title: Patient-Specific Cardiac Parametrization from Eikonal Simulations

text: Simulations in cardiac electrophysiology use the bidomain equations to describe the electrical potential in the heart. If only the electrical activation sequence in the heart is needed, then the full bidomain equations can be substituted by the Eikonal equation which allows much faster responses w.r.t. the changed material parameters in the equation. We use our Eikonal solver optimized for memory usage and parallelization. Patient-specific simulations in cardiac electrophysiology require patient-specific conductivity parameters which are not accurately available in vivo. One chance to improve the given conductivity parameters consists in comparing the computed activation sequence on the heart surface with the measured ECG on the torso mapped onto this surface. By minimizing the squared distance between the measured solution and the Eikonal computed solution we are able to determine the material parameters more accurately. To reduce the number of optimization parameters in this process, we group the material parameters and introduce a specific scaling parameter [Formula: see text] for each group. The minimization takes place w.r.t. the scaling [Formula: see text]. We solve the minimization problem by the BFGS method and adaptive step size control. The required gradient [Formula: see text] is computed either via finite differences or algorithmic differentiation using [Image: see text] in tangent as well as in adjoint mode. We present convergence behavior as well as runtime and scaling results.


title: Analysis of Checkpoint I/O Behavior

text: Nowadays, checkpoints have gained some relevance, given the increasing complexity of scientific applications for the use of many resources over a long period of time. Thus, in fault tolerance strategies, in addition to taking into account the impact that the application itself has on HPC systems, we must add the impact of the checkpoint. The checkpoint saves information about the application and the system in order to be able to restore the application, if necessary, in stable storage. The checkpoint can be considered as an intensive I/O application, so its storage need can have a great impact on the application. Therefore, in this paper, the analysis of the checkpoint’s I/O behavior is presented. The number of checkpoints to be performed in an application is often related to the maximum overhead that you want to introduce in the application. If we know the maximum overload the user wants to pay for and the overhead that a checkpoint introduces, we can calculate the number of checkpoints to be performed. This overhead depends significantly on the I/O operations. The PIOM-PX tool was used to analyze the spatial and temporal I/O patterns of the checkpoint. Based on this analysis, a model was designed to predict their behavior. This information is used to calculate the number of checkpoints to be performed in an application given a maximum overhead predefined by the user. This will allow us to understand what happens when a checkpoint is created in an HPC system, in order to make decisions that adapt to the user’s requirements.


title: Eigen-AD: Algorithmic Differentiation of the Eigen Library

text: In this work we present useful techniques and possible enhancements when applying an Algorithmic Differentiation (AD) tool to the linear algebra library Eigen using our in-house AD by overloading (AD-O) tool [Image: see text] as a case study. After outlining performance and feasibility issues when calculating derivatives for the official Eigen release, we propose Eigen-AD, which enables different optimization options for an AD-O tool by providing add-on modules for Eigen. The range of features includes a better handling of expression templates for general performance improvements as well as implementations of symbolically derived expressions for calculating derivatives of certain core operations. The software design allows an AD-O tool to provide specializations to automatically include symbolic operations and thereby keep the look and feel of plain AD by overloading. As a showcase, [Image: see text] is provided with such a module and its significant performance improvements are validated by benchmarks.


title: Cache-Aware Matrix Polynomials

text: Efficient solvers for partial differential equations are among the most important areas of algorithmic research in high-performance computing. In this paper we present a new optimization for solving linear autonomous partial differential equations. Our approach is based on polynomial approximations for exponential time integration, which involves the computation of matrix polynomial terms ([Image: see text]) in every time step. This operation is very memory intensive and requires targeted optimizations. In our approach, we exploit the cache-hierarchy of modern computer architectures using a temporal cache blocking approach over the matrix polynomial terms. We develop two single-core implementations realizing cache blocking over several sparse matrix-vector multiplications of the polynomial approximation and compare it to a reference method that performs the computation in the traditional iterative way. We evaluate our approach on three different hardware platforms and for a wide range of different matrices and demonstrate that our approach achieves time savings of up to 50% for a large number of matrices. This is especially the case on platforms with large caches, significantly increasing the performance to solve linear autonomous differential equations.


title: Object-Oriented Internet Reactive Interoperability

text: Information and Communication Technology has provided society with a vast variety of distributed applications. By design, the deployment of this kind of application has to focus primarily on communication. This article addresses research results on the systematic approach to the design of the meaningful Machine to Machine (M2M) communication targeting distributed mobile applications in the context of new emerging disciplines, i.e. Industry 4.0 and Internet of Things. This paper contributes to the design of a new architecture of mobile IoT solutions designed atop of the M2M communication and composed as multi-vendor cyber-physicals systems. The described reusable library supporting this architecture designed using the reactive interoperability archetype proves that the concept enables a systematic approach to the development and deployment of software applications against mobile IoT solutions based on international standards. Dependency injection and adaptive programming engineering techniques have been engaged to develop a full-featured reference application program and make the proposed solution scalable and robust against deployment environment continuous modifications. The article presents an executive summary of the proof of concept and describes selected conceptual and experimental results achieved as an outcome of the open-source project Object-Oriented Internet targeting multi-vendor plug-and-produce interoperability scenario.


title: Visual Self-healing Modelling for Reliable Internet-of-Things Systems

text: Internet-of-Things systems are comprised of highly heterogeneous architectures, where different protocols, application stacks, integration services, and orchestration engines co-exist. As they permeate our everyday lives, more of them become safety-critical, increasing the need for making them testable and fault-tolerant, with minimal human intervention. In this paper, we present a set of self-healing extensions for Node-RED, a popular visual programming solution for IoT systems. These extensions add runtime verification mechanisms and self-healing capabilities via new reusable nodes, some of them leveraging meta-programming techniques. With them, we were able to implement self-modification of flows, empowering the system with self-monitoring and self-testing capabilities, that search for malfunctions, and take subsequent actions towards the maintenance of health and recovery. We tested these mechanisms on a set of scenarios using a live physical setup that we called SmartLab. Our results indicate that this approach can improve a system’s reliability and dependability, both by being able to detect failing conditions, as well as reacting to them by self-modifying flows, or triggering countermeasures.


title: Analytical Techniques for the Identification of a Musical Score: The Musical DNA

text: In the information age, one of the main research field that is being developed is the one related to how to improve the quality of the search engine as regards knowing how to manage the information contained in a document in order to extract its content and interpret it. On the one hand due to the heterogeneity of the information contained on the web (text, image, video, musical scores), and on the other hand to satisfy the user who generally searches for information of a very different type. This paper describes the development and evaluation of an analytical method for the analysis of musical score considered in its symbolic level. The developed method is based on the analysis of the fundamental elements of the musical grammar and takes into account the distance between the sounds (which characterize a melody) and their duration (which makes the melody active and alive). The method has been tested on a set of different musical scores, realizing an algorithm in order to identity a musical score in a database.


title: Aerodynamic Shape Optimization for Delaying Dynamic Stall of Airfoils by Regression Kriging

text: The phenomenon of dynamic stall produce adverse aerodynamic loading which can adversely affect the structural strength and life of aerodynamic systems. Aerodynamic shape optimization (ASO) provides an effective approach for delaying and mitigating dynamic stall characteristics without the addition of auxiliary system. ASO, however, requires multiple evaluations time-consuming computational fluid dynamics models. Metamodel-based optimization (MBO) provides an efficient approach to alleviate the computational burden. In this study, the MBO approach is utilized for the mitigation of dynamic stall characteristics while delaying dynamic stall angle of the flow past wind turbine airfoils. The regression Kriging metamodeling technique is used to approximate the objective and constrained functions. The airfoil shape design variables are described with six PARSEC parameters. A total of 60 initial samples are used to construct the metamodel, which is further refined with 20 infill points using expected improvement. The metamodel is validated with the normalized root mean square error based on 20 test data samples. The refined metamodel is used to search for the optimal design using a multi-start gradient-based method. The results show that an optimal design with a [Formula: see text] delay in dynamic stall angle as well a reduction in the severity of pitching moment coefficients can be obtained.


title: An Effective Stable Numerical Method for Integrating Highly Oscillating Functions with a Linear Phase

text: A practical and simple stable method for calculating Fourier integrals is proposed, effective both at low and at high frequencies. An approach based on the fruitful idea of Levin, which allows the use of the collocation method to approximate the slowly oscillating part of the antiderivative of the desired integral, allows reducing the calculation of the integral of a rapidly oscillating function (with a linear phase) to solving a system of linear algebraic equations with a triangular or Hermitian matrix. The choice of Gauss-Lobatto grid nodes as collocation points let to increasing the efficiency of the numerical algorithm for solving the problem. To avoid possible numerical instability of the algorithm, we proceed to the solution of a normal system of linear algebraic equations.


title: Cast Shadow Generation Using Generative Adversarial Networks

text: We propose a computer graphics pipeline for 3D rendered cast shadow generation using generative adversarial networks (GANs). This work is inspired by the existing regression models as well as other convolutional neural networks such as the U-Net architectures which can be geared to produce believable global illumination effects. Here, we use a semi-supervised GANs model comprising of a PatchGAN and a conditional GAN which is then complemented by a U-Net structure. We have adopted this structure because of its training ability and the quality of the results that come forth. Unlike other forms of GANs, the chosen implementation utilises colour labels to generate believable visual coherence. We carried forth a series of experiments, through laboratory generated image sets, to explore the extent at which colour can create the correct shadows for a variety of 3D shadowed and un-shadowed images. Once an optimised model is achieved, we then apply high resolution image mappings to enhance the quality of the final render. As a result, we have established that the chosen GANs model can produce believable outputs with the correct cast shadows with plausible scores on PSNR and SSIM similarity index metrices.


