title: Look Deep into the New Deep Network: A Measurement Study on the ZeroNet

text: ZeroNet is a new decentralized web-like network of peer-to-peer users created in 2015. ZeroNet, called an open, free and robust network, has attracted a rising number of users. Most of P2P networks are applied into file-sharing systems or data-storage system and the characteristics of these networks are widely investigated. However, there are obvious differences between ZeroNet and conventional P2P networks. Existing researches rarely involve ZeroNet and the characteristics and robustness of ZeroNet are unknown. To tackle the aforementioned problem, the present study measures the ZeroNet peer resources and site resources separately, and at the same time, proposes collection methods for both. No like other simulation experiments, the experiments of this paper are set on real-world environment. This is also the first measurement study about ZeroNet. Experimental results show that the topology of the peer network in ZeroNet has scarce edges, short distances and low clustering coefficients, and its degree distribution exhibits a special distribution. These indicate that the peer network of ZeroNet has poor robustness and the experimental results of the ZeroNet resilience verify this issue. In addition, this paper represents an improved peer exchange method to enhance the robustness of the ZeroNet. We also measure the topology characteristics, languages, sizes and versions of the sites in ZeroNet. We find that the size of the sites and the client version are also the reasons for the low robustness of the ZeroNet.


title: Reproducibility of Computational Experiments on Kubernetes-Managed Container Clouds with HyperFlow

text: We propose a comprehensive solution for reproducibility of scientific workflows. We focus particularly on Kubernetes-managed container clouds, increasingly important in scientific computing. Our solution addresses conservation of the scientific procedure, scientific data, execution environment and experiment deployment, while using standard tools in order to avoid maintainability issues that can obstruct reproducibility. We introduce an Experiment Digital Object (EDO), a record published in an open science repository that contains artifacts required to reproduce an experiment. We demonstrate a variety of reproducibility scenarios including experiment repetition (same experiment and conditions), replication (same experiment, different conditions), and propose a smart reuse scenario in which a previous experiment is partially replayed and partially re-executed. The approach is implemented in the HyperFlow workflow management system and experimentally evaluated using a genomic scientific workflow. The experiment is published as an EDO record on the Zenodo platform.


title: Autonomous Vehicles as Local Traffic Optimizers

text: This paper explores the interaction between autonomous and human-driven cars on a microscopic level using an agent-based traffic simulator. More specifically, it deals with the design of driving logic models of “socially-aware” autonomous vehicles that can improve the performance of surrounding vehicles on the road. Congestion waves, which are created as a result of an abrupt stopping or a car joining a highway, are a known phenomenon in current traffic systems. Experiments performed, demonstrate how the presence of intelligent social vehicles on the road can reduce such effects by acting as a flexible medium between human-driven cars. Metrics to evaluate benefits ot our AV behaviour models under various states of traffic conditions/congestion are also proposed. Finally, results showing the effectiveness of these models are presented.


title: Learning Mixed Traffic Signatures in Shared Networks

text: On shared, wide-area networks (WAN), it can be difficult to characterise the current traffic. There can be different protocols in use, by multiple data streams, producing a mix of different traffic signatures. Furthermore, bottlenecks and protocols can change dynamically. Yet, if it were possible to determine the protocols (e.g., congestion control algorithms (CCAs)) or the applications in use by the background traffic, appropriate optimisations for the foreground traffic might be taken by operating systems, users, or administrators. We extend previous work in predicting network protocols via signatures based on a time-series of round-trip times (RTT). Gathering RTTs is minimally intrusive and does not require administrative privilege. Although there have been successes in using machine learning (ML) to classify protocols, the use cases have been relatively simple or have focused on the foreground traffic. We show that both k-nearest-neighbour (K-NN) with dynamic time warp (DTW), and multi-layer perceptrons (MLP), can classify (with useful accuracy) background traffic signatures with a range of bottleneck bandwidths.


title: Modeling Helping Behavior in Emergency Evacuations Using Volunteer’s Dilemma Game

text: People often help others who are in trouble, especially in emergency evacuation situations. For instance, during the 2005 London bombings, it was reported that evacuees helped injured persons to escape the place of danger. In terms of game theory, it can be understood that such helping behavior provides a collective good while it is a costly behavior because the volunteers spend extra time to assist the injured persons in case of emergency evacuations. In order to study the collective effects of helping behavior in emergency evacuations, we have performed numerical simulations of helping behavior among evacuees in a room evacuation scenario. Our simulation model is based on the volunteer’s dilemma game reflecting volunteering cost. The game theoretic model is coupled with a social force model to understand the relationship between the spatial and social dynamics of evacuation scenarios. By systematically changing the cost parameter of helping behavior, we observed different patterns of collective helping behaviors and these collective patterns are summarized with a phase diagram.


