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The analyses highlight the major limitations of current stethoscopes and the significant improvements the proposed system makes in challenging settings by minimizing both distortion of lung sounds and contamination by ambient noise.In this paper, we propose a novel method named Biomedical Confident Itemsets Explanation (BioCIE), aiming at post-hoc explanation of black-box machine learning models for biomedical text classification. Using sources of domain knowledge and a confident itemset mining method, BioCIE discretizes the decision space of a black-box into smaller subspaces and extracts semantic relationships between the input text and class labels in different subspaces. Confident itemsets discover how biomedical concepts are related to class labels in the black-box's decision space. BioCIE uses the itemsets to approximate the black-box's behavior for individual predictions. Optimizing fidelity, interpretability, and coverage measures, BioCIE produces class-wise explanations that represent decision boundaries of the black-box. Results of evaluations on various biomedical text classification tasks and black-box models demonstrated that BioCIE can outperform perturbation-based and decision set methods in terms of producing concise, accurate, and interpretable explanations. BioCIE improved the fidelity of instance-wise and class-wise explanations by 11.6% and 7.5%, respectively. It also improved the interpretability of explanations by 8%. BioCIE can be effectively used to explain how a black-box biomedical text classification model semantically relates input texts to class labels. The source code and supplementary material are available at https//github.com/mmoradi-iut/BioCIE.We present adversarial event prediction (AEP), a novel approach to detecting abnormal events through an event prediction setting. Given normal event samples, AEP derives the prediction model, which can discover the correlation between the present and future of events in the training step. MSL6 In obtaining the prediction model, we propose adversarial learning for the past and future of events. The proposed adversarial learning enforces AEP to learn the representation for predicting future events and restricts the representation learning for the past of events. By exploiting the proposed adversarial learning, AEP can produce the discriminative model to detect an anomaly of events without complementary information, such as optical flow and explicit abnormal event samples in the training step. We demonstrate the efficiency of AEP for detecting anomalies of events using the UCSD-Ped, CUHK Avenue, Subway, and UCF-Crime data sets. Experiments include the performance analysis depending on hyperparameter settings and the comparison with existing state-of-the-art methods. The experimental results show that the proposed adversarial learning can assist in deriving a better model for normal events on AEP, and AEP trained by the proposed adversarial learning can surpass the existing state-of-the-art methods.To address the architecture complexity and ill-posed problems of neural networks when dealing with high-dimensional data, this article presents a Bayesian-learning-based sparse stochastic configuration network (SCN) (BSSCN). The BSSCN inherits the basic idea of training an SCN in the Bayesian framework but replaces the common Gaussian distribution with a Laplace one as the prior distribution of the output weights of SCN. Meanwhile, a lower bound of the Laplace sparse prior distribution using a two-level hierarchical prior is adopted based on which an approximate Gaussian posterior with sparse property is obtained. It leads to the facilitation of training the BSSCN, and the analytical solution for output weights of BSSCN can be obtained. Furthermore, the hyperparameter estimation process is derived by maximizing the corresponding lower bound of the marginal likelihood function based on the expectation-maximization algorithm. In addition, considering the uncertainties caused by both noises in the real-world data and model mismatch, a bootstrap ensemble strategy using BSSCN is designed to construct the prediction intervals (PIs) of the target variables. The experimental results on three benchmark data sets and two real-world high-dimensional data sets demonstrate the effectiveness of the proposed method in terms of both prediction accuracy and quality of the constructed PIs.This article investigates the adaptive resilient event-triggered control for rear-wheel-drive autonomous (RWDA) vehicles based on an iterative single critic learning framework, which can effectively balance the frequency/changes in adjusting the vehicle's control during the running process. According to the kinematic equation of RWDA vehicles and the desired trajectory, the tracking error system during the autonomous driving process is first built, where the denial-of-service (DoS) attacking signals are injected into the networked communication and transmission. Combining the event-triggered sampling mechanism and iterative single critic learning framework, a new event-triggered condition is developed for the adaptive resilient control algorithm, and the novel utility function design is considered for driving the autonomous vehicle, where the control input can be guaranteed into an applicable saturated bound. Finally, we apply the new adaptive resilient control scheme to a case of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.Distance metric learning (DML) aims to learn a distance metric to process the data distribution. However, most of the existing methods are kNN DML methods and employ the kNN model to classify the test instances. The drawback of kNN DML is that all training instances need to be accessed and stored to classify the test instances, and the classification performance is influenced by the setting of the nearest neighbor number k. To solve these problems, there are several DML methods that employ the SVM model to classify the test instances. However, all of them are nonconvex and the convex support vector DML method has not been explicitly proposed. In this article, we propose a convex model for support vector DML (CSV-DML), which is capable of replacing the kNN model of DML with the SVM model. To make CSV-DML can use the most kernel functions of the existing SVM methods, a nonlinear mapping is used to map the original instances into a feature space. Since the explicit form of nonlinear mapped instances is unknown, the original instances are further transformed into the kernel form, which can be calculated explicitly.