Now showing 1 - 2 of 2
  • Publication
    Context-Aware Mixed Reality Data Visualization for Decision Support and Explanations
    (University College Dublin. School of Computer Science, 2022) ;
    Augmented Reality (AR), as a novel data visualisation tool, is advantageous in revealing spatial data patterns and data-context associations. Recent research has identified AR data visualisation as a promising approach to increasing decision-making efficiency and effectiveness. Existing literature also presented numerous possibilities for applying AR data visualisation in various Decision Support Systems (DSSs) to enhance knowledge conveying and comprehension. However, several essential issues impeded the popularization of AR-based DSS in people's daily life. In information-rich environments where various high-volume datasets are updating at high velocity, users may easily perceive information overload if the decision support datasets are not filtered and visualised appropriately. Information overloading issues will harm users' understanding of the data and thus hinder decision efficiency. Moreover, prior studies have indicated the issue of low recommendation adoption rates and found such issues can be more severe for users who lack sufficient education and training in relevant domains. Accordingly, a successful DSS needs to provide understandable and explainable decision support data with the ability to handle dynamically changing environments and the changing requirements of even non-expert user groups'. Therefore, being aware of the changing contexts of the decision environment and decision makers will be an essential capability for modern DSS. Context awareness has been combined with mobile AR to facilitate ubiquitous visualisations that support personalization, selective sharing, and interaction of contents. Prior works demonstrated the potential of utilizing context-aware AR to support decision-making. However, the AR-based DSS area is still at the stage of preliminary attempts to show the potential and possibilities. No thorough reviews have been presented to investigate the design methodologies and comprehensively evaluate relevant techniques and theories. Several compelling challenges are still not addressed. Therefore, this area's profound values and abundant possibilities have not been revealed to boost its popularisation in important industries and people's daily lives. Therefore, this thesis aims to push the AR-based DSS research area to the next stage by filling the research gaps and addressing the compelling challenges in this area. It will present context-aware AR solutions to improve visualisation relevance, immediacy, and interaction intuitiveness in various decision contexts for non-expert users. To achieve this research goal, this thesis will first review the state of the art in AR-based DSS areas and redefine the context-awareness methodology for decision support. With this theoretical background, this thesis proposes several research questions and corresponding context-aware solutions. The second part of this thesis will present multiple context-aware AR technologies and applications to manifest the feasibility of these solutions in various decision-making scenarios. Next, the third part of this thesis will present several studies to prove the values and advantages of these context-aware AR-based DSS solutions to address the compelling challenges identified before. These studies provide multiple findings to show how these novel technologies exploit various contextual data to solve information overloading and low recommendation adoption issues. With these findings, this thesis demonstrates that context-aware AR data visualisation may enhance the entire decision support process, including dataset generation, filtering, localisation, visualisation, and interactions. Also, these contributions made by this thesis will hopefully advance this field with enhanced decision outcomes, friendly user experience, and lower entry barriers. With such improvements, AR-based DSS will be ubiquitously applied in people's daily lives.
  • Publication
    CodEX: Source Code Plagiarism Detection Based on Abstract Syntax Trees
    (CEUR Workshop Proceedings, 2018-12-07) ; ;
    CodEX is a source code search engine that allows users to search a repository of source code snippets using source code snippets as the query also. A potential use for such a search engine is to help educators identify cases of plagiarism in students' programming assignments. This paper evaluates CodEX in this context. Abstract Syntax Trees (ASTs) are used to represent source code files on an abstract level. This, combined with node hashing and similarity calculations, allows users to search for source code snippets that match suspected plagiarism cases. A number of commonly-employed techniques to avoid plagiarism detection are identified, and the CodEX system is evaluated for its ability to detect plagiarism cases even when these techniques are employed. Evaluation results are promising, with 95% of test cases being identified successfully.