Research Publications 2022
- Aizawa, A., Mandl, T., Carevic, Z., Hinze, A., Mayr, P., & Schaer, P. (2022). JCDL '22: Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries.New York, NY, USA: ACM. doi: 10.1145/3529372
- Bainbridge, D. (2022). Building digital library collections with Greenstone 3 tutorial. In Proc ACM/IEEE Joint Conference on Digital Libraries (JCDL '22). Cologne, Germany: ACM. doi:10.1145/3529372.3530944
- Bainbridge, D., Hilbing, G., Jiang, M., Hu, Y., Layne-Worthey, G., & Downie, J. S. (2022). A Study on the Accuracy of OCR-based and NLP-based detection of Japanese Text in the HathiTrust Extracted Features v2.0 Dataset. In Digital Humanities (pp. 620-621). Tokyo. Retrieved from https://dh2022.dhii.asia/dh2022bookofabsts.pdf
- Barcellos, M., & Sherry, J. (2022). TPC Chairs' Message. In Proc 18th International Conference on emerging Networking EXperiments and Technologies (CoNEXT 2022) (pp. VII-VIII). Rome, Italy. doi:10.1145/3555050.fm
- Barracchia, E. P., Pio, G., Bifet, A., Gomes, H. M., Pfahringer, B., & Ceci, M. (2022). LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding. Information Sciences, 606, 702-721. doi:10.1016/j.ins.2022.05.079
- Boeira, F., Asplund, M., & Barcellos, M. (2022). POSTER: No Doppelgänger: Advancing mobile networks against impersonation in adversarial scenarios. In Proc 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '22) (pp. 280-281). San Antonio, TX, USA. doi:10.1145/3507657.3529651
- Bowen, J., & Hinze, A. (2022). Participatory data design: managing data sovereignty in IoT solutions. Interacting with Computers, 31, 14 pages. doi:10.1093/iwc/iwac031
- Bowen, J., Weyers, B., & Liu, B. (2022). Creating formal models from informal design artefacts. International Journal of Human–Computer Interaction, 39(15), 3141-3158. doi:10.1080/10447318.2022.2095833
- Britten, D., & Reeves, S. (2022). Modelling a Blockchain for Smart Contract Verification using DeepSEA. In C. Artho, & P. C. Ölveczky (Eds.), Proc 8th ACM SIGPLAN International Workshop on Formal Techniques for Safety-Critical Systems (FTSCS '22) (pp. 88-94). Auckland, NZ: ACM. doi:10.1145/3563822.3568011
- Britten, D., Sjöberg, V., & Reeves, S. (2022). Provably Correct Smart Contracts: An Approach using DeepSEA. In A. Potanin (Ed.), Companion Proceedings of the 2022 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (pp. 5-6). Auckland, NZ: ACM. doi:10.1145/3563768.3564116
- Cassales, G., Gomes, H. M., Bifet, A., Pfahringer, B., & Senger, H. (2022). Balancing performance and energy consumption of bagging ensembles for the classification of data streams in edge computing. IEEE Transactions on Network and Service Management, 20(3), 3038-3054. doi:10.1109/TNSM.2022.3226505
- Chanajitt, R., Pfahringer, B., & Gomes, H. M. (2022). A comparison of neural network architectures for malware classification based on Noriben operation sequences. In E. Pimenidis, P. J. C. Angelov, & A. A. M. Papaleonidas (Eds.), Proc Artificial Neural Networks and Machine Learning (ICANN 2022) Part I, LNCS 13530 (pp. 428-440). Bristol, UK: Springer. doi:10.1007/978-3-031-15919-0_36
- Chanajitt, R., Pfahringer, B., Gomes, H. M., & Yogarajan, V. (2022). Multiclass malware classification using either static opcodes or dynamic API calls. In H. Aziz, D. Corrêa, & T. French (Eds.), Proc 35th Australasian Joint Conference on Advances in Artificial Intelligence (AI 2022) LNAI 13728 (pp. 427-441). Perth, WA, Australia. doi:10.1007/978-3-031-22695-3_30
- Corrick, T., & Kumar, V. (2022). Design and architecture of Progger 3: A low-overhead, tamper-proof provenance system. In G. Wang, K. K. R. Choo, R. K. L. Ko, Y. Xu, & B. Crispo (Eds.), Proc 1st International Conference on Ubiquitous Security (UbiSec 2021), Communications in Computer and Information Science 1557 (pp. 189-202). Guangzhou, China. doi:10.1007/978-981-19-0468-4_14
- Diaz, J., Bravo-Marquez, F., & Poblete, B. (2022). Language modeling on location-based social networks. ISPRS International Journal of Geo-Information, 11(2), 147. doi:10.3390/ijgi11020147
- Falconer, J. R., Frank, E., Polaschek, D. L. L., & Joshi, C. (2022). Methods for eliciting informative prior distributions: A critical review. Decision Analysis, 19(3), 189-204. doi:10.1287/deca.2022.0451
