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    <title>STORRE Collection: Electronic copies of Computing Science and Mathematics conference papers and proceedings.</title>
    <link>http://hdl.handle.net/1893/478</link>
    <description>Electronic copies of Computing Science and Mathematics conference papers and proceedings.</description>
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        <rdf:li rdf:resource="http://hdl.handle.net/1893/38347" />
        <rdf:li rdf:resource="http://hdl.handle.net/1893/38262" />
        <rdf:li rdf:resource="http://hdl.handle.net/1893/38059" />
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    <dc:date>2026-10-03T18:11:28Z</dc:date>
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  <item rdf:about="http://hdl.handle.net/1893/38347">
    <title>Bridging Learning Outcomes and Module Specifications: GenAI Framework for Curriculum Development in CS Education</title>
    <link>http://hdl.handle.net/1893/38347</link>
    <description>Title: Bridging Learning Outcomes and Module Specifications: GenAI Framework for Curriculum Development in CS Education
Author(s): Elawady, Mohamed; Ali, Hazrat
Abstract: Recent developments in generative artificial intelligence (GenAI) have enabled the production of high-quality content in many domains , including education and computing. These advances offer opportunities to support tutors in curriculum development and to enhance the learning experience for students. This paper presents an end-to-end pipeline that automatically suggests a set of module specifications based on user input and structured guidelines.</description>
    <dc:date>2026-09-03T00:00:00Z</dc:date>
  </item>
  <item rdf:about="http://hdl.handle.net/1893/38262">
    <title>Optimizing Appliance Scheduling for Solar Energy Management Us-ing Metaheuristic Algorithms</title>
    <link>http://hdl.handle.net/1893/38262</link>
    <description>Title: Optimizing Appliance Scheduling for Solar Energy Management Us-ing Metaheuristic Algorithms
Author(s): Ahmed, Hiba; Brownlee, Alexander E I; Adair, Jason; Powers, Simon T
Abstract: Solar energy generation is often misaligned with when households use power, creating a scheduling challenge. We optimize appliance start times in an island microgrid setting to minimize user dissatisfaction while promoting solar use and respecting system constraints. A sequential multi-day scheduling framework using Iterated Local Search (ILS) and Simulated Annealing (SA) considers power consumption, active duration, inverter size, battery limits, and solar forecasts, opening potential to explore trade-offs between cost, system size, and satisfaction.</description>
    <dc:date>2026-08-13T00:00:00Z</dc:date>
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  <item rdf:about="http://hdl.handle.net/1893/38059">
    <title>Empowering Stakeholders with Participatory Auditing of Predictive AI: Perspectives from End-Users and Decision Subjects without AI Expertise</title>
    <link>http://hdl.handle.net/1893/38059</link>
    <description>Title: Empowering Stakeholders with Participatory Auditing of Predictive AI: Perspectives from End-Users and Decision Subjects without AI Expertise
Author(s): Di Campli San Vito, Patrizia; Fringi, Eva; Johnston, Penny; Bezerra, Leonardo C T; Aristodemou, Marios; Shahandashti, Siamak F; O'Hara, Emily; Fiona Whyte, Laura; Luo, Lin; Wong, Mark; Soufan, Ayah; Moshfeghi, Yashar; Stumpf, Simone
Abstract: Artificial intelligence (AI) applications have become ubiquitous in their impact on individuals and society, highlighting a crucial need for their responsible development. Recent research has called for participatory AI auditing, empowering individuals without AI expertise to audit AI applications throughout the entire AI development pipeline. Our work focuses on investigating how to support these kinds of auditors through participatory AI auditing tools and processes. We conducted a series of co-design workshops, using two health-related predictive AI applications as examples. Our results show that participants wanted to be part of AI audits, and were insightful in identifying the potential impacts of applications, but needed to be assisted in conducting audits, especially how to measure impacts. Importantly, participants provided examples of impacts not considered in current risk/harm taxonomies. Our findings provide implications for the design of tools and processes to empower everyone to contribute to responsible AI development in the future.</description>
    <dc:date>2026-04-01T00:00:00Z</dc:date>
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  <item rdf:about="http://hdl.handle.net/1893/37758">
    <title>Simulated performance of antenna position estimation through sub-sampled exponential analysis</title>
    <link>http://hdl.handle.net/1893/37758</link>
    <description>Title: Simulated performance of antenna position estimation through sub-sampled exponential analysis
Author(s): Weideman, Rina-Mari; Louw, Ridalise; Knaepkens, Ferre; de Villiers, Dirk; Cuyt, Annie; Lee, Wen-shin; Wijnholds, Stefan J.
Abstract: Antenna position estimation is an important problem in large irregular arrays where the positions might not be known very accurately from the start. In a previous paper we presented a method using harmonically related signals transmitted from an Unmanned Aerial Vehicle (UAV), with the added advantage that the UAV can be in the near-field of the receiving antenna array. It was shown that the method delivers excellent results using ideal synthetic data with added noise. In this paper we continue the work by simulating the problem in a full-wave solver. Although the results are less accurate than when synthetic data are used, due to the effects of mutual coupling, the method still performs satisfactorily, with errors smaller than 4% of the smallest transmitted wavelength.</description>
    <dc:date>2022-09-28T00:00:00Z</dc:date>
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