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Most studies on machine learning in sensing systems focus on low-level perception tasks that process raw sensory data within a short time window.
F. Ingelrest, G. Barrenetxea, G. Schaefer, M. Vetterli, O. Couach, and M. Parlange, “Sensorscope: Application-specific sensor network for environmental monitoring,” ACM Transactions on Sensor Networks (TOSN) , vol. 6, no. 2, pp. 1–32, 2010
2010
Earlier work this paper cites.
F.-T. Sun, Y.-T. Yeh, H.-T. Cheng, C. Kuo, and M. Griss, “Nonparametric discovery of human routines from sensor data,” in 2014 IEEE international conference on pervasive computing and communications (PerCom) . IEEE, 2014, pp. 11–19
2014
Earlier work this paper cites.
N. Banovic, T. Buzali, F. Chevalier, J. Mankoff, and A. K. Dey, “Modeling and understanding human routine behavior,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems , 2016, pp. 248–260
2016
Earlier work this paper cites.
T. Han, K. Muhammad, T. Hussain, J. Lloret, and S. W. Baik, “An efficient deep learning framework for intelligent energy management in iot networks,” IEEE Internet of Things Journal , vol. 8, no. 5, pp. 3170–3179, 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Weber, F. Banihashemi, P. Mandl, H.-A. Jacobsen, and R. Mayer, “Overcoming data scarcity through transfer learning in co2-based building occupancy detection,” in Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation , 2023, pp. 1–10
2023
Earlier work this paper cites.
X. Xu, X. Liu, H. Zhang, W. Wang, S. Nepal, Y. Sefidgar, W. Seo, K. S. Kuehn, J. F. Huckins, M. E. Morris et al. , “Globem: Cross-dataset generalization of longitudinal human behavior modeling,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 6, no. 4, pp. 1–34, 2023
2023
Earlier work this paper cites.
2023
Cited alongside, same era.
S. Mirchandani, F. Xia, P. Florence, D. Driess, M. G. Arenas, K. Rao, D. Sadigh, A. Zeng et al. , “Large language models as general pattern machines,” in 7th Annual Conference on Robot Learning , 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. Xue and F. D. Salim, “Utilizing language models for energy load forecasting,” in Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation , 2023, pp. 224–227
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Ouyang, Z. Xie, H. Fu, S. Cheng, L. Pan, N. Ling, G. Xing, J. Zhou, and J. Huang, “Harmony: Heterogeneous multi-modal federated learning through disentangled model training,” in Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services , 2023, pp. 530–543
2023
Later among the works it cites.
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2023
Cited alongside, same era.
J. V. Jeyakumar, A. Sarker, L. A. Garcia, and M. Srivastava, “X-char: A concept-based explainable complex human activity recognition model,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , vol. 7, no. 1, pp. 1–28, 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Later among the works it cites.
Y. Chen, H. Xie, M. Ma, Y. Kang, X. Gao, L. Shi, Y. Cao, X. Gao, H. Fan, M. Wen et al. , “Automatic root cause analysis via large language models for cloud incidents,” 2024
2024
Closest in time.
“ollama,” https://github.com/ollama/ollama, 2024
2024
Closest in time.