Property:References

From ISLAB/CAISR
Jump to navigationJump to search

This is a property of type Text.

previous 202050100250500next 20
Showing 20 pages using this property.
A
Automated Inference regarding Goals in Elite Football Data +
https://www.worldatlas.com/articles/what-are-the-most-popular-sports-in-the-world.html https://www.alliedmarketresearch.com/football-market-A11328 Jordet, G., Aksum, K. M., Pedersen, D. N., Walvekar, A., Trivedi, A., McCall, A., ... & Priestley, D. (2020). Scanning, contextual factors, and association with performance in english premier league footballers: an investigation across a season. Frontiers in psychology, 11, 553813. Decroos, T., & Davis, J. (2019, September). Player vectors: Characterizing soccer players’ playing style from match event streams. In Joint European conference on machine learning and knowledge discovery in databases (pp. 569-584). Springer, Cham.  +
Automatic Generation of Realtime Machine Learning Architectures +
Subbaraj, H., 2020. Using Dataflow for Machine Learning Inference. Anderson, J., Alkabani, Y. and El-Ghazawi, T., 2019. Towards Energy-Quality Scaling in Deep Neural Networks. IEEE Design & Test.  +
Automatic Idea Detection from social media for Controlling and Preventing Healthcare-Associated Infections (with funding opportunity) +
Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018). Nguyen, Dat Quoc, Thanh Vu, and Anh Tuan Nguyen. "BERTweet: A pre-trained language model for English Tweets." arXiv preprint arXiv:2005.10200 (2020). Gould, Dinah, et al. "Electronic hand hygiene monitoring: accuracy, impact on the Hawthorne effect and efficiency." Journal of Infection Prevention 21.4 (2020): 136-143. Christensen, Kasper, et al. "How good are ideas identified by an automatic idea detection system?." Creativity and Innovation Management 27.1 (2018): 23-31.  +
Automatic Machine Learning (AUTO-AUTO-ENCODER!) +
The following paper summarises the algorithm configuration in the different domain : http://aad.informatik.uni-freiburg.de/papers/16-AUTOML-AutoNet.pdf This paper presents the initial idea behind Bayesian optimization for estimating parameter: https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf Previous master thesis on applying autoencoder for histogram data: Robin Ng, “Efficient Implementation of Histogram Dimension Reduction using Deep Learning”, 2017.  +
Autonomous Trust and Access Control in Coalition IoBT Networks +
Trust-based Blockchain Authorization for IoT https://arxiv.org/pdf/2104.00832 Blockchain-based Decentralized Trust Management in IoT: Systems, Requirements and Challenges https://link.springer.com/article/10.1007/s40747-023-01058-8 UAVouch https://ieeexplore.ieee.org/document/9448085  +
Autonomy-Aware Trust Modeling in Heterogeneous IoBT - Teams​ +
1. Fernandes, R., Hieb, M. R., & Costa, P. C. “Levels of Autonomy: Command and Control of Hybrid Forces”, 21st ICCRTS, 2016. 2. Dunin-Keplicz, B., & Verbrugge, R. “Teamwork in Multi-agent Systems: A formal approach”, John Wiley & Sons, 2011. 3. Alberts, D. S. “The Agility Advantage: A Survival Guide for Complex Enterprises and Endeavors”, CCRP Publication Series, 2011.  +
B
Barcode mapping in warehouses +
