The MNE7 Objective 3.4 Cyber Situational Awareness: LOE First Impression Experiment Report (FIER)

2013 ◽  
Author(s):  
Auvo Viita-aho ◽  
Anne Koskinen-Kannisto
Keyword(s):  
Situational Awareness ◽  
First Impression

How Socially Relevant Visual Characteristics of Avatars Influence Impression Formation

2010 ◽  
Vol 22 (1) ◽  
pp. 37-43 ◽  
Author(s):  
David Weibel ◽  
Daniel Stricker ◽  
Bartholomäus Wissmath ◽  
Fred W. Mast
Keyword(s):  
Impression Formation ◽  
Pupil Size ◽  
Conscious Perception ◽  
Viewing Angle ◽  
Frontal View ◽  
Computer Mediated ◽  
First Impression ◽  
Visual Characteristics ◽  
Socially Relevant ◽  
Blink Frequency

Like in the real world, the first impression a person leaves in a computer-mediated environment depends on his or her online appearance. The present study manipulates an avatar’s pupil size, eyeblink frequency, and the viewing angle to investigate whether nonverbal visual characteristics are responsible for the impression made. We assessed how participants (N = 56) evaluate these avatars in terms of different attributes. The findings show that avatars with large pupils and slow eye blink frequency are perceived as more sociable and more attractive. Compared to avatars seen in full frontal view or from above, avatars seen from below were rated as most sociable, self-confident, and attractive. Moreover, avatars’ pupil size and eyeblink frequency escape the viewer’s conscious perception but still influence how people evaluate them. The findings have wide-ranging applied implications for avatar design.

Maintaining Situational Awareness: The Role of Visual Attention

1999 ◽  
Author(s):  
Alex Chaparro ◽  
Loren Groff ◽  
Kamala Tabor ◽  
Kathy Sifrit ◽  
Leo J. Gugerty
Keyword(s):  
Visual Attention ◽  
Situational Awareness

Review on Fog Based Spectrum Sensing for Artificial Intelligence

2018 ◽  
pp. 66-70
Author(s):  
A. Rethina Palin ◽  
I. Jeena Jacob
Keyword(s):  
Bandwidth Allocation ◽  
Situational Awareness ◽  
Cluster Head ◽  
Mesh Network ◽  
Network Systems ◽  
Markov Logic ◽  
Wireless Mesh ◽  
Local Environments ◽  
Reactive Routing ◽  
The Hierarchical Structure

Wireless Mesh Network (MWN) could be divided into proactive routing, reactive routing and hybrid routing, which must satisfy the requirements related to scalability, reliability, flexibility, throughput, load balancing, congestion control and efficiency. DMN (Directional Mesh Network) become more adaptive to the local environments and robust to spectrum changes. The existing computing units in the mesh network systems are Fog nodes, the DMN architecture is more economic and efficient since it doesn’t require architecture- level changes from existing systems. The cluster head (CH) manages a group of nodes such that the network has the hierarchical structure for the channel access, routing and bandwidth allocation. The feature extraction and situational awareness is conducted, each Fog node sends the information regarding the current situation to the cluster head in the contextual format. A Markov logic network (MLN) based reasoning engine is utilized for the final routing table updating regarding the system uncertainty and complexity.

Situational Awareness Applied to Dynamic Security in Power Systems

2017 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Sandra Camila Garzon ◽  
Mario Alberto Rios ◽  
Oscar Gomez
Keyword(s):  
Power Systems ◽  
Situational Awareness ◽  
Dynamic Security

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

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