docid: lwxmjbjm

title: GPU-Accelerated RDP Algorithm for Data Segmentation

text: The Ramer-Douglas-Peucker (RDP) algorithm applies a recursive split-and-merge strategy, which can generate fast, compact and precise data compression for time-critical systems. The use of GPU parallelism accelerates the execution of RDP, but the recursive behavior and the dynamic size of the generated sub-tasks, requires adapting the algorithm to use the GPU resources efficiently. While previous research approaches propose the exploitation of task-based parallelism, our research advocates a general fine-grained solution, which avoids the dynamic and recursive execution of kernels. The segmentation of depth images, a typical application used on autonomous driving, reaches speeds of almost 1000 frames per second for typical workloads using our massively parallel proposal on low-consumption, embedded GPUs. The GPU-accelerated solution is at least an order of magnitude faster than the execution of the same program on multiple CPU cores with similar energy consumption.


title: Utilizing GPU Performance Counters to Characterize GPU Kernels via Machine Learning

text: GPU computing kernels are relatively simple to write if achieving the best performance is not of the highest priority. However, it can quickly become a much more daunting task when users try to tune and optimize their kernels to obtain the highest performance. This is due to GPUs’ massive degree of parallelism, complex memory hierarchy, fine grain synchronization, and long memory access latency. Hence, users must carry out the complex tasks of profiling, analyzing, and tuning to reduce performance bottlenecks. Today’s GPUs can generate hundreds of performance events that comprehensively quantify the behavior of a kernel. Instead of relying on experts’ manual analysis, this paper targets using machine learning methods to generalize GPU performance counter data to determine the characteristics of a GPU kernel as they will reveal possible reasons for low performance. We choose a set of problem-independent counters as our inputs to design and compare three machine learning methods to automatically classify the execution behavior of a kernel. The experimental results on stencil computing kernels and sparse matrix multiplications show the machine learning models’ good accuracy, and demonstrate a feasible approach that is capable of classifying a kernel’s characterizations and suggesting changes to a skilled user, who can subsequently improve kernel performance with less guessing.


title: QEScalor: Quantitative Elastic Scaling Framework in Distributed Streaming Processing

text: Recently, researchers usually use the elastic scaling techniques as a powerful means of the distributed stream processing systems to deal with the high-speed data stream which arrives continuously and fluctuates constantly. The existing methods allocate the same amount of resources to the instances of the same operator, but they ignore the correlation between the operator performance and resource provision. It may lead to the waste of the resources caused by the over-provision or the huge overhead of the scheduling caused by the under-provision. To solve the above problems, we present a quantitative elastic scaling framework, named QEScalor, to allocate resources for the operator instances quantitatively based on the actual performance requirements. The experimental results show that compared with the existing works, the QEScalor can not only achieve resource-efficient elastic scaling with lower cost, but also it can enhance the total performance of the DSPAs.


title: Modelling the Effects of Antibiotics on Gut Flora Using a Nonlinear Compartment Model with Uncertain Parameters

text: We present a mathematical model of a microbial community in the human colon, including transport, exchange and metabolic processes. The colon is represented as a bioreactor with separate lumen and mucus microhabitats. The microbial community in the colon is grouped into four biomass functional groups based on their metabolic activity. While computational models present a challenge in the selection of model parameters, they also provide a more systematic approach to addressing uncertainties when compared to in vivo and in vitro experiments. We conduct an exploratory study on the uncertainty of our input parameters and a simulation study of the perturbation and recovery of gut microbiota from antibiotic exposure. We consider our parameters as random variables drawn from a uniform random distribution to reflect the diversity of gut microbial composition and variability between individuals.


title: Detecting Critical Transitions in the Human Innate Immune System Post-cardiac Surgery

text: Coronary artery bypass grafting with cardiopulmonary bypass activates the human innate immune system (HIIS) and invokes a vigorous inflammatory response that is systemic. This massive inflammatory reaction can contribute to the development of postoperative complications that could topple the state of the system from health to disease, or even to some extent, death. The body, after all, is in a state where majority of its immune cell populations have been depleted, and sometimes needs days or even longer to recuperate. To obtain a deeper understanding on how HIIS responds to complications after cardiac surgery, we perturb the immune system model that we have developed in an earlier work in-silico by adding another source of inflammation triggering moieties (ITMs) hours after surgery in various regimes. A critical transition occurs upon the addition of a critical concentration of ITMs when the insult is sustained for approximately 3 h – a total concentration that corresponds to the fatal concentration of ITMs documented in literature. By perturbing HIIS in-silico with additional sources of ITMs to mimic persistent and recurring episodes of post-surgery complications, we are able to specify under which conditions critical transitions occur in HIIS, as well as pinpoint important blood parameters that exhibit critical transitions in our model. More importantly, by applying early warning signals on the clinical trial data used to calibrate and validate HIIS model, we are able to detect blood parameters that exhibit critical transitions in patients who died post-surgery, where pro-inflammatory cytokines are deemed potential markers for critical transitions.


