A Workflow Authorization Model Based on Role and Task and Constraints Specification

2005 ◽  
Vol 42 (11) ◽  
pp. 1946
Author(s):  
Guanglin Xing
Keyword(s):  
Model Based ◽  
Authorization Model ◽  
And Task

Designing A Healthcare Authorization Model Based On Cloud Authentication

2014 ◽  
Vol 20 (3) ◽  
pp. 365-379 ◽  
Author(s):  
Chin-Ling Chen ◽  
Tsai-Tung Yang ◽  
Fang-Yie Leu ◽  
Yi-Li Huang
Keyword(s):  
Model Based ◽  
Authorization Model

The Grid Task Attemper Layer Model Based on Agent Role

2014 ◽  
Vol 543-547 ◽  
pp. 3641-3645
Author(s):  
Ming Sun ◽  
Chen Han Wu
Keyword(s):  
Grid Computing ◽  
Grid System ◽  
Layer Model ◽  
Single Node ◽  
Model Based ◽  
And Task

In this paper the new grid task attemper is discussed. It combined the grid computing with role layer resource organize, role working flow and role collaboration. Consequently, the tactical computing functions has been shifted from single node running into simultaneous multi-node operation. Thus, the problems caused by large amount of data and complicated operation, such as poor speed, high request for server equipment and task attemper intermission etc, in the traditional grid system can be solved effectively.

scholarly journals Distinct replay signatures for planning and memory maintenance

2021 ◽  
Author(s):  
G. Elliott Wimmer ◽  
Yunzhe Liu ◽  
Daniel McNamee ◽  
Raymond Dolan
Keyword(s):  
Multivariate Analysis ◽  
Learning Task ◽  
Relative Strength ◽  
Task Demands ◽  
Reward Learning ◽  
Planning Strategy ◽  
Model Based ◽  
Human Participants ◽  
Structure Knowledge ◽  
And Task

Theories of neural replay propose that it supports a range of different functions, most prominently planning and memory maintenance. Here, we test the hypothesis that distinct replay signatures relate to planning and memory maintenance. Our reward learning task required human participants to utilize structure knowledge for 'model-based' evaluation, while maintaining knowledge for two independent and randomly alternating task environments. Using magnetoencephalography (MEG) and multivariate analysis, we found neural evidence for compressed forward replay during planning and backward replay following reward feedback. Prospective replay strength was enhanced for the current environment when the benefits of a model-based planning strategy were higher. Following reward receipt, backward replay for the alternative, distal environment was enhanced as a function of decreasing recency of experience for that environment. Consistent with a memory maintenance role, stronger maintenance-related replay was associated with a modulation of subsequent choices. These findings identify distinct replay signatures consistent with key theoretical proposals on planning and memory maintenance functions, with their relative strength modulated by on-going computational and task demands.

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