title: Conversational Interface for Managing Non-trivial Internet-of-Things Systems

text: Internet-of-Things has reshaped the way people interact with their surroundings. In a smart home, controlling the lights is as simple as speaking to a conversational assistant since everything is now Internet-connected. But despite their pervasiveness, most of the existent IoT systems provide limited out-of-the-box customization capabilities. Several solutions try to attain this issue leveraging end-user programming features that allow users to define rules to their systems, at the cost of discarding the easiness of voice interaction. However, as the number of devices increases, along with the number of household members, the complexity of managing such systems becomes a problem, including finding out why something has happened. In this work we present Jarvis, a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. A proof-of-concept was used to carry out a quasi-experiment with non-technical participants that provides evidence that such approach is intuitive enough to be used by common end-users.


title: Trilateration-Based Multilevel Method for Minimizing the Lennard-Jones Potential

text: Simulating atomic evolution for the mechanics and structure of materials presents an ever-growing challenge due to the huge number of degrees of freedom borne from the high-dimensional spaces in which increasingly high-fidelity material models are defined. To efficiently exploit the domain-, data-, and approximation-based hierarchies hidden in many such problems, we propose a trilateration-based multilevel method to initialize the underlying optimization and benchmark its application on the simple yet practical Lennard-Jones potential. We show that by taking advantage of a known hierarchy present in this problem, not only a faster convergence, but also a better local minimum can be achieved comparing to random initial guess.


title: IoT-Based Cow Health Monitoring System

text: Good health and wellbeing of animals are essential to dairy cow farms and sustainable production of milk. Unfortunately, day-to-day monitoring of animals condition is difficult, especially in large farms where employees do not have enough time to observe animals and detect first symptoms of diseases. This paper presents an automated, IoT-based monitoring system designed to monitor the health of dairy cows. The system is composed of hardware devices, a cloud system, an end-user application, and innovative techniques of data measurements and analysis algorithms. The system was tested in a real-life scenario and has proved it can effectively monitor animal welfare and the estrus cycle.


title: Application of Underdetermined Differential Algebraic Equations to Solving One Problem from Heat Mass Transfer

text: This paper addresses a mathematical model of the boiling of subcooled liquid in an annular channel. The model is presented by a mixed system of ordinary differential equations, algebraic relations and a single partial differential equation, which, written together, can be viewed as an underdetermined differential algebraic equation with a partial differential equation attached. Using the tools of the differential algebraic equation theory, we reveal some important qualitative properties of this system, such as its existence domain, and propose a numerical method for its solution. The numerical experiments demonstrated that within the found existence domain the mathematical model adequately represents real-life boiling processes that occur in the experimental setup.


title: Minimizing Material Consumption of 3D Printing with Stress-Guided Optimization

text: 3D printing has been widely used in daily life, industry, architecture, aerospace, crafts, art, etc. Minimizing 3D printing material consumption can greatly reduce the costs. Therefore, how to design 3D printed objects with less materials while maintain structural soundness is an important problem. The current treatment is to use thin shells. However, thin shells have low strength. In this paper, we use stiffeners to stiffen 3D thin-shell objects for increasing the strength of the objects and propose a stress guided optimization framework to achieve minimum material consumption. First, we carry out finite element calculations to determine stress distribution in 3D objects and use the stress distribution to guide random generation of some points called seeds. Then we map the 3D objects and seeds to a 2D space and create a Voronoi Diagram from the seeds. The stiffeners are taken to be the edges of the Voronoi Diagram whose intersections with the edges of each of the triangles used to represent the polygon models of the 3D objects are used to define stiffeners. The obtained intersections are mapped back to 3D polygon models and the cross-section size of stiffeners is minimized under the constraint of the required strength. Monte-Carlo simulation is finally introduced to repeat the process from random seed generation to cross-section size optimization of stiffeners. Many experiments are presented to demonstrate the proposed framework and its advantages.


title: Improved Two-Step Binarization of Degraded Document Images Based on Gaussian Mixture Model

text: Image binarization is one of the most relevant preprocessing operations influencing the results of further image analysis conducted for many purposes. During this step a significant loss of information occurs and the use of inappropriate thresholding methods may cause difficulties in further shape analysis or even make it impossible to recognize different shapes of objects or characters. Some of the most typical applications utilizing the analysis of binary images are Optical Character Recognition (OCR) and Optical Mark Recognition (OMR), which may also be applied for unevenly illuminated natural images, as well as for challenging degraded historical document images, considered as typical benchmarking tools for image binarization algorithms. To face the still valid challenge of relatively fast and simple, but robust binarization of degraded document images, a novel two-step algorithm utilizing initial thresholding, based on the modelling of the simplified image histogram using Gaussian Mixture Model (GMM) and the Monte Carlo method, is proposed in the paper. This approach can be considered as the extension of recently developed image preprocessing method utilizing Generalized Gaussian Distribution (GGD), based on the assumption of its similarity to the histograms of ground truth binary images distorted by Gaussian noise. The processing time of the first step, producing the intermediate images with partially removed background information, may be significantly reduced due to the use of the Monte Carlo method. The proposed improved approach leads to even better results, not only for well-known DIBCO benchmarking databases, but also for more demanding Bickley Diary dataset, allowing the use of some well-known classical binarization methods, including the global ones, in the second step of the algorithm.


title: A Stochastic Birth-Death Model of Information Propagation Within Human Networks

text: The fixation probability of a mutation in a population network is a widely-studied phenomenon in evolutionary dynamics. This mutation model following a Moran process finds a compelling application in modeling information propagation through human networks. Here we present a stochastic model for a two-state human population in which each of N individual nodes subscribes to one of two contrasting messages, or pieces of information. We use a mutation model to describe the spread of one of the two messages labeled the mutant, regulated by stochastic parameters such as talkativity and belief probability for an arbitrary fitness r of the mutant message. The fixation of mutant information is analyzed for an unstructured well-mixed population and simulated on a Barabási-Albert graph to mirror a human social network of [Formula: see text] individuals. Chiefly, we introduce the possibility of a single node speaking to multiple information recipients or listeners, each independent of one another, per a binomial distribution. We find that while in mixed populations, the fixation probability of the mutant message is strongly correlated with the talkativity (sample correlation [Formula: see text]) and belief probability ([Formula: see text]) of the initial mutant, these correlations with respect to talkativity ([Formula: see text]) and belief probability ([Formula: see text]) are weaker on BA graph simulations. This indicates the likely effect of added stochastic noise associated with the inherent construction of graphs and human networks.


title: Information Theory-Based Feature Selection: Minimum Distribution Similarity with Removed Redundancy

text: Feature selection is an important preprocessing step in pattern recognition. In this paper, we presented a new feature selection approach in two-class classification problems based on information theory, named minimum Distribution Similarity with Removed Redundancy (mDSRR). Different from the previous methods which use mutual information and greedy iteration with a loss function to rank the features, we rank features according to their distribution similarities in two classes measured by relative entropy, and then remove the high redundant features from the sorted feature subsets. Experimental results on datasets in varieties of fields with different classifiers highlight the value of mDSRR on selecting feature subsets, especially so for choosing small size feature subset. mDSRR is also proved to outperform other state-of-the-art methods in most cases. Besides, we observed that the mutual information may not be a good practice to select the initial feature in the methods with subsequent iterations.


title: Swarm Intelligence Approach for Rational Global Approximation of Characteristic Curves for the Van der Waals Equation of State

text: The Van der Waals (VdW) equation of state is a popular generalization of the law of ideal gases proposed time ago. In many situations, it is convenient to compute the characteristic curves of the VdW equation of state, called binodal and spinodal curves. Typically, they are constructed through data fitting from a collection of data points represented in the two-dimensional pressure-volume plane. However, the resulting models are still limited and can be further enhanced. In this paper, we propose to extend this polynomial approach by using a rational function as a fitting function. In particular, we consider a rational free-form Bézier curve, which provides a global approximation to the shape of the curve. This rational approach is more flexible than the polynomial one owing to some extra parameters, the weights. Unfortunately, data fitting becomes more difficult as these new parameters have also to be computed. In this paper we address this problem through a powerful nature-inspired swarm intelligence method for continuous optimization called the bat algorithm. Our experimental results show that the method can reconstruct the characteristic curves with very good accuracy.


title: Weighted Clustering for Bees Detection on Video Images

text: This work describes a bee detection system to monitor bee colony conditions. The detection process on video images has been divided into 3 stages: determining the regions of interest (ROI) for a given frame, scanning the frame in ROI areas using the DNN-CNN classifier, in order to obtain a confidence of bee occurrence in each window in any position and any scale, and form one detection window from a cloud of windows provided by a positive classification. The process has been performed by a method of weighted cluster analysis, which is the main contribution of this work. The paper also describes a process of building the detector, during which the main challenge was the selection of clustering parameters that gives the smallest generalization error. The results of the experiments show the advantage of the cluster analysis method over the greedy method and the advantage of the optimization of cluster analysis parameters over standard-heuristic parameter values, provided that a sufficiently long learning fragment of the movie is used to optimize the parameters.


