Technical advancements allow for embodied virtual agents to not only be increasingly human-like, but also to behave and look like particular individuals. As biases towards self-similarity have been found in human-human studies, it is of interest to explore to what extent this applies to virtual characters (VCs). This work set out to extend on previous research that has investigated the effects of facial self-similarity in VCs, and explore it in the context of empathic emotion. For this aim, a method for creating facially similar virtual characters was developed and a user study conducted where 13 participants were told autobiographical stories by a virtual character that either did or did not resemble them facially and/or in gender category. The participants' first impressions and emotional responses were measured. The results showed that even though similarity was not explicitly perceived, a bias might exist towards more positive impressions of self-similar characters, especially in terms of gender category. Regarding the emotional responses, the results did not allow for discovering any difference between conditions but pointed to some interesting differences in comparison to what was hypothesized. The immense ways in which the appearances of virtual characters can be altered provides possibilities to influence the interaction with them. However, although biases might exist on a general level, it is difficult to predict the human responses in individual cases. Virtual characters might make possible a more human-like interaction with technology, however, it might also mean that our reactions to them are influenced by more parameters and our relations to them become even more like those with other humans: complex. / Den tekniska utvecklingen möjliggör numera att virtuella agenter kan göras inte bara människolika, utan även lika specifika individer i hur de beter sig och ser ut. Då tidigare studier påvisat att människor ofta föredrar personer som i någon mån liknar dem själva, är det intressant att utforska i vilken utsträckning detta även gäller virtuella karaktärer. Detta arbete hade som mål att undersöka effekterna av visuella likheter mellan människor och virtuella karaktärer, med fokus på ansikten och genus och i en kontext där empati är betydande. En metod för att konstruera virtuella karaktärer som hade visuella likheter med specifika användare utvecklades, och en användarstudie med 13 deltagare genomfördes. I det konstruerade scenariot berättade en karaktär, som hade likheter med användaren antingen gällande ansiktets utseende och/eller genus, självbiografiska historier. Intrycket av karaktären och den emotionella responsen mättes. Resultaten visade att den visuella likheten inte uppfattades explicit. Dock fanns tendenser som pekar på att likheter framför allt när det gäller sociala kategorier som genus, kan ha en positiv påverkan på hur virtuella karaktärer uppfattas. Det gick inte att upptäcka några skillnader mellan betingelserna gällande den emotionella responsen, men resultaten påvisade intressanta avvikelser från de förväntade reaktionerna. Möjligheterna att designa och anpassa virtuella karaktärer till olika individer och situationer ökar, vilket kan utnyttjas för att försöka påverka hur människor förhåller sig till och interagerar med dem. Det är dock svårt att förutsäga hur människor kommer att reagera och relatera till en virtuell karaktär utifrån generella tendenser, vilket denna studie visar. Virtuella karaktärer kan möjliggöra en mer människolik interaktion med teknik, men det innebär också att många parametrar är inblandade och att relationerna med tekniken blir liksom relationerna mellan människor: komplexa.

This thesis presents a method for integrating heterogeneous gene/protein datasets at the functional level based on Gene Ontology term similarity. Often biologists want to integrate heterogeneous data sets obtain from different biological samples. A major challenge in this process is how to link the heterogeneous datasets. Currently, the most common approach is to link them through common reference database identifiers which tend to result in small number of matching identifiers. This is due to lack of standard accession schemes. Due to this problem, biologists may not recognize the underlying biological phenomena revealed by a combination of the data but by each data set individually. We discuss an approach for integrating heterogeneous datasets by computing the similarity among them based on the similarity of their GO annotations. Then we group the genes and/or proteins with similar annotations by applying a hierarchical clustering algorithm. The results demonstrate a more comprehensive understanding of the biological processes involved.

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Authentication in digital systems is still prominently done through passwords. These passwords should simultaneously be easy to remember, unique, and change over time. Humans, however, have a limited ability to remember complex passwords. To make this easier, users often adopt schemes where a base word is only modified slightly. While such schemes can easily fulfil basic password requirements based on length or the symbols used, they can leave users vulnerable. Leaked passwords, even expired ones, can be exploited by malicious actors and a single compromised account can cascade to multiple services.  We propose a v-gram based approach to detect similarity with a set of passwords, which could be used to improve user password habits. The proposed scheme utilizes a Cuckoo Filter, which allows for inherent obfuscation of the stored passwords and the integration of encryption techniques natively. The system could for example be embedded in a password manager to inform users when they are using a password that is too similar to a previous password. This work comprises an analysis of several aspects of the system in order to assess its suitability.  A Cuckoo Filter using a single byte fingerprint for each v-gram can achieve load factors exceeding 95%, while maintaining a false positivity rate of less than 3%. The computational cost of guessing a password based on the information stored within the filter is relatively low. While the false positivity rate of the filter and the size of the alphabet have an impact, they are only logarithmically proportional to the cost, and the attack is considered a significant vulnerability. Nevertheless, the proposed system can be a viable alternative for detecting similarity between passwords — if configured correctly — and could be used to guide user behaviour to more secure password habits.

