Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captures spatial and radio geometries so that UEs close in space are close in the channel chart. In this paper, we demonstrate that autoencoder (AE)-based CC can be augmented with side information that is obtained during the CSI acquisition process. More specifically, we propose to include pairwise representation constraints into AEs with the goal of improving the quality of the learned channel charts. We show that such representation-constrained AEs recover the global geometry of the learned channel charts, which enables CC to perform approximate positioning without global navigation satellite systems or supervised learning methods that rely on extensive and expensive measurement campaigns.
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Control charting methods for autocorrelated cyber vulnerability data
Control charting cyber vulnerabilities is challenging because the same vulnerabilities can remain from period to period. Also, hosts (personal computers, servers, printers, etc.) are often scanned infrequently and can be unavailable during scanning. To address these challenges, control charting of the period-to-period demerits per host using a hybridmoving centerline residual-based and adjusted demerit (MCRAD) chart is proposed. The intent is to direct limited administrator resources to unusual cases when automatic patching is insufficient. The proposed chart is shown to offer superior average run length performance compared with three alternative methods from the literature. The methods are illustrated using three datasets.
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- Award ID(s):
- 1409214
- PAR ID:
- 10058429
- Date Published:
- Journal Name:
- Quality engineering
- Volume:
- 28
- Issue:
- 3
- ISSN:
- 1436-2457
- Page Range / eLocation ID:
- 313-328
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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