title: Impression Curve as a New Tool in the Study of Visual Diversity of Computer Game Levels for Individual Phases of the Design Process

text: Impression curve is a widely used method in urban and landscape design to assess visual diversity of the space. In these studies, the method is applied for game level design. The goal of conducted research was the analysis of space perception in successive design phases related to the process of game environment formation. Next steps of the design process define the space burdened with more and more information. It aims to evaluate if initial assumptions, made by a designer at the beginning of the designing process, are maintained with the increase in the number of details and the content of locations. These studies are also a background for research in automation of visual diversity assessment. This, in turn, is related to making a player focused and interested during a gameplay, by the means of space defining an action scene. By applying a method from domain of urban planning and architecture in human-computer interaction (HCI) studies related to virtual space, we show that both - defining the surroundings and its impact on recipient - are subject to the same rules in either case.


title: Genetic Learning Particle Swarm Optimization with Interlaced Ring Topology

text: Genetic learning particle swarm optimization (GL-PSO) is a hybrid optimization method based on particle swarm optimization (PSO) and genetic algorithm (GA). The GL-PSO method improves the performance of PSO by constructing superior exemplars from which individuals of the population learn to move in the search space. However, in case of complex optimization problems, GL-PSO exhibits problems to maintain appropriate diversity, which leads to weakening an exploration and premature convergence. This makes the results of this method not satisfactory. In order to enhance the diversity and adaptability of GL-PSO, and as an effect of its performance, in this paper, a new modified genetic learning method with interlaced ring topology and flexible local search operator has been proposed. To assess the impact of the introduced modifications on performance of the proposed method, an interlaced ring topology has been integrated with GL-PSO only (referred to as GL-PSOI) as well as with a flexible local search operator (referred to as GL-PSOIF). The new strategy was tested on a set of benchmark problems and a CEC2014 test suite. The results were compared with five different variants of PSO, including GL-PSO, GGL-PSOD, PSO, CLPSO and HCLPSO to demonstrate the efficiency of the proposed approach.


title: Medical Image Enhancement Using Super Resolution Methods

text: Deep Learning image processing methods are gradually gaining popularity in a number of areas including medical imaging. Classification, segmentation, and denoising of images are some of the most demanded tasks. In this study, we aim at enhancing optic nerve head images obtained by Optical Coherence Tomography (OCT). However, instead of directly applying noise reduction techniques, we use multiple state-of-the-art image Super-Resolution (SR) methods. In SR, the low-resolution (LR) image is upsampled to match the size of the high-resolution (HR) image. With respect to image enhancement, the upsampled LR image can be considered as low quality, noisy image, and the HR image would be the desired enhanced version of it. We experimented with several image SR architectures, such as super-resolution Convolutional Neural Network (SRCNN), very deep Convolutional Network (VDSR), deeply recursive Convolutional Network (DRCN), and enhanced super-resolution Generative Adversarial Network (ESRGAN). Quantitatively, in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), the SRCNN, VDSR, and DRCN significantly improved the test images. Although the ERSGAN showed the worst PSNR and SSIM, qualitatively, it was the best one.


title: Comparative Analysis of Time Series Databases in the Context of Edge Computing for Low Power Sensor Networks

text: Selection of an appropriate database system for edge IoT devices is one of the essential elements that determine efficient edge-based data analysis in low power wireless sensor networks. This paper presents a comparative analysis of time series databases in the context of edge computing for IoT and Smart Systems. The research focuses on the performance comparison between three time-series databases: TimescaleDB, InfluxDB, Riak TS, as well as two relational databases, PostgreSQL and SQLite. All selected solutions were tested while being deployed on a single-board computer, Raspberry Pi. For each of them, the database schema was designed, based on a data model representing sensor readings and their corresponding timestamps. For performance testing, we developed a small application that was able to simulate insertion and querying operations. The results of the experiments showed that for presented scenarios of reading data, PostgreSQL and InfluxDB emerged as the most performing solutions. For tested insertion scenarios, PostgreSQL turned out to be the fastest. Carried out experiments also proved that low-cost, single-board computers such as Raspberry Pi can be used as small-scale data aggregation nodes on edge device in low power wireless sensor networks, that often serve as a base for IoT-based smart systems.


title: Deep Learning Assisted Memetic Algorithm for Shortest Route Problems

text: Finding the shortest route between a pair of origin and destination is known to be a crucial and challenging task in intelligent transportation systems. Current methods assume fixed travel time between any pairs, thus the efficiency of these approaches is limited because the travel time in reality can dynamically change due to factors including the weather conditions, the traffic conditions, the time of the day and the day of the week, etc. To address this dynamic situation, we propose a novel two-stage approach to find the shortest route. Firstly deep learning is utilised to predict the travel time between a pair of origin and destination. Weather conditions are added into the input data to increase the accuracy of travel time predicition. Secondly, a customised Memetic Algorithm is developed to find shortest route using the predicted travel time. The proposed memetic algorithm uses genetic algorithm for exploration and local search for exploiting the current search space around a given solution. The effectiveness of the proposed two-stage method is evaluated based on the New York City taxi benchmark dataset. The obtained results demonstrate that the proposed method is highly effective compared with state-of-the-art methods.


title: Improving Coverage Area in Sensor Deployment Using Genetic Algorithm

text: Wireless sensor networks (WSN) are a collection of autonomous nodes with a limited battery life. They are used in various fields such as health, industry, home automation. Due to their limited resources and constraints, WSNs face several problems. One of these problems is the optimal coverage of a observed area. Indeed, whatever the domain, ensuring optimal network coverage remains a very important issue in WSNs, especially when the number of sensors is limited. In this paper, we aim to cover a two-dimensional Euclidean area with a given number of sensors by using genetic algorithm in order to find the best placement to ensure a good network coverage. The maximum coverage problem addressed in this paper is based on the calculation of the total area covered by deployed sensor nodes. We first, define the problem of maximum coverage. For a given number of sensors, the proposed algorithm find the best position to maximize the sensor area coverage. Finally, the results show that the proposed method well maximize the sensor area coverage.


title: Depth Map Estimation with Consistent Normals from Stereo Images

text: The total variation regularization of non-convex data terms in continuous variational models can be convexified by the so called functional lifting, which may be considered as a continuous counterpart of Ishikawa’s method for multi-label discrete variational problems. We solve the resulting convex continuous variational problem by the augmented Lagrangian method. Application of this method to the dense depth map estimation allows us to obtain a consistent normal field to the depth surface as a byproduct. We illustrate the method with numerical examples of the depth map estimation for rectified stereo image pairs.


title: Reduced-Cost Constrained Modeling of Microwave and Antenna Components: Recent Advances

text: Electromagnetic (EM) simulation models are ubiquitous in the design of microwave and antenna components. EM analysis is reliable but CPU intensive. In particular, multiple simulations entailed by parametric optimization or uncertainty quantification may considerably slow down the design processes. In order to address this problem, it is possible to employ fast metamodels. Here, the popular solution approaches are approximation surrogates, which are versatile and easily accessible. Notwithstanding, the major issue for conventional modeling methods is the curse of dimensionality. In the case of high-frequency components, an added difficulty are highly nonlinear outputs that need to be handled. A recently reported constrained modeling attempts to broaden the applicability of approximation surrogates by confining the surrogate model setup to a small subset of the parameter space. The said region contains the parameter vectors corresponding to high-quality designs w.r.t. the considered figures of interest, which allows for a dramatic reduction of the number of training samples needed to render reliable surrogates without formally restricting the parameter ranges. This paper reviews the recent techniques employing these concepts and provides real-world illustration examples of antenna and microwave structures.


title: A Relaxation Algorithm for Optimal Control Problems Governed by Two-Dimensional Conservation Laws

text: We develop a class of numerical methods for solving optimal control problems governed by nonlinear conservation laws in two space dimensions. The relaxation approximation is used to transform the nonlinear problem to a semi-linear diagonalizable system with source terms. The relaxing system is hyperbolic and it can be numerically solved without need to either Riemann solvers for space discretization or a non-linear system of algebraic equations solvers for time discretization. In the current study, the optimal control problem is formulated for the relaxation system and at the relaxed limit its solution converges to the relaxed equation of conservation laws. An upwind method is used for reconstruction of numerical fluxes and an implicit-explicit scheme is used for time stepping. Computational results are presented for a two-dimensional inviscid Burgers problem.


title: Impact of Long-Range Dependent Traffic in IoT Local Wireless Networks on Backhaul Link Performance

text: Performance evaluation in Internet of Things (IoT) networks is becoming more and more important due to the increasing demand for quality of service (QoS). In addition to basic statistical properties based on the distribution of interarrival times of packets, actual network traffic exhibits correlations over a wide range of time scales associated with long-range dependence (LRD). This article focuses on examining the impact of both LRD and number of nodes that transmit packets in a typical IoT wireless local network, on performance of the backhaul link. The analysis of latency and packet loss led to an interesting observation that the aggregation of packet streams, originating from single nodes, lowers the importance of LRD, even causing an underestimation of performance results when compared to the queueing system with Markovian input.


