Uncertainty on Discrete-Event System Simulation

Uncertainty Propagation methods are well-established when used in modeling and simulation formalisms like differential equations. Nevertheless, until now there are no methods for Discrete-Dynamic Systems. Uncertainty-Aware Discrete-Event System Specification (UA-DEVS) is a formalism for modeling Discrete-Event Dynamic Systems that include uncertainty quantification in messages, states, and event times. UA-DEVS models provide a theoretical framework to describe the models’ uncertainty and their properties. As UA-DEVS models can include continuous variables and non-computable functions, their simulation could be non-computable. For this reason, we also introduce Interval-Approximated Discrete-Event System Specification (IA-DEVS), a formalism that approximates UA-DEVS models using a set of order and bounding functions to obtain a computable model. The computable model approximation produces a tree of all trajectories that can be traversed from the original model and some erroneous ones introduced by the approximation process. We also introduce abstract simulation algorithms for IA-DEVS, present a case study of UA-DEVS, its IA-DEVS approximation and, its simulation results using the algorithms defined.

Decision making at unsignalized inner city intersections using discrete events systems

Autonomous driving is a promising technology to, among many aspects, improve road safety. There are however several scenarios that are challenging for autonomous vehicles. One of these are unsignalized junctions. There exist scenarios in which there is no clear regulation as to is allowed to drive first. Instead, communication and cooperation are necessary to solve such scenarios. This is especially challenging when interacting with human drivers. In this work we focus on unsignalized T-intersections. For that scenario we propose a discrete event system (DES) that is able to solve the cooperation with human drivers at a T-intersection with limited visibility and no direct communication. The algorithm is validated in a simulation environment, and the parameters for the algorithm are based on an analysis of typical human behavior at intersections using real-world data.

The Impact of Enforced Working from Home on Employee Job Satisfaction during COVID-19: An Event System Perspective

During the COVID-19 pandemic, working from home (WFH) became the only option for many organizations, generating increasing interest in how such arrangements impact employee job satisfaction. Adopting an event system perspective, this study employed an online survey to capture the WFH experiences of 256 workers from 66 Chinese enterprises during the pandemic. Using fuzzy-set qualitative comparative analysis (fsQCA), the study examined how satisfaction was affected by five job characteristics when working from home: longevity (time), home workspace suitability (space), job autonomy (criticality), digital social support (novelty) and monitoring mechanisms (disruption). The findings reveal that three configurations promote employee job satisfaction and that a suitable home workspace is a core condition. In the absence of a suitable workspace, digital social support and an appropriate monitoring mechanism, long-term WFH was found to undermine job satisfaction. However, job autonomy is not a necessary condition for employee job satisfaction. These findings have clear implications for theory and practice.

A stochastic matching model on hypergraphs

Motivated by applications to a wide range of areas, including assemble-to-order systems, operations scheduling, healthcare systems, and the collaborative economy, we study a stochastic matching model on hypergraphs, extending the model of Mairesse and Moyal (J. Appl. Prob.53, 2016) to the case of hypergraphical (rather than graphical) matching structures. We address a discrete-event system under a random input of single items, simply using the system as an interface to be matched in groups of two or more. We primarily study the stability of this model, for various hypergraph geometries.

LDES: Detector Design for Version Number Attack Detection using Linear Temporal Logic based on Discrete Event System

The Internet Engineering Task Force (IETF) has defined routing protocols for Low Power and Lossy Networks (RPL) for constrained devices. RPL constructs DODAGs (Destination Oriented Directed Acyclic Graphs), to optimize routing. RPL ensures acyclic topology with the DODAG version number. However, the control message's DODAG version number is not authenticated. So, RPL is vulnerable to topological inconsistency attack known as DODAG Version Number (DVN) attack. DVN attack creates a packet delay, packet loss, cyclic topology, etc., in the network. This paper proposes a method for detecting DODAG version number attacks. Several existing schemes to defend against the DVN, such as cryptographic techniques, trust-based, threshold-based and mitigation are computationally intensive or require protocol modification. DVN does not change the packet format or sequence of packets, but can still perform attacks and hence fall under the category of stealthy attacks, which are difficult to detect using traditional Intrusion Detection System$'$s (IDS). Discrete-Event System (DES) based IDS have been applied in the literature for stealthy attacks that achieve low overhead, low false alarm rate, etc. However, the construction of DES-based IDS for network protocol may lead to errors, as modelling is manual. The resulting IDS, therefore, is unable to guarantee its correctness. This paper proposes Linear Temporal Logic (LTL) based DES paradigm to detect DVN. LTL-based paradigm facilitates formal verification of the DES-based IDS, and hence the correctness of the scheme is ascertained. The proposed technique is simulated using the Contiki cooja simulator. When the percentage of spiteful nodes in the network increases, the true positive rate, and packet delivery rate drops, while the false positive rate and control message overhead increase. The memory requirement for sending the packets and verifying the nodes is minimal. The LTL-based IDS has been formally verified using NuSMV to ensure the correctness of the framework.

Individual Differences and Changes in Self-Reported Work Performance During the Early Stages of the COVID-19 Pandemic

This longitudinal study examines how three dimensions of self-reported work performance, including task proficiency, adaptivity, and proactivity, changed between December 2019 and September 2020 in Germany. Based on event system and transition theories, we expected work performance to decline because of the “lockdown” between early April and early May 2020 and to subsequently increase when restrictions were eased between early May and early September 2020. Additionally, we hypothesized that high levels of core self-evaluations (i. e., employees’ fundamental evaluations about themselves) buffer the decline and strengthen the recovery in work performance. Data were collected from N = 591 full-time employees across eight measurement points. Results based on discontinuous latent growth curve modeling largely supported the expected trajectories in work performance. Moreover, core self-evaluations positively predicted the levels of work performance as well as the slopes indicating recovery in task proficiency and adaptivity, but not proactivity.

A Parallel Event System for Large-Scale Cloud Simulations in DISSECT-CF

Discrete Event Simulation (DES) frameworks gained significant popularity to support and evaluate cloud computing environments. They support decision-making for complex scenarios, saving time and effort. The majority of these frameworks lack parallel execution. In spite being a sequential framework, DISSECT-CF introduced significant performance improvements when simulating Infrastructure as a Service (IaaS) clouds. Even with these improvements over the state of the art sequential simulators, there are several scenarios (e.g., large scale Internet of Things or serverless computing systems) which DISSECT-CF would not simulate in a timely fashion. To remedy such scenarios this paper introduces parallel execution to its most abstract subsystem: the event system. The new event subsystem detects when multiple events occur at a specific time instance of the simulation and decides to execute them either on a parallel or a sequential fashion. This decision is mainly based on the number of independent events and the expected workload of a particular event. In our evaluation, we focused exclusively on time management scenarios. While we did so, we ensured the behaviour of the events should be equivalent to realistic, larger-scale simulation scenarios. This allowed us to understand the effects of parallelism on the whole framework, while we also shown the gains of the new system compared to the old sequential one. With regards to scaling, we observed it to be proportional to the number of cores in the utilised SMP host.

Computational challenges to test and revitalize Claude Lévi-Strauss transformational methodology

The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in his structural analysis of myth. After an analysis of Lévi-Strauss’s transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment. The validation of the method involves a discrete-event system specification simulation based on the extension of discrete-event system specification models dedicated to provide a dynamic Google Earth visualization of the selected myth. Future work around the discrete-event system specification formalism in anthropology is described as well as future applications regarding the impact of computational models (discrete-event system specification formalism, Bayesian inferences, and object-oriented features) to new contemporary anthropological domains.