title: heFFTe: Highly Efficient FFT for Exascale

text: Exascale computing aspires to meet the increasing demands from large scientific applications. Software targeting exascale is typically designed for heterogeneous architectures; henceforth, it is not only important to develop well-designed software, but also make it aware of the hardware architecture and efficiently exploit its power. Currently, several and diverse applications, such as those part of the Exascale Computing Project (ECP) in the United States, rely on efficient computation of the Fast Fourier Transform (FFT). In this context, we present the design and implementation of heFFTe (Highly Efficient FFT for Exascale) library, which targets the upcoming exascale supercomputers. We provide highly (linearly) scalable GPU kernels that achieve more than [Formula: see text] speedup with respect to local kernels from CPU state-of-the-art libraries, and over [Formula: see text] speedup for the whole FFT computation. A communication model for parallel FFTs is also provided to analyze the bottleneck for large-scale problems. We show experiments obtained on Summit supercomputer at Oak Ridge National Laboratory, using up to 24,576 IBM Power9 cores and 6,144 NVIDIA V-100 GPUs.


title: A Massively Parallel Algorithm for the Three-Dimensional Navier-Stokes-Boussinesq Simulations of the Atmospheric Phenomena

text: We present a massively parallel solver using the direction splitting technique and stabilized time-integration schemes for the solution of the three-dimensional non-stationary Navier-Stokes-Boussinesq equations. The model can be used for modeling atmospheric phenomena. The time integration scheme utilized enables for efficient direction splitting algorithm with finite difference solver. We show how to incorporate the terrain geometry into the simulation and how to perform the domain decomposition. The computational cost is linear [Formula: see text] over each sub-domain, and near to [Formula: see text] in parallel over 1024 processors, where N is the number of unknowns and c is the number of cores. This is even if we run the parallel simulator over complex terrain geometry. We analyze the parallel scalability experimentally up to 1024 processors over a PROMETHEUS Linux cluster with multi-core processors. The weak scalability of the code shows that increasing the number of sub-domains and processors from 4 to 1024, where each processor processes the subdomain of [Formula: see text] internal points ([Formula: see text] box), results in the increase of the total computational time from 120 s to 178 s for a single time step. Thus, we can perform a single time step with over 1,128,000,000 unknowns within 3 min. The number of unknowns results from the fact that we have three components of the velocity vector field, one component of the pressure, and one component of the temperature scalar field over 256,000,000 mesh points. The computation of the one time step takes 3 min on a Linux cluster. The direction splitting solver is not an iterative solver; it solves the system accurately since it is equivalent to Gaussian elimination. Our code is interfaced with the mesh generator reading the NASA database and providing the Earth terrain map. The goal of the project is to provide a reliable tool for parallel, fully three-dimensional computations of the atmospheric phenomena.


title: Sparse Matrix-Based HPC Tomography

text: Tomographic imaging has benefited from advances in X-ray sources, detectors and optics to enable novel observations in science, engineering and medicine. These advances have come with a dramatic increase of input data in the form of faster frame rates, larger fields of view or higher resolution, so high performance solutions are currently widely used for analysis. Tomographic instruments can vary significantly from one to another, including the hardware employed for reconstruction: from single CPU workstations to large scale hybrid CPU/GPU supercomputers. Flexibility on the software interfaces and reconstruction engines are also highly valued to allow for easy development and prototyping. This paper presents a novel software framework for tomographic analysis that tackles all aforementioned requirements. The proposed solution capitalizes on the increased performance of sparse matrix-vector multiplication and exploits multi-CPU and GPU reconstruction over MPI. The solution is implemented in Python and relies on CuPy for fast GPU operators and CUDA kernel integration, and on SciPy for CPU sparse matrix computation. As opposed to previous tomography solutions that are tailor-made for specific use cases or hardware, the proposed software is designed to provide flexible, portable and high-performance operators that can be used for continuous integration at different production environments, but also for prototyping new experimental settings or for algorithmic development. The experimental results demonstrate how our implementation can even outperform state-of-the-art software packages used at advanced X-ray sources worldwide.


title: Narrow Passage Problem Solution for Motion Planning

text: The paper introduces a new randomized sampling-based method of motion planning suitable for the problem of narrow passages. The proposed method was inspired by the method of exit points for cavities in protein models and is based on the Rapidly Exploring Random Tree (RRT). Unlike other methods, it can also provide locations of the exact positions of narrow passages. This information is extremely important as it helps to solve this part of space in more detail and even to decide whether a path through this bottleneck exists or not. For data with narrow passages, the proposed method finds more paths in a shorter time, for data without narrow passages, the proposed method is slower but still provides correct paths.