title: Dual Formulation of the TV-Stokes Denoising Model for Multidimensional Vectorial Images

text: The TV-Stokes denoising model for a vectorial image defines a denoised vector field in the form of the gradient of a scalar function. The dual formulation naturally leads to a Chambolle-type algorithm, where the most time consuming part is application of the orthogonal projector onto the range space of the gradient operator. This application can be efficiently executed by the fast cosine transform taking advantage of the fast Fourier transform. Convergence of the Chambolle-type iteration can be improved by Nesterov’s acceleration.


title: Modified Binary Tree in the Fast PIES for 2D Problems with Complex Shapes

text: The paper presents a modified binary tree in the fast multipole method (FMM) included into the modified parametric integral equations system (PIES), called the fast PIES, in solving potential 2D boundary value problems with complex shapes. The modified binary tree proposed in this paper is built based on a one-dimensional reference system contrary to a quad-tree (based on a two-dimensional reference system) which is applied in the fast multipole boundary element method (FM-BEM). Application of the proposed tree allows reducing the number of numerical computations performed during its construction and fast multipole calculations in the fast PIES. The proposed modification of the tree in the fast PIES allows obtaining accurate solutions in engineering problems with complex shapes on a standard personal computer in a short time.


title: OpenGraphGym: A Parallel Reinforcement Learning Framework for Graph Optimization Problems

text: This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems. This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to rapidly plug in and test new RL algorithms and graph embeddings for graph optimization problems. This new open-source RL framework is targeted at achieving both high performance and high quality of the computed graph solutions. This RL framework forms the foundation of several ongoing research directions, including 1) benchmark works on different RL algorithms and embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations, as well as 3) performance evaluation on real-world graph solutions.


title: On the Potential of the Nature-Inspired Algorithms for Pure Binary Classification

text: With the advent of big data, interest for new data mining methods has increased dramatically. The main drawback of traditional data mining methods is the lack of comprehensibility. In this paper, the firefly algorithm was employed for standalone binary classification, where each solution is represented by two classification rules that are easy understandable by users. Implicitly, the feature selection is also performed by the algorithm. The results of experiments, conducted on three well-known datasets publicly available on web, were comparable with the results of the traditional methods in terms of accuracy and, therefore, the huge potential was exhibited by the proposed method.


title: Model-Based Sensitivity Analysis of Nondestructive Testing Systems Using Machine Learning Algorithms

text: Model-based sensitivity analysis is crucial in quantifying which input variability parameter is important for nondestructive testing (NDT) systems. In this work, neural networks (NN) and convolutional NN (CNN) are shown to be computationally efficient at making model prediction for NDT systems, when compared to models such as polynomial chaos expansions, Kriging and polynomial chaos Kriging (PC-Kriging). Three different ultrasonic benchmark cases are considered. NN outperform these three models for all the cases, while CNN outperformed these three models for two of the three cases. For the third case, it performed as well as PC-Kriging. NN required 48, 56 and 35 high-fidelity model evaluations, respectively, for the three cases to reach within [Formula: see text] accuracy of the physics model. CNN required 35, 56 and 56 high-fidelity model evaluations, respectively, for the same three cases.


title: Visual Analysis of Computer Game Output Video Stream for Gameplay Metrics

text: This work contains a solution for game metrics analysis based on a visual data stream dedicated for the player. The solution does not require interference in the programming code of the analyzed game and it is only based on image processing. It is possible to analyze several aspects of the game simultaneously, for example health/energy bars, current weapon used, number of objects worn (aid kits, ammunition). There have been presented methods using cascading classifiers and their training to detect the desired objects on the screen and to prepare data for other stages of processing, e.g. OCR. The effect of the methods is a gameplay chart that allows a thorough analysis of the player’s actions in the game world and his or her advancement. The solution is fast enough that it can be used not only in previously recorded gameplay analysis, but also in real time during simultaneous gameplay.


title: Parametric Learning of Associative Functional Networks Through a Modified Memetic Self-adaptive Firefly Algorithm

text: Functional networks are a powerful extension of neural networks where the scalar weights are replaced by neural functions. This paper concerns the problem of parametric learning of the associative model, a functional network that represents the associativity operator. This problem can be formulated as a nonlinear continuous least-squares minimization problem, solved by applying a swarm intelligence approach based on a modified memetic self-adaptive version of the firefly algorithm. The performance of our approach is discussed through an illustrative example. It shows that our method can be successfully applied to solve the parametric learning of functional networks with unknown functions.


title: Low Reynolds Number Swimming with Slip Boundary Conditions

text: We investigate the classical Taylor’s swimming sheet problem in a viscoelastic fluid, as well as in a mixture of a viscous fluid and a viscoelastic fluid. Extensions of the standard Immersed Boundary (IB) Method are proposed so that the fluid media may satisfy partial slip or free-slip conditions on the moving boundary. Our numerical results indicate that slip may lead to substantial speed enhancement for swimmers in a viscoelastic fluid and in a viscoelastic two-fluid mixture. Under the slip conditions, the speed of locomotion is dependent in a nontrivial way on both the viscosity and elasticity of the fluid media. In a two-fluid mixture with free-slip network, the swimming speed is also significantly affected by the drag coefficient and the network volume fraction.


title: A Random Line-Search Optimization Method via Modified Cholesky Decomposition for Non-linear Data Assimilation

text: This paper proposes a line-search optimization method for non-linear data assimilation via random descent directions. The iterative method works as follows: at each iteration, quadratic approximations of the Three-Dimensional-Variational (3D-Var) cost function are built about current solutions. These approximations are employed to build sub-spaces onto which analysis increments can be estimated. We sample search-directions from those sub-spaces, and for each direction, a line-search optimization method is employed to estimate its optimal step length. Current solutions are updated based on directions along which the 3D-Var cost function decreases faster. We theoretically prove the global convergence of our proposed iterative method. Experimental tests are performed by using the Lorenz-96 model, and for reference, we employ a Maximum-Likelihood-Ensemble-Filter (MLEF) whose ensemble size doubles that of our implementation. The results reveal that, as the degree of observational operators increases, the use of additional directions can improve the accuracy of results in terms of [Formula: see text]-norm of errors, and even more, our numerical results outperform those of the employed MLEF implementation.


title: Plane Space Representation in Context of Mode-Based Symmetry Plane Detection

text: This paper describes various representations of the space of planes. The main focus is on the plane space representation in the symmetry plane detection in [Formula: see text] where many candidate planes for many pairs of points of the given object are created and then the most often candidate is found as a mode in the candidate space, so-called Mode-based approach. The result depends on the representation used in the mode-seeking process. The most important aspect is how well distances in the space correspond to similarities of the actual planes with respect to the input object. So, we describe various usable distance functions and compare them both theoretically and practically. The results suggest that, when using the Mode-based approach, representing planes by reflection transformations is the best way but other simpler representations are applicable as well. On the other hand, representations using 3D dual spaces are not very appropriate. Furthermore, we introduce a novel way of representing the reflection transformations using dual quaternions.


title: Generating Random Floating-Point Numbers by Dividing Integers: A Case Study

text: A method widely used to obtain IEEE 754 binary floating-point numbers with a standard uniform distribution involves drawing an integer uniformly at random and dividing it by another larger integer. We survey the various instances of the algorithm that are used in actual software and point out their properties and drawbacks, particularly from the standpoint of numerical software testing and data anonymization.


title: Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networks

text: State-of-the-art simulations of detailed neurons follow the Bulk Synchronous Parallel execution model. Execution is divided in equidistant communication intervals, with parallel neurons interpolation and collective communication guiding synchronization. Such simulations, driven by stiff dynamics or wide range of time scales, struggle with fixed step interpolation methods, yielding excessive computation on intervals of quasi-constant activity and inaccurate interpolation of periods of high volatility in solution. Alternative adaptive timestepping methods are inefficient in parallel executions due to computational imbalance at the synchronization barriers. We introduce a distributed fully-asynchronous execution model that removes global synchronization, allowing for long variable timestep interpolations of neurons. Asynchronicity is provided by point-to-point communication notifying neurons’ time advancement to synaptic connectivities. Timestepping is driven by scheduled neuron advancements based on interneuron synaptic delays, yielding an exhaustive yet not speculative execution. Benchmarks on 64 Cray XE6 compute nodes demonstrate reduced number of interpolation steps, higher numerical accuracy and lower runtime compared to state-of-the-art methods. Efficiency is shown to be activity-dependent, with scaling of the algorithm demonstrated on a simulation of a laboratory experiment.


title: Trends in U.S. Trade Book Publisher Mergers and Acquisitions

text: Mergers and acquisitions (M&A) activity involving book publishers is a continuing feature of the U.S. commercial publishing landscape. Over the last 6 years, from the beginning of 2014 through the end of 2019, there were 120 control transactions involving U.S. trade publishers of all sizes: on average, twenty transactions annually. This activity has proceeded subject to random annual variation uncorrelated to industry performance or to macroeconomic factors; in the last 2 years activity has accelerated, with 27 completed transactions in 2019. Historically, in the wake of the 2008 financial crisis, trade publishing M&A activity stalled from the valuations achieved in 2006–2007, even as activity remained relatively steady. We anticipate that in the wake of the pandemic M&A activity will gradually increase as publishers seek stronger partners and increased scale. In the first 5 months of 2020, eight transactions were announced or completed, and ViacomCBS set the stage for a major transaction by announcing its intention to divest Simon & Schuster.


