Fully mining the differential features of different class samples in overlapping areas is the key and difficult point to improving imbalanced classification performance under complex distribution patterns. Although existing data-level and algorithm-level methods have achieved good results in dealing with overlapping problems, sample generation and classifier training heavily rely on distribution information, and the ability to mine the different information is limited. This paper proposes a dual imbalanced classification framework with feature transfer guided by memory compensation strategy, which enhances the model's ability to mine differential features by constructing a feature space with better inter-class separability. In the traditional classification branch, a feature extraction network maps original samples to feature space and a traditional classifier is used to classify the features. In the compensation classification branch, a feature memory module based on iterative clustering strategy is designed, separately obtaining and saving the correctly classified feature centers of different classes. Moreover, a feature transfer module based on vector combination theory is proposed, combining “push” and “pull” vectors to transfer the misclassified features to the non-overlapping areas corresponding to the same class feature memory module, thereby constructing a feature space with better inter-class separability. Finally, a classification compensation strategy based on feature similarity is designed, integrating the prediction results of the traditional classifier and feature memory module as the final classification results. Experimental results on 50 imbalanced datasets show the proposed method outperforms 28 typical imbalanced classification methods in F1-score and G-mean. Especially on 20 severely overlapping datasets, the performance improvement is more significant.

Generalized few-shot object detection aims to improve detection accuracy for novel classes while maintaining high performance on base classes. Traditional fine-tuning approaches often blur feature boundaries, leading to misclassification of novel samples as base classes or background. Additionally, differences in data distributions between base and novel classes can cause the model to “forget” base knowledge. This paper proposes a novel generalized few-shot detection method that leverages memory distillation of category prototypes. The approach includes two key components: a variational prototype refinement module (VPRM) and a memory bank of category prototypes (MBCP). The variational prototype refinement module introduces a class-agnostic feature fusion mechanism based on the original variational autoencoder. First, the mean and variance of the original distribution of base class are estimated in the base class training stage. The noise variables are converted into memory prototypes with strong generalization ability through reparameterization and stored. Second, the stored memory prototypes are fused with class-agnostic features of novel classes in the fine-tuning stage, which significantly alleviates the problem of base class bias when processing novel classes. In the base class training phase, the category prototype memory bank stores the base class memory prototypes extracted by the variational prototype refinement module and selects the best memory items by dynamically updating the category confidence and intersection-over-union threshold. This memory item can be used not only to constrain features of base classes to alleviate catastrophic forgetting of base classes but also to fuse with features of novel classes, adaptively extracting class-agnostic common information to strengthen the feature representation of the novel class. Experiments on PASCAL VOC and MS-COCO show superior average precision in both single-round and multi-round tests, outperforming existing state-of-the-art methods.

AUTOSAR enhances the management of complex automotive electrical and electronic architectures by improving the reusability and interchangeability of software modules between OEMs and suppliers. However, existing AUTOSAR modeling tools need help with non-intuitive representation and complicated architectural relationship modeling processes. In this paper, we propose a visual architecture modeling language to represent based on model-driven development of the system architecture design of vehicles. Our approach addresses these issues by implementing multi-dimensional visualization capabilities, incorporating two-dimensional graphical representations and detailed one-dimensional tabular displays. Furthermore, we introduce a practical'AUTOSAR meeting in the middle modeling method, which allows for separate modeling at different levels. This approach effectively harnesses the expertise of detailed bottom-level designers and high-level architects, improving efficiency in automotive system design. A detailed case study and evaluation substantiate the effectiveness of our modeling language in describing the system architecture.

With the trend of intelligence, automobile archi-tecture has become more complex. It is necessary to predict and discover reliability-related issues to reduce the cost of correction in the later period. In AUTOSAR-based automotive architecture design, software and hardware interaction, mid-dleware platform behavior, physical environment, and system usage profile affect the system's reliability. It is necessary to comprehensively consider these factors to predict the system's reliability reasonably. However, existing methods often overlook the influence of some factors, especially oversimplifying the control flow of the middleware platform in the system. Resulting in difficulty in effectively modeling the behavior of the AUTOSAR middleware platform in error propagation and its impact on system failure behavior. To analyze the impact of middleware platforms on failure behavior, this paper analyzes the impact of the AutoSAR middleware platform on application software faults based on error propagation methods. Then, expand the AUTOSAR meta-model to model reliability parameters and automatically convert the architecture model into a formal model for reliability prediction. Finally, the effectiveness of considering unreliable platform behavior modeling was verified through a car headlight design case study.

Along with the promotion of intelligent connected vehicles (ICVs), the problems of network attacks have rapidly increased, and thus the cybersecurity has drawn much attention. Unfortunately, although remarkable progress has been achieved both in technics and standard, it still remains vague for designing vehicular cybersecurity. In this article, the general technical profile of cybersecurity for ICVs has been comprehensively reviewed, including threat analysis and risk assessment, static defense, and intrusion detection. The potential attacking vulnerabilities for ICVs are summarized, within in-vehicle network and mobile networks. Then, the identity authentication and secure communication methods are introduced from static defense, where the conventional and novel intelligent approach are included. And the intrusion detection is introduced as the active methods, including conventional and novel ones. Moreover, the general procedure and management for designing the vehicular cybersecurity are also summarized according to the current standard system. It hopes that the review of research progress on technical method may help researchers and manufactures, and delivers the potential direction for future cybersecurity development.