Learning Social Links and Communities from Interaction, Topical, and Spatio-Temporal Information

The immense popularity of today's social networks has lead to the availability and accessibility of vast amounts of data created by users on a daily basis. Various types of information can be extracted from such data, for example, interactions among users, topics of user postings, and geographic locations of users. While most of the existing works on social network analysis, in particular those focusing on social links and communities, rely on explicit and static link structures among users, extracting knowledge from exploiting more features embedded in user-generated data is another important direction that only recently has gained more attention. Initial studies employing this approach show good results in terms of a better understanding latent interactions among users. In the context of this dissertation, multiple features embedded in user-generated data are investigated to develop new models and algorithms for (1) revealing hidden social links between users and (2) extracting and analyzing dynamic feature-based communities in social networks. We introduce two approaches for extracting and measuring interpretable and meaningful social links between users. One is based on the participation of users in threads of discussions. The other one relies on the social characteristics of users as reflected in their postings. A novel probabilistic model called rLinkTopic is developed to address the problem of extracting a new type of feature-based community called regional LinkTopic: a community of users that are geographically close to each other over time, have common interests indicated by the topical similarity of their postings, and are contextually linked to each other. Based on the rLinkTopic model, a comprehensive framework called ErLinkTopic is developed that allows to extract and capture complex changes in the features describing regional LinkTopic communities, for example, the community membership of users and topics of communities. Our framework provides a novel basis for important studies such as exploring social characteristics of users in geographic regions and predicting the evolution of user communities. For each approach developed in this dissertation, extensive comparative experiments are conducted using data from real-world social networks to validate the proposed models and algorithms in terms of effectiveness and efficiency. The experimental results are further discussed in detail to show improvements over existing approaches and the applicability and advantages of our models in terms of learning social links and communities from user-generated data

A full permutation polygon synthetic indicator (FPPSI) approach for measuring and evaluating city prosperity: case study in Da Nang City, Vietnam

Economic growth has dominated development strategies and goals for many years, but prosperity encompasses more than that. In 2013, UN-Habitat proposed the City Prosperity Initiative (CPI) as a tool to quantify cities' prosperity and sustainable development. The CPI is accompanied by six essential components with 62 indicators associated with the urban settlement, incorporating productivity, infrastructure development, quality of life, social inclusion, environmental sustainability, and urban governance and legislation. The research aims to use the indicators of CPI and adopted the Full Permutation Polygon Synthetic Indicator method to measure and evaluate the level of prosperity of Da Nang City of Vietnam with data from 2004 to 2019. According to the findings of our study, the value of a synthetic indicator for the prosperity of Da Nang City increased, from 0.34 in 2004 to 0.36 in 2009, 0.43 in 2014, and 0.45 in 2019, which indicates a moderate level of wealth. On the one hand, Da Nang City has high levels of quality of life, equity and social inclusion, and urban governance and legislation. However, the city still has modest determinants of prosperity in terms of the environment, productivity and infrastructure. The Full Permutation Polygon Synthetic Indicator technique provides a comprehensive solution that illustrates the system integration idea. As a result, the proposed methodology offers a potential foundation for decision-making to promote sustainable urban development strategies and assess the effectiveness of these action

Vortex flow generator utilizing synthetic jets by diaphragm vibration

This paper develops a millimeter scale fully packaged device in which a vortex flow of high velocity is generated inside a chamber. Under the actuation by a lead zirconate titanate (PZT) diaphragm, a flow circulates with increasing velocity after each actuating circle to form a vortex in a cavity named as the vortex chamber. At each cycle, the vibration of the PZT diaphragm creates a small net air flow through a rectifying nozzle, generates a synthetic jet which propagates by a gradual circulation toward the vortex chamber and then backward the feedback chamber. The design of such device is firstly conducted by a numerical analysis whose results are considered as the base of our experimental set-up. A vortex flow generated in the votex chamber was observed by a high-speed camera. The present approach which was illustrated by both the simulation and experiment is potential in various applications related to the inertial sensing, fluidic amplifier and micro/nano particle trapping and mixing

A circulatory ionic wind for inertial sensing application

A novel gyroscope using circulatory electro-hydrodynamics flow in a confined space is presented for the first time. The configuration of the new gyroscope includes three point-ring corona discharge actuators that generate ion flows in three separated sub-channels. The three ion flows then merge together when going through a nozzle of the main chamber entrance and create a jet flow. In the new configuration, the residual charge of ion wind flow is removed by a master-ring electrode located at one end of the main chamber. Under the effect of the angular speed of gyroscope, the jet flow is deflected and this deflection is sensed using hotwires. The results, which are consistently acquired by both the numerical simulation and experiment on our prototypes, demonstrate the repeatability and stability of the new approach. Since the ion wind can be generated by a minimum power, the present configuration-based device does not require any vibrating component. Thus, the device is robust, cost, and energy-effective

Computation of limit and shakedown using the NS-FEM and second-order cone programming

This paper presents a novel numerical procedure for computation of limit and shakedown using node-based smoothed finite element method (NS-FEM) in combination with second-order cone programming (SOCP). The obtained discretization formulation is then cast in a form which involves second-order cone constraints, ensuring that the underlying optimization problem can be solved by highly efficient primal-dual interior point algorithm. Furthermore, in the NS-FEM, the system stiffness matrix is computed using the smoothed strains over the smoothing domains associated with nodes. This ensures that the size of the resulting optimization problem is kept to a minimum. The efficiency of the present approach is illustrated by examining several numerical examples