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Searching large digital repositories can be extremely frustrating, as common list-based formats encourage users to adopt a convenience-sampling approach that favours chance discovery and random search, over meaningful exploration. We have designed a methodology that allows users to visually and thematically explore corpora, while developing personalised holistic reading strategies. We describe the results of a three-phase qualitative study, in which experienced researchers used our interactive visualisation approach to analyse a set of publications and select relevant themes and papers. Using in-depth semi-structured interviews and stimulated recall, we found that users: (i) selected papers that they otherwise would not have read, (ii) developed a more coherent reading strategy, and (iii) understood the thematic structure and relationships between papers more effectively. Finally, we make six design recommendations to enhance current digital repositories that we have shown encourage users to adopt a more holistic and thematic research approach.
Finding the semantically accurate answer is one of the key challenges in advanced searching. In contrast to keyword-based searching, the meaning of a question or query is important here and answers are ranked according to relevance. It is very natura
Mixed reality (MR) technology development is now gaining momentum due to advances in computer vision, sensor fusion, and realistic display technologies. With most of the research and development focused on delivering the promise of MR, there is only
Ranked search results have become the main mechanism by which we find content, products, places, and people online. Thus their ordering contributes not only to the satisfaction of the searcher, but also to career and business opportunities, education
Each claim in a research paper requires all relevant prior knowledge to be discovered, assimilated, and appropriately cited. However, despite the availability of powerful search engines and sophisticated text editing software, discovering relevant pa
Although Vietnamese is the 17th most popular native-speaker language in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it. One of the re