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No. of bitstreams: 2 DissertacaoLuisRojasComFichaCatalograficaEFolhaAprovacao.pdf: 6768066 bytes, checksum: 5c26bd8a9fe369e787ba394d81fd07f3 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-03-23 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Code mutation and metamorphism have been successfully employed to create and proliferate new malware instances from existing malicious code. With such techniques, it is possible to modify a code’s structure without altering its original functions, so, new samples can be made that lack structural and behavioral patterns present in knowledge bases of malware identification systems, which hinders their detection. Previous research endeavors addressing metamorphic malware detection can be grouped into two categories: identification through code signature matching and detection based on models of classification. Matching code signatures presents lower false positive rates in comparison with models of classification, since such structures are resilient to the effects of metamorphism and allow better discrimination among instances, however, temporal complexity of matching algorithms prevents the application of such technique in real detection systems. On the other hand, detection based on classification models present less algorithmic complexity, however, a models’ generalization capacity is affected by the versatility of patterns that can be obtained by applying techniques of metamorphism. In order to overcome such limitations, this work presents methods for metamorphic malware identification through matching annotated data dependency graphs, extracted from known malwares and suspicious instances in the moment of analysis. To deal with comparison algorithms’ complexity, using these methods on real detection systems, the databases of graphs were indexed using machine learning algorithms, resulting in multiclass classification models that discriminated among malware families based on structural features of graphs. Experimental results, employing a prototype of the proposed methods from a database of 40,785 graphs extracted from 4,530 malware instances, presented detection times below 150 seconds for all instances, as well as higher average accuracy than 56 evaluated commercial malware detection systems. / A mutação de código e o metamorfismo têm sido empregados com sucesso para a criação e proliferação de novas instâncias de malware a partir de códigos maliciosos existentes. Com estas técnicas é possível modificar a estrutura de um código sem alterar as funcionalidades originais para obter novas instâncias que não se encaixam nos padrões estruturais e de comportamento presentes em bases de conhecimento dos sistemas de identificação de malware, dificultando assim a detecção. Pesquisas anteriores que abordam a detecção de malware metamórfico podem ser agrupadas em: identificação por meio do matching de assinaturas de código e detecção baseada em modelos de classificação. O matching de assinaturas de código tem apresentado taxas de falsos positivos inferiores às apresentadas pelos modelos de classificação, uma vez que estas estruturas são resilientes aos efeitos do metamorfismo e permitem melhor discriminação entre as instâncias. Entretanto a complexidade temporal dos algoritmos de comparação impedem a aplicação desta técnica em sistemas de detecção reais. Por outro lado, a detecção baseada em modelos de classificação apresenta menor complexidade algorítmica, porém a capacidade de generalização dos modelos se vê afetada pela versatilidade de padrões que podem ser obtidos por médio da aplicação de técnicas de metamorfismo. Para superar estas limitações, este trabalho apresenta uma metodologia para a identificação de malware metamórfico através da comparação de grafos de dependência de dados anotados extraídos de malwares conhecidos e de instâncias suspeitas no momento da análise. Para lidar com a complexidade dos algoritmos de comparação, permitindo assim a utilização da metodologia em sistemas de detecção reais, as bases de grafos são indexadas empregando algoritmos de aprendizagem de máquina, resultando em modelos de classificação multiclasse que discriminam entre famílias de malwares a partir das características estruturais dos grafos. Resultados experimentais, utilizando um protótipo da metodologia proposta sobre uma base composta por 40,785 grafos extraídos de 4,530 instâncias de malwares, mostraram tempos de detecção inferiores aos 150 segundos para processar todas as instâncias e de criação dos modelos inferiores aos 10 minutos, bem como acurácia média superior à maioria de 56 ferramentas comerciais de detecção de malware avaliadas.

