ABSTRACT Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for expert systems that enable continuous and interpretable cardiac monitoring. We present ArmFormer, a knowledge‐driven expert system that leverages Transformer‐based reasoning for robust cardiac event detection from wearable armband electrocardiogram signals. The model integrates domain‐guided multi‐scale patch encoding to capture waveform morphology and rhythm dependencies, while local gated Transformer blocks enhance temporal continuity and suppress noise‐induced variability. A lead‐wise attention mechanism coupled with gradient‐based visualisation provides interpretability by highlighting clinically relevant regions such as QRS complexes, P waves, and ST segments. On an in‐house cohort of 99 subjects comprising 6211 normal and 10,030 abnormal 10‐s segments, ArmFormer achieved 91.66% accuracy, 91.57% F1‐score, 91.41% sensitivity, and 97.36% AUC under a subject‐exclusive protocol that prevents patient‐level information leakage. Compared with convolutional and residual baselines, AUC improved by up to 5%, while floating‐point operations and parameters were reduced by 29‐fold and 47‐fold, respectively, achieving 2.58 ms inference latency per segment. External validation on CPSC2018 and Chapman showed accuracies of 83.65% and 94.69%, with AUCs of 96.69% and 99.38%, respectively. By combining domain‐guided encoding, noise‐robust temporal reasoning, and interpretable attention, ArmFormer provides a practical and reliable framework for expert‐level cardiac event detection in wearable monitoring scenarios.

Whilst Convolutional Neural Network (CNN)-based object tracking methods can achieve promising results on traditional well-lit datasets, it is challenging to accurately locate targets in low-light images taken in nighttime scenes, even for state-of-the-art (SOTA) trackers. Existing solutions often disregard potential image features beneficial for object tracking or focus solely on improving human perception, making it difficult to balance image enhancement and object tracking tasks. To address this issue and attain reliable nighttime unmanned aerial vehicle (UAV) tracking, we propose a lightweight Pyramid Attention-based low-light image enhancer, which serve as a plug-and-play solution before the trackers. In addition, we introduce a Pyramid Attention Module (PAM) to enhance the capability for multi-scale feature representation of images as image features are difficult to distinguish under low-light conditions. Experimental results reflect the effectiveness of our method in dealing with poor illumination situations.

Networks are one of the most powerful structures for modeling problems in the real world. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding learns from feature representations of nodes and links in a network, and converts it to vectors. Community structure is an important feature of the network, which represents the relationship among nodes and attracts the attention of relevant researchers. Many algorithms have been developed to identify the community structure. These algorithms usually identify different communities in the network, generating different types of information. In this paper, we propose a "Community Splitter" model based on random walk and RNN (Recurrent Neural Networks) that combines the node information generated by multiple community detection algorithms to improve node representation and link prediction. Extensive experiments on nine real datasets demonstrate that our proposed Community Splitter model has a significant prediction power compared to state-of-the-art link prediction models.