125 research outputs found
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
Um método supervisionado para encontrar variáveis discriminantes na análise de problemas complexos : estudos de caso em segurança do Android e em atribuição de impressora fonte
Orientadores: Ricardo Dahab, Anderson de Rezende RochaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A solução de problemas onde muitos componentes atuam e interagem simultaneamente requer modelos de representação nem sempre tratáveis pelos métodos analíticos tradicionais. Embora em muitos caso se possa prever o resultado com excelente precisão através de algoritmos de aprendizagem de máquina, a interpretação do fenómeno requer o entendimento de quais são e em que proporção atuam as variáveis mais importantes do processo. Esta dissertação apresenta a aplicação de um método onde as variáveis discriminantes são identificadas através de um processo iterativo de ranqueamento ("ranking") por eliminação das que menos contribuem para o resultado, avaliando-se em cada etapa o impacto da redução de características nas métricas de acerto. O algoritmo de florestas de decisão ("Random Forest") é utilizado para a classificação e sua propriedade de importância das características ("Feature Importance") para o ranqueamento. Para a validação do método, dois trabalhos abordando sistemas complexos de natureza diferente foram realizados dando origem aos artigos aqui apresentados. O primeiro versa sobre a análise das relações entre programas maliciosos ("malware") e os recursos requisitados pelos mesmos dentro de um ecossistema de aplicações no sistema operacional Android. Para realizar esse estudo, foram capturados dados, estruturados segundo uma ontologia definida no próprio artigo (OntoPermEco), de 4.570 aplicações (2.150 malware, 2.420 benignas). O modelo complexo produziu um grafo com cerca de 55.000 nós e 120.000 arestas, o qual foi transformado usando-se a técnica de bolsa de grafos ("Bag Of Graphs") em vetores de características de cada aplicação com 8.950 elementos. Utilizando-se apenas os dados do manifesto atingiu-se com esse modelo 88% de acurácia e 91% de precisão na previsão do comportamento malicioso ou não de uma aplicação, e o método proposto foi capaz de identificar 24 características relevantes na classificação e identificação de famílias de malwares, correspondendo a 70 nós no grafo do ecosistema. O segundo artigo versa sobre a identificação de regiões em um documento impresso que contém informações relevantes na atribuição da impressora laser que o imprimiu. O método de identificação de variáveis discriminantes foi aplicado sobre vetores obtidos a partir do uso do descritor de texturas (CTGF-"Convolutional Texture Gradient Filter") sobre a imagem scaneada em 600 DPI de 1.200 documentos impressos em 10 impressoras. A acurácia e precisão médias obtidas no processo de atribuição foram de 95,6% e 93,9% respectivamente. Após a atribuição da impressora origem a cada documento, 8 das 10 impressoras permitiram a identificação de variáveis discriminantes associadas univocamente a cada uma delas, podendo-se então visualizar na imagem do documento as regiões de interesse para uma análise pericial. Os objetivos propostos foram atingidos mostrando-se a eficácia do método proposto na análise de dois problemas em áreas diferentes (segurança de aplicações e forense digital) com modelos complexos e estruturas de representação bastante diferentes, obtendo-se um modelo reduzido interpretável para ambas as situaçõesAbstract: Solving a problem where many components interact and affect results simultaneously requires models which sometimes are not treatable by traditional analytic methods. Although in many cases the result is predicted with excellent accuracy through machine learning algorithms, the interpretation of the phenomenon requires the understanding of how the most relevant variables contribute to the results. This dissertation presents an applied method where the discriminant variables are identified through an iterative ranking process. In each iteration, a classifier is trained and validated discarding variables that least contribute to the result and evaluating in each stage the impact of this reduction in the classification metrics. Classification uses the Random Forest algorithm, and the discarding decision applies using its feature importance property. The method handled two works approaching complex systems of different nature giving rise to the articles presented here. The first article deals with the analysis of the relations between \textit{malware} and the operating system resources requested by them within an ecosystem of Android applications. Data structured according to an ontology defined in the article (OntoPermEco) were captured to carry out this study from 4,570 applications (2,150 malware, 2,420 benign). The complex model produced a graph of about 55,000 nodes and 120,000 edges, which was transformed using the Bag of Graphs technique into feature vectors of each application with 8,950 elements. The work accomplished 88% of accuracy and 91% of precision in predicting malicious behavior (or not) for an application using only the data available in the application¿s manifest, and the proposed method was able to identify 24 relevant features corresponding to only 70 nodes of the entire ecosystem graph. The second article is about to identify regions in a printed document that contains information relevant to the attribution of the laser printer that printed it. The discriminant variable determination method achieved average accuracy and precision of 95.6% and 93.9% respectively in the source printer attribution using a dataset of 1,200 documents printed on ten printers. Feature vectors were obtained from the scanned image at 600 DPI applying the texture descriptor Convolutional Texture Gradient Filter (CTGF). After the assignment of the source printer to each document, eight of the ten printers allowed the identification of discriminant variables univocally associated to each one of them, and it was possible to visualize in document's image the regions of interest for expert analysis. The work in both articles accomplished the objective of reducing a complex system into an interpretable streamlined model demonstrating the effectiveness of the proposed method in the analysis of two problems in different areas (application security and digital forensics) with complex models and entirely different representation structuresMestradoCiência da ComputaçãoMestre em Ciência da Computaçã
