Abstract

Precomputed Radiance Transfer (PRT) can achieve high-quality renders of glossy materials at real-time framerates. PRT involves precomputing a k-dimensional transfer vector or a k × k- matrix of Spherical Harmonic (SH) coefficients at specific points for a scene depending on whether the material is diffuse or glossy respectively. Most prior art precomputes values at vertices of the mesh and interpolates color for interior points. They require finer mesh tessellations for high-quality renders. In this work, we introduce transfer textures for decoupling mesh resolution from transfer storage and sampling specifically benefiting the glossy renders. Dense sampling of the transfer is possible on the fragment shader while rendering with the use of transfer textures for both diffuse as well as glossy materials, even with a low tessellation. This simultaneously provides high render quality and frame rates.

Abstract

Recently, Internet of Drones (IoD) has emerged as an important topic to research in academy and industry due to its generic ser- vices for various drone applications. In an IoD environment, the deployed drones and the ground station server (GSS) communicate over an open network which leads to security and privacy breaches. To mitigate these issues, a blockchain driven authentication and key establishment protocol for secure communication in IoD has been designed (in short, it is called as BAKP-IoDA). The security analysis of BAKP-IoDA shows its robustness against different at- tacks. During the performance comparison, it has been observed that BAKP-IoDA maintains superior security along with extra func- tionality features with the competing schemes. Additionally, the blockchain based practical demonstration of BAKP-IoDA shows its impact on various important network performance parameters, like computation time and transactions per second

Abstract

Home Handbook of Real-Time Computing Reference work entry Hierarchical Scheduling Jin Hyun Kim, Deepak Gangadharan, Kyong Hoon Kim, Insik Shin & Insup Lee Reference work entry First Online: 09 August 2022 793 Accesses Abstract This chapter presents two hierarchical scheduling analysis paradigms for a uniprocessor hierarchical scheduling system: compositional framework (CF) and real-time calculus (RTC). Each paradigm uses different techniques for resource models and schedulability analysis: CF uses supply-bound functions (sbf) and demand-bound functions (dbf), whereas RTC computes a lower-bound of service curve that satisfies the demand of a given workload. This chapter describes both CF and RTC approaches and various schedulability analysis techniques for hierarchical scheduling systems. These techniques are described based on bounded delay resource model, periodic resource model, explicit deadline periodic model, and arrival and service curves. Finally, this chapter also describes optimality results and compares CF and RTC approaches.

Abstract

Extreme events are defined as events that largely deviate from the nominal state of the system as observed in a time series. Due to the rarity and uncertainty of their occurrence, predicting extreme events has been challenging. In real life, some variables (passive variables) often encode significant information about the occurrence of extreme events manifested in another variable (active variable). For example, observables such as temperature, pressure, etc., act as passive variables in case of extreme precipitation events. These passive variables do not show any large excursion from the nominal condition yet carry the fingerprint of the extreme events. In this study, we propose a reservoir computation-based framework that can predict the preceding structure or pattern in the time evolution of the active variable that leads to an extreme event using information from the passive variable. An appropriate threshold height of events is a prerequisite for detecting extreme events and improving the skill of their prediction. We demonstrate that the magnitude of extreme events and the appearance of a coherent pattern before the arrival of the extreme event in a time series aect the prediction skill. Quantitatively, we confirm this using a metric describing the mean phase dierence between the input time signals, which decreases when the magnitude of the extreme event is relatively higher, thereby increasing the predictability skill

Abstract

In the field of complex dynamics, multistable attractors have been gaining a significant attention due to its unpredictability in occurrence and extreme sensitivity to initial conditions. Co-existing attractors are abundant in diverse systems ranging from climate to finance, ecological to social systems. In this article, we investigate a data-driven approach to infer different dynamics of a multistable system using echo state network (ESN). We start with a parameter-aware reservoir and predict diverse dynamics for different parameter values. Interestingly, machine is able to reproduce the dynamics almost perfectly even at distant parameters which lie considerably far from the parameter values related to the training dynamics. In continuation, we can predict whole bifurcation diagram significant accuracy as well. We extend this study for exploring various dynamics of multistable attractors at unknown parameter value. While, we train the machine with the dynamics of only one attarctor at parameter p, it can capture the dynamics of co-existing attractor at a new parameter value p+∆p. Continuing the simulation for multiple set of initial conditions, we can identify the basins for different attractors. We generalize the results by applying the scheme on two distinct multistable systems.