Orientador: Ricardo da Silva Torres / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-19T04:44:52Z (GMT). No. of bitstreams: 1 Akune_FernandoCesar_M.pdf: 1241917 bytes, checksum: b220cb9e6aac3f8136585dedd9cd0da9 (MD5) Previous issue date: 2011 / Resumo: A disseminação de grandes coleções de arquivos de imagens, músicas e vídeos tem aumentado a demanda por métodos de indexação e sistemas de recuperação de informações multimídia. No caso de imagens, os sistemas de busca mais promissores são os sistemas baseados no conteúdo, que ao invés de usarem descrições textuais, utilizam vetores de características, que são representações de propriedades visuais, como cor, textura e forma. O emparelhamento dos vetores de características da imagem de consulta e das imagens de uma base de dados é implementado através da busca por similaridade. A sua forma mais comum é a busca pelos k vizinhos mais próximos, ou seja, encontrar os k vetores mais próximos ao vetor da consulta. Em grandes bases de imagens, um índice é indispensável para acelerar essas consultas. O problema é que os vetores de características podem ter muitas dimensões, o que afeta gravemente o desempenho dos métodos de indexação. Acima de 10 dimensões, geralmente é preciso recorrer aos métodos aproximados, sacrificando a eficácia em troca da rapidez. Dentre as diversas soluções propostas, existe uma abordagem baseada em curvas fractais chamadas curvas de preenchimento do espaço. Essas curvas permitem mapear pontos de um espaço multidimensional em uma única dimensão, de maneira que os pontos próximos na curva correspondam a pontos próximos no espaço. O grande problema dessa alternativa é a existência de regiões de descontinuidade nas curvas, pontos próximos dessas regiões não são mapeados próximos na curva. A principal contribuição deste trabalho é um método de indexação de vetores de características de alta dimensionalidade, que utiliza uma curva de preenchimento do espaço e múltiplos representantes para os dados. Esse método, chamado MONORAIL, gera os representantes explorando as propriedades geométricas da curva. Isso resulta em um ganho na eficácia da busca por similaridade, quando comparado com o método de referência. Outra contribuição não trivial deste trabalho é o rigor experimental usado nas comparações: os experimentos foram cuidadosamente projetados para garantir resultados estatisticamente significativos. A escalabilidade do MONORAIL é testada com três bases de dados de tamanhos diferentes, a maior delas com mais de 130 milhões de vetores / Abstract: The spread of large collections of images, videos and music has increased the demand for indexing methods and multimedia information retrieval systems. For images, the most promising search engines are content-based, which instead of using textual annotations, use feature vectors to represent visual properties such as color, texture, and shape. The matching of feature vectors of query image and database images is implemented by similarity search. Its most common form is the k nearest neighbors search, which aims to find the k closest vectors to the query vector. In large image databases, an index structure is essential to speed up those queries. The problem is that the feature vectors may have many dimensions, which seriously affects the performance of indexing methods. For more than 10 dimensions, it is often necessary to use approximate methods to trade-off effectiveness for speed. Among the several solutions proposed, there is an approach based on fractal curves known as space-filling curves. Those curves allow the mapping of a multidimensional space onto a single dimension, so that points near on the curve correspond to points near on the space. The great problem with that alternative is the existence of discontinuity regions on the curves, where points near on those regions are not mapped near on the curve. The main contribution of this dissertation is an indexing method for high-dimensional feature vectors, using a single space-filling curve and multiple surrogates for each data point. That method, called MONORAIL, generates surrogates by exploiting the geometric properties of the curve. The result is a gain in terms of effectiveness of similarity search, when compared to the baseline method. Another non-trivial contribution of this work is the rigorous experimental design used for the comparisons. The experiments were carefully designed to ensure statistically sound results. The scalability of the MONORAIL is tested with three databases of different sizes, the largest one with more than 130 million vectors / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