AIMS-project, http://islab.hh.se/mediawiki/AIMS ROS - Robot Operating System, http://www.ros.org/ ZBar bar code reader, http://zbar.sourceforge.net/ Stampfer, D.; Lutz, M.; Schlegel, C., "Information driven sensor placement for robust active object recognition based on multiple views," Technologies for Practical Robot Applications (TePRA), 2012 IEEE International Conference on , vol., no., pp.133,138, 23-24 April 2012, doi: 10.1109/TePRA.2012.6215667 Karpischek, S., Michahelles, F., Fleisch, E., “my2cents: enabling research on consumer-product interaction”, Pers Ubiquit Comput (2012) 16:613–622, DOI 10.1007/s00779-011-0426-9 Han, Y., Sumi, Y., Matsumoto, Y., and And, N, “.Acquisition of Object Pose from Barcode for Robot Manipulation”, I. Noda et al. (Eds.): SIMPAR 2012, LNAI 7628, pp. 299–310, 2012. G Meng, S Darman, “Label and Barcode Detection in Wide Angle Image”, Master Thesis, Halmstad University, Sweden, http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-23979  +
Beyond 5G baseband processing on a multicore architecture +
* Larsson, Erik G., et al. "Massive MIMO for next generation wireless systems." IEEE communications magazine 52.2 (2014): 186-195. * Malkowsky, Steffen. Massive MIMO: Prototyping, Proof-of-Concept and Implementation. Diss. University of Lund, 2019.   +
Biases in electronic health records +
1. Verheij, Robert A., et al. "Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse." Journal of medical Internet research 20.5 (2018). 2. Gianfrancesco, Milena A., et al. "Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data." JAMA internal medicine (2018). 3. Johnson, Alistair EW, et al. "MIMIC-III, a freely accessible critical care database." Scientific data 3 (2016): 160035.  +
Blockchain-Assisted Data Integrity in Eventually Consistent IoBT Systems +
An Efficient Lightweight Blockchain for Decentralized IoT. https://arxiv.org/abs/2508.19219 A Scalable Blockchain Based Framework for Efficient IoT Data Management Using Lightweight Consensus. https://www.nature.com/articles/s41598-024-58578-7.pdf TON_IoT dataset (2021) – has telemetry and cyberattack traces for IoT/IIoT. https://ieee-dataport.org/documents/toniot-datasets  +
Body posture alignment feedback using xAI +
Vivek Anand Thoutam, Anugrah Srivastava, Tapas Badal, Vipul Kumar Mishra, G. R. Sinha, Aditi Sakalle, Harshit Bhardwaj, Manish Raj, "Yoga Pose Estimation and Feedback Generation Using Deep Learning", Computational Intelligence and Neuroscience, vol. 2022, Article ID 4311350, 12 pages, 2022. https://doi.org/10.1155/2022/4311350 Cooney, Martin & Pihl, J & Larsson, H & Orand, A & Aksoy, Eren. (2019). Exercising with an "Iron Man": Design for a Robot Exercise Coach for Persons with Dementia. 10.13140/RG.2.2.14286.61765. Chaudhari, Ajay, et al. "Yog-guru: Real-time yoga pose correction system using deep learning methods." 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. https://doi.org/10.1109/ICCICT50803.2021.9509937  +
C
Chess playing humanoid robot by vision +
https://www.youtube.com/watch?v=gXOkWuSCkRI  +
Coalition Interoperability in Multi-Domain Trust Frameworks​ +
1. Fernandes, R., Hieb, M. R., & Costa, P. C. “Levels of Autonomy: Command and Control of Hybrid Forces”, 21st ICCRTS, 2016. 2. Dunin-Keplicz, B., & Verbrugge, R. “Teamwork in Multi-agent Systems: A formal approach”, John Wiley & Sons, 2011. 3. Alberts, D. S. “The Agility Advantage: A Survival Guide for Complex Enterprises and Endeavors”, CCRP Publication Series, 2011.  +
Comparative study of an automated testing coverage for a TCP/IP stack implementation +
Wojciech Mostowski, Thomas Arts, and John Hughes. Modelling of Autosar Libraries for Large Scale Testing. Proceedings, 2nd Workshop on Models for Formal Analysis of Real Systems (MARS 2017), Uppsala, Sweden, April 2017, Volume 244 of EPTCS. http://ceres.hh.se/mediawiki/images/b/bb/Mostowski_mars2017.pdf Thomas Arts and John Hughes (2016): How Well are Your Requirements Tested? In: 2016 IEEE International Conference on Software Testing, Verification and Validation, pp. 244–254, doi:10.1109/ICST.2016.23.  +
