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Role engineering, a critical task in role-based access control systems, is the process of identifying a complete set of roles that accurately reflect the structure of an organization. Role mining, a data-driven approach, utilizes data mining techniques on user-permission assignments represented as binary data to automatically derive these roles. However, relying solely on data-driven methods often leads to the generation of a large set of roles lacking interpretability. To address this limitation, this thesis presents a role mining algorithm, whose results can be viewed as an initial step in the role engineering process, in order to streamline the task of defining semantically meaningful roles, where human analysis is an inevitable post-processing step. The algorithm is based on hierarchical clustering analysis, and its main objective is identifying a sufficiently small set of roles that cover as large a proportion of the user-permission assignments as possible. To evaluate the performance of the algorithm, multiple real-world data sets representing diverse access control scenarios are utilized. The evaluation focuses on comparing various binary similarity measures, with the goal of determining the most suitable characteristics of a binary similarity measure to be used for role mining. The evaluation of different binary similarity measures provides insights into their effectiveness in achieving accurate role definitions to be used as a foundation for constructing meaningful roles. Ultimately, this research contributes to the advancement of role mining methodologies, facilitating improved access control systems that align with organizational needs and enhance security and efficiency. / Role engineering går ut på att identifiera en komplett uppsättning roller som återspeglar strukturen i en organisation och är en viktig uppgift när organisationer övergår till rollbaserad åtkomstkontroll. Role mining är en datadriven metod som använder data mining-tekniker på användarnas behörighetstilldelningar för att automatiskt härleda dessa roller. Dessa tilldelningar kan representeras som binär data. Att enbart förlita sig på datadrivna metoder leder dock ofta till att en stor uppsättning svårtolkade roller genereras. För att adressera denna begränsning har en role mining-algoritm utvecklas i det här arbetet. Genom att applicera algoritmen på den binära tilldelningsdatan kan de erhållna resultaten betraktas som ett inledande steg i role engineering-processen. Syftet är att effektivisera arbetet med att definiera semantiskt meningsfulla roller, där mänsklig analys är en oundviklig fas. Algoritmen är baserad på hierarkisk klustring och har som huvudsyfte att identifiera en lagom stor uppsättning roller som täcker så stor del av behörighetstilldelningarna som möjligt. För att utvärdera algoritmens prestanda appliceras den på flertalet datamängder insamlade från varierande verkliga åtkomstkontrollsystem. Utvärderingen fokuserar på att jämföra olika binära likhetsmått med målet att bestämma de mest lämpliga egenskaperna för ett binärt likhetsmått som ska användas för role mining. Utvärderingen av olika binära likhetsmått ger insikter i deras effektivitet att uppnå korrekta rolldefinitioner som kan användas som grund för att konstruera meningsfulla roller. Denna forskning bidrar till framsteg inom role mining och syftar till att underlätta övergången till rollbaserad åtkomstkontroll samt förbättra metoderna för att identifiera roller som överensstämmer med organisationsbehov och förbättrar säkerhet och effektivitet.

This thesis deals with the problem with cluster analysis and biosignal classification options. The principle of cluster analysis, methods for calculating distances between objects and the standard process in the implementation of clustering are described in the first part. For biosignals processing,it is necessary to get familiar with the primary parameters of these signals in the following sections of thesis, process biosignals and methods for recording of action potentials described. Based on studying different clustering methods is presented a program with the applied method kmedoid in the next section of this thesis. The steps of this program are described in detail and in the end of thesis functionality is tested on a database of signals ÚBMI.