- Gunasekara, N., Gomes, H. M., Pfahringer, B., & Bifet, A. (2022). Online hyperparameter optimization for streaming Neural Networks. In Proc 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). Padua, Italy: IEEE. doi:10.1109/IJCNN55064.2022.9891953
- Gunasekara, N., Gomes, H., Bifet, A., & Pfahringer, B. (2022). Adaptive Neural Networks for Online Domain Incremental Continual Learning. In P. Pascal, & D. Ienco (Eds.), Pro 25th International Conference on Discovery Science (DS 2022), LNAI 13601 (pp. 89-103). Montpellier, France. doi:10.1007/978-3-031-18840-4_7
- Gunasekara, N., Gomes, H., Bifet, A., & Pfahringer, B. (2022). Adaptive online domain incremental continual learning. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Proc 31st International Conference on Artificial Neural Networks (ICANN'22), Part 1, LNCS 13529 (pp. 491-502). Bristol, UK. doi:10.1007/978-3-031-15919-0_41
- Haseeb, J., Malik, S. U. R., Mansoori, M., & Welch, I. (2022). Corrigendum to ‘Probabilistic modelling of deception-based security framework using markov decision process’ [Computers & Security 115 (2022)/102599] (Computers & Security (2022) 115, (S0167404821004223), (10.1016/j.cose.2021.102599)). Computers and Security, 117. doi:10.1016/j.cose.2022.102689
- Haseeb, J., Malik, S. U. R., Mansoori, M., & Welch, I. (2022). Probabilistic modelling of deception-based security framework using markov decision process. Computers and Security, 115. doi:10.1016/j.cose.2021.102599
- Haseeb, J., Mansoori, M., Hirose, Y., Al-Sahaf, H., & Welch, I. (2022). Autoencoder-based feature construction for IoT attacks clustering. Future Generation Computer Systems, 127, 487-502. doi:10.1016/j.future.2021.09.025
- Hinze, A., Vanderschantz, N., Sijnja, N., Rogers, B., & Cunningham, S. J. (2022). Physical metadata visualisation: The knitted personal library. In Proc 16th International Conference on Tangible, Embedded, and Embodied Interaction (TEI'22), Article 78. Daejeon, Republic of Korea: ACM. doi:10.1145/3490149.3505582
- Hinze, A., Vanderschantz, N., Timpany, C., Cunningham, S. J., Saravani, S., & Wilkinson, C. (2022). A study of mobile app use for teaching and research in higher education. Technology, Knowledge and Learning, 28, 1271-1299. doi:10.1007/s10758-022-09599-6
- Hirsz, M., Hunt, L., Mayo, M., & Chepulis, L. (2022). Symptoms associated with colorectal cancer in patients referred to secondary care. New Zealand Medical Journal, 135(1554), 137-139.
- Holmes, G., Frank, E., Fletcher, D., & Sterling, C. (2022). Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models. In Proc 27th International Conference on Intelligent User Interfaces (IUI '22) (pp. 584-593). New York, NY, USA: ACM. doi:10.1145/3490099.3511110
- König, J. L., Wu, S., Fitzgerald, A., Franken, M., & Witten, I. H. (2022). F-Lingo: Leveraging Smart CALL for massive open online courses. In J. Colpaert, & G. Stockwell (Eds.), Smart CALL: Personalization, contextualization, & socialization (pp. 293-319). London: Castledown. doi:10.29140/9781914291012-13
- König, J., Hinze, A., & Bowen, J. (2022). Workload categorization for hazardous industries: the semantic modelling of multi-modal physiological data. Future Generation Computer Systems, 141, 369-381. doi:10.1016/j.future.2022.11.019
- Li, L., Franken, M., & Wu, S. (2022). Sentence initial lexical bundles in Chinese and New Zealand PhD theses in the discipline of General and Applied Linguistics. Australian Review of Applied Linguistics. doi:10.1075/aral.21018.li
- Li, M., Frank, E., & Pfahringer, B. (2023). Large scale K-means clustering using GPUs. Data Mining and Knowledge Discovery, 37, 67-109. doi:10.1007/s10618-022-00869-6
- Lim, N., Bifet, A., Bull, D., Frank, E., Jia, Y., Montiel, J., & Pfahringer, B. (2023). Showcasing the TAIAO project: providing resources for machine learning from images of New Zealand's natural environment. Journal of the Royal Society of New Zealand, 53(1), 69-81. doi:10.1080/03036758.2022.2118321
- Lorier, C., Luckie, M., Barcellos, M., & Nelson, R. (2022). Shoehorn: Towards portable P4 for low cost hardware. In 2022 IFIP Networking Conference. IEEE. doi:10.23919/ifipnetworking55013.2022.9829819