Comprehending low-dimensional manifolds of temporal data from the home +
Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605. Lundström, J., Järpe, E., & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert Systems with Applications, 55, 429-440. Rauber, P. E., Falcão, A. X., & Telea, A. C. (2016). Visualizing time-dependent data using dynamic t-SNE. Proc. EuroVis Short Papers, 2(5). Cheng, J., Liu, H., Wang, F., Li, H., & Zhu, C. (2015). Silhouette analysis for human action recognition based on supervised temporal t-sne and incremental learning. IEEE Transactions on Image Processing, 24(10), 3203-3217.  +
Conditional GAN for better embedding and generation of medical codes +
Data: https://mimic.mit.edu/docs/about/ papers: https://dspace.mit.edu/handle/1721.1/128349 https://proceedings.neurips.cc/paper/2019/file/254ed7d2de3b23ab10936522dd547b78-Paper.pdf https://www.sciencedirect.com/science/article/pii/S0957417421000233  +
Conflict-free Replicated Data Type (CRDT)-based Distributed Trust Propagation in Partitioned Networks +
1. Fernandes, R., Hieb, M. R., & Costa, P. C. “Levels of Autonomy: Command and Control of Hybrid Forces”, 21st ICCRTS, 2016. 2. Dunin-Keplicz, B., & Verbrugge, R. “Teamwork in Multi-agent Systems: A formal approach”, John Wiley & Sons, 2011. 3. Alberts, D. S. “The Agility Advantage: A Survival Guide for Complex Enterprises and Endeavors”, CCRP Publication Series, 2011.  +
Consensus clustering for categorizing orthogonal vehicle operations +
- Some slides: https://www.siam.org/meetings/sdm11/clustering.pdf - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012, April). Discovering multiple clustering solutions: Grouping objects in different views of the data. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on (pp. 1207-1210). IEEE. - Hu, J., & Pei, J. (2017). Subspace multi-clustering: a review. Knowledge and Information Systems, 1-28. - Yang, S., & Zhang, L. (2017). Non-redundant multiple clustering by nonnegative matrix factorization. Machine Learning, 106(5), 695-712. - Dang, X. H., & Bailey, J. (2015). A framework to uncover multiple alternative clusterings. Machine Learning, 98(1-2), 7-30. - Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4. - Qi, Z., & Davidson, I. (2009, June). A principled and flexible framework for finding alternative clusterings. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 717-726). ACM. - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210). - Cui, Y., Fern, X. Z., & Dy, J. G. (2007). Non-redundant multi-view clustering via orthogonalization. In IEEE International Conference on Data Mining (ICDM), (pp. 133-142). - Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.  +
Constrained dynamic path planning for truck and trailer +
R. Siegwart and I. R. Nourbakhsh,Introduction to Autonomous Mobile Robots. Scituate, MA, USA: Bradford Company, 2004 A. Nozad, Heavy vehicle path stability control for collision avoidance applications," Master's thesis, Chalmers university of technology, 2011 J. G. Fernandez, A vehicle dynamics model for driving simulators," Master's thesis, Chalmers university of technology, 2012  +
Coordinated Multi-Drone Pattern Formation with Crazyflie & Lighthouse +
Crazyswarm/Crazyswarm2 (multi-robot micro-quadrotor control) https://www.bitcraze.io/tag/swarm/ Bitcraze Lighthouse positioning (setup & calibration) https://www.bitcraze.io/documentation/tutorials/getting-started-with-lighthouse/ Olfati-Saber, “Flocking for multi-agent dynamic systems” (IEEE, 2006) Ren & Beard, Distributed Consensus in Multi-vehicle Cooperative Control  +
Retrieved from "https://mw.hh.se/caisr/index.php?title=Property:References&oldid=1824"