Abstract: Social media tools have changed our world due to the way they convey information between individuals; this has led to many social movements either starting on social media or being organised and managed through this medium. At times however, certain human-induced events can trigger Human Security Threats such as Personal Security, Health Security, Economic Security or Political Security. The aim of this paper is to propose a holistic Data Analysis Framework for examining Social Movements and detecting pernicious threats to National Security interests. As a result of this, the proposed framework focuses on three main stages of an event (Detonating Event, Warning Period and Crisis Interpretation) to provide timely additional insights, enabling policy makers, first responders, and authorities to determine the best course of action. The paper also outlines the possible computational techniques utilised to achieve in depth analysis at each stage. The robustness and effectiveness of the framework are demonstrated by dissecting Warning Period scenarios, from real-world events, where the increase of Human Security aspects were key to identifying likely threats to National Security.

Abstract: Retweet prediction is a fundamental and crucial task in social networking websites as it may influence the process of information diffusion. Existing prediction approaches consider social network structure, but social context has not been fully considered. It is important to incorporate social contextual information into retweet prediction. Besides, the sparsity of retweet data severely disturb the performance of these models. In this paper, we propose a novel retweet prediction framework based on probabilistic matrix factorization model to integrate the observed retweet data, social influence and message embeddings semantic to improve the accuracy of prediction. We then incorporate these regularization terms into the objective function. Comprehensive experiments on the real-world dataset clearly validate both the effectiveness and efficiency of our model compared with several state-of the-art baselines.

Abstract: As one of the most important properties of the power grid, the voltage load plays an important role in the cascading failure of the smart grid and load redistribution can accelerate the speed of the failure by triggering more nodes to overload and fail. The subnet structure and different coupling modes also affect the robustness of the smart grid. However, the research on the effect of load, subnet structure and coupling mode on the cascading failure of the smart grid is still rare. In this paper, the smart grid with two-way coupling link consists of a power grid with small world topology and a communication network with scale- free topology. An improved load-capacity model is applied to overload- induced failure in the power grid and node importance (NI) is used as an evaluation index to assess the effect of nodes on the power grid and communication network. We propose three kinds of coupling modes based on NI of nodes between the cyber and physical subnets, i.e., Random Coupling in Subnets (RCIS), Assortative Coupling in Subnets (ACIS) and Disassortative Coupling in Subnets (DCIS). In order to improve the robustness of the smart grid, a cascading failure model based on load redistribution is proposed to analyze the influence of different coupling modes on the cascading failure of the smart grid under both a targeted attack and random attack. Some findings are summarized as: (I) The robustness of the smart grid is improved by increasing the tolerance α. (II) ACIS applied to the bottom-up coupling link is more beneficial in enhancing the robustness of the smart grid than DCIS and RCIS, regardless of a targeted attack or random attack.

Abstract: Volunteering and community reaction is known to be an essential part of response to critical events. With the rapid evolution of new means of communication, it has transformed accordingly. A new category of volunteers emerged – those that are not in the proximity to the area of emergency but willing to help the affected. Widely known as digital volunteers, they help aggregate, disseminate and distribute information to increase and maintain the awareness of stakeholders and resourceful individuals about the situation. There has been an upsurge of investigations of roles, timelines and aggregate characteristics of emergent communication. Compared to that, characteristics of crisis-related social media posts that predict wider social response to date have been studied modestly. In this research we are studying the process of reaction of potential digital volunteers to different extreme situations in social media platform.

Abstract: We present a novel computational pheromone model for describing dynamic network behaviors in terms of transition, persistency, and hosting. The model consists of a three-dimensional force-directed graph with bi-directional pheromone deposit and decay paths. A data compression algorithm is developed to optimize computational performance. We applied the model for visual analysis of a Malware Distribution Network (MDN), a connected set of maliciously compromised domains used to disseminate malicious software to victimize computers and users. The MDN graphs are extracted from crowdsourcing datasets from Google Safe Browsing (GSB) reports with attributions from VirusTotal report site. Our research shows that this novel approach reveals patterns of topological changes of the network over time, including the existence of persistent subnetworks and individual TLDs’ critical to the successful operation of MDNs, and the dynamics of the topological changes on daily basis. From the visualization, we observed notable clustering effects, and also noticed life span patterns for high edge count malware distribution clusters.

