Background: Cloud computing’s rapid growth has highlighted the need for efficientresource allocation. While cloud platforms offer scalability and cost-effectiveness for a variety of applications, managing resources to match dynamic workloads remains a challenge. Auto-scaling, the dynamic allocation of resources in response to real-time demand and performance metrics, has emerged as a solution. Traditional rule-based methods struggle with the increasing complexity of cloud applications. Machine Learning models offer promising accuracy by learning from performance metrics and adapting resource allocations accordingly.  Objectives: This thesis addresses the topic of cloud environments auto-scaling recommendations emphasizing the integration of Machine Learning models and significant application metrics. Its primary objectives are determining the critical metrics for accurate recommendations and evaluating the best recommendation techniques for auto-scaling. Methods: The study initially identifies the crucial metrics—like CPU usage and memory consumption that have a substantial impact on auto-scaling selections through thorough experimentation and analysis. Machine Learning(ML) techniques are selected based on literature review, and then further evaluated through thorough experimentation and analysis. These findings establish a foundation for the subsequent evaluation of ML techniques for auto-scaling recommendations. Results: The performance of Random Forests (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) are investigated in this research. The results show that RF have higher accuracy, precision, and recall which is consistent with the significance of the metrics which are identified earlier. Conclusions: This thesis enhances the understanding of auto-scaling recommendations by combining the findings from metric importance and recommendation technique performance. The findings show the complex interactions between metrics and recommendation methods, establishing the way for the development of adaptive auto-scaling systems that improve resource efficiency and application functionality.

Stylometry is the field of research aimed at defining features for quantifying writing style, and the most studied question in stylometry has been authorship attribution, where given a set of texts with known authorship, we are asked to determine the author of a new unseen document. In this study a number of lexical and syntactic stylometric feature sets were extracted for two datasets, a smaller one containing 27 books from 25 authors, and a larger one containing 11,063 books from 316 authors. Neural networks were used to transform the features into embeddings after which the nearest neighbor method was used to attribute texts to their closest neighbor. The smaller dataset achieved an accuracy of 91.25% using frequencies of 50 most common functional words, dependency relations, and Part-of-speech (POS) tags as features, and the larger dataset achieved 69.18% accuracy using a similar feature set with 100 most common functional words. In addition to performing author attribution, a user test showed the potentials of the model in generating author similarities and hence being useful in an applied setting for recommending books to readers based on author style. / Stilometri eller stilistisk statistik är ett forskningsområde som arbetar med att definiera särdrag för att kvantitativt studera stilistisk variation hos författare. Stilometri har mest fokuserat på författarbestämning, där uppgiften är att avgöra vem som skrivit en viss text där författaren är okänd, givet tidigare texter med kända författare. I denna stude valdes ett antal lexikala och syntaktiska stilistiska särdrag vilka användes för att bestämma författare. Experimentella resultat redovisas för två samlingar litterära verk: en mindre med 27 böcker skrivna av 25 författare och en större med 11 063 böcker skrivna av 316 författare. Neurala nätverk användes för att koda de valda särdragen som vektorer varefter de närmaste grannarna för de okända texterna i vektorrummet användes för att bestämma författarna. För den mindre samlingen uppnåddes en träffsäkerhet på 91,25% genom att använda de 50 vanligaste funktionsorden, syntaktiska dependensrelationer och ordklassinformation. För den större samlingen uppnåddes en träffsäkerhet på 69,18% med liknande särdrag. Ett användartest visar att modellen utöver att bestämma författare har potential att representera likhet mellan författares stil. Detta skulle kunna tillämpas för att rekommendera böcker till läsare baserat på stil.

