Updating Bayesian networks using crowds

Title:

Updating Bayesian networks using crowds

Creator:

Sarma, H., Samaddar, A.B., Porzel, R., Smeddinck, J.D., and Malaka, R.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:8766a8c4-2350-4ba6-981f-0dc3fe2347f7
uuid:8766a8c4-2350-4ba6-981f-0dc3fe2347f7
doi:10.14311/NNW.2017.27.028

Subject:

CPT, Bayesian network, crowdsourcing, human computation, exercise instructions, collective intelligence, and automatic update

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Physical exercise instruction sheets are difficult to understand. In general, considerable information is hidden in these types of instruction sheets, which also makes them difficult for machines to understand. Major missing information types include the source and destination location of a human movement. Here we present a Bayesian network to extract the implicit or missing information from typical exercise instruction sheets. We proposed two different kind of Bayesian networks which consists of three and four variables respectively. The network with three variable are designed to for single exercise instruction with single action or pose and the other one designed for single or multiple sentence with two actions or poses. The conditional probability table (CPT) is the backbone of the Bayesian network. At the start, the CPT is updated from our physical exercise instruction sheet corpus (PEISC). Keeping the Action and Bodypart fixed, we have developed our CPT using a unique approach, i.e., crowdsourcing, where we have developed a CPT update system using 13 different exercises consisting of 44 different exercise videos. Using this system based on the rating of a participant of the video the specific variable of that CPT is updated automatically in the Bayesian network. We also updated the Action variable, which consists of 14 different values (action verbs) using crowdsourcing with a human computation approach.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

529-540

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2017 Volume:27 | Number:5

Harvested from:

CDK

Metadata only:

false