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. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central Taiwan in the months when land-sea breeze dominates the accumulation of PM[Formula: see text].

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. The paper proposes a composite neural network model, the Remote Transported Pollutants (RTP) model, for such long-range pollutant transportation that predicts more accurate local PM2.5 concentrations given such satellite data. The proposed RTP model integrates several deep learning components and learns from the heterogeneous features of various domains. We also detected remote transportation pollution events (RTPEs) at two reference sites from the AOD data. Extensive experiments using real-world data show that the proposed RTP model outperforms the base model that does not account for RTPEs by 17%-30%, 23%-26% and 18%-22% and state-of-the-art models that account for RTPEs by 12%-22%, 12%-14%, and 10%-11% at +4h to +24h, +28h to +48 hours, and +52h to +72h hours respectively.

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. We propose embedding modules to transform Windows API function parameters, registry, filenames, and URLs into low-dimension vectors, while still preserving the closeness property. Moreover, we utilize an attention mechanism to capture the relations between generated tags and certain API invocation calls. Results show that the most possible malicious actions are identified by TagSeq. Examples and a case study demonstrate that the proposed embedding modules preserve semantic-physical relations and that the predicted tags reflect malicious intentions. We believe this work is suitable as a tool to help security analysts recognize malicious behavior and intent with easy-to-understand tags.