Abstract: One of the critical issues when adopting Bayesian networks (BNs) to model dependencies among random variables is to “learn” their structure. This is a well-known NP-hard problem in its most general and classical formulation, which is furthermore complicated by known pitfalls such as the issue of I-equivalence among different structures. In this work we restrict the investigation to a specific class of networks, i.e., those representing the dynamics of phenomena characterized by the monotonic accumulation of events. Such phenomena allow to set specific structural constraints based on Suppes’ theory of probabilistic causation and, accordingly, to define constrained BNs, named Suppes-Bayes Causal Networks (SBCNs). Within this framework, we study the structure learning of SBCNs via extensive simulations with various state-of-the-art search strategies, such as canonical local search techniques and Genetic Algorithms. This investigation is intended to be an extension and an in-depth clarification of our previous works on SBCN structure learning. Among the main results, we show that Suppes’ constraints do simplify the learning task, by reducing the solution search space and providing a temporal ordering on the variables, which simplifies the complications derived by I-equivalent structures. Finally, we report on tradeoffs among different optimization techniques that can be used to learn SBCNs.

Abstract: We focus on a surface evolution problem where the local surface speed depends on a computationally expensive scalar function with non-local properties. The local surface speed must be re-evaluated in each time step, even for non-moving parts of the surface, due to possibly changed properties in remote regions of the simulation domain. We present a method to evaluate the surface speed only on a sparse set of points to reduce the computational effort. This sparse set of points is generated according to application-specific requirements using an iterative partitioning scheme. We diffuse the result of a constant extrapolation in the neighborhood of the sparse points to obtain an approximation to a linear interpolation between the sparse points. We demonstrate the method for a surface evolving with a local surface speed depending on the incident flux from a source plane above the surface. The obtained speedups range from 2 to 8 and the surface deviation is less than 3 grid-cells for all evaluated test cases.

Abstract: We propose a methodology to generate an accurate and efficient reconstruction of radiated fields based on high order interpolation. As the solution is obtained with the convolution by a smooth but potentially high frequency oscillatory kernel, our basis functions therefore incorporate plane waves. Directional interpolation is shown to be efficient for smart directions. An adaptive subdivision of the domain is established to limit the oscillations of the kernel in each element. The new basis functions, combining high order polynomials and plane waves, provide much better accuracy than low order ones. Finally, as standard visualization softwares are generally unable to represent such fields, a method to have a well-suited visualization of high order functions is used. Several numerical results confirm the potential of the method.

Abstract: In the paper we present a case study of the aero-structural analysis and optimization for the wing design using the modular analysis and unified derivatives (MAUD). Wing design is one of the essential procedures of aircraft manufactures and it is a compromise between many competing factors and constraints. As a result, the efficient numerical optimized methods are important to speed-up the design procedures special for the the design parameter of order~$\cal O$(10-100). In the aero-structural optimization, the derivatives can be calculated by simply applying the finite-difference methods. However, the finite difference methods are in general significantly more expensive, requiring at least one additional flow solution per parameter. By using the method of modular analysis and unified derivatives (MAUD), we can unify all methods for computing total derivatives using a single equation. The derivatives can be automatically uniform calculated [1]as \frac{\partial R}{\partial u} \frac{du}{dr} =\Psi = \frac{\partial R^T}{\partial u}\frac{du^T}{dr} The wing design requires a set of benchmark cases for the shape optimization. In the paper, we choice the Common Research Model (CRM) geometry which was developed by NASA Langely Research Center and Amers Research Center for applied CFD validation studies. In this paper we only focus on preliminary designs on the static aeroelastic analysis for example the aileron reversal analysis [2]. We use the open source code OpenMDAO [3] as the framework for the implementation of MAUD applied for the wing design. OpenMDAO provides a library of sparse solvers and optimizers designed to work with its distributed-memory, sparse data-passing scheme. We present performance comparisons for a typical production problem and a summary of the experiences gained. References: [1] J. R. R. A. Martins and J. T. Hwang, Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models, AIAA Journal, 51(1):2582--2599, 2013 [2] Mengmeng Zhang, Contributions to Variable Fidelity MDO Framework for Collaborative and Integrated Aircraft Design, Doctoral Thesis, Royal Institute of Technology KTH, Stockholm, Sweden, 2015. [3] OpenMDAO, http://openmdao.org/

