detection response
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2022 ◽  
pp. 156-172
Author(s):  
Aristides Dasso ◽  
Ana Funes

Threat and Risk Assessment is an important area in cybersecurity. It covers multiple systems and organizations where cybersecurity is significant, such as banking, industry, SCADA, Energy Management System, among many others. The chapter presents a method to help assessing threats and risks associated with computer and networks systems. It integrates the Framework for Improving Critical Infrastructure Cybersecurity—developed by the National Institute of Standards and Technology—with a quantitative method based on the use of a Continuous Logic, the Logic Scoring of Preference (LSP) method. LSP is a method suitable for decision making that provides the guidelines to produce a model to assist the expert in the process of assessing how much a product or system satisfy a number of requirements, in this case associated to the identification, protection, detection, response and recovery of threat and risks in an organization.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yijing Zhang ◽  
Jinfei Ma ◽  
Chi Zhang ◽  
Ruosong Chang

AbstractWith the continuous improvement of automated vehicles, researchers have found that automated driving is more likely to cause passive fatigue. To explore the impact of automation and scenario complexity on the passive fatigue of a driver, we collected electroencephalography (EEG), detection-response task (DRT) performance, and the subjective report scores of 48 drivers. We found that in automated driving under monotonic conditions, after 40 min, the alpha power of the driver’s EEG indicators increased significantly, the accuracy of the detection reaction task decreased, and the reaction time became slower. The receiver characteristic curve was used to calculate the critical threshold of the alpha power during passive fatigue. The determination of the threshold further clarifies the occurrence time and physiological characteristics of passive fatigue and improves the passive fatigue theory.


2021 ◽  
Vol 37 (3) ◽  
pp. 321-337
Author(s):  
Marcin Paprzycki ◽  
Maria Ganzha ◽  
Katarzyna Wasielewska ◽  
Piotr Lewandowski

Current software projects require continuous integration during their whole lifetime. In this context, different approaches regarding introduction of DevOps and DevSecOps strategies have been proposed in the literature. While DevOps proposes an agile methodology for the development and instantiation of software platforms with minimal impact in any kind of operations environment, this contribution proposes the introduction of DevOps methodology for Next Generation IoT deployments. Moreover, novelty of the proposed approach lies in leveraging DevSecOps in different stages and layers of the architecture. In particular, the present work describes the different DevSecOps methodology tasks, and how the security is included on pre-design activities such as planning, creation or adaptation, the design and implementation, as well as on post-implementation activities such as detection, response. Without proper consideration of security and privacy best practices identified in this article, the continuous delivery of services using DevOps methodologies may create risks and introduce different vulnerabilities for Next Generation IoT deployments.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257201
Author(s):  
Yijing Zhang ◽  
Jinfei Ma ◽  
Chunyang Pan ◽  
Ruosong Chang

With ongoing improvements in vehicle automation, research on automation trust has attracted considerable attention. In order to explore effects of automation trust on drivers’ visual distraction, we designed a three-factor 2 (trust type: high trust group, low trust group) × 2 (video entertainment: variety-show videos, news videos) × 3 (measurement stage: 1–3) experiment. 48 drivers were recruited in Dalian, China for the experiment. With a driving simulator, we used detection-response tasks (DRT) to measure each driver’s performance. Their eye movements were recorded, and automation-trust scale was used to divide participants into high trust group and low trust group. The results show that: (1) drivers in the high trust group has lower mental workload and paid more attention to visual non-driving-related tasks; (2) video entertainment also has an impact on distraction behavior, variety-show videos catch more attention than news videos. The findings of the present study indicate that drivers with high automation trust are more likely to be involved in non-driving-related visual tasks.


Author(s):  
Holland M. Vasquez ◽  
Justin G. Hollands ◽  
Greg A. Jamieson

Some previous research using a new augmented reality map display called Mirror-in-the-Sky (MitS) showed that performance was worse and mental workload (MWL) greater with MitS relative to a track-up map for navigation and wayfinding tasks. The purpose of the current study was to determine—for both MitS and track-up map—how much performance improves and MWL decreases with practice in a simple navigation task. We conducted a three-session experiment in which twenty participants completed a route following task in a virtual environment. Task completion times and collisions decreased, subjective MWL decreased, and secondary task performance improved with practice. The NASA-TLX Global ratings and Detection Response Task Hit Rates showed a larger decrease in MWL with MitS than the track-up map. Additionally, means for performance and workload measures showed that differences between the MitS and track-up map decreased in the first session. In later sessions the differences between the MitS and track-up map were negligible. As such, with practice performance and MWL may be comparable to a traditional track-up map.


Recycling ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 54
Author(s):  
Giuseppe Bonifazi ◽  
Ludovica Fiore ◽  
Riccardo Gasbarrone ◽  
Pierre Hennebert ◽  
Silvia Serranti

In this work, the application of Short-Wave Infrared (SWIR: 1000–2500 nm) spectroscopy was evaluated to identify plastic waste containing brominated flame retardants (BFRs) using two different technologies: a portable spectroradiometer, providing spectra of single spots, and a hyperspectral imaging (HSI) platform, acquiring spectral images. X-ray Fluorescence (XRF) analysis was preliminarily performed on plastic scraps to analyze their bromine content. Chemometric methods were then applied to identify brominated plastics and polymer types. Principal Component Analysis (PCA) was carried out to explore collected data and define the best preprocessing strategies, followed by Partial Least Squares—Discriminant Analysis (PLS-DA), used as a classification method. Plastic fragments were classified into “High Br content” (Br > 2000 mg/kg) and “Low Br content” (Br < 2000 mg/kg). The identified polymers were acrylonitrile butadiene styrene (ABS) and polystyrene (PS). Correct recognition of 89–90%, independently from the applied technique, was achieved for brominated plastics, whereas a correct recognition ranging from 81 to 89% for polymer type was reached. The study demonstrated as a systematic utilization of both the approaches at the industrial level and/or at laboratory scale for quality control can be envisaged especially considering their ease of use and the short detection response.


2021 ◽  
Vol 17 (7) ◽  
pp. e1009096
Author(s):  
Gustav Markkula ◽  
Zeynep Uludağ ◽  
Richard McGilchrist Wilkie ◽  
Jac Billington

Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate–the visual looming–of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.


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