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Research Publications 2019

  • Alyamani, H. J., Hinze, A., Smith, S., & Kavakli, M. (2019). Preference feedback for driving in an unfamiliar traffic regulation. In H-P. Lam & S. Mistry (Eds.), Proc Australian Symposium on Service Research and Innovation (ASSRI 2018) LNBIP 367 (pp. 35-49).Wollongong, NSW, Australia: Springer.  doi:10.1007/978-3-030-32242-7_4
  • Anderson, R., Koh, Y. S., Dobbie, G., & Bifet, A. (2019). Recurring concept meta-learning for evolving data streams. Expert Systems with Applications, 138. doi:10.1016/j.eswa.2019.112832
  • Apperley, M. (2019). Modelling Fractal-Structured Smart Microgrids: Exploring signals and protocols. In M. Negnevitsky, & V. Sultan (Eds.), Proc ENERGY 2019, The Ninth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (pp. 13-17). Athens, Greece: IARIA.
  • Bahri, M., Maniu, S., & Bifet, A. (2019). A sketch-based naive bayes algorithms for evolving data streams. In Proc IEEE International Conference on Big Data, Big Data 2018 (pp. 604-613). Seattle, WA, USA. doi:10.1109/BigData.2018.8622178
  • Bainbridge, D. (2019). Digital libraries: mission accomplished?. SRELS Journal of Information Management, 56(4), 159-170. doi:10.17821/srels/2019/v56i4/146594
  • Bainbridge, D. (2019). Disassembling the software architecture of digital libraries: getting more out of the building blocks. SRELS Journal of Information Management, 56(5), 222-239. doi:10.17821/srels/2019/v56i5/147812
  • Bainbridge, D. (2019). Redefining digital library boundaries: being more than an information silo. SRELS Journal of Information Management, 56(6), 274-287. doi:10.17821/srels/2019/v56i6/149766
  • Bainbridge, D., Nichols, D. M., Hinze, A., & Downie, J. S. (2019). Using the HTRC Data Capsule Model to promote reuse and evolution of experimental analysis of digital library data: a case study of topic modeling. In Proc 19th ACM/IEEE Joint Conference on Digital Libraries (JCDL 2019) (pp. 463-464). Champaign, IL, USA: IEEE. doi:10.1109/jcdl.2019.00124
  • Barcellos, M., & Aranha, D. F. (2019). Research in security and privacy in Brazil. IEEE Security and Privacy, 16(6), 14-21. doi:10.1109/MSEC.2018.2874855
  • Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Boosting decision stumps for dynamic feature selection on data streams. Information Systems 83, 13-29. doi:10.1016/j.is.2019.02.003
  • Barddal, J. P., Enembreck, F., Gomes, H. M., Bifet, A., & Pfahringer, B. (2019). Merit-guided dynamic feature selection filter for data streams. Expert Systems with Applications 116, 227-242. doi:10.1016/j.eswa.2018.09.031
  • Bifet, A., Berlingerio, M., Gama, J., Read, J., & Ryan, E. (2019). Big data mining (KDD Bigmine-19). In CEUR Workshop Proceedings 2579.
  • Bifet, A., Carvalho, A., Ferreira, C., & Gama, J. (2019). Special track on data streams. In Proc ACM Symposium on Applied Computing Part F147772 (pp. 556).
  • Bifet, A., Hammer, B., & Schleif, F. M. (2019). Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. In Proc 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2019) (pp. 421-430).
