Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning

2020 ◽  
pp. 96-105
Author(s):  
Tong Li ◽  
Kai Xuan ◽  
Zhong Xue ◽  
Lei Chen ◽  
Lichi Zhang ◽  
...  
Keyword(s):  
Context Learning ◽  
Label Transfer ◽  
Mr Segmentation

Spatial context learning and switching strategy

2006 ◽  
Author(s):  
Nobutaka Endo ◽  
Walter R. Boot ◽  
Arthur F. Kramer ◽  
Alejandro Lleras ◽  
Takatsune Kumada
Keyword(s):  
Spatial Context ◽  
Switching Strategy ◽  
Context Learning

Preservice Music Teacher Identity Construction Through Metaphor

2020 ◽  
pp. 105708372098227
Author(s):  
Cynthia L. Wagoner
Keyword(s):  
Preservice Teachers ◽  
Identity Construction ◽  
Teacher Identity ◽  
Instrumental Music ◽  
Methods Course ◽  
Learning Context ◽  
Context Learning ◽  
Instrumental Methods ◽  
The Impact ◽  
Authentic Context

I investigated how preservice instrumental music teachers understand and describe their teacher identity through the use of metaphor in a one-semester instrumental methods course emphasizing authentic context learning. Twenty-five third-year instrumental methods course music education students created a personal metaphor to explore their professional identity construction. Preservice teacher metaphors were revisited throughout the semester, while students participated in an authentic context learning experience in an urban instrumental music classroom. Data sources included student artifacts, informal interviews, and observation/field notes. The impact of teaching within an authentic learning context appears to enrich the ways in which preservice teachers are able to articulate details of their metaphor descriptions. Through their reflections across the semester, preservice teachers demonstrated how personal metaphors were used to restructure their understandings of teacher identity and capture some of the complexities of becoming teachers.

Adaptive Context Learning Network for Crowd Counting

2020 ◽  
Author(s):  
Zhao Liu ◽  
Guanqi Zeng ◽  
Zunlei Feng ◽  
Rong Zhang ◽  
Mingli Song ◽  
...  
Keyword(s):  
Crowd Counting ◽  
Learning Network ◽  
Context Learning

scholarly journals Cardiac MR segmentation based on sequence propagation by deep learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0230415
Author(s):  
Chao Luo ◽  
Canghong Shi ◽  
Xiaoji Li ◽  
Dongrui Gao
Keyword(s):  
Deep Learning ◽  
Cardiac Mr ◽  
Mr Segmentation

Service productivity as mutual learning

2015 ◽  
Vol 7 (2/3) ◽  
pp. 296-311 ◽  
Author(s):  
Christian Grönroos ◽  
Katri Ojasalo
Keyword(s):  
Open Systems ◽  
Cost Savings ◽  
Service Organizations ◽  
Content Type ◽  
Mutual Learning ◽  
Context Learning ◽  
Starting Point ◽  
Service Production ◽  
Service Productivity ◽  
Internal Efficiency

Purpose – The purpose of this paper is to analyse the mutual learning implications for service productivity of the characteristics of service and service production. Design/methodology/approach – This is a conceptual paper. The starting point is, first of all, that productivity as a management concept should help a firm to manage its economic profit, and secondly, that service organizations are open systems, where the customers participate as co-producers and are exposed to the firm’s production resources and processes. Unlike in manufacturing, to understand productivity in service organizations as a means of managing profit, cost effects and revenue effects of changes in the productions system cannot be separated. Due to the interaction between customers and the firm’s resources during service production, dialogical collaboration between them develops. This enables mutual learning. Findings – Given the social dynamics in service production processes, four learning processes that influence service productivity are identified. Two processes enhance the organizations’s internal efficiency (cost savings), and two enhance its external effectiveness (perceived quality, revenue generation); two are organization-driven, two are customer-driven. Research limitations/implications – The mutual learning model demonstrates how the service provider by learning from the dynamics of service encounters in many ways can manage the productivity of the organizations’s processes. It shows that learning enables improvement of service productivity through effects enhancing both internal efficiency and external effectiveness. Originality/value – In a productivity context, learning has not earlier been studied as a mutual learning phenomenon.

Cybersecurity educational games: a theoretical framework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mark I Hwang ◽  
Susan Helser
Keyword(s):  
Learning Outcomes ◽  
Learning Theory ◽  
Computer Games ◽  
Educational Games ◽  
Theoretical Framework ◽  
User Characteristics ◽  
Content Type ◽  
Context Learning ◽  
Game Context ◽  
Design And Testing

Purpose Computer games that teach cybersecurity concepts have been developed to help both individuals and organizations shore up their defence against cybercrimes. Evidence of the effectiveness of these games has been rather weak, however. This paper aims to guide the design and testing of more effective cybersecurity educational games by developing a theoretical framework. Design/methodology/approach A review of the literature is conducted to explore the dependent variable of this research stream, learning outcomes and its relationship with four independent variables, game characteristics, game context, learning theory and user characteristics. Findings The dependent variable can be measured by five learning outcomes: information, content, strategic knowledge, eagerness to learn/time spent and behavioral change. Game characteristics refer to features that contribute to a game’s usefulness, interactivity, playfulness or attractiveness. Game context pertains to factors that determine how a game is used, including the target audience, the skill involved and the story. Learning theory explains how learning takes place and can be classified as behaviorism, cognitivism, humanism, social learning or constructivism. User characteristics including gender, age, computer experience, knowledge and perception, are attributes that can impact users’ susceptibility to cybercrimes and hence learning outcomes. Originality/value The framework facilitates taking stock of past research and guiding future research. The use of the framework is illustrated in a critique of two research streams. Multiple research directions are discussed for continued research into the design and testing of next-generation cybersecurity computer games.

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