Deep neural networks (DNNs) is susceptible to surrogate attacks, where adversaries use surrogate data and corresponding outputs from the target model to build their own stolen model. Model stealing attacks jeopardize model privacy and model owners' commercial benefits. To address this issue, this paper proposes a hybrid protection approach-Maximize the confidence differences between benign samples and adversarial samples (MCD), to protect models from theft. Firstly, the LogitNorm approach is used to overcome the overconfidence problem in adversary query classification. Then, samples are divided into four groups according to ES and RS. Different groups are poisoned by different degrees. In addition to enhancing defensive performance and accounting for model integrity, the MCD uses a trigger to confirm the cloned model's owner. Experimental results show that the MCD defends against a variety of original models and attack techniques well. Against KnockoffNets and DFME attacks, the MCD yields an average defense performance of 54.58 % on five datasets, which is a great improvement over other defenses. Compared to other poisoning techniques, the Strong Poisoning (SP) module reduces the adversary's accuracy by 48.23 % on average. Additionally, the MCD overcomes the issue of OOD overconfidence while safeguarding the model accuracy in OOD detection and reduces the misclassification rate of ID samples for multiple OOD datasets.

Wood drying technology plays a key role in extending service lifetime of wood as the moisture content has a great influence on the wood quality. This paper presents a moisture content prediction model based on the deep long short-term memory (LSTM) autoencoders with stochastic sensitivity (DLASS) to extract a hidden representation of input data. The DLASS uses multiple LSTM encoders to learn more informative hidden representations from unseen samples, which are then decoded using multiple LSTM decoders. The DLASS is trained by minimizing the perturbed error from historical moisture content data. Furthermore, a nonlinear fully connected feedforward neural network as a regression layer is applied to predict moisture content using hidden representations learned by the DLASS. The DLASS is applied to real-world industrial data of Sugi wood processed by a drying kiln made by SECEA from August 4 to 19, 2008. Multiple test cases and comparisons with existing classical and state-of-the-art models show that the DLASS model yields more accurate moisture content prediction results and has high generalization capability. To be specific, the DLASS yields the lowest MAE (0.026), MAPE (0.280), and RMSE (0.058) for predicting moisture content during the wood drying process.

MOTIVATION: Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. RESULTS: In this study, we present the EBD , a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"-the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. AVAILABILITY AND IMPLEMENTATION: EBD is available at http://www.eyeseeworld.com/ebd/index.html.