Confirmation and the computational paradigm (or: Why do you think they call itartificial intelligence?)

Modern paradigms of motor control and rehabilitation are analyzed in the paper. Two main paradigms, i. e. computational approach and dynamical system approach are engaged in rivalry in motor control and learning research at present. From the standpoint of computational paradigm the principal mechanism of motor control and learning consists in the ability of the brain “to calculate” (acting as some kind of biological computer). According to the paradigm of dynamical systems the mechanism of motor control is time dependent. In other words, it can be different each time. The main principles of motor control and properties of movements are given considerable attention in the paper. Besides, modern methods of motor rehabilitation after stroke are emphasized in the paper. Fitting of neuroprosthesis and restoration of damaged neural cells are significant maiden steps in modern science. The scientists are engaged in search for: a) constraining such mechanism prosthesis that would submit to the efforts of human will and b) restoring neural cells damaged because of the brain stroke suffered.Keywords: motor control, rehabilitation, stroke.

Enabling Large Scale Deep Learning on Smart Device by Exploiting Edge-Cloud Computational Paradigm

10.1109/vtc2021-fall52928.2021.9625365 ◽

2021 ◽

Author(s):

Tryan Aditya Putra ◽

Syahidah Izza Rufaida ◽

Jenq-Shiou Leu

Keyword(s):

Deep Learning ◽

Large Scale ◽

Smart Device ◽

Computational Paradigm

Informational Technology Computational Paradigm

Encyclopedia of Quality of Life and Well-Being Research ◽

10.1007/978-94-007-0753-5_102014 ◽

2014 ◽

pp. 3272-3272

Keyword(s):

Informational Technology ◽

Computational Paradigm

Intermezzo

It Began with Babbage ◽

10.1093/oso/9780199309412.003.0010 ◽

2014 ◽

Author(s):

Subrata Dasgupta

Keyword(s):

High School ◽

World War Ii ◽

A Priori ◽

World War ◽

Structure Of Scientific Revolutions ◽

Scientific Context ◽

Computational Paradigm ◽

Doctoral Work ◽

Mature Science ◽

Dominant Paradigm

By the end of World War II, independent of one another (and sometimes in mutual ignorance), a small assortment of highly creative minds—mathematicians, engineers, physicists, astronomers, and even an actuary, some working in solitary mode, some in twos or threes, others in small teams, some backed by corporations, others by governments, many driven by the imperative of war—had developed a shadowy shape of what the elusive Holy Grail of automatic computing might look like. They may not have been able to define a priori the nature of this entity, but they were beginning to grasp how they might recognize it when they saw it. Which brings us to the nature of a computational paradigm. Ever since the historian and philosopher of science Thomas Kuhn (1922–1996) published The Structure of Scientific Revolutions (1962), we have all become ultraconscious of the concept and significance of the paradigm, not just in the scientific context (with which Kuhn was concerned), but in all intellectual and cultural discourse. A paradigm is a complex network of theories, models, procedures and practices, exemplars, and philosophical assumptions and values that establishes a framework within which scientists in a given field identify and solve problems. A paradigm, in effect, defines a community of scientists; it determines their shared working culture as scientists in a branch of science and a shared mentality. A hallmark of a mature science, according to Kuhn, is the emergence of a dominant paradigm to which a majority of scientists in that field of science adhere and broadly, although not necessarily in detail, agree on. In particular, they agree on the fundamental philosophical assumptions and values that oversee the science in question; its methods of experimental and analytical inquiry; and its major theories, laws, and principles. A scientist “grows up” inside a paradigm, beginning from his earliest formal training in a science in high school, through undergraduate and graduate schools, through doctoral work into postdoctoral days. Scientists nurtured within and by a paradigm more or less speak the same language, understand the same terms, and read the same texts (which codify the paradigm).

A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention

BioMedical Engineering OnLine ◽

10.1186/s12938-020-00787-y ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Kunjan D. Rana ◽

Sheraz Khan ◽

Matti S. Hämäläinen ◽

Lucia M. Vaina

Keyword(s):

Real Time ◽

Spatial Attention ◽

Dynamic Allocation ◽

Computational Paradigm

VANET-Based Volunteer Computing (VBVC): A Computational Paradigm for Future Autonomous Vehicles

IEEE Access ◽

10.1109/access.2020.2974500 ◽

2020 ◽

Vol 8 ◽

pp. 71763-71774

Author(s):

Ayesha Amjid ◽

Abid Khan ◽

Munam Ali Shah

Keyword(s):

Autonomous Vehicles ◽

Volunteer Computing ◽

Computational Paradigm

Brain-inspired computational paradigm dedicated to fault diagnosis of PEM fuel cell stack

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2016.11.043 ◽

2017 ◽

Vol 42 (8) ◽

pp. 5410-5425 ◽

Cited By ~ 11

Author(s):

Zhixue Zheng ◽

Simon Morando ◽

Marie-Cécile Pera ◽

Daniel Hissel ◽

Laurent Larger ◽

...

Keyword(s):

Fuel Cell ◽

Fault Diagnosis ◽

Pem Fuel Cell ◽

Fuel Cell Stack ◽

Computational Paradigm

A computational paradigm for multiresolution topology optimization (MTOP)

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-009-0443-8 ◽

2009 ◽

Vol 41 (4) ◽

pp. 525-539 ◽

Cited By ~ 102

Author(s):

Tam H. Nguyen ◽

Glaucio H. Paulino ◽

Junho Song ◽

Chau H. Le

Keyword(s):

Topology Optimization ◽

Computational Paradigm ◽

Multiresolution Topology Optimization

A Perforating Tool Kit as a Computational Paradigm

10.2118/180302-ms ◽

2016 ◽

Cited By ~ 2

Author(s):

Jim Wight ◽

G. G. Craddock ◽

Dennis Haggerty ◽

Fabian Rojas ◽

Jonathan Montiverdi ◽

...

Keyword(s):

Computational Paradigm

INTERACTION-ORIENTED SOFTWARE DEVELOPMENT

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194001000530 ◽

2001 ◽

Vol 11 (03) ◽

pp. 259-279 ◽

Cited By ~ 4

Author(s):

MICHAEL N. HUHNS

Keyword(s):

Software Engineering ◽

Software Development ◽

Software Components ◽

New Approach ◽

Reusable Software ◽

Current State ◽

Computational Paradigm ◽

And Behavior

This paper describes a new approach to the production of robust software. We first motivate the approach by explaining why the two major goals of software engineering — correct software and reusable software — are not being addressed by the current state of software practice. We then describe a methodology based on active, cooperative, and persistent software components, i.e., agents, and show how the methodology produces robust and reusable software. We derive requirements for the structure and behavior of the agents, and report on preliminary experiments on applications based on the methodology. We conclude with a roadmap for development of the methodology and ruminations about uses for the new computational paradigm.