PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases

Dynamic Security Risk Evaluation via Hybrid Bayesian Risk Graph in Cyber-Physical Social Systems

© 2014 IEEE. Cyber-physical social system (CPSS) plays an important role in both the modern lifestyle and business models, which significantly changes the way we interact with the physical world. The increasing influence of cyber systems and social networks is also a high risk for security threats. The objective of this paper is to investigate associated risks in CPSS, and a hybrid Bayesian risk graph (HBRG) model is proposed to analyze the temporal attack activity patterns in dynamic cyber-physical social networks. In the proposed approach, a hidden Markov model is introduced to model the dynamic influence of activities, which then be mapped into a Bayesian risks graph (BRG) model that can evaluate the risk propagation in a layered risk architecture. Our numerical studies demonstrate that the framework can model and evaluate risks of user activity patterns that expose to CPSSs

Understanding musical genre preference evolution within a social network

Dissertation presented as partial requirement for obtaining the Master’s degree in Information Management, specialization in Knowledge Management and Business IntelligenceA música é um campo que simplesmente não pode ser desassociado dos aspetos sociais da vida. Durante a história da humanidade, a música mais popular consistiu sempre num reflexo dos diferentes aspetos da sociedade. Como tal, diferentes estudos foram feitos anteriormente que demonstram este reflexo e obtiveram diversas conclusões. Nesta tese, iremos contribuir para este campo através de uma análise da evolução das preferências de géneros musicais ao longo do tempo através de uma rede social. Usando dados obtidos através de uma experiência de evolução social com cerca de 80 participantes faremos uma análise dos dados existentes. De seguida, esta análise é tida em conta para definir os princípios necessários para representar e analisar a rede social existente. Após esta definição, iremos avaliar a homogeneização da rede social ao longo do tempo. Isto é, iremos avaliar a evolução das diferenças de preferências musicais entre indivíduos que estão ligados na rede social, de forma a perceber se existe alguma tendência de estas diminuírem ao longo do tempo. Um Sequential Algorithm, conhecido como Hidden Markov Model, é aplicado para prever mudanças nas preferências de géneros musicais, considerando as próprias preferências de cada individuo, bem como as preferências dos indivíduos com que este se encontra ligado na nossa rede social. O algoritmo Support Vector Machines é também utilizado para fazer o mesmo tipo de previsão que o modelo anterior servindo como comparação. Por último, discutimos o processo e as limitações que conduziram à definição final do nosso modelo e de forma a contextualizar os resultados que foram obtidos através deste. Em suma, esta tese procurar acrescentar ao trabalho existente em termos de preferências de géneros musicais através de uma avaliação destes dentro do contexto de uma rede social e tendo também em conta a evolução destas ao longo do tempo.Music is a field that simply cannot be disassociated with the social aspects of life. Throughout human history, popular music has always been a reflection of the different aspects of society. As such, there is an interesting amount of studies available that showcase this reflection and draw multiple types of insights. In this thesis, we will look to contribute to this field by assessing the evolution of musical genre preferences over time throughout a social network. Using data obtained through a social evolution experiment of around 80 different individuals we will make an initial assessment of our existing data. This evaluation is then taken into consideration in the next phase of our work where we define the principles necessary to represent and analyse the existing social network. Afterwards, we will showcase a representation of this network, as well as analyse it using various metrics and sub-structures commonly applied in Social Network Analysis. After this, we will evaluate the homogenisation of a network as time goes on. In other words, we will assess the evolution of differences in preferences between individuals that were connected in the social network, in order to understand if there is a trend of these differences diminishing over time. A Sequential-Based algorithm, more specifically, a Hidden Markov Model is used to predict the change in musical genre preferences. This was done by considering each individual’s own preferences as well as the preferences of his connections within the social network with the ultimate goal of assessing how influential the network is in the evolution of a person’s musical genre preferences. To tackle the same research question and provide an alternative approach, as well as a comparison model, we used a Support Vector Machine model. Finally, we discuss the results and limitations that led to our model definition. Overall, this thesis seeks to build upon previous work regarding musical genre preferences by assessing these within the context of a network and taking into account the evolution of these over time

Markov field models of molecular kinetics

Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

Modeling Tolerance in Dynamic Social Networks

The study of social networks has become increasingly important in recent years. Multi-agent systems research has proven to be an effective way of representing both static and dynamic social networks in order to model and analyze many different situations. Previous implementations of multi-agent systems have observed a phenomenon called tolerance between agents through simulation studies, which is defined as an agent maintaining an unrewarding connection. This concept has also arisen in the social sciences through the study of networks. We aim to bridge this gap between simulation studies in multi-agent systems and real-world observations. This project explores how local interactions of autonomous agents in a network relate to the development of tolerance. We have developed a new model for multi-agent system interactions based on these observations. We also claim that tolerance is directly observable in real dynamic social networks, and the parameters that govern tolerance of a system can be estimated using a Hidden Markov Model