Profession


La neumología pos-COVID-19


Primary prevention: Vaccines and the allergist-immunologist's role


Valoriser le rôle de dispensateur du pharmacien d’officine


Elsewhere in The AGA Journals


Host immune responses to influenza infection and vaccines: Lessons learned for all viral pandemic challenges


title: SciNER: Extracting Named Entities from Scientific Literature

text: The automated extraction of claims from scientific papers via computer is difficult due to the ambiguity and variability inherent in natural language. Even apparently simple tasks, such as isolating reported values for physical quantities (e.g., “the melting point of X is Y”) can be complicated by such factors as domain-specific conventions about how named entities (the X in the example) are referenced. Although there are domain-specific toolkits that can handle such complications in certain areas, a generalizable, adaptable model for scientific texts is still lacking. As a first step towards automating this process, we present a generalizable neural network model, SciNER, for recognizing scientific entities in free text. Based on bidirectional LSTM networks, our model combines word embeddings, subword embeddings, and external knowledge (from DBpedia) to boost its accuracy. Experiments show that our model outperforms a leading domain-specific extraction toolkit by up to 50%, as measured by F1 score, while also being easily adapted to new domains.


title: Data Fitting by Exponential Sums with Equal Weights

text: In this paper, we introduce a Prony-type data fitting problem consisting in interpolating the table [Formula: see text] with [Formula: see text] in the sense of least squares by exponential sums with equal weights. We further study how to choose the parameters of the sums properly to solve the problem. Moreover, we show that the sums have some advantages in data fitting over the classical Prony exponential sums. Namely, we prove that the parameters of our sums are a priori well-controlled and thus can be found via a stable numerical framework, in contrast to those of the Prony ones. In several numerical experiments, we also compare the behaviour of both the sums and illustrate the above-mentioned advantages.


title: A Combination of Moment Descriptors, Fourier Transform and Matching Measures for Action Recognition Based on Shape

text: This paper presents an approach for human action recognition based on shape analysis. The purpose of the approach is to classify simple actions by applying shape descriptors to sequences of binary silhouettes. The recognition process consists of several main stages: shape representation, action sequence representation and action sequence classification. Firstly, each shape is represented using a selected shape descriptor. Secondly, shape descriptors of each sequence are matched, matching values are put into a vector and transformed into final action representation—we employ Fourier transform-based methods to obtain action representations equal in size. A classification into eight classes is performed using leave-one-out cross-validation and template matching approaches. We present results of the experiments on classification accuracy using moment-based shape descriptors (Zernike Moments, Moment Invariants and Contour Sequence Moments) and three matching measures (Euclidean distance, correlation coefficient and C1 correlation). Different combinations of the above-mentioned algorithms are examined in order to indicate the most effective one. The experiments show that satisfactory results are obtained when low-order Zernike Moments are used for shape representation and absolute values of Fourier transform are applied to represent action sequences. Moreover, the selection of matching technique strongly influences final classification results.


title: Bézier Surfaces for Modeling Inclusions in PIES

text: The paper presents the approach for solving 2D elastic boundary value problems defined in domains with inclusions with different material properties using the parametric integral equation system (PIES). The main feature of the proposed strategy is using Bézier surfaces for global modeling of inclusions. Polygonal inclusions are defined by bilinear surfaces, while others by bicubic surfaces. It is beneficial over other numerical methods (such as FEM and BEM) due to the lack of discretization. Integration over inclusions defined by surfaces is also performed globally without division into subareas. The considered problem is solved iteratively in order to simulate different material properties by applying initial stresses within the inclusion. This way of solving avoids increasing the number of unknowns and can also be used for elasto-plastic problems without significant changes. Some numerical tests are presented, in which the results obtained are compared with those calculated by other numerical methods.


title: Epigenetic Modification of Genetic Algorithm

text: The article presents a new operation in the genetic algorithm. This operation mimics the epigenetic process of cytosine methylation. The Epigenetic processes have a huge impact on the functioning of living organisms, but have not yet been reflected in the operations of genetic algorithms. In a study on the evaluation of the operation mimics epigenetics process were used genetic algorithm for Knapsack issue.


title: Calculating Reactive Power Compensation for Large-Scale Street Lighting

text: LED-based street lighting installations generate reactive power, particularly when they are dynamically dimmed. It contributes to power loss and efficiency reduction of the grid. The reactive power can be compensated by installing additional dynamically connected inductors in lighting control cabinets. However such an approach significantly increases the cost of the lighting infrastructure. The goal of this paper is to propose another, low cost approach to reactive power compensation for dynamically dimmed lighting installations. It is based on connecting fixed settings inductors at lighting control cabinets. The inductors settings are calculated by the proposed algorithm for city-scale lighting systems. Its objective is to completely eliminate capacitive reactive power and to keep inductive reactive power within acceptable limits.


title: Loop Aggregation for Approximate Scientific Computing

text: Trading off some accuracy for better performances in scientific computing is an appealing approach to ease the exploration of various alternatives on complex simulation models. Existing approaches involve the application of either time-consuming model reduction techniques or resource-demanding statistical approaches. Such requirements prevent any opportunistic model exploration, e.g., exploring various scenarios on environmental models. This limits the ability to analyse new models for scientists, to support trade-off analysis for decision-makers and to empower the general public towards informed environmental intelligence. In this paper, we present a new approximate computing technique, aka. loop aggregation, which consists in automatically reducing the main loop of a simulation model by aggregating the corresponding spatial or temporal data. We apply this approximate scientific computing approach on a geophysical model of a hydraulic simulation with various input data. The experimentation demonstrates the ability to drastically decrease the simulation time while preserving acceptable results with a minimal set-up. We obtain a median speed-up of 95.13% and up to 99.78% across all the 23 case studies.


title: Simulation Versus an Ordered–Fuzzy-Numbers-Driven Approach to the Multi-depot Vehicle Cyclic Routing and Scheduling Problem

text: It is an undeniable fact that material handling systems aim at supplying the right materials at the right locations at the right time. This fact creates the need for the design of logistic-train-fleet-oriented, distributed and scalability-robust control policies ensuring deadlock-free operations. The paper presents a solution to a multi-item and multi-depot vehicle routing and scheduling problem subject to fuzzy pick-up and delivery transportation time constraints. Since this type of problem can be treated as a fuzzy constraint satisfaction problem, a solution to it can be determined using both computer simulation and analytical ordered-fuzzy-numbers-driven calculations. The accuracy of both approaches is verified based on the results of multiple simulations. In this context, our contribution consists of proposing an alternative approach that allows avoiding time-consuming computer simulation-based calculations of logistic train fleet schedules.


title: GPU-Embedding of kNN-Graph Representing Large and High-Dimensional Data

text: Interactive visual exploration of large and multidimensional data still needs more efficient [Formula: see text] data embedding (DE) algorithms. We claim that the visualization of very high-dimensional data is equivalent to the problem of 2D embedding of undirected kNN-graphs. We demonstrate that high quality embeddings can be produced with minimal time&memory complexity. A very efficient GPU version of IVHD (interactive visualization of high-dimensional data) algorithm is presented, and we compare it to the state-of-the-art GPU-implemented DE methods: BH-SNE-CUDA and AtSNE-CUDA. We show that memory and time requirements for IVHD-CUDA are radically lower than those for the baseline codes. For example, IVHD-CUDA is almost 30 times faster in embedding (without the procedure of kNN graph generation, which is the same for all the methods) of the largest ([Formula: see text]) YAHOO dataset than AtSNE-CUDA. We conclude that in the expense of minor deterioration of embedding quality, compared to the baseline algorithms, IVHD well preserves the main structural properties of ND data in 2D for radically lower computational budget. Thus, our method can be a good candidate for a truly big data ([Formula: see text]) interactive visualization.


title: Radial Basis Functions Based Algorithms for Non-Gaussian Delay Propagation in Very Large Circuits

text: In this paper, we discuss methods for determining delay distributions in modern Very Large Scale Integration design. The delays have a non-Gaussian nature, which is a challenging task to solve and is a stumbling block for many approaches. The problem of finding delays in VLSI circuits is equivalent to a graph optimisation problem. We propose algorithms that aim at fast and very accurate calculations of statistical delay distributions. The speed of execution is achieved by utilising previously obtained analytical results for delay propagation through one logic gate. The accuracy is achieved by preserving the shapes of non-Gaussian delay distribution while traversing the graph of a circuit. The discussion on the methodology to handle non-Gaussian delay distributions is the core of the present study. The proposed algorithms are tested and compared with delay distributions obtained through Monte Carlo simulations, which is the standard verification procedure for this class of problems.


title: Picking Peaches or Squeezing Lemons: Selecting Crowdsourcing Workers for Reducing Cost of Redundancy

text: Crowdsourcing (CS) platforms are constantly gaining attention from both researchers and companies, due to the offered possibility of utilizing the “wisdom of crowds” in order to solve a great variety of problems. Despite the obvious advantages of such mechanisms, there are also numerous concerns regarding the quality assurance of work results produced by a large group of anonymous workers. In this work, we use data gathered from a real CS platform in order to study the performance of various approaches to worker selection, including a novel approach that utilizes automatic real-time monitoring of the produced results. We compare the performance of these mechanisms with respect to both overall cost and the accuracy of the final results to benchmark algorithms that aggregate results from a group of workers without pre-selection, relying solely on the “wisdom of crowd”. We find that our novel approach is capable of reducing the cost of obtaining high-quality aggregated results by a factor of four, without sacrificing quality.