title: Reconstruction of Low Energy Neutrino Events with GPUs at IceCube

text: IceCube is a cubic kilometer neutrino observatory located at the South Pole that produces massive amounts of data by measuring individual Cherenkov photons from neutrino interaction events in the energy range from few GeV to several PeV. The actual reconstruction of neutrino events in the GeV range is computationally challenging due to the scarcity of data produced by single events. This can lead to run times of several weeks for the state-of-the-art reconstruction method – Pegleg – on CPUs for typical workloads of many ten-thousand events. We propose a GPU version of Pegleg that probes the likelihood space with several hypotheses in parallel while adapting the amount of parallel sampled hypotheses dynamically in order to reduce computation time significantly. Our results show an average speedup of 14 (with a maximum of over 200) for 5262 reconstructed neutrino events of different flavors on a Titan V GPU compared to the multithreaded CPU version, which enables quicker and broader analysis of IceCube events.


title: Scalable Workflow-Driven Hydrologic Analysis in HydroFrame

text: The HydroFrame project is a community platform designed to facilitate integrated hydrologic modeling across the US. As a part of HydroFrame, we seek to design innovative workflow solutions that create pathways to enable hydrologic analysis for three target user groups: the modeler, the analyzer, and the domain science educator. We present the initial progress on the HydroFrame community platform using an automated Kepler workflow. This workflow performs end-to-end hydrology simulations involving data ingestion, preprocessing, analysis, modeling, and visualization. We demonstrate how different modules of the workflow can be reused and repurposed for the three target user groups. The Kepler workflow ensures complete reproducibility through a built-in provenance framework that collects workflow specific parameters, software versions, and hardware system configuration. In addition, we aim to optimize the utilization of large-scale computational resources to adjust to the needs of all three user groups. Towards this goal, we present a design that leverages provenance data and machine learning techniques to predict performance and forecast failures using an automatic performance collection component of the pipeline.


title: Boost and Burst: Bubbles in the Bitcoin Market

text: This study investigates bubbles and crashes in the cryptocurrency market. In particular, using the log-periodic power law, we estimate the critical time of bubbles in the Bitcoin market. The results indicate that Bitcoin bubbles clearly exist, and our forecast of critical times can be verified with high accuracy. We further claim that bubbles could originate from the mining process, investor sentiment, global economic trend, and even regulation. For policy makers, the findings suggest the necessity of monitoring the signatures of bubbles and their progress in the market place.


title: Using Individual-Based Models to Look Beyond the Horizon: The Changing Effects of Household-Based Clustering of Susceptibility to Measles in the Next 20 Years

text: Recent measles outbreaks in regions with a high overall vaccination coverage have drawn attention to other factors - aside from the overall immunity level - determining the spread of measles in a population, such as heterogeneous social mixing behavior and vaccination behavior. As households are an important context for measles transmission, the clustering of susceptible individuals within households can have a decisive effect on the risk for measles outbreaks. However, as the population ages and household constitutions change over the next 20 years, that effect may change as well. To adequately plan for the control and eventual elimination of measles, we need to understand how the effect of within-household susceptibility clustering will evolve. Individual-based models enable us to represent the different levels of heterogeneity in a population that are necessary to understand the spread of a disease in a highly immunized population. In this paper, we use such an individual-based model to investigate how the effect of household-based susceptibility clustering is expected to change over the next two decades in Flanders, Belgium. We compare different scenarios regarding the level of within-household susceptibility clustering for three different calendar years between 2020 and 2040, using projections for the age distribution of the population, the constitution of households and age-specific immunity levels. We find that a higher level of susceptibility clustering within households increases the risk for measles outbreaks and their potential to spread through the population, in current as well as in future populations.


title: Computational Analysis of the Adaptive Causal Relationships Between Cannabis, Anxiety and Sleep

text: In this paper an adaptive computational temporal-causal network model is presented to analyse the dynamic and adaptive relationships between cannabis usage, anxiety and sleep. The model has been used to simulate different well-known scenarios varying from intermittent usage to longer periods of usage interrupted by attempts to quit and to constant usage based on full addiction. It is described how the model has been verified and validated by empirical information from the literature.


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.


docid: nmhqqcpf

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.


docid: mikuptli

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.


docid: zcicimcf

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.


docid: kjmtspej

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.


docid: liwgsdfz

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


docid: gdtkzefz

title: Valoriser le rôle de dispensateur du pharmacien d’officine


Elsewhere in The AGA Journals


docid: jfdsymvx

title: 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.


docid: vjizwacb

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.