  • Boeira, F., Asplund, M., & Barcellos, M. (2019). Decentralized proof of location in vehicular Ad Hoc networks. Computer Communications, 147, 98-110. doi:10.1016/j.comcom.2019.07.024
  • Boeira, F., Asplund, M., & Barcellos, M. P. (2019). Mitigating position falsification attacks in vehicular platooning. In IEEE Vehicular Networking Conference, VNC, 2018-December (pp. 1-4). Taipei, Taiwan. doi:10.1109/VNC.2018.8628427
  • Boiko Ferreira, L. E., Murilo Gomes, H., Bifet, A., & Oliveira, L. S. (2019). Adaptive random forests with resampling for imbalanced data streams. In Proc International Joint Conference on Neural Networks 2019-July. doi:10.1109/IJCNN.2019.8852027
  • Booysen, M. J., Engelbrecht, J. A. A., Ritchie, M. J., Apperley, M., & Cloete, A. H. (2019). How much energy can optimal control of domestic water heating save? Energy for Sustainable Development, 51, 73-85. doi:10.1016/j.esd.2019.05.004
  • Boulegane, D., Bifet, A., & Madhusudan, G. (2019). Arbitrated Dynamic Ensemble with Abstaining for Time-Series Forecasting on Data Streams. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 1040-1045). doi:10.1109/BigData47090.2019.9005541
  • Boulegane, D., Radulovic, N., Bifet, A., Fievet, G., Sohn, J., Nam, Y., . . . Choi, D. W. (2019). Real-Time Machine Learning Competition on Data Streams at the IEEE Big Data 2019. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 3493-3497). doi:10.1109/BigData47090.2019.9006357
  • Bowen, J., & Hinze, A. (2019). Smarter software engineering methods for smart environments. In 2nd Workshop on Charting the Way towards Methods and Tools for Advanced Interactive Systems at EICS 2019. Valencia, Spain.
  • Bravo-Marquez, F., Frank, E., Pfahringer, B., & Mohammad, S. M. (2019). AffectiveTweets: a Weka package for analyzing affect in tweets. Journal of Machine Learning Research 20, 1-6. Retrieved from http://jmlr.org/papers/v20/18-450.html
  • Bravo-Marquez, F., Reeves, S., & Ugarte, M. (2019). Proof-of-learning: A blockchain consensus mechanism based on machine learning competitions. In Proc 2019 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPCON 2019) (pp. 119-124). East Bay, San Francisco, California, USA: IEEE. doi:10.1109/DAPPCON.2019.00023
  • Brockmann, P., Schuhbauer, H., & Hinze, A. (2019). Diversity as an advantage: An analysis of career competencies for it students. In 16th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2019 (pp. 209-216).
  • Burnside, M., Crocket, H., Mayo, M., Pickering, J., Tappe, A., & de Bock, M. (2019). Do it yourself automated insulin delivery: A leading example of the democratization of medicine. Journal of Diabetes Science and Technology. doi:10.1177/1932296819890623
  • Calude, A., Stevenson, L., Whaanga, H., & Keegan, T. T. (2019). The use of Māori words in National Science Challenge online discourse. Journal of the Royal Society of New Zealand, 1-18. doi:10.1080/03036758.2019.1662818
  • Calude, A., Stevenson, L., Whaanga, H., & Keegan, T. T. (2019). The use of Māori words in National Science Challenge online discourse. Journal of the Royal Society of New Zealand, 18 pages. doi:10.1080/03036758.2019.1662818
  • Carnein, M., Trautmann, H., Bifet, A., & Pfahringer, B. (2019). Towards automated configuration of stream clustering algorithms. In P. Cellier, & K. Driessens (Eds.), Machine Learning and Knowledge Discovery in Databases: Proc ECML PKDD 2019, Part 1 Vol. CCIS 1167 (pp. 137-143). Würzburg, Germany: Springer, Cham. doi:10.1007/978-3-030-43823-4_12
  • Cassales, G. W., Senger, H., De Faria, E. R., & Bifet, A. (2019). IDSA-IoT: An Intrusion Detection System Architecture for IoT Networks. In Proceedings - International Symposium on Computers and Communications 2019-June. doi:10.1109/ISCC47284.2019.8969609
  • Chehreghani, M. H., Abdessalem, T., & Bifet, A. (2019). Metropolis-hastings algorithms for estimating betweenness centrality. Advances in Database Technology - EDBT, 2019-March, 686-689. doi:10.5441/002/edbt.2019.87
  • Chehreghani, M. H., Bifet, A., & Abdessalem, T. (2019). Adaptive algorithms for estimating betweenness and k-path centralities. In Proc International Conference on Information and Knowledge Management (pp. 1231-1240). doi:10.1145/3357384.3358064
