Publications

Singh, AK & Zhu, H 2024, 'Human-Computer Interface in Smart Agriculture' in Encyclopedia of Smart Agriculture Technologies, Springer.
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Our reference work takes full advantage of this feature, which allows for continuous improvement or revision of published content electronically. The Editorial BoardDr. Irwin R. Donis-Gonzalez, University of California Davis, Dept.

Trianni, A, Bennett, N, Cantley-Smith, R, Cheng, C-T, Dunstall, S, Hasan, ASMM, Katic, M, Leak, J, Lindsay, D, Pears, A, Wheatland, FT, White, S & Zeichner, F 2024, 'Industry 4.0 for Energy Productivity: Insights and Future Perspective for Australia' in Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems, Springer Nature Switzerland, pp. 547-554.
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Cui, H, Han, M, Xu, F, Liu, Q & Saha, SC 2024, 'Scaling analysis of intrusion flow and thermal plume for Pr > 1 in the triangular cavity', International Journal of Thermal Sciences, vol. 195, pp. 108616-108616.
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Luo, Y, Chu, Y, Fu, X, Canning, J, Wang, J, Zhang, J, Yan, B, Wen, J, Wang, T & Peng, G-D 2024, '3D printing specialty multi-function twin core Bi/Er co-doped silica optical fibres for ultra-broadband polarized near infrared emission and sensing applications', Optics & Laser Technology, vol. 168, pp. 109817-109817.
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Qashlan, A, Nanda, P & Mohanty, M 2024, 'Differential privacy model for blockchain based smart home architecture', Future Generation Computer Systems, vol. 150, pp. 49-63.
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Wu, Y, Cao, J & Xu, G 2024, 'FASTER: A Dynamic Fairness-assurance Strategy for Session-based Recommender Systems', ACM Transactions on Information Systems, vol. 42, no. 1, pp. 1-26.
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When only users’ preferences and interests are considered by a recommendation algorithm, it will lead to the severe long-tail problem over items. Therefore, the unfair exposure phenomenon of recommended items caused by this problem has attracted widespread attention in recent years. For the first time, we reveal the fact that there is a more serious unfair exposure problem in session-based recommender systems (SRSs), which learn the short-term and dynamic preferences of users from anonymous sessions. Considering the fact that in SRSs, recommendations are provided multiple times and item exposures are accumulated over interactions in a session, we define new metrics both for the fairness of item exposure and recommendation quality among sessions. Moreover, we design a dynamic F airness- A ssurance ST rategy for s E ssion-based R ecommender systems ( FASTER ). FASTER is a post-processing strategy that tries to keep a balance between item exposure fairness and recommendation quality. It can also maintain the fairness of recommendation quality among sessions. The effectiveness of FASTER is verified on three real-world datasets and five original algorithms. The experiment results show that FASTER can generally reduce the unfair exposure of different session-based recommendation algorithms while still ensuring a high level of recommendation quality.