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In this article, the authors identify the correlative and complementary relations among multiple modalities. They then propose a multimodal ensemble fusion model to capture the complementary relation and correlative relation between two modalities (images and text) and explain why this ensemble fusion model works. Experimental results on the University of Illinois at Urbana-Champaign Image Sense Discrimination...
Anomaly detection in dynamic graphs is an emerging data mining research topic. However, applying anomaly detection to real-world industry problems such as insider threats, and banking fraud, is full of challenges. The multi-million dollar question is: What is a high-quality anomaly? In this paper we address the importance of reducing false positives and associating them with anomaly triage. After...
With the advent of abundant multimedia data on the Internet, there have been research efforts on multimodal machine learning to utilize data from different modalities. Current approaches mostly focus on developing models to fuse low-level features from multiple modalities and learn unified representation from different modalities. But most related work failed to justify why we should use multimodal...
Enterprises heavily rely on commercial security products to identify malicious domains for traffic blocking. However, new malicious domains emerge quickly and can remain undetected for extended time periods before security vendors identify them. This poses a serious threat to enterprise security. We believe that enterprises can take one step further than commercial tools/services by leveraging some...
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