Computing Conference Papers

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    Differences in e-government trust between people with high and low IT innovativeness
    (Australasian (ACIS) at AIS Electronic Library (AISeL), 2025-12-02) Dell, P.; Kabbar, Eltahir
    While it is important to understand citizens' trust in e-government, theory on this topic does not accommodate potential differences between innovators and non-innovators. To investigate factors of trust development for two groups (innovators and non-innovators) and produce a multigroup model we conducted an EFA and CFA, followed by Structural Equation Modelling to test hypotheses for each group. We used the Perceived Innovativeness in IT (PIIT) construct to identify innovators and non-innovators. Our findings demonstrate that e-government trust processes for innovators and non-innovators are different, and our model accounts for more than half the variance in Trust in e-Government. Key antecedents for innovators are Technology Self-Efficacy and Trust in Government, and for non-innovators are Technology Self-Efficacy and Social Influence. We discuss some of the implications of these findings for both theory and practice.
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    Integration of heterogeneous software using XML Webservice Middleware: Case study: WorkOS and PSA software
    (2022-11-30) Thirunahari, S.; Ramirez Prado, Guillermo; Barmada, Bashar; Unitec, Te Pūkenga; Te Pūkenga
    AGENDA Software integration overview About and OpenAir Timesheets Business problem Proposed methodology Software integration approaches Business goals and requirements Software integration method OpenAir API integration methods GraphQL API API integration trends and OpenAir integration & expected final product Conclusion Project timeline
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    Acoustic signal processing systems for intelligent beehive monitoring
    (Acoustical Society of New Zealand, 2022) Ardekani, Iman; Varastehpour, Soheil; Sharifzadeh, Hamid; Unitec, Te Pūkenga
    Bees, as pollinators and producers of honey and medicinal products, play a crucial role in human life and environmental sustainability. Emerging Smart Beekeeping technologies utilise various methodologies in apiology, agricultural science, computer science, and electrical engineering. A significant part of these technologies includes data-driven and intelligent condition monitoring systems that can ideally imitate expert beekeepers. This paper shows that the acoustic signals generated by bees form an efficient and reliable source of knowledge about the beehive and its bee colony. Also, it proposes an acoustic signal processing system for intelligent and data-driven beehive monitoring. The proposed system includes acoustic data acquisition, noise reduction, feature extraction and machine learning techniques for inferential or predictive data analysis. This system can be used for different monitoring purposes; however, this paper focuses on queenless beehive identification. Finally, this paper reports a flexible experimental setup for developing and testing intelligent beehive monitoring systems.
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    Bayesian active noise control
    (Acoustical Society of New Zealand, 2022-10) Ardekani, Iman; Varastehpour, Soheil; Sharifzadeh, Hamid; Unitec, Te Pūkenga
    Active Noise Control (ANC) is a challenging practical application of adaptive control systems. This paper approaches ANC from the perspective of the Bayesian Inverse Problems theory. The ANC underlying problem is initially formulated as a generic Bayesian inverse problem. A solution to this problem is then obtained using standard methods in the Bayesian Inverse Problems theory, resulting in a new adaptive algorithm for ANC. The results show the effectiveness of the chosen approach in creating new adaptive algorithms for ANC, one of which is presented in this paper. This algorithm can reach a probabilistic model for the optimal control systems, but conventional algorithms can reach only a deterministic model. Consequently, unlike other algorithms, the proposed algorithm can quantify the uncertainty associated with the adaptive control process in active noise control.
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    Smart beekeeping using IoT & ML
    (2022-12-02) Ardekani, Iman; Shakiba, Masoud; Unitec, Te Pūkenga; Te Pūkenga
    OUTLINE 1 Introduction 2 Intelligent beehive monitoring 3 Proposed system 4 Data collection 5 Conclusion