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Abstract-Large and continuously growing dynamic web content has created new opportunities for large-scale data analysis in the recent years. There is huge amount of information that the traditional web crawlers cannot access, since they use link analysis technique by which only the surface web can be accessed. Traditional search engine crawlers require the web pages to be linked to other pages via hyperlinks causing large amount of web data to be hidden from the crawlers. Enormous data is available in deep web that can be useful to gain new insight for various domains, creating need to access the information from the deep web by developing efficient techniques. As the amount of Web content grows rapidly, the types of data sources are proliferating, which often provide heterogeneous data. So we need to select Deep Web Data sources that can be used by the integration systems. The paper discusses various techniques that can be used to surface the deep web information and techniques for Deep Web Source Selection.

While describing visual data is a trivial task for humans, it is an intricate task for a computer. This is even more challenging if the visual data is a video. Comprehending a video and describing it is called Video Captioning. This involves understanding the semantics of a video and then generating humanlike descriptions of the video. It requires the collaboration of both research communities of computer vision and natural language processing. The captions generated by video captioning can be further utilized for video retrieval, summarization, question-answering, etc. Video Question-Answering (video-QA) involves querying the system to obtain an answer in response. This paper presents a brief survey of the video captioning techniques and a comprehensive review of existing techniques, datasets, and evaluation metrics for the task of video-QA. Video-QA techniques rely on the attention mechanism to generate relevant results. The presented survey shows that recent works on Memory Networks, Generative Adversarial Networks, and Reinforced Decoders, have the capability to handle the complexities and challenges of video-QA. Additionally, the graph-based methods, although less explored, give very promising results. In this article, we have discussed the emerging research directions and various application areas of video-QA.

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