  • Chehreghani, M. H., Bifet, A., & Abdessalem, T. (2019). An in-depth comparison of group betweenness centrality estimation algorithms. In Proc 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 2104-2113). Seattle, WA, USA. doi:10.1109/BigData.2018.8622133
  • Chehreghani, M. H., Bifet, A., & Abdessalem, T. (2019). DyBED: An Efficient Algorithm for Updating Betweenness Centrality in Directed Dynamic Graphs. In Proc IEEE International Conference on Big Data, Big Data 2018 (pp. 2114-2123). Seattle, WA, USA. doi:10.1109/BigData.2018.8622452
  • Chew, C., & Kumar, V. (2019). Behaviour based ransomware detection. In G. Lee, & Y. Jin (Eds.), Proc 34th International Conference on Computers and Their Applications (CATA 2019) Vol. 58 (pp. 127-136). EasyChair. doi:10.29007/t5q7
  • Coup, S., Vetrova, V., Frank, E., & Tappenden, R. (2019). Domain specific transfer learning using image mixing and stochastic image selection. In Sixth Workshop on Fine-Grained Visual Categorization (FGVC6), Computer Vision and Pattern Recognition Conference (EVPR 2019) (pp. 4 pages). Long Beach, CA. Retrieved from https://drive.google.com/file/d/14XiBpmeT4h7RUcJj6l7a5hf6xw7L493D/view
  • Cunningham, S. (2019). Interacting with personal music collections. In N. G. Taylor, C. Christian-Lamb, M. H. Martin, & B. Nardi (Eds.), Information in Contemporary Society. iConference 2019. LNCS 11420 (pp. 526-536). Cham: Springer. doi:10.1007/978-3-030-15742-5_50
  • Cunningham, S. J. (2019). An analysis of Arabic language music queries: Design considerations for an Arabic music digital library. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries 2019-June (pp. 331-332). doi:10.1109/JCDL.2019.00056
  • Daoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine 97, 204-214. doi:10.1016/j.artmed.2019.01.006
  • Daoud, M., Mayo, M., & Cunningham, S. J. (2019). RBFA: Radial Basis Function Autoencoders. In 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) (pp. 2966-2973). Wellington, NZ. doi:10.1109/CEC.2019.8790041
  • de Mello, R. F., Manapragada, C., & Bifet, A. (2019). Measuring the shattering coefficient of decision tree models. Expert Systems with Applications, 137, 443-452. doi:10.1016/j.eswa.2019.07.012
  • de Mello, R. F., Vaz, Y., Grossi, C. H., & Bifet, A. (2019). On learning guarantees to unsupervised concept drift detection on data streams. Expert Systems with Applications 117, 90-102. doi:10.1016/j.eswa.2018.08.054
  • Dekker, M., & Kumar, V. (2019). Using audio characteristics for mobile device authentication. Proc International Conference on Network and System Security (NSS 2019) LNCS 11928 (pp. 98-113). doi:10.1007/978-3-030-36938-5_6
  • Distante, D., Winckler, M., Bernhaupt, R., Bowen, J., Campos, J. C., Müller, F., . . . Voit, A. (2019). Trends on Engineering Interactive Systems: An overview of works presented in workshops at EICS 2019. In Proc ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS'19) (pp. 22:1-22:6). New York, NY, USA: ACM. doi:10.1145/3319499.3335655
  • Du, Y., Fang, M., Yi, J., Xu, C., Cheng, J., & Tao, D. (2019). Enhancing the robustness of neural collaborative filtering systems under malicious attacks. IEEE Transactions on Multimedia 21(3), 555-565. doi:10.1109/TMM.2018.2887018
  • Dyer, M., Dyer, R., Weng, M.-H., Wu, S., Grey, T., Gleeson, R., & García Ferrari, T. (2019). Framework for soft and hard city infrastructures. Proceedings of the Institution of Civil Engineers - Urban Design and Planning, 172(6) (pp. 219-227). doi:10.1680/jurdp.19.00021
  • Dyer, M., Dyer, R., Weng, M., Wu, S., & Garcia Ferrari, T. (2019). Urban narratives for city infrastructures. In Proc WEC2019: World Engineers Convention 2019 (pp. 1127-1134). Melbourne, Australia. Retrieved from https://search.informit.com.au/documentSummary;dn=969460565943813;res=IELENG
  • Fang, M., Zhou, C., Shi, B., Gong, B., Xu, J., & Zhang, T. (2019). Dher: Hindsight experience replay for dynamic goals. In 7th International Conference on Learning Representations, ICLR 2019.