Orientadores: Neucimar Jerônimo Leite, Hélio Pedrini / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-19T05:52:55Z (GMT). No. of bitstreams: 1 Valio_FelipeBraunger_M.pdf: 3505580 bytes, checksum: 3b20a046a5822011c617729904457d95 (MD5) Previous issue date: 2011 / Resumo: Detecção de textos em imagens é um problema que vem sendo estudado a várias décadas. Existem muitos trabalhos que estendem os métodos existentes para uso em análise de vídeos, entretanto, poucos deles criam ou adaptam abordagens que consideram características inerentes dos vídeos, como as informações temporais. Um problema particular dos vídeos, que será o foco deste trabalho, é o de detecção de legendas. Uma abordagem rápida para localizar quadros de vídeos que contenham legendas é proposta baseada em uma estrutura de dados especial denominada ritmo visual. O método é robusto à detecção de legendas com respeito ao alfabeto utilizado, ao estilo de fontes, à intensidade de cores e à orientação das legendas. Vários conjuntos de testes foram utilizados em nosso experimentos para demonstrar a efetividade do método / Abstract: Detection of text in images is a problem that has been studied for several decades. There are many works that extend the existing methods for use in video analysis, however, few of them create or adapt approaches that consider the inherent characteristics of video, such as temporal information. A particular problem of the videos, which will be the focus of this work, is the detection of subtitles. A fast method for locating video frames containing captions is proposed based on a special data structure called visual rhythm. The method is robust to the detection of legends with respect to the used alphabet, font style, color intensity and subtitle orientation. Several datasets were used in our experiments to demonstrate the effectiveness of the method / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

Mängden information på internet är enorm och bara forsätter att öka på både gott och ont. Framförallt kan det vara svårt för grupper såsom synskadade och personer med språksvårigheter att navigera sig och ta vara på all denna information. Därmed finns ett behov av väl fungerande sammanfattningsverktyg för dessa, men även för andra människor som snabbt behöver presenteras det viktigaste ur en uppsättning texter. Den här studien undersöker hur väl sammanfattningssystemet CogSum, som är baserat på Random Indexing, presterar med och utan rankningsalgoritmen PageRank aktiverat på nyhetstexter och texter från Försäkringskassan. Utöver detta används sammanfattningssystemet SweSum som en baslinje i undersökningen. Rapporten innefattar en teoretisk bakgrund som avhandlar automatisk textsammanfattning i stort vilket inkluderar olika utvärderingsmetoder, tekniker och sammanfattningssystem. Utvärderingen utfördes med hjälp av det automatiska utvärderingsverktyget KTHxc på nyhetstexterna och ett annat sådant, AutoSummENG, på Försäkringskassans texter. Studiens resultat påvisar att CogSum utan PageRank presterar bättre än CogSum med PageRank på 10 nyhetstexter medan det omvända gäller för 5 texter från Försäkringskassan. SweSum i sin tur erhöll det bästa resultatet för nyhetstexterna respektive det sämsta för texterna från Försäkringskassan.

The four pillars of material science and engineering namely structure, processing, properties and performance form the so-called material paradigm. At the heart of the material paradigm is materials characterization, which is used to measure and identify the relationships. Materials Characterization typically reconstructing the conditions giving rise to a measurement, a classic inverse problem. The solutions of these inverse problems are under or over determined and not unique. The solutions of these inverse problems can be greatly improved if accurate forward models exist for these characterization experiments. In this thesis, we will be focusing of developing forward models for electron diffraction modalities. Specifically, four different forward models for electron diffraction, namely the Electron Backscatter Diffraction, Electron Channeling Patterns, Precession Electron Diffraction and Transmission kikuchi Diffraction modalities are presented. Further, these forward models are applied to important materials characterization problems, including diffraction pattern indexing using the dictionary approach and forward model based orientation refinement. Finally, a novel pole figure inversion algorithm using the cubochoric representation and model based iterative reconstruction is also presented.

