Encoding edge type information in graphlets.

Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements

Ulisses NextGen

Dissertação de mestrado integrado em Informatics EngineeringNowadays data can have many different shapes and relations between itself, ontologies try to formalize the semantics subjacent to this data and make it understandable by humans and code alike. While code succeeds at parsing and interpreting this formalization traditional ontology formats can be tough for a human to understand without previously deepened knowledge of the ontologic paradigm and, even then, directly analyzing a format like RDF would be, at the very least, very tedious. This problem is not exclusive to ontologic data either as to make sense of big datasets, even in famously human readable formats like JSON, humans need visualizations and abstractions. This dissertation is a study on graph visualization of ontologic data and how abstractions can be used to convey information to the end user in meaningful ways The information gathered is then used to implement an application called "Ulisses NextGen" that can generate an easily navigable graph visualizing application with a strong focus to support ontological data but general enough to support any information that can be abstracted as a graph. The application is served as a javascript package to be used in anywhere on the web where it can be used best to reach the end user.Hoje em dia os dados podem ter muitas formas e relações diferentes entre si, as ontologias tentam formalizar a semântica subjacente a estes dados e torná-los compreensíveis tanto para o ser humano como para o código. Embora o código consiga análisar e interpretar facilmente esta formalização, os formatos tradicionais de ontologias podem ser difíceis de entender para um humano sem um con hecimento previamente aprofundado do paradigma ontológico e, mesmo assim, analisar directamente um formato como o RDF seria, no mínimo, muito tedioso. Este problema não é exclusivo dos dados ontológicos, existe tradicionalmente uma grande dificulade por parte do ser humano em interpretar grandes conjuntos de dados precisando de visualizações e abstracções. Esta dissertação é um estudo sobre a visualização gráfica de dados ontológicos e como as abstracções podem ser usadas para transmitir informação ao utilizador final de formas significativas A informação recolhida é então usada para implementar uma aplicação chamada "Ulisses NextGen" que gera um grafo facilmente navegável com um grande foco para suportar dados ontológicos mas geral o suficiente para suportar qualquer informação que possa ser abstraída como um grafo. A aplicação é servida como um pacote javascript para ser usado em qualquer lugar na web onde possa ser melhor utilizada para chegar ao utilizador final

Análise de vídeo sensível

Orientadores: Anderson de Rezende Rocha, Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Vídeo sensível pode ser definido como qualquer filme capaz de oferecer ameaças à sua audiência. Representantes típicos incluem ¿ mas não estão limitados a ¿ pornografia, violência, abuso infantil, crueldade contra animais, etc. Hoje em dia, com o papel cada vez mais pervasivo dos dados digitais em nossa vidas, a análise de conteúdo sensível representa uma grande preocupação para representantes da lei, empresas, professores, e pais, devido aos potenciais danos que este tipo de conteúdo pode infligir a menores, estudantes, trabalhadores, etc. Não obstante, o emprego de mediadores humanos, para constantemente analisar grandes quantidades de dados sensíveis, muitas vezes leva a ocorrências de estresse e trauma, o que justifica a busca por análises assistidas por computador. Neste trabalho, nós abordamos este problema em duas frentes. Na primeira, almejamos decidir se um fluxo de vídeo apresenta ou não conteúdo sensível, à qual nos referimos como classificação de vídeo sensível. Na segunda, temos como objetivo encontrar os momentos exatos em que um fluxo começa e termina a exibição de conteúdo sensível, em nível de quadros de vídeo, à qual nos referimos como localização de conteúdo sensível. Para ambos os casos, projetamos e desenvolvemos métodos eficazes e eficientes, com baixo consumo de memória, e adequação à implantação em dispositivos móveis. Neste contexto, nós fornecemos quatro principais contribuições. A primeira é uma nova solução baseada em sacolas de palavras visuais, para a classificação eficiente de vídeos sensíveis, apoiada na análise de fenômenos temporais. A segunda é uma nova solução de fusão multimodal em alto nível semântico, para a localização de conteúdo sensível. A terceira, por sua vez, é um novo detector espaço-temporal de pontos de interesse, e descritor de conteúdo de vídeo. Finalmente, a quarta contribuição diz respeito a uma base de vídeos anotados em nível de quadro, que possui 140 horas de conteúdo pornográfico, e que é a primeira da literatura a ser adequada para a localização de pornografia. Um aspecto relevante das três primeiras contribuições é a sua natureza de generalização, no sentido de poderem ser empregadas ¿ sem modificações no passo a passo ¿ para a detecção de tipos diversos de conteúdos sensíveis, tais como os mencionados anteriormente. Para validação, nós escolhemos pornografia e violência ¿ dois dos tipos mais comuns de material impróprio ¿ como representantes de interesse, de conteúdo sensível. Nestes termos, realizamos experimentos de classificação e de localização, e reportamos resultados para ambos os tipos de conteúdo. As soluções propostas apresentam uma acurácia de 93% em classificação de pornografia, e permitem a correta localização de 91% de conteúdo pornográfico em fluxo de vídeo. Os resultados para violência também são interessantes: com as abordagens apresentadas, nós obtivemos o segundo lugar em uma competição internacional de detecção de cenas violentas. Colocando ambas em perspectiva, nós aprendemos que a detecção de pornografia é mais fácil que a de violência, abrindo várias oportunidades de pesquisa para a comunidade científica. A principal razão para tal diferença está relacionada aos níveis distintos de subjetividade que são inerentes a cada conceito. Enquanto pornografia é em geral mais explícita, violência apresenta um espectro mais amplo de possíveis manifestaçõesAbstract: Sensitive video can be defined as any motion picture that may pose threats to its audience. Typical representatives include ¿ but are not limited to ¿ pornography, violence, child abuse, cruelty to animals, etc. Nowadays, with the ever more pervasive role of digital data in our lives, sensitive-content analysis represents a major concern to law enforcers, companies, tutors, and parents, due to the potential harm of such contents over minors, students, workers, etc. Notwithstanding, the employment of human mediators for constantly analyzing huge troves of sensitive data often leads to stress and trauma, justifying the search for computer-aided analysis. In this work, we tackle this problem in two ways. In the first one, we aim at deciding whether or not a video stream presents sensitive content, which we refer to as sensitive-video classification. In the second one, we aim at finding the exact moments a stream starts and ends displaying sensitive content, at frame level, which we refer to as sensitive-content localization. For both cases, we aim at designing and developing effective and efficient methods, with low memory footprint and suitable for deployment on mobile devices. In this vein, we provide four major contributions. The first one is a novel Bag-of-Visual-Words-based pipeline for efficient time-aware sensitive-video classification. The second is a novel high-level multimodal fusion pipeline for sensitive-content localization. The third, in turn, is a novel space-temporal video interest point detector and video content descriptor. Finally, the fourth contribution comprises a frame-level annotated 140-hour pornographic video dataset, which is the first one in the literature that is appropriate for pornography localization. An important aspect of the first three contributions is their generalization nature, in the sense that they can be employed ¿ without step modifications ¿ to the detection of diverse sensitive content types, such as the previously mentioned ones. For validation, we choose pornography and violence ¿ two of the commonest types of inappropriate material ¿ as target representatives of sensitive content. We therefore perform classification and localization experiments, and report results for both types of content. The proposed solutions present an accuracy of 93% in pornography classification, and allow the correct localization of 91% of pornographic content within a video stream. The results for violence are also compelling: with the proposed approaches, we reached second place in an international competition of violent scenes detection. Putting both in perspective, we learned that pornography detection is easier than its violence counterpart, opening several opportunities for additional investigations by the research community. The main reason for such difference is related to the distinct levels of subjectivity that are inherent to each concept. While pornography is usually more explicit, violence presents a broader spectrum of possible manifestationsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação1572763, 1197473CAPE