This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.

The complexity of data stored in large databases has increased at very fast paces. Hence, operations more elaborated than traditional queries are essential in order to extract all required information from the database. Therefore, the interest of the database community in similarity search has increased significantly. Two of the well-known types of similarity search are the Range (\'R IND. q\') and the k-Nearest Neighbor (\'kNN IND. q\') queries, which, as any of the traditional ones, can be sped up by indexing structures of the Database Management System (DBMS). Another way of speeding up queries is to perform query optimization. In this process, metrics about data are collected and employed to adjust the parameters of the search algorithms in each query execution. However, although the integration of similarity search into DBMS has begun to be deeply studied more recently, the query optimization has been developed and employed just to answer traditional queries. The execution of similarity queries, even using efficient indexing structures, tends to present higher computational cost than the execution of traditional ones. Two strategies can be applied to speed up the execution of any query, and thus they are worth to employ to answer also similarity queries. The first strategy is query rewriting based on algebraic properties and cost functions. The second technique is when external query factors are applied, such as employing the semantic expected by the user, to prune the answer space. This thesis aims at contributing to the development of novel techniques to improve the similarity-based query optimization processing, exploiting both algebraic properties and semantic restrictions as query refinements / A complexidade dos dados armazenados em grandes bases de dados tem aumentado sempre, criando a necessidade de novas operações de consulta. Uma classe de operações de crescente interesse são as consultas por similaridade, das quais as mais conhecidas são as consultas por abrangência (\'R IND. q\') e por k-vizinhos mais próximos (\'kNN IND. q\'). Qualquer consulta e agilizada pelas estruturas de indexação dos Sistemas de Gerenciamento de Bases de Dados (SGBDs). Outro modo de agilizar as operações de busca e a manutenção de métricas sobre os dados, que são utilizadas para ajustar parâmetros dos algoritmos de busca em cada consulta, num processo conhecido como otimização de consultas. Como as buscas por similaridade começaram a ser estudadas seriamente para integração em SGBDs muito mais recentemente do que as buscas tradicionais, a otimização de consultas, por enquanto, e um recurso que tem sido utilizado para responder apenas a consultas tradicionais. Mesmo utilizando as melhores estruturas existentes, a execução de consultas por similaridade tende a ser mais custosa do que as operações tradicionais. Assim, duas estratégias podem ser utilizadas para agilizar a execução de qualquer consulta e, assim, podem ser empregadas também para responder às consultas por similaridade. A primeira estratégia e a reescrita de consultas baseada em propriedades algébricas e em funções de custo. A segunda técnica faz uso de fatores externos à consulta, tais como a semântica esperada pelo usuário, para restringir o espaço das respostas. Esta tese pretende contribuir para o desenvolvimento de técnicas que melhorem o processo de otimização de consultas por similaridade, explorando propriedades algebricas e restrições semânticas como refinamento de consultas