Strengthening e-crime legislation in the UAE: learning lessons from the UK and the EU
The electronic revolution brought with it technological innovations that are now integral to communication, business, commerce and the workings of governments all over the world. It also significantly changed the criminal landscape. Globally it has been estimated that crime conducted via the internet (e-crime) costs more than €290 billion annually. Formulating a robust response to cybercrime in law is a top priority for many countries that presents ongoing challenges. New cybercrime trends and behaviours are constantly emerging, and debates surrounding legal provisions to deal with them by increasing online tracking and surveillance are frequently accompanied by concerns of the rights of citizens to freedom, privacy and confidentiality. This research compares the ways that three different legislative frameworks have been navigating these challenges. Specifically, it examines the legal strategies of the United Arab Emirates (UAE), the United Kingdom (UK) and the European Union (EU). The UAE is comparatively inexperienced in this area, its first law to address e-crime was adopted in 2006, sixteen years after the UK, and so the express purpose of this study is to investigate how e-crime legislation in the UAE can be strengthened. Drawing on a range of theoretical resources supplemented with empirical data, this research seeks to provide a comprehensive account of how key e-crime legislation has evolved in the UAE, the UK and the EU, and to evaluate how effective it has been in tackling cybercrime. Integral to this project is an analysis of some of the past and present controversies related to surveillance, data retention, data protection, privacy, non-disclosure and the public interest. An important corollary of this research is how e-crime legislation is not only aligned with political and economic aims, but when looking at the UAE, the discrete ways that legislation can be circumscribed by cultural, social and religious norms comes into focus
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society. This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
Transitioning to Good Health and Well-Being
Transitioning to Good Health and Well-Being addresses critical issues of health in the context of sustainability, which need to be tackled in order to achieve Agenda 2030. Acknowledging the dramatic improvements that have been made in the past decades with regards to health, we also face disparities that remain amongst and within countries. While life expectancy has more than doubled, we are, at the same time, confronted with the challenges that come along with population growth alongside environmental change, migration, ageing, and economic disparities. In its 2018 progress report concerning SDG 3, the UN stated that, while the quality of global health is increasing, “people are still suffering needlessly from preventable diseases”, both infectious and non-communicable, "and too many are dying prematurely". Although we are on the verge of eradicating, poliomyelitis, which disables 350’000 children each year, we continue to have few answers for outbreaks of emerging infectious diseases. Making progress against these outbreaks with strong health systems, particularly in neglected or inaccessible regions, is deeply connected to further issues targeted by the UN SDGs such as (restricted) access to clean water, healthy food, or continuing political instabilities as well as gender inequalities. Transitioning to Good Health and Well-Being, therefore, offers a vessel for a productive reflection and conversation on the meaning of and possibilities for global health, giving voice to a range of scholars, strategists and practitioners. Transitioning to Good Health and Well-Being is part of MDPI's new Open Access book series Transitioning to Sustainability. With this series, MDPI pursues environmentally and socially relevant research which contributes to efforts toward a sustainable world. Transitioning to Sustainability aims to add to the conversation about regional and global sustainable development according to the 17 SDGs. The book series is intended to reach beyond disciplinary, even academic boundaries
Transitioning to Good Health and Well-Being
At this purpose characterization, production, effectiveness, safety and use of natural fibers, block copolymeric nanoparticles and food packaging will be briefly described and discussed
Transitioning to Good Health and Well-Being
Transitioning to Good Health and Well-Being addresses critical issues of health in the context of sustainability, which need to be tackled in order to achieve Agenda 2030. Acknowledging the dramatic improvements that have been made in the past decades with regards to health, we also face disparities that remain amongst and within countries. While life expectancy has more than doubled, we are, at the same time, confronted with the challenges that come along with population growth alongside environmental change, migration, ageing, and economic disparities. In its 2018 progress report concerning SDG 3, the UN stated that, while the quality of global health is increasing, “people are still suffering needlessly from preventable diseases”, both infectious and non-communicable, "and too many are dying prematurely". Although we are on the verge of eradicating, poliomyelitis, which disables 350’000 children each year, we continue to have few answers for outbreaks of emerging infectious diseases. Making progress against these outbreaks with strong health systems, particularly in neglected or inaccessible regions, is deeply connected to further issues targeted by the UN SDGs such as (restricted) access to clean water, healthy food, or continuing political instabilities as well as gender inequalities. Transitioning to Good Health and Well-Being, therefore, offers a vessel for a productive reflection and conversation on the meaning of and possibilities for global health, giving voice to a range of scholars, strategists and practitioners. Transitioning to Good Health and Well-Being is part of MDPI's new Open Access book series Transitioning to Sustainability. With this series, MDPI pursues environmentally and socially relevant research which contributes to efforts toward a sustainable world. Transitioning to Sustainability aims to add to the conversation about regional and global sustainable development according to the 17 SDGs. The book series is intended to reach beyond disciplinary, even academic boundaries
- …