Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model

Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We propose a novel framework for multi-label classification, Multivariate Probit Variational AutoEncoder (MPVAE), that effectively learns latent embedding spaces as well as label correlations. MPVAE learns and aligns two probabilistic embedding spaces for labels and features respectively. The decoder of MPVAE takes in the samples from the embedding spaces and models the joint distribution of output targets under a Multivariate Probit model by learning a shared covariance matrix. We show that MPVAE outperforms the existing state-of-the-art methods on important computational sustainability applications as well as on other application domains, using public real-world datasets. MPVAE is further shown to remain robust under noisy settings. Lastly, we demonstrate the interpretability of the learned covariance by a case study on a bird observation dataset.

Predicting forest fire in Indonesia using remote sensing data

<p>Over the last decades we are seeing an increase in forest fires due to deforestation and climate change. In Southeast Asia, tropical peatland forest fires are a major environmental issue having a significant effect on the climate and causing extensive social, health and economical impacts. As a result, forest fire prediction has emerged as a key challenge in computational sustainability. Existing forest fire prediction systems, such as the Canadian Forest Fire Danger Rating System (Natural Resources Canada), are based on handcrafted features and use data from instruments on the ground. However, data from instruments on the ground may not always be available. In this work, we propose a novel machine learning approach that uses historical satellite images to predict forest fires in Indonesia. Our prediction model achieves more than 0.86 area under the receiver operator characteristic(ROC) curve. Further evaluations show that the model's prediction performance remains above 0.81 area under ROC curve even with reduced data. The results support our claim that machine learning based approaches can lead to reliable and cost-effective forest fire prediction systems.</p>

Industrial Big Data and Computational Sustainability: Multi-Method Comparison Driven by High-Dimensional Data for Improving Reliability and Sustainability of Complex Systems

Sustainable development is of great significance. The emerging research on data-driven computational sustainability has become an effective way to solve this problem. This paper presents a fault diagnosis and prediction framework for complex systems based on multi-dimensional data and multi-method comparison, aimed at improving the reliability and sustainability of the system by selecting methods with relatively superior performance. This study took the avionics system in the industrial field as an example. Based on the literature research on typical fault modes and fault diagnosis requirements of avionics systems, three popular high-dimensional data-driven fault diagnosis methods—support vector machine, convolutional neural network, and long- and short-term memory neural network—were comprehensively analyzed and compared. Finally, the actual bearing failure data were used for programming in order to verify and compare various methods and the process of selecting the superior method driven by high-dimensional data was fully demonstrated. We attempt to provide a sustainable development idea that continuously explores multi-method integration and comparison, aimed at improving the calculation efficiency and accuracy of reliability assessments, optimizing system performance, and ultimately achieving the goal of long-term improvement of system reliability and sustainability.