A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities simultaneously.Specifically,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.

Distribution and Collection of China's Dinosaurs

It is estimated that the world has discovered more than1000 species and 800 genera of dinosaurs.Chinese scientists have named more than 170 species of dinosaurs,with 17 genera and 44 species of dinosaur egg fossils,35genera and 39 species of dinosaur footprints,since their first discovery in China in 1902.

PM10 emissions from industrial coal-fired chain-grate boilers

Industrial coal-fired boiler is an important air pollutant emission source in China. The chain-grate boiler is the most extensively used type of industrial coal-fired boiler. An electrical low-pressure impactor, and a Dekati？ Low Pressure Impactor were applied to determine mass and number size distributions of PM10 at the inlet and the outlet of the particulate emission control devices at six coalfired chain-grate boilers. The mass size distribution of PM10 generated from coal-fired chain-grate boilers generally displays a bimodal distribution that contains a submicron mode and a coarse mode. The PM in the submicron mode for burning with raw coal contributes to 33% ± 10 % of PM10 emissions, much higher than those for pulverized boilers. And the PM in the submicron mode for burning with briquette contributes up to 86 % of PM10 emissions. Multiclones and scrubbers are not efficient for controlling PM10 emission. Their average collection efficiencies for sub-micron particle and super-micron particle are 34% and 78%, respectively. Operating conditions of industrial steam boilers have influence on PM generation. Peak of the submicron mode during normal operation period is larger than the start-up period.