Brain-Inspired Intelligent Systems for Daily Assistance Call for Papers

Brain-Inspired Intelligent Systems for Daily Assistance
Call for Papers

Ambient intelligence refers to a framework designed to augment the
level of interaction between individuals and their
environment. Typically sensors are positioned within an environment
providing the acquisition of continuous real-time data. The data are
typically consumed by an agent, which responds to the sensor input(s)
according to some prescribed rule base. The ultimate goal is to
provide a person/agent with information that enhances the ability of
the person/agent to interact more effectively within a prescribed
environment. Typical applications include remote healthcare
monitoring, robot monitoring and interaction at home, complex decision
making about emotions, and behaviour in humans and animals.

The confluence of the ambient intelligence, ubiquitous computing, and
related domains on the one hand and various cognitive computing,
neural-inspired algorithms (e.g., deep ANNs, deep RL), and
brain-intelligent systems on the other hand will assist us in
redefining person-interface cooperativity. More generally, we are
interested in discovering how these frameworks, when imbued within a
social neuroscience perspective, could enhance the quality of life
either at home or in a clinical environment for all individuals. For
example, in robotic assistance, better and faster algorithms for
learning, self-organization, and decision making can shorten the
critical time from detection to cognitive manipulation of the
environment, while dependent people are at risk. These can be people
with acquired brain injuries. In addition, accurate emotion
recognition systems of distressed individuals either adults or
children who cannot self-report information due to physical
deformation, shyness, or anxiety could result in more reliable
diagnostics in a clinical environment.

This special issue is expected to present original work on algorithms
and neural-inspired systems that flexibly adapt to new learning tasks,
learn from the environment using multimodal signals (e.g., neural,
physiological, and kinematic), and produce autonomous adaptive
agencies, which utilize cognitive and affective data, within a social
neuroscientific framework. These agencies should be capable of
acquiring data from a variety of inputs/sensors, generating models,
which become mutually interactive and assistive to all members of a
social construct (such as a classroom, hospital ward, encounter group,
and a family at home).

Potential topics include but are not limited to the following:

    New biological neural network models and training algorithms
    Innovative deep learning algorithms and architectures
    Progressive learning algorithms
    Complex environments
    Neural networks social robotics
    Self-organization, unsupervised, and semisupervised learning
    Brain-computer interfaces and assistive technologies
    Ambient assistive living (AAL)
    Innovative brain-neural computer interfaces
    Emotion recognition models and systems

Authors can submit their manuscripts through the Manuscript Tracking System at
Submission Deadline	Friday, 12 October 2018
Publication Date	March 2019

Papers are published upon acceptance, regardless of the Special Issue publication date.
Lead Guest Editor

    Anastassia Angelopoulou, University of Westminster, London, UK

Guest Editors

    Jose Garcia-Rodriguez, University of Alicante, Alicante, Spain
    Epameinondas Kapetanios, University of Westminster, London, UK
    Peter Roth, TU Graz University of Technology, Graz, Austria
    Kenneth Revett, HCL Infosystems Ltd., Boston, USA