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In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced soft-sensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method.
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Overheard: Audio-based Integral Event Inference
There is no doubt that the popularity of smart devices and the development of deep learning models bring individuals too much convenience. However, some rancorous attackers can also implement unexpected privacy inferences on sensed data from smart devices via advanced deep-learning tools. Nonetheless, up to now, no work has investigated the possibility of riskier overheard, referring to inferring an integral event about humans by analyzing polyphonic audios. To this end, we propose an Audio-based integraL evenT infERence (ALTER) model and two upgraded models (ALTER-p and ALTER-pp) to achieve the integral event inference. Specifically, ALTER applies a link-like multi-label inference scheme to consider the short-term co-occurrence dependency among multiple labels for the event inference. Moreover, ALTER-p uses a newly designed attention mechanism, which fully exploits audio information and the importance of all data points, to mitigate information loss in audio data feature learning for the event inference performance improvement. Furthermore, ALTER-pp takes into account the long-term co-occurrence dependency among labels to infer an event with more diverse elements, where another devised attention mechanism is utilized to conduct a graph-like multi-label inference. Finally, extensive real-data experiments demonstrate that our models are effective in integral event inference and also outperform the state-of-the-art models.
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- Award ID(s):
- 2416872
- PAR ID:
- 10634559
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Journal of Data and Information Quality
- ISSN:
- 1936-1955
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3695771