Improving Reuse of Distributed Transaction Software with Transaction-Aware Aspects

Implementing crosscutting concerns for transactions is difficult, even using Aspect-Oriented Programming Languages (AOPLs) such as AspectJ. Many of these challenges arise because the context of a transaction-related crosscutting concern consists of loosely-coupled abstractions like dynamically-generated identifiers, timestamps, and tentative value sets of distributed resources. Current AOPLs do not provide joinpoints and pointcuts for weaving advice into high-level abstractions or contexts, like transaction contexts. Other challenges stem from the essential complexity in the nature of the data, operations on the data, or the volume of data, and accidental complexity comes from the way that the problem is being solved, even using common transaction frameworks. This dissertation describes an extension to AspectJ, called TransJ, with which developers can implement transaction-related crosscutting concerns in cohesive and loosely-coupled aspects. It also presents a preliminary experiment that provides evidence of improvement in reusability without sacrificing the performance of applications requiring essential transactions. This empirical study is conducted using the extended-quality model for transactional application to define measurements on the transaction software systems. This quality model defines three goals: the first relates to code quality (in terms of its reusability); the second to software performance; and the third concerns software development efficiency. Results from this study show that TransJ can improve the reusability while maintaining performance of TransJ applications requiring transaction for all eight areas addressed by the hypotheses: better encapsulation and separation of concern; loose Coupling, higher-cohesion and less tangling; improving obliviousness; preserving the software efficiency; improving extensibility; and hasten the development process

Improving Reuse and Maintainability of Communication Software With Conversation-Aware Aspects

Inter-process communications (IPC) are ubiquitous in today’s software systems, yet they are rarely treated as first-class programming concepts. Implementing crosscutting concerns for message-based IPC are difficult, even using aspect-oriented programming languages (AOPL) such as AspectJ. Many of these challenges are because the context of a communication-related crosscutting concern is often a conversation consisting of message sends and receives. Hence, developers typically have to implement communication protocols manually using primitive operations, such as connect, send, receive, and close. This dissertation describes an extension to AspectJ, called CommJ, with which developers can implement communication-related concerns in cohesive and loosely coupled aspects. It then presents preliminary, but encouraging results from a subsequent study that begin by defining a reuse and maintenance quality model. Subsequently the results show seven different ways in which CommJ can improve the reusability and maintainability of applications requiring network communications

Evaluating and Mapping Internet Connectivity in the United States

We evaluated Internet connectivity in the United States, drawn from different definitions of connectivity and different methods of analysis. Using DNS cache manipulation, traceroutes, and a crowdsourced “site ping” method we identify patterns in connectivity that correspond to higher population or coastal regions of the US. We analyze the data for quality strengths and shortcomings, establish connectivity heatmaps, state rankings, and statistical measures of the data. We give comparative analyses of the three methods and present suggestions for future work building off this report

IntelligentAutonomous SystemsLearningSequential SkillsforRobot Manipulation Tasks

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