Property:References
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Latest revision as of 10:05, 23 October 2014
This is a property of type Text.
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(self-detection) K. Gold, B. Scassellati, Using probabilistic reasoning over time to self-recognize, Robotics and Autonomous Systems (2008), doi:10.1016/j.robot.2008.07.006
(body schema) Mai Hikita, Sawa Fuke, Masaki Ogino, Takashi Minato and Minoru Asada. Visual attention by saliency leads cross-modal body representation. IROS - 2008.
(anomaly detection) Takahiro Suzuki, Fumihiro Bessho, Tatsuya Harada and Yasuo Kuniyoshi. Visual Anomaly Detection under Temporal and Spatial Non-uniformity for News Finding Robot. IROS 2011.
(self-augmentation) Luzius Brodbeck and Fumiya Iida. Enhanced Robotic Body Extension with Modular Units, IROS 2012. +
A
Oktian, Y.E., Witanto, E.N. and Lee, S.G., 2021. A Conceptual Architecture in Decentralizing Computing, Storage, and Networking Aspect of IoT Infrastructure. IoT, 2(2), pp.205-221.
Kolhar, M., Al-Turjman, F., Alameen, A. and Abualhaj, M.M., 2020. A three layered decentralized IoT biometric architecture for city lockdown during COVID-19 outbreak. Ieee Access, 8, pp.163608-163617.
Da Xu, L., Lu, Y. and Li, L., 2021. Embedding blockchain technology into IoT for security: a survey. IEEE Internet of Things Journal. +
A Meta-Learning Approach for Preserving and Transferring Beneficial Behaviors in Asynchronous Multi-Agent Reinforcement Learning +
Mnih, V. et al. (2016). Asynchronous Methods for Deep Reinforcement Learning. ICML 2016.
Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017.
Gupta, A. et al. (2018). Meta-Reinforcement Learning of Structured Exploration Strategies. NeurIPS 2018.
Qi, P. (2024). Model Aggregation Techniques in Federated Learning: A Comprehensive Survey. Future Generation Computer Systems, 139, 1-15
Wu, H. et al. (2024). Adaptive Multi-Agent Reinforcement Learning for Flexible Resource Management. Applied Energy, 374, 121-135
OpenAI Gym. https://www.gymlibrary.ml/
Ray RLLib. https://docs.ray.io/en/latest/rllib.html
PyTorch. https://pytorch.org/ +
1. Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method. Physiological Measurement. 2016;37(8):1298.
2. Konkani A, Oakley B, Bauld TJ. Reducing hospital noise: a review of medical device alarm management. Biomedical Instrumentation & Technology. 2012;46(6):478-87.
3. Cvach M. Monitor alarm fatigue: an integrative review. Biomedical Instrumentation & Technology. 2012;46(4):268-77.
4. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet. Circulation. 2000;101(23):e215. +
1) Pezone, Francesco. "Semantic communication based on generative AI: a new approach to image compression and edge optimization." arXiv preprint arXiv:2502.01675 (2025).
2) Qiao, Li, Mahdi Boloursaz Mashhadi, Zhen Gao, Chuan Heng Foh, Pei Xiao, and Mehdi Bennis. "Latency-aware generative semantic communications with pre-trained diffusion models." IEEE wireless communications letters (2024).
3) Islam, Azharul, and KyungHi Chang. "Navigating the future of wireless networks: A multidimensional survey on semantic communications." ICT Express 10, no. 4 (2024): 747-773.
4) Li, Nan, Alexandros Iosifidis, and Qi Zhang. "Dynamic semantic compression for cnn inference in multi-access edge computing: A graph reinforcement learning-based autoencoder." IEEE Transactions on Wireless Communications (2024). +
Beth Logan et al. A Long-Term Evaluation of Sensing Modalities for Activity Recognition. Ubiquitous Computing. Lecture Notes in Computer Science vol. 4717, pp. 483-50, 2007.
Juan Carlos Augusto, Hideyuki Nakashima, Hamid Aghajan. Ambient Intelligence and Smart Environments: A State of the Art. Handbook of Ambient Intelligence and Smart Environments, pp 3-31, 2010. +
http://www.acumen-language.org/
http://en.wikipedia.org/wiki/SCARA SCARA +
Mnih, V. et al., Asynchronous Methods for Deep Reinforcement Learning. (2016)
Shen, H. et al., Towards Understanding Asynchronous Advantage Actor–Critic: Convergence and Linear Speedup. (2020)
Ma, J. et al., FedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting. (2024)
Wu, Y. et al., Uncertainty Weighted Actor–Critic for Offline Reinforcement Learning. (2021)
Kumar, A. et al., Adaptive aggregation for RL in average reward MDPs. (2012)
Littlestone, N. & Warmuth, M., The Weighted Majority Algorithm. (1994) +
SAS2-project, http://islab.hh.se/mediawiki/SAS2
ROS - Robot Operating System, http://www.ros.org/
OpenCv - http://opencv.org/
Nemati, Hassan, Åstrand, Björn (2014). Tracking of People in Paper Mill Warehouse Using Laser Range Sensor. 2014 UKSim-AMSS 8th European Modelling Symposium, EMS 2014, Pisa, Italy, 21-23 October, 2014.