Abstract

The role of topological heterogeneity in the origin of extreme events in a network is investigated here. The dynamics of the oscillators associated with the nodes are assumed to be identical and influenced by mean-field repulsive interactions. An interplay of topological heterogeneity and the repulsive interaction between the dynamical units of the network triggers extreme events in the nodes when each node succumbs to such events for discretely different ranges of repulsive coupling. A high degree node is vulnerable to weaker repulsive interactions, while a low degree node is susceptible to stronger interactions. As a result, the formation of extreme events changes position with increasing strength of repulsive interaction from high to low degree nodes. Extreme events at any node are identified with the appearance of occasional large-amplitude events (amplitude of the temporal dynamics) that are larger than a threshold height and rare in occurrence, which we confirm by estimating the probability distribution of all events. Extreme events appear at any oscillator near the boundary of transition from rotation to libration at a critical value of the repulsive coupling strength. To explore the phenomenon, a paradigmatic second-order phase model is used to represent the dynamics of the oscillator associated with each node. We make an annealed network approximation to reduce our original model and, thereby, confirm the dual role of the repulsive interaction and the degree of a node in the origin of extreme events in any oscillator associated with a node. ACKNOWLEDGMENTS

Abstract

The persistence of biodiversity of species is a challenging proposition in ecological communities in the face of Darwinian selection. The present article investigates beyond the pairwise competitive interactions and provides a novel perspective for understanding the influence of higher-order interactions on the evolution of social phenotypes. Our simple model yields a prosperous outlook to demonstrate the impact of perturbations on intransitive competitive higherorder interactions. Using a mathematical technique, we show how alone the perturbed interaction network can quickly determine the coexistence equilibrium of competing species instead of solving a large system of ordinary differential equations. It is possible to split the system into multiple feasible cluster states depending on the number of perturbations. Our analysis also reveals the ratio between the unperturbed and perturbed species is inversely proportional to the amount of employed perturbation. Our results suggest that nonlinear dynamical systems and interaction topologies can be interplayed to comprehend species’ coexistence under adverse conditions. Particularly our findings signify that less competition between two species increases their abundance and outperforms others

Abstract

This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed “all articles” and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) “all_articles” (1B) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 – 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 ±128.6, 77.6±125.3 and 255.6±205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0±8.4/day to 282.5±110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3±4 days, 20.6 days, and 2.3±2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2±1.4, 1.3±0.9, and 1.1±0.4 respectively. Subgroup analysis, PubMed review articles mean review <0|17±17|77> days; and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.

Abstract

In this smart and rapidly growing computing world, most of the organizations and companies needs to share necessary information (data) to third parties for their analysis which plays major role in decision making. Any data generally consists of sensitive (personal) information about people and organizations and when the sensitive information to be revealed to third parties, there is always chance of privacy loss. In this regard any organization or an individual requires the application of privacy preserving techniques to ensure data privacy, that means the data to be exchanged between two or more sites, without data loss or privacy violation. When data to be shared between multiple organizations or sites (parties) the privacy issues will be arises more. In this line we choose collaborative fuzzy clustering that gives better solutions to preserve privacy of data while sharing information between multiple parties to achieve …

Abstract

Machine learning (ML) is a subset of Artificial Intelligence (AI), which focuses on the implementation of some systems that can learn from the historical data, identify patterns and make logical decisions with little to no human interventions. Cyber security is the practice of protecting digital systems, such as computers, servers, mobile devices, networks and associated data from malicious attacks. Uniting cyber security and ML has two major aspects, namely accounting for cyber security where the machine learning is applied, and the use of machine learning for enabling cyber security. This uniting can help us in various ways, like it provides enhanced security to the machine learning models, improves the performance of the cyber security methods, and supports effective detection of zero day attacks with less human intervention. In this survey paper, we discuss about two different concepts by uniting cyber security and ML. We also discuss the advantages, issues and challenges of uniting cyber security and ML. Furthermore, we discuss the various attacks and provide a comprehensive comparative study of various techniques in two different considered categories. Finally, we provide some future research directions. © 2022 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Cyber security; Machine learning; Internet of Things (ioT); Privacy; Security; Intrusion detection