Protein sequencing has produced overwhelming amount of protein sequences, especially in the last decade. Nevertheless, the majority of the proteins' functional and structural classes are still unknown, and experimental methods currently used to determine these properties are very expensive, laborious and time consuming. Therefore, automated computational methods are urgently required to accurately and reliably predict functional and structural classes of the proteins. Several bioinformatics methods have been developed to determine such properties of the proteins directly from their sequence information. Such methods that involve signal processing methods have recently become popular in the bioinformatics area and been investigated for the analysis of DNA and protein sequences and shown to be useful and generally help better characterise the sequences. However, there are various technical issues that need to be addressed in order to overcome problems associated with the signal processing methods for the analysis of the proteins sequences. Amino acid indices that are used to transform the protein sequences into signals have various applications and can represent diverse features of the protein sequences and amino acids. As the majority of indices have similar features, this project proposes a new set of computationally derived indices that better represent the original group of indices. A study is also carried out that resulted in finding a unique and universal set of best discriminating amino acid indices for the characterisation of allergenic proteins. This analysis extracts features directly from the protein sequences by using Discrete Fourier Transform (DFT) to build a classification model based on Support Vector Machines (SVM) for the allergenic proteins. The proposed predictive model yields a higher and more reliable accuracy than those of the existing methods. A new method is proposed for performing a multiple sequence alignment. For this method, DFT-based method is used to construct a new distance matrix in combination with multiple amino acid indices that were used to encode protein sequences into numerical sequences. Additionally, a new type of substitution matrix is proposed where the physicochemical similarities between any given amino acids is calculated. These similarities were calculated based on the 25 amino acids indices selected, where each one represents a unique biological protein feature. The proposed multiple sequence alignment method yields a better and more reliable alignment than the existing methods. In order to evaluate complex information that is generated as a result of DFT, Complex Informational Spectrum Analysis (CISA) is developed and presented. As the results show, when protein classes present similarities or differences according to the Common Frequency Peak (CFP) in specific amino acid indices, then it is probable that these classes are related to the protein feature that the specific amino acid represents. By using only the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient, as biologically related features can appear individually either in the real or the imaginary spectrum. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Upon identification of a new protein, it is important to single out amino acid responsible for the structural and functional classification of the protein, as well as the amino acids contributing to the protein's specific biological characterisation. In this work, a novel approach is presented to identify and quantify the relationship between individual amino acids and the protein. This is successfully demonstrated over the analysis of influenza neuraminidase protein sequences. Characterisation and identification problem of the Influenza A virus protein sequences is tackled through a Subgroup Discovery (SD) algorithm, which can provide ancillary knowledge to the experts. The main objective of the case study was to derive interpretable knowledge for the influenza A virus problem and to consequently better describe the relationships between subtypes of this virus. Finally, by using DFT-based sequence-driven features a Support Vector Machine (SVM)-based classification model was built and tested, that yields higher predictive accuracy than that of SD. The methods developed and presented in this study yield promising results and can be easily applied to proteomic fields.

