Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news

Searching for Evidence of Scientific News in Scholarly Big Data

Public digital media can often mix factual information with fake scientific news, which is typically difficult to pinpoint, especially for non-professionals. These scientific news articles create illusions and misconceptions, thus ultimately influence the public opinion, with serious consequences at a broader social scale. Yet, existing solutions aiming at automatically verifying the credibility of news articles are still unsatisfactory. We propose to verify scientific news by retrieving and analyzing its most relevant source papers from an academic digital library (DL), e.g., arXiv. Instead of querying keywords or regular named entities extracted from news articles, we query domain knowledge entities (DKEs) extracted from the text. By querying each DKE, we retrieve a list of candidate scholarly papers. We then design a function to rank them and select the most relevant scholarly paper. After exploring various representations, experiments indicate that the term frequency-inverse document frequency (TF-IDF) representation with cosine similarity outperforms baseline models based on word embedding. This result demonstrates the efficacy of using DKEs to retrieve scientific papers which are relevant to a specific news article. It also indicates that word embedding may not be the best document representation for domain specific document retrieval tasks. Our method is fully automated and can be effectively applied to facilitating fake and misinformed news detection across many scientific domains