  • Fang, M., Zhou, T., Yin, J., Wang, Y., & Tao, D. (2019). Data Subset Selection With Imperfect Multiple Labels. IEEE Transactions on Neural Networks and Learning Systems 30(7), 2212-2221. doi:10.1109/tnnls.2018.2875470
  • Fitzgerald, A., König, J., & Witten, I. H. (2019). F-Lingo: Integrating lexical feature identification into MOOC platforms for learning professional and academic English. In Proc 2019 IEEE Learning With MOOCS (LWMOOCS 2019) (pp. 101-104). Milwaukee, WI. doi:10.1109/LWMOOCS47620.2019.8939658
  • Gomes, H. M., Bifet, A., Fournier-Viger, P., Granatyr, J., & Read, J. (2019). Network of experts: Learning from evolving data streams through network-based ensembles. Proc International Conference on Neural Information Processing (ICONIP 2019) LNCS 11953 (pp. 704-716). doi:10.1007/978-3-030-36708-4_58
  • Gomes, H. M., Bifet, A., Read, J., Barddal, J. P., Enembreck, F., Pfahringer, B., . . . Abdessalem, T. (2019). Correction to: Adaptive random forests for evolving data stream classification (Machine Learning, (2017), 106, 9-10, (1469-1495), 10.1007/s10994-017-5642-8). Machine Learning 108. (pp. 1877-1878). doi:10.1007/s10994-019-05793-3
  • Gomes, H. M., Mello, R. F. D., Pfahringer, B., & Bifet, A. (2019). Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 761-769). doi:10.1109/BigData47090.2019.9006366
  • Gomes, H. M., Read, J., & Bifet, A. (2019). Streaming random patches for evolving data stream classification. In Proceedings - IEEE International Conference on Data Mining, ICDM 2019-November (pp. 240-249). doi:10.1109/ICDM.2019.00034
  • Gouk, H., Pfahringer, B., & Frank, E. (2019). Stochastic gradient trees. In W. S. Lee, & T. Suzuki (Eds.), Proc 11th Asian Conference on Machine Learning (ACML 2019) PMLR 101 (pp. 1094-1109). Nagoya, Japan: PMLR. Retrieved from http://proceedings.mlr.press/v101/gouk19a.html
  • Grzenda, M., Gomes, H. M., & Bifet, A. (2019). Delayed labelling evaluation for data streams. Data Mining and Knowledge Discovery. doi:10.1007/s10618-019-00654-y
  • Hebert-Losier, K., Hanzlíková, I., Zheng, C., Streeter, L., & Mayo, M. (2019). The Deep Landing Error Scoring System calculation method can make an important difference! In XXVII Congress
  • Heydarian, P., & Bainbridge, D. (2019). Dastgàh recognition in Iranian music: different features and optimized parameters. In Proc 6th International Conference on Digital Libraries for Musicology (DLfM '19) (pp. 53-57). The Hague, Netherlands: ACM Press. doi:10.1145/3358664.3361873
  • Hinze, A., Bainbridge, D., Cunningham, S. J., Taube-Schock, C., Matamua, R., Downie, J. S., & Rasmussen, E. (2019). Capisco: low-cost concept-based access to digital libraries. International Journal on Digital Libraries 20(4), 307-334. doi:10.1007/s00799-018-0232-3
  • Hinze, A., Heese, R., Schlegel, A., & Paschke, A. (2019). Manual semantic annotations: User evaluation of interface and interaction designs. Journal of Web Semantics 58. doi:10.1016/j.websem.2019.100516
  • Hou, Y., Fang, M., Che, W., & Liu, T. (2019). A corpus-free State2Seq user simulator for task-oriented dialogue. In Proc China National Conference on Chinese Computational Linguistics (CCL 2019) LNCS 11856 (pp. 689-702). doi:10.1007/978-3-030-32381-3_55
  • Jaidka, S., Reeves, S., & Bowen, J. (2019). A coloured petri net approach for modelling and analyzing safety-critical interactive systems. In Proc 26th Asia-Pacific Software Engineering Conference (APSEC 2019) (pp. 347-354). Putrajaya, Malaysia. doi:10.1109/APSEC48747.2019.00054