This thesis aims to examine the intellectual property regimes in the Gulf Co-operation Council (GCC) states and assess the relationships between legislation, enforcement mechanisms and sharia law. The GCC states, currently Bahrain, Oman, Kuwait, Saudi Arabia, UAE and Qatar, all have varied mechanisms in place for both the implementation and enforcement of intellectual property rights. The thesis pays close attention to the evolution of intellectual property laws and regulations in the GCC states with particular interest directed towards the development of national intellectual property laws within the GCC states from the 1970’s onwards1. Intellectual property protection in the GCC states is considered from two perspectives. The first perspective addresses the international demand for higher standards of intellectual property protection in the GCC states. The second perspective defines intellectual property within the laws of Islam and explores the relationship between Islam and intellectual property. The latter part analyses religious influence, societal and cultural norms, economic reality and the developmental stage of each GCC state. It is an important area of study as developing Muslim countries are struggling with meeting international standards and a successful integrated framework will impact not only on GCC states but other Islamic states and as a result could potentially lead to more informed negotiation in trade agreements with developed states. The research argues there are systematic flaws in the GCC states adopting intellectual property laws which are in essence a procrustean modification of foreign laws which have developed from colonial occupation or laws taken from donor countries. The GCC legal systems of the states have evolved utilising different sets of legal principles and therefore it could be argued the foreign laws that have been adopted are somewhat unsuitable for the GCC states. The research has focused on the implications of the national and international legislative regimes on the protection of intellectual property rights on the GCC states. Consideration is given to compliance, mainly how compliant the GCC is to its World Trade Organisation (WTO) membership and Trade Related Intellectual Property Rights (TRIP’s) Agreement and to what extent the European Union (EU) and the United States (US) influence the intellectual property protection regimes in the GCC. The research has examined the development of the GCC in three distinct stages; pre-TRIPS, TRIPS compliance stage and TRIPS plus. Furthermore, the thesis argues that the somewhat simplistic formula of the GCC states passing a large number of intellectual property laws to appease the EU and US does not have the significant economic impact on the GCC economy as the international agreements would suggest. Not all trade is intellectual property related and not all foreign direct investment is contingent upon intellectual property protection. However, as the GCC states are largely oil dependent, they do need to diversify their trade and as such an intellectual property protection model that accounts for international intellectual property law and the bespoke cultural and religious views amongst GCC citizens can produce tangible results for both the GCC and its trading partners. What sets the research apart from previous research is two-fold. Firstly, the research is qualitative and has scratched beneath the surface of intellectual property law in the GCC and examined in detail the Islamic law principles that have been used to justify sharia compliance, the western perspective on international intellectual property and the impact of multilateral trade agreements. Secondly, the analysis of Islamic finance and the application of successful sharia compliant models in Islamic finance to intellectual property is innovative as it acts as a springboard to creating a modified sharia compliant intellectual property protection model. Finally, the thesis will conclude by making a series of recommendations to develop an integrated approach to intellectual property rights which takes into account; the structure of the GCC states, international agreements and pressures, the international institutions, Islamic finance and both societal and religious views.

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[pt] O varejo virtual tem sido um importante setor para dinamização da economia, cujo valor das transações em 2010 ficou em torno de R$10,6 bilhões. As lojas nesse segmento não possuem restrição de clientes ou de estoque, porém possuem consumidores pouco pacientes com várias outras lojas a sua disposição, sendo necessário que o item de seu interesse seja encontrado visível rapidamente. Buscando resolver este problema, foram desenvolvidos algoritmos de recomendação capazes de gerar listagens de produtos que fossem direcionados ao usuário. Os algoritmos de filtragem colaborativa são amplamente usados no varejo virtual, porém eles apresentam problemas devido a escala e esparsidade do banco de dados. Algoritmos baseados em conteúdo podem apresentar menor sensibilidade ao tamanho da base de dados, porém sua efetividade depende da existência de dados de usuários que comumente não estão presentes. Nesta tese, propõe-se um algoritmo híbrido que utiliza tanto a filtragem colaborativa quanto um algoritmo