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Magister Commercii - MCom / The notion of market efficiency posits that stock prices fully reflect all available information in a timely manner. The efficient market hypothesis (EMH) proposed by Fama (1970) systematically rules out the profitability of information driven investing, and implicitly promulgates a passive market capitalisation weighted investment strategy such as indexing. The appeal of passive strategies has largely been driven by the growth of passive tracking instruments, which allow investors to earn underlying index performance by purchasing a single security such as an exchange traded fund (ETF). On the contrary, proponents of behavioural finance suggest that investors are irrational and subject to psychological biases. Furthermore, the noisy market hypothesis of Siegel (2006) asserts that the deviations from the economic ideal of rationality proposed by the EMH, introduces noise in the market which could lead prices to deviate from their intrinsic values. The resultant drag in performance of market capitalisation weighted indices suggests that the optimal cap-weighted market portfolio promulgated by the modern portfolio theory (MPT) of Markowitz (1952), ceases to be the most mean-variance approach to asset allocation. With the goal of testing the applications of ETF’s, this study first evaluates the performance of passive sector ETF’s in the global equity market. In addition, motivated by the potential inefficiencies of capweighted portfolios, the study tests optimisation based asset allocation techniques, and technical analysis based market timing strategies. The study employs the S&P Global 1200 sector indices and their respective sector ETF’s to test their performances and applications in passive and active investment strategies, over the period from July 5th, 2002 to February 6th, 2015. The ETF’s are evaluated based on their tracking ability and price efficiency. All 10 sector ETF’s possess insignificant tracking errors and successfully replicate the performance of their underlying indices. In addition, the globalsector ETF’s are not price efficient over the study period, as they possess persistent price deviations from their net asset values (NAV’s). Furthermore, the ETF trading strategy based on the relationship between ETF returns and price deviations, proves to be effective in outperforming the passive buy and hold strategy in the majority of the sectors. The sector decomposition of the cap-weighted S&P Global 1200 index which is employed as the market proxy, reveals that its sector allocation remains fairly stable throughout the study period. In contrast, the optimal historical sector composition incurs large changes in sector exposure from year to year and provides substantially superior performance relative to the cap-weighted market portfolio. The cap-weighted portfolio tends to overweight cyclical sectors and underweight resilient sectors during major economic downturns. The long-only, long-short and market neutral strategies developed from the S&P Global 1200 index and its constituent sector indices provide exceptional risk-adjusted performance, and more meanvariance efficient portfolios than the cap-weighted market proxy. The relaxation of the longonly constraint also improves the optimised portfolios risk-adjusted performance, mainly through risk reduction benefits. The performance of the optimised global sector based portfolios also resembles the performances of the global style based optimised portfolios developed by Hsieh (2010), thereby suggesting that the two approaches are analogous. The 3 technical market timing strategies tested in this research provide varying results. The sector momentum portfolios experience significant positive returns during bull markets, however the portfolios incur significant drawdowns during periods of economic turmoil such as the 2008 global financial crisis. As a result, all sector momentum portfolios provide inferior risk-adjusted returns relative to the passive cap-weighted buy and hold strategy. The exponential moving average (EMA) trend timing strategy promulgated by Hsieh (2010) provides impressive risk-management attributes and superior risk-adjusted performance relative to passive buy and hold benchmarks. Similarly, the alternative technical charting heuristics trend timing strategy helps reduce drawdowns during market crashes, however the charting strategy provides inferior cost and risk-adjusted performance relative to the capweighted buy and hold approach due to larger timing errors and longer hedging periods in comparison to the EMA strategy. In addition, the global tactical sector allocation (GTSA) model tests the EMA and technical charting trend timing tools in the context of a global sector portfolio, and the model provides outstanding cost and risk-adjusted performances relative to the passive investing alternatives. The portfolio based GTSA model highlights the benefits of portfolio diversification and successfully hedges market exposure during economic downturns.