title: Hashing Based Prediction for Large-Scale Kernel Machine

text: Kernel Machines, such as Kernel Ridge Regression, provide an effective way to construct non-linear, nonparametric models by projecting data into high-dimensional space and play an important role in machine learning. However, when dealing with large-scale problems, high computational cost in the prediction stage limits their use in real-world applications. In this paper, we propose hashing based prediction, a fast kernel prediction algorithm leveraging hash technique. The algorithm samples a small subset from the input dataset through the locality-sensitive hashing method and computes prediction value approximately using the subset. Hashing based prediction has the minimum time complexity compared to the state-of-art kernel machine prediction approaches. We further present a theoretical analysis of the proposed algorithm showing that it can keep comparable accuracy. Experiment results on most commonly used large-scale datasets, even with million-level data points, show that the proposed algorithm outperforms the state-of-art kernel prediction methods in time cost while maintaining satisfactory accuracy.


title: Investigating the Benefit of FP16-Enabled Mixed-Precision Solvers for Symmetric Positive Definite Matrices Using GPUs

text: Half-precision computation refers to performing floating-point operations in a 16-bit format. While half-precision has been driven largely by machine learning applications, recent algorithmic advances in numerical linear algebra have discovered beneficial use cases for half precision in accelerating the solution of linear systems of equations at higher precisions. In this paper, we present a high-performance, mixed-precision linear solver ([Formula: see text]) for symmetric positive definite systems in double-precision using graphics processing units (GPUs). The solver is based on a mixed-precision Cholesky factorization that utilizes the high-performance tensor core units in CUDA-enabled GPUs. Since the Cholesky factors are affected by the low precision, an iterative refinement (IR) solver is required to recover the solution back to double-precision accuracy. Two different types of IR solvers are discussed on a wide range of test matrices. A preprocessing step is also developed, which scales and shifts the matrix, if necessary, in order to preserve its positive-definiteness in lower precisions. Our experiments on the V100 GPU show that performance speedups are up to 4.7[Formula: see text] against a direct double-precision solver. However, matrix properties such as the condition number and the eigenvalue distribution can affect the convergence rate, which would consequently affect the overall performance.


title: A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations

text: Differential privacy algorithm is an effective technology to protect data privacy, and there are many pieces of research about differential privacy and some practical applications from the Internet companies, such as Apple and Google, etc. By differential privacy technology, the data organizations can allow external data scientists to explore their sensitive datasets, and the data owners can be ensured provable privacy guarantees meanwhile. It is inevitable that the query results that will cause the error, as a consequence that the differential privacy algorithm would disturb the data, and some differential privacy algorithms are aimed to reduce the introduced noise. However, those algorithms just adopt to the simple or relative uniform data, when the data distribution is complex, some algorithms will lose efficiency. In this paper, we propose a new simple [Formula: see text]-differential privacy algorithm. Our approach includes two key points: Firstly, we used Laplace-based noise to disturb answer to reduce the error of the linear computation queries under intensive data items by workload-aware noise; Secondly, we propose an optimized workload division method. We divide the queries recursively to reduce the added noise, which can reduce computation error when there exists query hot spot in the workload. We conduct extensive evaluation over six real-world datasets to examine the performance of our approach. The experimental results show that our approach can reduce nearly 40% computation error for linear computation when compared with MWEM, DAWA, and Identity. Meanwhile, our approach can achieve better response time to answer the query cases compared with the start-of-the-art algorithms.


title: ITP-KNN: Encrypted Video Flow Identification Based on the Intermittent Traffic Pattern of Video and K-Nearest Neighbors Classification

text: As video dominates internet traffic, researchers tend to pay attention to video-related fields, such as video shaping, differentiated service, multimedia protocol tunneling detection. Some video-related fields, e.g., traffic measurement and the metrics for Quality of Experience, are based on video flow identification. However, video flow identification faces challenges. Firstly, the increasing adoption of Transport Layer Security makes payload-based methods no longer applicable. Secondly, traffic features differ when generated by different streaming protocols. This paper proposes a video flow identification method, called ITP-KNN, which utilizes the intermittent traffic pattern-related features (ITP) and the K-nearest neighbors (KNN) algorithm. The intermittent traffic pattern is caused by fragmented transmission, which is common among video streamings generated by different streaming protocols. Therefore, the intermittent traffic pattern is useful for overcoming the above challenges and then differentiating video traffic from not-video traffic. We develop a set of features to describe the intermittent traffic pattern. Preliminary results show the promise of ITP-KNN, yielding high identification recall and precision over a range of video content and encoding qualities.


title: A Stable Discontinuous Galerkin Based Isogeometric Residual Minimization for the Stokes Problem

text: We investigate a residual minimization (RM) based stabilized isogeometric finite element method (IGA) for the Stokes problem. Starting from an inf-sup stable discontinuous Galerkin (DG) formulation, the method seeks for an approximation in a highly continuous trial space that minimizes the residual measured in a dual norm of the discontinuous test space. We consider two-dimensional Stokes problems with manufactured solutions and the cavity flow problem. We explore the results obtained by considering highly continuous isogeometric trial spaces, and discontinuous test spaces. We compare by the Pareto front the resulting numerical accuracy and the computational cost, expressed by the number of floating-point operations performed by the direct solver algorithm.


title: An Adaptive Computational Network Model for Strange Loops in Political Evolution in Society

text: In this paper a multi-order adaptive temporal-causal network model is introduced to model political evolution. The computational network model makes use of Hofstadter’s notion of a Strange Loop and was tested and validated successfully to reflect political oscillations seen in presidential elections in the USA over time.


title: Uncertainty Quantification in Fractional Stochastic Integro-Differential Equations Using Legendre Wavelet Collocation Method

text: The paper aims to present an efficient numerical scheme to quantify the uncertainty in the solution of stochastic fractional integro-differential equations. The numerical scheme presented here is based on Legendre wavelets combined with block pulse functions using their deterministic and stochastic operational matrix of integration. The operational matrices are utilized to convert the stochastic fractional integro-differential equation to a linear system of algebraic equation. Finally, the accuracy and efficiency of the proposed scheme are investigated through numerical experiments.


title: Modelling Contextual Data for Smart Environments. Case Study of a System to Support Mountain Rescuers

text: Context-aware pervasive systems are complex, due to the need to gather detailed environmental information and to perform a variety of context reasoning processes in order to adapt behaviours accordingly. These operations are merged seamlessly. We show the feasibility and vitality of a fully designed system for mountain rescue operations, with various aspects of the contextual processing in middleware, as well as analyse its context life cycle. The system is verified through intensive experiments with a rich set of categorised context data. The contextual processing is shown in different weather scenarios. The service is geared towards software development, converging IoT (Internet of Things) and cloud computing with specific reference to smart application scenarios.


title: Modeling of Anti-tracking Network Based on Convex-Polytope Topology

text: Anti-tracking network plays an important role in protection of network users’ identities and communication privacy. Confronted with the frequent network attacks or infiltration to anti-tracking network, a robust and destroy-resistant network topology is an important prerequisite to maintain the stability and security of anti-tracking network. From the aspects of network stability, network resilience and destroy-resistance, we propose the convex-polytope topology (CPT) applied in the anti-tracking network. CPT has three main advantages: (1) CPT can easily avoid the threat of key nodes and cut vertices to network structure; (2) Even the nodes could randomly join in or quit the network, CPT can easily keep the network topology in stable structure without the global view of network; (3) CPT can easily achieve the self-optimization of network topology. Anti-tracking network based on CPT can achieve the self-maintenance and self-optimization of its network topology. We compare CPT with other methods of topology optimization. From the experimental results, CPT has better robustness, resilience and destroy-resistance confronted with dynamically changed topology, and performs better in the efficiency of network self-optimization.


title: On the Automated Assessment of Open-Source Cyber Threat Intelligence Sources

text: Global malware campaigns and large-scale data breaches show how everyday life can be impacted when the defensive measures fail to protect computer systems from cyber threats. Understanding the threat landscape and the adversaries’ attack tactics to perform it represent key factors for enabling an efficient defense against threats over the time. Of particular importance is the acquisition of timely and accurate information from threats intelligence sources available on the web which can provide additional intelligence on emerging threats even before they can be observed as actual attacks. In this paper, an approach to automate the assessment of cyber threat intelligence sources and predict a relevance score for each source is proposed. Specifically, a model based on meta-data and word embedding is defined and experimented by training regression models to predict the relevance score of sources on Twitter. The results evaluation show that the assigned score allows to reduce the waiting time for intelligence verification, on the basis of its relevance, thus improving the time advantage of early threat detection.


title: DeepAD: A Joint Embedding Approach for Anomaly Detection on Attributed Networks

text: Detecting anomalies in the attributed network is a vital task that is widely used, ranging from social media, finance to cybersecurity. Recently, network embedding has proven an important approach to learn low-dimensional representations of vertexes in networks. Most of the existing approaches only focus on topological information without embedding rich nodal information due to the lack of an effective mechanism to capture the interaction between two different information modalities. To solve this problem, in this paper, we propose a novel deep attributed network embedding framework named DeepAD to differentiate anomalies whose behaviors obviously deviate from the majority. DeepAD (i) simultaneously capture both of the highly non-linear topological structure and node attributes information based on the graph convolutional network (GCN) and (ii) preserve various interaction proximities between two different information modalities to make them complement each other towards a unified representation for anomaly detection. Extensive experiments on real-world attributed networks demonstrate the effectiveness of our proposed anomaly detection approach.