  • Jaidka, S., Reeves, S., & Bowen, J. (2019). Formal modelling of safety-critical interactive devices using coloured petri nets. In 8th Formal Methods for Interactive Systems Workshop (FMIS 2019). Porto, Portugal.
  • Jia, S., Luckie, M., Huffaker, B., Elmokashfi, A., Aben, E., Claffy, K., & Dhamdhere, A. (2019). Tracking the deployment of IPv6: Topology, routing and performance. Computer Networks, 165, 15 pages. doi:10.1016/j.comnet.2019.106947
  • Keegan, T. (2019). Ātea - a new avenue to store and engage with indigenous literature and culture. In Pacific Ancient and Modern Language Association (PAMLA) 117th Annual Conference. San Diego, California, USA.
  • Keegan, T.. (2019). Māori language procreation in social media. In 17th International Conference on Minority Languages (ICML XVII). Leeuwarden, Netherlands.
  • Keegan, T. T. (2019). Māori language procreation on social media. In 17th International Conference on Minority Languages (ICML XVII) (pp. 31). Leeuwarden, NL.
  • Keegan, T. T., & Laulaupea'alu, S. (2019). Cyber security vulnerabilities in Tonga. In B. Cusack, & R. Lutui (Eds.), Proc 2018 Cyber Forensic & Security International Conference (2018 CFSIC) (pp. 187-194). Conference held Tonga.
  • Koay, A., Welch, I., & Seah, W. K. G. (2019). Effectiveness of entropy-based features in high-and low-intensity DDoS attacks detection. In N. Attrapadung, & T. Yagi (Eds.), Proc 14th International Workshop on Security (IWSEC 2019), Advances in Information and Computer Security, LNCS 11689 (pp. 207-217). Tokyo, Japan: Springer. doi:10.1007/978-3-030-26834-3_12
  • König, J., Calude, A. S., & Coxhead, A. (2019). Using character-grams to automatically generate pseudowords and how to evaluate them. Applied Linguistics, amz045, 24 pages. doi:10.1093/applin/amz045
  • Kourtellis, N., de Francisci Morales, G., & Bifet, A. (2019). Analyzing big data streams with apache SAMOA. In Proc International Workshop on Modeling Social Media (MSM 2016), Behavioral Analytics in Social and Ubiquitous Environments, MUSE 2015 LNCS 11406 (pp. 44-67). doi:10.1007/978-3-030-34407-8_3
  • Kumar, V. (2019). A bilinear pairing based secure data aggregation scheme for WSNs. In Proc 15th International Wireless Communications & Mobile Computing Conference (IWCMC) (pp. 102-107). Tangier, Morocco: IEEE. doi:10.1109/iwcmc.2019.8766759
  • Lang, S., Bravo-Marquez, F., Beckham, C., Hall, M., & Frank, E. (2019). WekaDeeplearning4j: A deep learning package for weka based on Deeplearning4j. Knowledge-Based Systems. doi:10.1016/j.knosys.2019.04.013
  • Le Nguyen, M. H., Gomes, H. M., & Bifet, A. (2019). Semi-supervised Learning over Streaming Data using MOA. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 553-562). doi:10.1109/BigData47090.2019.9006217
  • Leathart, T., Frank, E., Pfahringer, B., & Holmes, G. (2019). Ensembles of nested dichotomies with multiple subset evaluation. In Q. Yang, Z. -H. Zhou, Z. Gong, M. -L. Zhang, & S. -J. Huang (Eds.), Advances in Knowledge Discovery and Data Mining: Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), LNCS 11439 Part I (pp. 81-93). Springer. doi:10.1007/978-3-030-16148-4_7
  • Leathart, T., Frank, E., Pfahringer, B., & Holmes, G. (2019). On calibration of nested dichotomies. In Q. Yang, Z. -H. Zhou, Z. Gong, M. -L. Zhang, & S. -J. Huang (Eds.), Advances in Knowledge Discovery and Data Mining: Proc 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), LNCS 11439 Part I (pp. 69-80). Springer. doi:10.1007/978-3-030-16148-4_6
