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Anomaly analysis is an important component of any surveillance system. In recent years, it has drawn the attention of the computer vision and machine learning communities. In this article, our overarching goal is thus to provide a coherent and systematic review of state-of-the-art techniques and a comprehensive review of the research works in anomaly analysis. We will provide a broad vision of computational models, datasets, metrics, extensive experiments, and what anomaly analysis can do in images and videos. Intensively covering nearly 200 publications, we review (i) anomaly related surveys, (ii) taxonomy for anomaly problems, (iii) the computational models, (iv) the benchmark datasets for studying abnormalities in images and videos, and (v) the performance of state-of-the-art methods in this research problem. In addition, we provide insightful discussions and pave the way for future work. 

In recent years, the need to exploit digitized document data has been increasing. In this paper, we address the problem of parsing digitized Vietnamese paper documents. The digitized Vietnamese documents are mainly in the form of scanned images with diverse layouts and special characters introducing many challenges. To this end, we first collect the UIT-DODV dataset, a novel Vietnamese document image dataset that includes scientific papers in Vietnamese derived from different scientific conferences. We compile both images that were converted from PDF and scanned by a smartphone in addition a physical scanner that poses many new challenges. Additionally, we further leverage the state-of-the-art object detector along with the fused loss function to efficiently parse the Vietnamese paper documents. Extensive experiments conducted on the UIT-DODV dataset provide a comprehensive evaluation and insightful analysis. 

Frog virus 3 (FV3) is the type species of the genus Ranavirus (family Iridoviridae). FV3 and FV3-like viruses are globally distributed infectious agents with the capacity to replicate in three vertebrate classes (teleosts, amphibians, and reptiles). At the cellular level, FV3 and FV3-like viruses can infect cells from virtually all vertebrate classes. To date, the cellular receptors that are involved in the FV3 entry process are unknown. Class A scavenger receptors (SR-As) are a family of evolutionarily conserved cell-surface receptors that bind a wide range of chemically distinct polyanionic ligands and can function as cellular receptors for other DNA viruses, including vaccinia virus and herpes simplex virus. The present study aimed to determine whether SR-As are involved in FV3 cellular entry. By using well-defined SR-A competitive and non-competitive ligand-blocking assays and absolute qPCR, we demonstrated that the SR-A competitive ligands drastically reduced the quantities of cell-associated viral loads in frog cells. Moreover, inducing the expression of a human SR-AI in an SR-A null cell line significantly increased FV3–cell association. Together, our results indicate that SR-As are utilized by FV3 during the cellular entry process.