Abstract: Flash calculations are a performance bottleneck of compositional flow simulations. Some work has demonstrated the feasibility of using sparse grid techniques to remove the bottleneck, but a complete realisation of the idea is still not available. Thus, this work fills the niche. By introducing a new concept of layer to sparse grid points, the sparse grid construction can become much efficient. As a result, a much easier data structure the array can be used to store the sparse grids. Compared with the popular data structures to store the sparse grids such as the hash table and the tree, the array can minimize the space size and the traversing time, and at the same time reduce the number of points in the sparse grids by removing the architecture ancestors in the tree, which in turn makes parallelization of flash calculations come true. All of them are not only contributions to flash calculations, but also contributions to existing sparse grid techniques. Moreover, both of the sparse grid construction and interpolation algorithms can be done in parallel. Different from the former parallel algorithms in sparse grid techniques, which have troubles in decomposing the domain equally and keeping load balance among the processors, our parallel algorithm can achieve load balance easily among the threads for any sparse grid configurations. Lastly, multicomponent experiments are also carried out to demonstrate the accuracy, correctness and efficiency of the algorithms.

Abstract: Displacement methane (CH4) by injection gases is regarded an effective way to exploit shale gas and sequestrate carbon dioxide (CO2). In this study, we use grand canonical Monte Carlo (GCMC) simulation to investigate the displacement CH4 by injection gases firstly. Then, molecular dynamics (MD) simulation is used to investigated the adsorption occurrence behavior of CH4 in different pore size. The shale model is constructed by organic and inorganic material, which is an original and comprehensive simplification for the real shale composition. The results show that both the displacement amount of CH4 and sequestration amount of CO2 see an upward trend with the increase of pore size. The CO2 molecules can replace the adsorbed CH4 from the adsorption sites directly. On the contrary, when N2 molecules are injected into the pores, these molecules can decrease the partial pressure of CH4. With the increase of the pores width, the adsorption oc-currence transfers from single adsorption layer to four adsorption layers. It is ex-pected that our work can reveal the mechanisms of adsorption and displacement of shale gas, which could provide a guidance and reference for displacement ex-ploitation of shale gas and sequestration of CO2.

Abstract: A coupled LBGK scheme, constituting of two independent distribution functions describing velocity and temperature respectively, is established in this paper. Chapman-Enskog expansion, a procedure to prove the consistency of this mesoscopic method with macroscopic conservation laws, is also conducted for both lattice scheme of velocity and temperature, as well as a simple introduction on the common used DnQb model. An efficient coding manner for Matlab is proposed in this paper, which improves the coding and calculation efficiency at the same time. The compact and efficient scheme is then applied in the simulation of Rayleigh-Benard convection, which is a natural heat convection problem common seen in modern industries. The results are interesting and reasonable, and meet the experimental data well. The stability of this scheme is also proved through different cases with a large range of Rayleigh number, until 2 million.

Abstract: Natural gas pipeline networks are the primary means of transporting natural gas, and safety is the priority in production operation. Investigating the vulnerability of natural gas pipeline networks can effectively identify weak links in the pipeline networks and is critical to the safe operation of pipeline networks. In this paper, based on network evaluation theory, a pipeline network topology-based natural gas pipeline network method to identify sources of vulnerability was developed. In this process, based on characteristics of actual flow in natural gas pipeline networks, network evaluation indices were improved to increase the accuracy of the identification of sources of vulnerability for natural gas pipeline networks. Based on the improved index, a topology-based identification process for sources of vulnerability for natural gas pipeline networks was created. Finally, the effec-tiveness of the proposed method was verified via pipeline network hydraulic simulation. The result shows that the proposed method is simple and can accu-rately identify sources of vulnerability in the nodes or links in natural gas pipeline networks.