  • Li, T., Xi, W., Fang, M., Xu, J., & Meng, M. Q. H. (2019). Learning to Solve a Rubik's Cube with a Dexterous Hand. In IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 (pp. 1387-1393). doi:10.1109/ROBIO49542.2019.8961560
  • Luckie, M., Beverly, R., Koga, R., Keys, K., Kroll, J. A., & claffy, K. (2019). Network hygiene, incentives, and regulation: deployment of source address validation in the Internet. In Proc 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19) (pp. 465-480). London, UK: ACM Press. doi:10.1145/3319535.3354232
  • Luckie, M., Huffaker, B., & claffy, K. (2019). Learning regexes to extract router names from hostnames. In Proc ACM 2019 Internet Measurement Conference (IMC'19) (pp. 337-350). Amsterdam, Netherlands: ACM Press. doi:10.1145/3355369.3355589
  • Mayo, M. (2019). Improving the robustness of the glycemic variability percentage metric to sensor dropouts in continuous glucose monitor data. In N. T. Nguyen, F. L. Gaol, T. P. Hong, & B. Trawinski (Eds.), Intelligent Information and Database Systems. (ACIIDS 2019) LNCS 11432 (pp. 373-384). Springer. doi:10.1007/978-3-030-14802-7_32
  • Mayo, M., & Daoud, M. (2019). Data normalisation using differential evolution and aggregated logistic functions. In Proc 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) (pp. 920-927). Wellington, NZ. doi:10.1109/CEC.2019.8790251
  • Mayo, M., & Yogarajan, V. (2019). A nearest neighbour-based analysis to identify patients from continuous glucose monitor data. In N. T. Nguyen, F. L. Gaol, T. P. Hong, & B. Trawinski (Eds.), , Intelligent Information and Database Systems. (ACIIDS 2019) LNCS 11432 (pp. 349-360). Springer. doi:10.1007/978-3-030-14802-7_30
  • Mayo, M., Chepulis, L., & Paul, R. G. (2019). Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning. PLOS ONE 14(12), e0225613. doi:10.1371/journal.pone.0225613
  • Montiel, J., Bifet, A., Losing, V., Read, J., & Abdessalem, T. (2019). Learning fast and slow: A unified batch/stream framework. In Proc 2018 IEEE International Conference on Big Data (Big Data 2018) (pp. 1065-1072). Seattle, WA, USA. doi:10.1109/BigData.2018.8622222
  • Müller, L., Luckie, M., Huffaker, B., Claffy, K., & Barcellos, M. (2019). Challenges in inferring spoofed traffic at IXPs. In Proc 15th International Conference on Emerging Networking Experiments And Technologies (CoNEXT '19) (pp. 96-109). Orlando, Florida: ACM Press. doi:10.1145/3359989.3365422
  • Neves, M., Huffaker, B., Levchenko, K., & Barcellos, M. (2019). Dynamic property enforcement in programmable data planes. In 2019 IFIP Networking Conference (IFIP Networking) (pp. 1-9). Warsaw, Poland: IEEE. doi:10.23919/ifipnetworking.2019.8816830
  • Podolskiy, V., Mayo, M., Koay, A., Gerndt, M., & Patros, P. (2019). Maintaining SLOs of cloud-native applications via self-adaptive resource sharing. In Proc 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) (pp. 72-81). Umeå, Sweden: IEEE. doi:10.1109/SASO.2019.00018
  • Qi, Y., Cheng, J., Chen, X., Cheng, R., Bifet, A., & Wang, P. (2019). Discriminative Streaming Network Embedding. Knowledge-Based Systems 190. doi:10.1016/j.knosys.2019.105138
  • Ray, A., Holder, L. B., & Bifet, A. (2019). Efficient frequent subgraph mining on large streaming graphs. Intelligent Data Analysis 23(1), 103-132. doi:10.3233/IDA-173705
  • Reeves, S. (2019). Usable-by-construction. In CEUR Workshop Proceedings Vol. 2503 (pp. 18-26).