Power, P. Wayne, and Johann A. Schoonees. "Understanding background mixture models for foreground segmentation." Proceedings image and vision computing New Zealand. Vol. 2002. 2002. +
SAS2-project, http://islab.hh.se/mediawiki/SAS2
ROS - Robot Operating System, http://www.ros.org/
OpenCv - http://opencv.org/
Lalonde, Jean-Francois; Vandapel, Nicolas; Huber, Daniel; Hebert, Martial; Natural terrain classification using three-dimensional ladar data for ground robot mobility, Journal of Field Robotics, Vol. 23, No. 10, pp. 839 - 861, November, 2006
Mosberger, Rafael; Vision-based human detection from mobile machinery in industrial environments, Thesis, Örebro University, Sweden, 2016
Saarinen, Jari P.; Andreasson, Henrik; Stoyanov, Todor; Lilienthal, Achim J.; 3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments, The International Journal of Robotics Research, Vol 32, Issue 14, pp. 1627 – 1644, 2013 +
https://dl.acm.org/doi/pdf/10.1145/3564625.3567988 +
Dekkers, G., Vuegen, L., van Waterschoot, T., Vanrumste, B., & Karsmakers, P. (2018). DCASE 2018 Challenge-Task 5: Monitoring of domestic activities based on multi-channel acoustics. arXiv preprint arXiv:1807.11246.
Dekkers, G., Lauwereins, S., Thoen, B., Adhana, M. W., Brouckxon, H., Van den Bergh, B., ... & Karsmakers, P. (2017). The SINS database for detection of daily activities in a home environment using an acoustic sensor network. Detection and Classification of Acoustic Scenes and Events 2017. +
J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. & Phys., vol. 32, no. 6, pp. 545–552, 2010.
J. J. Kavanagh and H. B. Menz, “Accelerometry: A technique for quantifying movement patterns during walking,” Gait & Posture, vol. 28, no. 1, pp. 1–15, 2008.
D. Lai, R. Begg, and M. Palaniswami, “Computational intelligence in gait research: A perspective on current app. And future challenges,” Info. Tech. in Biomed., IEEE Trans. on, vol. 13, no. 5, pp. 687–702, 2009. +
https://www.usenix.org/system/files/sec22-manandhar.pdf
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7579770&tag=1
https://dl.acm.org/doi/pdf/10.1145/3442381.3450048
https://dspace.networks.imdea.org/bitstream/handle/20.500.12761/690/On_The_Ridiculousness_of_Notice_and_Consent_2019_EN.pdf?sequence=1 +
Li, Z., Zhu, Y., & Van Leeuwen, M. (2023). A survey on explainable anomaly detection. ACM Transactions on Knowledge Discovery from Data, 18(1), 1-54.
Li, Z., & Van Leeuwen, M. (2023). Explainable contextual anomaly detection using quantile regression forests. Data Mining and Knowledge Discovery, 37(6), 2517-2563.
Pasini, K., Khouadjia, M., Same, A., Trépanier, M., & Oukhellou, L. (2022). Contextual anomaly detection on time series: a case study of metro ridership analysis. Neural Computing and Applications, 34(2), 1483-1507.
Han, X., Zhang, L., Wu, Y., & Yuan, S. (2023, October). On root cause localization and anomaly mitigation through causal inference. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 699-708).
Carvalho, J., Zhang, M., Geyer, R., Cotrini, C., & Buhmann, J. M. (2024). Invariant anomaly detection under distribution shifts: a causal perspective. Advances in Neural Information Processing Systems, 36.
Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016, July). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1).
Gallicchio, C. (2024). Euler state networks: Non-dissipative reservoir computing. Neurocomputing, 579, 127411. +
https://github.com/exathlonbenchmark/exathlon +
Learning Low-Dimensional Representation of Bivariate Histogram Data
https://ieeexplore.ieee.org/abstract/document/8464276 +
(1) https://www.youtube.com/watch?v=ZuydOEws92s <br>
(2) https://www.youtube.com/watch?v=sF2DeSPrGfc +
M. Goldstein and S. Uchida, "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data", PLOS ONE, vol. 11, no. 4, p. e0152173, 2016.
P. Arjunan, H. Khadilkar, T. Ganu, Z. Charbiwala, A. Singh and P. Singh, "Multi-User Energy Consumption Monitoring and Anomaly Detection with Partial Context Information", Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments - BuildSys '15, 2015.
D. Araya, K. Grolinger, H. ElYamany, M. Capretz and G. Bitsuamlak, "An ensemble learning framework for anomaly detection in building energy consumption", Energy and Buildings, vol. 144, pp. 191-206, 2017.
S. Rayana and L. Akoglu, "Less is More", ACM Transactions on Knowledge Discovery from Data, vol. 10, no. 4, pp. 1-33, 2016.
Huang, Huaming, "Rank Based Anomaly Detection Algorithms" (2013). Electrical Engineering and Computer Science - Dissertations.Paper 331. +
Lowe, David G. "Local feature view clustering for 3D object recognition." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001.
Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vision 60.2 (2004): 91-110.
Pontil, Massimiliano, and Alessandro Verri. "Support vector machines for 3D object recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 20.6 (1998): 637-646.
Pinto, Nicolas, David D. Cox, and James J. DiCarlo. "Why is real-world visual object recognition hard?." PLoS computational biology 4.1 (2008): e27.
Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma, A survey of content-based image retrieval with high-level semantics, Pattern Recognition, Volume 40, Issue 1, January 2007, Pages 262-282, ISSN 0031-3203
Mutch, Jim, and David G. Lowe. "Multiclass object recognition with sparse, localized features." Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 1. IEEE, 2006. +
