Using satellite data on remote transportation of air pollutants for PM2.5 prediction in northern Taiwan.

Accurate PM2.5 prediction is part of the fight against air pollution that helps governments to manage environmental policy. Satellite Remote sensing aerosol optical depth (AOD) processed by The Multi-Angle Implementation of Atmospheric Correlation (MAIAC) algorithm allows us to observe the transportation of remote pollutants between regions.

Influence of land-sea breeze on PM[Formula: see text] prediction in central and southern Taiwan using composite neural network.

PM[Formula: see text] prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground-level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features.

TagSeq: Malicious behavior discovery using dynamic analysis.

In recent years, studies on malware analysis have noticeably increased in the cybersecurity community. Most recent studies concentrate on malware classification and detection or malicious patterns identification, but as to malware activity, it still relies heavily on manual analysis for high-level semantic descriptions. We develop a sequence-to-sequence (seq2seq) neural network, called TagSeq, to investigate a sequence of Windows API calls recorded from malware execution, and produce tags to label their malicious behavior.