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1. Detecting Cyber Attacks in Smart Grids Using Semi-Supervised Anomaly Detection and Deep Representation Learning

   https://doi.org/10.3390/info12080328  

   Qi, Ruobin; Rasband, Craig; Zheng, Jun; Longoria, Raul (August 2021, Information)

   null (Ed.)

   Smart grids integrate advanced information and communication technologies (ICTs) into traditional power grids for more efficient and resilient power delivery and management, but also introduce new security vulnerabilities that can be exploited by adversaries to launch cyber attacks, causing severe consequences such as massive blackout and infrastructure damages. Existing machine learning-based methods for detecting cyber attacks in smart grids are mostly based on supervised learning, which need the instances of both normal and attack events for training. In addition, supervised learning requires that the training dataset includes representative instances of various types of attack events to train a good model, which is sometimes hard if not impossible. This paper presents a new method for detecting cyber attacks in smart grids using PMU data, which is based on semi-supervised anomaly detection and deep representation learning. Semi-supervised anomaly detection only employs the instances of normal events to train detection models, making it suitable for finding unknown attack events. A number of popular semi-supervised anomaly detection algorithms were investigated in our study using publicly available power system cyber attack datasets to identify the best-performing ones. The performance comparison with popular supervised algorithms demonstrates that semi-supervised algorithms are more capable of finding attack events than supervised algorithms. Our results also show that the performance of semi-supervised anomaly detection algorithms can be further improved by augmenting with deep representation learning. 

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2. Abstraction, validation , and generalization for explainable artificial intelligence

   https://doi.org/10.1002/ail2.37  

   Yang, Scott Cheng‐Hsin; Folke, Tomas; Shafto, Patrick (September 2021, Applied AI Letters)

   Abstract Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision‐making must be understandable to a wide range of stakeholders. Methods to explain artificial intelligence (AI) have been proposed to answer this challenge, but a lack of theory impedes the development of systematic abstractions, which are necessary for cumulative knowledge gains. We propose Bayesian Teaching as a framework for unifying explainable AI (XAI) by integrating machine learning and human learning. Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee. This formalization decomposes a wide range of XAI methods into four components: (a) the target inference, (b) the explanation, (c) the explainee model, and (d) the explainer model. The abstraction afforded by Bayesian Teaching to decompose XAI methods elucidates the invariances among them. The decomposition of XAI systems enables modular validation, as each of the first three components listed can be tested semi‐independently. This decomposition also promotes generalization through recombination of components from different XAI systems, which facilitates the generation of novel variants. These new variants need not be evaluated one by one provided that each component has been validated, leading to an exponential decrease in development time. Finally, by making the goal of explanation explicit, Bayesian Teaching helps developers to assess how suitable an XAI system is for its intended real‐world use case. Thus, Bayesian Teaching provides a theoretical framework that encourages systematic, scientific investigation of XAI. 

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3. Semi-supervised Wafer Map Pattern Recognition using Domain-Specific Data Augmentation and Contrastive Learning

   https://doi.org/10.1109/ITC50571.2021.00019  

   Hu, Hanbin; He, Chen; Li, Peng (October 2021, IEEE International Test Conference (ITC))

   Wafer map pattern recognition is instrumental for detecting systemic manufacturing process issues. However, high cost in labeling wafer patterns renders it impossible to leverage large amounts of valuable unlabeled data in conventional machine learning based wafer map pattern prediction. We proposed a contrastive learning framework for semi-supervised learning and prediction of wafer map patterns. Our framework incorporates an encoder to learn good representation for wafer maps in an unsupervised manner, and a supervised head to recognize wafer map patterns. In particular, contrastive learning is applied for the unsupervised encoder representation learning supported by augmented data generated by different transformations (views) of wafer maps. We identified a set of transformations to effectively generate similar variants of each original pattern. We further proposed a novel rotation-twist transformation to augment wafer map data by rotating each given wafer map for which the angle of rotation is a smooth function of the radius. Experimental results demonstrate that the proposed semi-supervised learning framework greatly improves recognition accuracy compared to traditional supervised methods, and the rotation-twist transformation further enhances the recognition accuracy in both semi-supervised and supervised tasks. 

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4. Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning

   Ajinkya Tejankar; Soroush Abbasi Koohpayegani; K L Navaneet; Kossar Pourahmadi Meibodi; Akshayvarun Subramanya; Hamed Pirsiavash (January 2022, European Conference on Computer Vision)

   We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluster images by pulling the embedding of a query image to be closer to its nearest neighbors (NNs). Since most NNs are close to the query by design, the averaging may not affect the embedding of the query much. On the other hand, far away NNs may not be semantically related to the query. We generalize the mean-shift idea by constraining the search space of NNs using another source of knowledge so that NNs are far from the query while still being semantically related. We show that our method (1) outperforms MSF in SSL setting when the constraint utilizes a different augmentation of an image from the previous epoch, and (2) outperforms PAWS in semi-supervised setting with less training resources when the constraint ensures that the NNs have the same pseudo-label as the query. 

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5. Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning

   K L Navaneet, Soroush Abbasi (October 2022, European Conference on Computer Vision)

   We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluster images by pulling the embedding of a query image to be closer to its nearest neighbors (NNs). Since most NNs are close to the query by design, the averaging may not affect the embedding of the query much. On the other hand, far away NNs may not be semantically related to the query. We generalize the mean-shift idea by constraining the search space of NNs using another source of knowledge so that NNs are far from the query while still being semantically related. We show that our method (1) outperforms MSF in SSL setting when the constraint utilizes a different augmentation of an image from the previous epoch, and (2) outperforms PAWS in semi-supervised setting with less training resources when the constraint ensures that the NNs have the same pseudo-label as the query. 

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