Productive Aging through Intelligent Personalized Crowdsourcing
Authors: Han Yu, Chunyan Miao, Siyuan Liu, Zhengxiang Pan, Nur Syahidah Khalid, Zhiqi Shen, Cyril Leung
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper outlines the Silver Productive (SP) mobile app, a system powered by the RTS-P intelligent personalized task sub-delegation approach with dynamic worker effort pricing functions. The SP mobile app is incorporated with the RTS-P approach to support productive aging through intelligent personalized crowdsourcing. It is the result of a collaboration with a local senior care community volunteer organization in Singapore. Figure 1 shows screenshots from the app demonstrating its main functionalities. |
| Researcher Affiliation | Academia | Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University, Singapore Department of Electrical and Computer Engineering, the University of British Columbia, Vancouver, Canada {han.yu, ascymiao, syliu, panz0012, nurs0027, zqshen}@ntu.edu.sg, cleung@ece.ubc.ca |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks are present in the paper. The RTS-P approach is described in narrative text. |
| Open Source Code | No | No statement or link indicating the release of open-source code for the described methodology. The paper does not mention any code availability. |
| Open Datasets | No | The paper mentions 'Based on the learning models derived from a large-scale user study (Yu, Salmon, and Leung 2015)' which refers to external work. However, this paper itself does not provide concrete access information (link, DOI, repository, or explicit citation for dataset access) for any publicly available or open dataset used for training or evaluation within this specific paper. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits. It describes a system and an approach but does not detail experimental data partitioning. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, or cloud instances) used for running experiments or developing the system are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or programming languages with versions) are mentioned in the paper. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for any experiments. It focuses on describing the approach and the mobile application. |