The advancement of the Internet of Medical Things (IoMT) has rendered the clustering method pivotal in medical data analysis. Nevertheless, the exposure of patient privacy during this process constitutes a significant security concern. To achieve privacy protection for cluster analysis while ensuring the accuracy of clustering results in IoMT, this paper proposes a Differential Privacy-based availability-enhanced $k$-modes Clustering scheme, named DPKC. First, we introduce partition entropy to quantify attribute weights and calculate the initial center based on density with distance. This can mitigate the impact of random initialization, thus improving the accuracy of the clustering results. Second, we introduce cluster weights to calculate the distance between data points and centers during allocation. This adjustment aims to reduce the difference in compactness between clusters. Third, we employ a geometric mechanism to inject noise into the frequency of attribute values, ensuring data privacy during the iteration process. Theoretical analysis proves that DPKC satisfies differential privacy and prevents information disclosure. Experimental results show that DPKC improves the F-measure metric by 7.36% compared to existing algorithms.

Catastrophic forgetting is currently the greatest challenge faced in Exemplar-Free Class Incremental Learning (EFCIL), which does not allow the replay of old data from previous tasks because of factors such as user privacy and device capacity limitations. In this paper, we propose a Comprehensive Ensemble Framework for exemplar-free Class Incremental Learning (CEFCIL), which includes an ensemble Nearest Class Mean (NCM) classifier based on the Mahalanobis metric with a given number of diversified base networks, a cached root model consisting of initialized base networks for root knowledge distillation, a dual knowledge distillation strategy, and a dimensional collapse prevention strategy. Across diverse experimental conditions, CEFCIL exhibits superior performance in EFCIL and possesses robust cross-domain capabilities.

We introduce the Open Sequential Repetitive Action Counting (OSRAC) task, which aims to count all repetitions and locate transition boundaries of sequential actions from large-scale video data, without relying on predefined action categories. Unlike the Repetitive Action Counting (RAC) task that focuses on a single-action assumption, OSRAC handles diverse and alternating repetitive action sequences in real-world scenarios, which is fundamentally more challenging. To this end, we propose UniCount, a universal system capable of counting multiple sequential repetitive actions from video data. Specifically, UniCount designs three primary modules: the Universal Repetitive Pattern Learner (URPL) to capture general repetitive patterns in alternating actions, Temporal Action Boundary Discriminator (TABD) to locate the action transition boundaries, and Dual Density Map Estimator (DDME) to achieve action counting and repetition segmentation. We also design a novel actionness loss to improve the detection of action transitions. To support this task, we conduct in-depth data analysis on existing RAC datasets and construct several OSRAC benchmarks (i.e., MUCFRep, MRepCount, and MInfiniteRep) by developing a pipeline on data processing and mining. We further perform comprehensive experiments to evaluate the effectiveness of UniCount. On MInfiniteRep, UniCount substantially improves the Off-By-One Accuracy (OBOA) from 0.39 to 0.78 and decreases the Mean Absolute Error (MAE) from 0.29 to 0.14 compared to counterparts. UniCount also achieves superior performance in open-set data, showcasing its universality.

With the increasing utilization of High-Speed Trains (HSTs), the need for a reliable and high-bandwidth Internet access under high-speed mobility scenarios has become more demanding. In static, walking, and low mobility environments, TCP/IP (transmission control protocol/Internet protocol) can work well. However, TCP/IP cannot work well in high-speed scenarios because of reliability and handoff delay problems. This is mainly because the mobile node is required to maintain the connection to the corresponding node when it handovers to another access point node. In this paper, we propose a named data networking wireless mesh network architecture for HST wireless communication (NDN-Mesh-T), which combines the advantages of Wireless Mesh Networks (WMNs) and NDN architectures. We attempt to solve the reliability and handoff delay problems to enable high bandwidth and low latency in Internet access in HST scenarios. To further improve reliability and bandwidth utilization, we propose a Direction-Aware Forwarding (DAF) strategy to forward Interest packet along the direction of the running train. The simulation results show that the proposed scheme can significantly reduce the packet loss rate by up to 51% compared to TCP/IP network architecture. Moreover, the proposed mechanism can reduce the network load, handoff delay, and data redundancy.