- Lubis, P. Y., & Shahri, B. (2022). Exploring student perceptions of product-service systems. In Research & Education in Design Conference 2022 Abstracts (pp. 12). Lisbon, Portugal. Retrieved from https://redesconference.fa.ulisboa.pt/
- Lubis, P. Y., & Shahri, B. (2022). Human-centered design as a qualitative research methodology in the area of public health. In Proc ECADE2022: The European Conference on Arts, Design and Education 2022. Porto, Portugal: The International Academic Forum(IAFOR). doi:10.22492/issn.2758-0989.2022.8
- Lubis, P. Y., Shahri, B., & Ramirez, M. (2022). Integration of human-centered design and design for sustainability tool: proposal of design for amelioration tool. MIX Sustentável, 8(2), 19-30. doi:10.29183/2447-3073.mix2022.v8.n2.19-30
- Lueg, C. P., Nichols, D., & Twidale, M. (2022). Warum eine bessere Usability die digitale Welt sozial gerechter macht ( Why better usability makes the digital world more socially just ). Bern University of Applied Sciences. Retrieved from https://www.societybyte.swiss/
- Lueg, C. P., Nichols, D., & Twidale, M. (2022). Why better usability makes the digital world more socially just. Bern University of Applied Sciences. Retrieved from https://www.societybyte.swiss/
- Lueg, C., Nichols, D., & Twidale, M. (2022). Usability matters—I’d like to complain about this user experience!. Information Matters, 2(3). Retrieved from https://informationmatters.org/
- Mazzola, F., Marcos, P., & Barcellos, M. (2022). Light, camera, actions: characterizing the usage of IXPs' action BGP communities. In Proc 18th International Conference on emerging Networking EXperiments and Technologies (CoNEXT '22) (pp. 196-203). Roma Italy: ACM. doi:10.1145/3555050.3569143
- Mazzola, F., Marcos, P., Castro, I., Luckie, M., & Barcellos, M. (2022). On the latency impact of remote peering. In O. Hohlfeld, G. Moura, & C. Pelsser (Eds.), Proc 23rd International Conference on Passive and Active Measurement (PAM 2022) LNCS 13210 (pp. 367-392). Cham: Springer. doi:10.1007/978-3-030-98785-5_16
- Mitchell, R., Cooper, J., Frank, E., & Holmes, G. (2022). Sampling permutations for Shapley value estimation. Journal of Machine Learning Research, 23(43), 1-46. Retrieved from https://jmlr.org/papers/volume23/21-0439/21-0439.pdf
- Mitchell, R., Frank, E., & Holmes, G. (2022). GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles. PeerJ Computer Science, 8. doi:10.7717/peerj-cs.880
- Mitchell, R., Stokes, D., Frank, E., & Holmes, G. (2022). Bandwidth-optimal random shuffling for GPUs. ACM Transactions on Parallel Computing, 9(1). doi:10.1145/3505287
- Muller, S., Yeo, A., Paruru, D., Kusabs, N., & Hinze, A. (2022). Multidisciplinary community-engaged learning pilot project with a New Zealand indigenous community: Opportunities and lessons learnt. In K. E. Zegwaard, & K. Hoskyn (Eds.), Refereed Proceedings of the 24th Work-Integrated Learning New Zealand Conference (pp. 19-25). Auckland, NZ.
- Pullar-Strecker, Z., Dost, K., Frank, E., & Wicker, J. (2022). Hitting the target: stopping active learning at the cost-based optimum. Machine Learning, 18 pages. doi:10.1007/s10994-022-06253-1
- Regenbrecht, H., Park, N., Duncan, S., Mills, S., Lutz, R., Lloyd-Jones, L., ..Keegan, TT . . Whaanga, H. (2022). Ātea Presence - Enabling virtual storytelling, presence, and tele-co-presence in an Indigenous setting. IEEE Technology and Society Magazine, 41(1), 32-42. doi:10.1109/MTS.2022.3147525
- Reijneveld, J. A., van Oostrum, M. J., Brolsma, K. M., Fletcher, D., & Oenema, O. (2022). Empower innovations in routine soil testing. Agronomy, 12(1). doi:10.3390/agronomy12010191
- Sahito, A., Frank, E., & Pfahringer, B. (2022). Better self-training for image classification through self-supervision. In G. Long, X. Yu, & S. Wang (Eds.), Proc 34th Australasian Joint Conference on Advances in Artificial Intelligence (AI 2021), LNAI 13151 (pp. 645-657). Sydney, Australia: Springer. doi:10.1007/978-3-030-97546-3_52
- Soo, C. -E. K. (2022). Co-WARe. In C. Soddu, & E. Colabella (Eds.), Proc XXV Annual International Generative Art Conference (GA2022) (pp. 74-78). Rome, Italy.