  • Ruckstuhl, K., Amoamo, M., Hart, N. H., Martin, W. J., Keegan, T. T., & Pollock, R. (2019). Research and development absorptive capacity: a Māori perspective. Kōtuitui : New Zealand Journal of Social Sciences Online, 14, 177-197. doi:10.1080/1177083X.2019.1580752
  • Sahito, A., Frank, E., & Pfahringer, B. (2019). Semi-supervised learning using Siamese networks. In J. Liu, & J. Bailey (Eds.), Proc 32nd Australasian Joint Conference on Advances in Artificial Intelligence (AI 2019), LNCS 11919 (pp. 586-597). Springer. doi:10.1007/978-3-030-35288-2_47
  • Sanger, R., Luckie, M., & Nelson, R. (2019). Identifying equivalent SDN forwarding behaviour. In Proc 2019 ACM Symposium on SDN Research (SOSR '19) (pp. 127-139). New York, NY: ACM. doi:10.1145/3314148.3314347
  • Soffer, P., Hinze, A., Koschmider, A., Ziekow, H., Di Ciccio, C., Koldehofe, B., . . . Song, W. (2019). From event streams to process models and back: challenges and opportunities. Information Systems 81, 181-200. doi:10.1016/j.is.2017.11.002
  • Song, F., Diao, Y., Read, J., Stiegler, A., & Bifet, A. (2019). EXAD: A system for explainable anomaly detection on big data traces. In IEEE International Conference on Data Mining Workshops, ICDMW 2018-November (pp. 1435-1440). doi:10.1109/ICDMW.2018.00204
  • Tabassum, G., Kulathuramaiyer, N., Harris, R., & Yeo, A. W. (2019). The indirect and intangible impacts of a telecentre on a rural community. Electronic Journal of Information Systems in Developing Countries 85(3). doi:10.1002/isd2.12087
  • Taia, I., Hinze, A., Vanderschantz, N., & Keegan, T. T. (2019). Maumahara Papahou: A mobile augmented reality memory treasure box based on Māori mnemonic aids. MAI Journal 8(2), 110-125. doi:10.20507/MAIJournal.2019.8.2.2
  • Tommasini, R., Blomqvist, E., Keskisärkkä, R., Valle, E. D., Calbimonte, J. P., & Bifet, A. (2019). Continuous analytics of web streams: Half-day tutorial at the web conference 2019. In The Web Conference 2019 - Companion of the World Wide Web Conference (WWW 2019) (pp. 1323-1325). San Francisco, USA: ACM. doi:10.1145/3308560.3320088
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  • Tsai, Y. -C. V., Gordon, A., Yeo, A., Khoo, E., Yu-Cheng, T., & Dobbie, G. (2019). Fostering students’ development of transferable skills: The experience of Auckland ICT Graduate School. In NZACE 2019: Our place in the future of work. Conference held at Te Papa, Wellington, New
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