participants
Computational Intelligence Technology Center (CITC) is the first big data R&D center to bridge the big data technology across industries in Taiwan. CITC aims to develop intelligent analytics technologies to boost Artificial Intelligence and big data core capabilities for local IT software companies, also to apply intelligent analytics to help the related industries to enhance their productivity and create new business opportunities. The cross-disciplinary approach enables CITC’s core technology capabilities to provide intelligent analytics and machine learning algorithms that industries need. CITC develops an open Artificial Intelligence and big data platform to create solutions that facilitate the innovative application for service design and business model for industries.
Marine litter has become a critical pollution issue which is worthy of global attention. Scientific studies show that artificial waste has posed a tremendous and irreversible threat to our environment and economy.
Compared to air and water pollution, the geographic scale of marine waste is far more larger, which results in difficulties for finding the connection between the pollution source and polluted area.
There are high discrepancies in artificial waste in appearances, size, weights and ingredients, which makes it challenging to identify these pollutants with single detector or utilizing methods commonly used in examining heavy metals and pesticide.
Although the reduction of artificial waste is the fundamental way to solve the pollution issue, however, it’s challenging and time-consuming. Thus, terminal solutions such as beach cleanup have played an important role in alleviating this crisis.
Beach Litter Rapid Assessment allows researchers to conduct sampling survey in a wide range of locations in a short period of time. It is one of the examination methods that helps quantify the marine wastes and assist scientists decide the location where beach cleanup should be held. The sampling survey is conducted by each observation station every ten kilometers of the shoreline. Taiwan has 121 observation stations in the 2210-kilometer coastline.
This topic focuses on exploiting the data collected by one observatory to predict the sampling survey results of nearby observatories. This method is expected to lower the amount of observation station and the involvement of human labor.
Reference
[1] https://www.sow.org.tw/sites/sow/files/hai_an_fei_qi_wu_kuai_shai_diao_cha_zhi_yin_190802.pdf
Ask HR for details.
After the participants uploaded their forecasts, the system will calculate the score based on Cohen’s kappa formula illustrated below:
$$ k=1- {\sum_{i=1}^{k} \sum_{j=1}^{k} w_{ij} x_{ij} \over \sum_{i=1}^{k} \sum_{j=1}^{k} w_{ij} m_{ij} } $$
