was around 80% while selecting only one Web service as a best from the list of available. Moreover, one strategy for measuring accuracy has also been developed, the main idea of which is the following: not one but several Web services, the difference in the response time of which does not exceed the boundary value, can be considered as optimal ones. According to this strategy, the maximum accuracy of selecting the best Web service was about 89%. In addition, a strategy for selecting the best Web service from the end-user side was developed to evaluate the performance of implemented model. Finally, it should also be mentioned that with the help of specific tool the input data for the experiments was generated, which allowed not only generating different input datasets without huge time consumption but also using the input data with the different type (linear, periodic) for experiments.

Which security holes and security methods do IEEE 802.11b and Bluetooth offer? Which standard provides best security methods for companies? These are two interesting questions that this thesis will be about. The purpose is to give companies more information of the security aspects that come with using WLANs. An introduction to the subject of WLAN is presented in order to give an overview before the description of the two WLAN standards; IEEE 802.11b and Bluetooth. The thesis will give an overview of how IEEE 802.11b and Bluetooth works, a in depth description about the security issues of the two standards will be presented, security methods available for companies, the security flaws and what can be done in order to create a secure WLAN are all important aspects to this thesis. In order to give a guidance of which WLAN standard to choose, a comparison of the two standards with the security issues in mind, from a company's point of view is described. We will present our conclusion which entails a recommendation to companies to use Bluetooth over IEEE 802.11b, since it offers better security methods.

The aim of this diploma thesis is to present the methodology that is used, to test the design and implementation of internal application controls in environment of large companies using ERP systems, especially in the environment of companies using SAP ECC. This methodology is described in the thesis. Practical task which is aimed at verifying the security level of SAP ECC in real business environment is also part of the thesis. The practical part is composed of a detailed description of IT auditors individual steps of the testing procedure, a list of security elements, which are subject to an audit procedures and documents required for verification of the control effectiveness implemented in clients environment. Furthermore, there is a summary and evaluation of the risk level associated with identified deficiencies. Part of the evaluation is a list of recommendations, which the company should apply to increase the efficiency of internal controls and thus achieve the optimal security level of SAP ECC. In the final section of the diploma thesis there is an analysis of the deficiencies elaborated. These deficiencies have been identified during the audit season in 2016 in environment of 20 large companies using this ERP system. Identified findings are classified according to the risk level. Another part of analysis are comprehensive recommendations that IT auditors provide to their clients in order to increase the security level of IT environment, especially in connection with the management and other activities related to financial data.

As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results. In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.

Numerous studies have concluded that roughly half of all implemented software requirements are never or rarely used in practice, and that failure to realise expected benefits is a major cause of software project failure. This thesis presents an exploration of these concepts, claims, and causes. It evaluates the literature s proposed solutions to them, and then presents a unified framework that covers additional concerns not previously considered. The value of a requirement is assessed often during the requirements engineering (RE) process, e.g., in requirement prioritisation, release planning, and trade-off analysis. In order to support these activities, and hence to support the decisions that lead to the aforementioned waste, this thesis proposes a framework built on the modelling languages of Goal Oriented Requirements Engineering (GORE), and on the principles of Value Based Software Engineering (VBSE). The framework guides the elicitation of a requirement s value using philosophy and business theory, and aims to quantitatively model chains of instrumental value that are expected to be generated for a system s stakeholders by a proposed software capability. The framework enriches the description of the individual links comprising these chains with descriptions of probabilistic degrees of causation, non-linear dose-response and utility functions, and credibility and confidence. A software tool to support the framework s implementation is presented, employing novel features such as automated visualisation, and information retrieval and machine learning (recommendation system) techniques. These software capabilities provide more than just usability improvements to the framework. For example, they enable visual comprehension of the implications of what-if? questions, and enable re-use of previous models in order to suggest modifications to a project s requirements set, and reduce uncertainty in its value propositions. Two case studies in real-world industry contexts are presented, which explore the problem and the viability of the proposed framework for alleviating it. The thesis research questions are answered by various methods, including practitioner surveys, interviews, expert opinion, real-world examples and proofs of concept, as well as less-common methods such as natural language processing analysis of real requirements specifications (e.g., using TF-IDF to measure the proportion of software requirement traceability links that do not describe the requirement s value or problem-to-be-solved). The thesis found that in general, there is a disconnect between the state of best practice as proposed by the literature, and current industry practice in requirements engineering. The surveyed practitioners supported the notion that the aforementioned value realisation problems do exist in current practice, that they would be treatable by better requirements engineering practice, and that this thesis proposed framework would be useful and usable in projects whose complexity warrants the overhead of requirements modelling (e.g., for projects with many stakeholders, competing desires, or having high costs of deploying incorrect increments of software functionality).

The issue of capital market protection, especially from manipulation with financial instruments' prices and abuse of inside information, forms the content of this diploma thesis. After the legal introduction of market manipulation in EU directives and regulations, which gives manipulation relevant context, there follows the analysis of particular forms of manipulation, which can be used to influence prices of investment instruments. Then, analysis of impact of investment recommendation to selected stock prices (i.e. NWR, ERSTE and Telefónica O2), which are traded on Czech stock market RM-System, is performed. Final chapter of the thesis handles the analysis of some market manipulation and insider trading cases from the past, together with the discussion of impacts of stricter regulation of financial markets to their efficient functioning.

Recommender systems support online customers by suggesting products and services of likely interest to them. Research in recommender systems is now starting to recognise the importance of multiple selection criteria and the role of customer context in improving the recommendation output. This thesis investigates the inclusion of criteria and context information in the recommendation process. Firstly, a novel technique for multi-criteria recommendation is proposed. It assumes that some selection criteria for an item (product or a service) will dominate the overall rating, and that these dominant criteria will be different for different users. Following this assumption, users are clustered based on their criteria preferences, creating a “preference lattice”. The recommendation output for a user is then based on ratings by other users from the same or nearby clusters. Secondly, a context similarity metric for context aware recommendation is presented. This metric can help improve the prediction accuracy in two ways. On the one hand, the metric can guide the aggregation of the feedback from similar context to improve the prediction accuracy. This aggregation is important because the recommendation generation based on prior feedback by similar customers reduces the quantum of feedback used, resulting in a reduction in recommendation quality. On the other hand, the value returned by the context similarity metric can also be used to indicate the importance of the context information in the prediction process for a context aware recommendation.The validation of the two proposed techniques and their applications are conducted in the service domain because the relatively high degree of user involvement attracts users to provide detailed feedback from multiple perspectives, such as from criteria and context perspectives. In particular, hotel services and web services areas are selected due to their different levels of maturity in terms of users’ feedback. For each area, this thesis proposes a different recommendation approach by combining the proposed techniques with a traditional recommendation approach. The thesis concludes with experiments conducted on the datasets from the two aforementioned areas to evaluate the proposed techniques, and to demonstrate the process and the effectiveness of the techniques-based recommendation approaches.