- Sun, Y., Pfahringer, B., Gomes, H. M., & Bifet, A. (2022). SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams. Data Mining and Knowledge Discovery, 36(5), 2006-2032. doi:10.1007/s10618-022-00858-9
- Trye, D., Calude, A., Keegan, T. T., & Julia, F. (2023). When loanwords are not lone words: Using networks and hypergraphs to explore Māori loanwords in New Zealand English. International Journal of Corpus Linguistics, 28(4), 461-499. doi:10.1075/ijcl.21124.try
- Trye, D., Keegan, T. T., Mato, P., & Apperley, M. (2022). Harnessing Indigenous Tweets: The Reo Māori Twitter corpus. Language Resources and Evaluation, 56, 1229-1268. doi:10.1007/s10579-022-09580-w
- Trye, D., Yogarajan, V., König, J., Keegan, T. T., Bainbridge, D., & Apperley, M. (2022). A hybrid architecture for labelling bilingual Māori-English tweets. In Y. He, H. Ji, S. Li, Y. Liu, & C. -H. Chang (Eds.), Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022 (pp. 119-130). Online: Association for Computational Linguistics. Retrieved from https://aclanthology.org/2022.findings-aacl.11.pdf
- Vanderschantz, N., Daly, N., & San, V. (2022). Typographic design in Māori-English bilingual picturebooks: some educational implications. Children's Literature in English Language Education, 10(1), 14-40. Retrieved from https://clelejournal.org/article-1-vanderschantz-daly-san/
- Wang, H., Fraser, H., Gouk, H., Frank, E., Pfahringer, B., Mayo, M., & Holmes, G. (2022). Experiments in cross-domain few-shot learning for image classification: extended abstract. In P. Brazdil, J. N. van Rijn, H. Gouk, & F. Mohr (Eds.), ECMLPKDD Workshop on Meta-Knowledge Transfer, Proceedings of Machine Learning Research Vol. 191 (pp. 81-83). Grenoble, France: PMLR. Retrieved from https://proceedings.mlr.press/v191/wang22a/wang22a.pdf
- Wang, H., Gouk, H., Fraser, H., Frank, E., Pfahringer, B., Mayo, M., & Holmes, G. (2023). Experiments in cross-domain few-shot learning for image classification. Journal of the Royal Society of New Zealand, 53(1), 169-191. doi:10.1080/03036758.2022.2059767
- Weng, M. H., Wu, S., & Dyer, M. (2022). Identification and visualization of key topics in scientific publications with transformer-based language models and document clustering methods. Applied Sciences (Switzerland), 12(21). doi:10.3390/app122111220
- Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2022). Data mining: Practical machine learning tools and techniques (Korean translation) (4th ed.). Korea: Acorn Publishing.
- Yeo, A., Hinze, A., Vanderschantz, N., Aporosa, A., & Paruru, D. (2022). Mobile app development: Work-integrated learning collaborations with Māori and Fijian partners. International Journal of Work-Integrated Learning, 23(2), 237-258.
- Yogarajan, V., Dobbie, G., Leitch, S., Keegan, T. T., Bensemann, J., Witbrock, M., . . . Reith, D. (2022). Data and model bias in artificial intelligence for healthcare applications in New Zealand. Frontiers in Computer Science, 4. doi:10.3389/fcomp.2022.1070493
- Yogarajan, V., Montiel, J., Smith, T., & Pfahringer, B. (2022). Predicting COVID-19 patient shielding: A comprehensive study. In Proc 34th Australasian Joint Conference on Advances in Artificial Intelligence (AI 2021) LNCS 13151 (pp. 332-343). Sydney, Australia: Springer International Publishing. doi:10.1007/978-3-030-97546-3_27
- Yogarajan, V., Pfahringer, B., Smith, T., & Montiel, J. (2022). Concatenating BioMed-Transformers to tackle long medical documents and to improve the prediction of tail-end labels. In E. Pimenidis, P. J. C. Angelov, & A. A. M. Papaleonidas (Eds.), Proc Artificial Neural Networks and Machine Learning (ICANN 2022) Part 1, LNCS 13530 (pp. 209-221). Cham: Springer. doi:10.1007/978-3-031-15931-2_18
- Zhang, Y., Pfahringer, B., Frank, E., Bifet, A., Lim, N. J. S., & Jia, Y. (2022). Repeated augmented rehearsal: A simple but strong baseline for online continual learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Proc 36th Conference on Advances in Neural Information Processing Systems (NeurIPS 2022) Vol. 35.
- Zheng, C., Pfahringer, B., & Mayo, M. (2022). Alzheimer's Disease detection via a surrogate brain age prediction task using 3D Convolutional Neural Networks. In Proc 22nd International Joint Conference on Neural Networks (IJCNN). Padua, Italy: IEEE. doi:10.1109/IJCNN55064.2022.9892974