131.
Pathak, A., Al-Anbagi, I.S., and Hamilton, H.J.
``Early-Stage Conflict Detection in HLF-Based Delay-Critical IoT Networks'',
Cyber Resilience Workshop (CRW),
IEEE Conference on Communications and Network Security (CNS),
Orlando, FL, USA,
October 5, 2023.
130.
Pathak, A., Al-Anbagi, I.S., and Hamilton, H.J.
``Trust-based Blockchain Mechanism for V2X Networks in a Smart Grid Environment'',
2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE),
Regina, SK, Canada,
September 24-27, 2023,
496-501.
129.
Beug, A., and Hamilton, H.J.,
``Splitting Large Convolutional Neural Network Layers to Run Real-Time Applications on Mixed-Reality Hardware '',
IEEE Conference on Virtual Reality,
Virtual, March 12-16, 2022.
110.
Cullimore, J., Hamilton, H.J., and Gerhard, D.,
``Directed Transitional Composition for Gaming and Adaptive Music Using Q-Learning.''
40th International Computer Music Conference /
11th Sound and Music Computing Conference,
(ICMC/SMC 2014),
Athens, Greece, September 14-20, 2014.
Some researchers have told us that they cannot find our algorithm in the above SDM paper. When our paper was accepted as a poster paper at the conference, we shortened it by keeping the theoretical work and deleting the algorithm and results. Thus, our UMining algorithm was not available in our published paper. In Liu, Y., Liao, W.-K., and Choudhary, A., ``A Fast High Utility Itemsets Mining Algorithm,'' Proceedings of the First International Workshop on Utiliy-based Data Mining, Chicago, Illinois, 2005, pp. 90-99, Liu, Liao, and Choudhary derived an algorithm from our theoretical work and called it ``Mining using Expected Utility'' (MEU). The MEU algorithm is not described in our SDM paper.
This situation has led to some confusion. Therefore, we are making the original submitted version of our paper available below. This version shows our UMining algorithm. If necessary, this work can be referenced via its web page.
However, we recommend that a later paper be referenced instead. An extended and improved description of our work is available as Yao, H. and Hamilton, H.J., "Mining Itemset Utilities from Transaction Databses, Data and Knowledge Engineering, Accepted October 2005. See our journal publications page: http://www.cs.uregina.ca/~hamilton/references/journals.html
11. Hilderman, R.J., and Hamilton, H.J.
``Distributed Application Entity Groups: Analysis and Simulation Results.''
In Proceedings of the 1995 IEEE International Phoenix Conference on
Computers and Communications, Phoenix, AZ, March, 1995, pp. 697-703.
5. Hamilton, H.J., and Dyck, J.M.
``IIPS: A Framework for Specifying Inductive-Inference Problems,''
in W.W. Koczkodaj, P.E. Lauer, and A.A. Toptsis, editors,
Computing and Information, ICCI'92, IEEE, Toronto, May, 1992,
pp. 240-243. Preliminary, short version of journal paper.
4. Hamilton, H.J., and Dyck, J.M.
``Using the IIPS Framework to Specify Machine-Discovery Problems,''
in Proceedings ICCI '92 Fourth International Conference on Computing
and Information. IEEE Computer Society Press, Toronto, May, 1992,
pp. 266-269.
2. Dyck, J.M., and Hamilton, H.J.
``Using Subgoal Ordering to Generate Programming Environment Routines,''
in Proc. of the Fourth International Symposium on Artificial
Intelligence: Applications in Informatics, ITESM (Monterrey) and
International Joint Conferences on Artificial Intelligence,
Cancun, Mexico, November, 1991, pp. 543-549;
also in Graduate Review, Centre for Systems Science, Simon Fraser University, November,
1991, pp. 122-134.
1. Hamilton, H.J.
``DATAX: A Framework for Searching for Regularity in Data,''
in CSCSI-90: Proceedings of the Eighth Biennial Conference
of the Canadian Society for Computational Studies of Intelligence,
Ottawa, May, 1990, pp. 196-203.
Other References:
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