L’imagerie de l’activité neuronale (AN) permet d’étudier le fonctionnement normal et pathologique du cerveau humain, en plus d’aider au diagnostic et à la planification d’interventions neurochirurgicales. L’électroencéphalographie (EEG) et l’imagerie par résonance magnétique fonctionnelle (IRMf) comptent parmi les modalités d’imagerie fonctionnelle les plus utilisées en recherche et en clinique. Plusieurs éléments de la structure cérébrale peuvent toutefois influencer les signaux mesurés, de sorte qu’ils ne reflètent pas uniquement l’AN. Il importe donc d’en tenir compte pour bien interpréter les résultats, surtout lorsqu’on compare des sujets à l’anatomie cérébrale très différente. En outre, la maturation, le vieillissement et certaines pathologies s’accompagnent de changements structurels du cerveau. Ceci complique l’analyse de données longitudinales et la comparaison d’un groupe cible avec un groupe contrôle. Or, notre compréhension des interactions structure-signal demeure incomplète et très peu d’études en tiennent compte. Mon projet de doctorat a consisté à étudier les impacts de la structure cérébrale sur les signaux d’EEG et d’IRMf ainsi qu’à explorer des pistes de solution pour s’en affranchir. J’ai d’abord étudié l’effet de l’amincissement cortical dû au vieillissement sur la désynchronisation liée à l’événement (« event-related desynchronization » - ERD) en EEG. Les résultats ont mis en lumière une relation linéaire négative entre l’ERD et l’épaisseur corticale, ce qui a permis de corriger les signaux par régression. J’ai ensuite étudié l’impact de la présence de veines sur la réponse BOLD (blood-oxygen-level dependent) mesurée en IRMf suite à une stimulation visuelle. Ces travaux ont démontré que la densité veineuse locale, qui varie fortement d’une région et d’un sujet à l’autre, corrèle positivement avec l’amplitude et le délai de la réponse BOLD. Finalement, j’ai adapté une technique de classification de données visant à améliorer la détection des régions du cortex activées en IRMf. Cette méthode permet d’éviter plusieurs problèmes de l’analyse classique en IRMf, de réduire l’impact de la structure cérébrale sur les résultats obtenus et d’établir des cartes d’activité cérébrale contenant plus d’information. Globalement, ces travaux contribuent à l’amélioration de notre compréhension des interactions structure-signal en EEG et en IRMf, ainsi qu’au développement de méthodes d’analyse réduisant leur impact sur l’interprétation des données en termes d’AN. / Abstract : Imaging neural activity allows studying normal and pathological function of the human brain, while also being a useful tool for diagnosis and neurosurgery planning. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are some of the most commonly used functional imaging modalities, both in research and clinic. Many aspects of cerebral structure can however influence the measured signals, so that they do not only reflect neural activity. Taking them into account is therefore of import to correctly interpret results, especially when comparing subjects displaying large differences in brain anatomy. In addition, maturation, aging as well as some pathologies are associated with changes in brain structure. This acts as a confounding factor when analysing longitudinal data or comparing target and control groups. Yet, our understanding of structure-signal relationships remains incomplete and very few studies take them into account. My Ph.D. project consisted in studying the impacts of cerebral structure on EEG and fMRI signals as well as exploring potential solutions to mitigate them. In that regard, I first studied the effect of age-related cortical thinning on event-related desynchronization (ERD) in EEG. Results allowed identifying a negative linear relationship between ERD and cortical thickness, enabling signal correction using regression. I then investigated how the presence of veins in a region impacts the blood-oxygen-level dependent (BOLD) response measured in fMRI following visual stimulation. This work showed that local venous density, which strongly varies across regions and subjects, correlates positively with the BOLD response amplitude and delay. Finally, I adapted a data clustering technique to improve the detection of activated cortical regions in fMRI. This method allows eschewing many problematic assumptions used in classical fMRI analyses, reducing the impacts of cerebral structure on results and establishing richer brain activity maps. Globally, this work contributes to further our understanding of structure-signal interactions in EEG and fMRI as well as to develop analysis methods that reduce their impact on data interpretation in terms of neural activity.

Taxonomic classification has always been important to the study of any biological system. Many biological species will go unclassified and become lost forever at the current rate of classification. The current state of computer technology makes image storage and retrieval possible on a global level. As a result, computer-aided taxonomy is now possible. Content based image retrieval techniques utilize visual features of the image for classification. By utilizing image content and computer technology, the gap between taxonomic classification and species destruction is shrinking. This content based study utilizes the Fourier Descriptors of fifteen known landmark features on three Carpiodes species: C.carpio, C.velifer, and C.cyprinus. Classification analysis involves both unsupervised and supervised machine learning algorithms. Fourier Descriptors of the fifteen known landmarks provide for strong classification power on image data. Feature reduction analysis indicates feature reduction is possible. This proves useful for increasing generalization power of classification.