title: Deep Analytics for Management and Cybersecurity of the National Energy Grid

text: The United States’s energy grid could fall into victim to numerous cyber attacks resulting in unprecedented damage to national security. The smart concept devices including electric automobiles, smart homes and cities, and the Internet of Things (IoT) promise further integration but as the hardware, software, and network infrastructure becomes more integrated they also become more susceptible to cyber attacks or exploitation. The Defense Information Systems Agency (DISA)’s Big Data Platform (BDP), deep analytics, and unsupervised machine learning (ML) have the potential to address resource management, cybersecurity, and energy network situation awareness. In this paper, we demonstrate their potential using the Pecan Street data. We also show an unsupervised ML such as lexical link analysis (LLA) as a causal learning tool to discover the causes for anomalous behavior related to energy use and cybersecurity.


title: Malicious Domain Detection Based on K-means and SMOTE

text: The Domain Name System (DNS) as the foundation of Internet, has been widely used by cybercriminals. A lot of malicious domain detection methods have received significant success in the past decades. However, existing detection methods usually use classification-based and association-based representations, which are not capable of dealing with the imbalanced problem between malicious and benign domains. To solve the problem, we propose a novel domain detection system named KSDom. KSDom designs a data collector to collect a large number of DNS traffic data and rich external DNS-related data, then employs K-means and SMOTE method to handle the imbalanced data. Finally, KSDom uses Categorical Boosting (CatBoost) algorithm to identify malicious domains. Comprehensive experimental results clearly show the effectiveness of our KSDom system and prove its good robustness in imbalanced datasets with different ratios. KSDom still has high accuracy even in extremely imbalanced DNS traffic.


title: Innovativeness Analysis of Scholarly Publications by Age Prediction Using Ordinal Regression

text: In this paper we refine our method of measuring the innovativeness of scientific papers. Given a diachronic corpus of papers from a particular field of study, published over a period of a number of years, we extract latent topics and train an ordinal regression model to predict publication years based on topic distributions. Using the prediction error we calculate a real-number based innovation score, which may be used to complement citation analysis in identifying potential breakthrough publications. The innovation score we had proposed previously could not be compared for papers published in different years. The main contribution we make in this work is adjusting the innovation score to account for the publication year, making the scores of papers published in different years directly comparable. We have also improved the prediction accuracy by replacing multiclass classification with ordinal regression and Latent Dirichlet Allocation models with Correlated Topic Models. This also allows for better understanding of the evolution of research topics. We demonstrate our method on two corpora: 3,577 papers published at the International World Wide Web Conference (WWW) between the years 1994 and 2019, and 835 articles published in the Journal of Artificial Societies and Social Simulation (JASSS) from 1998 to 2019.


title: Joint Entity Linking for Web Tables with Hybrid Semantic Matching

text: Hundreds of millions of tables on the World-Wide Web contain a considerable wealth of high-quality relational data, which has already been viewed as an important kind of sources for knowledge extraction. In order to extract the semantics of web tables to produce machine-readable knowledge, one of the critical steps is table entity linking, which maps the mentions in table cells to their referent entities in knowledge bases. In this paper, we propose a novel model JHSTabEL, which converts table entity linking into a sequence decision problem and uses hybrid semantic features to disambiguate the mentions in web tables. This model captures local semantics of the mentions and entities from different semantic aspects, and then makes full use of the information of previously referred entities for the subsequent entity disambiguation. The decisions are made from a global perspective to jointly disambiguate the mentions in the same column. Experimental results show that our proposed model significantly outperforms the state-of-the-art methods.


title: Adaptive and Efficient Transfer for Online Remote Visualization of Critical Weather Applications

text: Critical weather applications such as cyclone tracking require online visualization simultaneously performed with the simulations so that the scientists can provide real-time guidance to decision makers. However, resource constraints such as slow networks can hinder online remote visualization. In this work, we have developed an adaptive framework for efficient online remote visualization of critical weather applications. We present three algorithms, namely, most-recent, auto-clustering and adaptive, for reducing lag between the simulation and visualization times. Using experiments with different network configurations, we find that the adaptive algorithm strikes a good balance in providing reduced lags and visualizing most representative frames, with up to 72% smaller lag than auto-clustering, and 37% more representative than most-recent for slow networks.


title: Developing a Decision Support App for Computational Agriculture

text: In the age of climate change, increasing populations and more limited resources, efficient agricultural production is being sought by farmers across the world. In the case of smallholder farms with limited capacity to cope with years of low production, this is even more important. To help to achieve this aim, data analytics and decision support systems are being used to an ever greater extent. For rice/shrimp farmers in the Mekong Delta, Vietnam, trying to tune the conditions so that both crops can be successfully grown simultaneously is an ongoing challenge. In this paper, the design and development of a smartphone app, from a well researched Bayesian Belief Network, is described. This now gives farmers the ability to make better informed planting and harvesting decisions. The app has been initially well received by water management practitioners and farmers alike.


title: NURBS Curves in Parametric Integral Equations System for Modeling and Solving Boundary Value Problems in Elasticity

text: This paper presents a way to improve the boundary shape modeling process in solving boundary value problems in elasticity. The inclusion of NURBS curves into the mathematical formalism of the parametric integral equations system method (PIES) is proposed. The advantages of such an application are widely discussed. Recently, the Bezier curves, mainly the cubic curves (of third-degree), were used. The segments of the boundary shape were modeled by such curves (with ensuring continuity at the connection points). Using NURBS curves, the boundary shape can be modeled with only one curve. So, continuity is automatically ensured. Additionally, the second degree NURBS curve is enough to obtain the shape with high accuracy (better than cubic Bezier curves). The NURBS curve is defined by points, their weights and knots vector. Such parameters significantly improve the shape modification process, which can directly improve e.g. the shape identification process. To examine the impact of modeling accuracy on the final PIES solutions, examples described by the Navier-Lamé equations were used. To improve calculations, the PIES method using NURBS curves was implemented as a computer program. Then, it was decided to verify the accuracy of the obtained solutions. For comparison, the solutions were also obtained using analytical solutions, boundary element method, and PIES method (with the Bezier curves). An improvement in the boundary shape modeling was noticed. It significantly affects the accuracy of solutions. As a result, the consumption of computer resources was reduced, while the process of boundary shape modeling and the accuracy of the obtained results were improved.


title: Data-Driven Partial Differential Equations Discovery Approach for the Noised Multi-dimensional Data

text: Data-driven methods provide model creation tools for systems, where the application of conventional analytical methods is restrained. The proposed method involves the data-driven derivation of a partial differential equation (PDE) for process dynamics, which can be helpful both for process simulation and studying. The paper describes the progress made within the PDE discovery framework. The framework involves a combination of evolutionary algorithms and sparse regression. Such an approach gives more versatility in comparison with other commonly used methods of data-driven partial differential derivation by making fewer restrictions on the resulting equation. This paper highlights the algorithm features which allow the processing of data with noise, which is more similar to the real-world applications of the algorithm.


title: Ant Colony Optimization Implementation for Reversible Synthesis in Walsh-Hadamard Domain

text: Reversible circuits are one of the technologies that can provide future low energy circuits. The synthesis of an optimal reversible circuit for a given function is an np-hard problem. The meta-heuristic approaches are one of the most promising methods for these types of optimization problems. In this paper, a new approach for ACO reversible synthesis is presented. Usually, authors build an ACO system with the use of truth table or permutation representation of the reversible function. In this work, a Walsh spectral representation of a Boolean function is used. This allows dividing search spaces into smaller “promising” areas with well-defined transition operations between them. As a result, we can minimize the enormous search space and generate better solutions than obtained by ACO synthesis with classical reversible function representation. The proposed approach was applied to benchmark reversible functions of 4,5 and 6 variables and compared to other meta-heuristic results and best-known solutions.


title: Remarks on Kaczmarz Algorithm for Solving Consistent and Inconsistent System of Linear Equations

text: In this paper we consider the classical Kaczmarz algorithm for solving system of linear equations. Based on the geometric relationship between the error vector and rows of the coefficient matrix, we derive the optimal strategy of selecting rows at each step of the algorithm for solving consistent system of linear equations. For solving perturbed system of linear equations, a new upper bound in the convergence rate of the randomized Kaczmarz algorithm is obtained.


title: Numerical Computation for a Flow Caused by a High-Speed Traveling Train and a Stationary Overpass

text: In the United Kingdom, old infrastructure facilities along train tracks like overpasses should be repaired as soon as possible. In order to design them more optimally, we need to know the flow field around such facilities. In this study, a traveling train and a stationary overpass were reproduced in a computational domain, and the flow field around the train and the overpass was simulated with the MCD method and the Expanded Sliding Mesh approach. In the conventional Sliding Mesh approach, sliding planes must be completely adjacent to other Sliding planes, that is, Sliding planes cannot be adjacent to both other Sliding planes and outer boundaries simultaneously. Then, the Expanded Sliding Mesh approach was proposed. The results given by using the MCD method and the Expanded Sliding Mesh approach were compared with the experimental results given by Baker, and their good qualitative and quantitative agreements were shown in almost cases. Only in the case that the overpass is very close to the train, the effects of boundary layers on laminar flow and turbulence need to be considered for more precise computation.