Submitted by Fabricia Fialho Reginato (fabriciar) on 2015-08-26T23:54:22Z No. of bitstreams: 1 TiagoWiedemann.pdf: 5635533 bytes, checksum: c0d3805abbcaf56aa36da4d8422457b6 (MD5) / Made available in DSpace on 2015-08-26T23:54:22Z (GMT). No. of bitstreams: 1 TiagoWiedemann.pdf: 5635533 bytes, checksum: c0d3805abbcaf56aa36da4d8422457b6 (MD5) Previous issue date: 2014 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / CNPQ – Conselho Nacional de Desenvolvimento Científico e Tecnológico / A Computação Ubíqua, que estuda formas de integrar a tecnologia ao cotidiano das pessoas, é uma área que vem crescendo nos últimos anos, especialmente devido ao desenvolvimento de tecnologias como a computação móvel. Um dos aspectos fundamentais para o desenvolvimento deste tipo de aplicação é a questão da Sensibilidade ao Contexto, que permite a uma aplicação adaptar o seu funcionamento conforme a situação na qual o usuário se encontra no momento. Com esta finalidade, diversos autores apresentaram definições formais sobre o que é um contexto e como representá-lo. A partir desta formalização começaram a ser desenvolvidas técnicas para análise de dados contextuais que propunham a realização de predições e inferências, entre outras análises. Esta dissertação especifica um framework denominado SIMCOP (SIMilar Context Path) para a realização da análise de similaridade entre sequências de contextos visitados por uma entidade. Este tipo de análise permite a identificação de contextos semelhantes com a intenção de prover funcionalidades como a recomendação de entidades e/ou contextos, a classificação de entidades e a predição de contextos. Um protótipo do framework foi implementado, e a partir dele foram desenvolvidas duas aplicações de recomendação, uma delas por um desenvolvedor independente, através do qual foi possível avaliar a eficácia do framework. Com o desenvolvimento desta pesquisa comprovou-se, conforme demonstrado nas avaliações realizadas, que a análise de similaridade de contextos pode ser útil em outras áreas além da computação ubíqua, como a mineração de dados e os sistemas de filtragem colaborativa, entre outras áreas, onde qualquer conjunto de dados que puder ser descrito na forma de um contexto, poderá ser analisado através das técnicas de análise de similaridade implementadas pelo framework. / The Ubiquitous Computing, that studies the ways to integrate technology into the people’s everyday life, is an area that has been growing in recent years, especially due to the development of technologies such as mobile computing. A key for the development of this type of application is the issue of context awareness, which enables an application to self adapt to the situation in which the user is currently on. To make this possible, it was necessary to formally define what is a context and how to represent it . From this formalization, techniques for analyzing contextual data have been proposed for development of functions as predictions or inferences. This paper specifies a framework called SIMCOP (SIMilar Context Path ) for performing the analysis of similarity between sequences of contexts visited by an entity. This type of analysis enables the identification of similar contexts with the intention to provide features such as the recommendation of entities and contexts, the entities classification and the prediction of contexts. The development of this research shows that the contexts similarity analysis can be useful in other areas further the ubiquitous computing, such as data mining and collaborative filtering systems. Any data type that can be described as a context, can be analyzed through the techniques of similarity analysis implemented by the framework, as demonstrated in the assessments.

The complexity of data stored in large databases has increased at very fast paces. Hence, operations more elaborated than traditional queries are essential in order to extract all required information from the database. Therefore, the interest of the database community in similarity search has increased significantly. Two of the well-known types of similarity search are the Range (\'R IND. q\') and the k-Nearest Neighbor (\'kNN IND. q\') queries, which, as any of the traditional ones, can be sped up by indexing structures of the Database Management System (DBMS). Another way of speeding up queries is to perform query optimization. In this process, metrics about data are collected and employed to adjust the parameters of the search algorithms in each query execution. However, although the integration of similarity search into DBMS has begun to be deeply studied more recently, the query optimization has been developed and employed just to answer traditional queries. The execution of similarity queries, even using efficient indexing structures, tends to present higher computational cost than the execution of traditional ones. Two strategies can be applied to speed up the execution of any query, and thus they are worth to employ to answer also similarity queries. The first strategy is query rewriting based on algebraic properties and cost functions. The second technique is when external query factors are applied, such as employing the semantic expected by the user, to prune the answer space. This thesis aims at contributing to the development of novel techniques to improve the similarity-based query optimization processing, exploiting both algebraic properties and semantic restrictions as query refinements / A complexidade dos dados armazenados em grandes bases de dados tem aumentado sempre, criando a necessidade de novas operações de consulta. Uma classe de operações de crescente interesse são as consultas por similaridade, das quais as mais conhecidas são as consultas por abrangência (\'R IND. q\') e por k-vizinhos mais próximos (\'kNN IND. q\'). Qualquer consulta e agilizada pelas estruturas de indexação dos Sistemas de Gerenciamento de Bases de Dados (SGBDs). Outro modo de agilizar as operações de busca e a manutenção de métricas sobre os dados, que são utilizadas para ajustar parâmetros dos algoritmos de busca em cada consulta, num processo conhecido como otimização de consultas. Como as buscas por similaridade começaram a ser estudadas seriamente para integração em SGBDs muito mais recentemente do que as buscas tradicionais, a otimização de consultas, por enquanto, e um recurso que tem sido utilizado para responder apenas a consultas tradicionais. Mesmo utilizando as melhores estruturas existentes, a execução de consultas por similaridade tende a ser mais custosa do que as operações tradicionais. Assim, duas estratégias podem ser utilizadas para agilizar a execução de qualquer consulta e, assim, podem ser empregadas também para responder às consultas por similaridade. A primeira estratégia e a reescrita de consultas baseada em propriedades algébricas e em funções de custo. A segunda técnica faz uso de fatores externos à consulta, tais como a semântica esperada pelo usuário, para restringir o espaço das respostas. Esta tese pretende contribuir para o desenvolvimento de técnicas que melhorem o processo de otimização de consultas por similaridade, explorando propriedades algebricas e restrições semânticas como refinamento de consultas