The advent of smart TVs has reshaped the TV-consumer interaction by combining TVs with mobile-like applications and access to the Internet. However, consumers are still unable to seamlessly interact with the contents being streamed. An example of such limitation is TV shopping, in which a consumer makes a purchase of a product or item displayed in the current TV show. Currently, consumers can only stop the current show and attempt to find a similar item in the Web or an actual store. It would be more convenient if the consumer could interact with the TV to purchase interesting items. Towards the realization of TV shopping, this dissertation proposes a scalable multimedia content processing framework. Two main challenges in TV shopping are addressed: the efficient detection of products in the content stream, and the retrieval of similar products given a consumer-selected product. The proposed framework consists of three components. The first component performs computational and temporal aware multimedia abstraction to select a reduced number of frames that summarize the important information in the video stream. By both reducing the number of frames and taking into account the computational cost of the subsequent detection phase, this component component allows the efficient detection of products in the stream. The second component realizes the detection phase. It executes scalable product detection using multi-cue optimization. Additional information cues are formulated into an optimization problem that allows the detection of complex products, i.e., those that do not have a rigid form and can appear in various poses. After the second component identifies products in the video stream, the consumer can select an interesting one for which similar ones must be located in a product database. To this end, the third component of the framework consists of an efficient, multi-dimensional, tree-based indexing method for multimedia databases. The proposed index mechanism serves as the backbone of the search. Moreover, it is able to efficiently bridge the semantic gap and perception subjectivity issues during the retrieval process to provide more relevant results.

L'objectif de cette thèse est de proposer une solution d'indexation ``scalable'' et basée objet de flux vidéos HD compressés avec Motion JPEG2000. Dans ce contexte, d'une part, nous travaillons dans le domaine transformé hiérachique des ondelettes 9/7 de Daubechies et, d'autre part, la représentation ``scalable'' nécessite des méthodes en multirésolution, de basse résolution vers haute résolution. La première partie de ce manuscrit est dédiée à la définition d'une méthode d'extraction automatique des objets en mouvement. Elle repose sur la combinaison d'une estimation du mouvement global robuste et d'une segmentation morphologique couleur à basse résolution. Le résultat est ensuite affiné en suivant l'ordre des données dans le flux scalable. La deuxième partie est consacrée à la définition d'un descripteur sur les objets précédemment extraits, basé sur les histogrammes en multirésolution des coefficients d'ondelettes. Enfin, les performances de la méthode d'indexation proposée sont évaluées dans le contexte de requêtes scalables de recherche de vidéos par le contenu. / This thesis aims at proposing a solution of scalable object-based indexing of HD video flow compressed by MJPEG2000. In this context, on the one hand, we work in the hierarchical transform domain of the 9/7 Daubechies' wavelets and, on the other hand, the scalable representation implies to search for multiscale methods, from low to high resolution. The first part of this manuscript is dedicated to the definition of a method for automatic extraction of objects having their own motion. It is based on a combination of a robust global motion estimation with a morphological color segmentation at low resolution. The obtained result is then refined following the data order of the scalable flow. The second part is the definition of an object descriptor which is based on the multiscale histograms of the wavelet coefficients. Finally, the performances of the proposed method are evaluated in the context of scalable content-based queries.

The purpose of dissertation is to utilize connotative messages for enhancing image retrieval and browsing. By adopting semiotics as a theoretical tool, this study explores problems of image retrieval and proposes an image retrieval model. The semiotics approach conceptually demonstrates that: 1) a fundamental reason for the dissonance between retrieved images and user needs is representation of connotative messages, and 2) the image retrieval model which makes use of denotative index terms is able to facilitate users to browse connotatively related images effectively even when the users' needs are potentially expressed in the form of denotative query. Two experiments are performed for verifying the semiotic-based image retrieval model and evaluating the effectiveness of the model. As data sources, 5,199 records are collected from Artefacts Canada: Humanities by Canadian Heritage Information Network, and the candidate terms of connotation and denotation are extracted from Art & Architecture Thesaurus. The first experiment, by applying term association measures, verifies that the connotative messages of an image can be derived from denotative messages of the image. The second experiment reveals that the association thesaurus which is constructed based on the associations between connotation and denotation facilitates assigning connotative terms to image documents. In addition, the result of relevant judgments presents that the association thesaurus improves the relative recall of retrieved image documents as well as the relative recall of browsing sets. This study concludes that the association thesaurus indicating associations between connotation and denotation is able to improve the accessibility of the connotative messages. The results of the study are hoped to contribute to the conceptual knowledge of image retrieval by providing understandings of connotative messages within an image and to the practical design of image retrieval system by proposing an association thesaurus which can supplement the limitations of the current content-based image retrieval systems (CBIR).