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A Framework for the Inference of Sensing Measurements Based on Correlation

Autor: Rahul Urgaonkar, Simone Silvestri, Murtaza Zafer, Bong Jun Ko

Publikováno v: ACM Transactions on Sensor Networks. 15:1-28

Sensor networks are commonly adopted to collect a variety of measurements in indoor and outdoor settings. However, collecting such measurements from every node in the network, although providing high accuracy and resolution of the phenomena of intere

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cb53f4ede47b714b374fb0c00c746c2c
https://doi.org/10.1145/3272035

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Overcoming Noisy and Irrelevant Data in Federated Learning

Autor: Bong Jun Ko, Shiqiang Wang, Changchang Liu, Kin K. Leung, Tiffany Tuor

Publikováno v: ICPR

Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and communication bandwidth restrictions. Federated learning is an effective w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41ebed52f7cd78f298af78895761a48b
https://doi.org/10.1109/icpr48806.2021.9412599

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Acoustic anomaly detection system

Autor: Wei-Han Lee, Keith Grueneberg, Xiping Wang, Shiqiang Wang, Eduardo Morales, David Wood, Jae-wook Ahn, Bong Jun Ko

Publikováno v: SenSys

Acoustic signals contain rich information of the environment. They can be used for detecting anomalous events such as in automated machine monitoring. In this demonstration, we present our acoustic anomaly detection system that captures acoustic sign

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1077b7dcfb93c23697ebb892c6f0ec6f
https://doi.org/10.1145/3356250.3361963

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Exact Incremental and Decremental Learning for LS-SVM

Autor: Changchang Liu, Wei-Han Lee, Bong Jun Ko, Shiqiang Wang, Kin K. Leung

Publikováno v: ICIP

In this paper, we present a novel incremental and decremental learning method for the least-squares support vector machine (LS-SVM). The goal is to adapt a pre-trained model to changes in the training dataset, without retraining the model on all the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2cd4fd142b0c5c9229dcb85a9ea17c39
https://doi.org/10.1109/icip.2019.8803291

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On Data Summarization for Machine Learning in Multi-organization Federations

Autor: Dave Conway-Jones, Shiqiang Wang, Bong Jun Ko, Ting He

Publikováno v: SMARTCOMP

Machine learning is a promising technology for many modern applications. To train an effective machine learning model, a large amount of data is required. However, data may be created in different organizations and sharing data across organizational

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ca1215f09bbc594b3b6dec173804b5f9
https://doi.org/10.1109/smartcomp.2019.00030

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