Abstract: Due to complicated rheological behaviors and elastic effect of viscoelastic fluids, there are very few literatures reporting the large-eddy simulation (LES) studies on turbulent drag-reduction (DR) mechanism of viscoelastic fluids. In addition, these few studies are limited within the low Reynolds number situations. In this paper, LES approach is employed to further investigate the flow characteristics and DR mechanism of high Reynolds viscoelastic turbulent drag-reducing flow. To improve the accuracy of LES, an N-parallel FENE-P constitutive model based on multiple relaxation times and an improved mixed subgrid-scale (SGS) model are both utilized. DR rate and velocity fluctuations under different calculation conditions are analyzed. Contributions of different shear stresses on frictional resistance coefficient, and turbulent coherent structures which are closely related to turbulent burst events are investigated in details to further reveal the DR mechanism of high Reynolds viscoelastic turbulent drag-reducing flow. Especially, the different phenomena and results between high Reynolds and low Reynolds turbulent flows are addressed. This study is expected to provide beneficial guidance to the engineering application of turbulent DR technology.

Abstract: In time-domain goal-oriented adaptivity, it is essential to represent the error in the quantity of interest as an integral over the whole space-time domain. In that way, we can express the global error as a sum of local element contributions, and perform adaptivity. A space-time variational formulation provides such integral representation. Many authors employ implicit methods in time for performing goal-oriented adaptivity, like Backward Euler or Crank-Nicholson, as it is well known that these methods have variational structure. The Galerkin formulation of explicit methods in time for partial differential equations, however, remains elusive. In this work, we first construct a Petrov-Galerkin formulation for parabolic problems that is equivalent to the Forward Euler method in time (first order Runge-Kutta). Then, we derive an error representation and an explicit goal-oriented adaptive algorithm, enabling dynamic meshes in space. Some numerical results are provided for the 1D advection-diffusion equation to illustrate the proposed explicit algorithm. Finally, we provide an overview of how to build other Runge-Kutta methods using a variational formulation and follow a similar goal-oriented procedure.

Abstract: We focus on three-dimensional isogeometric analysis with tensor product C^k B-spline basis functions. We solve the computational problem with multi-frontal direct solver using the ordering obtained from the element partition trees. The trees have particular elements at the leaves, the entire mesh at the roof, and recursive partitions at internal nodes. The ordering obtained by browsing the tree in post-order, from leaves up to the root, results in a lower computational cost of the LU factorization than state-of-the art orderings obtained from the spare matrix analysis. We propose the algorithm that plots the map of FLOPS per mesh nodes using the trees, which in turn allows to identify the computationally expensive mesh nodes. Finally, we modify the algorithm of refined isogeometric analysis to introduce C0 separators at expensive mesh nodes, which reduces the cost of the solver by order of magnitude, while maintaining the numerical accuracy.

Abstract: To describe the diversity of opinions and dynamics of their changes in a society, there exist different approaches — from macroscopic laws of political processes to individual–based cognition and perception models. In this paper, we propose mesoscopic individual–based model of opinion dynamics which tackles the role of context by considering influence of different sources of information during life cycle of agents. The model combines several sub–models such as model of gen-eration and broadcasting of messages by mass media, model of daily activity, contact model based on multiplex network and model of information processing. To show the applicability of the approach, we present two scenarios illustrating the effect of the conflicting strategies of informational influence on a population and polarization of opinions about topical subject.

Abstract: In the paper, heuristic algorithm for tensor product approximation with B-spline basis functions of three-dimensional material data is presented. The algorithm has an application as a preconditioner for implicit dynamics simulations of a non-linear flow in heterogeneous media using alternating directions method. As the simulation use case non-stationary problem of liquid fossil fuels exploration with hydraulic fracturing is considered. Presented algorithm allows to approximate the permeability coefficient function as a tensor product what in turn allows for implicit simulations of the Laplasjan term in the partial differential equation. In the consequence the number of time steps of the non-stationary problem can be reduced, while the numerical accuracy is preserved.