title: A Current Task-Based Programming Paradigms Analysis

text: Task-based paradigm models can be an alternative to MPI. The user defines atomic tasks with a defined input and output with the dependencies between them. Then, the runtime can schedule the tasks and data migrations efficiently over all the available cores while reducing the waiting time between tasks. This paper focus on comparing several task-based programming models between themselves using the LU factorization as benchmark. HPX, PaRSEC, Legion and YML+XMP are task-based programming models which schedule data movement and computational tasks on distributed resources allocated to the application. YML+XMP supports parallel and distributed tasks with XscalableMP, a PGAS language. We compared their performances and scalability are compared to ScaLAPACK, an highly optimized library which uses MPI to perform communications between the processes on up to 64 nodes. We performed a block-based LU factorization with the task-based programming model on up to a matrix of size [Formula: see text]. HPX is performing better than PaRSEC, Legion and YML+XMP but not better than ScaLAPACK. YML+XMP has a better scalability than HPX, Legion and PaRSEC. Regent has trouble scaling from 32 nodes to 64 nodes with our algorithm.


title: Microservice Disaster Crash Recovery: A Weak Global Referential Integrity Management

text: Microservices which use polyglot persistence (using multiple data storage techniques) cannot be recovered in a consistent state from backups taken independently. As a consequence, references across microservice boundaries may break after disaster recovery. In this paper, we give a weak global consistency definition for microservice architectures and present a recovery protocol which takes advantage of cached referenced data to reduce the amnesia interval for the recovered microservice, i.e., the time interval after the most recent backup, during which state changes may have been lost.


title: A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression

text: Inverse Distance Weighting (IDW) is a widely adopted interpolation algorithm. This work presents a novel formulation for IDW which is derived from a weighted linear regression. The novel method is evaluated over study cases related to elevation data, climate and also on synthetic data. Relevant aspects of IDW are preserved while the novel algorithm achieves better results with statistical significance. Artifacts are alleviated in interpolated surfaces generated by the novel approach when compared to the respective surfaces from IDW.


title: Computation of the Airborne Contaminant Transport in Urban Area by the Artificial Neural Network

text: Providing the real-time working system able to localize the dangerous contaminant source is one of the main challenges of the cities emergency response groups. Unfortunately, all proposed up to now frameworks capable of estimating the contamination source localization based on recorded by the sensors network the substance concentrations are not able to work in real-time. The reason is the significant computational time required by the applied dispersion models. In such reconstruction systems, the parameters of the given dispersion model are sampled to fit the model output to the registrations; thus, the dispersion model is run tens of thousands of times. In this paper, we test the possibility of training an artificial neural network (ANN) to effectively simulate the atmospheric toxin transport in the highly urbanized area. The use of a fast neural network in place of computationally costly dispersion models in systems localizing the source of contamination can enable its fast response time. As a training domain, we have chosen the center of London, as it was used in the DAPPLE field experiment. The training dataset is generated by the Quick Urban & Industrial Complex (QUIC) Dispersion Modeling System. To achieve the ANN capable of estimating the contaminant concentration, we tested various ANN structures, i.e., numbers of ANN layers, neurons, and activation functions. The performed tests confirm that trained ANN has the potential to replace the dispersion model in the contaminant source localization systems.


title: Parameterizations and Lagrange Cubics for Fitting Multidimensional Data

text: This paper discusses the issue of interpolating data points in arbitrary Euclidean space with the aid of Lagrange cubics [Formula: see text] and exponential parameterization. The latter is commonly used to either fit the so-called reduced data [Formula: see text] for which the associated exact interpolation knots remain unknown or to model the trajectory of the curve [Formula: see text] passing through [Formula: see text]. The exponential parameterization governed by a single parameter [Formula: see text] replaces such discrete set of unavailable knots [Formula: see text] ([Formula: see text] - an internal clock) with some new values [Formula: see text] ([Formula: see text] - an external clock). In order to compare [Formula: see text] with [Formula: see text] the selection of some [Formula: see text] should be predetermined. For some applications and theoretical considerations the function [Formula: see text] needs to form an injective mapping (e.g. in length estimation of [Formula: see text] with any [Formula: see text] fitting [Formula: see text]). We formulate and prove two sufficient conditions yielding [Formula: see text] as injective for given [Formula: see text] and analyze their asymptotic character which forms an important question for [Formula: see text] getting sufficiently dense. The algebraic conditions established herein are also geometrically visualized in 3D plots with the aid of Mathematica. This work is supplemented with illustrative examples including numerical testing of the underpinning convergence rate in length estimation [Formula: see text] by [Formula: see text] (once [Formula: see text]). The reparameterization has potential ramifications in computer graphics and robot navigation for trajectory planning e.g. to construct a new curve [Formula: see text] controlled by the appropriate choice of interpolation knots and of mapping [Formula: see text] (and/or possibly [Formula: see text]).


title: G-protein-coupled Receptors in Fungi

text: G-protein-coupled receptors (GPCRs) are the largest family of transmembrane receptors in fungi. These receptors have an important role in the transduction of extracellular signals into intracellular sites in response to diverse stimuli. They enable fungi to coordinate cell function and metabolism, thereby promoting their survival and propagation, and sense certain fundamentally conserved elements, such as nutrients, pheromones, and stress, for adaptation to their niches, environmental stresses, and host environment, causing disease and pathogen virulence. This chapter highlights the role of GPCRs in fungi in coordinating cell function and metabolism. Fungal cells sense the molecular interactions between extracellular signals. Their respective sensory systems are described here in detail.


title: Exploring Musical Structure Using Tonnetz Lattice Geometry and LSTMs

text: We study the use of Long Short-Term Memory neural networks to the modeling and prediction of music. Approaches to applying machine learning in modeling and prediction of music often apply little, if any, music theory as part of their algorithms. In contrast, we propose an approach which employs minimal music theory to embed the relationships between notes and chord structure explicitly. We extend the Tonnetz lattice, originally developed by Euler to introduce a metric between notes, in order to induce a metric between chords. Multidimensional scaling is employed to embed chords in twenty dimensions while best preserving this music-theoretic metric. We then demonstrate the utility of this embedding in the prediction of the next chord in a musical piece, having observed a short sequence of previous chords. Applying a standard training, test, and validation methodology to a dataset of Bach chorales, we achieve an accuracy rate of 50.4% on validation data, compared to an expected rate of 0.2% when guessing the chord randomly. This suggests that using Euler’s Tonnetz for embedding provides a framework in which machine learning tools can excel in classification and prediction tasks with musical data.


title: Personality Recognition from Source Code Based on Lexical, Syntactic and Semantic Features

text: Automatic personality recognition from source code is a scarcely explored problem. We propose personality recognition with handcrafted features, based on lexical, syntactic and semantic properties of source code. Out of 35 proposed features, 22 features are completely novel. We also show that n-gram features are simple but surprisingly good predictors of personality and present results arising from joint usage of both handcrafted and baseline features. Additionally we compare our results with scores obtained within the Personality Recognition in SOurce COde track during Forum for Information Retrieval Evaluation 2016 and set up state-of-the-art results for conscientiousness and neuroticism traits.


title: Numerical Modeling of the Two-Phase Flow of Water with Ice in the Tom River

text: A new mathematical model and a numerical method were constructed for numerical investigation of a two-phase turbulent flow in an open channel. Solid particles with a density close to that of water were considered a continuous phase with effective properties. This new model is based on a continuum-mechanics approach, a hydrostatic assumption, and equations averaged by the flow depth. Turbulent closure of the equations was done with a two-parameter k − ε turbulence model modified by Pourahmadi and Humphrey to account for the influence of the particles on the turbulent structure of the flow. The new numerical method is based on partial elimination algorithm for computing areas of the two-phase flow free of ice particles and uses semi-implicit approximation in time. The influence of the dynamic parameters of the dispersed phase on the structure of the flow was also investigated by computing several scenarios of the flow in an open channel with a 90-degree bend. Applications of the approach to the modeling of riverside flooding due to sudden increase in the river depth after a release of an ice jam illustrate the capabilities of the model.


title: Advantage of Using Spherical over Cartesian Coordinates in the Chromosome Territories 3D Modeling

text: This paper shows results of chromosome territory modeling in two cases: when the implementation of the algorithm was based on Cartesian coordinates and when implementation was made with Spherical coordinates. In the article, the summary of measurements of computational times of simulation of chromatin decondensation process (which led to constitute the chromosome territory) was presented. Initially, when implementation was made with the use of Cartesian Coordinates, simulation takes a lot of time to create a model (mean 746.7[sec] with the median 569.1[sec]) and additionally requires restarts of the algorithm, also often exceeds acceptable (given a priori) time for the computational experiment. Because of that, authors attempted changing the coordinate system to Spherical Coordinates (in a few previous projects it leads to improving the efficiency of implementation). After changing the way that 3D point is represented in 3D space the time required to make a successful model reduced to the mean 25.3[sec] with a median 18.5[s] (alongside with lowering the number of necessary algorithm restarts) which gives a significant difference in the efficiency of model’s creation. Therefore we showed, that a more efficient way for implementation was the usage of spherical coordinates.