Semantic Component Selection Based on Non-Functional Requirements

Reusing existing software components in place of requiring the implementation of new components can reduce the complexity of the software development process. However, for a software component to be effectively identified and selected for reuse, we need a good understanding of both the functional and non-functional requirements of the component needed, and the components available. Functional requirements specify what a software component does and non-functional requirements specify how a software component achieves its goals. Non-functional requirements are typically complex, and difficult to both understand and effectively articulate. Requirements engineering provides a solution to easing this process, and involves performing the following reasoning steps: elicitation, analysis and description. However, the output of these steps is based on reasoning that requires manual, expensive and error-prone techniques. To solve such drawbacks, this thesis describes a framework that provides the necessary tools and techniques for automating reasoning including: an ontology for non-functional requirements as a conceptual model for reasoning; and a search algorithm that matches the best component according to the reasoning process outputs. To validate our framework, we develop an implementation that supports semantic search within a repository to locate matches based on a user query, validated with experimental findings on a repository consisting of 50 individual component descriptions. Our findings demonstrate the benefit obtained from using an ontology, by minimizing the cost and complexity of analysing non-functional requirements. Our algorithm is capable of running a complex query, for example, supporting 5 non-functional requirements with total 16 prerequisites against a repository of 1000 components can run in 1750 second. It would be impossible for a field expert to compute a complex query in this time frame.Thesis (MCompSc) -- University of Adelaide, School of Computer Science, 201

Automated identification of astronauts on board the International Space Station: A case study in space archaeology

We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions

First Approximation of Population Distributions on the International Space Station

This paper presents an analysis of data derived from thousands of publicly available photographs showing life on the International Space Station (ISS) between 2000 and 2020. Our analysis uses crew and locational information from the photographs’ metadata to identify the distribution of different population groups—by gender, nationality, and space agency affiliation—across modules of the ISS, for the first time. Given the significance of the ISS as the most intensively inhabited space habitat to date, an international cooperative initiative involving 26 countries and five space agencies, and one of the most expensive building projects ever undertaken by humans, developing an understanding of which people are using different parts of the space station is critical for future usage of this and other stations. This study also sheds light on problems faced by future space station designers who are concerned with optimal usage of their habitats. The data from this investigation have been permanently deposited with Open Context. It is freely available for use under a Creative Commons license (CC BY 4.0) at https://doi.org/10.6078/M7668B9H

First Approximation of Population Distributions on the International Space Station

This paper presents an analysis of data derived from thousands of publicly available photographs showing life on the International Space Station (ISS) between 2000 and 2020. Our analysis uses crew and locational information from the photographs’ metadata to identify the distribution of different population groups—by gender, nationality, and space agency affiliation—across modules of the ISS, for the first time. Given the significance of the ISS as the most intensively inhabited space habitat to date, an international cooperative initiative involving 26 countries and five space agencies, and one of the most expensive building projects ever undertaken by humans, developing an understanding of which people are using different parts of the space station is critical for future usage of this and other stations. This study also sheds light on problems faced by future space station designers who are concerned with optimal usage of their habitats. The data from this investigation have been permanently deposited with Open Context. It is freely available for use under a Creative Commons license (CC BY 4.0) at https://doi.org/10.6078/M7668B9H

Automated Identification of Astronauts on Board the International Space Station: A Case Study in Space Archaeology

We develop and apply a deep learning-based computer vision pipeline to automatically identify crew members in archival photographic imagery taken on-board the International Space Station. Our approach is able to quickly tag thousands of images from public and private photo repositories without human supervision with high degrees of accuracy, including photographs where crew faces are partially obscured. Using the results of our pipeline, we carry out a large-scale network analysis of the crew, using the imagery data to provide novel insights into the social interactions among crew during their missions