Group recommendation using feature space representing behavioral tendency and power balance among members

2011 ◽  
Author(s):  
Shunichi Seko ◽  
Takashi Yagi ◽  
Manabu Motegi ◽  
Shinyo Muto
Keyword(s):  
Feature Space ◽  
Power Balance ◽  
Group Recommendation

The Utility of an Intermediate Representation of Feature Space: Lessons From Fingerprint Examiners

2012 ◽  
Author(s):  
Tom Busey ◽  
Chen Yu ◽  
Francisco Parada ◽  
Brandi Emerick ◽  
John Vanderkolk
Keyword(s):  
Feature Space ◽  
Intermediate Representation

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.

HIGH PREVALENCE OF MALNUTRITON AND WORM INFECTION AMONG VAN KIEU AND PAKOH CHILDREN AGED 12-36 MONTHS IN DAKRONG DISTRICT, QUANG TRI PROVINCE

2012 ◽  
pp. 129-134
Author(s):  
Thi Lan Tran ◽  
Thi Huong Le ◽  
Xuan Ninh Nguyen
Keyword(s):  
Cross Sectional Study ◽  
Age Group ◽  
Infection Status ◽  
Sectional Study ◽  
Group Recommendation ◽  
Related Factors ◽  
Normal Children ◽  
Cross Sectional ◽  
High Prevalence ◽  
Very High

Objectives: Assess the nutritional status, worm infection status and some related factors among children aged 12-36 months of Dakrong district, Quang Tri province. Subject and method: A cross sectional study was carried out in 2010, in 680 children aged 12-36 months in 4 communes of Dakrong district, Quang Tri province. Results: The malnutrition rate was 55.0% for underweight, 66.5% for stunting and 16.2% for wasting. The prevalence of malnutrition increases by age group. The prevalence of worm infection was 31.6%, the highest prevalence was belong to Ascaris infection (24.6%), followed by Hookworm and Trichuris (6.5% and 6.2%, respectively). The prevalence of worm infection among children under two is very high (27.0%). The prevalence of worm infection was distributed quite equally between the malnutrition children group and normal children group. Recommendation: Early deworming forchildren from 12 months should be considered as important strategy against the malnutrition of children in Dakrong district, Quang Tri province

Model selection of extreme learning machine based on latent feature space

2013 ◽  
Vol 33 (6) ◽  
pp. 1600-1603
Author(s):  
Wentao MAO ◽  
Zhongtang ZHAO ◽  
Huanhuan HE
Keyword(s):  
Model Selection ◽  
Extreme Learning Machine ◽  
Feature Space ◽  
Learning Machine ◽  
Selection Of

Towards Feature-space Emotional Speech Adaptation for TDNN based Telugu ASR systems

2019 ◽  
Author(s):  
Vishnu Vidyadhara Raju V ◽  
Krishna Gurugubelli ◽  
Mirishkar Sai Ganesh ◽  
Anil Kumar Vuppala
Keyword(s):  
Feature Space ◽  
Emotional Speech
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