Sparse Bayesian Tensor Completion for Data Recovery in Intelligent IoT Systems
Bibliographic record
Abstract
Intelligent Internet of Things (IoT), is an emerging paradigm that integrates lightweight intelligence algorithms to various IoT devices to provide convenient and intelligent services for modern life and production. For this purpose, data should be efficiently processed to explore the hidden information to elevate the intelligence of services. However, the IoT data are collected from a complex environment with high speed, and high noise, which inevitably brings problems about missing and imparting challenges to the progression of intelligent IoT services. To recover the missing data with higher precision and provide data cornerstones for intelligent IoT systems, a sparse Bayesian tensor completion (SBTC) method is proposed in this article. With the hierarchical sparse prior, the proposed tensor completion model can obtain the underlying low-rank structure from the incomplete tensor, thereby recovering missing data with high accuracy. For model learning, a variational Bayesian inference method is developed in the frequency domain, which improves the model’s efficiency. The model proposed is within a fully Bayesian framework, thereby endowing the model with commendable robustness. The superiority of our model is fully demonstrated by comparing other state-of-the-art methods on synthetic data, traffic data, logistics data, and visual data. In particular, on traffic data and video data, our method has improved by at least 2% and 10dB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".