Predicting enzyme-substrate interactions has long been a fundamental problem in biochemistry and metabolic engineering. While existing methods could leverage databases of expert-curated enzyme-substrate pairs for models to learn from known pair interactions, the databases are often sparse, i.e., there are only limited and incomplete examples of such pairs, and also labor-intensive to maintain. This lack of sufficient training data significantly hinders the ability of traditional enzyme prediction models to generalize to unseen interactions. In this work, we try to exploit chemical reaction equations from domain-specific databases, given their easier accessibility and denser, more abundant data. However, interactions of multiple compounds, e.g., educts and products, with the same enzymes create complex relational data patterns that traditional models cannot easily capture. To tackle that, we represent chemical reaction equations as triples of (educt, enzyme, product) within a knowledge graph, such that we can take advantage of knowledge graph embedding (KGE) to infer missing enzyme-substrate pairs for graph completion. Particularly, in order to capture intricate relationships among compounds, we propose our knowledge-enhanced hypergraph model for enzyme prediction, i.e., Hyper-Enz, which integrates a hypergraph transformer with a KGE model to learn representations of the hyper-edges that involve multiple educts and products. Also, a multi-expert paradigm is introduced to guide the learning of enzyme-substrate interactions with both the proposed model and chemical reaction equations. Experimental results show a significant improvement, with up to a 88% relative improvement in average enzyme retrieval accuracy and 30% improvement in pair-level prediction compared to traditional models, demonstrating the effectiveness of our approach.

Recent progress in adversarial attacks on 3D point clouds, particularly in achieving spatial imperceptibility and high attack performance, presents significant challenges for defenders. Current defensive approaches remain cumbersome, often requiring invasive model modifications, expensive training procedures or auxiliary data access. To address these threats, in this paper, we propose a plug-and-play and non-invasive defense mechanism in the spectral domain, grounded in a theoretical and empirical analysis of the relationship between imperceptible perturbations and high-frequency spectral components. Building upon these insights, we introduce a novel purification framework, termed PWAVEP, which begins by computing a spectral graph wavelet domain saliency score and local sparsity score for each point. Guided by these values, PWAVEP adopts a hierarchical strategy, it eliminates the most salient points, which are identified as hardly recoverable adversarial outliers. Simultaneously, it applies a spectral filtering process to a broader set of moderately salient points. This process leverages a graph wavelet transform to attenuate high-frequency coefficients associated with the targeted points, thereby effectively suppressing adversarial noise. Extensive evaluations demonstrate that the proposed PWAVEP achieves superior accuracy and robustness compared to existing approaches, advancing the state-of-the-art in 3D point cloud purification. Code and datasets are available at https://github.com/a772316182/pwavep

Cloud computing has become a dominant paradigm for outsourcing computationally intensive tasks. However, the lack of transparency in remote execution poses significant challenges to integrity and trust. Verifiable computation (VC) mitigates these issues by enabling clients to efficiently verify outsourced results. Existing VC schemes often incur high verification costs, scale poorly, and exhibit low computational efficiency. We propose the Parallel and Verifiable Polynomial Computation (PVPC) scheme, designed for secure, privacy-preserving, and scalable polynomial evaluation in heterogeneous cloud-edge environments. PVPC reformulates polynomial evaluation as matrix-vector operations combined with random sparse blinding, enabling parallel execution across multiple sub-servers while preserving input privacy. It achieves sublinear complexity in the polynomial degree through row-wise factorization and enables aggregated batch verification via bilinear pairings, thereby reducing per-task verification overhead. We present formal correctness and security proofs under the Computational Diffie-Hellman assumption, and evaluate PVPC against state-of-the-art VC protocols. Experimental results show that PVPC substantially outperforms existing schemes in computation, verification, and recovery phases-particularly for high-degree polynomials-while maintaining practical communication costs. These features make PVPC well-suited for large-scale, latency-sensitive, and distributed verifiable computing applications.

Advanced persistent threats (APT) pose a major cybersecurity concern due to their covert nature and targeted attacks on enterprises, industries, and national infrastructures. Orchestrated by well-organised hacker groups, these threats leverage sophisticated malware, which makes detection and source tracing challenging. However, characteristic patterns within the malware used by each hacker group allow for classification and analysis. In this study, we present an innovative APT classification system that leverages the temporal dependencies of API call sequences through a hybrid deep learning model. By integrating a one-dimensional convolutional neural network (CNN) with a bidirectional long short-term memory (BiLSTM) network, enhanced with an attention mechanism, our system effectively captures the nuanced behaviours of malware. This model allows for a refined understanding of APT malware, offering both high accuracy and practical utility. The system is tested on a dataset of 12 different malware families, and the results show high accuracy and practical utility.

Existing geometric knowledge graph embedding methods employ various relational transformations, such as translation, rotation, and projection, to model different relation patterns, which aims to enhance the expressiveness of models. In contrast to current approaches that treat the expressiveness of the model as a binary issue, we aim to delve deeper into analyzing the level of difficulty in which geometric knowledge graph embedding models can represent relation patterns. In this paper, we provide a theoretical analysis framework that measures the expressiveness of the model in relation patterns by quantifying the size of the solution space of linear equation systems. Additionally, we propose a mechanism for imposing relational constraints on geometric knowledge graph embedding models by setting “traps” near relational optimal solutions, which enables the model to better converge to the optimal solution. Empirically, we analyze and compare several typical knowledge graph embedding models with different geometric algebras, revealing that some models have insufficient solution space due to their design, which leads to performance weaknesses. We also demonstrate that the proposed relational constraint operations can improve the performance of certain relation patterns. The experimental results on public benchmarks and relation pattern specified dataset are consistent with our theoretical analysis.

The development of a high-performance energy management strategy is of significant importance for reducing the operational costs of fuel cell hybrid electric vehicles. Current energy management strategies lack the quantification of the cost of energy source degradation and suffer from insufficient global optimality. Therefore, this study first quantifies the driving costs, including energy source degradation, and introduces a hierarchical energy management strategy. Specifically, the upper-level driving condition predictor provides accurate driving condition prediction information, while the lower-level power distribution controller uses the established driving cost to create a reward function. By optimizing the overall driving costs of the vehicle within a broad range of driving conditions, the developed energy management strategy combines good global optimality with high computational efficiency, demonstrating potential for practical applications.