Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralized risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralized risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralized risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralized risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralized risks. Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.

Hyperspectral image feature extraction plays a crucial role in reducing the redundancy and correlation among spectral bands while preserving the essential information. Knowledge-driven feature extraction methods, such as spectral indices (SIs), leverage the interaction mechanisms between electromagnetic waves and materials to enhance the characteristic attributes of ground objects through band operations. These methods offer key advantages, including strong physical interpretability, simple construction, and robust scene reusability. However, most of the existing SIs still rely on expert knowledge tailored to specific scenarios, leading to inherent limitations, such as subjectivity, high time consumption, and implementation complexity. In this article, to address these challenges, we propose a hyperspectral image multi-agent deep reinforcement learning feature extraction (HI-MAFE) algorithm, aiming to alleviate the burden of manual SIs design by human experts. HI-MAFE employs a heuristic “generation-selection” strategy to simulate the decision-making process of domain experts, with specifically designed deep reinforcement learning (DRL) models for both the generation and selection steps. To accelerate exploration in a high-dimensional action space, the model incorporates a multi-agent deep reinforcement learning (MADRL) framework. The experimental results demonstrate the effectiveness and superiority of the proposed algorithm for hyperspectral image classification. The proposed HI-MAFE framework leverages DRL to autonomously generate meaningful environmental interpretation from spectral data, thereby reducing the reliance on manually designed SIs. This research can inspire future work in SIs construction and complement the limitations of data-driven approaches.

Automatic building localization and extraction based on high-resolution remote sensing images is of great importance to city mapping and smart city management. Extracted buildings with high precisions and fine boundaries contribute to the vectorization operation and, thus, the digital line graph (DLG) production. In this regard, a comprehensive framework Poly-BRBLE is proposed, combining a boundary refinement-based individual building localization and extraction model BRBLE as well as a particularly revised regularization method. The BRBLE is mainly composed of a multiscale feature fusion and propagation module and a coarse-to-fine mask optimization module, which allow the model to identify buildings from similar backgrounds and extract them with precise boundaries. Comparisons were made between BRBLE and other classical and state-of-the-art models on the WHU building dataset, the Chinese building instance segmentation dataset, and the Inria Polygon dataset, which demonstrated that BRBLE outperformed the second-best model by 2%, 1.5%, and 1.2%, respectively, in${\text {AP}}_{\text {mask}}$, and the advantage was further enlarged on large objects to 5.2%, 1.8%, and 2%. Moreover, we built the SH building dataset, which contained complex-shaped buildings and mixed-built environments, where it was demonstrated that BRBLE outperformed the second-best model by 3.4% in${\text {AP}}_{\text {mask}}$. We further compared the precisions of the building footprints obtained by the Poly-BRBLE and other different methods, where Poly-BRBLE showed a superior performance, with an${\text {AP}}_{\text {mask}}$score of 60.4%, which demonstrated that it is capable of extracting complicated buildings, such as high-rise buildings and villas, and factories of multiple shapes, even though the images were off-nadir.

Convolutional neural networks (CNNs) have achieved success in HSI classification, but the performance is constrained by the limited reception field. In this regard, vision transformer is introduced recently, which is of powerful capabilities in long-range feature extraction for HSI classification. However, transformers are computation intensive and poor for local feature extraction. The motivation for this study is to build a lightweight hybrid model, which ensembles the respective inductive bias from CNNs and global receptive field from transformers. In this work, we propose a concise and efficient framework—the spatial-spectral feature extraction network with patch attention module (PASSNet), to simultaneously extract both local and global features. Specifically, we design an innovative plugin called patch attention module (PAM), which can be easily integrated into both CNNs and transformers blocks to extract spatial-spectral features from multiple spatial perspectives. Besides, a novel partial convolution operation is introduced, with a reduced computational cost than vanilla convolution operation. Through coupling the local attention from the CNNs with the global receptive fields in the transformers, the proposed PASSNet exhibits a superior classification performance on three well-known datasets with a small training sample size.

In closed-loop feedback control systems, faults are propagated through the feedback link, which eventually leads to the abnormality of the entire system. Generally, it is very difficult to identify the faults of systems under the influence of the closed-loop feedback link. The existence of redundancy improves the reliability of the system. Meanwhile, it also poses new challenges to the fault diagnosis of multiple redundant systems. In this regard, a causality-based method is proposed for the fault diagnosis of closed-loop feedback control system with multiple modular redundancy. The dynamic Bayesian networks for fault diagnosis are established based on sensor data and system parameters. The networks consist of four layers, which are sensors, performances, monitors, and faults, respectively. Furthermore, the conditional probabilities of the fault nodes are calculated by Noisy-OR and Noisy-MAX models. The proposed method can dynamically evaluate system performance and integrate other monitoring information as evidence to assist faults diagnosis and location. A double modular redundant control system for a subsea blowout preventer is used as a case to demonstrate the proposed method, and the results show that the proposed method has high accuracy. The influence of sampling frequency, noise, and redundancy mode on diagnosis results is studied and discussed in the case study.

The Bayesian network (BN) is a powerful model for probabilistic knowledge representation and inference and is increasingly used in the field of reliability evaluation. This paper presents a bibliographic review of BNs that have been proposed for reliability evaluation in the last decades. Studies are classified from the perspective of the objects of reliability evaluation, i.e., hardware, structures, software, and humans. For each classification, the construction and validation of a BN-based reliability model are emphasized. The general procedural steps for BN-based reliability evaluation, including BN structure modeling, BN parameter modeling, BN inference, and model verification and validation, are investigated. Current gaps and challenges in reliability evaluation with BNs are explored, and a few upcoming research directions that are of interest to reliability researchers are identified.