Conventional heterogeneous computing systems built on PCIe interconnects suffer from inefficient fine-grained host-device interactions and complex programming models. In recent years, many proprietary and open cache-coherent interconnect standards have emerged, among which compute express link (CXL) prevails in the open-standard domain after acquiring several competing solutions. Although CXL-based coherent heterogeneous computing holds the potential to fundamentally transform the collaborative computing mode of CPUs and XPUs, research in this direction remains hampered by the scarcity of available CXL-supported platforms, immature software/hardware ecosystems, and unclear application prospects. This paper presents Cohet, the first CXL-driven coherent heterogeneous computing framework. Cohet decouples the compute and memory resources to form unbiased CPU and XPU pools which share a single unified and coherent memory pool. It exposes a standard malloc/mmap interface to both CPU and XPU compute threads, which share a single per-process page table for user applications, leaving the OS dealing with smart memory allocation, page auto-migration, and management of heterogeneous resources. This design significantly simplifies heterogeneous parallel programming to a level comparable to homogeneous programming. To facilitate Cohet research, we also present a fullsystem cycle-level simulator named SimCXL, which is capable of modeling all CXL sub-protocols and device types. SimCXL has been rigorously calibrated against a real CXL testbed with various CXL memory and accelerators, showing an average simulation error of 3 %. Our evaluation reveals that CXL.cache reduces latency by 68 % and increases bandwidth by$14.4 \times$compared to DMA transfers at cacheline granularity. Building upon these insights, we demonstrate the benefits of Cohet with two killer apps, which are remote atomic operation (RAO) and remote procedure call (RPC). Compared to PCIe-NIC design, CXL-NIC achieves a 5.5 to$40.2 \times$speedup for RAO offloading and an average speedup of$\mathbf{1. 8 6} \times$for$\mathbf{R P C}$(de)serialization offloading.

Graph neural networks (GNNs) have been widely applied in the Internet of Things (IoT) for the intelligent analysis of data collected by sensors, particularly complex relationships and dependent information between IoT devices. However, recent studies have shown that GNNs are vulnerable to adversarial attacks, which significantly limits their application in safety-critical IoT systems such as smart health monitoring, traffic monitoring, and autonomous driving. To address this issue, in addition to the low feature similarity, this study examines the vulnerability of GNNs empirically and reveals that adversarial perturbations against GNNs tend to have low structural proximity in local neighborhoods. Thus, a natural approach for defending GNNs against adversarial attacks is to utilize the related high-order robust information of the perturbed graphs. In this study, we construct auxiliary views with high-order structure and feature similarity from a perturbed graph and propose a multi-view ensemble learning-based robust graph convolutional network (MV-RGCN). Each base model in the MV-RGCN aggregates the adversarial perturbed graph and the constructed view through an adaptive aggregation mechanism, thereby eliminating the impact of adversarial perturbations. Robust representations of the base models are then integrated using an adaptive ensemble mechanism to generate predictions. Extensive experiments under adversarial attack scenarios demonstrate that the MV-RGCN outperforms state-of-the-art methods and can achieve satisfactory performance without affecting its accuracy on the original graph data. This code is available at https://github.com/thomaslok0516/MVRGCN.