Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.Comment: Accept by AAAI 2023. Code is available at https://github.com/thuiar/Robust-MS

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs

Is there an association between mild cognitive impairment and dietary pattern in chinese elderly? Results from a cross-sectional population study

<p>Abstract</p> <p>Background</p> <p>Diet has an impact on cognitive function in most prior studies but its association with Mild Cognitive Impairment (MCI) in Chinese nonagenarians and centenarians has not been explored.</p> <p>Methods</p> <p>870 elder dujiangyan residents aged 90 years or more in 2005 census were investigated at community halls or at home. They underwent the Mini-Mental State Examination (MMSE) for assessment of cognitive function and replied to our questionnaire comprised of 12 food items and other risk factors. MCI was defined by two steps: first, subjects with post-stroke disease, Alzheimer's disease or Parkinson's disease and MMSE< 18 were excluded; and then subjects were categorized as MCI (MMSE scores between 19 and 24) and normal (MMSE scores between 25 and 30). Logistic regression models were used to analyze the association between diet and the prevalence of MCI. The model was adjusted for gender, ages, systolic blood pressure, diastolic blood pressure, body mass index, fasting plasma glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, smoking habits, alcohol and tea consumption, educational levels and exercise in baseline dietary assessment.</p> <p>Results</p> <p>364 elderly finally included, 108 (38.71%) men and 171 (61.29%) women of whom were classified as MCI. A significant correlation between MCI and normal in legume was observed (OR, 0.84; 95%CI, 0.72-0.97), and also in animal oil (any oil that obtained from animal substances) (OR, 0.93; 95%CI, 0.88-0.98). There was no statistical difference of other food items between normal and MCI.</p> <p>Conclusions</p> <p>Among Chinese nonagenarians and centenarians, we found there were significant associations between inadequate intake of legume and animal oil and the prevalence of MCI. No significant correlation between other food items and the prevalence of MCI were demonstrated in this study.</p

Uncovering Insurance Fraud Conspiracy with Network Learning

Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.Comment: Accepted by SIGIR '19. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 201

Explicit Haze & Cloud Removal for Global Land Cover Classification

Haze and clouds in Earth's atmosphere obstruct a seamless monitoring of our planet via optical satellites. Prior work shows that models can learn to adapt and perform remote sensing downstream tasks even in the presence of such sensor noise. So what are the auxiliary benefits of incorporating an explicit cloud removal task, and what is its relation to other tasks in the remote sensing pipeline? We address these questions and show that explicit cloud removal makes models for land cover classification furthermore robust to haze and clouds. Finally, we explore the relation to a self-supervised pre-text task (including abundant cloudy data) and demonstrate how to further ease the need for costly annotations on the land cover classification task