Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments demonstrate that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in terms of imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.

Identifying and linking the same users across different social platforms is crucial for understanding user behavior and preferences. However, cross-domain datasets exhibit diverse characteristics, such as varying check-in frequencies, significant disparities in data precision, and distinct distributions. Existing trajectory representations rely on recurrent neural network, which fails to dynamically learn multi-dimensional feature relations and capture high-order associations. Furthermore, current methods for integrating trajectory information fails to capture the complex relations and dynamic variations among cross-domain mobility trajectories. To this end, we propose the Hierarchical Spatio-Temporal Enhanced Attention Hypergraph Network (StarNet). This model dynamically regulates the multi-dimensional features of trajectories through a locally enhanced spatiotemporal graph neural network. Meanwhile, StarNet employs a hypergraph network enhanced by a global spatiotemporal to capture high-order associations between cross-domain trajectories. The fusion enhancement association integrates local and global information, which enables this model to link user identities. Extensive experiments on two well-known LBSN cross-domain datasets reveal that StarNet outperforms state-of-the-art baselines in the accuracy of user identity linkage.

Super-enhancers (SEs) are typically located in the regulatory regions of genes, driving high-level gene expression. Identifying SEs is crucial for a deeper understanding of gene regulatory networks, disease mechanisms, and the development and physiological processes of organisms, thus exerting a profound impact on research and applications in the life sciences field. Traditional experimental methods for identifying SEs are costly and time-consuming. Existing methods for predicting SEs based solely on sequence data use deep learning for feature representation and have achieved good results. However, they overlook biological features related to physicochemical properties, leading to low interpretability. Additionally, the complex model structure often requires extensive labeled data for training, which limits their further application in biological data. In this paper, we integrate the strengths of different models and proposes an ensemble model based on an integration strategy to enhance the model’s generalization ability. It designs a multi-angle feature representation method that combines local structure and global information to extract high-dimensional abstract relationships and key low-dimensional biological features from sequences. This enhances the effectiveness and interpretability of the model’s input features, providing technical support for discovering cell-specific and species-specific patterns of SEs. We evaluated the performance on both mouse and human datasets using five metrics, including area under the receiver operating characteristic curve accuracy, and others. Compared to the latest models, EnsembleSE achieved an average improvement of 4.5% in F1 score and an average improvement of 8.05% in recall, demonstrating the robustness and adaptability of the model on a unified test set. Source codes are available at https://github.com/2103374200/EnsembleSE-main.

Missing data in spatiotemporal systems presents a significant challenge for modern applications, ranging from environmental monitoring to urban traffic management. The integrity of spatiotemporal data often deteriorates due to hardware malfunctions and software failures in real-world deployments. Current approaches based on machine learning and deep learning struggle to model the intricate interdependencies between spatial and temporal dimensions effectively and, more importantly, suffer from cumulative errors during the data imputation process, which propagate and amplify through iterations. To address these limitations, we propose CoFILL, a novel Conditional Diffusion Model for spatiotemporal data imputation. CoFILL builds on the inherent advantages of diffusion models to generate high-quality imputations without relying on potentially error-prone prior estimates. It incorporates an innovative dual-stream architecture that processes temporal and frequency domain features in parallel. By fusing these complementary features, CoFILL captures both rapid fluctuations and underlying patterns in the data, which enables more robust imputation. The extensive experiments reveal that CoFILL's noise prediction network successfully transforms random noise into meaningful values that align with the true data distribution. The results also show that CoFILL outperforms state-of-the-art methods in imputation accuracy. The source code is publicly available at https://github.com/joyHJL/CoFILL.

The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial cybersecurity. However, mainstream datasets widely used in academia and industry (e.g., Drebin) exhibit significant limitations: on one hand, their heavy reliance on VirusTotal's multi-engine aggregation results introduces substantial label noise; on the other hand, outdated samples reduce their temporal relevance. Moreover, automated labeling tools (e.g., AVClass2) suffer from suboptimal aggregation strategies, further compounding labeling errors and propagating inaccuracies throughout the research community.