Effective large-scale process optimization in manufacturing industries
requires close cooperation between different human expert parties who encode
their knowledge of related domains as Bayesian network models. For instance,
Bayesian networks for domains such as lithography equipment, processes, and
auxiliary tools must be conjointly used to effectively identify process
optimizations in the semiconductor industry. However, business confidentiality
across domains hinders such collaboration, and encourages alternatives to
centralized inference. We propose CCBNet, the first Confidentiality-preserving
Collaborative Bayesian Network inference framework. CCBNet leverages secret
sharing to securely perform analysis on the combined knowledge of party models
by joining two novel subprotocols: (i) CABN, which augments probability
distributions for features across parties by modeling them into secret shares
of their normalized combination; and (ii) SAVE, which aggregates party
inference result shares through distributed variable elimination. We
extensively evaluate CCBNet via 9 public Bayesian networks. Our results show
that CCBNet achieves predictive quality that is similar to the ones of
centralized methods while preserving model confidentiality. We further
demonstrate that CCBNet scales to challenging manufacturing use cases that
involve 16-128 parties in large networks of 223-1003 features, and decreases,
on average, computational overhead by 23%, while communicating 71k values per
request. Finally, we showcase possible attacks and mitigations for partially
reconstructing party networks in the two subprotocols.

制造业需要大规模流程优化，而 CCBNet 是首个保密的协作贝叶斯网络推断框架，利用秘密共享安全分析各方模型的联合知识，实现与集中式方法相似的预测质量。

CCBNet：保密的协作贝叶斯网络推理

CCBNet: Confidential Collaborative Bayesian Networks Inference

Manufacturing industries require efficient and voluminous production of
high-quality finished goods. In the context of Industry 4.0, visual anomaly
detection poses an optimistic solution for automatically controlling product
quality with high precision. Automation based on computer vision poses a
promising solution to prevent bottlenecks at the product quality checkpoint. We
considered recent advancements in machine learning to improve visual defect
localization, but challenges persist in obtaining a balanced feature set and
database of the wide variety of defects occurring in the production line. This
paper proposes a defect localizing autoencoder with unsupervised class
selection by clustering with k-means the features extracted from a pre-trained
VGG-16 network. The selected classes of defects are augmented with natural wild
textures to simulate artificial defects. The study demonstrates the
effectiveness of the defect localizing autoencoder with unsupervised class
selection for improving defect detection in manufacturing industries. The
proposed methodology shows promising results with precise and accurate
localization of quality defects on melamine-faced boards for the furniture
industry. Incorporating artificial defects into the training data shows
significant potential for practical implementation in real-world quality
control scenarios.

基于视觉异常检测和无监督类别选择的缺陷定位自编码器在制造业中提高缺陷检测的准确度和可靠性。