Proportional-Integral-Derivative (PID) controllers are instrumental in managing industrial processes. Their effectiveness hinges on the precision of their tuned parameters. Consequently, it becomes essential to monitor their performance frequently and re-tune them regularly to ensure optimal operation. However, conventional tuning methods require significant effort and expertise. This research tackles this challenge by using historical data to construct machine learning (ML) models, coupled with optimization algorithms, to streamline PID tuning. Specifically, we propose integrating an Explainable Boosting Machine (EBM) as an ML model and harnessing Bayesian Optimization (BO) within a comprehensive PID tuning framework. EBM stands out for its ease of construction and accuracy. The synergistic combination of EBM and BO yields an effective solution, as demonstrated through a case study involving black liquor concentration control in a multiple-effect evaporator system within kraft pulp manufacturing.

Object detection is critical in autonomous driving, and it is more practical yet challenging to localize objects of unknown categories: an endeavour known as Class-Agnostic Object Detection (CAOD). Existing studies on CAOD predominantly rely on RGB cameras, but these frame-based sensors usually have high latency and limited dynamic range, leading to safety risks under extreme conditions like fast-moving objects, overexposure, and darkness. In this study, we turn to the event-based vision, featured by its sub-millisecond latency and high dynamic range, for robust CAOD. We propose Detecting Every Object in Events (DEOE), an approach aimed at achieving high-speed, class-agnostic object detection in event-based vision. Built upon the fast event-based backbone: recurrent vision transformer, we jointly consider the spatial and temporal consistencies to identify potential objects. The discovered potential objects are assimilated as soft positive samples to avoid being suppressed as backgrounds. Moreover, we introduce a disentangled objectness head to separate the foreground-background classification and novel object discovery tasks, enhancing the model's generalization in localizing novel objects while maintaining a strong ability to filter out the background. Extensive experiments confirm the superiority of our proposed DEOE in both open-set and closed-set settings, outperforming strong baseline methods.

Recent research has shown that an adversary can use a surrogate model to steal the functionality of a target deep learning model even under the black-box condition and without data curation, while the existing defense mainly relies on API poisoning to disturb the surrogate training. Unfortunately, due to poisoning, the defense is achieved at the price of fidelity loss, sacrificing the interests of honest users. To solve this problem, we propose an Adversarial Fine-Tuning (AdvFT) framework, incorporating the generative adversarial network (GAN) structure that disturbs the feature representations of out-of-distribution (OOD) queries while preserving those of in-distribution (ID) ones, circumventing the need for OOD sample collection and API poisoning. Extensive experiments verify the effectiveness of the proposed framework. Code is available at github.com/Hatins/AdvFT.

Deep Neural Network (DNN) models have offered powerful solutions for a wide range of tasks, but the cost to develop such models is nontrivial, which calls for effective model protection. Although black-box distribution can mitigate some threats, model functionality can still be stolen via black-box surrogate attacks. Recent studies have shown that surrogate attacks can be launched in several ways, while the existing defense methods commonly assume attackers with insufficient in-distribution (ID) data and restricted attacking strategies. In this paper, we relax these constraints and assume a practical threat model in which the adversary not only has sufficient ID data and query times but also can adjust the surrogate training data labeled by the victim model. Then, we propose a two-step categorical inference poisoning (CIP) framework, featuring both poisoning for performance degradation (PPD) and poisoning for backdooring (PBD). In the first poisoning step, incoming queries are classified into ID and (out-of-distribution) OOD ones using an energy score (ES) based OOD detector, and the latter are further classified into high ES and low ES ones, which are subsequently passed to a strong and a weak PPD process, respectively. In the second poisoning step, difficult ID queries are detected by a proposed reliability score (RS) measurement and are passed to PBD. In doing so, the first step OOD poisoning leads to substantial performance degradation in surrogate models, the second step ID poisoning further embeds backdoors in them, while both can preserve model fidelity. Extensive experiments confirm that CIP can not only achieve promising performance against state-of-the-art black-box surrogate attacks like KnockoffNets and data-free model extraction (DFME) but also work well against stronger attacks with sufficient ID and deceptive data, better than the existing dynamic adversarial watermarking (DAWN) and deceptive perturbation defense methods. PyTorch code is available athttps://github.com/Hatins/CIP_master.git.