O cultivo de camarão branco Litopennaeus vannamei tem provado ser um sector promissor para a economia do nordeste do Brasil. Contudo, a criação de camarão branco no Brasil tem sido afetada negativamente pela ocorrência de doenças virais, ameaçando a sua expansão e sustentabilidade. Por esta razão, depreende-se a importância da elaboração de um sistema de vigilância capaz de detectar e definir a ausência de doenças virais de elevado impacto econômico. O modelo estocástico AquaVigil é aqui implementado para avaliar prospectivamente diferentes estratégias de vigilância para determinar a ausência de doença e identificar a estratégia exigindo menor esforço de amostragem e simultaneamente, fazer o melhor uso dos recursos disponíveis através da implementação de vigilância baseada em risco. O estudo apresentado exemplifica a aplicação regional do sistema proposto para o estado do Ceará, podendo ser aplicado a outros estados do Brasil. O modelo AquaVigil pode analisar qualquer sistema de vigilância baseada em risco semelhante àquele aqui considerado. A criação de camarão no nordeste do Brasil tem sido alvo de vários desafios, desde a ocorrência de doenças virais a mudanças no acesso aos mercados internacionais. Tendo em consideração as dificuldades encontradas pela aquicultura de camarão no nordeste do Brasil, facilmente se compreende a importância de caracterizar e melhor compreender este setor e assim assegurar o seu desenvolvimento sustentável. Para este fim, foram aplicados métodos de análise de correspondência multipla e clustering particional a dados recolhidos durante um levantamento nacional de fazendas de carcinicultura de forma a obter informação necessária para caracterizar tendências e identificar falhas e necessidades existentes. Esta informação será útil no momento de melhorar o manejo das fazendas e elaborar legislação a favor do desenvolvimento do setor / The farming of Pacific white shrimp Litopennaeus vannamei in northeast Brazil, has proven to be a promising sector. However, the farming of Pacific white shrimp in Brazil has been affected negatively by the occurrence of viral diseases, threatening this sector\'s expansion and sustainability. For this reason, the drafting of a surveillance system for early detection and definition of freedom from viral diseases, whose occurrence could result in high economic loses, is of the utmost importance. The stochastic model AquaVigil was implemented to prospectively evaluate different surveillance strategies to determine freedom from disease and identify the strategy with the lowest sampling efforts, making the best use of available resources through risk-based surveillance. The worked example presented was designed for regional application for the state of Ceará and can easily be applied to other Brazilian states. The AquaVigil model can analyze any risk-based surveillance system that considers a similar outline to the strategy here presented. In recent years, shrimp aquaculture has faced many challenges, ranging from the occurrence of viral diseases to changes in market access. Considering the past and present challenges faced by the shrimp farmers in Northeast Brazil it is easily understood that the comprehensive characterization of the shrimp farming is of the utmost importance when striving for sustainable development. To this aim, the exploratory data analysis methods of multiple correspondence analysis and partitional clustering were applied to the data collected through a national census to extract the greatest amount of information and profile shrimp farms, identifying gaps and needs. The results of the analysis will contribute to improve management practices and policy-making for sustainable shrimp farming in Northeast Brazil

Made available in DSpace on 2014-12-17T15:42:03Z (GMT). No. of bitstreams: 1 MariaFPV.pdf: 6083202 bytes, checksum: 8b9199df5753800b615ab0a90bae3e8e (MD5) Previous issue date: 2007-12-10 / Heavy metals can cause problems of human poisoning by ingestion of contaminated food, and the environment, a negative impact on the aquatic fauna and flora. And for the presence of these metals have been used for aquatic animals biomonitoramento environment. This research was done in order to assess the environmental impact of industrial and domestic sewage dumped in estuaries potiguares, from measures of heavy metals in mullet. The methods used for these determinations are those in the literature for analysis of food and water. Collections were 20 samples of mullet in several municipality of the state of Rio Grande do Norte, from the estuaries potiguares. Were analyzed the content of humidity, ash and heavy metals. The data were subjected to two methods of exploratory analysis: analysis of the main components (PCA), which provided a multivariate interpretation, showing that the samples are grouped according to similarities in the levels of metals and analysis of hierarchical groupings (HCA), producing similar results. These tests have proved useful for the treatment of the data producing information that would hardly viewed directly in the matrix of data. The analysis of the results shows the high levels of metallic species in samples Mugil brasiliensis collected in Estuaries /Potengi, Piranhas/A?u, Guara?ra / Papeba / Ar?s and Curimata? / Os metais pesados podem provocar problemas de intoxica??o humana pela ingest?o de alimentos contaminados e para o meio ambiente, uma repercuss?o negativa ? fauna e flora aqu?ticas. E para detectar a presen?a destes metais t?m-se utilizado animais aqu?ticos para o biomonitoramento ambiental. Esta pesquisa foi feita com o intuito de se avaliar o impacto ambiental de esgotos dom?sticos e industriais despejados nos estu?rios potiguares, a partir das medidas de metais pesados em tainha. Os m?todos utilizados para estas determina??es s?o aqueles constantes da literatura para an?lise de alimentos e de ?gua. Foram coletas 20 amostras de tainha em diversos munic?pios do Estado do Rio Grande do Norte, oriundas dos estu?rios potiguares. Foram analisados os teores de umidade, cinzas e metais pesados. Os dados foram submetidos a dois m?todos de an?lises explorat?rios: an?lise de componentes principais (PCA), que proporcionou uma interpreta??o multivariada, mostrando que as amostras s?o agrupadas de acordo com as similaridades de teores de metais e an?lise hier?rquica de agrupamentos (HCA), produzindo resultados semelhantes. Estas an?lises mostraram-se ?teis para o tratamento dos dados produzindo informa??es que dificilmente seriam visualizados diretamente na matriz de dados. A an?lise dos resultados mostra os altos teores de esp?cies met?licas em amostras coletadas em tainhas nos Estu?rios Potengi, Piranhas/A?u, Guara?ra/Papeba/Ares e Curimata?

