Distributional Shift Adaptation using Domain-Specific Features

Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming big data can be Out-Of-Distribution (OOD), rendering these algorithms ineffective. Prior solutions to the OOD challenge seek to identify invariant features across different training domains. The underlying assumption is that these invariant features should also work reasonably well in the unlabeled target domain. By contrast, this work is interested in the domain-specific features that include both invariant features and features unique to the target domain. We propose a simple yet effective approach that relies on correlations in general regardless of whether the features are invariant or not. Our approach uses the most confidently predicted samples identified by an OOD base model (teacher model) to train a new model (student model) that effectively adapts to the target domain. Empirical evaluations on benchmark datasets show that the performance is improved over the SOTA by ~10-20

Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data.Comment: Accepted in IEEE Big Data 2

FBG-Based Creep Analysis of GFRP Materials Embedded in Concrete

This paper presents a typical study regarding the creep interaction behavior between prestressed glass fiber reinforced polymer (GFRP) bar and concrete when this GFRP bar is subjected to a constant external pullout force. A number of optical fiber Bragg grating (FBG) sensors were mounted on GFRP bar surface by using an innovative installation method to measure strain distributions. Test results indicate that the complicated interaction at GFRP bar-concrete interface can be evaluated using a transitional factor. Variation trends of this transitional factor indicate three typical zones characterized by different strain/stress variation trends of the GFRP bar when prestress values are sustained at specific levels. These three typical zones include stress release zone, stress transition zone, and continuous tension zone. Test results also suggest that the instant stress loss at the interaction interface between concrete and GFRP bar was quite limited (less than 5%) in present test. Contributed proportion of each GFRP bar section was obtained to represent the creep behavior of the GFRP bar embedded in concrete. This investigation improved the understanding of the short-term interaction behavior between prestressed GFRP bar and concrete