baseado em conteúdo para permitir boas recomendações em bases de dados esparsas e de grande porte. O algoritmo baseado em conteúdo faz uso de números fuzzy e técnicas de marketing para guiar sua recomendação apenas com base nos itens comprados pelo usuário, sem necessidade de quaisquer outros dados pessoais do usuário. O algoritmo proposto foi testado em bases de dados sintética e real, sendo comparado com um filtro colaborativo padrão para avaliar seu desempenho.Os resultados obtidos demonstram que o algoritmo híbrido proposto apresentou um desempenho superior ao do filtro colaborativo padrão em ambas as base de dados, apresentando invariância à esparsidade da base de dados. / [en] The virtual retail has been an important sector at Brazilian economy, being a USD 6.23 billion market in 2010, having 30 percent expansion on that period. The companies in such segment don t have client or product restrictions due to physical limitations. On the other hand, the consumers of this kind of retail have several options to buy and little patience to keep searching on the same website. The companies need to define which item will be shown to the consumer before he leaves for the next competitor. Several recommendation algorithms were developed to generate products list directed to the consumer. Nowadays the algorithms for collaborative filtering are well spread in virtual retail, but they have problems caused exactly by the huge quantity of data that exist on virtual retail. Content based algorithms are less sensitive to the size of the database, but their effectiveness depends on the existence of user data, which usually are not available. This thesis proposes a hybrid algorithm which uses both collaborative filtering and a content based algorithm to allow recommendations in huge sparse databases. The content base algorithm uses fuzzy numbers and marketing techniques to guide the recommendation using only the items brought by the user, without the need for further personal data from the consumer. The proposed algorithm was tested in both artificial and real databases, compared with a benchmark collaborative filter. The collected results show that the proposed hybrid algorithm provides superior performance than the benchmark collaborative filter in both databases, generating good results and presenting sparsity invariance. The proposed algorithm also solves problems of initialization, neighborhood transitivity and in cases when new users or items are inserted on database.

Rapporten är skriven för ett högskoleingenjörsexamensarbete på kungliga tekniska högskolan, inom programmet maskinteknik, med inriktning industriell ekonomi och produktion. Bakgrunden av detta arbete ska ge läsaren en grund för de olika modellerna, elbilar och vätgasbilar samt väcka ett intresse för att bevara säkerheten med valet av bil. Syftet och målet med denna studie har varit att presentera en jämförelsestudie, gällande elbilar och vätgasbilar, samt svara på frågeställningarna som arbetet tagit fram. Resultatet av arbetet bygger på både FMEA- analyser för vätgasbilar och elbilar, samt jämförelsematris som ger en förtydligad bild på skillnader mellan elbilar och vätgasbilar, ur vissa valda funktioner. En förtydligad bild av FMEA analysen har byggt, genom att tillämpa ett paretodiagram som beskriver de olika risker och prioritering som finns för respektive modell. Rekommendationer och ytterligare säkerhetsarbeten för att minimera dessa risker beskrivs i FMEA analysen, utifrån indata och beskrivningar från tidigare rapporter, samt kunskap från studier. Resultatet från FMEA- analysen, paretodiagrammet, samt jämförelsematrisen visar att vätgasbilar är en säkrare modell och har en framtid eftersom utvecklingsmöjligheterna fortfarande finns, då dessa är nya på marknaden. Vätgasbilen är även mindre riskbenägen modell jämfört med elbilar, detta kan man visa med hjälp av RPN-talet, som är lägre för vätgasbilar, i jämförelse med elbilarnas RPN-tal. / The background of this thesis will give the reader the basis for the models of electric and hydrogen fueled vehicles. The purpose and goal of this study has been to present a comparative study regarding electric and hydrogen vehicles, and to answer the questions that the study has raised. The results of the work are based on both FMEA analysis for hydrogen and electric vehicles, as well as a comparison matrix that provides a clarified picture of the differences between electric vehicles and hydrogen vehicles, through certain selected factors. A clarified picture of the FMEA analysis results has been built by applying a pareto diagram that describes the different risks of each model and also what their priorities are. Recommendations and additional safety work to minimize these risks are suggested and described in the FMEA analysis, based on input data and descriptions from previous reports, as well as gained knowledge from studies. The results from the FMEA analysis, pareto-diagram and the comparison matrix shows that hydrogen vehicles are a less risk-prone model compared to electric vehicles and have a bright future as development opportunities still exist, this due to the fact that they are still new in the automotive industry. This can be proved with the help of the RPN number for hydrogen vehicles, which is lower compared to the RPN number of electric vehicles.