No description available.

O cultivo de camarão branco Litopennaeus vannamei tem provado ser um sector promissor para a economia do nordeste do Brasil. Contudo, a criação de camarão branco no Brasil tem sido afetada negativamente pela ocorrência de doenças virais, ameaçando a sua expansão e sustentabilidade. Por esta razão, depreende-se a importância da elaboração de um sistema de vigilância capaz de detectar e definir a ausência de doenças virais de elevado impacto econômico. O modelo estocástico AquaVigil é aqui implementado para avaliar prospectivamente diferentes estratégias de vigilância para determinar a ausência de doença e identificar a estratégia exigindo menor esforço de amostragem e simultaneamente, fazer o melhor uso dos recursos disponíveis através da implementação de vigilância baseada em risco. O estudo apresentado exemplifica a aplicação regional do sistema proposto para o estado do Ceará, podendo ser aplicado a outros estados do Brasil. O modelo AquaVigil pode analisar qualquer sistema de vigilância baseada em risco semelhante àquele aqui considerado. A criação de camarão no nordeste do Brasil tem sido alvo de vários desafios, desde a ocorrência de doenças virais a mudanças no acesso aos mercados internacionais. Tendo em consideração as dificuldades encontradas pela aquicultura de camarão no nordeste do Brasil, facilmente se compreende a importância de caracterizar e melhor compreender este setor e assim assegurar o seu desenvolvimento sustentável. Para este fim, foram aplicados métodos de análise de correspondência multipla e clustering particional a dados recolhidos durante um levantamento nacional de fazendas de carcinicultura de forma a obter informação necessária para caracterizar tendências e identificar falhas e necessidades existentes. Esta informação será útil no momento de melhorar o manejo das fazendas e elaborar legislação a favor do desenvolvimento do setor / The farming of Pacific white shrimp Litopennaeus vannamei in northeast Brazil, has proven to be a promising sector. However, the farming of Pacific white shrimp in Brazil has been affected negatively by the occurrence of viral diseases, threatening this sector\'s expansion and sustainability. For this reason, the drafting of a surveillance system for early detection and definition of freedom from viral diseases, whose occurrence could result in high economic loses, is of the utmost importance. The stochastic model AquaVigil was implemented to prospectively evaluate different surveillance strategies to determine freedom from disease and identify the strategy with the lowest sampling efforts, making the best use of available resources through risk-based surveillance. The worked example presented was designed for regional application for the state of Ceará and can easily be applied to other Brazilian states. The AquaVigil model can analyze any risk-based surveillance system that considers a similar outline to the strategy here presented. In recent years, shrimp aquaculture has faced many challenges, ranging from the occurrence of viral diseases to changes in market access. Considering the past and present challenges faced by the shrimp farmers in Northeast Brazil it is easily understood that the comprehensive characterization of the shrimp farming is of the utmost importance when striving for sustainable development. To this aim, the exploratory data analysis methods of multiple correspondence analysis and partitional clustering were applied to the data collected through a national census to extract the greatest amount of information and profile shrimp farms, identifying gaps and needs. The results of the analysis will contribute to improve management practices and policy-making for sustainable shrimp farming in Northeast Brazil