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pro vyhledávání: '"XUE, Hao"'
To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous pr
Externí odkaz:
http://arxiv.org/abs/2412.19841
Autor:
Lin, Xiachong, Prabowo, Arian, Razzak, Imran, Xue, Hao, Amos, Matthew, Behrens, Sam, Salim, Flora D.
Incorporating AI technologies into digital infrastructure offers transformative potential for energy management, particularly in enhancing energy efficiency and supporting net-zero objectives. However, the complexity of IoT-generated datasets often p
Externí odkaz:
http://arxiv.org/abs/2412.14175
Addressing the challenges of irregularity and concept drift in streaming time series is crucial in real-world predictive modelling. Previous studies in time series continual learning often propose models that require buffering of long sequences, pote
Externí odkaz:
http://arxiv.org/abs/2411.07413
Autor:
Lin, Xiachong, Prabowo, Arian, Razzak, Imran, Xue, Hao, Amos, Matthew, Behrens, Sam, Salim, Flora D.
The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building manag
Externí odkaz:
http://arxiv.org/abs/2411.08888
Traditional POI recommendation systems often lack transparency, interpretability, and scrutability due to their reliance on dense vector-based user embeddings. Furthermore, the cold-start problem -- where systems have insufficient data for new users
Externí odkaz:
http://arxiv.org/abs/2410.20643
In this work, we bridge the gap between wearable sensor technology and personalized AI assistants by enabling Large Language Models (LLMs) to understand time-series tasks like human activity recognition (HAR). Despite the strong reasoning and general
Externí odkaz:
http://arxiv.org/abs/2410.10624
Occupation information can be utilized by digital assistants to provide occupation-specific personalized task support, including interruption management, task planning, and recommendations. Prior research in the digital workplace assistant domain req
Externí odkaz:
http://arxiv.org/abs/2407.18518
Video language continual learning involves continuously adapting to information from video and text inputs, enhancing a model's ability to handle new tasks while retaining prior knowledge. This field is a relatively under-explored area, and establish
Externí odkaz:
http://arxiv.org/abs/2406.13123
Autor:
Yin, Du, Deng, Jinliang, Ao, Shuang, Li, Zechen, Xue, Hao, Prabowo, Arian, Jiang, Renhe, Song, Xuan, Salim, Flora
Training models on spatio-temporal (ST) data poses an open problem due to the complicated and diverse nature of the data itself, and it is challenging to ensure the model's performance directly trained on the original ST data. While limiting the vari
Externí odkaz:
http://arxiv.org/abs/2406.12709
Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets have limitati
Externí odkaz:
http://arxiv.org/abs/2406.12693